Networks, Institutions, and Encounters: Information Flow in Early-Modern Markets
Emily Erikson
Yale University
Sampsa Samila
National University Singapore
This is a draft. 18 December 2015
Word count (not including title page or abstract): 9,966
Key words: Development, Institutions, Social Networks, Comparative-Historical, Early
Modern
Corresponding author: Emily Erikson, 493 College St., Department of Sociology, Yale
University, New Haven, CT, 06520-8265, 203 432 6332, emily.erikson@yale.edu
The authors would like to thank John Campbell, David Krackhardt, Richard Lachmann,
James Lincoln, Adam Slez, Mattias Smangs, and participants of the Networks and
Governance Conference at NUS Business School, for insightful comments and feedback.
1
Abstract: New Institutionalism currently dominates research into the transition into
capitalism, market economies, and economic development. Emerging economies are
characterized by the absence of adequate legal infrastructures; thus it has been important
to identify early state and non-state alternatives to modern legal systems in order to
understand the process of economic development. Personal relationships, or network
relations, have played a central role in this research -- as informal substitutes for legal
systems that operate through reputation and closure mechanisms. Social networks,
however, have at least two aspects: they can produce social closure or distribute
information widely via heterogeneous and transient ties, i.e. weak ties. Weak ties are
generally presumed to require institutional supports, such as a modern legal system, that
create an atmosphere of generalized trust. Counter to these assumptions, we find an
instance in which transient encounters give rise to the widespread diffusion of
information via network ties in a weak institutional environment.
2
New institutionalism currently dominates research on the transition to capitalism, market
economies, and economic development. Emerging economies are generally characterized
by the absence of adequate legal infrastructures, thus it has been important to identify
early state and non-state alternatives to modern legal systems in order to understand the
process of economic development. In response, a large literature bridging the social
sciences has emerged on the evolution and importance of principal-agency relations,
contract enforcement, and property rights (Acemoglu, Johnson, Robinson 2001; Adams
1994a 1994b 1996; Campbell and Lindberg 1990, Campbell, Lindberg, and
Hollingsworth 1991, Erikson 2014; Erikson and Bearman 2006; Greif 1993 2006a 2006b;
Greif, Milgrom & Weingast 1994, Hall and Soskice 2001; Kiser 1994, Milgrom, North &
Weingast 1990; Nee 1992, North 1990 1991; North, Wallis, Weingast 2009, Stasavage
2003 2011, Thelen 2004, White 1985).1
Within this literature, network ties have been identified as key mechanisms in the
decentralized enforcement of commercial norms. For example, Janet Landa has shown
how ethnic ties serve as an alternative to contract law for Chinese traders in Java (1981),
and Avner Greif has shown how reputation functioned as a regulative mechanism for
Maghribi traders in the eleventh century (1993). These network mechanisms rely on
social closure where information is transferred across the strong ties that result from
densely-interwoven clusters that facilitate the group’s ability to monitor the behavior of
its members. Ultimately, however, social closure limits the process of market expansion,
by restricting both transactions and information to small groups. Thus it is generally
accepted that the transition to a system that protects property rights and enforces
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contracts through impersonal and rational legal processes is necessary for continued
market expansion (Dixit 2004, Hillmann and Aven 2011, Li 1999, North 1991).
Once legal systems are in place, theory suggests that strong third-party enforcement
increases the likelihood of generalized trust, leading individuals to venture out of their
small, closed groups and engage with a larger, more diverse population. These strong
institutional settings thus facilitate the creation of weak ties. Once in place, informal,
non-kin, non-institutional ties transfer and circulate information within a large population
by providing random links that dramatically alter structures of information from
collections of discrete cave worlds into one large and connected small world (Granovetter
1973, Burt 1995, Watts 1999). This transition then promotes further expansion,
innovation and increased rates of development via the positive benefits of increased weak
tie interactions.
Perhaps because of a widespread belief that largely transient, informal, non-kin relations
are an outgrowth of the modernization process, rather than drivers of economic expansion,
research on this second aspect of networks is often restricted to contemporary sites
located in the later stages of capitalist development. Historical comparative network
research has however demonstrated that network ties function in ways other than
regulatory mechanisms and that weak ties – as opposed to cohesive clusters – can be
associated with pre-modern economic development. Padgett and Powell have in
particular turned the tables on the presumption that institutions expand the scope of
relations by showing how the layering of intersectional relations that bridge different
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aspects of social, political and economic life are central to the generation of institutions
themselves (Padgett and Powell 2012). Other works are suggestive in finding an
association between one-shot, heterogeneous exchange and early periods of economic
development, in for example medieval Genoa (Van Doosselaere 2009) and late
seventeenth and early eighteenth century Bristol (Trapido 2013) – both sites of significant
commercial growth. Despite these works, there is a lack of research that systematically
considers the role of weak ties and transient encounters in processes of economic
development prior to or outside of settings with strong institutional frameworks and an
absence of work that directly considers whether networks served predominantly as
regulative devices via social closure mechanisms or were in fact conduits for the
dispersion of information, in the manner of weak ties, in weak institutional contexts. Such
a test would however shed significant light on how networks function to spur economic
development – if indeed they do have a positive role in this process.
Our goal is to test patterns of network usage for evidence of either social closure or
broader information diffusion processes in a moment of early modern commercial
expansion. For our analysis, we used data from the trade of the English East India
Company (1601-1835), which was a significant actor in a key moment of global market
expansion and early-modern commercial development. The analysis proceeds in three
stages. We first conduct a statistical analysis of how captains chose intermediary ports
along their voyages and in particular whether information carried by social networks was
a factor. We proceed to a structural analysis of a network of informal ties in the Company
in order to evaluate the proposition that network ties acted as informal substitutes for
5
legalistic regulatory mechanisms – as with Avner Greif’s proto-coalitions (1993). Selfmonitoring small groups, such as Greif’s proto-coalitions, should be evident in persistent
micro-level patterns of reciprocity, transitivity, generalized exchange, and triadic closure.
