Human Capital and Industrialization:
Theory and Evidence from the Enlightenment∗
Mara P. Squicciarini
Nico Voigtländer
KULeuven and FWO
UCLA and NBER
March 2014
Abstract
While human capital is one of the strongest predictors of economic development today, its importance for the Industrial Revolution is typically assessed as minor. We differentiate average human
capital (worker skills) from upper tail knowledge both theoretically and empirically. We introduce
the two types of human capital in a simple multi-sector model of industrialization that differentiates
between traditional and modern manufacturing. While average worker skills are more important
in the former, upper tail knowledge raises efficiency in the latter. The model predicts that the local
presence of knowledge elites is unimportant in the pre-industrial era, but drives growth thereafter;
worker skills, in contrast, are not crucial for growth. To measure the historical presence of knowledge elites, we use city-level subscriptions to the famous Encyclopédie in mid-18th century France.
We show that subscriber density is a strong predictor of city growth after 1750, but not before the
onset of French industrialization. Variables that proxy for the level of development confirm this
pattern: soldier height and industrial activity are strongly associated with subscriber density after,
but not before, 1750. Literacy, on the other hand, does not predict growth. Finally, joining data on
British patents with a large French firm survey from 1837, we show that locations with higher subscriber density were significantly more productive in operating innovative industrial technology.
JEL: J24, N13, O14, O41
Keywords: Human Capital, Industrial Revolution, Regional Development
∗
We would like to thank Ran Abramitzky, Quamrul Ashraf, Sascha Becker, Leonardo Bursztyn, Davide Cantoni,
Matteo Cervellati, Nick Crafts, Paola Giuliano, Stelios Michalopoulos, Suresh Naidu, Nathan Nunn, Enrico Spolaore,
Jo Swinnen, Joachim Voth, Fabian Waldinger, Ludger Woessmann, and Noam Yuchtman, as well as seminar audiences
at IPEG Barcelona, KU Leuven, and the University of Munich for helpful comments and suggestions. We are grateful
to Tomas E. Murphy for sharing digitized data of Annuaires Statistiques de la France, to Carles Boix for sharing data
on proto-industrialization in France, to Jeremiah Dittmar for his data on ports and navigable rivers, and to Daniel
Hicks for sharing geo-coded data on soldier height from Komlos (2005).
The genre of modern industrial production requires extended knowledge of mechanics, notion of calculus, great dexterity at work, and enlightenment in the underlying principles
of the crafts. This combination of expertise . . . has only been achieved in this [18th century] period, where the study of science has spread widely, accompanied by an intimate
relationship between savants and artisans. (Chaptal, 1819, p.32)
1
Introduction
While a rich literature shows that human capital is strongly associated with economic development
in the modern world,1 its role during the Industrial Revolution is typically described as minor. In
Britain – the cradle of industrialization – education was inessential for economic growth (Mitch,
1993; Clark, 2005); and referring to Europe in general, Galor (2005) points out that "in the first
phase of the Industrial Revolution . . . [h]uman capital had a limited role in the production process,
and education served religious, social, and national goals."2 However, these observations are based
on education or skills of the average worker. This may veil the role of scientifically savvy engineers
and entrepreneurs at the top of the skill distribution. Mokyr (2005a) stresses the importance of this
"density in the upper tail" for innovation and the diffusion of technology during industrialization.
Recent research on contemporaneous economies chimes in, underlining the importance of math
and science skills, or of entrepreneurial skills, in addition to average education (Hanushek and
Kimko, 2000; Gennaioli, Porta, Lopez-de-Silanes, and Shleifer, 2013).
In this paper, we ask whether distinguishing between upper-tail and average skills may reinstate the importance of human capital during the transition from stagnation to growth. Answering
this question hinges on a historical proxy for the thickness of the upper tail, i.e., the presence of
knowledge elites.3 We use a novel measure from the eve of industrialization in mid-18th century
France: subscriptions to the Encyclopédie – the cornerstone of Enlightenment, representing the
most important collection of scientific and technological knowledge at the time. One of the publishers kept a list of all (more than 8,000) subscriptions to the most prominent edition. Based on
this information, we calculate subscriber density for almost 200 French cities and use it as a proxy
for the local concentration of knowledge elites. Figure 1 shows that regions with high and low
1
See, among many others, Barro (2001), Krueger and Lindahl (2001), Cohen and Soto (2007), Hanushek and
Woessmann (2008), and Ciccone and Papaioannou (2009).
2
Galor (2005, p.205). The low schooling rate in Britain underlines this point: as late as 1855, at the end of the first
Industrial Revolution, primary schooling enrollment was 11% (Flora, Kraus, and Pfenning, 1983). Focusing on an
alternative skill measure in a panel of European countries, Allen (2003, p.433) concludes that "literacy was generally
unimportant for growth."
3
Following Mokyr (2005a), we use a broad definition of "upper tail knowledge": it reflects an interest in scientific
advances, motivated by the Baconian notion that knowledge is at the heart of material progress. This concept comprises
innovative and entrepreneurial capabilities in adopting and improving new technology, but also lower access costs to
modern techniques; it is thus compatible with Mokyr’s notion of (economically) ‘useful knowledge’. When referring
to the local presence of people embodying such knowledge, we use the term "knowledge elite".
1
values are relatively evenly distributed and often immediately adjacent. In addition, subscriber
density is uncorrelated with literacy rates in the same period (see Figures 1 and A.4), allowing us
to differentiate between average and upper tail skills.
To guide our analysis, we build a simple multi-sector, multi-region model of industrialization
in the spirit of Hansen and Prescott (2002). The model features an agricultural sector, as well as
traditional and modern manufacturing. Distinguishing between the latter two is crucial for our
results; it allows upper tail and average skills to have different effects for each type of manufacturing. We assume that upper tail knowledge raises the efficiency of entrepreneurs that operate
modern manufacturing. Average worker skills, on the other hand, raise productivity in all sectors,
and this effect is particularly strong in traditional manufacturing (O’Rourke, Rahman, and Taylor,
2008). Technological progress takes the form of exogenous efficiency increases in modern industrial technology. This makes modern manufacturing more attractive, especially so in regions with
a thick upper tail of knowledge. Thus, income and industrial employment grow faster in these regions. However, this relationship holds only after the modern technology has reached a sufficiently
high level of efficiency; in pre-industrial times, there is no association between upper tail knowledge and growth because modern manufacturing is too inefficient to compete with its traditional
counterpart.
The model predicts very different effects for average human capital. Because it is particularly useful in traditional manufacturing, high endowment with worker skills attracts employment
away from the other sectors. This effect is strong for agriculture (the least intensive in worker
skills), and it also holds to a limited degree for modern industrial production (with an intermediate
dependence on worker skills). This leads to the predictions that i) pre-industrial manufacturing
is predominantly located in areas with higher average worker skills; ii) this pattern prevails after industrialization; and iii) because high average skills reduce the share of modern industrial
production, they are (moderately) negatively associated with growth.
We collect a host of outcome variables to test the model predictions. First, using city size in
France between 1400 and 1850, we find that subscriber density was strongly associated with city
growth after 1750 – when French industrialization started – but not before that date. Figure 2 illustrates this finding, using a consistent set of cities that are observed over our full sample period. The
coefficient on subscriber density is almost six times larger after 1750 – it rises from 0.037 to 0.218,
and the difference is highly significant, with the two 95% confidence intervals not overlapping. In
contrast, 18th century literacy (proxying for average human capital) is negatively associated with
city growth during industrialization. We obtain the same results when using pre-1750 membership in scientific societies as an alternative proxy for upper tail knowledge. Exploiting the panel
dimension, we use city fixed effects to control for unobserved characteristics. This specification
2
confirms our results, while placebo treatments with cutoff dates prior to 1750 yield no effects of
subscriber density on city growth. Second, soldier height – proxying for income – at the French
department level was significantly higher in areas with more encyclopedia subscriptions in the
early 19th century; for soldier height before 1750, this relationship disappears. Literacy, on the
other hand, is strongly positively correlated with soldier height both before and after industrialization. Third, mid-19th century census data for almost 90 French departments reveal that those
with higher subscriber density had significantly higher disposable income, industrial wages, and
industrial employment. In addition, the wage effect is strongest within modern industries (such
as textiles), and it is muted for sectors that saw less rapid technological change before 1850, such
as food processing and agriculture. On the other hand, schooling goes hand-in-hand with higher
wages in all sectors, even in agriculture. Fourth, in the pre-industrial era, proto-industrial activity was higher in regions that had higher literacy as early as 1686, but it was not associated with
subscriber density. In sum, results from different sources and periods strongly support our model
predictions: Upper tail knowledge is strongly associated with growth during industrialization. Average human capital, in contrast, was associated with higher income and wage levels before and
after industrialization; but it cannot explain modern growth.
Our results can be interpreted in the context of Mokyr’s (2002; 2005b) seminal work on the
importance of upper tail knowledge for modern growth. Importantly, we do not argue that the
encyclopedia caused such knowledge at the local level. In fact, knowledge elites were present
prior to 1750, and their spatial distribution was stable over time – we show that early scientific
societies are typically found in the same locations that later saw high subscriber density.4 Our
argument is that subscriber density reflects this distribution of knowledge elites: a substantial share
of subscribers were progressively minded and scientifically interested noblemen, administrative
elites, and entrepreneurs.5 These elites, in turn, were crucially involved in industrial development.
While local upper tail knowledge has deeper historical roots (that go beyond the scope of this
paper), we show that it was only ‘activated’ after it became economically useful – with the advent
of modern innovative technology.
Further to the historical support for the mechanism, we also collect novel data to show that
knowledge elites were important for the adoption and diffusion of innovative industrial technol4
The spatial dispersion of scientific activity in early modern Europe is well-documented; it was driven by a variety
of factors, most of them unrelated to pre-industrial economic activity: Livingstone (2003, p.181) observes that "the
Scientific Revolution bore the stamp of ... local arenas of engagement. In some cases a maritime culture was the chief
engine power behind the cultivation of scientific pursuits; in some a courtly culture predominated; in others religious
conviction was the molding agent; in yet others economic ambitions provided both impetus and constraint."
5
In this dimension, our empirical analysis is similar in spirit to Voigtländer and Voth (2012), who take the spatial
pattern of anti-Semitism as given and use historical persecution of Jews to measure it. In a similar vain, we take the
spatial dispersion of scientific activity as given, and argue that subscriber density is a powerful proxy to capture it.
3
ogy.6 We use a survey of more than 14,000 French firms in 1837, which lists the sector of operation. Based on sector-specific British patent data, we split these firms into ‘modern’ (innovative)
and ‘old’ (traditional). As a first step, we confirm that the predominant share of technologies illustrated in the encyclopedia indeed belonged to ‘modern’ sectors. Next, we show that subscriber
density had a particularly strong effect on firm productivity in ‘modern’ sectors, even after controlling for sector fixed effects. This is in line with the mechanism that upper tail knowledge favored
the adoption and diffusion of innovative industrial technology.
By exploiting variation within a centralized state, our analysis is not subject to the typical
limitations of cross-country studies, such as differences in property rights or the rule of law.7 At
the local level – in addition to standard characteristics (e.g., proxies for trade or early book printing) – we control for a number of potential confounding factors. We find that the location of
proto-industrial centers is uncorrelated with subscriber density, so that pre-industrial development
cannot explain our results. Another concern is that the encyclopedia may merely have been a decorative item in the bookshelves of the nobility, who also used their financial resources for industrial
investment. Controlling for the local density of noble families does not influence our results. This
suggests that not the nobility in general, but – in line with historical accounts – a progressive scientifically savvy subset of the nobility, together with lower social classes, drove industrialization.
The finding that areas with a progressive elite developed faster might by confounded by the ‘Reign
of Terror’ in 1792-94, when alleged reactionary individuals were executed on a massive scale. We
geocode data on approximately 13,000 executions; our results are unchanged when controlling for
these. Finally, we control for total French book sales at the city level in the 18th century. While
these are strongly correlated with encyclopedia subscriber density, they do not affect growth.
Our paper is related to a large literature on the transition from stagnation to growth (for an
overview see Galor, 2011), and in particular to the role of human capital during industrialization. For England – the technological leader – the consensus view is that formal education did not
contribute to economic growth (Mokyr, 1990; Mitch, 1993; Crafts, 1996; Clark, 2005). For the
follower countries, the evidence is mixed. O’Rourke and Williamson (1995) and Taylor (1999)
6
Modern technology was often imported from Britain. For its implementation, French entrepreneurs would regularly seek advise from British technicians. In the early 19th century, 10,000-20,000 English artisans – many of them
mechanics – worked in France (Kelly, Mokyr, and Ó Grada, 2014). The same authors also point out that "the French
learned quickly, and as soon as local workmen had acquired the basic skills, the senior British operative became more
of a rarity." However, it is important to note that France did not merely adopt British technology. Instead, it became an
important source of innovations itself: "Technological progress became indigenous, built in to the economy, so that ...
France became at mid-century a centre of invention and diffusion for modern technologies" (Crouzet, 2003, p.234).
7
As Braudel (1982, p.323) documents, France was a strong centralized state, allowing for little local variation in
institutions. In addition, while 1789 brought a dramatic change in the political regime, our results hold for both the
1750-1800 and the 1800-1850 subperiods.
4
conclude from country-level cross-sectional and panel analyses that human capital was not a crucial driver of catch-up in the 19th century. In contrast, Becker, Hornung, and Woessmann (2011)
document that elementary education predicts employment in metals and other industries, but not
in textiles in 19C Prussia. This is in line with O’Rourke et al. (2008), who emphasize that industrial innovation in sectors such as textiles was initially unskill-biased, reducing the demand
for skilled workers.8 We shed new light on this debate by distinguishing between average and
upper-tail skills. Mokyr (2005b) underlines the role of the latter, arguing that the expansion and
accessibility of ‘useful knowledge’ during the period of Enlightenment was a cornerstone of industrial development. Mokyr and Voth (2009, p.35) conclude that "the Industrial Revolution was
carried not by the skills of the average or modal worker, but by the ingenuity and technical ability
of a minority." Kelly et al. (2014) also emphasize the importance of highly skilled, technically
capable individuals. In the contemporaneous context, Hanushek and Woessman (2012) show that
the share of cognitively high-performing students is strongly associated with growth, independent
of basic literacy. Our paper also relates to a literature showing that book production had a positive
impact on pre-industrial economic development (Baten and van Zanden, 2008; Dittmar, 2011).9
Our main explanatory variable, encyclopedia subscriber density, offers two important advantages
over printing locations or local book production. First, it is a more precise measure, identifying
readers within the narrow category of scientific publications.10 Second, subscriptions measure the
local demand for knowledge, rather than the supply of books from printing locations.
