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Fragmented Networks and Entrepreneurship in Late Imperial Russia


Abstract and Figures

Emergent economies suffer from underdeveloped market infrastructures and insufficient public institutions to enforce contract commitments and property rights. Informal reputation-based arrangements may substitute for government enforcement but they require close-knit networks that enable monitoring. Economic development also requires access to capital, information and other resources, which is enabled by wide-reaching and diverse networks, and not by closure. How is entrepreneurship possible given these conflicting demands? We examine how partnership networks and reputation channel the mobilization of capital for new enterprises, using quantitative information on 4,172 corporate partnerships during the industrialization of late imperial Russia (1869-1913). We find that reputation is locally effective in small and homogeneous network components. By contrast, founders in the largest components that form the network core raise more capital from investors but benefit less from reputation and more from brokerage opportunities and ties that reach diverse communities.
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Fragmented Networks And Entrepreneurship In Late Imperial Russia
Henning Hillmann
University of Mannheim
Brandy L. Aven
Carnegie Mellon University
We are particulary grateful to Rodrigo Canales, Christina Gathmann, Steve Nafziger, and Kate
Stovel for their extensive comments and suggestions. We also wish to thank Frank Dobbin,
Roberto Fernandez, Neil Fligstein, Heather Haveman, Doug McAdam, Jim Moody, Aldo
Musacchio, Susan Olzack, Woody Powell, Denis Trapido, Andy Walder, Ezra Zuckerman, and
seminar participants at Berkeley, the University of Chicago, Harvard University, the University
of Mannheim, MIT, and Stanford University for their helpful critiques. Stan Markuze
contributed invaluable research assistance. For all remaining errors, we alone are responsible.
Direct correspondence to Henning Hillmann, Department of Sociology, School of Social
Sciences, University of Mannheim, D-68131 Mannheim. E-mail:
Emergent economies suffer from underdeveloped market infrastructures and insufficient public
institutions to enforce contract commitments and property rights. Informal reputation-based
arrangements may substitute for government enforcement but they require close-knit networks
that enable monitoring. Economic development also requires access to capital, information and
other resources, which is enabled by wide-reaching and diverse networks, and not by closure.
How is entrepreneurship possible given these conflicting demands? We examine how partnership
networks and reputation channel the mobilization of capital for new enterprises, using
quantitative information on 4,172 corporate partnerships during the industrialization of late
imperial Russia (1869-1913). We find that reputation is locally effective in small and
homogeneous network components. By contrast, founders in the largest components that form
the network core raise more capital from investors but benefit less from reputation and more from
brokerage opportunities and ties that reach diverse communities.
A central role of the state is the provision of stable institutions that ensure property rights,
enforce contracts, and minimize transaction costs. Historical evidence supports this view of the
economic role of the state and shows that reliable market-supporting institutions are vital to
economic development. The challenge for emerging and transition economies is that they often
suffer from insufficiently developed and unreliable public institutions that do not support
economic transactions (Haber, Razo, and Maurer 2003; Mokyr 2009; North 1981). How, then, is
economic development, and entrepreneurship in particular, possible in the absence of strong
public institutions? Recent studies suggest that privately organized substitutes may emerge in
settings where the public provision of adequate institutional scaffolding for economic activity
fails. Such informal substitutes depend on information and trust within reputation-based
networks. For example, medieval Maghribi traders formed a coalition based on their close-knit
ethnic networks to monitor and sanction the agents they employed in overseas ports (Greif 2006).
Agents who were found to have embezzled goods or proceeds for their own gain were ostracized
by the entire coalition of traders, and lost their reputation as reliable agents. Such enforcement
through reputation works as long as agents value the long-term benefits of their reputation more
than the short-term gains from embezzlement. Similar arrangements include the reliance on
collective reputation among loan-seeking firms during Mexico's modern industrialization
(Maurer and Sharma 2001), the enforcement of communal norms among neighboring farmers
to contain potential conflicts arising from cattle trespassing in present-day rural California
(Ellickson 1991), and trust-based business networks to cope with information asymmetries in
developing markets in Africa (Fafchamps 2004).
Various definitions of reputation exist in the extant literature (Greif 2006; Podolny 2005;
Stuart, Hoang, and Hybels 1999). Differences in detail aside, most social scientists agree upon
two aspects of reputation: first, knowing a business partner's past behavior mitigates uncertainty
about his future performance; second, reputation demonstrates the person's credibility as an
honest business partner and reduces the uncertainty associated with trusting him. The first aspect
considers reputation as an individual-level signal that potential partners and investors rely on to
assess an entrepreneur's abilities and qualities. The second aspect points to collective reputation-
mechanisms that a community of merchants relies on to monitor and sanction opportunistic
behavior. The individual and collective uses of reputation are closely linked, and we will explore
We argue that the composition of social structures – and not just the positions and
strategies of individual entrepreneurs – that underpinned such reputation-mechanisms was key to
their success in encouraging entrepreneurship. While context-specific variation in reputation-
based institutions exists, they typically hinge on network closure because it enables local
monitoring. Closure in social structure implies that an entrepreneur's contacts are also linked with
each other. Close bonds within a community of merchant entrepreneurs ensure that members
know how their exchange partners behaved in the past, whether the behavior complied with
community norms or not, and if these partners should be trusted in future transactions (Burt
2005; Coleman 1990). Yet, emergent economies and transition societies tend to suffer both from
the absence of reliable government institutions and from fragmentation into diverse and often
opposing interest groups (Weingast 1997). Affiliations that may bridge the divisions rarely exist.
Because reputation-based monitoring mechanisms rest on closure in local networks their benefits
as substitutes for missing public institutions may work well in local communities but do not
extend beyond their boundaries. And because closure keeps knowledge and resources within local
communities, it alone will not promote economic development – but it facilitates strategies for
staying in business and maintaining the economic status quo.
The creation of informal arrangements for coordination and monitoring may be
adequate local responses to the lack of strong public institutions. Yet to advance economic
development, a different pattern of social networks is required. Collecting novel information,
securing credit, and attracting capital from potential partners and investors are all essential for
entrepreneurship and development in emergent economic settings (Fafchamps 2004).
Development beyond the economic status quo mandates social structures that span diverse
communities of entrepreneurs. Local closure is the opposite of network reach and hinders such
allocation of resources. By contrast, wide-spanning networks foster economic development
because they encourage brokerage between diverse social circles and open access to the resources,
valuable opportunities, and novel information that lie beyond local closure and would otherwise
be inaccessible to entrepreneurs (Burt 2005).1
To summarize our theoretical discussion so far, we argue that the social organizational
demands of private-order institutions reveal a dilemma that is particularly salient for societies in
transition as they struggle to overcome their economic backwardness, understood here as an
insufficient ability or willingness of governments to maintain reliable market-supporting
institutions as public goods (Haber, Razo, and Maurer 2003). Because their public institutions are
insufficiently developed, emergent economies should be prime candidates for the creation of
privately organized alternatives. And yet, their challenge is that the network alternatives – closure
and reach – compete with each other. Their entrepreneurs face the double-bind of either
partnering within their tight-knit enclaves to ensure social control and reduce risk, or partnering
across social boundaries to expand capital access but making themselves potentially vulnerable to
If these arguments are correct, then which of the alternative strategies will be more
successful for teams of entrepreneurs to pursue will depend on the particular opportunity
structure they are embedded in. Because the reliance on reputation-mechanisms for monitoring
rests on local closure we would expect that reputation-based strategies of entrepreneurship are
1 Again, what distinguishes our argument from otherwise similar approaches (e.g. Burt 2005) is that we
emphasize the potential of different network structures as foundations for private-order responses to the
absence of reliable public institutions, and not just their strategic options for individual entrepreneurs.
most successful in densely knit local networks, and to be less salient in large cohesive networks
that span across a variety of diverse clusters. Because network closure primarily contributes to the
economic status quo, we would also expect that new ventures are more successful in attracting
capital and other critical resources when they operate in more wide-spanning networks that are
populated by entrepreneurs coming from diverse origins. These expectations delineate the
possibilities and limitations of private-order arrangements to effectively support economic
activities within the fragmented networks that tend to characterize emergent societies.
We consider the general question of economic development and entrepreneurship in the
absence of strong institutions in the historical setting of corporate industrialization in late
imperial Russia from 1869 to 1913. The period marked a profound transition for Russian
economic development, leading from the Great Reforms, including the emancipation of enserfed
peasants, to the Great War in 1913, which brought both the Tsarist Empire and corporate
capitalism in Russia to an end. During this period, key corporate sectors such as the railway,
textile, and metallurgy industries experienced rapid economic growth, but at the same time had
to confront glaring constitutional deficiencies that undermined the effective organization of
corporate capitalism (Crisp 1976; Gregory 1994). Particularly consequential impediments to
industrial development included arbitrary government decisions in economic policy,
discrimination of Jewish entrepreneurs and other ethnic minorities, and unreliable enforcement
of property rights (Gatrell 1995; Owen 1991a, 2005). The economic constraints coupled with
ethnic, religious, and regional rivalries among the various merchant-industrialist factions (Clowes,
Kassow, and West 1991; Joffe 1984; Rieber 1982). Within this setting, we focus on the founding
of the large corporations that were primarily responsible for Russia's industrialization (large joint-
stock companies and industrial partnerships accounted for 75% of the total corporate capital
invested in companies in 1900, and for 86% in 1914; see Crisp 1976). We examine how
corporate founders relied on their reputation and their networks of business partnerships to
mobilize the necessary capital for new ventures. Supporting empirical evidence for our
theoretical argument comes from our analysis of the founding activities of some 11,545 elite
entrepreneurs connected through the networks of their partnerships in 4,172 chartered firms
known to have operated in 1869-1913.
Three central results emerge from our study. First, the Russian corporate network was
indeed fragmented into a widespread and well-connected core and a periphery that consisted of
hundreds of scattered small components without any relational bridges connecting them. Local
network closure characterized the small components in the periphery whereas far-reaching
connections and brokerage opportunities characterized the core. Notably, the extent of network
fragmentation changed little over the 1869-1913 period.
Second, homophily based on common ethnicity, regional origins, and shared experience
in similar industries guided the composition of founding teams within the isolated and tight-knit
clusters in the periphery. In contrast, the network core embedded entrepreneurs who came from
more diverse ethnic and regional backgrounds and who invested in diverse industrial sectors.
Likewise, the division between core and periphery in the corporate network did not map directly
onto the economic and political geography of the Russian Empire, but crosscut geographic
locations and their ethnic and socio-economic boundaries instead.2
We are less interested in answering where the network fragmentation comes from.3
Instead, we seek to understand how different network patterns influence the mobilization of
resources and capital for the founding of new ventures. We find, third, that cooperation with
founding partners who have earned a reputation of successful entrepreneurship in the past did
2 Unless noted otherwise, whenever we use the terms "core" and "periphery", we refer to network analytic
concepts and not to locations in the Russian industrial or political geography.
3 The lack of adequate data means that an investigation of the origins of entrepreneurial networks in our
historical case is beyond the scope of this article. Recent work on network formation in general, and the
origins of homophily in particular, suggests an endogenous relationship between people's preferences and
network structure: their preferences lead people to particular positions and exchange partners just as much
as their existing networks shape those preferences (Kossinets and Watts 2009; Wimmer and Lewis 2010).
indeed raise the amount of capital mobilized for present ventures. More important, the effect of
reputation on capital mobilization was significantly greater within the small and more
homogenous components of founders in the periphery than within the network core. But
founding partnerships in the core benefitted from their opportunities for brokerage between
diverse groups of enterprising merchants: on average, ventures in the wide-spanning network
core attracted significantly more capital than ventures in the periphery.
The contribution of the present study is twofold. First and substantively, our finding that
the corporate network, and especially its core, did not correspond to Russia's industrial and
political geography suggests that economic partnerships may have served as alternative affiliations
that bridged the political and regional divisions in the multi-ethnic Russian Empire. Second, our
historical case also offers a more general lesson for understanding how cooperation through
private-order arrangements may support entrepreneurship in the absence of reliable government
institutions. The mere availability of privately organized substitutes for public institutions will not
be sufficient. Whether their support will be successful or not depends on the particular
composition of the opportunity structure within which entrepreneurs find themselves. Closure in
the relationships among entrepreneurs facilitates collective monitoring through reputation costs,
but its scope is unlikely to extend beyond local communities. On the other hand, access to
information, capital, credit, and other valuable resources necessitates wide-spanning relationships
that reach diverse communities of merchant entrepreneurs.