We then consider whether the network ties were associated with the distribution of new
information and the distribution of information to new market participants, as would be
consistent with a role more closely analogous to Mark Granovetter’s weak ties (1973) or
the random component of Duncan Watts’s small-world structures (1999).
Our results indicate that information carried by networks was influential in captains’
decisions about where to trade early in the life of the Company but, as the principals in
London tightened their control later on, this effect goes away. In the second stage of
analysis on patterns of network use, we do not find evidence of small groups enforcing
fair exchange and dyadic trust processes or reputation mechanisms -- suggesting that ties
in this setting are not substituting for formal regulatory institutions via mechanisms of
social closure. And in the last stage of analysis, we find that network use increases for
new market participants and in wartime conditions, indicating that transient, weak ties
arose in response to the influx of (1) new information and (2) new individuals seeking
information. Together, the analyses indicate that fleeting and heterogeneous interactions
had a significant impact on early modern trade even in a context with weak enforcement
capacity. These results suggest that strong regulatory institutions are not always
necessary to coerce cooperative behavior in developing economies -- particularly if one
considers the exchange of valuable information as indicative more general levels of
cooperation -- and that generalized information exchange and weak ties effects may play
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important roles in developing economies if an infrastructure encouraging information
exchange is in place.
Institutional and Historical Context:
In the seventeenth century, several European nations created large-scale chartered
organizations to pursue long-distance trade. The English East India Company was created
on December 31, 1600 with monopoly privileges to the overseas trade to East Africa, the
Middle East, and Asia. It existed as a monopoly until 1833, at which time it withdrew
from trade and focused on the governance of its colonial possessions in India, acquired
with few exceptions after 1757. The English Company was a major force in overseas
trade and one of the largest organizations in Britain. In 1801, the Company was
employing 145 ships, docking at over 96 ports, and delivering total exports valued at
2,515,817GBP (Bowen 2005, Erikson 2014).
The Company did not, however, create what can be characterized as a strong institutional
setting, particularly during its years as a commercial power. We define a strong
institutional setting as one in which some governance structure has the capacity to
effectively monitor behavior, punish deviations, and thereby enforce rules and regulations.
As a rule, the early modern period is characterized by low central authority and weak
institutions in comparison to the developed legal framework and market institutions of
the modern era. Early modern chartered companies were particularly plagued with
structural conditions that inhibited their ability to effectively discipline agents (Adams
7
1996, Carlos and Nicholas 1990, Carlos 1992, Norton 2014). Agents operated thousands
of miles from principals, who could neither directly observe agents nor distribute
punishments until the voluntary return of agents, years later.
While the Company suffered continued difficulties in controlling it agents until its
dissolution, the nature of company operations changed in the late eighteenth century,
subjecting the employees to increased scrutiny. In 1757 the Company took possession of
Bengal, beginning a process of colonial expansion that ultimately resulted in the British
Raj. As colonial rulers, the Company gained access to an alternative and vast stream of
revenue in the form of land taxes. The Company became less dependent on trade, and
therefore the activity of their ships, for profits, the institutional infrastructure of the
Company’s overseas locations were built up, and the British government became directly
involved in the monitoring of company activities overseas. By 1776 we can see this
instituted in internal company legislation restricting the autonomy of captains (Cartwright
1788). In our statistical analysis, we will use the year 1776 as the dividing line between a
weak and a comparatively strong institutional environment.
Infrastructure:
Although the regulatory framework was weak, the East India Company did provide
important infrastructural elements. The most salient was the system of factories the
Company constructed in the ports frequented by their ships. 2 The factories served as
warehouses, business offices, living quarters, and mess halls (Cotton 1949). With the
8
exception of the minimal crew needed to safeguard the ship in harbor and captains or
officers affluent enough to have retained private lodgings, all company employees
resided in the factory, which also served as the social center of company life abroad. All
employees were required to take meals at the factory, and religious services on premises
were obligatory though often avoided. Factories were largely closed to the rest of the
population of the port, and thus concentrated captains’ exposure to relevant information
from other ships that had been engaged in similar travels. The close quarters of life in the
factories provided an ideal context for the diffusion of information about trading
prospects for English vessels. Furthermore, the close quarters made it unlikely that the
captains could strategically withhold information from each other or systematically
falsify it by virtue of the group nature of interactions, which were not confined to
captains, but included extensive contact between crew and officers. In effect, they served
as network foci with low external overlap, structures that encourage weak ties formation
and promote information diffusion (Feld 1981).
Additionally, the factories provided a context in which encounters between individuals
were not easily planned in advance. Captains were responding to company orders, crew
needs, personal interest, fluctuating weather, and volatile market conditions. Together,
these conditions made planning deliberate meetings extremely difficult. Instead, factories
served as a platform facilitating encounters and interactions between a diverse and
changing population of company employees.
Data and Variables:
9
The English East India Company is an exemplary case for considering the role of
informal networks in market expansion because of its central importance to the history of
global economic development and market expansion. It is also an exceptional case
because the data preserved on its operations is both detailed and systematic. These
characteristics allow for the reconstruction of processes of network transmission and
provide a rare opportunity for examining informal mechanisms in pre-modern commerce.