Relative to this literature, we make several contributions. First, we show that differentiating
between modern and traditional manufacturing, as well as between average and upper tail skills,
yields important novel predictions for the role of human capital in a workhorse model of unified
growth (Hansen and Prescott, 2002). Second, to the best of our knowledge this paper is the first to
empirically differentiate between average worker skills (literacy/schooling) and upper tail knowledge in the historical context of industrialization. To this effect, we introduce a novel proxy –
the local density of encyclopedia subscriptions. Third, we provide strong support for the model
predictions, using a variety of outcome variables such as city growth, soldier height, disposable
income, and local wages. We thus establish a striking novel fact: encyclopedia subscriptions are
strongly positively associated with growth and income after 1750, but not before the onset of in8
This led to the famous incident of the Luddites – skilled textile workers – wrecking modern machinery that was
operated by unskilled labor.
9
Cantoni and Yuchtman (2014) show that universities fostered commercial activity as early as in medieval times.
10
Total book production, in contrast, contains books from cooking manuals to religious works. For example, books
about natural science, math, and engineering account for less than 5% of overall book sales by the large publishing
house STN in the late 18th century, while 70% of all sales occurred in Belles lettres (e.g., romans and poetry), history,
and religion.
5
dustrialization. Fourth, we shed light on the channel, showing that subscriber density predicts
higher firm productivity in modern, innovative industries, but not in traditional sectors. Along this
dimension, our study is the first to provide systematic evidence for Mokyr’s (2005b) hypothesis on
the importance of the spread of ‘useful knowledge’ for industrialization. Finally, we show that –
in contrast to upper tail knowledge – basic education was related to economic performance both
before and after industrialization, but not to growth.11
The paper is organized as follows: Section 2 discusses the historical background of industrialization in France, as well as encyclopedia subscriptions as a proxy for the presence of knowledge
elites. Section 3 presents a model of industrialization that illustrates our main argument. In Section
4 we describe our data. Section 5 presents our empirical results. Section 6 concludes by discussing
the implications of our findings for the literature on human capital and development.
2
Historical Background: Industrialization and Upper Tail Knowledge
In this section, we discuss the role of upper tail knowledge during the French industrialization. We
also provide background on the Encyclopédie, and discuss why its subscriptions are a good proxy
for the local presence of knowledge elites.
2.1
Industrialization in France
Starting in the mid-18th century, France experienced significant economic growth; its industrial
output more than doubled during this century (Rostow, 1975), and machines were introduced in
the main industrial sectors – textiles and metallurgy (Ballot, 1923). By the mid-19th century,
France had become "a centre of invention and diffusion of modern technology" (Crouzet, 2003,
p.234).12
At the micro level, French firms were smaller than their English counterparts: most of them
were family owned; in 1865 they had 9.5 workers on average (Verley, 1985), and in 1901, 71
percent had no hired employees (Nye, 1987). However, the small size of French firms is not a signal
of inefficiency or lack of entrepreneurial skills, since they suited the economic and technological
conditions of the time and "would not have stood to gain much in efficiency by being larger" (Nye,
1987, p. 668). For example, firms would often not expand because the political environment made
11
The cross-sectional result is in line with Hornung (2014), who shows that the migration of skilled Huguenots to
Prussia had strong positive effects on the productivity of textile firms around 1800, i.e., before Prussia industrialized.
12
Figure A.1 in the appendix shows that GDP per capita was relatively stable until approximately 1750, and then
started to grow steadily. Timing the French industrial takeoff is difficult, as it lacks a clear structural break (Roehl,
1976); the predominant "moderate revisionist" view describes steady growth until the mid-19th century, only interrupted during the two decades after the revolution in 1789 (Crouzet, 2003). Growth was particularly strong between
1815 and the 1840s, then slowed down during a depression around 1875-95, which was followed by another period of
rapid growth (Belle époque). On average, French GDP per capita grew as fast as its British counterpart over the period
1820-1913, although French population grew at a markedly lower rate (Maddison, 2001).
6
investments risky. Overall, France had its own path to industrialization, which was different, but
not inferior to the British one (Crouzet, 2003).
Many French firms adopted new British technology (Milward and Saul, 1973). Even during the
Napoleonic wars, they were aware of the latest British inventions and discoveries. British knowhow reached France via several channels: First, scientific reports published by learned societies,
an intense correspondence among "industrially minded" people in the two countries, as well as
industrial spies sending regular reports on English technology drove the rapid transfer of industrial
knowledge (Mokyr, 2005b; Horn, 2006). Learned circles played a central role in the local spread of
this knowledge. Second, there were widespread initiatives to materially import English machines,
and third, thousands of British workers were hired by French producers with the specific aim to
gain access to technical knowledge (Horn, 2006). Finally, the state and provincial governments
also played an important role in French industrialization, by supporting scientific institutions, by
bringing together entrepreneurs and scientists, but also by fostering adoption of machines and
expertise from abroad. These policies were put into practice at the national and local level by a
commercial, industrial, and scientific elite (Chaussinand-Nogaret, 1985; Horn, 2006).
2.2
Enlightenment, Upper Tail Knowledge, and the Encyclopédie
The Enlightenment was a period of intellectual and cultural revolution in Western history, stretching from the late 17th throughout the 18th century, stressing the importance of reason and science,
as opposed to faith and tradition. A fundamental part was what Mokyr labeled ‘Industrial Enlightenment’, which "bridges between the Scientific and Industrial Revolution" (Mokyr, 2005a, p.22),
between the intellectual movement and economic growth.13 The link between enlightenment and
growth followed two steps: the expansion of propositional knowledge with practical applications,
and the reduction of access costs to existing knowledge – once established, knowledge had to be
disseminated as much as possible, in order to become economically useful. Despite its efforts to
popularize and spread knowledge, the Enlightenment never became a mass movement. It was confined to a small elite that, nevertheless, played a crucial role in fostering industrial development
and economic growth.
The Encyclopédie
In the culturally vibrant atmosphere of 18th century France, Diderot and d’Alembert launched the
ambitious project of the Great Encyclopédie – the "most paradigmatic Enlightenment triumph"
(Mokyr, 2005b, p.285). Following Lord Francis Bacon’s conceptual framework, Diderot’s objec13
The roots of the Industrial Enlightenment go back to the ideas of Lord Francis Bacon, who "aimed at expanding
the set of useful knowledge and applying natural philosophy to solve technological problems and bring about economic
growth" (Mokyr, 2005b, p.326).
7
tive was to classify all domains of human knowledge in one single source, easily accessible to
everybody. The focus was on knowledge derived from empirical observation, as opposed to superstition.14 While mercantilistic ideas were still widespread, and artisans and guilds kept secrecy
over knowledge, Diderot and the Philosophes believed that scientific knowledge should not be a
private good, confined to a few people, but disseminated as widely as possible (Mokyr, 2005a).
The Encyclopédie went through several editions and reprints. The government initially refused
to allow official sales, and most copies went to customers outside France. Correspondingly, the first
(the Encyclopédie by Diderot and d’Alembert) and the second (the Geneva reprint) editions sold
together only 3,000 copies in France. Moreover, the first two editions were luxury items that did not
penetrate far beyond the restricted circle of courtiers, salon lions, and progressive parlementaires.
This changed radically with the Quarto (1777-1780) and the Octavo editions (1778-1782).15 These
became wide-spread throughout the country and largely reached middle class budgets (Darnton,
1973). Since our proxy for local knowledge elites is based on subscriptions to the Quarto, we
discuss this edition in more detail.16
The Quarto edition
The Quarto edition of the Encyclopédie is particularly useful as a proxy for local knowledge elites
for several reasons. It represents the turning point when the Encyclopédie moved to a phase of
diffusion of Enlightenment on a massive scale. The Quarto was designed to be affordable for middle class readers. Correspondingly, its format was smaller than the luxurious Folio of Diderot, the
quality of the paper poorer, and the price lower. The publishers described their price discrimination strategy as follows: "The in-folio format will be for ‘grands seigneurs’ and libraries, while the
in-quarto will be within the reach of men of letters and interested readers [‘amateurs’] whose fortune is less considerable."17 This strategy proved extremely successful: the Quarto had the highest
14
The articles of the Encyclopédie often resulted provocative to authorities. For example, in the engraving of the
Tree of Knowledge, theology is considered a simple branch of Philosophy. This led to the imprisonment of the editor
and the publishers; the Pope condemned the Encyclopédie, and it was suppressed by a royal decree (Vogt, 1982).
15
The names Quarto and Octavo refer to the printing format: A Quarto sheet is folded twice, creating four leaves;
an Octavo is folded three times, creating eight leaves. The central figure behind the Quarto edition was the French
entrepreneur Charles-Joseph Panckoucke (1736-1798), who had bought the plates of the Encyclopédie from the original publishers, together with the rights to future editions. Administrative obstacles in selling the Quarto appear to
have been minor. While the Encyclopédie was officially illegal until 1789, the government had relaxed its censorship.
In addition, Panckoucke had good connections with the government, and did not hesitate to lobby and bribe public
officials (Vogt, 1982).
16
The Quarto edition comprised 36 volumes of text and 3 volumes of illustrative plates. Since these were typically
not delivered in one chunk, readers of the Encyclopédie are commonly referred to as ‘subscribers’.
17
Société Tipographique de Neuchâtel (the publisher) in a letter to Rudiger of Moscow, May 31, 1777; cited after
Darnton (1973, p.1349). The Quarto cost only one fifth of the first original folio. While unaffordable to lower social
classes and workers, the lower price put it well within the reach of the provincial middle class. According to Darnton’
(1973) calculations, a Quarto cost the equivalent of 10 weeks of income of a prosperous parish priest, and about 5
8
sales in France among all editions. The low cost of the Quarto rendered budget constraints less
important, so that scientific interest, rather than deep pockets, determined subscriptions.
Crucially for our study, one of the publishers, Duplain, secretly kept a list of subscriptions,
which survived in the archives of the Société Tipographique de Neuchâtel (STN). This list contains
the name of booksellers (but not subscribers), their city, and the number of sets they purchased for
retail among their local clients. Darnton (1973) provides this list, which comprises a total of
8,011 subscriptions, out of which 7,081 were sold in France – in 118 cities.18 The list contains
all subscriptions to the Quarto in France, so that we can safely assume that cities which are not
listed had zero subscribers. Subscriptions were not confined to major cities; instead, they were
distributed across the whole French territory (see Figure 1).
Subscriptions to the Quarto edition and local knowledge elites
Does a higher frequency of subscriptions at any one location reflect a broader interest in upper
tail knowledge?19 Characteristics of individual subscribers are key to shed light on this question.
While Duplain’s secret list does not allow for a systematic analysis because it only contains the
names of local booksellers, individual information exists in a few cases. For Besançon, a list
of individual subscribers has survived. Darnton (1973, p.1350) summarizes these by profession
and social status: 11% belonged to the first estate (clergy) and 39% to the second estate (nobility); the remaining one half of the 137 subscribers belonged to lower social ranks, including the
bourgeoisie.20 Professionals, merchants, and manufacturers account for 17% of the total (23 subscriptions). Thus, an important share of subscribers can be directly identified as economic agents
(from lower ranks) involved in the French industrialization. Their share is likely a lower bound for
the importance of the encyclopedia in the business community, because many subscribers in the
upper class (‘nobility’ – the largest single category) were also active businessmen (Horn, 2006).21
For example, Chaussinand-Nogaret (1985, p.87) argues that
Over a whole range of activities and enterprises nobles, either alone or in association
with members of the greater business bourgeoisie, showed their dynamism, their taste for
weeks of income of a provincial bourgeois living off his rents.
18
Lyon with 1,078 and Paris with 487 subscriptions are at the top of this list; at the opposite end of the spectrum,
there are 22 towns with fewer than 5 subscriptions.
19
For example, it is possible that wealthy people merely bought the encyclopedia to decorate their bookshelves.
However, according to Darnton (1973, p.1352), if anything, the opposite was probably the case: "far more people
must have read the Encyclopédie than owned it, as would be common in an era when books were liberally loaned and
when ‘cabinets litteraires’ were booming."
20
This may be a lower bound for other locations. The second estate is probably over-represented in this sample –
Besançon was a garrison town, and almost half of the subscriptions in this category went to noblemen in the army.
21
This reflects the revisionist view that replaced earlier – often Marxist – interpretations of the nobility exemplifying
an aristocratic tyranny of arrogance and decadence. An impartial reading of the historical account shows that "nobles
of the eighteenth century had been as modern and progressive as anyone." (Smith, 2006, p.2).
9
invention and innovation, and their ability as economic leaders: ... their ability to direct
capital..., to choose investments according to their productiveness and their modernity,
and ... to transmute the forms of production into an industrial revolution.
It is, however, important to note that only a progressive subset of the nobility was involved in industrial activities.22 The same subset was also heavily engaged in the Enlightenment (ChaussinandNogaret, 1985, p.73). This underlines Mokyr’s (2005b) view that upper tail knowledge and industrialization were closely entwined.
In addition, many subscriptions went to high public officials and parliamentarians – about 28%
in Besançon (Darnton, 1973, p.1350). Enlightened elites in the provincial administration were
often involved in fostering local industrialization. For example, in Rouen and Amiens, they established the Bureau of Encouragement that gathered businessmen, manufacturers, local savants,
and provincial authorities in an effort to assist technological advance (Horn, 2006, p.81). Finally,
the Encyclopédie – and especially the Quarto – also reached non-subscribers in the lower ranks
of society, via indirect access (Roche, 1998). Organized lectures, symposia, and public experiments were booming in France during the Enlightenment, and public readings of the Encyclopédie
were organized by scientific societies, libraries, lodges, and coffeehouses (Darnton, 1979; Mokyr,
2005b).23
One prominent example for the link between science and entrepreneurship in France is JeanAntoine Chaptal. Chaptal considered science to be inseparable from technology, and the key to
foster industrial development. He was well-connected in the French network of savants, which
entertained an intensive exchange with international scientists such as James Watt, and stimulated
the application of science to industry in France. Chaptal pursued this cause both as a public figure
and as a private entrepreneur. As a public official, he created favorable economic and bureaucratic conditions for entrepreneurs, for example by founding the Conseils d’Agriculture, des Arts
et Commerce, and the Société d’Encouragement pour l’Industrie National, where scientists, industrialists, and bureaucrats were brought together. He also subsidized promising artisans, engineers,
and industrialists, and gave pubic lectures on chemistry and experimental physics. As a private
22
As Chaussinand-Nogaret (1985, p.90) puts it: "In the economic sphere, ...it is clear that the whole of nobility was
not involved, but only the part that can be considered its natural elite, ... because of its ... openness to the progressive
tendencies of the age."