On the eve of the Great War in 1913, Russia ranked as the fifth largest industrial power on par
with Austria-Hungary and following behind the United States, the United Kingdom, France, and
Germany. Between 1885 and 1913, Russia's average annual growth rate of total product (3.25%)
was exceeded only by the United States, Canada, Australia (all three experiencing significant in-
migration in contrast to Russia's out-migration), Japan, and Sweden. Where Russia lagged behind
its competitors was in its economic performance on a per capita basis. At 101.4 rubles per capita,
Russia's GNP in 1913 compared unfavorably with Germany's 300.4 rubles, the United
Kingdom's 460.6 rubles, and the United States' 682.2 rubles. One important reason was that
output growth combined with exceptionally rapid population growth (Gregory 1994). The
population within the Russian Empire increased by nearly 140% from 74.1 million in 1860 to
175.1 million in 1914, the most rapid population growth in Europe. The average rate of
urbanization in the provinces of European Russia increased little from 9.9% in 1863 to merely
14.4% in 1914 (Crisp 1976). The share of agriculture relative to industrial production reflects the
lasting rural concentration. Russia remained the world's largest grain producer in 1861-1913, but
a minor producer of industrial commodities. Although 75% of its labor force was engaged in
agriculture the grain output per capita was well below the output of France, Germany, and the
United States, and roughly equal to Austria-Hungary. Russia's per capita output in industrial
products was only half of Austria-Hungary's in 1913 (Gregory 1994).
Few historians would deny the central role the state played in Russia's economic
development. The ability to maintain market-supporting institutions and policies is arguably the
main economic reason why states exist (North 1981). In Russia's case, the historical evidence
indicates that, after the defeat in the Crimean War (1853-56) and an ensuing economic
stagnation, the tsarist regime was forced to adopt social reforms and industrial modernization if it
wanted to live up to its ambitions of great power status (Eklof, Bushnell, and Zakharova 1994;
Gatrell 1986). Earlier work such as Gerschenkron's (1962) characterized the ambitions of an
active Russian state that was eager to close the economic gap separating it from competing
countries: “Economic development in a backward country such as Russia can be viewed as a
series of attempts to find – or to create – substitutes for those factors which in more advanced
countries had substantially facilitated economic development, but which were lacking in
conditions of Russian backwardness” (Gerschenkron 1962, p.123). Gerschenkron argued that the
tsarist state was particularly successful in its interventions in two key areas of industrialization.
One area was the attraction of foreign entrepreneurial expertise and capital, aided by the
introduction of a stable gold-backed currency whose absence before 1897 Gerschenkron
regarded as one of the important reasons for Russia's belated industrialization. The other area
concerned the role of the state as a substitute entrepreneur. To promote industrialization, so
Gerschenkron's argument, the tsarist government erected tariff barriers for imported
commodities, expanded railroad construction, subsidized private enterprise, and reserved
contracts for military equipment for domestic firms.
More recent explorations in economic history find less empirical support for
Gerschenkron's positive view of the state in Russia's economic development (Gatrell 1986;
Gregory 1994; Owen 2005). Kahan (1989, p.96) shows that the government spent “only a minute
part of its budget expenditures ... for purposes of developing the industrial sector.” The evidence
also indicates that tsarist tariff policies were motivated as much by fiscal needs as by aims to
protect domestic industries, as Gerschenkron implied (Kahan 1989). Others have also challenged
Gerschenkron's positive interpretation of the state's policy toward foreign investment and the
promotion of entrepreneurship. McKay (1970) and Carstensen (1983, 1984) demonstrate that
expectations of high returns in an emerging mass market attracted foreign investors more than
skillful campaigns of Russian government officials. Few would therefore question the inflow of
foreign investment.4 But, as documented by Owen (1991a, 2005) and others (Gatrell 1995; Crisp
1976), industrialists repeatedly complained that the tsarist state failed to guarantee and enforce
4 McKay (1970, pp. 25-29) reports that foreign ownership of common stock in industrial corporations
increased from 17% in 1880 to 47% in 1914, and that foreign investments accounted for 55% of new
capital in 1893-1900 and for 50% in 1909-13 (see also Carstensen 1983).
property rights. Instead of implementing policies that supported entrepreneurship, the tsarist
administration undermined it through often arbitrary legislation.5
In a 1899 memorandum, even the Minister of Finance Sergei Witte lamented how
bureaucratic idiosyncrasies strangled foreign investments: “All foreign companies are subject to
Russian laws and regulations as well as ordinances and rules which may be subsequently issued. In
permitting the activities of foreign companies in Russia, the government retains the right to revoke at
any time that permission and to demand the liquidation of any company. Obviously, every detail of the
influx of foreign capital into Russia is kept under strictest control by the central and local
authorities” (quoted in Von Laue 1963, p.181). Such hindrances to property rights were not
limited to foreign investors. An 1899 Ministry of Finance memorandum reveals that preventing
“the encroachment of undesirable elements” was the motivation behind increasingly severe
measures to limit the rights to property, residence and managerial functions of Jewish
entrepreneurs (cited in Owen 1991a, p. 122; see Nathans 2002; Rogger 1986). Even ethnic
Russian entrepreneurs like Moscow merchant F.V. Chizhov deplored the “stupidity, conceit, and
ignorance of the army of pen-pushers” in the imperial bureaucracy and their arbitrary
governance (quoted in Rieber 1982, p.176). Just as their Jewish business partners elsewhere,
leading Muscovite merchants faced persecution simply because they did not adhere to the
Orthodox Church, but to the sectarian Old Belief. They had to confront corrupt and inefficient
government officials, disruptive labor policies, and police controls. They risked arrest for
advocating their radical slavophile politics and their opposition to the detested Petersburg
5 The state prohibited foreigners from owning shares or holding managerial positions in the trade and
shipping industry on the Caspian sea (law of November 1869); Siberian gold mining was restricted to
ethnic Russians (law of January 1885); the law of March 1887 prohibited foreign companies from owning
or leasing rural land in Poland, the eight western Russian provinces, Bessarabia, Courland, and Livonia; if
Russian companies held land in these areas, foreigners could not own shares; the law of December 1888
prohibited foreigners from acquiring land for mining in Poland; corporations with Jewish or foreign
stockholders could not purchase real estate in Turkistan (law of November 1893); the law of June 1899
closed managerial positions to foreigners in the western provinces, Don Military Region, Caucasus,
Turkestan, and Amur region (Owen 1991a, ch.5).
bureaucrats (Rieber 1982). By 1913, property rights were still not fully enforced against
government violations. In a State Duma speech during the same year, Aleksandr Konovalov, a
textile magnate and prominent member of the progressive liberal party, demanded “to replace
the 'arbitrariness of the administrative authorities with the creation of firm norms of legality [...],
equal for all' and to eliminate 'red tape and tutelage' from the administration of corporate
enterprise” (quoted in Owen 1991a, p.169). Such was the broader institutional setting and
economic climate within which Russian and foreign entrepreneurs pursued their enterprises. It
combined a merchantry weakened by fragmentation into competing interest groups, unreliable
political institutions, and economic growth that was often hindered by poorly formulated
commercial policies of the imperial administration.
Supporting evidence for our arguments comes from the RUSCORP database (Owen 1992). It
contains rich quantitative information on the nature of all for-profit corporations founded in the
Russian Empire from the time of Peter the Great to the Great War. To examine entrepreneurship
in late imperial Russia, we use the information on the company profiles of share partnerships and
joint-stock companies recorded in their corporate charters. Russian corporate law distinguished
the large corporation and the share partnership from the small business and trading firm ( torgovyi
dom) that only required a contract, signed by all partners and registered with the local municipal
clerk (Owen 1991a). The RUSCORP data set does not include the various mid-level trading firms
and small family businesses because they did not require an imperial charter.6
6 However, Owen (1991a, p.11) suggests that "their aggregate economic importance remained minor." He
notes that despite the large number of over 9,000 small trading firms in 1914, their entire basic capital
stock of 333.1 million rubles was dwarfed by the stock value of 4.6 billion rubles of the 2,263 industrial
corporations in the same year. Further, corporations accounted for 74% (in 1900) and 86% (in 1914) of
the capital invested in industrial and commercial companies (Crisp 1976, p.113). Moreover, small
partnership firms typically existed only for short periods because changes in partners required the
dissolution of the firm and a new contract (Owen 1992, p.20).
Our empirical focus on large corporations and partnerships implies that we are
considering the pursuits of the business elite. One reason for focusing on elites is that we are also
interested in the political consequences of the merchants' economic activities, and the extant
literature recognizes that the merchant elite carried most of the political weight in the Russian
business community (Rieber 1982; Owen 2005). Within this empirical scope, we consider all
incorporations from the late 1860s to the eve of the Great War in 1913, a crucial transition
period for Russian economic development that witnessed the consequences of the Great Reforms
under Alexander II, an unprecedented rise of heavy industry and the inflow of foreign
investment (Eklof, Bushnell, and Zakharova 1994; Carstensen 1983, 1984; McKay 1970). Figure
1 illustrates the rise in the number of corporate foundings in the Russian Empire during the
1869-1913 period.7
<Figure 1 about here>
The founding of both forms of large corporations, the share partnership (tovarishchestvo na
paiakh) and the joint-stock company (aktsionernoe obshchestvo), required the approval of the central
government, which granted charters only to enterprises that it deemed to be of national
economic importance. All corporate charters had to be signed by the tsar. Members of the
merchant guilds or any free estate could participate, and all members of a corporation enjoyed
the privilege of limited liability (Owen 1991a).8 The corporate law of 1836 regulated
incorporation. It introduced the concession system, which required every proposed founding
charter to be reviewed by the appropriate ministry and state council before being signed by the
tsar. To attract entrepreneurs and their capital, the ministerial review could also entail the
7 We start our periodization in 1869 because reliable price indices for deflating basic capital are not
available for earlier years.
8 Entrepreneurs in Saint-Petersburg favored joint-stock companies. Typically, a large number of shares at
small individual values were issued to raise basic capital for large projects such as railroads, steamship lines
or banks. Muscovite merchants preferred share partnerships, which typically entailed more intimate and
family-business relationships and were established to provide limited liability for smaller ventures than the
large joint-stock enterprises. Partnerships issued fewer shares at much higher values (5,000 to 10,000
rubles per share) compared to joint-stock companies (Crisp 1976; Owen 1991a).
granting of monopoly rights, tax exemptions, and other privileges if the ministers saw great
significance in the proposed enterprise. Other articles exemplified the regulative nature of the
government’s policies: primarily to limit stock-jobbing and speculation, no company could start
its operations before all shares were sold and payments collected; unnamed shares and futures
were banned; founders could become members of the board but could not purchase more than
one-fifth of the total share capital; and the annual general assembly of stockholders exercised the
primary authority, including the election of the board, the general strategy, and the decision to
dissolve the company. Once confirmed by the tsar’s signature, the rules set forth in the founding
charter could not be changed without the permission of the appropriate government authorities.
With few exceptions, these legal regulations remained in place unaltered until the end of the
tsarist regime (Owen 1991a).
Company Data
Our outcome variable of interest is the amount of basic capital raised by a company's founders
and recorded in its corporate charter. The basic capital recorded in the charter is best interpreted
as a potential for attracting financial commitments from investors. But the critical point here is
that a company could not start its operations before all shares were sold and payments collected.
As the kind of ruble – silver, copper, or paper assignat – and the values of shares routinely varied
from charter to charter, even within the same year, all capital values are normalized according to
the standard ruble of account (Owen 1992). We then deflated all capital values using the
standard Saint-Petersburg Institute of Economic Research retail price index (Gregory 1982;
Strumilin 1966). All capital values are denoted in thousands of rubles with 1913 as the base year.
Where basic capital consisted of both stocks and bonds, the sum of both amounts is used. Table
1 reports descriptive statistics for this and all other company-level variables we use in our analysis.
<Table 1 about here>
Founder Data
The advantage of the dataset for examining entrepreneurship is that it provides matching
information on the characteristics of individual founders (to the extent that they are documented
in the corporate charters or can be unambiguously established from secondary sources). In table
2, we compare summary statistics for the amount of basic capital, ethnic and social status
background for founders who participated in no more than one corporate founding (n=8,709,
columns 1-3) and serial entrepreneurs (n=768, columns 4-6) involved in the founding of several
enterprises. The panel data yield multiple observations for serial entrepreneurs as they participate
in successive foundings of companies over time. The noteworthy difference here is that, on
average, serial founders were involved in corporate foundings with significantly larger amounts of
basic capital than one-time founders, perhaps as an effect of learning-by-doing (column 7).9
Table 2 details the founding activities of serial entrepreneurs in late imperial Russia. The
mobilization of capital was not the only, but certainly one of the most critical parts of their
activity (McKay 1970; Guroff and Carstensen 1983; Owen 2005). The information that
potential founding partners and investors had access to beyond their local contact networks was
often too diffuse to assess the prospects of new ventures (Stuart, Hoang, and Hybels 1999). We
reason that a founding team's past success in mobilizing capital for a new enterprise contributes
to a reputation of successful entrepreneurship that present investors may interpret as an indicator
of the founders' performance potential. Investors may expect that past success leads to future
success.10 Put differently, the amount of capital founders mobilized for a new enterprise may also
be interpreted as the revealed preference of investors to promote that company. The founding of
9 The difference is slightly smaller when we compare the capital of one-time founders to serial founders in
their first venture (mean capital=2074.158; s.e.=158.9146; t-ratio=-2.6942).