The data used here was compiled from the ships’ logs and books stored by the Company
and preserved in the British Library and National Maritime Museum. The record of the
4,725 commercial voyages of East India Company ships were assembled in the volume,
A Catalogue of the East India Company Ships’ Journals and Logs, 1600-1834
(Farrington 1999), which includes a systematic record of the ship, captain, ports, and
travel dates (to the day of arrival at each port) of each listed voyage. This volume is the
most exhaustive and systematic collection of data on East India Company voyages in
existence. It combines the information from previous research with primary evidence
drawn from the extensive holding of the British Library (which holds over 14 kilometers
of shelves devoted to East India Company papers) as well as the National Maritime
Museum. A stratified sample of 107 of the original ships’ logs, available in the British
Library’s India Office Records collection, confirmed the accuracy of the Catalogue
record of ports and dates. There were no inconsistencies between the original logs and the
data recorded in the Farrington catalogue. The data generally only report arrival dates,
but a sample of trips had complete information. We used this information to estimate the
10
time it took to travel between ports and the average stay in ports, which we then used to
estimate the departure times from ports. The names and identities of all captains were
extensively cross-checked using A Biographical Index of East India Company Maritime
Service Officers, 1600-1834 (Farrington 1999). Additional supplementary data was
introduced from the Great Powers Wars Dataset (Levy 1989). 3 Ports were located
geographically using a combination of historical atlases and online data repositories.
Travel to a Port:
The statistical model we use estimates the likelihood that certain conditions will
encourage travel to a particular port out of a set of alternative ports. When a captain is on
a voyage from England to Asia and back, he must stop at various ports along the way. At
each port, he has considerable flexibility in choosing which port to travel to next.4 We are
modeling this choice: which port will the ship travel to next. The model requires the
specification of a set of ports that were potential destinations. Beginning with the full set
of ports east of the Cape recorded in the East India Company ship logs, we exclude ports
based on two criteria. First, we exclude any ports that were never visited from the current
port as infeasible, whether due to distance, currents, winds, or other reasons. For example,
if Aden never appeared as the next port following Hong Kong in the ship logs, we
exclude Aden from any choice set involving a captain in Hong Kong. Second, we exclude
each port not visited in the time period more than five years prior to the first recorded
visit to that port and more than five years after the last recorded visit to that port. These
ports may not yet have been established, may not have been known to the British, or may
have been commercially uninteresting.
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Hence, a case in the data is a captain at an intermediate port on a voyage and choosing
which port to go to next. Each observation within the case then is a potential next port
that the captain could have chosen and the dichotomous dependent variable indicates
which port was chosen.
Networks:
Our primary concern is to assess whether information exchange between captains was
influencing their decisions about which ports to visit. For that purpose, we constructed
the variable social network, measured as a voyage and port-specific dummy that captures
the potential transfer of information about ports between ships. Let k indicate the focal
captain’s ship on its current voyage, i indicate the port where the ship is currently docked,
and J indicate the set of possible destination ports where the ship could proceed from this
port. Then, the variable social networkijk for each j  J is one if k encountered another
ship in port i that had traveled to port j earlier in its current voyage. If there was no
information available about the possible destination port via other ships co-located at the
current port, the variable is given a value of zero. The variable is dichotomous, so
additional or repeated information about a particular port does not increase its value over
one.5
An illustration is presented in Figure 1. Here the two ships the Airly Castle and the
Hawkesbury are represented as in harbor together at the port of Surat. At Surat the
captains, officers, and crew would have encountered each other repeatedly at the
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Company factory and various social events. The factory served as living quarters,
business office, and social center for employees in port. Here the ties represent the
direction of travel (which will not be the case in later network representations). The Airly
Castle traveled to Surat from Goa. The Hawkesbury traveled to Surat from Madras. After
encountering the Hawkesbury at Surat, the Airly Castle sailed to Malacca, and the
Hawkesbury sailed to Goa. The model captures the decision moment of the captain at a
port. If we constrain the choice set to the ports in the example for convenience, the
Hawkesbury may travel Surat-to-Malacca, Surat-to-Goa, or Surat-to-Madras. The social
network variable for Surat-to-Malacca and Surat-to-Madras would be zero since no
information was transmitted about these ports by other ships. The network variable for
Surat-to-Goa would be coded as one as the Hawkesbury was exposed to information
about Goa via the Airly Castle. The network variable for the Airly Castle’s potential trip,
Surat-to-Malacca would also be coded as one.
Journals and accounts of the time indicate that information about ports was exchanged
between captains and officers. Examples may be found in the Journals of Nicholas
Buckeridge and secondary works such as Jean Sutton’s East India Company’s Maritime
Service, and K.N. Chaudhuri’s Trading World of Asia and the English East India
Company (Buckeridge 1973: 63, Sutton 2010: 59, and Chaudhuri 1978, p. 204)
-----------------------Figure 1 about here
------------------------
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In additional models, we break networks down into three different types: encounters,
activated ties, and experienced alter. Encounters and activated ties are mutually
exclusive categories, where encounters are instances in which a captain was exposed to
information via another captain and had not previously acted on information provided by
that captain. This could be either the first time the captains encounter each other or it
could be that they had met before, but the focal captain had not visited a port about which
the other captain carried information.
Activated ties are instances in which the focal captain met another captain and had
previously acted on information provided by the other captain. That is, the focal captain
had previously chosen to go to a port about which the other captain had brought
information. In these cases, the network exchange had already occurred between the two
focal captains. Both variables could take the value one simultaneously if the focal captain
received information about a port from a captain with whom he had had a prior
information exchange and from another captain from whom he had not had a prior
exchange.
Experienced alter is a dichotomous variable indicating the experience level of the alter
captain in a network exchange. Since reputation and trust take time to develop via
repeated interactions or demonstrations of responsible behavior, this is a possible
indicator of the salience of these social mechanisms. Although the results hold for a
continuous measure of experience, we use a binary measure to increase comparability
14
with the other central variables of interest. We considered captains on their third or later
voyage as experienced alters, but the results were substantively similar if we used the
second or fourth voyage as the cutoff.