23
Certainly, reading or hearing about a new technology was not sufficient to be able to adopt and operate it. However,
scientific publications and lectures made technological know-how available on a large scale, breaking the exclusive
transmission from master to apprentice (Mokyr, 2005b). The details needed for actual adoption of new technologies
were then typically found elsewhere, such as embodied in ‘imported’ British experts. For example, Fox (1984, p.143)
describes the case of the British engineer Job Dixon, who came from Manchester to Cernay in southern Alsace in 1820
and joined the firm Risler frères. This firm had been founded only two years earlier as the first machine-builder in the
region. It subsequently became the main supplier of the latest spinning and weaving machinery for the region, serving
also as a training ground for French engineers.
10
entrepreneur, Chaptal built the largest factory for chemical products in France (Horn and Jacob,
1998). The example of Chaptal illustrates that the effect of knowledge elites on local industrial
development could go both via scientifically savvy public officials supporting entrepreneurship,
and via savants themselves operating businesses.24
In sum, the encyclopedia had a broad spectrum of readers from the knowledge elite that were
both directly and indirectly involved in industrialization. This supports both our use of subscriber
density as a proxy for local upper tail knowledge, and the hypothesis that this knowledge was
crucial in fostering economic growth.
3
Model
Our model features n = 1, ..., N regions with given land endowment. In each region, there is a
continuum of households i ∈ [0, 1] that are infinitely lived and supply labor inelastically. There
is no saving, so that all income is consumed in each period. In addition, we assume that households are immobile, operating within their region of origin. Thus, wages can differ across regions.
However, goods are traded so that output prices are equalized across regions. In any given period,
households optimally choose between working in three sectors: agriculture (A), traditional manufacturing (T ), and modern manufacturing (M ). We can thus think of households as firms. We
keep the model tractable by following Hansen and Prescott (2002) in assuming that agricultural
and (both) manufacturing goods are perfect substitutes.25
Each household’s choice between the three modes of production depends on two dimensions
of the region-specific knowledge distribution. First, average worker skills, hn , affect the efficiency
of production in each sector, but to varying degrees. The effect of hn on productivity is smallest
in agriculture. In addition, in line with the historical evidence (c.f. Clark, 2005; O’Rourke et al.,
2008), we assume that the effect of worker skills on productivity is stronger in traditional than modern manufacturing. Second, each household receives a draw of upper tail (or scientific) knowledge,
si , which affects the efficiency of operating modern manufacturing M . In other words, modern
manufacturing is relatively less intensive in average worker skills, but more intensive in upper tail
knowledge. This reflects Mokyr’s (2005a) observation that modern technology was "simpler to
use if more complex to build."26 To clearly distinguish between their effects on development, we
24
Another example is the Duke d’Orléans, who invested in several textile firms, adopting modern machinery from
Britain. He also introduced steam-engines into cotton spinning in France (Chaussinand-Nogaret, 1985), and set up a
soda-making facility together with chemist Nicolas Leblanc (1742-1806) (Horn, 2006).
25
Thus, our results are driven only by the supply side. Introducing demand-side factors – such as increasing relative
demand for manufacturing as income rises – would actually strengthen our results, by further raising income in regions
that industrialize more heavily.
26
Mokyr (2005a, p.45) describes this notion in more detail: "An economy that is growing technologically more
sophisticated and more productive may end up using techniques that are more difficult to invent and artefacts that are
11
model hn and si as independently distributed.27
All relevant cross-sectional predictions of our model can be derived in partial equilibrium,
taking the price of output as given and normalizing it to P = 1. We use the model to analyze
how average worker skills and upper tail knowledge affect the distribution of income and labor
shares across regions. The efficiency of modern manufacturing, AM , plays a central role in this
analysis. Following Hansen and Prescott (2002) and Kelly et al. (2014), we assume that AM grows
exogenously over time, and that this reflects the expansion of useful knowledge during the period
of Industrial Enlightenment.
3.1
Distribution of Scientific Knowledge
Worker skill levels hn vary across regions n. For simplicity, we assume that hn is the same for
all households within a given region.28 On the other hand, scientific knowledge varies across
households (firms) within regions, and the underlying distribution itself differs across regions. The
local density of scientific knowledge (i.e., the knowledge elite) is reflected by the thickness of
the upper tail in a Pareto distribution – and thus by a single region-specific parameter βn . The
probability that any given household i in region n draws scientific knowledge si above S is given
by
( )βn
1
Fn (si ≥ S) =
; βn ≥ 1
(1)
S
where S > 1 and βn > 1. Note that a smaller βn implies a thicker upper tail and thus a higher
probability of large si draws.
Given their human capital endowments hn and si , families optimally choose between the three
technologies. Next, we describe these technologies (or sectors) and how they differ in their dependence on the two types of human capital.
3.2
Production
At the beginning of a period, each household i chooses a single sector of production. Technology
in all sectors exhibits constant returns, so that the scale of production is not important.29 Each
household supplies one unit of labor. We denote total labor in sector j ∈ {A, T, M } in region n
by Lj,n . Because each region is populated by a mass one of households, sector-specific labor is
equivalent to the labor shares: Lj,n = lj,n , and labor market clearing implies lA,n + lT,n + lM,n = 1.
more complex in design and construction, but may be easier to actually use and run on the shop floor".
27
This also reflects the empirical evidence presented in Figure A.4 in the appendix, which shows that our historical
proxies for the two types of human capital, literacy and subscriber density, are uncorrelated across French regions.
28
Using randomly distributed worker skills within regions would not yield additional insights in our analysis.
29
Thus, we can assume without loss of generality that each household represents a firm. This also reflects the
historical evidence discussed in Section 2 that the majority of French firms in the 19th century were family-owned
(Nye, 1987).
12
Next, we characterize the production technologies used by the three sectors. In each of these,
worker skills hn enter production by augmenting the efficiency of labor, but with a varying degree
across sectors. Agricultural output in region n is given by
YA,n = AA hnγA LαA,n Xn1−α ,
(2)
where XN is land endowment in region n, and γA reflects the sensitivity of agricultural productivity with respect to worker skills.30 We assume that there are no property rights to land, so that
household income in agriculture is given by the average product yA,n = YA,n /LA,n :31
(
yA,n =
AA hγnA
Xn
LA,n
)1−α
(
=
AA hγnA
xn
lA,n
)1−α
,
(3)
where xn is land per household. Note that agricultural income increases if the agricultural labor
share lA,n declines, because this leaves more land for each remaining agricultural household in
region n. Thus, growth in manufacturing can indirectly raise wages in agriculture, by boosting the
land-labor ratio.
Traditional manufacturing is produced using only (skill-augmented) labor. The production
function is YT,n = AT hγnT LT,n , and correspondingly, household income is given by
yT,n = AT hγnT
(4)
where γT is the sensitivity of productivity in sector T with respect to worker skills. Finally, we turn
to modern manufacturing M . In this sector, productivity is affected by both region-specific worker
skills, hn , and the operating household’s draw of scientific knowledge, si . The latter is similar in
spirit to the entrepreneurial ability to adopt and run modern technology. We thus follow Gennaioli
et al. (2013) in assuming that both types of human capital enter the production function in a multiplicative fashion: YM,n (i) = AM s(i)hγnM LM,n (i). Thus, household income when operating M is
given by:
yM,n (i) = AM s(i)hγnM
(5)
where γM is the elasticity of productivity with respect to worker skills. Note that, in contrast to A
and T , income in modern manufacturing is heterogenous across households (even within regions),
30
In growth models and development accounting, h typically multiplies L directly, since it already reflects the
impact of schooling on productivity via Mincerian returns (c.f. Bils and Klenow, 2000). By using different γj for
j ∈ {A, T, M }, we allow these returns to vary across sectors, i.e., we allow for sector-specific returns to worker skills.
31
This assumption does not affect our results, but it simplifies the analysis. Alternatively, we could assume that land
for agriculture must be rented, and that households keep their wage income.
13
due to the different draws of s(i). AM in (5) reflects the state of modern technology. In this setup,
AM and s(i) are complementary: increasing AM raises the return to scientific knowledge. Thus, a
thick upper tail in the distribution Fn (s) affects the economy more strongly if AM is larger.32 This
is at the core of our mechanism.
The assumption that s(i) raises productivity in M reflects several possible mechanisms: first,
more scientifically savvy people are more likely to know about the existence of new technologies,
which reduces their search costs and raises the likelihood of adoption.33 Second, they can be expected to operate modern technology more efficiently because of a better understanding of the
underlying processes. Third, higher scientific knowledge makes further innovative improvements
more likely – this is in line with Mokyr’s (2005a) argument that technological progress often came
in the form of micro-inventions by implementation of broader technological concepts. Interpreting s(i) broadly as entrepreneurial ability reflects all three elements mentioned above.34 For our
argument, the exact mechanism by which scientific knowledge affects the productivity of modern
technology is not crucial.
Finally, we describe our assumptions for each sector’s sensitivity with respect to worker skills
and scientific knowledge. Given that pre-industrial manufacturing was typically performed by
skilled artisans, we assume that it was more skill-intensive than agriculture, γT > γA . In addition, consistent with the historical evidence that in the first phase of industrialization, industrial
technology used unskilled workers relatively more intensively, (O’Rourke et al., 2008), we assume γM < γT . When comparing all three sectors, we assume that agriculture was the least skill
intensive, so that γA < γM < γT . Finally, we implicitly assume that scientific knowledge affected productivity only in modern manufacturing – s(i) is present only in (5). While basic worker
skills mattered for agriculture and traditional manufacturing, these sectors saw substantially less
innovation during industrialization so that advanced knowledge arguably played a limited role.35
32
Note that, strictly speaking, AM itself is not the technological frontier because the Pareto distribution Fn (s)
delivers draws s(i) > 1. Instead, AM can be interpreted as the state of technology that adopters can build on. Savvy
entrepreneurs with high s(i) profit particularly strongly from high AM .
33
As Mokyr (2000, p.30) put it: "Of course I do not argue that one could learn a craft just from reading an encyclopedia article (though some of the articles in the Encyclopédie read much like cookbook entries). But ... once the
reader knew what was known, he or she could look for details elsewhere."
34
Note that at the regional level, our setup is also compatible with knowledge spillovers: high draws of s(i) are
more likely where the density of scientific knowledge is larger. Part of this effect could come from households without
a scientifically savvy member nevertheless having access to modern technology via other interested people in their
environment. Similarly, firms may profit from an ‘enlightened’ local administration that fosters industrialization, as
suggested by the historical evidence in Section 2.2.
35
This pattern is clearly borne out by innovations exhibited at world fairs: Among the 6,377 British exhibits in 1851,
very few belonged to traditional sectors: only 261 (or 4.1%) were agricultural machinery, and 140 (2.2%) belonged to
food processing. At the other end of the spectrum, modern sectors made up the large majority of innovations: textiles
alone accounted for more than 26%, and engines and scientific instruments for another 15% (Moser, 2012, Table 3).
14
3.3
The Choice of Technology
Each household chooses a sector j of production, so as to maximize income yj,n (i), j ∈ {A, T, M }.
In the following, we analyze how worker skills and scientific knowledge affect the sectoral allocation of labor within each regions n. This effect depends on the efficiency of modern manufacturing, AM . We examine two regimes: initially, AM is low, representing the pre-industrial period.
Subsequently, AM grows exogenously, reflecting technological improvements during the scientific
revolution and the early phases of industrialization.
Low efficiency of modern manufacturing
Low values of AM imply that modern manufacturing is unattractive, so that only households with
extremely high draws s(i) will employ this technology. The main sectors in the economy are thus
A and T . Within each region, labor mobility ensures that household income is equalized in these
two sectors, yA,n = yT,n . Using (3) and (4), this implies
(
lA,n =
AA γA −γT
h
AT n
1
) 1−α
tn
(6)
Thus, the agricultural employment share is higher in more land abundant regions, and it is lower
where workers have higher average skill levels hn (since γA < γT ).
Modern manufacturing is used by a household i if yM,n (i) ≥ max{yA,n , yT,n }. Since yA,n =
yT,n , we can use (4) and (5) to derive the threshold of scientific knowledge at which households in
region n are indifferent between A and M : s∗n = AAMT hγnT −γM . Using (1), the share of households
with scientific knowledge draws above this threshold (i.e., those using M ) is given by
(
lM,n =
AM γM −γT
h
AT n
)βn
(7)
The labor share in traditional manufacturing follows as lT,n = 1 − lA,n − lM,n . Finally, average household income in region n is given by the employment-weighted average across the three
sectors, yn = lA,n yA,n + lT,n yT,n + lM,n yM,n , where yM,n is the average income of all households
using modern manufacturing in region n.36
When AM rises, modern manufacturing is adopted by an increasing fraction of households,
thus attracting more workers. These are initially withdrawn from traditional manufacturing.37 For
36
This follows from the aggregate
from modern manufacturing in region n, divided by the mass of house∫ ∞ income
1
γM βn
holds using M : yM,n = lM,n
A
sh
M
∗
β
n s n +1 ds, where the last term is the pdf of s in a Pareto distribution with
sn
(
) β1
1
n
1
n
AM hγnM lM,n
.
parameter βn . Solving the integral and using s∗n = (1/lM,n ) βn yields yM,n = βnβ−1
37
The intuition is as follows: As long as T operates, yA,n = yT,n must hold. Suppose that M attracted workers from
15
sufficiently high values of AM , traditional manufacturing disappears, while agriculture continues
to be operated. We analyze this case next.
High efficiency of modern manufacturing
When T is not operated anymore, a household i chooses M if yM,n (i) ≥ yA,n . Using (3) and (5),
this implies
(
)1−α
tn
γM
γA
(8)
AM s(i)hn ≥ AA hn
lA,n
Let sen denote the threshold level of scientific knowledge at which households are indifferent besn )βn . Consetween A and M . Using this in (1) determines the labor share in M : lM,n = (1/e
quently, focusing on the household at the margin where (8) holds with equality, we can substitute
1
s(i) = sen = (1/lM,n ) βn . In addition, since lT,n = 0, we can substitute lA,n = 1 − lM,n in (8). This
yields:
(
)1−α
1
AM γM −γA 1 − lM,n
(9)
hn
= (lM,n ) βn
AA
tn
∗
∗
∗
This equation has a unique solution lM,n
.38 As for the remaining variables, lA,n
= 1 − lM,n
follows
immediately, and average household income in region n is given by yn = lA,n yA,n + lM,n yM,n ,
where average income from modern manufacturing in region n, yM,n , is computed as described
in footnote 36. To differentiate between the two cases where T does (or not) operate in region
bM,n , such that below this level, (7) applies, and
n, Appendix A.1 derives the threshold level A
otherwise, (9).