10 We presume that information on mobilized capital was public. Tsarist corporate law required founders
“not only to record all stock purchases in a special sealed book (shnurovaia kniga )… , but also to account for
the money thus collected in another book and to leave both books open for public inspection on the
premises of the local municipal government until the subscription of shares has been completed” (Owen
1991a, p.28). Referring to the case of Belgian entrepreneurs operating in Russia, McKay (1970, pp. 83-85)
argues that signals of success travelled fast in close-knit groups of investors.
the Moscow-Tashkent Silk Company by such eminent Muscovite merchants as Fedor Chizhov
and Timofei Morozov illustrates the importance of reputation, but also how looming business
failure could undermine it. While faith in past achievements encouraged the founding associates
to promote this silk cultivation enterprise, the government's insistence on publishing its accounts
"aroused the investors' fears that the apparent lack of success in that risky undertaking might
damage their reputations and weaken public confidence in their other enterprises" (Rieber 1982,
We consider the reputation effect of previous success on future capital mobilization for
each partner in the founding team.11 In particular, we measure success in a previous founding (t-
1) as the amount of capital raised relative to the median amount of basic capital assembled by
other founding teams in the same industry, location, and decade.12 For example, the 4,423,000
rubles (1913 ruble value) that one Benedikt Givartovskii and his fourteen partners mobilized in
1875 to establish the First Moscow Streetcar Company exceeded the median basic capital of
other new corporations in the transportation sector in the Moscow center region in the 1870s,
and therefore we define this founding as successful.13 An important concern here is to what extent
the potential to attract contributions to basic capital may also tell us anything about corporate
11 Measuring reputation is meaningful only for persons with at least two observations over time. We find
too many changes in membership composition over time for teams (rather than individual founders) to be
an appropriate unit of analysis: only 15.7% of serial founders continue collaborations with previous
partners (table 2); merely 4.3% of founding teams reoccur with the same partners.
12 To ensure sufficient observations for calculating median capital, we combine the more fine-grained
industry classifications in table 1 into six larger categories: mining (n=234), construction (n=74),
manufacturing (n=2,318), transportation (n=332), wholesale (n=207), and finance(n=350). The only small
category remaining is Public Administration (n=6). We exclude the six unclassifiable enterprises in table 1.
13 We code previous success as a binary indicator to distinguish founders with a good reputation from
others since unit changes in a continuous capital variable do not make this distinction. A continuous
measure of capital also misses the variation in the distribution of basic capital across industries, and
within industries over time. Figure A.2 in the appendix illustrates this variation in capital for the
transportation industry, including railways and river shipments. Clearly, the capital of other corporations
within the same industry and period should be the comparison set for selecting the appropriate cut-off
value. A higher cut-off than the median capital amount (e.g. the upper 80% or 90% in the distribution) is
unlikely to change our findings. Our regression results already show that reputation is positively related to
capital mobilization when we use such a relatively low benchmark as the median capital. Our findings
would be even stronger if we would select a less conservative cut-off than the median.
economic performance because basic capital itself is not a direct measure of a firm's
performance. Our robustness analysis in the appendix demonstrates that success in the
mobilization of basic capital is indeed systematically related to performance: the survival rate for
companies we coded as successful is significantly higher than the rate for companies that raised
less capital (figure A.1).
The results in table 2 further show that about 36% of the 1,832 observed founding
activities of all serial entrepreneurs in our sample enjoyed such success in past foundings and in
20% of the cases, the founding team included at least one previously successful partner (column
5). The remaining variables in table 2 consider continued collaborations, which may have
signaled the benefits of trust-filled relationships, and the frequency, timing, and diversity of
foundings by individual entrepreneurs.
<Table 2 about here>
Network Data
To assess the salience of variation in network patterns for entrepreneurship, we coded affiliation
networks of co-founding ties among individual founders and the companies in whose founding
teams they participated. For our entire period of interest (1869-1913), we have cumulative
network data on the affiliations among 11,545 founders and 4,172 companies.14 Since we are
interested in the extent and persistence of structural cohesion versus fragmentation over time, we
split these data into period-specific networks. Ideally, we will want a division into periods that
does not artificially create fragmentation by cutting off observed ties. We also need a sufficient
number of discrete periods to reveal potential changes in the network patterns. Our solution is a
periodization based on the observed duration between subsequent foundings (see table 2). On
average, foundings were about four to five years apart. We opted for a conservative estimate of
network fragmentation and split the time-axis into five eight-year periods. Eight years are about
14 The numbers in tables 1 and 2 are smaller because information is missing for some variables.
twice the average duration between foundings, and comfortably include the majority of founding
sequences of individual entrepreneurs within each period (the last period, 1909-1913, contains
only five years, but a much larger number of founders than the other periods). Because we do not
know when partnership ties ended for all corporations in our data, this periodization is likely to
be biased towards cohesion among founders, and therefore yields conservative estimates of
For each period, we first constructed a binary founder-by-company matrix with founders
arrayed in rows and companies in columns. Each cell reports if an entrepreneur was a member
of the company’s founding team or not. We then transformed the eight matrices into a
symmetric founder-by-founder matrix and a corresponding symmetric company-by-company
matrix. Within each founder-by-founder network, pairs of founders are linked to the extent that
they were partners in the founding of the same companies. Entries are equal to 0 absent such co-
founding ties. The corresponding company-by-company networks record the number of founders
that each pair of companies has in common and documents the extent of interlock between
founding teams.
The graph in figure 2 maps the cumulative founder-by-founder network over the entire
1869-1913 period. The nodes represent individual entrepreneurs, linked by their joint
membership in the same founding teams.15 We use a graphing algorithm that draws the network
in such a way that the distance between founders is proportional to the shortest path linking
them. To avoid placing nodes too close to each other, the algorithm minimizes variation in the
length of lines. Immediately visible is the partition of the network into a well-connected core
(gray and black nodes) around a cohesive main component (black nodes only) and a surrounding
fragmented network periphery (white nodes) consisting of a multitude of small components that
15 Normalizing tie-strength is not an issue. First, we are interested in the shape of network patterns and
not in the strength of dyadic ties. Second, dyadic tie-strength varied little: the maximum tie value for the
entire 1869-1913 period equals 4; and only 178 out of all 38,456 ties among the 11,545 founders have a
value greater than 1.
are disconnected from each other.16 Here we should remind the reader that positions in the core
and periphery of the corporate partnership network should not be interpreted as being
congruent with locations in the economic and political geography of the Russian Empire, such as
the Muscovite core versus regional peripheries (Rieber 1982). If we define the geographic core to
include Moscow and its surrounding provinces, then cross-classification reveals no significant
overlap between geographic location and network position: in our panel data, 60.5% of
observations located in the geographic periphery belong to the network core. Similarly, 57.6% of
observations in the Moscow core occupy positions in the network core. Hence, differences
between geographic locations are not reflected in corresponding differences between network
positions (chi2(1)=1.1377; p=.286).17 The finding does not imply that geography and regional
identities played no role in the economic relationships we examine here. But it suggests that a
substantial number of entrepreneurs did not rely solely on their regional attachments when they
selected partners for their corporate founding teams.
<Figure 2 about here>
Likewise, we do not simply confound the periphery with one-time founders and the core
with serial entrepreneurs: 31.5% of the observed founding activities of one-time founders in our
sample are embedded in the network core, and 39.6% of the founding activities of serial
16 Because a large number of mutually reachable founders characterizes a cohesive network core (Moody
and White 2003), we define the core as consisting of all components that are equal to or larger than the
95th percentile of component sizes in each period (see table 4). All other components and isolates belong to
the periphery.
17 Results refer to the serial founders included in our regressions (n=1,832). The geographic indicator
equals 1 if a venture is located in one of the following provinces, and equals 0 otherwise: Vladimir,
Voronezh, Kaluga, Kostroma, Kursk, Moscow, Orel, Penza, Riazan, Tambov, Tver, Tula, Iaroslavl. The
results are similar if we include one-time founders (chi2(1)=.0030; p=.956), or include Petersburg, Odessa
and their surrounding provinces in the geographic core (chi2(1)=.3731; p=.541). Consequently,
interpretations of differences in the economic geography between the Russian heartland and peripheral
regions are not well suited to explain the split within the corporate network into a cohesive core and a
fragmented periphery. Such interpretations refer to differential stages of industrial development between
the geographic center and the periphery, comparative advantages gained through regional specialization,
differences in the stratification of ethnicities between the Russian heartland and the peripheral provinces,
and differences in the timing of geopolitical incorporation of peripheral regions into the Russian imperial
polity (Bassin 1999; Brower 2003; Kappeler 2001; Weeks 1996).
entrepreneurs occur in the periphery. Nor are core positions and success in mobilizing capital
perfectly overlapping: 34.3% of the founding activities of founders we classified as not successful
are embedded in the core, and 34.8% of those we coded as successful occur in the periphery.
We first consider to what extent a reputation of past success contributed to the mobilization of
basic capital, a central activity of merchant entrepreneurs (Guroff and Carstensen 1983; McKay
1970). As noted earlier, we suggest that founding team partners and investors may interpret a
founder’s past success in raising capital for his previous enterprises as an indicator of his
performance potential. That is, current partners and investors consider the revealed preference
of past sponsors as a cue to decide whether they should promote the new enterprise by the same
founder or not. Reputation in this sense is used to cope with the uncertainty of future
performance. Continued success in raising sufficient funds for various enterprises expresses a
reputation in another sense as well: to the extent that other promoters are repeatedly willing to
offer their support, they signal that such founders are credible and trustworthy business partners
who do not deceive their investors or founding partners. Network closure that embeds founders
and investors through a high density of ties supports this credibility because it facilitates both the
enforcement of collective norms against fraud and the flow of information about credible
partners among potential promoters. An illustration of such cohesion are the Old Believer
merchants who "trusted each other ... because a network of personal relationships ... provided
crucial financial and commercial support, including interest-free loans, so that ostracism on
account of dishonesty toward a coreligionist meant economic ruin" (Owen, in Guroff and
Carstensen 1983, p.60). Crisp (1976, p.114) similarly notes of the developing credit market that
“on the local but to an overwhelming extent also on the regional and national level face to face
relations or recommendations of persons of proven probity were the basis of credit.” Relational
closure thus increases the costs associated with losing one’s reputation (Burt 2005; Coleman 1990;
Greif 2006). We argue that such a reputation mechanism operated as a private-order safeguard
against the often widespread embezzlement of initial share capital when reliable public
institutions to protect stockholders' rights were lacking.18
<Table 3 about here>
In table 3, we consider this reputation mechanism. We present least squares estimates of
the influence of partners' prior success and repeated partnerships on the mobilization of basic
capital for new enterprises founded by serial entrepreneurs. Again, we focus on serial founders
because only they could have built a reputation for success over time and engaged in repeated
partnerships with other founders.19 The dependent variable in all specifications is the variation in
logged basic capital, standardized and deflated to 1913 rubles.20 All regressions include fixed-
effects for years in which corporations were founded to control for year-specific impacts on the
amount of capital raised. We use Hubert and White robust variance estimates to adjust for non-
independence among observations within the same founding team.21
Recall that we measure success in a previous founding (t-1) as the value of capital raised
relative to the median basic capital mobilized by other founding teams in the same industry,
location, and decade. In columns (1) and (2) in table 3, we find that the reputation effect of
previous success is indeed far from trivial. Having one or more successful partners on one’s
founding team significantly22 increased the value of basic capital between 14% and 17% as
18 Owen (1991a, p.29) notes that “corporate founders had learned a clever way to benefit at the expense of
the stockholders: to bestow upon themselves, free of charge, a large portion of the corporation’s initial
stock as compensation for their entrepreneurial efforts. Having invested nothing of their own, they could
dispose of the company quickly, taking a profit on the sale of their shares to the public.”
19 Robustness checks demonstrate that the estimates for our main variables (reputation and network
position) in tables 3 and 5 retain their direction and magnitude if we include both one-time and serial
founders (results available from the authors).
20 We estimate capital on a logarithmic scale because the distribution of basic capital is highly skewed.
21 Estimating period-specific autocorrelation models to control for network dependencies also confirms the
least square results.
22 Strictly speaking, our regressions are based on the population of serial entrepreneurs, and not on a
random sample of that population. Whenever we refer to the statistical significance of estimated
compared to founding teams that lacked partners with a history of success (we address the
potential endogeneity in the relationship between capital mobilization and partner choice in the
appendix. Our robustness checks demonstrate that the choice of founding partners was not
primarily dictated by requirements to raise sufficient funds for an enterprise).