Captain Experience:
Considering the effect the focal captain’s level of experience on their use of networks
allows us to evaluate the importance of long-term reputation and trust mechanisms on
network use. Since coalitions and network closure take time to develop, if they were the
driving forces of network use, we would expect network usage to increase with
increasing level of experience, as with experienced alter. Captain experience is likely to
accumulate non-linearly over careers with the first completed voyage being the most
important. Hence, we measured captain experience by comparing those captains on their
first voyage with those on later voyages. First voyage is a dummy variable that takes the
value one if this was the first voyage for that particular captain.
New Information:
In times of war, the movement of enemy ships and potential capture of friendly ports
introduced an even more pronounced element of dynamism of overseas trade. There was
a great need for updated information on safe passages and ports. There were several wars
between European powers during the period of the East India Companies, as well as wars
with Asian powers. The dummy variable, War, indicates whether Britain was at war with
France, Holland, or both and excludes confrontations with Asian powers, which were
confined to land battles and therefore had limited impact on the passage of ships. The
15
data was gathered from the Great Powers Wars dataset (Levy 1989). The variable war
has one value in any given year and is the same across all captains and ports.
Control Variables:
Additionally we control for a set of variables that could have been driving the choice by a
captain to go to particular port and potentially confound our estimates. There are two
main concerns: First, some ports were simply more accessible, more important, and better
known than other ports, and hence we could conflate a captain’s choice to go to one of
those ports with our network information measure. Second, while sources of information
outside of other captains were very limited, they were not entirely absent. In particular,
the London head office often directed the captains to specific ports. The captains also
developed their own knowledge of ports through travel, and knowledge of some target
ports may have been common knowledge in the current port.
We use target port fixed effects to control for the fact that some ports were more easily
navigable or better trading ports for a variety of reasons. These are fixed effects for each
port in the choice set. We also control for the distance between ports (measured in
kilometers) from the current port to each of the alternative ports, as captains were both
more likely to go to a port that was closer and also to receive information from that port.
The variable formal orders captures whether the captain received orders from the
principals in London to travel to a destination port or not. This is a dichotomous variable.
Personal experience controls for the likelihood that captains preferred to travel to ports to
16
which they themselves had already traveled, either because of their increased knowledge
of the ports, business contacts, or other issues. This variable is also dichotomous and
indicates whether a captain had previously been to the potential destination port.
Some ports were more easily accessible from the current port due to winds and currents
but also may have been better known in the current port at the particular time. We control
for this by measuring the traffic between the current port and the target port in question in
a ten-year interval, lagged by one year. That is, we count the total traffic from the current
port P1 to each alternative P2 in the choice set from t-1 years to t-10 years. This is
represented by port-to-port traffic. The focal captain could have also learned about the
ports from other captains in London prior to his departure or during his visit to other ports
prior to the current one. We control for this by measuring the total number of visits to
each target port in a ten-year interval, lagged by two years, i.e., from t-2 to t-11. This is
represented in target port traffic. The lag of two years was chosen to measure the
information transmission back in London prior to departure or in other ports earlier on the
same voyage. Information about more recent visits by other captains to the target port
was most likely to come through meeting those captains in the current port. The duration
of ten years for both variables was chosen as the best fit to the data after trying five,
seven, ten, twelve, and fifteen-year durations. The results were substantively similar
across the durations.
Methods and Results:
Statistical Model of Port Travel:
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To model the decision to travel to a port we employ a conditional logit discrete-choice
model. The conditional logit has a relatively straightforward structure. Given a set of M
alternatives, a vector xij of variables related to the jth alternative in the ith decision
moment, and a vector β of coefficients, the probability of choosing alternative j is as
follows:
𝑝𝑖𝑗 =
𝑒 𝐱𝑖𝑗 𝛃
𝐱𝑖𝑙 𝛃
∑𝑀
𝑙=1 𝑒
The estimation then is by maximum likelihood, i.e., choosing the vector of coefficients to
maximize the probability of the observed choices.
Daniel McFadden originally proposed the conditional logit (1974) to analyze the factors
driving a decision made from a discrete set of alternatives. This model allows us to
consider the impact of exposure to network information on travel to a port, and more
importantly the factors that interact with network exposure to make travel to a port more
likely. The model centers its comparison on the decision made versus the decisions that
could have been made. In this case, the model compares the port to which the captain
chose to travel against the set of ports that were not chosen.
----------------------Table 2 about here
-----------------------
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Prior studies support the idea that captain-to-captain information transfer affected trade
patterns (Erikson 2014, Erikson and Samila 2015a 2015b, Mentz 2005, Ogborn 2007,
Sutton 2010: 22). We include an additional test of this relationship by estimating the
effect of the institutional environment and the relationship between social network ties
(encounters and activated ties) on the decision to travel to a port.
Table 2 presents the results. Model 1 uses the entire sample and produces the expected
results. Captains were more likely to go to nearby ports, currently popular ports, ports to
which they were ordered to go, and ports to which they had been in the past. There is a
network effect: captains were more likely to go to a port if they encountered another
captain who had earlier on the same voyage been to that port. The estimated coefficient
for social networks is 0.0698. Since this is a dichotomous variable, it indicates
approximately a 7.2% increase in the odds of travel to a port (e0.0698 ~ 1.072) when
exposed to information via another ship versus not being exposed to information via
another ship.