3.4
Model Predictions
We now summarize the predictions of our model for manufacturing employment shares as well
as household income in a cross-section of regions with different endowments of worker skills,
and different sizes of the knowledge elite (represented by the thickness of the upper tail in Fn ).
We define the "knowledge elite" as those households with scientific knowledge draws s ≥ 10,
i.e., ten times larger than the mode of the underlying Pareto distribution Fn .39 We derive four
propositions. The first two analyze the cross-sectional effect of knowledge elites for the cases of
A. Then lA,n would decline, and according to (3), yA would increase, while yT,n in (4) would remain unchanged.
Thus, households would immediately stop operating T . Instead, rising AM leads to a slow decline of lT,n , but
(initially) not of lA,n .
38
The left-hand-side (LHS) of (9) is strictly decreasing in lM,n , while the right-hand-side (RHS) is strictly increasing. In addition, as lM,n converges to zero from above, the LHS converges to a positive number, while the RHS
converges to zero. Thus, there is a unique solution where the RHS cuts the LHS from below.
39
Alternative cutoffs do not change our qualitative results. Note that higher βn in (1) imply smaller elites. Our empirical proxy for the knowledge elite – encyclopedia subscriber density – varies between 0 and 15 per 1,000 inhabitants
(see Figure A.3). Using βn ∈ [1.8, 6] replicates this range in our model.
16
relatively low AM (before the Industrial Revolution), and for high AM (the industrial period). The
third proposition analyzes the role of worker skills, and the fourth focuses on the predictions for
growth. We discuss the intuition behind each proposition in the text; formal proofs are provided in
Appendix A.
Proposition 1. Low AM : If modern technology M is still inefficient (low AM ), then labor shares
in manufacturing and average income in a region are not (or at best weakly) affected by the size
of the local knowledge elite.
Proof: See Appendix A.2.
Figure 3 provides an illustration of Proposition 1. Under reasonable parameter choices, neither
labor shares nor income are affected by the percentage of the population belonging to the knowledge elite.40 Intuitively, if AM is low, only households (firms) with extremely high draws s(i)
would find it profitable to operate M . However, these represent a minuscule share in the far upper
tail of the distribution Fn . In other words, even within the knowledge elite essentially nobody finds
it worthwhile to adopt M . The steam engine may serve as an illustration. While early designs date
back to the 17th century, they were far from being economically viable (low AM ). A continuous
stream of inventions and improvements throughout the 18th century raised AM and eventually led
to the widespread use of the steam engine, beginning in the late 18th century. The next proposition
refers to this period, when the frontier of modern technology had expanded substantially, making
it competitive with traditional manufacturing.
Proposition 2. High AM : After the frontier in modern manufacturing moved out (AM became
high), a larger local knowledge elite is associated with:
(a) higher labor shares in modern manufacturing (lM,n ), and, for sufficiently high AM , higher
employment shares in manufacturing overall (combined T and M )
(b) higher wages in modern manufacturing (yM,n ), but not in traditional manufacturing (yT,n )
(c) higher regional income yn , in part due to a second-order positive effect on income in agriculture
Proof: See Appendix A.3.
Intuitively, the larger AM the lower is the threshold sen for adopting M . Therefore, with a high
state of modern technology AM , a notable fraction of households will be above the threshold. This
fraction (lM,n ) is higher in regions with a larger knowledge elite. This is illustrated in Figure 4. In
regions with a small elite, few firms adopt M , and traditional manufacturing T still operates. For a
40
We choose γA = 0.3, γM = 0.5, and γT = 0.7, as well as a labor share in agriculture α = 0.6. We normalize
land and population to 1 and choose the efficiencies AA and AT such that the labor share in (overall) manufacturing
is 12% – this corresponds to the urbanization rate in France in 1700 (total city population from Bairoch, Batou, and
Chèvre (1988) divided by French population from McEvedy and Jones (1978)). In Figures 3 and 4, we keep average
worker skills constant at hn = 1.1 and use the range of βn ∈ [1.8, 6] explained in footnote 39.
17
larger elite (thicker knowledge upper tail), modern manufacturing crowds out its traditional counterpart, and it also attracts labor from agriculture. This has a positive indirect effect on agricultural
wages, because withdrawing workers from A raises the land-labor ratio. Finally, a thicker upper
tail does not affect yT , but it raises the income of the average household that operates M , yM,n .41
Altogether, average incomes are therefore higher in regions with a larger knowledge elite. Next,
we analyze the effect of worker skills on income and employment.
Proposition 3. Effect of worker skills: Higher average worker skills hn in region n lead to:
(a) higher overall employment shares in manufacturing (combined T and M )
(b) (slightly) lower employment in modern manufacturing
(c) higher regional income yn , and in particular, higher income in all sectors A, T , and M .
Proof: See Appendix A.4.
Figure 5 illustrates these predictions.42 Worker skills are most important in traditional manufacturing. Thus, higher hn leads to a concentration of employment in this sector, crowding out both
agriculture and modern manufacturing. This effect is strong for A, but weak for M , because the
latter is more similar to traditional manufacturing in its dependence on worker skills.43 However,
the effect of larger hn on overall manufacturing employment is unambiguously positive. Also,
because production in all sectors is positively affected by hn , an increase in worker skills raises
incomes across the board.
Finally, we analyze the effect of an expansion of the scientific frontier, which is reflected by
exogenously growing efficiency in modern manufacturing AM . The following proposition shows
that, despite production knowledge being non-rival and available to all regions, it has differential
effects on economic growth.
Proposition 4. Suppose that AM grows exogenously. Then:
(a) Initially, when AM is still small, growing AM will not (or to a very limited extent) translate
into economic growth.
(b) For sufficiently large levels (AM /AT >> 0), further advances in AM will lead to growth in
manufacturing employment and regional income yn
41
This is due to two effects: a thicker upper tail implies that there are more households above the threshold, and
these also have a larger average distance to the threshold (conditional on having passed it).
42
We use the parameters described in footnote 40, but now keep βn constant at 3. The range hn ∈ (1.0, 1.3) on the
horizontal axis is chosen as follows: the average return to schooling in a cross-section of countries is about 0.1 (Bils and
Klenow, 2000). In our sample, literacy in 1686 varies between 0 and 60% across French departments, and we assume
that literacy is equivalent to 5 years of schooling. With this, we obtain an upper bound of exp(0.1 · 0.6 · 5) ≈ 1.3, and
a lower bound of exp(0) = 1. Changing these values does not affect our qualitative results.
43
The closer γM is to γT , the weaker is the (negative) relationship between hn and lM,n – see equations (7) and (9).
If we chose γM = γT , hn does not affect lM,n .
18
(c) Growth is faster where the knowledge elite is larger; however, average worker skills hn have
a (small) negative effect on growth.
Proof: See Appendix A.5.
The right panels of Figures 3-5 illustrate this proposition, showing the effect of a 5% increase
in AM on average household income. The basic mechanism works as follows: higher aggregate
efficiency in modern manufacturing is particularly useful to regions that adopt more of this technology, i.e., those with a larger knowledge elite. However, this growth effect only sets in after AM is
sufficiently high to make modern manufacturing economically viable for elite entrepreneurs. This
explains why knowledge elites do not affect growth when AM is low (Figure 3), while they have a
strong impact when AM is larger (Figure 4). On the other hand, higher average worker skills favor
traditional manufacturing, binding employment in a sector that does not profit from the expanding
technological frontier. Consequently, growth is slowed down in regions with higher hn .44
Summing up, as compared to the previous theoretical literature, our model provides a more
differentiated view on the role of human capital during industrialization. Distinguishing between
worker skills and upper tail (scientific) knowledge allows us to derive predictions that differ importantly for the two types of human capital. To test the model’s predictions, we collect a rich dataset
covering industrialization in France.
4
Data
In this section we describe our data, beginning with our main variable – encyclopedia subscriber
density. We then turn to a set of city-level and regional variables that reflect French development before, during, and after industrialization. We also analyze whether subscriber density varies
systematically with other local characteristics.
4.1
Main Explanatory Variables: Subscriptions and Scientific Societies
Our main dataset consists of 193 French cities for which Bairoch et al. (1988) report population in
1750. Among these, 85 have recorded subscriptions to the encyclopedia.45 According to Duplain’s
list (see Section 2), there were 7,081 subscriptions to the Encyclopédie’s Quarto Edition in France.
Altogether, we match 6,944 subscriptions (or 98% of the total) to the cities in our sample. Since
Duplain’s list covers the universe of subscriptions, we can safely assume that the 108 cities covered
by Bairoch et al. (1988), but not mentioned in Duplain’s list, had no subscribers. In the following,
44
As shown in the right panel of Figure 5, the negative growth effect of hn is quantitatively small, because the
(negative) effect of hn on employment in M is also minor.
45
In total there are 118 cities with subscriptions to the Encyclopédie listed in Darnton (1973); 12 of these are not
reported in Bairoch et al. (1988), and the remaining 21 can be matched to Bairoch et al. (1988), but population data
are not available in 1750.
19
we use Subsn to denote overall subscribers in city n. Since all subscriptions to the Quarto in
Duplain’s list were sold at the same price, including shipment (Darnton, 1979, p.264), we arguably
measure local demand rather than supply. This differentiates our empirical approach from previous
studies that build on local printing activity (c.f. Baten and van Zanden, 2008; Dittmar, 2011).
Because larger cities will mechanically tend to have more subscribers, we normalize subscriptions by population in 1750. Subscriptions per capita (among cities with above-zero entries) varied substantially, from 0.5 per 1,000 in Strasbourg to 16.3 in Valence; Paris belonged
to the lower tercile of this distribution, with 0.85 subscriptions per 1,000. To reduce the influence of extreme values, we use log-subscriber density as our baseline variable: lnSubDensn =
ln(Subsn /pop1750
+ 1), where pop1750
is city population in 1750.46
n
n
We also use an earlier indicator for upper tail knowledge: a dummy for cities hosting a scientific society before 1750, as well as the number of ordinary members of scientific societies at
the city level. These data are from McClellan (1985). To proxy for average worker skills, we use
department-level data on male literacy in 1686 and 1786 from Furet and Ozouf (1977). Literacy
rates reflect the percentage of men able to sign their wedding certificate. For 1837, departmentlevel schooling data are available from Murphy (2010), computed as the ratio of students to schoolage population (5 to 15 years).
4.2
Outcome Variables
Our first outcome variable is city growth – a widely used proxy for economic development (Acemoglu, Johnson, and Robinson, 2005; Dittmar, 2011). We use the panel of city population from
Bairoch et al. (1988), which includes cities that reached (at least once) 5,000 inhabitants between
1000 and 1800. Bairoch et al. (1988) report city size for every 100 years until 1700, and for every
50 years thereafter until 1850.
To test the model predictions on income levels, we need a proxy that is observed before and
after industrialization. Following a rich literature in economic history, we use soldier height (c.f.
Steckel, 1983; Brinkman, Drukker, and Slot, 1988; Komlos and Baten, 1998).47 We obtain conscript height before 1750 at the department level from almost 30,000 individual records collected
by Komlos (2005). These reflect conscriptions over the first half of the 18th century. We filter
out time- and age-specific patterns in recruitment by using the residual variation in height after
controlling for decade fixed effects and conscript age – see Appendix B.3 for more detail. As our
46
Adding the number 1 ensures that the measure is also defined for cities with zero subscriptions. Appendix B.2
provides further detail and distribution plots.
47
Cross-sectional analyses typically document a strong positive correlation between height and per capita income
(see Steckel, 2008, for a recent survey of the literature and empirical evidence). In longitudinal studies, the relationship
is less clear, since it can also be affected by income inequality, volatility, and food prices (Komlos, 1998). We thus
only exploit the variation in height across French regions, but not within regions over time.
20
first proxy for income after industrialization, we use department level soldier height from Aron,
Dumont, and Le Roy Ladurie (1972) for the period 1819-1826. In addition, we use disposable
income in 1864 from Delefortrie and Morice (1959).
Next, we use wages in industry and agriculture (measured in 1852) from Goreaux (1956),
employment shares in industry and agriculture (in 1876) from Service de la Statistique Général de
France (1878), and wages in the textile and the food industry (in 1864) from Delefortrie and Morice
(1959). Finally, we perform a detailed within-sector analysis, using local wages and value added
per worker as a proxy for productivity. The underlying data are from Chanut, Heffer, Mairesse,
and Postel-Vinay (2000), who cleaned and digitalized a survey of 14,238 firms over the period
1839-1847. The data were collected by the Statistique de la France at the arrondissement (county)
level, and categorize firms into 16 industrial sectors.
4.3
Control Variables
In the following, we briefly describe our control variables. Appendix B.4 provides more detailed
descriptions and sources. Our baseline set of controls includes various geographic characteristics
of cities, such as dummies for cities with ports on the Atlantic Ocean and on the Mediterranean
Sea, as well as for cities located on navigable rivers. Following Dittmar (2011), we also include a
dummy for cities that had a university before 1750, a printing press between 1450 and 1500, and
the log number of editions printed before 1501. To control for cultural and language differences,
we construct a dummy for cities located in non-French speaking departments.
We also control for a number of potential confounding factors: First, encyclopedia subscriptions may have been high where book sales in general were high – thus not reflecting upper tail
knowledge but a broader interest in reading. We obtain book sales to each French city over the
period 1769-1794 from the FBTEE (2012) project that reconstructed the book trade of the important Swiss publishing house STN (which also published the Encyclopédie). This source covers the
sales of more than 400,000 copies, for which the location of the buyer (private or book stores, with
the vast majority located in France) is available.48 A second potential concern is that the nobility
may have both purchased a disproportionate amount of encyclopedias and funded industrialization.
We obtain the number of noble families in each department from the Almanach de Saxe Gotha, the
most important classification of European royalty and nobility. Altogether, our sample contains
more than 1,000 noble families in 1750, in 88 French departments. Third, encyclopedia sales may
have been high where industrialization was already on the way in the mid-17th century. To account
for this possibility, we use data on proto-industrialization in France, as used by Abramson and Boix
48
This data is available at http://chop.leeds.ac.uk/stn/. The STN book sales are directly comparable to our data on
encyclopedia subscriptions: both occurred during the same period, are shipped from one place (Switzerland and Lyon)
towards all of France, and both reflect local demand for books (sales) rather than supply (printing).
