# Random Connections and Violence in the French Revolution

Brenda Van Coppenolle, Oliver Vanden Eynde

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PARIS SCHOOL OF ECONOMICS
ECOLE D'ECONOMIE DE PARIS

WORKING PAPER N° 2026-05

Random Connections and Violence in the French Revolution

Brenda Van Coppenolle
Oliver Vanden Eynde

JEL Codes: D74, D72, N40

Keywords: Historical political economy, French revolution, Networks
Random Connections and Violence
in the French Revolution

Brenda Van Coppenolle*, Oliver Vanden Eynde,† ‡

Friday 13th February, 2026

Abstract

Networks can help actors survive violence in revolutions. Connections could protect against reprisals and provide information. However, personal networks are not randomly assigned, making it difficult to causally trace their role. The French Constituent Assembly of 1789 randomly assigned legislators to smaller groups, which we use to assess outcomes in the ensuing revolution such as violent death and emigration. Violent death was contagious among the nobility in these networks. However, connections to two key leaders, Lafayette and Robespierre, were protective against violent death, encouraging emigration, regardless of ideological differences. We argue that nobles drew an informational advantage from their connections.

JEL Codes: D74, D72, N40

Keywords: Historical political economy, French revolution, Networks

<footnote>*CEVIPOF, Sciences Po Paris. Email: brenda.vancoppenolle@sciencespo.fr</footnote>

<footnote>†Paris School of Economics, CNRS, and CEPR. Email: o.vandeneynde@psemail.eu</footnote>

<footnote>‡This research was supported by ERC-2022-STG POLLOT project 101077623, funded by the European Union.
Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the
European Union or the ERCEA. Neither the European Union nor the granting authority can be held responsible
for them. This research was also supported by EUR grant ANR-17-EURE-0001 at PSE. We thank participants of
conferences for helpful comments and suggestions. Sofija Riegger, Etienne Chastel and Blanca Gonzalez Martinez
provided excellent research assistance.</footnote>
# 1 Introduction

Violence can spiral out of control during regime transitions. In such circumstances, political actors must fear for their own safety, and personal networks can play a crucial role in how they cope with these risks. However, networks are non-randomly assigned and hard to measure, which makes it particularly difficult to evaluate their role.

The French Constituent Assembly, which assembled in 1789, employed a peculiar procedure to randomly assign more than 1,200 legislators, or "Constituants", to smaller committee-like groups called *bureaus*. In the initial weeks of the Constituent Assembly, they met almost daily, with no difference in their tasks, only in their composition. In this paper, we use the variation in personal connections between legislators created by this as-good-as-random assignment to smaller groups. We focus on a long-term outcome in the ensuing revolution: violent death, by execution or massacres.

Our empirical findings show that the random networks that were formed in the 1789 Constituent Assembly affected an individual's likelihood of dying during the Terror, a period of spiraling violence. Among the highly targeted nobility, we confirm that violent death was contagious. We also find that two key figures, the Marquis de Lafayette and Maximilien Robespierre, affected revolutionary fates among the nobility, each lowering their random connections' chances of dying a violent revolutionary death. Arguably, nobles were at higher risk of prosecution, but they also had more resources to anticipate and flee the Terror. The value of personal connections to either of these key leaders also encouraged emigration in this group, and to some extent ideological choices such as club membership or army enlistment against the Republic. We conclude from this evidence that the bureaus, which had an integrating function in the earlier weeks of the assembly, provided intricate private information within networks. The resulting personal connections were protective, not necessarily because of direct help from either key leader, but rather because of the informational value these networks provided to the actors involved.

To motivate our focus on Robespierre and Lafayette, it is worth noting that these revolutionary leaders have the longest biographies in our dataset. To document how exceptional the influence of these two leaders was, we compare their real effects to placebo effects and to
effects of all other Constituents. Our results are robust to controlling for imbalances across
bureaus, and different dependent variable measurement strategies. The effects are larger for
the earlier bureau assignments as opposed to bureaus that formed later in the year, when the
bureaus are known to have met much more infrequently, and may have provided less valuable
private information.

The French Revolution is one of the most extensively studied historical events. However,
our paper is the first to study how connections in the 1789 Constituent Assembly shaped
the fate of political actors. Our paper contributes to recent work in political economy which
analyzes the causes of popular protest and violence in this canonical case of revolution, for
example through networks of ex-combatants in the American independence struggle (Jha and
Wilkinson, 2023). Essentially, we identify peer effects from a political experiment with in-
stitutional design. Peer effects are usually studied in settings where assignment to groups is
unavoidable (e.g., classes in schools), which means that evidence of peer effects does not al-
ways have clear policy implications (Angrist, 2014). In contrast, the peer effects we study arise
from deliberately created discussion groups. The bureau system of the French Constituent
Assembly inspired many other institutional experiments across Europe throughout the 19th
century and beyond (Cirone and Van Coppenolle, 2026). Identifying the role of connections in
managing personal risk during severe political crises is not only important to understand the
past, but remains timely today with social media networks connecting actors as never before.

2 The Political Economy of Leadership in Networks

Our paper brings together two strands of the political economy literature that study the role of
leaders. First of all, there is a literature that highlights how individuals can change economic
outcomes and the course of history. For example, it has been shown that leaders can affect
their country's economic growth (Jones and Olken, 2005; Easterly and Pennings, 2020). This
influence could operate through the identity of leaders, as there is work that shows that the
educational background (Besley et al., 2011) and gender Dube and Harish (2020) of leaders
play a role. Leaders can also serve as role models and change social norms (Acemoglu and
Jackson, 2015; Dippel and Heblich, 2021; Cagé et al., 2021). Finally, leaders may be able to
influence outcomes through their networks. Bai et al. (2023) show that Zeng Guofan’s elite networks during the Taiping Rebellion (1850–1864) influenced the subsequent distribution of political power in China. Our paper studies the role of leaders on more direct and more individual historical outcomes, namely how people who were in contact with key leaders of the revolution weathered this period of intense personal danger and upheaval.

Our paper relates to a second literature on political leaders - one that documents the im-
portance of political connections. A large literature studies how political connections enable
corruption (e.g., Lehne et al., 2018) and generate private rents (e.g., Cingano and Pinotti, 2013).
A more narrow literature studies the role of political connections in the persistence and power
of elites. The work of Bai et al. (2023), which we cited earlier, fits into this category: it shows
that links to a military leader affected participation in the Taiping rebellion and the subse-
quent power of elites. Studying the post-Second World War political purges of Vichy regime
parliamentarians, Aidt et al. (2022) show that those with a legal background similar to their
judges were more often acquitted in trials. This mechanism contributed to the persistence
of elites with a law background. Finally, Naidu et al. (2021) show how the structure of elite
networks in Haiti determined participation in a coup as well as the economic benefits derived
from this political transition. In contrast to the collective action dynamics within the elite, our
paper focuses on the more narrow question of individual safety during the reign of terror that
was unleashed by the French revolution, although we similarly track contagion using a social
network analysis.