The result lends systematic support to our reputation argument. Still, variation in
mobilizing capital may have been a result of unobserved individual heterogeneity across
founders. Some may have been particularly skillful in promoting their enterprises compared to
others who lacked such qualities. One way to control for individual-level skill differences is to
exploit the panel nature of our data by using fixed effects estimates.23 However, skill and related
sources of heterogeneity across individual founders may vary over time. We therefore use the
number of previous foundings that an entrepreneur was involved in as an additional time-varying
indicator of his founding experience beyond the time-invariant qualities captured by our fixed-
effects estimates. The underlying assumption is that the number of foundings reflects an
entrepreneur's learning-by-doing experience.24 The results in columns (3) and (4) in table 3
demonstrate that the positive influence of a partner's reputation on a company's capitalization
coefficients, we have in mind a general statistical model where the reputation of founders predicts their
potential to mobilize capital. What we observe in the Russian case is one realization of the underlying
stochastic process that relates reputation and capital mobilization. The null-hypothesis is that reputation is
unrelated to capital mobilization. What makes this interpretation probabilistic is not that our inferences
are based on a random sample, but that some random component ("chance") may reveal that variation in
reputation is unrelated to the amount of capital raised.
23 We find that within-founder variation is sufficient to employ fixed-effects specifications. First, we include
only serial founders in our regressions because measuring reputation requires at least two observations per
founder. Second, in model (3) in table 3, 99% of all observations represent at least two events per founder.
Merely ten out of all 1,832 observations represent singular occurrences. We observe these ten serial
founders only once because their other founding events occur before 1869 and are left-censored. Third,
model (4) adds continued partnerships as a covariate, which excludes each individual's initial founding
because it cannot continue any previous partnership. Consequently, 588 out of the 1,077 observations in
model (4) represent singular occurrences. But even this constraint still leaves us with within-founder
variation for about half of the 1,077 observations included in the regression.
24 We also estimated linear and quadratic trends for the number of years since the first founding as
controls for individual-level heterogeneity. Using these alternative indicators of time-varying skills
confirms the direction, magnitude and significance of our main effects.
holds even if we take such individual-level heterogeneity into account: joining with previously
successful partners still increases the value of basic capital significantly by at least 16%.
Besides individual differences, a self-selection mechanism may generate our results, where
entrepreneurs will weigh the opportunity costs of founding a new venture before committing
themselves to it. Previously successful founders may only engage in new enterprises that promise
to be successful as well, and this preference may generate the positive relationship between past
success and present capital mobilization we observe. We find no supporting evidence for this self-
selection: a full third (33.24%) of previously successful founders engage in a non-successful
founding, whereas previously unsuccessful founders are equally likely to transition to successful
(48.88%) and non-successful (51.12%) foundings (chi-2(df=1) = 36.21; p<.0001). In addition,
founding in this context is not an individual decision, which is precisely the reason why we focus
on having reputable partners on a team: 89% of all founder observations had at least one partner;
table 1 reports a mean of three partners per venture; and our observed founding teams included
up to 70 partners.
An additional strategy for merchant entrepreneurs to pursue beyond the reliance on
successful founding careers is to continue a past partnership, possibly because it proved to be an
exceptionally productive one. Especially in settings where public institutions are weak, continued
partnerships provide opportunities for forging trust-filled relationships as substitutes for legal
safeguards (Fafchamps 2004). To compare the role of reputation with the potential advantages of
continued partnerships we use a binary measure: it equals 1 if a founder keeps collaborating with
partners from previous enterprises, and equals 0 otherwise. The results in table 3 reveal a positive
influence of such repeated partnerships, which is roughly similar in magnitude to the effect of a
positive reputation, excepting the fixed-effects estimates. Still, the estimates for our main variable
of interest, a reputation of past success, remain robust. In sum, reputation, understood as a signal
of a successful entrepreneurial career of a credible founder, indeed had a significant positive
influence on the mobilization of basic capital across varying specifications.25
Network Fragmentation
One starting point for our argument was the observation that emergent societies are typically
characterized by fragmented social structures. They therefore often lack the globally cohesive
networks necessary for effective enforcement through reputation-based private-order institutions.
Our next task is to document the extent of fragmentation within the co-founding networks
among Russian entrepreneurs. The graph in figure 2 obtains its structure primarily from the
absence of relationships between components. This pattern reveals that fragmentation existed,
particularly among founders located in the network periphery. The results in table 4 support this
visual observation with quantitative evidence for the persistence of structural fragmentation in
each period. Column 1 in table 4 reports the number of founders in each period-specific co-
founding network. Not all firms in our sample were large joint-stocks, and it was possible for a
single entrepreneur to initiate a founding. Yet, the low counts of isolated founders in column 2
indicate that it was uncommon to establish companies single-handedly instead of forming
partnerships. This finding also demonstrates that any observed lack of overall cohesion does not
stem simply from the presence of a large proportion of isolated founders.26
<Table 4 about here>
25 Using interactions between reputation and period indicators shows no evidence that the relationship
between reputation and capital mobilization changed over time, as a consequence of shifting government
policies when a new tsar ascended to the throne in 1881 and after 1894. When added to the estimations in
table 3 (and table 5 below), none of the period interactions yield significant effects, whereas all coefficients
for the main reputation effect remain robust (results available from the authors).
26 The share of isolated founders increases over time, from 1% to 15%. But column 3 in table 4 shows that
this increase has little impact on the proportion of mutually unreachable pairs. The increase in isolates did
not help to overcome fragmentation, but little evidence suggests that they were primarily responsible for
the lack of cohesion.
An intuitive measure of fragmentation is the proportion of founder pairs that are unable
to reach each other through their network ties, either directly or through third parties. Networks
become more fragmented as the proportion of mutually unreachable dyads reaches a value of 1.
Column 3 documents that fragmentation was indeed pervasive across all periods because few
entrepreneurs were linked through their co-founding ties. The measure offers preliminary
evidence but less detail about the underlying pattern of ties. Affiliation networks invariably
generate a pattern of local clustering that reflects underlying group membership. Still, we may
ask, for example, if bridges and brokers help to cluster the linked founders into a few large
groups, or if founders evenly distribute themselves across a large number of small groups.
A useful alternative measure of fragmentation that takes the topology of networks into
account is the number and size of components. Substantively, components identify subgroups in
a network such that each member of a component can reach every other member by at least one
pathway, using one’s direct contacts and their subsequent contacts (Moody and White 2003). The
important point for our purpose is that components are mutually exclusive subgroups with no
bridges between them. Consequently, a network that consists of a large number of distinct
components exhibits structural fragmentation. Columns 4 through 10 in table 4 report the
number and membership sizes of components in each period-specific network.
Three findings emerge. First, all co-founding networks break into a large number of small
components relative to the total number of founders, indicating fragmentation. Many
entrepreneurs in late imperial Russia were thus embedded in co-founding groups that included
only a few members and rarely interlocked with each other (columns 8 and 9).27
Second, in each period, we find that the size of the largest (main) component, and hence
the proportion of founders embedded within it, is small relative to the overall network size
27 As there are also hundreds of companies in each period network, such splintering may not come as a
surprise. But it takes only a few founders involved in more than one company to fill positions as cutpoints
that connect such separate components.
(column 5 in table 4). By definition, a large proportion of nodes in the main component of a
network implies that the majority of founders is connected. Consequently, our result is yet
another indicator of fragmentation because the larger proportion of founders is instead located
in the hundreds of scattered smaller components shown in figure 2.28 This picture also contrasts
with recent studies of similar collaboration networks such as co-authorship networks, the
production of Broadway musicals, or biotechnology organizations that find up to 53% (Moody
2004), 94% (Uzzi and Spiro 2005), and even 98.6% (Powell et al. 2005) of nodes connected
within the main component. For comparison, even in the most cohesive network we observe (in
the first period, 1869-1876), only 33% of all founders are contained within the main component.
The contrast and significance of fragmentation is even more pronounced once we consider the
mere 2.2% to 6.6% of founders who are located in the main component in the other periods. 29
Third, we find that overall integration between components did not increase, and
consequently that fragmentation persisted over time. Exempting the more cohesive first period
(1869-1876), the percent of mutually unreachable founders remains consistently at 99%. The
percent of founders in the main component similarly stays at the same low end between 2.2%
and 6.6%. Likewise, the variation in the descriptive statistics for component sizes across periods is
small. If anything, fragmentation increased over time, considering the noteworthy change
following the first period.
28 Robustness checks using network simulations to assess the significance of the low proportion of founders
in the main component show that the observed proportions are substantially lower than those expected by
chance (results available from the authors).
29 One reviewer wondered if the large main component in the first period (1869-1876) shapes our
findings. In all our regressions, we include dummy indicators for each year to control for potential period
effects. Across all OLS specifications, only the year 1870 is significantly related to capital mobilization. We
also re-estimated all regressions without members of the first period network to assess potential cohort
effects. We excluded founders who began their careers before 1869, but were still active in 1869-1876, and
all founders who began their founding activity in 1869-1876. The results in table A.4 in the appendix
demonstrate that our inferences are not merely an outcome of selection on members of the first period
cohort: the estimates for our main variables of interest remain consistent in their direction and
significance with the results obtained from our full sample (tables 3 and 5), and shift only marginally in
their magnitude (the only exception is the loss in statistical significance of the network core coefficients).
To the best of our knowledge, no particular historical circumstances existed during the first period that
explain the size of the main component in the first period and its decline in later periods.
The last row in table 4 documents that these results are not mere artifacts of our chosen
periodization, which may have arbitrarily cut off collaborative ties and induced fragmentation.
The most conservative approach to address this concern is to neglect the decay of ties and
founders altogether: the statistics for the entire 1869-1913 period network clearly show that 98%
of all founders still cannot reach each other. Likewise, merely 14.4% of founders in the main
component is still a substantially lower percentage compared to the 53% to 98.6% found in
previous studies of affiliation networks (Moody 2004; Powell et al. 2005; Uzzi and Spiro 2005).
Capital Mobilization In Core And Periphery
The next intuitive question is whether reputation was equally salient for mobilizing capital in the
core and periphery of the Russian partnership network or not. Again, the important point here is
that reputation requires a particular social structural foundation – namely network closure – to
work as a credible enforcement mechanism (Coleman 1990). By contrast, the greater diversity
and connectivity through bridges and brokers in the core suggests that core positions offered
better access to diverse sources of capital than the periphery. Our second task in this section is to
provide direct evidence that capital mobilization varied systematically with the pattern of social
relationships in which founders were embedded (Stuart, Hoang, and Hybels 1999). Ultimately,
we seek to answer the question which social organizational foundations are better suited to
support successful founding strategies in settings where network fragmentation combined with
weak institutional support.
<Table 5 about here>
The results in column (1) in table 5 address the first question, to what extent reputation
was equally salient for founders positioned in core and periphery. We use the same OLS set-up as
in table 3, this time adding the effect of membership in the network core and its interaction with
a founding partner's reputation of success. The evidence reveals that founders who were
embedded in the core enjoyed a comparatively small additional benefit from also having partners
with a good reputation on their founding teams: for them, successful entrepreneurship in the past
increased the expected basic capital by 19%. In the periphery, the effect of reputation was twice
as large: there, having a partner who was known as a successful founder raised the basic capital
by 39%. The results do not imply that, in the core, reputation and a founder's network position
were direct substitutes for each other. But they do imply that the role of reputation for capital
mobilization was significantly less salient in the core compared to the network periphery. Again,
to understand the differential influence of reputation we suggest to look beyond individual
behavior and positioning, and focus on the broader pattern of affiliations within which founders
are embedded: the closed pattern of affiliations in the periphery supports monitoring and
sanctioning based on reputation costs, and such network closure is significantly less prevalent in
the core of the partnership network.
The main effect of core membership in table 5 indicates that, all else equal, core positions
should have been more desirable than peripheral ones because they offered better opportunities
for raising more capital. Compared to peripheral locations, founding teams in core positions did
enjoy an 11% increase in expected basic capital. But if the more lucrative positions in the core
are not accessible, local closure in the periphery, based on homophily in partner choice or other
grounds, may become valuable.30 Locally at least, reputation is significantly more relevant for
raising capital within the small yet tightly knit networks we find in the periphery. There, news
about fraudulent behavior travels instantaneously and the capacity for sanctioning is much
greater than in the wide-reaching core component (Coleman 1990).
When we consider the social organizational basis of entrepreneurship, a simple indicator
that distinguishes occupants of core and peripheral positions is arguably not the most fine-
30 Low mobility rates from the network periphery to the core indicate such entry-barriers. Over all periods,
we find considerable movement from the core to the periphery, but on average only 25% of all founders in
the periphery moved into the network core in a subsequent period.
grained representation of differences in their underlying patterns of affiliations. To get at more
nuanced micro-level network correlates of capital mobilization we measure network constraint
(Burt 1992). In our case, founders are constrained to the extent that their partners are also
partnering with each other – if all of one's partner do so, local closure results and channels for
reaching information and resources become redundant, yet enforcement is eased. This scenario
mostly reflects the social structure among entrepreneurs in the periphery. In contrast, founders
are less constrained when they join with partners who are otherwise not connected with each
other. This condition describes the potential for network reach through bridging across diverse
groups that we observe primarily in the core. The measure thus has the advantage of capturing
both local closure and opportunity structures for reach through brokerage in a single statistic.