21
(2013). These allow us to compute the number of mines, forges, iron trading locations, and textile
manufactures prior to 1500 for each department. Finally, another potential concern is related to the
‘Reign of Terror’ in 1792-94, when alleged counter-revolutionaries were murdered on a massive
scale. If these were concentrated in less progressive areas (i.e., with low subscriptions), executions
may explain slower economic progress there. To control for this possibility, we process data on
approximately 13,000 executions, assigning them to each department.
In order to guarantee consistency with our main explanatory variable, we calculate the local
density of scientific society members, total book sales, noble families in 1750, proto-industrial
locations, and executions during the ‘Reign of Terror’ in the same way as for subscriptions:
ln(1 + x/popn,1750 ). Finally, Appendix B.5 describes how we aggregate city-level variables to
the arrondissement and department level.
4.4
Balancedness
Do other town characteristics vary systematically with encyclopedia subscriptions? In Table 1 we
regress our main explanatory variable lnSubDens on a variety of controls (one-by-one, including
a dummy for Paris). We begin with our baseline controls in the first two columns. Column 1 uses
all cities, while column 2 uses only those with above-zero subscriptions. Few control variables
show a consistent pattern. City size is significantly positively correlated with lnSubDens in col
1, but significantly negatively in col 2.49 Seaports are essentially uncorrelated with subscriber
density. The coefficient on navigable rivers is significant in col 1, but switches signs and becomes
insignificant in col 2. The correlation with university and printing press dummies, as well as with
books printed before 1500, are all positive (and significant in col 1), as one should expect since
they reflect local advanced knowledge. In addition, cities in non-French speaking areas have a
smaller proportion of subscribers, which is also to be expected given that the encyclopedia was
published in French.50
Next, columns 3-5 in Table 1 report the coefficients of regressing subscriber density on our
proxies for average worker skills, as well as a variety of potentially confounding variables.51 Literacy rates in both 1686 and 1786, as well as schooling rates in 1837, are not significantly correlated
with subscriber density in any specification. On the other hand, overall (STN) book purchases are
49
Below, we confirm that our results hold in both the full sample and the Subsn > 0 sample. Note that this
implicitly addresses potential unobserved factors that are associated with city size, since these should also change the
way in which they affect our results between the two samples.
50
There were a number of regions in 18C France that spoke different languages such as Alsacien and Basque. The
corresponding six departments comprise 24 cities in our sample, out of which 6 had above-zero subscriptions.
51
Since most of these variables are observed at the department level, we compute subscriber density at the department level in the same way as for cities (see Appendix B.5). The department-level data comprise 88 observations
in the cross-section, and 66 out of these had above-zero subscriptions. Col 3 reports coefficients for all departments
without controls, col 4 adds controls, and col 5 restricts the sample to departments with above-zero subscriptions.
22
strongly and positively associated with encyclopedia subscriptions. This suggests that locations
with a greater interest in reading also host more people with scientific interests. In other words, the
upper tail of the knowledge distribution is thicker where the distribution in general is shifted to the
right. In our empirical analysis below we show that subscriber density strongly dominates general
book purchases as an explanatory variable for development. Next, the correlation between subscriber density and proto-industrialization is small and insignificant. This makes reverse causality
unlikely – the concern that encyclopedia sales may have been higher in areas with early industrial
activity. The local density of noble families is positively associated with subscriber density, with
a significant coefficient in col 5. This is in line with nobles being prominent subscribers to the
encyclopedia. Finally, the density of ‘counter-revolutionaries’ killed during the Reign of Terror
is positively, but not significantly correlated with subscriber density. Appendix B.6 provides further tests of balancedness, comparing cities with and without subscriptions, as well as those with
above- and below-median subscriber density. These additional tests confirm the pattern described
above: the few city characteristics that vary systematically with subscriber density are those that
one should expect if subscriptions reflect the size of the local knowledge elite.
5
Empirical Results
In this section we present our empirical results. We test the predictions of the model by estimating
equations of the form
yn = β · Sn + γhn + δXn + εn ,
(10)
where Sn denotes different measures for the size of the knowledge elite in location n, i.e., the
density of encyclopedia subscribers and members of scientific societies; hn denotes proxies for
average human capital, such as literacy and schooling; Xn is a vector of control variables, and εn
represents the error term. We use a variety of outcome variables yn , depending on the proposition
that we analyze. Propositions 1-4 imply the following expected coefficients: First, when using city
growth as an outcome, we expect β = 0 and γ = 0 prior to 1750, as well as β > 0 and a small
negative γ after 1750. Second, for income proxies such as soldier height, we again expect β = 0
prior to 1750 and β > 0 afterwards, but γ > 0 in both periods. Third, the same is true when
we use measures for manufacturing activity (e.g., wages and employment) – with an additional
dimension: after 1750, we only expect β > 0 for modern manufacturing, but β = 0 for traditional
manufacturing.
5.1
City Growth
City growth is a widely used proxy for economic development (DeLong and Shleifer, 1993; Acemoglu et al., 2005; Dittmar, 2011). Following this approach, we use the outcome variable gpopnt ,
23
the log difference in city population between two periods t and t − 1 (mostly 100-year intervals).
Figure 6 shows the distribution of gpopnt for different subscriber densities. During industrialization in France, cities with encyclopedia subscriptions grew substantially faster: the city growth
distribution is markedly shifted to the right. In addition, this shift is more pronounced for cities
with above-median subscriptions per capita. In the following, we analyze this pattern in more
detail and account for possible confounding factors, such as initial city size.
Cities with vs. without Subscriptions: Matching Estimation
Between 1750 and 1850, cities with above-zero subscriptions to the encyclopedia grew at approximately double the rate as compared to those without subscribers (0.51 vs. 0.26 log points).
However, merely comparing the two subsets is problematic, because larger cities are more likely
to have at least one subscriber. As a first pass at this issue, we use propensity score matching,
comparing cities with and without subscriptions of very similar size. The results are reported in
Table 2.52 Panel A shows results for a variety of specifications for the period 1750-1850. In col
1, we use the full sample and match by initial population; col 2 excludes the 10% smallest and
largest cities in 1750. In cols 3 and 4 we introduce geographic latitude and longitude as additional
matching variables.53 We thus compare nearby cities with similar population size, accounting for
regional unobservables. The results are stable throughout: French cities with subscriptions grew
approximately 0.15 log points faster than those of comparable size without subscriptions. This
difference is statistically and economically significant: average city growth over this period was
0.37 log points.
In Panel B of Table 2, we repeat the analysis for the pre-industrial period. The difference in
growth is now substantially smaller and statistically insignificant, and for 1400-1500, the coefficient is even negative and significant. The matching results support our model prediction that a
knowledge elite – as proxied by encyclopedia subscriptions – fostered economic growth during,
but not before industrialization.
52
Following Abadie, Drukker, Herr, and Imbens (2004), we use the three nearest neighbors. Our results are robust
to alternative numbers of neighbors. We define ‘treatment’ as cities with above-zero subscriptions and report average
treatment of the treated (ATT) effects. Finally, we exclude the top and bottom 1-percentile of city growth rates for each
respective period. This avoids that extreme outliers due to population changes of very small cities (for example, from
1,000 to 4,000 or vice-versa) drive our results. In the OLS analysis below, we address this issue by using population
weights; in propensity score matching, weighting by city size is not feasible.
53
The average population difference between matched cities with and without subscriptions is 6,500 inhabitants in
col 1, and 500 inhabitants in col 2. When matching by geographic location (col 3), matched cities are on average less
than 30 miles apart.
24
Subscriber Density
We now turn to our main explanatory variable, subscriber density lnSubDensn , using OLS regressions. This offers several advantages over the previous matching exercise: it exploits the full
variation in subscriber density (instead of only a dummy), we can examine the coefficient on controls to see how they affect city growth, and we can use population-based weights to reduce the
noise in growth rates due to population changes in small cities – there is more reliable information
in a city growing from 100,000 to 200,000 inhabitants, than in one growing from 1,000 to 2,000.
Table 3, Panel A, presents our main OLS results for city growth. Subscriber density is strongly
and positively associated with city growth over the period 1750-1850 (columns 1-4). Atlantic and
Mediterranean ports have a similar effect; the former is in line with Acemoglu et al. (2005). The
negative coefficient on initial population (after controlling for other characteristics) provides some
evidence for conditional convergence (Barro and Sala-i-Martin, 1992). We also include a dummy
for Paris – the capital and by far the largest city. In addition, we control for the effect of local
languages: because the Encyclopédie was written in French, subscriptions were lower in areas
with different languages: 0.50 per 1,000 inhabitants, versus 1.83 in French-speaking cities. Both
Paris and cities in non-French speaking areas grew faster than average between 1750 and 1850.54
Finally, we include a dummy for cities that had a printing press in 1500 and control for the log
number of editions printed by that date. This replicates the specification in Dittmar (2011), who
shows that an early adoption of printing had strong positive effects on long-term city growth in
Europe. Within France, the pattern is less robust – the dummy for cities hosting a printing press
changes signs depending on the period. In sum, including a rich set of control variables does
not affect the size or statistical significance of the coefficient on lnSubDens. A one standard
deviation increase in subscriber density is associated with a city growth rate that is higher by 8.216.9 percentage points (0.17-0.35 standard deviations).
Columns 5-8 repeat the analysis for the pre-industrial period. The coefficients on lnSubDens
are now small and insignificant. In addition, where sample size and specification are comparable,
the difference between the post- and pre-1750 coefficients is strongly significant: comparing the
coefficients on lnSubDens in cols 3 and 5, the 95% confidence interval does not overlap. In Panel
B of Table 3 we repeat all city growth regressions for the subset of cities with above-zero subscriptions. The results closely resemble those of Panel A: subscriber density is strongly and significantly
associated with city growth after 1750, but not before – in fact, in Panel B the coefficients for the
two periods prior to industrialization are now negative (and insignificant).
54
The latter is mostly due to 6 Alsacien-speaking cities in the Rhine area. This area saw rapid growth during the
industrialization. When including a separate Rhine-area dummy, the coefficient of non-French speaking falls to less
than one half its original size, while the coefficient on lnSubDensn is unchanged.
25
Literacy and additional controls
We now turn to the role of average worker skills (measured by literacy) for city growth. Table
4 reports the results; it also introduces additional controls – the potential confounding factors
discussed in Section 4.3.55 The coefficients on literacy are negative throughout, and statistically
significant in some specifications. According to the point estimates, the effect of literacy is also
quantitatively small: a one standard deviation increase in literacy is associated with a decline in
city growth by 0.11 standard deviations; for lnSubDens, the point estimates imply an increase by
approximately 0.33 standard deviations.
Among the additional controls, only proto-industrialization is positively and significantly associated with city growth (col 2 in Table 4). This confirms the findings in Abramson and Boix
(2013). Nevertheless, controlling for proto-industrial activity does not alter the coefficient on subscriber density – in fact, the two measures are (weakly) negatively correlated (see Table 1). The
standardized beta coefficient on proto-industrial activity is 0.17, and thus half the size of the subscriber density effect. Nobility density is not associated with city growth (col 3). This is in line
with the historical evidence discussed in Section 2.2 that only a progressive subset of the nobility
was involved in industrial activity. Next, while overall book purchases are strongly correlated with
lnSubDens (see Table 1), they do not affect city growth (col 4). The coefficient on executions
during the reign of terror (cols 5) is also small and insignificant. Finally, column 6 includes all
potential confounding factors together, confirming the previous results. Importantly, in all regressions the coefficients on lnSubDens remain highly significant and quantitatively similar to our
baseline results in Table 3. In sum, the results in Table 4 strongly support our model predictions
that there is a crucial difference between average worker skills and upper tail knowledge.
Alternative specifications
So far, we have shown that the association between subscriber density and city growth was strong
after 1750, and weak before that date. Next, we analyze systematically whether the observed increase in the coefficient was statistically significant. Table 5 replicates the specification from Nunn
and Qian (2011), using log population as dependent variable in a panel setting. This specification
also includes city fixed effects, absorbing all unobserved city characteristics that do not vary over
time. We find that the interaction of lnSubDens with a post-1750 dummy is highly significant and
positive, with a magnitude that is very similar to the previous growth regressions. This finding is
robust to including interactions of the post-1750 dummy with our baseline controls (col 2), as well
as with our additional controls (col 3). The baseline result also holds in a balanced sample that
55
Standard errors are now clustered at the department level, since this is the geographical unit at which literacy and
most confounding factors are observed.
26
only includes cities where population is observed in every sample year between 1500 and 1850
(col 4). Finally, columns 5 and 6 report the results for placebo ‘treatments’, when moving the
industrialization cutoff to 1600 or 1700. Both yield small and insignificant coefficients.
In Table 6 we show that our results also hold for the two sub-periods 1750-1800 (col 1) and
1800-1850 (col 2). The coefficients on lnSubDens are highly significant and of a similar magnitude.56 Splitting the period of French industrialization into pre- and post-1800 also allows for
an additional check: in col 3, we control for growth in the earlier period. This variable should
capture unobserved (and persistent) drivers of city growth. Including it does not change the coefficient on lnSubDens in the later period. This further alleviates the concern that unobserved city
characteristics may be behind our results.
We perform a number of additional checks in Appendix B.7. First, we run our city growth
regressions for the period 1750-1850, using four different sub-samples, each including those cities
for which population data are available in the year 1400, 1500, 1600, and 1700 respectively. The
coefficients on lnSubDens are very similar to our baseline results and are always significant at the
1% level. Next, our results are almost identical when pooling growth over the period 1400-1750
and comparing it with growth in 1750-1850 (as visualized in Figure 2). In addition, we use two
alternative measures of subscriber density: one that is not log-based, and another one that allows
for variation in subscriber density across cities without subscription, assigning lower densities to
larger cities. All our results continue to hold. Finally, in line with the model predictions, we find
no clear relationship between early literacy (recorded in 1686) and city growth prior to 1750.
Scientific societies
Since data on encyclopedia subscriptions are from the second half of the 18th century (1777-80),
local adoption of industrial technology may already have been on the way when subscriptions
occurred. Thus, reverse causality is a concern, because successful early industrial entrepreneurs
may have been among the subscribers. In the following, we address this issue by using an earlier
proxy for knowledge elites: the presence and membership of scientific societies in each city before
1750 from McClellan (1985). Altogether, there are 22 cities with scientific societies founded
before 1750. This alternative variable is strongly associated with our main measure: cities hosting
a scientific society were over three times more likely to also have encyclopedia subscriptions, and
they had almost four times more subscribers per capita (see Table A.6 in the Appendix).