However, because it is difficult to systematically measure personal networks and pinpoint
their specific influences, empirical studies that have assessed the process that links individual-
level networks to macro-level political economy consequences such as violence are limited. In
terms of the specific context of the French revolution, (Jha and Wilkinson, 2023) have shown
that veterans of the American war of independence were more supportive of democratic re-
form, and their home districts were more likely to revolt. These effects are likely to be driven
by a combination of the personal experience of fighting in the Independence War and the net-
works that its veterans formed. In contrast, the networks created by the bureaus are the result
of the deliberate design of legislative institutions - and they have particular policy relevance
for that reason.

Even in instances where political connections can be measured or proxied, these connec-
tions are rarely randomly assigned. In the Florentine republic the appointment process of the
Gonfaloniere, the head of the executive body, or the Signoria, involved a random element.
Abramson (2023) shows how random leadership shocks affected exchange rates, while Belloc
et al. (2022) have identified the long-term effects on Florence's wealth distribution of the cap-
ture and dismantlement of the lottery effects by the Medici family. In other work on historical
lotteries, Cirone and Van Coppenolle (2018) have shown how random allocation of legisla-
tors helped them gain political experience and advance their careers in 19th century France,
while linking the abolishment of such unpredictable lotteries to capture by parties (Cirone and
Van Coppenolle, 2019). Our study similarly exploits random connections between legislators
of the French Constituent Assembly - the first time that such randomness was used at such a
large scale and with such long-lasting consequences.

Our paper also addresses the broader question of how people use their social networks when they are exposed to personal security risks. In a recent and related study, Buggle et al. (2023) show that the social networks of Jews in Germany played a role in their decisions to flee. Like these authors, we find that social networks shape the outcomes of individuals who are at risk of extreme violence, even if the exact nature of the peer effects is different in our setting. Of course, the context of the French Revolution is unique in many dimensions, and we add to recent work studying this turning point in world history through a political economy lens. For example, Chambru et al. (2024) find that the establishment of new departmental capitals during the French Revolution increases taxation and investments in law enforcement in the areas surrounding these capitals. In the period leading up to the Revolution, Giommoni and Tabellini (2025) show that regions who were subject to a higher salt tax experienced more revolts between 1750 and 1789. Instead, our work highlights how the French Revolution was also a period of profound political innovations. We document how the random assignment of politicians into small groups introduced network effects that shaped the fate of these politicians in the subsequent period of intense conflict.
# 3 Historical Background

In the *Etats Généraux*, later Constituent Assembly, of 1789 the total number of deputies elected for the non-privileged third Estate or the *Third* totalled the sum of the equal number of deputies elected for the privileged orders, the *Nobility* and the *Clergy* (e.g., Lefebvre, 1947). The meeting was held in Versailles and not in Paris for safety and convenience: there was ample space to accommodate the 1,200 deputies to be called, and its proximity to the Royal palace ensured the king could continue his daily hunting habit (e.g., Schama, 1989).

## 3.1 Constituent Assembly Procedures

In May and June, the Third estate stalled to convince the other orders to deliberate jointly. In these early weeks, the crucial procedural choices, that we analyze the consequences of in this paper, were made - first in the June meetings of the Third Estate, and later in the National Assembly which merged the three orders together in earnest in July.

Early in June the Third decided, despite their delay tactic, that it would be harmless to hold pre-deliberation on different subjects in smaller groups "où tous les objets intéressants seraient discutés avant d'être soumis à la deliberation" (Pierre, 1893, 743). Moreover, this temporary institutional fix offered the convenient solution of helping the assembly to figure out the opinions of the large group of about 600 deputies, who obviously could not all speak or be heard at the same time, nor be officially counted, as long as the Third refused to start deliberations.

The third order followed a pre-existing French procedural tradition but made an important, highly consequential tweak: To assign people to deliberate and take votes in smaller rooms, they decided on a mechanism they considered to be as-good-as-random. It would also be much easier to take part in a discussion with thirty members. The random assignment would ensure that deputies from across the country would meet and exchange opinions, regardless of background, status, power or wealth, breaking potentially corrupting provincial interests. After the orders united in a single assembly, the bureaus were redrawn (Poudra and Pierre, 1878, 495). In July, ten bureaus were added to the total and each would henceforth have forty instead of thirty members, a mix of all three orders. The bureaus were to be redrawn monthly, and the bureaus were indeed renewed in August, and also in September and December (Castaldo,
1989). During the crucial month of July, unrest, conspiracy theories, and violence increased, a period referred to by historians as 'the great fear', *la Grande Peur* (e.g. Martin, 2024; Lefebvre, 1932). During the July month, following the storming of the Bastille on the 14th of July, tensions increased among the Constituants as they witnessed or heard of increasing lawlessness. In this conspiratorial climate, some of the deputies would no longer have returned to their lodgings (Tackett, 1996, 163-171). During this time, they did not accomplish much, but met very often in their smaller bureaus.<fnref n="1" />

Increasing pressures forced the king and the Constituent Assembly to relocate to Paris in October 1789. It seems the bureaus lost their role soon before or after this move, which severely disrupted the assembly's work for a while.

## 3.2 The Terror after the Constituent Assembly

Under increasing unrest on the streets, particularly after the king had fled Paris in June of 1791, the Constituent Assembly concluded its work and prescribed new elections. By September 1791 the First Legislative was elected and in place. None of the Constituants had been eligible for election to this first legislative assembly. By the end of 1792, after the First Legislative had provisionally suspended the king, a second constitution with no mention of the monarchy was prepared in its successor, the National Convention.

The Terror as a time period is broadly considered to have started after the September massacres of 1792 took place, to have intensified under the government by the Committee of Public Safety which was established in April 1793, and to have ended in 1794 after the death of Robespierre in July. Between 10 August and 20 September 1792 a first wave of what would later be called the Terror took place. The second, and more extensively organized violence took place after 5 September 1793. It was this phase which involved the single legislative chamber infamously prosecuting, trying and executing punishments by sending many citizens, in particular nobles, to the guillotine.

<footnote n="1">¹In contrast to the full assembly meetings, where the public was always present, these smaller meetings would have been more closed and sheltered from public view.</footnote>
# 4 Mechanisms of Connections

In what ways should we expect these random networks to have mattered? Early scholars of the bureau-system have emphasized how little we really understand about these early social contacts among legislators, but how important they are likely to have been in the huge assembly meeting. In particular, in July, all kinds of different legislators would have had ample opportunity to get to know each other in their smaller bureau (Dawson, 1978, 161, 171). We hypothesize that this intensive contact during the tense July and August months would have reinforced the legislators' personal social networks with one another within their bureaus. While bureaus were drawn also in September, and in December, it is unclear whether they fulfilled a similarly integrating function as the earlier, summer bureaus.

We analyse different dependent variables and aim to exploit the variation in how the bureaus were used - more intensively over the first two months, and much more fleetingly over the next two assignments - to tentatively shed light on which of the mechanisms below receives most evidence.