As indicated earlier, the institutional control in the East India Company experienced a
considerable shift with the acquisition of the colonial possessions. These allowed the
principals in London a different source of income, namely taxation of those possessions,
and thus changed the bargaining power between the captains and the principals. In 1776
the principals forbade the captains to deviate from their routes. Model 2 considers the
pre-colonial era, before 1776, when the institutional control was weaker and captains had
greater freedom to craft their voyages. The results are similar except the network effect is
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larger, reflecting the captains’ greater freedom to act on current information. The
estimation now suggests a 13.2% increase in the odds of travel to a port when meeting
another captain who had recently visited that port. Model 3 presents the results for the
colonial era, from 1776 onwards, and shows how increasing institutional strength had
marked effects. Captains were less likely to go to nearby ports or to popular ports and
more likely to go where they were ordered and where they had been in the past. The
network effect turns insignificant and mildly negative, suggesting that captains were not
acting on current information carried by other captains.
Combined, these results suggest that the weak institutional environment of the East India
Company during the pre-colonial era allowed information exchange between captains to
thrive and the greater institutional control during the colonial era in the least stamped out
the captains’ ability to act on the information brought by other captains.
Structural Analysis:
Our next objective is to establish whether the exchange of information between captains
was constrained to small groups with the capacity to monitor each other over time. If
group sanctioning, reputation mechanisms, and peer monitoring promoted cooperation
between captains, we should see evidence in the form of small and persistent groups. We
consider the occurrence and persistence of reciprocity, transitivity, generalized exchange
patterns, and triadic closure, as these micro-level patterns would be present in networks
characterized by small, cohesive groups.
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----------------------Figure 2 about here
-----------------------
Figure 2 represents the ship-to-ship network of activated network ties that occurred in the
pre-colonial period (1601-1776). Nodes represent ships, and ties represent instances in
which exposure to recent information about a port via another ship resulted in travel to
that port. Arcs capture the set of social network variables (encounters, activated ties,
experienced alter) in which the network variable took on a value of one and the outcome
variable (travel to a port) took on a value of one. Ties are directed by the flow of
information, where information travels clockwise from source to target. Instances in
which formal orders and personal information overlapped with network information are
not included in this network.
In order to control for various structural factors that could confound measures of
clustering or reciprocity, we used the structure of interactions to constrain the
construction of network simulations, which serve as a baseline comparison. In the
simulation, ties are drawn at random at the same annual rate that tie activation occurred in
the observed network. The ties, however, are randomly drawn from the set of possible
ship-to-ship ties, determined by the encounter set. Thus the randomly drawn ties only
occur between ships that did in fact encounter each other at port. We then use these
randomly constructed networks to evaluate the properties of the observed network. Thus
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the artifactual structural properties of the network created by patterns of ship traffic are
held constant across the observed and constructed networks.
Figure 3 represents the results of comparing measures of reciprocity and transitivity
across the observed ship-to-ship network and the binned distribution of results for 1,000
simulated networks. In Figures 3, 4, 5, and 6 the large gray circle represents the observed
value, and the black bars represent the simulated values (which may be interpreted
similarly to expected values). The x-axis gives the value of the statistic, and the y-axis
represents the number of observations that returned that value. For example, the observed
value is one on the y-axis because there is only one observed network.
------------------------Figure 3 about here
-------------------------
Reciprocity is a measure of the proportion of ties that are reciprocated or the probability
that when a tie from A to B is present, a tie from B to A will also be present. As is
evident in the first panel of Figure 3, the observed network shows little tendency towards
reciprocity beyond what is present in the simulated networks. Panel two of figure 3
presents average local transitivity, similar to the global clustering coefficient (Watts and
Strogatz 1998). This measure calculates the probability that two neighbors of a node are
connected. The measure treats ties as undirected. This second panel indicates that the
observed network has higher rates of transitivity (or clustering) than would occur by
chance.
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Although Figure 3 supplies a preliminary indication that proto-coalitions may be driving
network transmission in this instance of early modern trade, further investigation of the
micro-structural properties reveals a different story. A triadic census provides a more
detailed means by which to consider the micro properties of the network. A triadic census
enumerates the number of different types of possible structural patterns between all
groups of three nodes in a given network. It is particularly useful because the triad is the
smallest level at which patterns of group interaction may be observed. In a directed
network, there are sixteen different possible patterns between nodes, ranging from a
complete absence of ties between nodes to the fully connected triad in which all nodes
are connected to each other node via reciprocated ties. Figures 4 through 6 display the
relevant results from the triadic census.
A typical marker of group cohesion and solidarity is a pattern of generalized exchange,
where one individual takes from an alter and passes information along to another in the
group (Bearman 1997). This type of exchange reduces the importance of dyads, while
simultaneously reinforcing group solidarity. Acts are reciprocated, but by the group
rather than a specific individual – thus the pattern indicates a strong group presence and
the likelihood that individuals are tied to the group rather than the individuals that
compose it. Evidence of generalized exchange patterns within triads would indicate that
merchant groups were a significant factor in the informal transmission of information in
this period; however, triads with a pattern of generalized exchange have an unusually low
rate of occurrence in the observed network. All triads in which actors are both givers and
23
receivers outside of reciprocal relations occur at a significantly lower rate in the observed
network than in the simulated random networks. The distributions and observed values
are shown in Figure 4 for the representative triad types. These are generalized exchange
patterns for the target node, which is the node that occurs at the top of the triad; thus
reciprocal exchange can exist between the two connected alters (the bottom-level node)
and not change the interpretation of whether the target node (top-level node) is engaged
in generalized exchange. Please note that the scale of the x-axis varies considerably
across the different graphs as more densely connected triads occur at lower rates in both
the observed and simulated networks.