Table 7 repeats our city growth regressions, using early scientific societies as explanatory vari56
The raw correlation of city growth over the two periods is small: 0.07. One possible explanation is a structural
break after the French Revolution. Nevertheless, this did not change the way in which knowledge elites affected city
growth – that is, even if the cross-sectional pattern of growth changed, cities with higher subscriber density grew faster
in both periods.
27
able. In Panel A we use propensity score matching, comparing cities of similar size and geographic
location that hosted a scientific society prior to 1750 with cities that did not. Over the industrialization period (cols 1-2), French cities hosting a scientific society grew 0.2 log points faster than
those without; the growth differential is statistically significant and is robust to adding geographic
latitude and longitude as matching variables. It is also very similar in magnitude to the results in
Table 2. Throughout the pre-industrial period (cols 3-6), the growth differential is insignificant. In
Panel B, we presents OLS results, using the local density of members, lnM embDens, as explanatory variable.57 Membership density in scientific societies is strongly and positively associated
with city growth over the period 1750-1850 (columns 1-2), but not before (columns 3-6). The
results based on early scientific societies thus confirm our main results.
5.2
Income and Industrialization
We now turn to the effect of subscriber density on income and industrial employment in the 19th
century, using a variety of outcome variables. The corresponding data is available at the department
level, which we use as our main unit of analysis in the following.
Soldier Height
We begin by using a common proxy for income – soldier height. This variable is available in
France before and after 1750, allowing us to test the income effects that our model predicts for
worker skills and knowledge elites before and after the onset of industrialization. Columns 1-4 in
Table 8 show that conscript height in 1819-26 is strongly positively associated with both subscriber
density and literacy. This holds even when we control for historical height (cols 3-4). The point
estimates imply that a one standard deviation increase in lnSubDens is associated with an increase
in average soldier height by 0.3cm (or 0.25 standard deviations). Columns 5 and 6 show that
soldier height prior to 1750 is also positively associated with literacy, but not with lnSubDens.58
The results on soldier height are strong evidence in favor of Propositions 1-3.
Disposable Income and Employment Shares
In columns 1 and 2 of Table 9 we show that encyclopedia subscriptions in the mid-18th century
predict disposable income in 1864, i.e., about a century later. The point estimate in col 1 implies
that a one standard deviation increase in lnSubDens is associated with 6.5 percent (0.29 standard
deviations) higher income. To proxy for average worker skills, we can now use department-level
57
This variable is calculated in the same way as lnSubDens (see Section 4.1). The two measures are strongly
correlated, with a coefficient of 0.313 (p-value 0.0001).
58
Table A.9 in the appendix reports further robustness checks, showing that all results hold when regressing conscript height separately on lnSubDens and Literacy, and when weighting regressions by the number of soldiers for
which height is observed in each department.
28
schooling rates, which is available for 1837. This variable is positively (but not significantly)
associated with income. Next, we use employment shares in 1876 as dependent variables. In line
with Proposition 2, subscriber density predicts lower employment shares in agriculture (cols 3-4
in Table 9), and higher industrial employment (cols 5-6). The same pattern holds for schooling
rates, confirming Proposition 3. All regressions in Table 9 control for population density in order
to account for differences in urban vs. rural areas. As expected, industrial employment is larger
in more densely populated areas. Controlling for potential confounding factors such as nobility
density, overall book sales, or density of proto-industrial activity (in the even columns) does not
change our results.
Sectoral Wages
In Table 10 we test our model’s predictions for wages after the onset of industrialization, using
department-level data from the mid-19th century. Columns 1-4 show that subscriber density is
not associated with wages in agriculture – the coefficients are small and insignificant; however,
it is a positive (and in col 3 significant) predictor of industrial wages. The industry wages in
columns 3 and 4 reflect combined traditional and modern manufacturing. In columns 5-8, we
separate the two by using wages in the textile (modern) and food (traditional) industries. We
find that the effect of lnSubDens is positive and significant for the textile sector (and larger than
the previous coefficient for industry overall); and it is minuscule and insignificant for the food
processing sector. This underlines the differential effect of upper tail knowledge – in line with our
model, it is important for modern industrial technology but not for traditional modes of production.
Finally, the coefficient on schooling is positive and strongly significant throughout columns 1-8.59
5.3
Innovativeness and the Role of Knowledge Elites
Our results suggest that knowledge elites are strongly associated with higher firm productivity in
modern manufacturing, but not in traditional manufacturing. In the following, we analyze one
mechanism that can explain this pattern: upper tail knowledge was important for the adoption,
diffusion, and further improvement of innovative industrial technology (Mokyr, 2005a,b). For
entrepreneurs and scientists in France, staying up-to-date with the latest British inventions ensured
a competitive advantage, and scientific publications were an important source of this information
(Horn, 2006). In the following, we implement a three-step argument. We first use British patent
data to split industrial sectors into modern and traditional. We then show that the dominant share
of technologies described and illustrated in the encyclopedia was indeed modern.60 Finally, we
59
In Table A.10 in the appendix we provide additional evidence in support of our model: the density of protoindustrial activity (i.e., prior to industrialization) is strongly associated with literacy, but not with subscriber density.
60
We do not argue that the encyclopedia was the only publication that illustrated recent innovations. However, its
subscribers were also more likely to read other scientific publications, and to be involved in innovation. This underlines
29
show that subscriber density predicts firm productivity in modern, but not in traditional sectors.
From English Patents to Plates in the Encyclopedia
Nuvolari and Tartari (2011) provide data on the share of "inventive output" of 21 British industrial
sectors for the period 1617-1841. This measure is based on reference-weighted patents, adjusted
for the sector-specific frequency of patenting rates and citations. For example, textiles have the
highest score, accounting for 16.6% of total inventive output; and pottery, bricks and stones are at
the lower end with a share of 1.4%.
We then use the British patent data to analyze whether ‘modern’ sectors were disproportionately represented in the encyclopedia. The encyclopedia used plates to illustrate crafts, processes,
and inventions. We obtain detailed information on 2,575 illustrative plates accompanying 326
encyclopedia entries (see Appendix B.9 for sources and further detail). Thus, on average each
entry was accompanied by 8 illustrative plates. About half of these describe manufacturing technologies, and entries include examples such as cloth cutting and figuring or machines to evacuate
water from a mine. We match each entry to one of the 21 British industrial sectors, which allows
us to split entries and plates into ‘modern’ and ‘old’ technologies, corresponding to above- and
below-median share of total inventive output. The share of inventive output was more than five
times higher in the modern category (8.4% vs. 1.6%), and more than two thirds of all plates dedicated to manufacturing described ‘modern’ technologies (see Table A.11 in the appendix). Thus,
there is strong evidence that the encyclopedia indeed spread predominantly knowledge on modern,
innovative industrial technology.
Knowledge Elites and Productivity in Modern vs. Traditional Manufacturing
We now analyze the relationship between subscriber density and firm productivity (proxied by
wages) in ‘modern’ versus ‘old’ manufacturing sectors. This analysis builds on data from a French
industrial survey of more than 14,000 firms in 1839-47, which reports the sector and firm location
by arrondissement.61 We run the following regression separately for ‘modern’ and ‘old’ sectors:
ln (wagejn ) = β · lnSubDensn + γ · schoolingn + δXn + ϕ · sizejn + αj + εjn ,
(11)
where wagejn is the average male wage paid by firms operating in sector j in arrondissement n,
and the vector Xn includes our baseline controls used above, as well as total population and the
urbanization rate in order to control for agglomeration effects. In addition, we control for sector
fixed effects (αj ), and for average firm size (sizejn ) to capture scale effects.
the character of our subscriber density measure as a proxy for local knowledge elites.
61
French arrondissements are comparable to US counties – there were altogether 356 arrondissements in 86 departments in the mid-19th century. Appendix B.10 describes the firm survey in more detail and shows how we match
French to British sectors. It also lists the resulting consistent 8 sectors and their share of ‘inventive output’.
30
If upper tail knowledge affected development by raising the productivity in innovative technology, we expect the coefficient β to be larger when running (11) for ‘modern’ as opposed to ‘old’
sectors. Table 11 presents compelling evidence in support of this hypothesis. Subscriber density
is a strong positive predictor of firm productivity in the ‘modern’ sectors (col 1). This holds after
adding our baseline controls (col 3), and also when including additional controls and sector fixed
effects (cols 5 and 7). The last specification exploits only within-sector variation across arrondissements; the point estimate can be interpreted as follows: suppose that we "move" a representative
firm in a given ‘modern’ sector from an arrondissements without subscriptions (the lower half of
subscriber density) to one in the 90th percentile of subscriber density. Then its output per worker
would increase by 13 percent. For ‘old’ sectors, on the other hand, the coefficients on subscriber
density are small and almost always insignificant (odd columns).62 In line with our model, schooling is positively associated with firm wages in both ‘old’ and ‘modern’ manufacturing.
Finally, we analyze the effect of encyclopedia subscriptions on wages within individual sectors,
running regression (11) separately for different sectors j. Table 12 reports the results, ranking
sectors by the size of the coefficient on lnSubDens. The top four sectors are all ‘modern’, and
the bottom four are all ‘old’. In particular, the coefficient on subscriber density is large within
sectors that saw rapid innovation during industrialization, such as textiles or transportation (the
first steamboat was built in France in 1783). For the least innovative sectors (leather, mining,
ceramics and glass), subscriber density is only weakly related to wages. Column 3 shows that the
number of observations and the R2 are similar for ‘modern’ and ‘old’ sectors. Thus, overall fit or
small samples do not drive the differences in coefficients. Table 12 also revisits the concern that
unobserved local wealth may drive our results, i.e., that the rich could afford the encyclopedia and
also had the financial means to invest in industrial machines. We use two proxies for an industry’s
dependence on up-front investment: the number of steam engines (col 4), and the number of other
engines such as wind and water mills (col 5). Interestingly, both measures for up-front investment
tend to be higher in sectors where the effect of encyclopedia subscriptions is weaker (such as
metal and leather). This makes financial abundance an unlikely confounding factor. In sum, our
analysis suggests that knowledge elites supported industrialization by raising the local productivity
in innovative modern technology.
5.4
Discussion: Interpretation and Limitations of Results
Our empirical analysis follows the common approach to regress growth rates on initial levels of
human capital (Barro, 2001). It thus also shares the common limitation that skills are not assigned
62
Note that this difference is not due to noisier estimates – the standard errors are very similar to those for ‘modern’
sectors, and the number of observations is actually larger for ‘old’ sectors. In Table A.13 we show that these results
are also robust to including regional fixed effects.
31
exogenously to different locations, making causal inference difficult. In the following, we interpret
our results accordingly.
We have documented a striking pattern: encyclopedia subscriptions are strongly associated
with economic growth and income after 1750, but not before the onset of industrialization in
France. Our interpretation is not that the encyclopedia turned its readers into entrepreneurs, or
that it caused local upper tail knowledge. Instead, we use subscriber density as an indicator for
the local presence of knowledge elites. This culture of knowledge was likely locally persistent and
existed prior to the encyclopedia: we documented that pre-1750 membership in scientific societies
is a strong predictor of encyclopedia subscriptions. Our analysis is thus similar to a difference-indifference (DID) approach where the ‘treatment’ (upper tail knowledge) is a latent, persistent local
characteristic whose effect is turned on only after the emergence of modern technology renders
upper tail knowledge economically ‘useful’ (Mokyr, 2005b).63 This limits the set of potential confounding factors – to drive our results, they would have to be correlated with subscriber density,
affect growth via channels unrelated to upper tail knowledge, and be ‘switched on’ around 1750.
We have analyzed alternative explanations that may fit this pattern – institutions, nobility, and
‘Reign of Terror’ – and concluded that they are unlikely to account for our results.64 While we
cannot fully exclude the possibility of yet other unobservables, we believe that the most reasonable
interpretation of our results is the one that relies on overwhelming support from the historical
literature: knowledge elites played a crucial role for economic growth once industrial technology
became economically viable in the 18th century. The underlying mechanism is not confined to
inventors or scientists actively improving technology, but it also comprises lower access costs
(e.g., via information networks in the knowledge elite) and higher efficiency at adopting complex
modern techniques (Mokyr, 2005b). Thus, our interpretation emphasizes a broad concept of upper
tail knowledge – but one that is clearly distinct from ordinary worker skills.
Average worker skills also show a stable local pattern over time – the department-level literacy
rate in 1686 has a correlation coefficients of 0.84 with literacy in 1786, and of 0.65 with schooling in 1837. We confirm the model prediction that these measures are positively associated with
development both before and after industrialization began. While endogeneity is a concern for
these cross-sectional results, it can hardly explain our finding that literacy was not, or if anything
63
In DID terminology: by comparing locations with high (‘treated’) and low (‘control’) subscriber density, we
account for unobserved country-wide changes during industrialization; and by comparing the relationship between
subscriber density and economic outcomes before and after 1750, we control for unobserved local characteristics.
64
We also demonstrated that subscriber density was unrelated to most observable local characteristics at the eve of
French industrialization, and that it was not associated with economic performance prior to 1750. In addition, 18C
subscriber density does not predict mid-19C productivity across the board, but only where we should expect it: within
innovative modern sectors.
32
negatively, associated with city growth during industrialization.
In sum, by predicting when the two types of human capital will and will not affect economic development, our model allows us to run a number of implicit checks. These are all confirmed by the
data. The empirical results thus strongly support our theoretical setup that differentiates between
average and upper tail skills, and its prediction that only the latter was crucial for industrialization.
6
Conclusion
An ample literature has highlighted the importance of human capital for economic development
in the modern world. However, its role during the Industrial Revolution is typically found to be
minor. Thus, a crucial driver of modern growth appears to be unrelated to the onset of growth itself,
and thus to the greatest structural break in economic history that led to a vast dispersion of per
capita income (Galor, 2011). Previous studies have typically used literacy or elementary education
to measure human capital at the eve of the Industrial Revolution. We show that distinguishing
between average worker skills and upper tail knowledge is crucial, reinstating the role of human
capital for industrialization.
We extend a workhorse model of the industrial revolution by splitting manufacturing into a
traditional and a modern sector. This allows us to introduce two assumptions that are motivated by
ample historical evidence: i) worker skills were needed in both sectors, and if anything, somewhat
more so in the traditional one, ii) upper tail knowledge was only important in the modern sector.