First, social interactions can reinforce integration, coordination, and the development of social capital (Glaeser et al., 2002). All of these may make individuals more likely to cooperate or protect one another at times of political upheaval (Aidt et al., 2022; Naidu et al., 2021). In addition, social networks can also help to diffuse private information (e.g., Banerjee et al., 2013).<fnref n="2" /> In a context of extreme political tension, among the most important private information that could be revealed and shared in social networks are individuals' deeply-held political opinions. It is not obvious how sharing of information within social networks affects the personal risks of their members. On the one hand, the revelation of ideologies in social networks could make targeting more likely within these networks once the revolution took a violent turn. On the other hand, it may make people who share the same private signal more likely to act in a similar way, while or even before the violence spirals out of control. The peer effects documented by Buggle et al. (2023) among Jews in Nazi Germany played exactly this role. Among French revolutionary deputies, learning from peers may have led to similar choices and polit-

<footnote n="2">²This feature of social networks has been studied closely by development economists as it is important for the adoption of policy interventions.</footnote>
ical trajectories, and it could explain why random assignment during the 1789 Assembly has long-term effects. Finally, the bureaus may have offered a venue not only for updating beliefs about others' privately held opinions, but also for persuasion by such ideas. Indeed, there is a very extensive literature in political science on the role of randomisation and persuasion in deliberative assemblies (e.g., Gastil, 2018). If persuasion indeed took place, connected individuals may also have first acted more similarly over the next few years, not just from privately updating but from partisan coordination, explaining why some may have met a similar fate by the end of the revolution.

# 5 Empirical Strategy

Our paper relies on the as-good-as-random allocation of Constituents to different bureaus.
First, we set out which data has been preserved and how we complete missing information.
Then, we detail our identification design.

## 5.1 Data

The data used in this project is derived from different sources. First, there is the list of deputies who served in the Etats Generaux, elected at different points in 1789 following different procedures. We matched this list to the lists of members of the bureaus, of which five different sets are known to have survived. The last assignment to bureaus was conducted in December 1789. For that reason, we consider a cross-section of members who served in the Constituent Assembly at some point in 1789 (excluding those who joined later). For each of these individuals we know the department and the order they were elected to represent (Clergy, Nobility, or Third), their full name, their birth and death dates (and therefore, age), as well as some information from a biographical record (such as bio length in number of characters). The list of deputies and their biographies were drawn from the lists that the French National Assembly provides on its website. Our final sample contains 1,256 unique members elected to French Constituent Assembly in 1789.

We consider violent death as a crucial long-term revolutionary outcome. This main depen-
dent variable captures the cause and timing of death of Constituents. We code if the official Assembly biographies contain any of a given set of words that point to a violent death during the Revolution.<fnref n="3" /> We also measure if a deputy was arrested during the Revolution.<fnref n="4" /> Finally, we measure whether Constituents emigrated.<fnref n="5" /> Table A8 presents an overview of dependent variables. In addition, we can use individual characteristics measured from Lemay’s 1989 *Dictionaire de Constituants*.<fnref n="6" /> We consider these characteristics for balance tests: An overview can be found in table A7.

Membership of the bureaus was collected or reconstructed from the National Archives. The July lists for the full assembly are missing, but could be reconstructed using the assignment algorithm - calling for the 1st, 31st, 61st, and so on to be united in the first bureau, etc. - and surviving, known details on about 18.7% of the bureau memberships in that month.<fnref n="7" /> The August list, an undated list which is likely to have been used to determine bureau membership in September, and a final list dated December 1789, could also be located in the National Archives and from sources in the BnF (Bibliothèque Nationale de France).

## 5.2 Research Design

We consider a natural experiment in the very early days of the Constituent Assembly, when deputies less familiar with each other cooperated intensively in the smaller bureaus. The membership of these bureaus was as-good-as-randomly assigned. We call such connections *random connections*.

Leveraging the quasi-random assignment of deputies to groups as determined by the algorithm, we estimate the exposure effect through the bureaus as follows:

<footnote n="3">³From the more extensive biographies in Lemay’s 1989 *Dictionaire de Constituants*, an alternative variable *Executed* is hand-coded and defined as officially tried and sentenced to execution.</footnote>

<footnote n="4">⁴Using the more extensive biographies in Lemay’s 1989 *Dictionaire de Constituants* we further code whether the deputy was "Ever recorded as having been arrested/imprisoned in the course of the Revolutionary period". We take the maximum value of both measures.</footnote>

<footnote n="5">⁵Using the more extensive biographies in Lemay’s 1989 *Dictionaire de Constituants* we further code whether and when the deputy emigrated out of France in the course of the Revolutionary period. We take the maximum value of both measures.</footnote>

<footnote n="6">⁶These individual characteristics were almost certainly determined before they entered the Constituent Assembly.</footnote>

<footnote n="7">⁷We know the ranking of the Third Estate in 17 surviving June lists, whose order was kept intact in the July list. Using surviving information scattered in different sources in the National Archives, BnF, the PV, and AP, it was easy to verify that the privileged Estates were listed first by each department. We used the order of Brette (1896), to the extent confirmed by Lemay (1989).</footnote>
$$DV_i = \alpha + \beta_1 Exposure_i + \beta_2 Exposure_i \times Order_i + X_i + \epsilon_i \quad (1)$$

$DV_i$ is our outcome of interest, and a binary indicator for violent death, arrest, or emigration. $i$ is a French Constituent Assembly member in 1789. $Exposure_i$ is a measure of the exposure of deputy $i$ in the randomly composed bureaus to specific revolutionary leaders, or to the number of individuals with the same outcomes within a bureau. We allow for different exposure windows in the analysis: either June-July, June-August, June-September, or June-December.<fnref n="8" /> We also control for a set of individual-level fixed effects $X_i$, which include order fixed effects, $Order_i$ (i.e., the elected order of the deputy: Clergy, Nobility, or Third). We include the interaction between the exposure measure and an individual deputy $i$'s order to allow for heterogeneous treatment effects by order. Conditioning the treatment effect of personal connections on a deputy's order is necessary because we know that individuals from different orders were targeted differently throughout the Terror period and they had different opportunities for emigration throughout the French Revolution. Finally, we control for department fixed effects ($Department_i$), and for the interaction between order and department, as we know that the algorithm only assigned deputies randomly after conditioning on these characteristics. We cluster our standard errors by bureau-month.

