Networks, Institutions, and Encounters: Information Flow in Early-Modern Markets

Emily EriksonYale University

Sampsa SamilaNational 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.

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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.

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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

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legalistic regulatory mechanisms – as with Avner Greif’s proto-coalitions (1993). Self-

monitoring 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

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agents (Adams 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

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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:

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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

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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)

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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

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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

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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

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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

∑l=1

M

ex il β

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.

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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,

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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

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

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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

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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

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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

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.

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Figure 3 about here

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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

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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.

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

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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

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.

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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

24

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

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 non-

random 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.

-----------------------

25

Figure 6 about here

-----------------------

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,

26

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

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

27

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

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 pre-

colonial 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

-----------------------

28

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

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.

29

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.

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

30

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

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 (ABCA) 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

31

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

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 third-

party 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.

32

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

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,

33

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

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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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

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Figure 5. Triads: Susceptibles

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Figure 6. Triads: Clustering

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Tables:

Table 1. Descriptive StatisticsMean S.D. Min Max

Distance 2658.44 2095.1 3.25 12404.25Port-to-Port Traffic 2.64 11.55 0 215Target Port Traffic 23.61 40.16 0 268Formal Order 0.05 0.21 0 1Personal Experience 0.09 0.29 0 1Social Network 0.1 0.3 0 1Experienced Alter 0.05 0.21 0 1Encounter 0.09 0.28 0 1Activated Tie 0.02 0.13 0 1First Voyage 0.41 0.49 0 1War 0.57 0.49 0 1

Table 2: Conditional Logit Regressions

(1) (2) (3)Entire Period Pre-Colonial Era Colonial Era

Distance (log) -0.280*** -0.322*** -0.241***

(-28.42) (-20.31) (-19.71)

Port-to-Port Traffic (log) 0.929*** 0.966*** 0.885***

(70.24) (42.85) (53.93)

Target Port Traffic (log) 0.0483*** 0.0623*** -0.135***

(3.60) (3.06) (-5.99)

Formal Order 1.948*** 1.869*** 2.131***

(33.52) (26.11) (21.34)

Personal Experience 0.392*** 0.208*** 0.541***

(10.29) (3.69) (11.15)

Social Network 0.0698** 0.124** -0.0479(2.00) (2.50) (-0.99)

Target Port Dummies Yes Yes YesLog-likelihood -25258.6 -13242.8 -11636.3

Captains 1572 752 879N 336442 167289 169153Robust standard errors, clustered by captain, t statistics in parentheses, * p < 0.10, ** p < 0.05, *** p < 0.01

45

46

Table 3: Conditional Logit Regressions

(4) (5) (6) (7) (8) (9)

Distance (log) -0.322*** -0.322*** -0.321*** -0.321*** -0.322*** -0.322***

(-20.31) (-20.31) (-20.25) (-20.26) (-20.35) (-20.31)

Port-to-Port Traffic (log) 0.966*** 0.966*** 0.966*** 0.966*** 0.967*** 0.967***

(42.85) (42.92) (42.84) (42.90) (42.62) (42.65)

Target Port Traffic (log) 0.0623*** 0.0618*** 0.0614*** 0.0611*** 0.0620*** 0.0609***

(3.06) (3.04) (3.01) (3.00) (3.04) (2.99)

Formal Order 1.869*** 1.868*** 1.866*** 1.865*** 1.871*** 1.867***

(26.11) (26.15) (26.14) (26.17) (26.14) (26.18)

Personal Experience 0.208*** 0.222*** 0.209*** 0.220*** 0.209*** 0.222***

(3.69) (3.91) (3.68) (3.86) (3.70) (3.88)

Social Network 0.124** 0.00727 -0.00741 -0.0912

(2.50) (0.10) (-0.12) (-1.22)

Social Network x First Voyage 0.229** 0.194**

(2.48) (2.05)

Social Network x War 0.263*** 0.233***

(2.98) (2.58)

Experienced Alter 0.0160 0.0780

(0.18) (0.85)

Encounter 0.152*** -0.0791

(2.67) (-0.93)

Activated Tie -0.0748 -0.279*

(-0.66) (-1.73)

Encounter x First Voyage 0.185*

(1.83)

Activated Tie x First Voyage 0.192

(0.86)

Encounter x War 0.234**

(2.37)

Activated Tie x War 0.157

(0.79)

Target Port Dummies Yes Yes Yes Yes Yes Yes

Log-likelihood -13242.8 -13239.5 -13238.4 -13236.1 -13241.0 -13234.0

Captains 752 752 752 752 752 752

N 167289 167289 167289 167289 167289 167289Robust standard errors, clustered by captain, t statistics in parentheses, * p < 0.10, ** p < 0.05, *** p < 0.01

47

48

Endnotes:

49

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 The methods used to compile these data are described in detail by Levy (1988).4 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).

7 The English Company did not own its ships, but leased them from powerful merchants operating out of London. Captains contracted with these ship owners.