Since technological progress during industrialization was concentrated in the modern sector, the
model predicts that upper tail knowledge was crucial for economic take-off. On the other hand,
spatial differences in average worker skills can explain income dispersion in the cross-section,
but are unrelated to growth. We present strong empirical support for these predictions, using
literacy and elementary schooling to proxy for average worker skills, and the novel measure of
18C encyclopedia subscriptions to capture local knowledge elites. Importantly, we do not argue
that average worker skills were altogether unimportant; we show that they were strongly correlated
with income levels before and after industrialization, but not with growth. In contrast, subscriber
density is not associated with income or industrial activity prior to 1750, but is a strong predictor
of growth thereafter, and of income levels and industrial activity in the 19th century. In line with
our theoretical prediction, subscriber density is a particularly strong predictor of firm productivity
in innovative modern sectors.
Our results have important implication for economic development: while improvements in basic schooling raise wages, greater worker skills alone are not sufficient to trigger a transition to
sustained industrial growth. Instead, upper tail skills – even if confined to a small entrepreneurial
elite – are crucial, fostering growth via the innovation and diffusion of modern technology. In
33
this respect, our findings resemble those in today’s economies, where the existence of a social
class with high education is crucial for development (Acemoglu, Hassan, and Robinson, 2011),
entrepreneurial skills matter beyond those of workers (Gennaioli et al., 2013), and scientific education is key (Hanushek and Kimko, 2000).
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38
FIGURES
Subscriber density
Literacy in 1786
Subs. per 1,000
Literacy 1786
no data
[0 − 0.3]
(0.3 − 0.5]
(0.5 − 0.7]
(0.7 − 0.9]
no data
[0 − 0.2]
(0.2 − 1.0]
(1.0 − 3.5]
(3.5 − 15.2]
Figure 1: Encyclopedia subscriber density and literacy rates
Notes: The left panel shows the spatial distribution of encyclopedia subscribers per 1,000 city inhabitants
in the second half of the 18th century. The right panel shows the distribution of literacy rates (percentage of
males signing their marriage certificate) across French departments in 1786. Both variables are described
in detail in Section 4.1. Figure A.4 in the appendix plots the two variables against each other, show that
they are not correlated.
39
.6
.3
0
−.6
−.3
City growth (residual)
.6
.3
0
−.3
coef = .037, (robust) se = .034, t = 1.10
−1
−.5
0
.5
−.9
−.9
−.6
City growth (residual)
.9
1750 – 1850
.9
1400 – 1750
1
1.5
coef = .218, (robust) se = .051, t = 4.28
−1
Subscriber density (residual)
−.5
0
.5
1
1.5
Subscriber density (residual)
Figure 2: Encyclopedia subscriptions and city growth – before and after 1750
Notes: The figure plots average annual city growth in France against encyclopedia subscriber density
(lnSubDens), after controlling for our baseline controls (those listed in the left panel of Table 1). The left
panel uses the pre-industrial period 1400-1750. The right panel examines the same cities over the period
of French industrialization, 1750-1850. Average annual city growth is more than twice as high in the latter
period, from 0.23% to 0.48%.
Employment Shares
Income
0.9
2.2
2.5
0.8
0.5
0.4
0.2
0.1
0
0
1.8
1.5
1.6
1
1.4
0.3
2
Income growth (right axis)
1.2
Traditional Manufacturing
Avg. Income
Agriculture
Modern Manufacturing
1
0.5
Income growth rate
0.6
HH income
Employment Share
2
Agriculture
0.7
0
−0.5
Traditional Manufacturing
0.5
1
0
1.5
0.5
1
1.5
%population in knowledge elite
%population in knowledge elite
Figure 3: Low state of modern technology AM
Notes: The figure shows the employment shares (left panel), as well as sector-specific income and growth
(right panel) for the case of low aggregate efficiency in modern manufacturing (low AM ). ‘Growth’
corresponds to the effect of a 5% increase in AM on average household income. The horizontal axis
represents the mass of the local knowledge elite, which is proxied by the percentage of the population
in region n with knowledge draws s ≥ 10, i.e., ten times larger than the mode of the underlying Pareto
distribution given by equation (1). Due to the low AM , even households with high draws s do not find
adoption of modern manufacturing profitable, so that the thickness of the upper tail does not affect labor
shares or income. See Proposition 1 and the corresponding discussion in the text for further detail.
40
Employment Shares
Income
2.2
0.8
Agriculture
2
2
Modern Manufacturing
1.8
0.6
HH income
Employment Share
0.7
2.5
0.5
0.4
1.5
Income growth (right axis)
1.6
1
1.4
0.3
Modern Manufacturing
0.5
Avg. Income
All manufacturing
0.2
1.2
Income growth rate (%)
0.9
0
Agriculture
0.1
Traditional Manufacturing
0
0
0.5
1
1
−0.5
Traditional Manufacturing
0
1.5
0.5
1
1.5
%population in knowledge elite
%population in knowledge elite
Figure 4: High state of modern technology AM
Notes: The figure shows employment shares, as well as sector-specific income and growth for a high
state of modern manufacturing technology (high AM ). See Figure 3 for the definition of ‘growth’ and a
description of the horizontal axis. The thicker the upper tail of the knowledge elite, the more households
receive draws at which modern manufacturing is adopted, so that its labor share rises. More employment in
highly productive modern manufacturing also raises average income and income growth. See Proposition
2 and the corresponding discussion in the text for further detail.
Employment Shares
Income
1
2.2
2.5
0.9
Agriculture
2
1.8
0.6
0.5
0.4
All manufacturing
1.5
Modern Manufacturing
1.6
1
Income growth (right axis)
1.4
0.5
0.3
1.2
0.2
Avg. Income
Agriculture
Income growth rate (%)
2
0.7
HH income
Employment Share
0.8
0
Traditional Manufacturing
0.1
1
Traditional Manufacturing −0.5
Modern Manufacturing
0
1
1.05
1.1
1.15
1.2
1.25
1
1.3
1.05
Worker skills h
1.1
1.15
1.2
1.25
1.3
Worker skills hn
n
Figure 5: Effect of worker skills
Notes: The figure shows employment shares, as well as sector-specific income and growth as a function of
average worker skills in region n, hn . Larger hn favors employment in traditional manufacturing because
this is the sector that is most intensive in worker skills. The effect of hn on employment in modern
manufacturing is minuscule. Income in all sectors rises with hn , but income growth (defined in the note to
Figure 3) is not affected. See Proposition 3 and the corresponding discussion in the text for further detail.
41
1
.8
Kernel density
.4
.6
0
.2
No subscriptions
Subscriptions p.c. > 0, below−median
Subscriptions p.c. > 0, above−median
−.5
0
.5
City growth 1750−1850
1
1.5
Figure 6: Subscriptions and city growth, 1750-1850
Notes: The figure shows the Kernel density of city growth over the
period 1750-1850 for three subsets of French cities: 108 cities without
encyclopedia subscriptions, as well as 43 (42) cities with above-zero
subscriptions and below-median (above-median) subscriber density.
42
TABLES
Table 1: Correlations with subscriber density (lnSubDens)
Baseline Controls
Cities included:
Worker skills and additional controls
(1)
(2)
All
Subs>0
Departments included:
(3)
(4)‡
(5)‡
All
All
Subs>0
Population in 1750
∗∗∗
0.374
(0.073)
∗∗
-0.234
(0.110)
Literacy 1686
0.551
(0.593)
0.844
(0.639)
-0.132
(0.721)
Atlantic Port
0.081
(0.207)
-0.222
(0.213)
Literacy 1786
0.290
(0.345)
0.363
(0.336)
0.021
(0.375)
Mediterranean Port
0.022
(0.276)
-0.129
(0.223)
School Rate 1837
0.363
(0.350)
0.369
(0.340)
0.187
(0.406)
Navigable River
0.422∗∗
(0.202)
-0.167
(0.210)
lnSTNBooksDens
0.232∗∗∗
(0.048)
0.232∗∗∗
(0.055)
0.163∗∗∗
(0.059)
University
1.030∗∗∗
(0.186)
0.316
(0.194)
lnProtoIndDens
-0.027
(0.830)
-0.058
(0.851)
-0.573
(0.825)
Non French-Speaking
-0.376∗∗
(0.146)
-0.719∗∗
(0.297)
lnNoblesDens
0.232
(0.278)
0.206
(0.318)
0.646∗∗∗
(0.237)
Printing Press
0.712∗∗∗
(0.170)
0.203
(0.182)
lnExecuteDens
0.278∗∗
(0.118)
0.205
(0.136)
0.236
(0.142)
ln(Books Printed 1500)
0.171∗∗∗
(0.061)
0.039
(0.066)
193
85
75/88
75/88
59/64
Observations
Notes: The table shows the coefficients of individual regression of subscriber density to the Quarto edition of the
Encyclopédie, lnSubDens, on a variety of city characteristics. lnSubDens is computed as described in Section 4.1.
Population in 1750 measures urban population (in thousands) for the cities in our sample. Atlantic Port, Mediterranean Port and Navigable River are dummies taking value 1 for cities with ports on the Atlantic Ocean or on the
Mediterranean Sea or located on a navigable river. University is a dummy taking value 1 for cities which hosted a
University before 1750. Non French Speaking is a dummy for six French departments who spoke a language other
than French. Printing Press is a dummy taking value 1 for cities where a printing press was established before 1500.
Ln(Books Printed 1500) represents the log number of editions printed before 1501. Additional controls comprise the
following variables (see Section 4.3 for sources and detail): Literacy in 1686 and 1786 measure the percentage of
men signing their wedding certificate in the respective year. SchoolRate in 1837 measures the ratio of students to
school-age population (5 to 15 years) in 1836-37. lnST N BooksDens represents the density of book sales to France
from the Swiss publishing house Société Typographique de Neuchâtel (STN). lnP rotoIndDens is an index of protoindustrialization in France that includes the number of mines, forges, iron trading locations, and textile manufactures
before 1500. lnN oblesDens reflects the density of noble families in each French department. lnExecuteDens measures the density of people executed during the reign of Terror. In columns 1-2, all regressions are run at the city level,
in columns 3-5 at the department level. Robust standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01.
‡
Includes baseline controls.
43
Table 2: Matching estimation by city size and location
Dependent variable: log city growth over the indicated period
(1)
(2)
(3)
(4)
PANEL A: Period 1750-1850
City size percentiles incl.:
ISubs>0
Matching variables
Population
Location
Observations
All
10-90 pct
∗∗
∗∗
All
∗∗∗
10-90 pct
0.146
(0.07)
0.155
(0.07)
0.267
(0.07)
0.163∗∗
(0.07)
X
X
177
154
X
X
167
X
X
144
PANEL B: Pre-1750
ISubs>0
Matching variables
Population
Location
Observations
1700-1750
1600-1700
1500-1600
1400-1500
0.087
(0.06)
0.034
(0.17)
0.181
(0.17)
-0.567∗∗∗
(0.21)
X
X
129
X
X
58
X
X
43
X
X
37
Notes: All regressions are run by propensity score matching at the city level, excluding the 1% of log city growth
outliers, and using the three nearest neighbors when there are more than 50 observations and the two nearest neighbors
otherwise. Treatment variables is the indicator variable ISubs>0 , which takes on value 1 if a city had above-zero
subscriptions to the encyclopedia (see the notes to Table 1 and Section 4.1 for detail). Columns 1 and 2 in Panel A
use city population as matching variable. Columns 3 and 4, as well as Panel B, add geographic longitude and latitude
(location) as matching variables. Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01.
44
Table 3: Encyclopedia subscriptions and city growth, before and after 1750
Dependent variable: log city growth over the indicated period
Period 1750-1850
(1)
(2)
(3)
Pre-1750
(4)
(5)
(6)
(7)
(8)
[unweighted]
1700-1750
1600-1700
1500-1600
1400-1500
PANEL A: All cities
lnSubDens
0.100∗∗
(0.039)
0.176∗∗∗
(0.034)
0.169∗∗∗
(0.033)
0.204∗∗∗
(0.036)
0.008
(0.037)
0.060
(0.119)
0.056
(0.087)
0.115
(0.167)
lnP opinitial
0.055∗∗∗
(0.014)
-0.081∗
(0.043)
-0.089∗
(0.048)
-0.156∗∗∗
(0.051)
-0.058
(0.040)
-0.456∗∗
(0.186)
-0.376∗∗∗
(0.087)
-0.287∗∗
(0.134)
Atlantic Port
0.223∗∗∗
(0.085)
0.242∗∗
(0.094)
0.349∗∗
(0.162)
0.087
(0.101)
-0.124
(0.239)
0.372∗∗
(0.145)
0.256
(0.272)
Mediterranean Port
0.768∗∗∗
(0.081)
0.794∗∗∗
(0.091)
0.752∗∗∗
(0.142)
-0.203∗∗
(0.094)
0.784∗
(0.398)
0.309∗
(0.161)
0.716∗∗
(0.277)
Navigable River
0.094
(0.069)
0.068
(0.072)
0.134∗
(0.069)
0.001
(0.076)
0.222
(0.191)
-0.017
(0.131)
0.221
(0.291)
University
-0.034
(0.051)
-0.063
(0.067)
-0.123
(0.084)
0.122∗
(0.069)
0.299∗
(0.173)
-0.108
(0.117)
-0.049
(0.271)
Paris
0.590∗∗∗
(0.136)
0.610∗∗∗
(0.132)
0.760∗∗∗
(0.171)
-0.020
(0.135)
0.638
(0.478)
1.237∗∗∗
(0.321)
0.402
(0.582)
Non French Speaking
0.344∗∗∗
(0.090)
0.330∗∗∗
(0.097)
0.428∗∗∗
(0.145)
0.100
(0.129)
-0.438
(0.376)
0.078
(0.272)
0.156
(0.373)
Printing Press in 1500
0.093
(0.094)
0.188∗
(0.098)
-0.078
(0.083)
-0.628∗∗
(0.272)
0.448∗∗
(0.182)
-0.063
(0.351)
ln(Books Printed 1500)
-0.001
(0.020)
0.006
(0.025)
0.029∗
(0.017)
0.162∗∗
(0.065)
-0.040
(0.040)
0.020
(0.067)
0.36
193
0.27
193
0.17
148
0.55
56
0.53
45
0.31
39
R2
Observations
0.12
193
0.36
193
PANEL B: Only cities with positive subscriptions
lnSubDens
Controls
2
R
Observations
0.090
(0.063)
0.139∗∗∗
(0.048)
0.117∗∗
(0.045)
0.086∗
(0.044)
-0.069
(0.053)
-0.061
(0.129)
0.018
(0.094)
0.175
(0.213)
X
X
X
X
X
X
X
X
0.08
85
0.46
85
0.48
85
0.38
85
0.32
76
0.64
44
0.64
36
0.30
32
Notes: All regressions are run at the city level and are weighted (except for Column 4) by initial population of the
respective period. The dependent variable in the different columns is log city population growth over the period
indicated in the header. The main period of French industrialization is 1750-1850; city growth in earlier periods is
used as a placebo. ‘Controls’ are the same as in the corresponding columns of Panel A. For details on lnSubDens
and the other explanatory variables see the notes to Table 1, Section 4 and Appendix B.4. Robust standard errors in
parentheses. * p<0.1, ** p<0.05, *** p<0.01.