### 5.2.1 Random Connections to Individuals

In our main results, we focus on two specific individual exposure effects. So, $Exposure_i^{Lafayette} = 1$ if a deputy $i$ randomly met Lafayette in his bureaus over a given time period (between June and $t_{max}$), and 0 otherwise. Likewise, $Exposure_i^{Robespierre}$ measures exposure to Robespierre. We decided to focus on these two historically important individuals because they were key leaders in the revolution. This is supported by the fact that they are the two individuals with the longest biographies in our dataset.<fnref n="9" />

<footnote n="8">⁸Note that the measure is cumulative only from July to August for the members of the Clergy and Nobility, who entered only in July.</footnote>

<footnote n="9">⁹We also attempted to measure who became an important individual using only information from the network (by considering all global brokers of the network at the end of December after a social network analysis, i.e. those with the top 2.5% of betweenness scores), but we do not find a systematic influence of such individuals.</footnote>
### 5.2.2 Random Connections to Networks

We also focus on network exposure effects in general, and for clarity of presentation, we consider these results first. This exposure measure is defined as:

$$Exposure_i = \sum_{t=June}^{t_{max}} \sum_{j \neq i} DV_j * (bureau_{i,t} = bureau_{j,t}) \quad (2)$$

Hence, Exposure$_i$ measures the total number of other members j with outcome DV$_j$ = 1 whom i randomly met in the same bureaus (bureau$_{i,t}$ ∈ {1, 2, ..., 30}) between June and month t$_{max}$. Depending on t$_{max}$, we use cumulative measures for the networks built up to July, up to August, up to September and up to December. In the case of violent death, we call this variable "other violent deaths".

Equation 1 now corresponds to an outcome-on-outcome leave-one-out design, in which the peer effect is determined by an intra-class correlation coefficient. Angrist (2014) formulates a powerful criticism of peer effect studies, highlighting how the effects they estimate are rarely socially significant. This is an important concern in settings when peer effects are estimated for existing, fixed groups like neighbourhoods or class groups, because it is very hard to understand through which policy-relevant factors (if any) the peer effects operate. In our setting, the groups are the direct result of a policy intervention, i.e. an institutional feature that is controlled by decision-makers. They force deputies to interact during a limited period of time in the relatively controlled environment of the Assembly. Even if we cannot identify the precise mechanisms through which social effects operate, any evidence of correlated outcomes is not obvious and important in this context. It is worth noting that our exposure measure based on specific individuals (discussed in section 5.2.1) corresponds to an exogenous peer-effect design, and has a more straight-forward causal interpretation.

### 5.2.3 Balance Checks

Random connections in bureaus were formed among at most forty deputies at a time. By definition, the random networks created were, therefore, very similar. By design, within the order of a deputy, there is little to no variation in most commonly considered network centrality
measures in studies of social networks in economic history (Esteves and Geisler Mesevage, 2019). In appendix A we present an illustration of the social network, as well as summary statistics.

In support of our research design, we tested for systematic imbalances across bureau draws that result in placebo treatment effects on baseline characteristics. Here, we estimated our main equation 1 for each of the individual characteristics, and for both $Exposure_i^{Lafayette}$ and $Exposure_i^{Robespierre}$. The results are presented in figure A7 in the appendix. The p-values show broad variation. When there are significant correlations, below the cut-off value of 5% or 10% significance, these relationships are not stable across exposure windows. In a robustness check, we control for these imbalances in the relevant specifications.

# 6 Results

We now summarize our main findings on random connections in the French revolution.

## 6.1 Random Connections: Network contagion

We first evaluate whether these random networks built in 1789 mattered for how violence spread once the revolution took a more violent turn. Table 1 presents the results. The nobility is the reference category, while the interaction effects highlight differential effects for the other orders. For each additional violent death in a noble Constituent's network, his probability of dying a violent death increases by about 1%. This effect is similar in size and significant within the networks at the end of August, September and December (columns 2, 3, and 4 of table 1). This effect is largely mitigated for the other two orders, the Clergy and the Third Estate, and their total effects do not differ significantly from zero. The mean network constructed by August contains about 5 violent deaths, whereas the mean number of violent deaths in the larger networks created at the end of December is about 10. A noble with 10 violent deaths in his network by December therefore had an about 15% higher probability of dying a violent death himself, compared to an otherwise similar counterfactual noble without any deaths in his random network. Given that on average about 11% of the noble Constituents would die
violent death during the revolution, these effects are sizable.

<table><caption>Table 1: Violent death contagion in random networks</caption><thead><tr><th></th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td><strong>Other violent deaths</strong></td><td>-0.012<br>[0.023]</td><td><strong>0.011**</strong><br><strong>[0.005]</strong></td><td><strong>0.015***</strong><br><strong>[0.002]</strong></td><td><strong>0.014***</strong><br><strong>[0.005]</strong></td></tr><tr><td>×Clergy</td><td>-0.006<br>[0.025]</td><td>-0.018***<br>[0.005]</td><td>-0.023***<br>[0.002]</td><td>-0.019***<br>[0.005]</td></tr><tr><td>×Third</td><td>0.016<br>[0.023]</td><td>-0.004<br>[0.007]</td><td>-0.010**<br>[0.004]</td><td>-0.011*<br>[0.006]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,079</td></tr></tbody></table>

Note: Dependent variable is whether the Constituent died a violent death, with the treatment the sum of all other violent deaths in his random network. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.

Could these results be due to random chance? We created 1,000 alternative datasets in which we randomly drew groups under the same conditions as the actual random connections built during the Revolution.<fnref n="10" /> This thought experiment underlines that the size and the relatively narrow confidence intervals around this true treatment effect, compared to those of placebo draws (see figure 1 below), are exceptional.<fnref n="11" /> We conclude that the effect we find is unlikely due to random chance.

<footnote n="10">¹⁰We created 1,000 placebo datasets whereby the same number of bureaus were created using the actual algorithm employed, but by randomly drawing a different department to start with, e.g. starting with (randomly drawn) Orléans instead of (real) Agen. See figure A8 in the appendix for a plot of these effects.</footnote>

<footnote n="11">¹¹See appendix figures A9, A10 and A11 for similar graphs for all other cumulative time periods.</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 1: T-statistic of true violent death effect compared to those of 1000 placebos</figcaption></figure>

Note: T-statistics of total effects of other violent deaths within a random network, by order: Clergy (left), Nobles (mid) and Third (right). The dependent variable is violent death. Figures depict t-statistics of the cumulative treatment effects within random networks until September. The true t-statistic is a large X colored red; similar t-statistics for 1000 placebo random draws are depicted in grey.

A violent death is a peculiar outcome, which remains - even in the context of the French revolution - quite rare: About 6% of all deputies elected to the Constituent Assembly would die a violent death during the revolution.<fnref n="12" /> Yet these peer effects are sizeable.

## 6.2 Random Connections: Key Leaders

We next evaluate whether randomly connecting to specific historical figures at the start of the Revolution was decisive for the subsequent fate of Constituents during the Terror.