<Table 6 about here>
Table 6 presents means comparisons for founders' constraints in core and periphery for
each period network (columns 3-5).31 In all periods, the extent of network reach and brokerage
opportunities was consistently greater in the core than in the periphery (recall that higher
constraint indicates closure and less brokerage).32 Columns (6)-(8) in table 6 indicate that founders
in the core were able to translate these social-relational opportunities into economic advantages.
During most years, core entrepreneurs raised significantly larger amounts of basic capital than
31 We also considered ethnic brokerage, using Gould and Fernandez' (1989) triadic measure, and the
number of cutpoints per component. The alternative measures confirm the constraint results in table 6.
For the most conservative 1869-1913 network, mean brokerage scores equal 9.171 (sd=2.110) in the core
and .106 (sd=.013) in the periphery (t=-6.231); the average number of cutpoints per component equals
57.981 (sd=63.129) in the core and .064 (sd=.255) in the periphery (t=-81.164).
32 Size differences may explain greater reach in the core. By chance alone, bridging ties are more likely
between large founding teams than between small teams. On average, founders in the core have 13
partners whereas founders in the periphery have 3 partners during 1869-1913. However, we estimate
network effects at the individual level, and it does not necessarily follow that a founder will initiate more
ties to outsiders just because he is a member of a large founding team. Likewise, normalizing tie strength
by team-size requires some arbitrary cut-off to define when a tie exists. We opt for a non-parametric
solution and control for founders' number of partners in our regressions. We also control for industry
sectors, in particular finance, which mobilized more capital and attracted more founders than other
sectors, especially in the network core.
their peers in the periphery (using our reputation variable instead of mean capital likewise shows
a significantly higher proportion of successful founders in the core than in the periphery).
Returning to table 5, the regressions in columns (2) and (3) provide further evidence for
the benefits of network reach in a multivariate specification. Column (2) adds founder's constraint
to the interaction of reputation and core membership. Increasing constraint in one's partnership
network shows the expected negative effect on basic capital. Column (3) sets the differential
influence of reputation in core and periphery aside and focuses on the main effects of constraint
and reputation. Again, both a partner's reputation of previous success and the lack of network
reach as measured by constraint have the expected effects on capital mobilization: having
successful partners increased capital by about 15% while an increase in constraint by one
standard deviation (=.242) reduced the expected capital by about 6%. In sum, these results
delineate the social organizational foundations of entrepreneurship when the surrounding
networks are fragmented. On a global scale, network reach across diverse groups in the core
offers a clear advantage over the closed and isolated clusters of founders in the periphery. But as
noted above, where barriers to enter partnerships in the core are too high, it may become a
locally rational strategy for peripheral founders to rely on closure and reputation for the
mobilization of capital.33
To illustrate the bridging activities of brokers and their contribution to the pattern
observed in the core, figure 3 shows the personal network of an exemplary entrepreneur who
occupied a prominent mediating position. Timofei Savvich Morozov belonged to the leading
33 Possible entry-barriers to core positions included hinderances to mobility across ethnic boundaries that
were built into rigid systems of ethnic stratification in some provinces, or anti-semitic policies lobbied for
by slavophile industrialists (Kappeler 2001; Weeks 1996). But anecdotal evidence suggests that attitudes
toward Jewish entrepreneurs were ambivalent: Muscovites urged the police to protect Jewish merchants
from anti-semitic rioters whenever the losses affected their own business; but when Jews were competitors,
Russian merchants petitioned the government to expel them from Moscow (Owen 1981). Our quantitative
evidence indicates that anti-semitic sentiments were not strong enough to exclude Jews from core
positions: in our panel data, 68% of the Jewish entrepreneurs still belonged to the network core, whereas
only 59% among non-Jewish (mostly ethnic Russian) founders held positions in the core (chi2(1)=7.6606;
industrialists of Moscow's entrepreneurial group. In politics, Morozov and his peers were ardent
defendants of a romanticized pan-Slavic nationalism against the perceived threat of foreign
competition and influence. Morozov was also closely tied to other prominent Muscovite
merchant families through shared religious adherence to the Old Belief and several marriages of
his large family (Rieber 1982). His diverse entrepreneurial activities in five different industries
and four different provinces reflected his central mediating position in the co-founding network:
the scores for both his constraint (.071) and his broker role between ethnic groups (298.350) place
him in the top 5th percentile of network mediators in the core.34 He made good use of this social
capital as he served as the president of the Moscow Exchange Society in 1870-76. The Exchange
Society served as a political organization to unify the Moscow entrepreneurial elite and advocate
their commercial interests, and thus required considerable brokerage skills to bridge its diverse
factions. Under Morozov's leadership, membership in the Exchange Society swelled to 1,500,
indicating that it was capable to successfully represent diverse interest groups (Owen 1981;
Rieber 1982).
<Figure 3 about here>
Founders And Investors
The RUSCORP database includes all founders who are listed in the company charters. As
mentioned earlier, tsarist corporate law since 1836 prescribed that founders could not hold more
than one-fifth of a company's initial share capital (Owen 1991a). Founders had to rely on outside
investors for the remaining proportion of basic share capital. Share partnerships (about 35% of
firms in our data) may have been an exception because they issued few and expensive shares so
that their main founders may have also served as investors. All our estimations include controls
34 For comparison, the mean constraint equals .760 (sd = .242; median = .889), and the mean number of
ethnic brokerage opportunities equals 72.344 (sd = 350.983; median = 12.5).
for variation in organizational form.35 We also have data on the number and ruble value of shares
that each corporation issued, and we include this information as control variables in our analysis.
The size of the contribution that founders seek from outside investors will also depend on the
type of business and the industry sector it operates in. We also control for these two variables in
all our estimates of reputation and network position. The one information we do not have are
microdata on investor behavior. Hence, we cannot identify individual investors and how they
allocated their contributions across different enterprises, unless they also appear as founders of
other companies in the database.
To what extent may this data limitation affect our findings? Investors may weave an
alternative network through their joint sponsoring of multiple firms. Such joint investment ties
would offer new opportunities for contact and thereby help to overcome the fragmentation we
observe in the corporate co-founding network. The reasoning here is that we may fail to
recognize the true extent of cohesion—rather than fragmentation—in the corporate network
because we are missing adequate information on the networking activities of investors. However,
the more investors commit themselves (and their capital) to an enterprise, the more we would
expect them to seek a central position within that enterprise that permits them to influence its
strategy. If investment ties are indeed so salient for corporate activities that they channel the
formation of corporate networks, then the patterns of co-founding ties we observe in our data
should reflect precisely the patterns of these investment relations. Direct historical evidence for
our interpretation comes from contemporary Petersburg businessmen who stated that "in our
country very often one and the same capitalist invests large amounts in several joint-stock
35 An alternative explanation for the positive relationship between partner's past success and capital
mobilization proposes that previously successful founders reinvested funds into their new ventures. This
mechanism would apply primarily to share partnerships where founders also acted as main investors. But
robustness checks of our results in table 3, using interactions between past success and organizational
form, yield no significant effects for partners with past success in share partnerships.
enterprises on the condition, which is completely understandable, that he participate in the
management of these enterprises" (quoted in Owen 1991a, p.167).
Alternative Sources Of Affiliation
Until now, our focus on co-founding ties has excluded alternative networks besides investor
relations that merchant entrepreneurs in Russia may have relied on to bridge the structural holes
that separated them within their business partnership networks. 36
One interpretation considers that prior or concurrent ties established through kinship,
neighborhood, or previous collaborations in the same industrial sector, among others, constrain
or create opportunities for assembling founding team partnerships (Ruef, Aldrich and Nancy
2003). If, for example, Russian entrepreneurs subscribed to taste-based discrimination against
other ethnicities and preferred fellow Russians as partners instead, their co-founding networks
should have become increasingly patterned along ethnic boundaries. In this scenario,
relationships based on shared ethnicity or kinship would have bridged structural holes in co-
founding networks (but may have given rise to ethnically homogenous clusters in the extreme).
Another interpretation emphasizes not the congruence of categorical affiliations and
networks, but cohesion through multiple crosscutting ties. Here, founders who are disconnected in
one network setting may still be linked through alternative ties elsewhere (Gould 1995). For
instance, the formation of some business partnerships may have cut across, and thereby helped to
overcome the diverse regional, ethnic, and religious rivalries that otherwise pitted entrepreneurial
groups in tsarist Russia against each other (Joffe 1984; Owen 1991b; Rieber 1982). Whereas the
36 Antecedent collaboration in small businesses may have given rise to the corporate partnerships we
observe and to unobserved cohesion. Unfortunately, we lack adequate data on small businesses because
they did not require imperial chartering. However, if small-scale business partnerships did indeed lead to
large corporations, then the corporate networks should reflect any cohesion created by these prior
first interpretation identifies categorical homogeneity in separated local network clusters, the second
interpretation emphasizes global cohesion based on diversity in categories and networks.
<Figure 4 about here>
Figure 4 displays the extent of such overlap between co-founding networks and salient
categorical affiliations within the network core and periphery over the entire 1869-1913 period.37
Recall that we define the network core as consisting of all partnership components that are equal
to or larger than the 95th percentile of component sizes, and consider all other components and
isolates as belonging to the periphery.
Economic historians of late tsarist Russia have long noted that rivalries between industrial
regions helped to prevent the emergence of a cohesive class identity among merchant
entrepreneurs. Because the Russian Empire was so expansive, geographic distance may have
hindered collaboration across locales and amplified regional fragmentation (Joffe 1984; Owen
1991b; Rieber 1982). We have information on industrial sectors and the location of corporate
headquarters for 4,172 companies active in 1869-1913. The first two comparisons to the left in
figure 4 focus on the 1,694 interlocks among corporations and consider to what extent connected
founding teams were located within the same industries and regions. For example, a link between
a Saint Petersburg bank and a Moscow Railroad company implies that their founders established
businesses in at least two different regions and industries.
Sixty-six percent (=1,114 ties) of the founding team interlocks reached across separate
industrial sectors.38 Earlier, in the data section, we have already documented that the network
37 We have also calculated the proportion of within-category partnership ties separately for each of the six
periods in table 4. The period-specific results confirm the pattern in figure 4. We thus opt for the more
parsimonious cross-sectional presentation. A single cross-section is also conservative because it does not
censor observations of bridges across categories.
38 We may observe fragmentation and a small proportion of founders in the main component because we
examine various industries at once, and not a single organizational field as in other studies (Powell et al.
2005). Consequently, we should expect fragmentation because companies in similar industries cluster
together with few bridges across clusters. The large proportion of co-founding ties across industrial sectors
in figure 4 suggests that little evidence exists for this concern.
positions of individual founders do not map directly onto geographic locations. Here, we likewise
find that about 50% (=840 ties) of all corporate interlocks link teams across regional boundaries.
The main result, then, is that neither geographic distance and regional rivalry nor industry sector
boundaries prevented a considerable number of founding collaborations across these divisions.
Consequently, neither was primarily responsible for the observed fragmentation.
Figure 4 further reveals that industrial and regional diversity was more common in the
network core than in the periphery. Whereas 43% of all 484 co-founding ties within the
periphery connected companies operating in the same industries, 69% of all 1,210 co-founding
ties in the core linked companies in different industries.39 Similarly, regional segregation was
significantly more pronounced in the periphery, where 62% of co-founding ties remained within
the same location. In contrast, 54% of corporate interlocks within the core bridged across
different regions. All differences in proportions are significant (p < .010).
Our findings look similar for co-founding ties across kinship and ethnicity among
individual entrepreneurs to the right in figure 4. As in other historical contexts, family-owned
firms and merchant dynasties played an important role in the Russian business world (Adams
2005; Owen 1981; Rieber 1982). In Moscow and elsewhere, “leading business families were
relatively old families, with a strong sense of continuity and personal pride in family
achievements” (Ruckman 1984, p.6). The first noteworthy founder-level result in figure 4
indicates, however, that kinship is the least salient category for creating business partnerships. In
39 Industry concentration seems unlikely to have generated the differences in connectivity between
network core and periphery. We do find a significantly (z = -28.707; p < .000) higher concentration in the
finance sector among core founders (32% of foundings) than in the periphery (11%), and our regression
estimates reveal that foundings in the finance sector mobilized about 20% more capital than foundings in
manufacturing (the comparison category). Banks certainly played a leading role in Russia's economic
development (Crisp 1976; Owen 1991a). However, our endogeneity checks in the appendix demonstrate
that capital needs did not dictate partner choice, and were therefore unlikely to have shaped differences in
network patterns. Similarly, a means comparison of network constraint does not offer strong evidence that
founders in the finance sector (mean constraint = .626; sd = .319) enjoyed more broker opportunities than
founders in other industries (mean constraint = .698; sd = .277).
the periphery, only 35% of co-founding ties connect founders who share the same last name. 40
Once again, this result contrasts significantly with the network core where merely 15% of
partnership ties formed among kin.