45
Table 4: Literacy and additional controls
Dependent variable: log city growth, 1750-1850
lnSubDens
Literacy 1786
(1)
(2)
(3)
(4)
(5)
(6)
0.180∗∗∗
(0.040)
-0.209
(0.142)
0.176∗∗∗
(0.038)
-0.276∗∗
(0.133)
1.107∗∗∗
(0.363)
0.176∗∗∗
(0.041)
-0.208
(0.144)
0.198∗∗∗
(0.042)
-0.156
(0.135)
0.179∗∗∗
(0.040)
-0.185
(0.144)
0.183∗∗∗
(0.040)
-0.209
(0.127)
1.068∗∗∗
(0.355)
0.114
(0.118)
-0.020
(0.021)
0.032
(0.037)
-0.029
(0.044)
X
0.41
166
lnProtoIndDens
lnNoblesDens
0.085
(0.135)
lnSTNBooksDens
-0.025
(0.021)
lnExecuteDens
ln(Pop 1750)
Baseline Controls
R2
Observations
-0.075∗
(0.043)
X
0.38
166
-0.067
(0.040)
X
0.40
166
-0.061
(0.055)
X
0.38
166
-0.053
(0.041)
X
0.39
166
0.037
(0.037)
-0.072
(0.044)
X
0.39
166
Notes: All regressions are run at the city level and are weighted by city population in 1750. The dependent variable is
log city population growth in 1750-1850. ‘Baseline Controls’ include all variables listed in Table 1 (left panel). For
details on lnSubDens and the other explanatory variables see notes to Table 1, Section 4 and Appendix B.4. Robust
standard errors in parentheses (clustered at the department level). * p<0.1, ** p<0.05, *** p<0.01.
46
Table 5: Panel regressions with city population, 1500-1850
Dependent variable: log city population
(1)
(2)
(3)
(4)
(5)
(6)
Baseline: treatment post-1750
Placebo treatment periods
[balanced] post-1600
post-1700
lnSubDensxP ost
Baseline Controls
Additional Controls
City FE
Time Period FE
R2
Observations
0.106∗∗∗
(0.029)
0.136∗∗∗
(0.031)
X
0.164∗∗
(0.070)
X
0.038
(0.075)
X
0.063
(0.039)
X
X
X
0.100∗∗
(0.044)
X
X
X
X
X
X
X
X
X
X
X
X
0.86
846
0.87
846
0.88
732
0.85
270
0.87
846
0.87
846
Notes: All regressions are run at the city level. The dependent variable is the log of city population in the years 1500,
1600, 1700, 1750, 1800, and 1850. The P ost indicator variable takes value zero for the periods 1500-1750, and one
for the periods 1800 and 1850. ‘Baseline Controls’ include all variables listed in Table 1 (left panel); ‘Additional
Controls’ are those used in in Table 4. For details on lnSubDens and controls see the notes to Table 1, Section 4 and
Appendix B.4. Robust standard errors in parentheses (clustered at the department level in col 3). * p<0.1, ** p<0.05,
*** p<0.01.
Table 6: City growth over the sub-periods 1750-1800 and 1800-1850
Dependent variable: log city growth over the indicated period
Period
(1)
(2)
(3)
1750-1800
1800-1850
1800-1850
Control for prior growth
lnSubDens
∗∗∗
0.108
(0.035)
∗∗
0.058
(0.026)
-0.141∗
(0.082)
Growth 1750-1800
Baseline Controls
R2
Observations
0.073∗∗∗
(0.027)
X
X
X
0.29
192
0.41
192
0.42
192
Notes: All regressions are run at the city level over the period indicated in the table header. ‘Baseline Controls’
include all variables listed in Table 1 (left panel), as well as log population in 1750. Col 4 uses predicted city growth
from the regression in col 1. For details on lnSubDens and controls see the notes to Table 1, Section 4 and Appendix
B.4. Robust standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01.
47
Table 7: Scientific societies and city growth
Dependent variable: log city population
(1)
(2)
(3)
(4)
(5)
(6)
1750-1850
1750-1850
1700-1750
1600-1700
1500-1600
1400-1500
PANEL A: Matching Estimation
IScient.Scociety>0
Matching variables
Population
Location
Observations
0.204∗∗
(0.098)
0.193∗∗
(0.095)
0.028
(0.083)
0.167
(0.151)
0.195
(0.137)
-0.324
(0.220)
X
X
X
175
X
X
136
X
X
58
X
X
44
X
X
41
0.058
(0.190)
X
0.56
52
0.211
(0.179)
X
0.53
41
-0.270
(0.474)
X
0.32
37
185
PANEL B: OLS Estimation
lnMembDens
Baseline Controls
R2
Observations
∗
0.171
(0.088)
0.10
185
0.287∗∗∗
(0.080)
X
0.32
185
0.069
(0.105)
X
0.19
140
Notes: All regressions are run at the city level. The dependent variable in the different columns is log city population
growth over the period indicated in the header. In Panel A, all regressions are run by propensity score matching,
excluding the 1% of log city growth outliers, and using the three nearest neighbors when there are more than 50
observations and the two nearest neighbors otherwise. Treatment variable is the indicator IScient.Society>0 , which
takes on value 1 if a city hosted a scientific society before 1750 (see Section 4 for details). Column 1 uses city
population as matching variable. Columns 2–6 add geographic longitude and latitude (location) as matching variables.
In Panel B, all regressions are run by OLS and are weighted by initial population of the respective period. ‘Baseline
Controls’ include all variables listed in Table 1 (left panel). In Column 1, only Pop 1750 is included as control. For
details on lnM embDens and controls see the notes to Table 1, Section 4 and Appendix B.4. Standard errors in
parentheses.* p<0.1, ** p<0.05, *** p<0.01.
48
Table 8: Soldiers height before and after 1750
Dependent variable: Soldier height in cm
(1)
(2)
(3)
(4)
Period 1819-1826
lnSubDens
Literacy
∗∗∗
∗∗∗
0.416
(0.136)
2.805∗∗∗
(0.398)
0.427
(0.139)
3.063∗∗∗
(0.363)
0.42
77
X
0.60
77
Height pre-1750
Baseline Controls
R2
Observations
∗∗∗
0.403
(0.139)
2.463∗∗∗
(0.459)
0.303
(0.193)
0.46
77
(5)
(6)
Pre-1750
∗∗∗
0.423
(0.141)
2.858∗∗∗
(0.393)
0.170
(0.147)
X
0.61
77
0.116
0.100
(0.115) (0.111)
1.043∗ 1.033∗
(0.525) (0.571)
0.06
74
X
0.16
74
Notes: All regressions are run at the French department level and include a dummy for Paris (Department Seine). The
dependent variable in cols 1-4 is soldier height as reported by Aron et al. (1972). In Columns 5-6, the dependent
variable is average soldier height recorded over the period 1716-49 and collected by Komlos (2005). To account for
variation in height and soldier age within this period, we control for age, age squared, and birth decade (see Appendix
B.3 for detail). We exclude departments with data for less than 20 soldiers (Table A.9 reports results when using
all departments and weighting by the number of soldiers). ‘Baseline Controls’ include all variables listed in Table 1
(left panel); we use Literacy in 1786 in Colums 1-4 and Literacy in 1686 in Columns 5-6. For details on lnSubDens,
Literacy and controls see the notes to Table 1, Section 4 and Appendix B.4. Original city-level variables are aggregated
to the department level as described in Appendix B.5. Robust standard errors in parentheses. * p<0.1, ** p<0.05, ***
p<0.01.
49
Table 9: Disposable income and employment shares in industry and agriculture
(1)
Dep. var.
(2)
(3)
ln(disposable
School Rate 1837
Population Density 1876
Baseline Controls
Additional Controls
R2
Observations
0.084∗∗
(0.033)
0.181
(0.130)
-0.016
(0.046)
X
0.30
84
(5)
(6)
Employment shares in 1876
income, 1864)
lnSubDens
(4)
Agriculture
0.078
-0.031∗
(0.053) (0.016)
0.141 -0.170∗∗∗
(0.125) (0.059)
-0.044 -0.205∗∗∗
(0.047) (0.034)
X
X
X
0.31
0.66
74
85
-0.033
(0.022)
-0.143∗∗
(0.058)
-0.175∗∗∗
(0.034)
X
X
0.69
74
Industry
0.020∗
(0.011)
0.101∗∗∗
(0.037)
0.132∗∗∗
(0.028)
X
0.56
85
0.032∗∗
(0.013)
0.108∗∗∗
(0.039)
0.120∗∗∗
(0.027)
X
X
0.64
74
Notes: All regressions are run at the French department level and include a dummy for Paris (Department Seine).
‘Baseline Controls’ include all variables listed in Table 1 (left panel). ‘Additional Controls’ are those used in in Table
4. For details on lnSubDens and controls see the notes to Table 1, Section 4 and Appendix B.4. Original city-level
variables are aggregated to the department level as described in Appendix B.5. Robust standard errors in parentheses.
* p<0.1, ** p<0.05, *** p<0.01.
Table 10: Subscriber density and department-level wages in different sectors
Dependent variable: log wages, in the indicated sectors
(1)
(2)
Agriculture
(3)
(4)
All Industry
(5)
(6)
Textile
(7)
(8)
Food Processing
-0.015
0.052∗∗
0.026
0.092∗∗∗ 0.090∗
0.014
0.012
(0.034) (0.020) (0.029) (0.029) (0.048) (0.035) (0.041)
School Rate 1837
0.386∗∗∗ 0.201∗∗∗ 0.181∗∗∗ 0.301∗∗∗ 0.307∗∗ 0.343∗∗∗ 0.365∗∗∗
(0.083) (0.056) (0.057) (0.106) (0.117) (0.091) (0.106)
Population Density 1876
0.004
0.038
0.022
0.171∗∗∗ 0.171∗∗∗
0.103
0.055
(0.040) (0.039) (0.038) (0.041) (0.051) (0.076) (0.087)
Baseline Controls
X
X
X
X
X
X
X
Additional Controls
X
X
X
X
R2
0.51
0.58
0.49
0.50
0.46
0.47
0.33
0.32
Observations
79
69
79
69
83
73
84
74
Notes: All regressions are run at the French department level and include a dummy for Paris (Department Seine).
‘Baseline Controls’ include all variables listed in Table 1 (left panel). ‘Additional Controls’ are those used in in Table
4. For details on lnSubDens and controls see the notes to Table 1, Section 4 and Appendix B.4. Original city-level
variables are aggregated to the department level as described in Appendix B.5. Robust standard errors in parentheses.
* p<0.1, ** p<0.05, *** p<0.01.
lnSubDens
0.035
(0.029)
0.413∗∗∗
(0.086)
0.021
(0.049)
X
50
Table 11: Subscriber density and average local firm productivity in 1837
Dep. Var.: log wages (arrondissement average, for firms in ‘modern’ and ‘old’ sectors)
Sector
lnSubDens
(1)
(2)
modern
old
∗∗∗
0.097
(0.013)
0.252∗∗∗
(0.076)
X
∗∗
0.032
(0.013)
0.238∗∗∗
(0.066)
X
(3)
(4)
(5)
(6)
(7)
(8)
modern
old
modern
old
modern
old
∗∗∗
0.073
(0.019)
0.249∗∗∗
(0.073)
X
X
0.068
(0.019)
0.270∗∗∗
(0.093)
X
X
X
0.020
(0.018)
0.219∗∗∗
(0.066)
X
X
X
∗∗∗
0.064
0.018
(0.019) (0.018)
School Rate 1837
0.259∗∗∗ 0.202∗∗∗
(0.085) (0.069)
Size and Pop. Controls
X
X
Baseline Controls
X
X
Additional Controls
X
X
Sector FE
X
X
2
R
0.22
0.22
0.28
0.28
0.30
0.29
0.43
0.32
Observations
633
794
428
541
392
501
392
501
Notes: All regressions are run at the French arrondissement level and include a dummy for Paris (Department Seine).
The dependent variable is the log of average male wages across all firms in a sector j in arrondissement n (see Section
B.10 for detail). ‘Baseline Controls’ include all variables listed in Table 1 (left panel). ‘Additional Controls’ are those
used in in Table 4. For details on lnSubDens and controls see the notes to Table 1, Section 4 and Appendix B.4.
Original city-level variables are aggregated to the arrondissement level as described in Appendix B.5. Standard errors
(clustered at the department level) in parentheses. * p<0.1, ** p<0.05, *** p<0.01.
51
0.025
(0.017)
0.259∗∗∗
(0.069)
X
X
∗∗∗
Table 12: Subscriber density and firm productivity within individual industries
(1)
(2)
(3)
(4)
(5)
2
Sector Name
Sector
type
Coefficient
lnSubDens
R
Engines per 1,000
Obs. Steam
Others
Transportation Equipment
modern
0.114∗∗∗
(0.026)
0.63
39
2
9
Printing Technology, and
Scientific Instruments
modern
0.103∗∗∗
(0.021)
0.20
221
1
27
Textile and Clothing
modern
0.067∗∗∗
(0.018)
0.28
298
3
11
Furniture and Lighting
modern
0.056∗
(0.033)
0.54
75
13
14
Metal and Metal Products
old
0.042∗
(0.024)
0.16
273
6
36
Leather
old
0.041∗
(0.022)
0.19
165
3
72
Mining
old
0.038∗∗
(0.018)
0.31
188
3
19
Ceramics and Glass
old
0.003
(0.020)
0.27
168
4
16
Notes: For each sector, column 1 specifies whether the sector belongs to the ‘modern’ or ‘old’ manufacturing classification (see Section 5.3 for detail). Sectors are ranked by the size of the coefficient of lnSubDens, reported in column
2 – where the dependent variable is the logarithm of male wages at the arrondissement level. For each regression,
column 3 reports the R2 and the number of observations. In all regressions, we control for ‘Size and Pop. Controls’,
as in Table 11 (cols 1-2). For details on lnSubDens, and controls see the notes to Table 1, Section 4, and Appendix
B.4 and B.10. Standard errors (clustered at the department level) in parentheses. * p<0.1, ** p<0.05, *** p<0.01.
Columns 4-5 show the average number of steam engine and of other engines per 1,000 workers (see Appendix B.10
for detail).
52