We find that random personal connections to either of two important revolutionary leaders lowered the probability of dying a violent death during the revolution, particularly for the highly prosecuted group of nobles. Exposure to Lafayette, at any point, lowers the nobility's chances by about 20-23% (see table 2, panel A). Randomly connecting to Robespierre does so too, by about 20-30% but only in the first two months when the bureaus worked intensively (see table 2, panel B). Any third estate deputies unfortunate enough to have been in the bureau of Lafayette over this time period were clearly less lucky, as their chances of violent death increased, not lowered. Unfortunately, Robespierre and Lafayette shared the same bureau in August, making it hard to attribute any effects within the unique networks they built over the first two assignments to either of them separately. Interestingly, the protective effect for the nobility of connecting with Robespierre disappears if we add the last two assignments and consider the network at the end of December 1789. However, we can offer a qualitative explanation

<footnote n="12">¹²By emigrating a Constituent could avoid this outcome. After dropping emigrants from the sample, we evaluated the contagion effect of other violent deaths on arrest, which returns positive estimates (see appendix table A10).</footnote>
nation for this change. Until the end of August the bureaus can be considered to have done substantively important work preparing legislative work. After early September, they only fulfilled quick, procedural functions, making the contact between deputies briefer and providing them with less information about the political opinions of their random connections from September onward. Robespierre in particular was known to dislike the bureaus, preferring open debate in the larger Assembly which in contrast to the bureaus did not exclude the public from its meetings.

<table><caption>Table 2: Lafayette and Robespierre exposure effect on violent death</caption><thead><tr><th>Violent Death</th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td><strong>Panel A: Lafayette</strong><br>Exposure to Lafayette</td><td>-0.232***<br>[0.058]</td><td>-0.198***<br>[0.036]</td><td>-0.177***<br>[0.036]</td><td>-0.222***<br>[0.064]</td></tr><tr><td>×Clergy</td><td>0.099<br>[0.060]</td><td>0.198**<br>[0.077]</td><td>0.240***<br>[0.057]</td><td>0.256***<br>[0.054]</td></tr><tr><td>×Third</td><td>0.442***<br>[0.078]</td><td>0.336***<br>[0.101]</td><td>0.283***<br>[0.087]</td><td>0.285***<br>[0.086]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,076</td></tr><tr><td><strong>Panel B: Robespierre</strong><br>Exposure to Robespierre</td><td>-0.333*<br>[0.165]</td><td>-0.214**<br>[0.095]</td><td>-0.001<br>[0.135]</td><td>-0.023<br>[0.100]</td></tr><tr><td>×Clergy</td><td>0.333**<br>[0.147]</td><td>0.286***<br>[0.081]</td><td>-0.013<br>[0.174]</td><td>0.010<br>[0.147]</td></tr><tr><td>×Third</td><td>0.325*<br>[0.168]</td><td>0.211**<br>[0.097]</td><td>-0.009<br>[0.149]</td><td>0.025<br>[0.113]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,078</td></tr></tbody></table>

Note: Dependent variable is whether the Constituent died a violent death, with the treatment exposure to Lafayette (panel A) or Robespierre (panel B) in his random network. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.
## 6.3 Robustness

One concern is that these two leaders are not the only ones with this particular effect on life outcomes. Therefore, we check the value of connections to every single Constituent; we run equation 1 for exposure to each individual deputy (excluding them from the corresponding sample). We plot their exposure effects on violent death for the reference group of the nobility in figure A13, which also highlights the effects for our two selected historical figures, Lafayette and Robespierre, for easy comparison.<fnref n="13" /> While the effects are distributed around 0, relatively few individuals have negative exposure effects like our two key leaders. We conclude from this additional check that our random networks were not decisive in determining who would be key leaders in the revolution. Still, for the two Constituents who turned out to be the most important leaders of the revolution, the random network appears to have shaped the fate of nobles.

Another concern is that our findings are driven by confounding factors. When looking at pre-determined individual characteristics, we did not find perfect balance across treated and control individuals. However, we confirm that our main results hold controlling for these unbalanced characteristics (see tables A17 and A18 in the appendix).

In another robustness check, we ensured that the protective effect of personal connections holds not just for dying a violent death, but in particular for execution, in automatically coded or hand-coded biographies. The relevant tables A11 and A12 (as well as tables A13 and A14 for hand-coded, alternative biographies) can be found in the appendix. Sharing a bureau with Lafayette in July or August decreases a noble's probability of execution by about 18%, while the proportion for Robespierre is exactly the same as for the violent death outcome: a decrease of about 21%, for nobles.

## 6.4 Mechanisms

Why did exposure to these key leaders decrease the chances of these nobles to die a violent death? Lafayette emigrated by August 1792, so any effect on other Constituents is unlikely to have been direct. It could be that nobles used their private information about either figure

<footnote n="13">¹³Similar graphs for June-July, June-Sept and June-Dec can be found in appendix figures A12, A14 and A15.</footnote>
to update their privately-held beliefs after history had taken an eventful turn in 1792, leading them to emigrate like Lafayette himself. Indeed, exposure to either Lafayette or to Robespierre also increased the probability that a Constituant noble would emigrate, significantly so in all but one specification (see table 3). Again, we argue that it is likely that Constituants held information from their interactions with either key leader in the bureaus, which made them more likely to emigrate.

<table><caption>Table 3: Lafayette and Robespierre exposure effect on emigration</caption><thead><tr><th>Emigrated</th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td><strong>Panel A: Lafayette</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Exposure to Lafayette</td><td>0.364***<br>[0.067]</td><td>0.462***<br>[0.111]</td><td>0.309**<br>[0.141]</td><td>0.311**<br>[0.141]</td></tr><tr><td>×Clergy</td><td>-0.406***<br>[0.136]</td><td>-0.533**<br>[0.198]</td><td>-0.178<br>[0.300]</td><td>-0.220<br>[0.258]</td></tr><tr><td>×Third</td><td>-0.421***<br>[0.075]</td><td>-0.497***<br>[0.095]</td><td>-0.325**<br>[0.143]</td><td>-0.337**<br>[0.144]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,076</td></tr><tr><td><strong>Panel B: Robespierre</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Exposure to Robespierre</td><td>0.286**<br>[0.119]</td><td>0.433***<br>[0.138]</td><td>0.367***<br>[0.121]</td><td>0.215<br>[0.150]</td></tr><tr><td>×Clergy</td><td>-0.366**<br>[0.136]</td><td>-0.562**<br>[0.207]</td><td>-0.440***<br>[0.144]</td><td>-0.211<br>[0.178]</td></tr><tr><td>×Third</td><td>-0.310**<br>[0.119]</td><td>-0.454***<br>[0.139]</td><td>-0.399***<br>[0.120]</td><td>-0.247*<br>[0.143]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,078</td></tr></tbody></table>

Note: Dependent variable is whether the Constituant emigrated, with the treatment exposure to Lafayette (panel A) or Robespierre (panel B) in his random network. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.

What can explain why these nobles with connections were targeted differently, or were more likely to emigrate? We consider a variable *Moderate Revolutionaries* indicating deputies
who ever formed part of the Club of 1789 or of the Feuillants, a more moderate breakaway
group from the Jacobin club. Table 4 (panel A) confirms that among the more moderate radicals
following Lafayette in either or both of these political clubs were those who had been randomly
exposed to him in a bureau in July, August or September. These nobles were about 18-27% more
likely to join the Club of 1789 or the Feuillants. By comparison, about a quarter of Constituants
joined the Feuillants, mostly from the Third Estate, but also about 14% of nobles did so.<fnref n="14" /> In
addition, there is some evidence that the nobles who randomly connected to Lafayette became
opposed to the revolution and left the country to join the Army of Princes (see the sizes of the
relevant coefficient in table 4 panel B).<fnref n="15" /> The Army of the Princes was formed of battalions
loyal to the royal Princes who had escaped and fought the revolutionary assembly.