Equally crucial for merchant entrepreneurs and for tsarist Russia in general were the
boundaries that separated the diverse ethnic groups the empire was composed of (Kappeler
2001; Weeks 1996).41 Self-selection, fueled by ethnic prejudice, was evident such that merchant
entrepreneurs often preferred co-ethnic business partners (Owen 1991b; Rieber 1982). As noted
earlier, increasingly repressive legislation against foreign entrepreneurs and ethnic minorities,
especially against Jews, restricted many corporate activities to ethnic Russians (Nathans 2002;
Rogger 1986). The proportions reported in figure 4 suggest that ethnicity was indeed a correlate
of corporate partnerships: about 60% of co-founding ties linked entrepreneurs who shared the
same ethnic origin. Yet, the difference in diversity between core and periphery that is so
substantial for all other categories seems less clear with respect to ethnicity. 42
40 Systematic kinship information is not available for all founders. We therefore coded kinship ties through
a careful matching on surnames using both a name recognition algorithm and case-by-case inspection. We
then identified components in this kinship network as our proxy for families. This coding is obviously a
proxy, but the bias can be in both directions: not all founders who share the same surname are necessarily
relatives; but relatives need not share the same surname. Matching on last names may also obscure
Russification of ethnic minorities (Brower 2003; Kappeler 2001; Weeks 1996). However, our main point is
that business partners were less likely to be relatives than expected, based on the lack of similarity in
names. Our result is in fact conservative: if we were able to correct the Russification bias, then the names
of ethnic minority members that currently look similar to Russian names will differ from those Russian
names. Consequently, there would be even less similarity in last names among business partners than we
41 Ethnicity and our kinship coding are strongly correlated: over all six period networks, 92% of kinship
ties linked co-ethnic founders, on average (sd = 3.1%; max = 95.7%, min = 87.5%).
42 Until the end of the third period (1885-92), co-ethnic partnership ties were more prevalent in the
periphery than in the core, revealing a pattern similar to within-industry, within-location, and kinship ties
in figure 4. The tsarist government's policies against ethnic minority entrepreneurs became particularly
severe by the fourth period (1893-1900). This shift toward more severe restrictions coincided with an
increase in co-ethnic partnership ties in the network core, from 44% of all partnership ties in 1885-92 to
68% in 1893-1900. In the periphery, the proportion of co-ethnic ties decreased from 67% to 60%,
possibly because Russian founders who wished to continue their partnerships with ethnic minority
entrepreneurs had to move from prominent network core positions to peripheral locations that were less
exposed to government sanctions.
In sum, these results show that the extent of fragmentation we identified earlier is not just
a by-product of our focus on co-founding partnerships. Certainly, alternative ties and group
memberships may have bridged the structural holes and supported cohesion within the co-
founding network. Such alternative bridges were significantly more common within the network
core than within the periphery. It was primarily in the core that founders from diverse
backgrounds – different regions, industries, and families – formed business partnerships with each
other, and thereby contributed to social cohesion. Ethnicity, in contrast, was a less likely source of
global cohesion. In both core and periphery, founders were more likely to eschew partners who
did not share their ethnic origins, a selection that contributed to fragmentation along ethnic
Finally, the majority of founders (68% in the entire 1869-1913 window) were located in
the periphery of the co-founding network, and here their dominant strategy of partnership
choice was homophily, not diversity. They preferred business partners who shared their
background, a strategy that reinforced local closure, but undermined the global cohesion that
would enable them to reach distant and diverse clusters of entrepreneurs. In the absence of
reliable public institutions to protect property rights and contract commitment, such reliance on
trustworthy neighbors and one’s family may have made perfect sense for entrepreneurs in the
periphery. But the unintended consequence of this homophily strategy in their local circles was
the reproduction of precisely the network fragmentation their strategy was designed to cope with
in the first place. One obvious question is why the structural holes between components were so
persistent over time. If network reach was really so beneficial, why do we observe so little change
toward cohesion over time? Our evidence suggests that a local preference for similar business
partners was one, but certainly not the only important influence that blocked substantial changes
in the network structure.
Foreign and Ethnic Minority Entrepreneurs
One may wonder to what extent the presence or absence of foreign founders in our dataset
influences our findings. The role of foreign entrepreneurship within late tsarist Russia has long
been recognized in the literature (Carstensen 1983, 1984; McKay 1970; Rieber 1982). But as we
emphasize in these pages, arbitrary decisions by the tsarist administration often hindered the
entrepreneurial activities of foreigners. Still, within the limits prescribed by these restricting
policies, foreigners could and did become partners on corporate founding teams (Owen 1991a,
1992). Within the sample of 1,077 serial entrepreneurs that enter our regressions in tables 3 and
5, we were able to identify 58 of these observations as foreign founders, using the information on
citizenship in the RUSCORP database. Because we have so few non-Russian citizens, we briefly
summarize the robustness checks we undertook. Foreign founders in our sample do not occupy
significantly different positions in the corporate network than their Russian peers. Sixty percent
among foreign founders belong to the network core, which contrasts little with the 63% core
members among Russian entrepreneurs (chi-2=.1788, p=.672). Virtually no difference exists in
the average network constraint between foreigners (constraint=.6766) and Russians
(constraint=.6765; t-value=-.0014). Foreign founders are engaged in more brokerage
opportunities between different ethnic groups (mean brokerage score=244.3) than their Russian
counterparts (mean brokerage score=126.3), but not significantly so (t-value=-1.2809). Likewise,
controlling for foreigners has little impact on our multivariate results. We replicated all capital
regressions with an added covariate for foreign citizenship (the number of observations is too
small to distinguish between nationalities). As suggested in the literature, foreign entrepreneurs
display a significant positive effect on capital mobilization when they are added to the
specifications in columns (1) and (2) in table 3 (the coefficients equal .246 (se=.102) and .264
(se=.121)). Controlling for foreign citizenship of founders in table 5 also yields significant and
positive impacts on capital (the coefficients are .256 (se=.120) in column (1), .242 (se=.120) in
column (2), and .242 (se=.120) in column (3)). But the important point here is rather that all of
our main effects—reputation and network position—on capital mobilization remain virtually
unchanged in direction, magnitude, and significance.
As with foreign citizens, restrictive policies of the Russian state toward ethnic minority
entrepreneurs shifted over time. To what extent did the policy shifts influence entrepreneurial
networks? Our results in the appendix tables A.1 and A.2 demonstrate that increasingly severe
discriminatory policies of the tsarist state did have a clear effect. For example, some of the most
stringent policies restricted the rights of Jewish entrepreneurs, which significantly reduced the
formation and continuation of partnership ties between ethnic-Russians and Jews.43 By contrast,
the results of our network analyses show that the overall pattern of the entrepreneurial network
remains largely unchanged over the entire period. Combining both findings suggests that state
policies primarily affected what kind of persons (their ethnic and religious origins) these
entrepreneurs could choose as their partners, but also that policy shifts seem to have done little to
change the way in which entrepreneurial networks were arranged. Put differently, the structural
properties of entrepreneurial networks remained intact, but the state was able to influence who
could occupy the individual positions within it.
Summary of Results
The motivation for our analysis has been a twin challenge for emergent economies. In the
absence of sufficiently developed public institutions to enforce contract commitments and
property rights, reputation and the control benefits of cohesive networks may provide private-
order substitutes (Fafchamps 2004; Greif 2006). However, industrial growth and firm
43 Data limitations make an analysis of discriminatory policies much more tractable for Jews than for other
minority groups.
development also require access to diverse capital sources, which necessitates far-reaching
networks to discover funding opportunities. The problem is that these two entrepreneurial
agendas pursue different strategies – either a focus on enforcing contract commitments, or a focus
on locating capital and other resources. Likewise, their social organizational prerequisites differ –
they require either networks that are cohesive enough to permit effective monitoring and
sanctioning, or networks that are sufficiently widespread so that exchange partners may identify
potential capital sources. How, then, is efficient economic organization possible under such
Our historical setting of late imperial Russia is such a case, where weak public institutions,
arbitrary governance, and fragmentation into competing ethnic, religious, and regional interest
groups characterized an emergent economy (Gatrell 1995; Kappeler 2001). We have examined
rich historical evidence on corporate entrepreneurship, particularly how varying patterns of
partnership networks offered different opportunities for the allocation of basic capital. The
evidence indicates that reputation-based arrangements were most effective in small and isolated
peripheral components, composed of founders who shared similar ethnic, kinship, and regional
origins. Here network closure and homophily combined to yield capital benefits from reputation.
Yet, within the wide-spanning network core with its diverse membership, brokerage and bridging
opportunities were more effective for capital mobilization than reputation effects. Lastly, we also
find that core entrepreneurs tended to be more successful in raising basic capital than their
competitors in the periphery.
Lessons From Historical Corporate Networks
Our argument applies primarily to similar settings where states are either too weak to sufficiently
uphold contract enforcement and property rights as public goods, or where governments grant
property rights only selectively to particular ethnic, regional, or religious interest groups, and
pursue policies that exclude members of other groups (Haber, Razo, and Maurer 2003). A
comprehensive comparison with the role of entrepreneurial networks in other historical or
contemporary settings is beyond the scope of the present study. Nevertheless, two aspects of the
relationship between such networks and their institutional environment are worth emphasizing
because evidence from industrialization in other settings echoes developments we identified in
our historical case.
First, we may expect that variation in institutional conditions across countries yields
corresponding differences in the organization of entrepreneurial networks. Yet, recent studies of
the historical development of such networks in the United States and other western economies
have also found evidence that these networks varied substantially in their cohesiveness – even
when they were located in the same industrial regions within countries, and thus faced similar
challenges of economic development (Morck 2005; Safford 2009). The important implication
here is that the spurts of technological advances that are so characteristic of industrializing
economies may not be sufficient to push the entire organization of corporate networks toward
the more cohesive patterns we observe in the core of the Russian network. That industrialization
alone may not necessarily lead to an efficient organization of entrepreneurial networks explains
to some extent why we observe so little change in the Russian corporate networks despite the
extensive industrialization of the economy around the turn of the century. The lesson from the
present study and comparable cases is that, without reliable market-supporting institutions, the
demand persists for the tightly-knit and reputation-based local clusters we find in the periphery
of the Russian network (Fafchamps 2004). Evidence from other economies that experienced their
initial industrial development at about the same time as Russia supports this inference. Recent
work on interlocking boards of joint-stock companies in Latin America, for example, shows that
business networks in Mexico formed around dense and exclusive personal connections that
substituted for the reliance on the formal legal system. In contrast, in Brazil, formal institutions
existed that promoted financial markets and facilitated the allocation of capital, and Brazilian
corporate networks consisted of connected yet widespread clusters (Musacchio and Read 2007).
These comparative insights and our findings suggest that the extent to which market-
supporting institutions exist is at least as critical for the organization of corporate networks and
economic growth as the more technological aspect of industrial development (Mokyr 2009).
Stable formal institutions are so important for efficient economic organization because “a market
meant much more than just effective demand, or the use of money in exchange, or even good
transport facilities, but also confidence in the stability of the currency, a proper credit
organisation, a system of reliable and enforceable law, and knowledge which came from the
experience of the operation of market forces” (Crisp 1976, p.218). In general, with the provision
of such formalized institutions, we would expect a movement toward the cohesive yet wide-
reaching networks we find in the Russian network core. At the same time, we would expect the
reliance on close-knit and reputation-based network circles to become less salient for successful
The second aspect of entrepreneurial networks we emphasize concerns how varying
patterns of corporate relationships relate to economic and political outcomes. Our evidence
reveals that the core networks, composed of diverse overlapping affiliations, had a clear
performance advantage over the closed and relatively homogeneous clusters in the network
periphery in Russia. This finding indicates that the success of entrepreneurs is contingent on the
particular arrangement of the opportunity structure in which they are embedded. 44 Again, we
find parallel developments in other historical instances of regional industrial development. For
example, tracing the roots of industrial development as far back as the mid-eighteenth century,
Safford (2009) documents that differences in their social structures led communities in the
American Rustbelt to respond differently to the challenges of a post-industrial world. Responses
44 This insight is not limited to economics. Recent research in political sociology shows that the
crosscutting of multiple networks facilitated political mobilization (e.g. Gould 1995).
were successful in communities whose multiple and crosscutting business, political and civic
networks enabled cooperation between various interest groups that otherwise would have been
divided by their regional, ethnic and class-based alignments. In contrast, neighboring
communities that lacked such crosscutting networks eventually fragmented and fell behind in
regional renewal.