----------------------Figure 4 about here
-----------------------
A more typical pattern for the observed network is one in which one ego draws
information from more than one source. Figure 5 presents the results for triads with high
in-degree for one node. These results indicate that a significant micro-level factor
contributing to the density of the total network is an individual-level property. Triads in
which one individual has taken information from more than one alter occur in the
observed network at nearly double the rate they occur in the simulated networks. This
pattern suggests that susceptible individuals account for a large proportion of the density
and cohesion of the observed network – as they connect otherwise disjoint actors. This
24
finding is particularly interesting given the importance of susceptible individuals in
seeding network effects (Watts and Dodds 2007).
----------------------Figure 5 about here
-----------------------
Figure 6 represents the four out of seven triad configurations in which at least one
connection occurs between each node pair. Two additional connected triads occur in the
previous figures: 030T in figure 5; and 030C and 120C in Figure 4. As is apparent and
consistent with the transitivity measure, figure 6 reinforces the finding that there is nonrandom propensity toward clustering; however it should be noted that the number of
connected triads is low overall – across both simulated and the observed networks. For
example, the observed number of triads with generalized exchange pattern 021U is 368
lower than the mean count of 021U in all simulated networks, where the observed value
of fully connected triads (300) is only six more than what occurs in simulated networks.
It should be noted that less frequent patterns have a relatively small cumulative impact on
the network.
----------------------Figure 6 about here
-----------------------
25
These patterns also, of course, have a temporal dimension – in particular the elapsed
duration of time between the ties within triads. If clusters represent a coercive,
sanctioning group process where individuals form and maintain reputations that enable
long-term access to valued information, clusters should arise over time and contain ties to
alters that occur over the span of careers – as individuals dip back into the pool of their
trusted group of alters. This pattern, however, is not evident in the fully connected triads,
where it would be most likely to occur. There are four separate fully connected triads in
the observed network (each is enumerated more than once in the census). Four connected
triads contain 24 relations. Only four of these relations occur in a different year than the
other ties within the triad, and these were in the year immediately prior to or following
the year in which the other ties were formed. There are no cases in which additional ties
between the involved nodes occurred before or after the relations that compose the
observed triad. It follows that the clusters neither represent nor translate into sustained
cooperation between individuals.
In sum, although the network shows a small tendency toward clustering, closer
investigation does not indicate that small groups capable of monitoring and enforcement
through repeated interactions encouraged the transmission of information between ships.
Instead we see that most of the cohesion was produced by individuals taking information
from more than one source. Generalized exchange patterns were extremely low,
indicating that group processes were not paramount and reinforcing the finding that
individuals were more likely to be takers – not givers and takers.6 The patches of high
density information transfer that occur are not the result of long-term cooperation
26
unfolding over time, but are instead a product of transient acts of collaboration, perhaps
in response to bursts of news about temporary market conditions. We explore this
possibility further in the next section.
Further Statistical Analysis of Port Travel:
In the above section, we found little evidence that social closure mechanisms and
coalition formation were driving the micro-processes behind the exchange of information
within the East India Company trade. This analysis does not address the question what
did drive the use of social networks. In this section we turn to possible triggers that could
have interacted with and stimulated the use of networks by modeling the decision-process
of captains. Since the previous analysis indicates that small group processes are not
driving network exchange, we consider individual and environmental level factors that
are likely to have been associated with increasing rates of network activation.
The conditional logit model conditions on the characteristics that are common across all
choices and only alternative-specific variables can directly enter the regression as there is
a fixed effect for each choice situation. In a conditional logit model we are predicting
which alternative, in this case a port, out of a set alternatives is chosen and hence
variables that have the same value for all alternatives cannot be estimated, as they are
fully collinear with the fixed effects. Indeed, the fixed effects do much more than these
variables would do and hence using them instead of the fixed effects included in
conditional logit would be a clear step backwards. In our case, the variables that do not
vary within each choice set include those related to the time period, the current port i, and
27
the captain. Hence, for instance the “main effects” of first voyage and war cannot and
should not be estimated in the regressions.
What we are interested in is how these variables --- first voyage and war --- might have
influenced the evaluation of alternatives through affecting the use of networks. We want
to know if war for instance made it more likely that captains relied on networks to choose
their trading ports. To achieve this, we use interactions of these variables with the
alternative-specific variables, specifically networks. That is, to see if war influenced the
use of networks from the current port i to the alternative port j, the product social network
x war is estimated. Thus the interaction terms included in the model will be of central
interest to our analysis. Table 3 presents the results from the regressions for the precolonial era when networks were active in the East India Company. Model 4 gives the
baseline estimation and is a replication of Model 2 of Table 2.
----------------------Table 3 about here
-----------------------
Model 5 provides additional evidence against a reputation mechanism. The results here
suggest that the effect of networks is mostly driven by captains early on in their careers.
Hence, increasing time spent overseas did not increase the likelihood of using network
28
ties, in fact the opposite seems to be the case. This bears on the social closure hypothesis
since, if individuals were giving information to a select group in the hopes of receiving
information, we would expect to see this reciprocated in the later years of their career;
however, we do not see this happening. Since the need for information about ports and
trade opportunities would have been most important early on in a captain’s career, high
network use in the first couple of voyages is consistent with our expectation that
networks here were mainly sources of timely information through transient, chance
encounters with other captains.
In Model 6 we add environmental uncertainty as a factor that could have increased the
importance of the flow of information between captains. Specifically, Model 7 includes
an interaction between Social Network on one hand and the variable War on the other. In
the case of war, the location of enemy ships and safety of ports that may have fallen into
the hands of enemies changed the baseline knowledge necessary to function effectively in
the overseas trade. The estimates suggest that a large proportion of network usage
happened at times of war. This evidence is consistent with the idea that temporary
circumstances -- rather than long-term coalitions and enforcement mechanisms -- drove
network use.
In Model 7 we include both the first voyage and war variables at once. The results are
consistent with the above, confirming that the results are not spuriously caused by
correlations between the variables. It appears that network activation was almost entirely
driven by captains early in their careers and during wartime.