We interpret these results as evidence supporting the information-mechanism we out-
lined in our theoretical framework. Among the nobles, exposure to Lafayette also affected
behaviour, and arguably beliefs.

<footnote n="14">¹⁴Robespierre exposure had a negative effect on joining the Feuillants, for nobles who connected to him in July only, see table A15 in the appendix.</footnote>

<footnote n="15">¹⁵A similar analysis for nobles who gained Robespierre exposure shows a mostly significant, positive effect in appendix table A16.</footnote>
<table><caption>Table 4: Moderate Revolutionaries, Army of the Princes & Exposure to Lafayette</caption><thead><tr><th></th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A: DV Moderate Revolutionaries</strong></td></tr><tr><td><em>Exposure to Lafayette</em></td><td>0.270***<br>[0.066]</td><td>0.221***<br>[0.061]</td><td>0.177***<br>[0.039]</td><td>0.131<br>[0.085]</td></tr><tr><td>×Clergy</td><td>-0.375***<br>[0.096]</td><td>-0.354***<br>[0.074]</td><td>-0.339***<br>[0.038]</td><td>-0.268***<br>[0.069]</td></tr><tr><td>×Third</td><td>-0.101<br>[0.077]</td><td>-0.171***<br>[0.061]</td><td>-0.149**<br>[0.071]</td><td>-0.112<br>[0.110]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,079</td></tr><tr><td colspan="5"><strong>Panel B: DV Army</strong></td></tr><tr><td><em>Exposure to Lafayette</em></td><td>0.354***<br>[0.034]</td><td>0.368***<br>[0.009]</td><td>0.267***<br>[0.023]</td><td>0.288***<br>[0.059]</td></tr><tr><td>×Clergy</td><td>-0.442***<br>[0.067]</td><td>-0.454***<br>[0.040]</td><td>-0.321***<br>[0.076]</td><td>-0.326***<br>[0.087]</td></tr><tr><td>×Third</td><td>-0.354***<br>[0.034]</td><td>-0.401***<br>[0.035]</td><td>-0.293***<br>[0.041]</td><td>-0.307***<br>[0.067]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,079</td></tr></tbody></table>

Note: Dependent variable is whether the Constituent became a moderate revolutionary (panel A), or enlisted in an army opposed to the republic (panel B), with the treatment exposure to Lafayette in his random network. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. \*< 0.1, \*\*< 0.05 and \*\*\*< 0.01.
# 7 Conclusion

Social networks can play a crucial role in revolutions, with personal connections determining whether key political actors can survive the regime change. In this paper, we studied random networks among members of the 1789 French Constituent Assembly. We found that violent death was contagious within random networks - at least for the highly targeted nobility. We also investigated random exposure to two key revolutionary leaders, Robespierre and Lafayette. Personal connections to either key leader seem to have been protective, but mostly for networks built during the months of intensive contact within these networks. We argue there is little evidence that Constituants received direct help from these personal connections, but that it is more likely that they shared similar, private, information about the revolutionary leaders. They formed beliefs during months of intensive contact with the revolutionary leaders, which they acted on later on after the Revolution had taken a violent turn. We also find some evidence that connecting to Lafayette influenced nobles to become more moderate. Exposed nobles also became more likely to enlist in enemy armies against the republic. These results highlight that social networks among political actors can shape their survival risks and strategies in periods of severe political upheaval.
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# Online appendix

## List of Figures

<table><tr><td>1</td><td>T-statistic of true violent death effect compared to those of 1000 placebos</td><td>15</td></tr><tr><td>A6</td><td>Bureau-Network in December</td><td>4</td></tr><tr><td>A7</td><td>Balance</td><td>5</td></tr><tr><td>A8</td><td>True violent death effect compared to this effect in 1000 placebo random draws</td><td>6</td></tr><tr><td>A9</td><td>T-statistic of true violent death effect compared to those of 1000 placebos (July)</td><td>7</td></tr><tr><td>A10</td><td>T-statistic of true violent death effect compared to those of 1000 placebos (Aug)</td><td>7</td></tr><tr><td>A11</td><td>T-statistic of true violent death effect compared to those of 1000 placebos (Dec)</td><td>7</td></tr><tr><td>A12</td><td>Violent Death and Random Connections in June or July</td><td>11</td></tr><tr><td>A13</td><td>Violent Death and Random Connections in June, July or August</td><td>11</td></tr><tr><td>A14</td><td>Violent Death and Random Connections in June, July, August or September</td><td>12</td></tr><tr><td>A15</td><td>Violent Death and Random Connections in June, July, August, September or December</td><td>12</td></tr></table>

## List of Tables

<table><tr><td>1</td><td>Violent death contagion in random networks</td><td>14</td></tr><tr><td>2</td><td>Lafayette and Robespierre exposure effect on violent death</td><td>16</td></tr><tr><td>3</td><td>Lafayette and Robespierre exposure effect on emigration</td><td>18</td></tr><tr><td>4</td><td>Moderate Revolutionaries, Army of the Princes &amp; Exposure to Lafayette</td><td>20</td></tr><tr><td>A7</td><td>Independent Variables</td><td>2</td></tr><tr><td>A8</td><td>Dependent Variables</td><td>2</td></tr><tr><td>A9</td><td>Summary statistics</td><td>3</td></tr><tr><td>A10</td><td>Violent death contagion in random networks on arrest, excl. emigrants</td><td>8</td></tr><tr><td>A11</td><td>Lafayette exposure effect on execution</td><td>8</td></tr><tr><td>A12</td><td>Robespierre exposure effect on execution</td><td>9</td></tr><tr><td>A13</td><td>Lafayette exposure effect on execution (handcoded Lemay bios)</td><td>9</td></tr><tr><td>A14</td><td>Robespierre exposure effect on execution (handcoded Lemay bios)</td><td>10</td></tr><tr><td>A15</td><td>Moderate Revolutionaries &amp; Exposure to Robespierre</td><td>13</td></tr><tr><td>A16</td><td>Army of Princes &amp; Robespierre Exposure</td><td>13</td></tr><tr><td>A17</td><td>Lafayette exposure effect on violent death, unbalanced controls</td><td>14</td></tr><tr><td>A18</td><td>Robespierre exposure effect on violent death, unbalanced controls</td><td>15</td></tr></table>
# A Appendix 1: Definitions, Summary Statistics, and Balance Checks

<table><caption>Table A7: Independent Variables</caption><thead><tr><td>Measurement</td><td>Variable list</td></tr></thead><tbody><tr><td>Ad-hoc</td><td>Clergy, Noble, Third, Society of 30, Army-background, Law, Assem. Exp., Arist. Title, Combat exp., Valois club, Freemason, Age, Department</td></tr></tbody></table>