These conditions resemble our finding that the relationships of corporate networks in
Russia did not map directly onto locations in the economic and political geography of the
empire, but crosscut their corresponding ethnic and socio-economic boundaries instead. This
result may be of substantive importance to those interested in the potential for economic and
political cooperation within such multi-ethnic policies as imperial Russia. Historians have long
noted the absence of a rising bourgeoisie in late imperial Russia. In contrast to Western Europe,
the middle classes in Russia were unable to turn their economic power into political influence. To
explain this lack of political engagement, historical interpretations have invoked several fissures
separating Russian merchant entrepreneurs: ethnic differences, religious segregation, and rivalries
between industrial regions in addition to inequality in social ranks (Clowes, Kassow, and West
1991; Rieber 1982). This is not the place for a detailed account of middle class politics in Russia
before the October Revolution. Still, our findings suggest that at least the various intersecting
relationships that formed the network core may have offered a promising social structural basis
for the formation of new identities that bridged existing ethnic, regional, and religious divisions.
Basic Capital And Economic Performance
One general concern is how basic capital at the time of founding relates to later corporate
performance. Systematic information on conventional indicators of performance such as labor
force size, the value of assets and stocks, annual sales figures or profits is either unavailable or
rudimentary at best. As an alternative measure of performance, entries in corporate directories
for the years 1869, 1874, 1892, 1905, and 1914 allow us to compare the survival rates of
corporate foundings we classified as successful and those we coded as non-successful. The
RUSCORP database does not have this survival information for every company in our sample, but
we were able to obtain it for 1,301 successful founding teams and for 1,096 non-successful ones,
all established between 1869 and 1913. For example, a company founded in 1870 is considered to
have survived until 1874 if it is listed in the corporate directory of that year. The company
survived further until 1892 if it is also listed in the 1892 directory. If it is not listed in the 1905
directory, it is coded as having failed by 1905. The information is not perfect because we cannot
specify in what year between 1892 and 1905 the company failed. Most likely, this discrete-time
measure of failure will bias corporate lifetime toward longer survival, but the bias is the same for
successful and non-successful foundings. Figure A.1 plots the resulting Kaplan-Meier survival
<Figure A.1 about here>
Clearly, our classification of success based on capital mobilization is a meaningful
indicator of future economic performance: successful corporate foundings survived at a
significantly higher rate than non-successful foundings (chi-2=10.52, p= .0012). If anything, our
result is conservative: the majority of foundings occur in later periods, and opportunities for
failure become fewer the closer the time of founding gets to 1914 (the censoring year). Hence, in
the first 10+ years after founding, the failure rates for the two categories are likely to appear more
equal than they really are. We therefore relied on the log-rank test to compare the equality of
survivor functions because it emphasizes differences toward the end of analysis time.
Still, some economic historians reason that the capital recorded in charters was a nominal
rather than a real value. Carstensen (1983) and Crisp (1976) argue that expansions of capital did
not reflect real investments but efforts to reduce a company’s tax rate, especially after tax
increases in 1906-1908: “In 1906 the progressivity of the tax was increased sharply, the marginal
rates reaching 24 percent on profits that exceeded 16 percent of nominal capital. To reduce
taxes, a company only needed to increase its capital” (Carstensen 1983, pp.143-45). We find no
evidence that supports this argument: throughout all specifications in table 3, none of the
indicators for 1906 and later years show a significant positive effect on basic capital, as one would
expect if founders systematically evaded taxes.
Even if we exclude tax evasion, the capital recorded in the charters may not express real
values of successful corporations but stock-jobbing by unscrupulous founders and speculators.
Again, our regressions offer robust evidence that a reputation of past success is a strong predictor
of future success. If large capitalizations merely indicated embezzlement, then we should not find
that the same founders were repeatedly able to win investors for their projects, assuming that
investors remembered past fraud.
Endogeneity Checks: Discriminatory Legislation And Partner Choice
Does the recruitment of reputable partners increase the amount of basic capital, as our
reasoning suggests, or do capital requirements for an intended enterprise dictate the choice of
founding partners who are able to raise sufficient funds? To disentangle the causal sequence, we
first identify an instrumental variable that significantly constrained the choice of business
partners but had no systematic impact upon the size of capital mobilized. In particular, we rely
on the introduction of arbitrary legislations whereby the tsarist government discriminated against
foreign and especially Jewish entrepreneurs. We demonstrate that these discriminatory policies
were systematically related to variation in the choice of partners. In a second step, we then
compare the effect of successful founding partners on the amount of capital raised before and after
the introduction of these arbitrary policies. The main idea is that, once they were enacted, these
legal discriminations severely limited the kinds of partners one could choose. Consequently,
whatever capitalization requirements existed, they could not have been primarily responsible for
the choice of business partners.
We consider the cumulative effect, by 1899, of successive government restrictions on
Jewish (and often Polish) corporate activities in the Russian Empire (detailed in Owen 1991a,
pp.122-49).45 Between 1864 and 1890 alone, a series of increasingly restrictive legal regulations
were enacted that prohibited Jewish entrepreneurs from leasing or owning landed property and
from taking up residence outside the Jewish Pale of Settlement.46 Corporate landholding also
became restricted to prevent Jews from acquiring land indirectly through company shares. From
1892 onwards, Jews were denied property rights pertaining to mining areas in Poland, and by
1899, most corporate managerial positions were closed to Jews.
<Table A.1 about here>
Table A.1 demonstrates that these discriminatory policies significantly reduced
partnership ties between ethnic-Russians and Jews (coding based on the ethnic classification of
founders in the corporate charters). In Russia at large, the percentage of ties that Russians
maintained with Jewish partners dropped significantly from 7.4% before 1899 to merely 4.1%
afterwards. The percentage decreased despite a 60% increase in the number of potential Jewish
partners. The decline in choosing Jewish founding partners was even more pronounced in
locations directly affected by the discriminatory laws: the percentage of partnership ties that
Russian founders maintained with Jews were significantly reduced by nearly a half, from about
17% before 1899 to about 9% afterwards.
45 We also attempted more fine-grained estimations of before/after effects of these policies, using more
than one point in time. Unfortunately, the data do not leave us with sufficient numbers of observations in
some periods to reliably estimate policy impacts.
46 The “Pale of Permanent Jewish Settlement” (cherta postoiannoi evreiskoi osedlosti) bounded the territory to
which the Russian state confined Jewish residence until 1917. It included the empire's fifteen western
provinces (roughly today's Lithuania, Belorussia, and Ukraine) plus the Kingdom of Poland. Few Jews
were permitted to reside outside of the Pale. The 1897 census estimated 5.2 million Jews in the Russian
Empire, about 4% of the entire population, making them the largest non-Slavic and non-Christian group
(Nathans 2002; Rogger 1986).
<Table A.2 about here>
Table A.2 demonstrates that other Russian founders could not adequately compensate the
loss of successful Jewish partners. In the entire Russian Empire, about 10% of Jewish founders
were involved in previously successful foundings before 1899, using our definition of success. In
contrast, merely 5% of the Russian founders in our sample can be considered successful before
1899. This significant difference disappeared with the full impact of discriminatory policies. After
1899, the proportion of successful Jewish founders was halved to 5% whereas the increase of
successful Russian founders was marginal, by less than 1%. The results are nearly identical for
those locations directly affected by the discriminatory policies. Both tables document that state-
sponsored discrimination did indeed shape Russian founders' choice of business partners, and
thus provides a suitable instrument for our robustness analysis.
In table A.3, we apply the same OLS specifications as in table 3. The difference is that we
compare the estimates before and after the full impact of discriminatory policies in those
locations that were directly affected by them. The post-1899 estimates take into account that
these policies significantly constrained partner choices, which in turn permits us to identify the
effect of reputable and successful partners on the mobilization of capital. 47
<Table A.3 about here>
The results in table A.3 confirm our inferences from table 3: joining with a reputable and
successful partner still significantly increases the amount of basic capital. The result remains
robust when we take legal restrictions on partner choice into account, indicating that variation in
partnering with successful founders is largely exogenous to capital requirements. The magnitude
of these estimates increases slightly after 1899, possibly because those previously successful
founders that were still available as partners made even more of a difference in capital
47 Ideally, we would use a two-stage specification, but founders in our data joined with multiple partners in
varying founding teams and years. Consequently, legal restrictions applied to some, but not all of their
partners. It thus remains unclear which partners should be selected to specify the IV model.
mobilization than before. A similar logic may explain the strong impact of continued
partnerships in column (4): if the choice of partners becomes increasingly restricted, then any
opportunity to continue a working partnership will be appreciated.
<Figure A.2 about here>
<Table A.4 about here>
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Fig. 1.—Number of corporate foundings within the Russian Empire, 1869-1913.
Fig. 2.—Cumulave founder-by-founder network, 1869-1913.
The nodes represent individual entrepreneurs, linked by their joint membership in the same founding teams. The black and gray nodes in the center
represent the network core. Black nodes are founders within the largest component. Gray nodes are founders within all other components that are
equal to or larger than the 95th percenle of component sizes. White nodes belong to the periphery, which consists of all components that are
smaller than the 95th percenle of component sizes. The network is drawn using a spring-embedding graphing algorithm such that the distance
between founders is proporonal to the shortest path linking them. To avoid placing nodes too close to each other, the algorithm minimizes variaon
in the length of lines.
T.S. Morozov
Fig. 3.—An exemplary entrepreneur in the core of the cumulave founder-by-founder network, 1869-1913.
The call-out illustrates the broker posion and personal network of Timofei Savvich Morozov, embedded in the wider co-founding
network (see main text for biographical details). For the graphing layout, see the note to figure 2.
Fig. 4.—Comparison of network core and periphery: Proporon of co-founding es within categories over enre period,
1869-1913. Significant differences in proporons: * p < .05; ** p < .01 (Chi-2, df = 1).
0.00 0.25 0.50 0.75 1.00
0 10 20 30 40 50
Years since corporate founding
Non−successful foundings Successful foundings
Kaplan−Meier Survival Esmates
Fig. A.1.—Basic capital as indicator of economic performance
The graph compares survival rates for corporate foundings coded as successful (their basic capital is equal
to or exceeds the median capital for all other corporaons founded in the same industry, region, and
decade) and foundings coded as non-successful (their basic capital is below the median capital).
N = 1,096 non-successful foundings (227 failures) vs. n = 1,301 successful foundings (223 failures).
Log-rank test of equality of survivor funcons: chi2 (df = 1) = 10.53 (p = .0012).
Fig. A.2.—Distribuon of logged basic capital of corporate foundings in the transportaon sector, by period.
The figure plots the distribuon of basic capital in the transportaon industry, including railways and river shipment. The Y-axis
represents logged basic capital amounts, standardized and deflated to 1913 rubles. The X-axis refers to the periodizaon we
use in our network analysis (see main text). The boxes enclose the interquarle range. The lines within the box areas represent
median values. The whiskers and dots represent outlying observaons that extent beyond the 75th and 25th percenles.
2 4 6 8 10 12
Log basic capital
1869−1876 1877−1884 1885−1892 1893−1900 1901−1908 1909−1913
... 680). Second, networks of economic relations can provide the basis for information transmission, circulating the types of reputational information that promotes cooperation in unregulated trade (Greif 2006;Hillmann and Aven 2011;Hillmann 2013). ...
... Consistent with our reasoning on the salience of risk for network reliance, Podolny (1994) finds that market actors depend more on repeated exchange partners when uncertainty is high. 7 A third network property that is often discussed is brokerage, where actors span otherwise disjointed network components (Granovetter 1973;Burt 1992;Hillmann and Aven 2011). Brokers gain access to diverse sources of information and resources that increase their relative position and "power" in exchange relations. ...
... Sociological theory on economic action posits that exogenous social networks are a prerequisite for trade (Granovetter 1985;Uzzi 1996;Hillmann and Aven 2011). The development of online markets is typically viewed as an exception to the rule, where exogenous social relations are initially mobilized to develop formal reputations (Kollock 1999) but are later rendered irrelevant (Diekmann et al. 2014;Przepiorka et al. 2017;Ladegaard 2020). ...
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How do illegal markets grow and develop? Using unique transaction-level data on 7,205 market actors and 16,847 illegal drug exchanges on a "darknet" drug market, the authors evaluate the network processes that shape online illegal drug trade and promote the growth of online illegal drug markets. Contrary to past research on online markets, the authors argue that the high-risk context of illegal trade enhances market actors' reliance on social relationships that emerge endogenously from transaction networks. The findings reveal a highly structured trade network characterized by dense clustering and frequent recurrent drug exchange. Dynamic network models reveal that both embeddedness and closure in exchange structure increase the hazard rate of illegal drug trade, with effect sizes comparable to formal reputations. These effects are pronounced in the early stages of market development but wane once the market reaches maturity. These findings demonstrate the powerful, temporally contingent, influence of transaction networks on illegal trade in online markets and reveal how endogenous networks of economic relations can promote risky exchange under conditions of relative anonymity and illegality. Governments play a key role in market development. States define the type of products for sale as well as the rules governing exchange. Markets also rely on 1 We are indebted to Srinivasan Parthasarathy and Mohit Jangid for coding assistance.