29
In Model 8, we divide the network variable into 3 categories: experienced alter,
encounters, and activated ties. As described in the variables section, encounters and
activated ties are mutually exclusive; experienced alter measure the voyage experience of
alter captains. As is evident, experienced alter and activated ties (i.e., the reoccurrence of
directed ties between captains) are not significant. Encounters – that is, ties without a
previous history of activation – are significant. Here again we see the evidence working
against a social closure hypothesis. If trust between specific alters was an important
factor in directing the trade, we should expect to see trust build up between two
individuals when one has had the opportunity to interact with another in the past;
however repeated ties are not significant. Instead first-time exchange is responsible for
the significance of the social networks variable – indeed the coefficient for encounters is
larger than the aggregated measure of the impact of social networks. It is important to
stress that captains often repeatedly use social networks; however they do not often
activate social networks from the same alter captain. Thus this is not evidence of a
tendency to avoid repeated use of networks altogether. It is also true that captains who
use networks on their first voyage return to the trade for a second voyage at a slightly
higher rate than captains who do not, so there is no evidence that bad information is
driving captains out of the trade: 53% of captains who used networks on their first
voyage returned for a second voyage compared to 50% of those who did not use
networks on their first voyage. Long-term dyadic relationships thus do not appear to be a
central mechanism fostering the diffusion of information. Reputation mechanisms also
are not supported, as experienced alter is not significant. Since reputations would build
30
over time as captains participated in the trade, we should expect to see more experienced
captains hold more sway over others; however the insignificance of experienced alter
shows that this is not the case.
Finally, Model 9 adds interactions of first voyage and war with the encounters and
activated ties, showing that only the interactions with encounters are significant for both
variables.
Conclusion:
In sum, the pattern of information exchange between ships indicates heterogeneous,
transient ties driven by the needs of new market participants and the influx of new
information. It does not indicate the presence of social closure mechanisms. Reciprocity
and generalized patterns of exchange (ABCA) are lower than expected by chance
given the structure of encounters. The small groups that do take shape in the network are
not durable; they quickly disband and disperse, typically within one trading season. Thus
dyadic trust formation and small-group enforcement do not seem to be responsible for the
exchange of information that does take place. Indeed, the longer one stays in the trade,
the less likely an actor is to draw information from a peer network – indicating, again,
that durable coalitions and mechanisms of reputation formation had little impact on the
exchange of valuable market and port information between ships. Instead the clustering
that occurs is related to transient waves of collaboration in wartime conditions and the
susceptibility of a class of inexperienced actors. Overall, these social networks played an
31
important role not as vehicles of small-group enforcement, but because of their potential
to expand the universe of information available to anyone individual.
The absence of evidence of social closure processes is particularly notable because of the
weak institutional context. This suggests, one, transient and heterogeneous cooperation in
developing contexts may be more common than often assumed and also that
decentralized, unregulated cooperation was a part of the dramatic expansion of trade in
the early modern era. Although there is long history of documenting fragmentation,
closure, and the erosion of social ties within weakly institutionalized contexts (dating to
Banfield 1958), we know of very little work that has effectively documented the robust
use of weak, transient, heterogeneous – and yet cooperative -- ties in weakly
institutionalized contexts. Clearly more work in this vein is necessary in order to
understand both the importance of weak ties in development and identify conditions that
produce (or perhaps allow) cooperative weak tie relations in the absence of strong thirdparty enforcement. These findings of course do not imply that informal sanctioning
systems or the rule of law are unimportant – only that sites which allow the possibility of
spontaneous collaboration may also be a independent factor in spurring economic
development.
This leads directly to our second observation. The factory system served as a mixing
device bringing otherwise disconnected individuals in close contact with each other,
while simultaneously maximizing exposure to relevant information about the trade in
English private goods (by concentrating the exposure of captains to other English private
32
traders, who were in fact employees of the Company). It appears that this infrastructure
encouraged a diversified flow of information within the trade system. It should also be
noted, although again it is not possible to test in this case, that the relative autonomy of
the actors and the relevance of the information to their informal pursuits may also be
related to their willingness to cooperate and exchange information. For example, it has
been shown that strong corporate governance can reduce the capacity for firms to create
horizontal ties (Ingram and Lifschitz 2006). Coercion and the threat of sanctions do not
always organically bring about decentralized cooperative relations. Indeed, coercion and
the threats of sanctions may undermine the possibility for actors to create decentralized
cooperative relations organically. A further possibility is that behavior in the presence
others -- crew, factory staff -- tends to follow norms (Goldfarb et al. 2015) or a shared
northern European identity facilitated cooperation.
A particularly important caveat is that we have only limited biographical information on
captains, thus it is possible that other small-group networks intersected with the network
we observe, and we are therefore capturing only portions of persistent small-groups that
did exist but overlap the boundary of our data. The limited information we have to test
this hypothesis does not bear it out. Captains are not more likely to use information from
other captains with the same surname or same ship owner.7 More importantly however,
although it is extremely plausible that external networks of relations would have served
as the basis of reciprocal relations or durable small coalitions of captains, we do not
observe these small groups forming in the trade itself. This is to say that if kinship ties
acted a significant constraint channeling information flow, we should see evidence of that
33
in strong and persistent dyadic relations whether or not we are able to observe that the
two individuals were related. We cannot rule out the existence of these other networks,
but can argue that they did not produce small-group patterns in this instance of early
modern overseas trade.
Thus, the research also bears upon our understanding of the role of companies,
commercial organizations, and factories in capitalist development. Companies are linked
to increased efficiency due to their ability to reduce transaction costs associated with
contract enforcement (Coase 1937, Williamson 1993 1998). Others have seen them as
vehicles for increasing employer control over workers (Marglin 1974). This research
supports an alternative view that emphasizes the role of commercial organizations in
serving as hubs for the distribution of information between skilled workers (Mokyr 2002).