<table><caption>Table A8: Dependent Variables</caption><thead><tr><td>Variable</td><td>Description</td></tr></thead><tbody><tr><td>Violent death</td><td>(auto-match) bios were automatically coded for executed or massacred: "assassiné", "condamné à mort", "empoisonné", "dévoré", "tué", "massacré", "guillotiné", "exécuté", "décapité". Reviewed, particularly for timing: All died in or before 1794</td></tr><tr><td>Emigration</td><td>(auto-match) bios were automatically coded for: "émigr", "partit", "se retira", "parvint à gagner", "quitter la France", "quitt". Reviewed.</td></tr><tr><td>Arrestation</td><td>(auto-match) bios were automatically coded for: "arrêté", "mis aux fers", "mis en prison", "mettre aux fers", "emprisonné", "enfermé", "Arrêté", "prison", "incarcéré", "détenu". Reviewed.</td></tr><tr><td>Arrested in Rev</td><td>(handcoded biography Lemay) Ever recorded as having been arrested/imprisoned in the course of the Revolutionary period.</td></tr><tr><td>Executed in Rev</td><td>(handcoded biography Lemay) officially tried and sentenced to execution.</td></tr><tr><td>Emigrated in Rev</td><td>(handcoded biography Lemay) Left France.</td></tr><tr><td>Arrested</td><td>Maximum of arrestation and arrested in Rev</td></tr><tr><td>Emigrated</td><td>Maximum of emigration and emigrated in Rev</td></tr><tr><td>Moderate revs</td><td>Biography Lemay lists membership of the Feuillants or Club of 1789</td></tr><tr><td>Army</td><td>Biography Lemay lists emigration to join the Armée des Princes, or Armée de Condé</td></tr></tbody></table>
<table><caption>Table A9: Summary statistics</caption><thead><tr><th>Variable</th><th>Obs</th><th>Mean</th><th>Std. dev.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Violent death</td><td>1,256</td><td>.0644904</td><td>.2457224</td><td>0</td><td>1</td></tr><tr><td>Emigrated</td><td>1,256</td><td>.3041401</td><td>.4602255</td><td>0</td><td>1</td></tr><tr><td>Arrested</td><td>1,256</td><td>.2253185</td><td>.4179583</td><td>0</td><td>1</td></tr><tr><td>Moderate revs</td><td>1,256</td><td>.2611465</td><td>.4394346</td><td>0</td><td>1</td></tr><tr><td>Army</td><td>1,256</td><td>.0636943</td><td>.2443048</td><td>0</td><td>1</td></tr><tr><td>Clergy</td><td>1,256</td><td>.2539809</td><td>.4354602</td><td>0</td><td>1</td></tr><tr><td>Noble</td><td>1,256</td><td>.2380573</td><td>.426064</td><td>0</td><td>1</td></tr><tr><td>Third</td><td>1,256</td><td>.5007962</td><td>.5001985</td><td>0</td><td>1</td></tr><tr><td>Other violent deaths (June-July)</td><td>1,256</td><td>2.985669</td><td>1.727615</td><td>0</td><td>8</td></tr><tr><td>Other violent deaths (June-Aug)</td><td>1,256</td><td>5.329618</td><td>2.639802</td><td>0</td><td>16</td></tr><tr><td>Other violent deaths (June-Sept)</td><td>1,256</td><td>7.68949</td><td>3.35507</td><td>0</td><td>20</td></tr><tr><td>Other violent deaths (June-July)</td><td>1,256</td><td>10.13774</td><td>3.705404</td><td>0</td><td>25</td></tr><tr><td>Other Emigrants (June-July)</td><td>1,256</td><td>12.22213</td><td>4.3415</td><td>0</td><td>24</td></tr><tr><td>Other Emigrants (June-Aug)</td><td>1,256</td><td>23.36306</td><td>6.444272</td><td>0</td><td>36</td></tr><tr><td>Other Emigrants (June-Sept)</td><td>1,256</td><td>34.62022</td><td>8.630246</td><td>0</td><td>52</td></tr><tr><td>Other Emigrants (June-Dec)</td><td>1,256</td><td>45.65525</td><td>9.77947</td><td>1</td><td>66</td></tr><tr><td>Exposure Lafayette (June-July)</td><td>1,256</td><td>.031051</td><td>.1735245</td><td>0</td><td>1</td></tr><tr><td>Exposure Lafayette (June-Aug)</td><td>1,256</td><td>.0605096</td><td>.2385235</td><td>0</td><td>1</td></tr><tr><td>Exposure Lafayette (June-Sept)</td><td>1,256</td><td>.0915605</td><td>.2885194</td><td>0</td><td>1</td></tr><tr><td>Exposure Lafayette (June-Dec)</td><td>1,256</td><td>.1218153</td><td>.3272026</td><td>0</td><td>1</td></tr><tr><td>Exposure Robespierre (June-July)</td><td>1,256</td><td>.0525478</td><td>.2232178</td><td>0</td><td>1</td></tr><tr><td>Exposure Robespierre (June-Aug)</td><td>1,256</td><td>.0820064</td><td>.2744837</td><td>0</td><td>1</td></tr><tr><td>Exposure Robespierre (June-Sept)</td><td>1,256</td><td>.1122611</td><td>.3158132</td><td>0</td><td>1</td></tr><tr><td>Exposure Robespierre (June-Dec)</td><td>1,256</td><td>.1417197</td><td>.3489014</td><td>0</td><td>1</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>Figure A6: Bureau-Network in December</figcaption></figure>

Note: Random networks in June (top left), July (top right), August (mid left), September (mid right) and December (bottom). Constituents who share(d) a bureau are linked by a grey line.

The networks were rolled-out by five different assignments in time, as depicted in figure A6. For example, degree - or the number of connections an individual possesses -, is by construction the same for individuals, as long as they were present throughout all assignments. The Third Estate members in our data have more connections due to the additional random assignment they experienced in June. A few individuals entered later on and, therefore, have fewer connections, these are the seemingly free-floating ones in figure A6. The median degree was 172 for Third Estate members by December, and 146 for the Clergy and Nobility members. One of the only measures that varies is betweenness - the number of pairs for whom their shortest path to each other crosses a given individual. If we calculate betweenness scores, we find that Robespierre is among the top-2.5% of these raw scores by the end of December.
<figure><img src="image_6.png" /><figcaption>Figure A7: Balance</figcaption></figure>

Note: Figure presents p-values from balance checks of ad-hoc, cross-order characteristics on main effects of treatment variables interacted by order: exposure to Robespierre or Lafayette. As in the main results, the Nobility is the reference group.
# B Appendix 2: Placebo random connections

We created 1,000 additional datasets whereby we randomly drew groups under the same conditions as the actual random connections built during the revolution we study here. We calculated the main effect for each of these samples and plotted the result in figure A8 below. We can see that the true effect for the nobility (in the middle) stands out as positive and significant compared to the placebo draws, regardless of whether we consider the network up to August (second row), September (third row) or December (bottom row). The third estate (on the right) has a similar treatment effect, but it fails to reach the conventional 5% level of statistical significance. The clergy (on the left) seems to have a lower probability of dying if more others died in their network - but the effect is again not significant.