... ethnicity, kinship) as signals of dependability (Gambetta, 2009;Smith and Papachristos, 2016;Bright et al., 2019). In these regards, the structural dynamics of illegal markets mirror pre-modern trade (Beckert and Wehinger, 2013, p. 17), where actors rely on social networks to build trust, surveil one another and circulate reputational information (Greif, 2006;Hillmann and Aven, 2011;Riberio, 2015). ...
... Like other forms of risky trade (Kollock, 1994;Greif, 2006;Hillmann and Aven, 2011), the central concern among illegal market actors is establishing the trustworthiness of trade partners (Gambetta, 2009;Beckert and Wehinger, 2013). However, unlike offline illegal markets where interpersonal relationships inform prices based on the perceived risks of associating with an untrustworthy exchange partner and the prospect of future trade (Moeller and Sandberg, 2019, pp. ...
... In offline markets, such as medieval trade networks (Greif, 2006;Hillmann and Aven, 2011), networkbased governance structures are regarded as those that rely on reputational arrangements to navigate trade. In online markets, reputational systems are given a formal character through sales ratings systems (Resnick and Zeckhauser, 2002;Diekmann et al., 2014;Przepiorka et al., 2017). ...
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Although economic sociology emphasizes the role of social networks for shaping economic action, little research has examined how network governance structures affect prices in the unregulated and high-risk social context of online criminal trade. We consider how overembeddedness-a state of excessive interconnectedness among market actors-arises from endogenous trade relations to shape prices in illegal online markets with aggregate consequences for short-term gross illegal revenue. Drawing on transaction-level data on 16 847 illegal drug transactions over 14 months of trade in a 'darknet' drug market, we assess how repeated exchanges and closure in buyer-vendor trade networks nonlinearly influence prices and short-term gross revenue from illegal drug trade. Using a series of panel models, we find that increases in closure and repeated exchange raise prices until a threshold is reached upon which prices and gross monthly revenue begin to decline as networks become overem-bedded. Findings provide insight into the network determinants of prices and gross monthly revenue in illegal online drug trade and illustrate how network structure shapes prices in criminal markets, even in anonymous trade environments.
... Networks are considered here as the relational capital to which an entrepreneur can turn to for advice and that are seen as sources of strategic external resources for entrepreneurs, which can be exploited at different stages (Cordova and Cancino, 2020). Likewise, these resources can be considered one of the most important sources of opportunities for entrepreneurs, since they contribute to their growth, development, and sustainability (Burt, 2005;Xiang, Guo, Wu and Sun, 2009;Hillmann and Aven, 2011). To this effect, Kuschel et al. (2017) point out that networks and individual characteristics significantly affect the financing opportunities reached by new businesses. ...
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This study aims to highlight cases of women-led business (WLB) that achieved high-growth in the short term, contradicting the traditional approach of female entrepreneurship associated with precariousness. This paper uses a multiple case study, exploring examples of WLBs in Chile and Peru. The results show some entrepreneurial behavior patterns among WLBs, such as experience in the field, growth-oriented strategy, service innovation, accelerated expansion, high-quality products, and a particular attitude toward entrepreneurship. This study shed additional light on how some WLBs are not linked to subsistence or low-impact, as well as how public policies must support high-growth WLBs. Emprendimiento femenino: Perspectiva de alto impacto basada en evidencia de Chile y Perú Resumen Este estudio se enfoca en caracterizar casos de negocios liderados por mujeres (NLM), que han alcanzado un alto crecimiento en el corto plazo, contradiciendo la aproximación tradicional del emprendimiento femenino asociado con la precariedad. El artículo utiliza un estudio de casos múltiples, explorando ejemplos de NLM en Chile y Perú. Los resultados muestran algunos patrones en el comportamiento emprendedor de los NLM, tales como la experiencia, estrategia de crecimiento, innovación, expansión acelerada, alta calidad, y actitud hacia el emprendimiento. Este estudio esclarece que los NLM no necesariamente están vinculados a la subsistencia y cómo las políticas públicas deben apoyar los NLM de alto crecimiento.
... Secondly, common characteristics, experiences, and/or background enable it to easily establish more cohesive ties and, as consequence, networks help organizations to procure information on competitors (Hillmann and Aven 2011;Ingram and Roberts 2000) and to generate trust between actors. Members of globally integrated NGOs regularly meet at various trainings, workshops or seminars, and are more likely to be familiar with each other rather than with their local colleagues from globally disintegrated civic organizations. ...
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How does international financial aid affect the cooperative behavior of local non-governmental organizations (NGOs)? Can NGOs, while turning global, preserve peer connections with local actors and be engaged in local issues? The civil society literature contains competing perspectives on and reports of how international financial aid may restructure local civic networks. Some scholars argue that international support comes at the expense of local integration as inclusion in global networks takes local NGOs out of the local context, while others find evidence that organizations do not have to face “a forced choice”, and may well be integrated both globally and locally. Drawing on this scholarship, we examine two hypotheses on how transnational funding influences cooperation patterns among NGOs. The hierarchy argument states that public entities tend to cooperate with internationally funded NGOs as external contact signals quality and trustworthiness. The segregation argument, on the contrary, suggests financial homophily according to which organizations are more likely to choose peers similar in sources of funding. To test these hypotheses, we apply Exponential Random Graph Models to the data on cooperation among 221 Kazakhstani NGOs. Results support the segregation hypothesis implying that NGOs are likely to have a bias towards similarly funded peers.
... Higher density, higher average degree, and smaller average path length could indicate a cohesive network. Furthermore, cluster coefficient could help assess the whole network as to whether the nodes are connected in dense pockets of interconnectivity (Watts & Strogatz, 1998); and fragmentation refers to the proportion of nodes that cannot reach each other directly or through a partner (Hillmann & Aven, 2011). ...
What predicts the formation and evolution of partnerships in unstable institutional contexts? We answer this question by examining the partnership field of environmental nonprofit organizations based in Lebanon. Employing descriptive and inferential network methods, we find organizational attributes such as scope, operations, and age to be significant predictors of partnership formation. In particular, organizations working in the same issue areas are more likely to partner with each other; age and scope complementarity also drives the partnership formation over time. Furthermore, the results reveal that organizations are more likely to form partnerships with their partners’ partners, and consequently stable clusters or subgroups emerge over time. These findings are suggestive but are the first to provide a multilevel analysis of nonprofit partnership formation and evolution.
Objective. The overarching goal of this thesis is to better understand not only the network dynamics which undergird the function and operation of cryptomarkets but the nature of consumer satisfaction and trust on these platforms. More specifically, I endeavour to push the cryptomarket literature beyond its current theoretical and methodological limits by documenting the network structure of a cryptomarket, the factors which predicts for vendor trust, the efficacy of targeted strategies on the transactional network of a cryptomarket, and the dynamics which facilitate consumer satisfaction despite information asymmetry. Moreover, we also aim to test the generalizability of findings made in prior cryptomarket studies (Duxbury and Haynie, 2017; 2020; Norbutas, 2018). Methods. I realize the aims of this research by using a buyer-seller dataset from the Abraxas cryptomarket (Branwen et al., 2015). Given the differences between the topics and the research questions featured, this thesis employs a variety of methodological techniques. Chapter two uses a combination of descriptive network analysis, community detection analysis, statistical modelling, and trajectory modelling. Chapter three utilizes three text analytic strategies: descriptive text analysis, sentiment analysis, and textual feature extraction. Finally, chapter four employs sequential node deletion pursuant to six law enforcement strategies: lead k (degree centrality), eccentricity, unique items bought/sold, cumulative reputation score, total purchase price, and random targeting. Results. Social network analysis of the Abraxas cryptomarket revealed a large and diffuse network where the majority of buyers purchased from a small cohort of vendors. This theme of preferential selection of vendors on the part of buyers is repeated in other findings within this study. More generally, the Abraxas transactional network can then be viewed as set of transactional islands as opposed to a large, densely connected conglomeration of vendors and buyers. With regard buyer feedback, buyers are generally pleased with their transactions on Abraxas as long as the product arrives on time and is as advertised. In general, vendors have a relatively low bar to achieve when it comes to satisfying their customers. Based on the results of the sequential node deletion, random targeting was found to be ineffective across the five outcome measures, producing minimal and a slow disruptive effect. Finally, these strategies are based on a power law where a small percentage of deleted nodes is responsible for an outsized proportion of the disruptive impact. Conclusion. As with all applied research examining emergent phenomena, this thesis lends itself to a more refined understanding of dark web cryptomarkets. While the results and conclusions drawn from these results are not perfectly generalizable to all cryptomarkets, they should serve to inform law enforcement on the dynamics which undergird these markets. To this extent, a sombre consideration of trust, consumer satisfaction, and tactical effectiveness of interventions is a necessary step towards the development of more effective countermeasures against these illicit online marketplaces. For law enforcement to be more effective against cryptomarkets, it is advised that an evidence-based approach be taken.
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This article outlines network research in German-speaking countries since the turn of the millennium. After briefly clarifying what is meant by network research in this context, it provides a short retrospective of German-language sociology’s contributions to network research in the previous century. It then focuses on the sociopolitical activities of sociological network research in German-speaking countries over the past 20 years. German-language network research exhibits two unique features in international comparison: a far-reaching debate on qualitative methods in network research (Section 4) and a theoretical debate on what has been coined relational sociology (Section 5). The article goes on to outline the contributions of network research to special sociologies in Section 6. Section 7 deals with the applications and developments of special methods of network research. The article ends with an outlook on the future of network research.
The historiographies of Mexico and Brazil have implicitly stated that business networks were crucial for the initial industrialization of these two countries. Recently, differing visions on the importance of business networks have arisen. In the case of Mexico, the literature argues that entrepreneurs relied heavily on an informal institutional structure to obtain necessary resources and information. In contrast, the recent historiography of Brazil suggests that after 1890 the network of corporate relations became less important for entrepreneurs trying to obtain capital and concessions, once the institutions promoted financial markets and easy entry for new businesses. Did entrepreneurs in Brazil and Mexico organize their networks differently to deal with the different institutional settings? We examine whether in Mexico businessmen relied more on networks of interlocking boards of directors and other informal arrangements to do business than in Brazil. Our hypothesis is confirmed by three related results: (1) the total number of connections (i.e., the density of the network) was higher in Mexico than Brazil; (2) in Mexico, there was one dense core network, while in Brazil we find fairly dispersed clusters of corporate board interlocks; and most importantly, (3) politicians played a more important role in the Mexican network of corporate directors than their counterparts in Brazil. Interestingly, even though Brazil and Mexico relied on very different institutional structures, both countries had similar rates of growth between 1890 and 1913. However, the dense and exclusive Mexican network might have ended up increasing the social and political tensions that led to the Mexican Revolution (1910–1920).
Mexico's initial industrialization was based on firms that were "grouped": that is, linked to other firms through close affiliations with a common bank. Most explanations for the prevalence of groups are based on increasing returns or missing formal capital markets. We propose a simpler explanation that better fits the facts of Mexican history. In the absence of secure property rights, tangible collateral could not credibly be offered to creditors; but there remained the possibility of using reputation as a form of intangible collateral. In such circumstances, firms had incentives to group together for purposes of mutual monitoring and insurance.
This book, first published in 2003, addresses a puzzle in political economy: why is it that political instability does not necessarily translate into economic stagnation or collapse? In order to address this puzzle, it advances a theory about property rights systems in many less developed countries. In this theory, governments do not have to enforce property rights as a public good. Instead, they may enforce property rights selectively (as a private good), and share the resulting rents with the group of asset holders who are integrated into the government. Focusing on Mexico, this book explains how the property rights system was constructed during the Porfirio Díaz dictatorship (1876–1911) and then explores how this property rights system either survived, or was reconstructed. The result is an analytic economic history of Mexico under both stability and instability, and a generalizable framework about the interaction of political and economic institutions.
This book focuses on the importance of ideological and institutional factors in the rapid development of the British economy during the years between the Glorious Revolution and the Crystal Palace Exhibition. Joel Mokyr shows that we cannot understand the Industrial Revolution without recognizing the importance of the intellectual sea changes of Britain's Age of Enlightenment. In a vigorous discussion, Mokyr goes beyond the standard explanations that credit geographical factors, the role of markets, politics, and society to show that the beginnings of modern economic growth in Britain depended a great deal on what key players knew and believed, and how those beliefs affected their economic behavior. He argues that Britain led the rest of Europe into the Industrial Revolution because it was there that the optimal intersection of ideas, culture, institutions, and technology existed to make rapid economic growth achievable. His wide-ranging evidence covers sectors of the British economy often neglected, such as the service industries.