Our findings stand in contrast, for example, to Sheilagh Ogilvie’s conclusion that
chartered companies primarily restricted the flow of market information (2011). While
Ogilvie may yet be right that information flow was restricted at the boundary of the firm,
the information flow within was significant and associated with market expansion.
34
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38
Figures:
Figure 1. Illustration of Operationalization of Social Networks
39
Figure 2. Network of Ship-to-Ship Ties
40
Figure 3. Observed and Simulated Measures of Reciprocity and Transitivity
41
Figure 4. Triads: Generalized Exchange
42
Figure 5. Triads: Susceptibles
43
Figure 6. Triads: Clustering
44
Tables:
Table 1. Descriptive Statistics
Mean
Distance
2658.44
Port-to-Port Traffic
2.64
Target Port Traffic
23.61
Formal Order
0.05
Personal Experience
0.09
Social Network
0.1
Experienced Alter
0.05
Encounter
0.09
Activated Tie
0.02
First Voyage
0.41
War
0.57
S.D.
2095.1
11.55
40.16
0.21
0.29
0.3
0.21
0.28
0.13
0.49
0.49
Min
3.25
0
0
0
0
0
0
0
0
0
0
Max
12404.25
215
268
1
1
1
1
1
1
1
1
Table 2: Conditional Logit Regressions
Distance (log)
Port-to-Port Traffic (log)
Target Port Traffic (log)
Formal Order
Personal Experience
Social Network
Target Port Dummies
Log-likelihood
Captains
N
(1)
Entire Period
-0.280***
(-28.42)
0.929***
(70.24)
0.0483***
(3.60)
1.948***
(33.52)
0.392***
(10.29)
0.0698**
(2.00)
Yes
-25258.6
1572
336442
(2)
Pre-Colonial Era
-0.322***
(-20.31)
0.966***
(42.85)
0.0623***
(3.06)
1.869***
(26.11)
0.208***
(3.69)
0.124**
(2.50)
Yes
-13242.8
752
167289
(3)
Colonial Era
-0.241***
(-19.71)
0.885***
(53.93)
-0.135***
(-5.99)
2.131***
(21.34)
0.541***
(11.15)
-0.0479
(-0.99)
Yes
-11636.3
879
169153
Robust standard errors, clustered by captain, t statistics in parentheses, * p < 0.10, ** p < 0.05, *** p < 0.01
45
Table 3: Conditional Logit Regressions
Distance (log)
Port-to-Port Traffic (log)
Target Port Traffic (log)
Formal Order
Personal Experience
Social Network
(4)
-0.322***
(-20.31)
0.966***
(42.85)
0.0623***
(3.06)
1.869***
(26.11)
0.208***
(3.69)
0.124**
(2.50)
Social Network x First Voyage
(5)
-0.322***
(-20.31)
0.966***
(42.92)
0.0618***
(3.04)
1.868***
(26.15)
0.222***
(3.91)
0.00727
(0.10)
0.229**
(2.48)
(6)
-0.321***
(-20.25)
0.966***
(42.84)
0.0614***
(3.01)
1.866***
(26.14)
0.209***
(3.68)
-0.00741
(-0.12)
0.263***
(2.98)
Social Network x War
(7)
-0.321***
(-20.26)
0.966***
(42.90)
0.0611***
(3.00)
1.865***
(26.17)
0.220***
(3.86)
-0.0912
(-1.22)
0.194**
(2.05)
0.233***
(2.58)
Experienced Alter
Encounter
Activated Tie
(8)
-0.322***
(-20.35)
0.967***
(42.62)
0.0620***
(3.04)
1.871***
(26.14)
0.209***
(3.70)
(9)
-0.322***
(-20.31)
0.967***
(42.65)
0.0609***
(2.99)
1.867***
(26.18)
0.222***
(3.88)
0.0160
(0.18)
0.152***
(2.67)
-0.0748
(-0.66)
0.0780
(0.85)
-0.0791
(-0.93)
-0.279*
(-1.73)
0.185*
(1.83)
0.192
(0.86)
0.234**
(2.37)
0.157
(0.79)
Yes
-13234.0
752
167289
Encounter x First Voyage
Activated Tie x First Voyage
Encounter x War
Activated Tie x War
Target Port Dummies
Log-likelihood
Captains
N
Yes
-13242.8
752
167289
Yes
-13239.5
752
167289
Yes
-13238.4
752
167289
Yes
-13236.1
752
167289
Yes
-13241.0
752
167289
Robust standard errors, clustered by captain, t statistics in parentheses, * p < 0.10, ** p < 0.05, *** p < 0.01
46
Endnotes:
1
For a comprehensive overview, see Hillmann 2013.
2
There were sites in which the Company was not allowed to erect settlements; however
in the overwhelming majority of instances in which ports were regularly visited by the
Company, a factory was constructed and inhabited by resident employees.
3
4
The methods used to compile these data are described in detail by Levy (1988).
We exclude all cases where the next port chosen was in the United Kingdom or west of
the Cape.
5
We made this decision after analysis showed that exposure to redundant information via
multiple sources was not significant in predicting travel to a port. The results were
qualitatively the same whether we used a dichotomous variable of exposure to
information about another port via networks or (log of) the count of other captains that
carried information about that port.
6
The rate of out-star triads is consistent with the simulated networks in the sparse case
(021D) and slightly higher than expected in the case of the clustered case (120D).
The English Company did not own its ships, but leased them from powerful merchants
operating out of London. Captains contracted with these ship owners.
7
47