<figure><img src="image_7.png" /><figcaption>Figure A8: True violent death effect compared to this effect in 1000 placebo random draws</figcaption></figure>

Note: Total effects of other violent deaths within a random network, by order: Clergy (left), Nobles (mid) and Third (right). The dependent variable is violent death. Figures on the top row depict cumulative treatment effects within random networks until July, second row until August, third row until September and bottom row until December. The true effect is colored red; similar effects for 1000 placebo random draws are depicted in grey.
<figure><img src="image_8.png" /><figcaption>Figure A9: T-statistic of true violent death effect compared to those of 1000 placebos (July)</figcaption></figure>

Note: T-statistics of total effects of other violent deaths within a random network, by order: Clergy (left), Nobles (mid) and Third (right). The dependent variable is violent death. Figures depict t-statistics of the cumulative treatment effects within random networks until July. The true t-statistic is a large X colored red; similar t-statistics for 1000 placebo random draws are depicted in grey.

<figure><img src="image_9.png" /><figcaption>Figure A10: T-statistic of true violent death effect compared to those of 1000 placebos (Aug)</figcaption></figure>

Note: T-statistics of total effects of other violent deaths within a random network, by order: Clergy (left), Nobles (mid) and Third (right). The dependent variable is violent death. Figures depict t-statistics of the cumulative treatment effects within random networks until August. The true t-statistic is a large X colored red; similar t-statistics for 1000 placebo random draws are depicted in grey.

<figure><img src="image_10.png" /><figcaption>Figure A11: T-statistic of true violent death effect compared to those of 1000 placebos (Dec)</figcaption></figure>

Note: T-statistics of total effects of other violent deaths within a random network, by order: Clergy (left), Nobles (mid) and Third (right). The dependent variable is violent death. Figures depict t-statistics of the cumulative treatment effects within random networks until December. The true t-statistic is a large X colored red; similar t-statistics for 1000 placebo random draws are depicted in grey.
## C Appendix 3: Additional analyses and robustness checks

<table><caption>Table A10: Violent death contagion in random networks on arrest, excl. emigrants</caption><thead><tr><th>Arrested</th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td>Other violent deaths</td><td>0.006<br>[0.067]</td><td>0.039<br>[0.028]</td><td>0.044**<br>[0.018]</td><td>0.056**<br>[0.021]</td></tr><tr><td>×Clergy</td><td>0.003<br>[0.076]</td><td>-0.005<br>[0.035]</td><td>-0.027<br>[0.028]</td><td>-0.033<br>[0.032]</td></tr><tr><td>×Third</td><td>-0.016<br>[0.068]</td><td>-0.035<br>[0.033]</td><td>-0.043*<br>[0.021]</td><td>-0.054**<br>[0.022]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sample excludes those who left</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>695</td><td>695</td><td>695</td><td>695</td></tr></tbody></table>

Note: Dependent variable is whether the Constituant was arrested, with the treatment all other violent deaths in his random network. Sample excludes emigrants. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.

<table><caption>Table A11: Lafayette exposure effect on execution</caption><thead><tr><th>Executed</th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td>Exposure to Lafayette</td><td>-0.197***<br>[0.053]</td><td>-0.177***<br>[0.040]</td><td>-0.155***<br>[0.023]</td><td>-0.201***<br>[0.065]</td></tr><tr><td>×Clergy</td><td>0.153**<br>[0.057]</td><td>0.238***<br>[0.067]</td><td>0.194***<br>[0.039]</td><td>0.228***<br>[0.051]</td></tr><tr><td>×Third</td><td>0.406***<br>[0.064]</td><td>0.314***<br>[0.094]</td><td>0.261***<br>[0.073]</td><td>0.265***<br>[0.082]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,079</td></tr></tbody></table>

Note: Dependent variable is whether the Constituant was executed (automatic coding), with the treatment exposure to Lafayette in his random network. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.
<table><caption>Table A12: Robespierre exposure effect on execution</caption><thead><tr><th>Executed</th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td><strong>Exposure to Robespierre</strong></td><td>-0.333*<br>[0.165]</td><td>-0.214**<br>[0.095]</td><td>-0.071<br>[0.098]</td><td>-0.056<br>[0.075]</td></tr><tr><td>×Clergy</td><td>0.199<br>[0.167]</td><td>0.214**<br>[0.083]</td><td>0.071<br>[0.105]</td><td>0.056<br>[0.077]</td></tr><tr><td>×Third</td><td>0.325*<br>[0.168]</td><td>0.211**<br>[0.097]</td><td>0.062<br>[0.109]</td><td>0.058<br>[0.081]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,079</td></tr></tbody></table>

Note: Dependent variable is whether the Constituant was executed (automatic coding), with the treatment exposure to Robespierre in his random network. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.

<table><caption>Table A13: Lafayette exposure effect on execution (handcoded Lemay bios)</caption><thead><tr><th>Executed (handcoded Lemay)</th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td><strong>Exposure to Lafayette</strong></td><td>-0.179***<br>[0.050]</td><td>-0.166***<br>[0.041]</td><td>-0.148***<br>[0.017]</td><td>-0.195***<br>[0.063]</td></tr><tr><td>×Clergy</td><td>0.135*<br>[0.069]</td><td>0.227***<br>[0.065]</td><td>0.187***<br>[0.033]</td><td>0.222***<br>[0.048]</td></tr><tr><td>×Third</td><td>0.365***<br>[0.071]</td><td>0.292***<br>[0.090]</td><td>0.246***<br>[0.066]</td><td>0.253***<br>[0.080]</td></tr><tr><td>Observations</td><td>1,067</td><td>1,067</td><td>1,067</td><td>1,067</td></tr></tbody></table>

Note: Dependent variable is whether the Constituant was executed (handcoded bios), with the treatment exposure to Lafayette in his random network. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.
<table><caption>Table A14: Robespierre exposure effect on execution (handcoded Lemay bios)</caption><thead><tr><th>Executed (handcoded Lemay)</th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td><strong>Exposure to Robespierre</strong></td><td>-0.333*<br>[0.165]</td><td>-0.214**<br>[0.095]</td><td>-0.074<br>[0.101]</td><td>-0.016<br>[0.075]</td></tr><tr><td>×Clergy</td><td>0.199<br>[0.167]</td><td>0.214**<br>[0.083]</td><td>0.074<br>[0.107]</td><td>0.016<br>[0.077]</td></tr><tr><td>×Third</td><td>0.325*<br>[0.168]</td><td>0.211**<br>[0.097]</td><td>0.064<br>[0.112]</td><td>0.022<br>[0.078]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,067</td><td>1,067</td><td>1,067</td><td>1,067</td></tr></tbody></table>

Note: Dependent variable is whether the Constituant was executed (handcoded bios), with the treatment exposure to Robespierre in his random network. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.
<figure><img src="image_11.png" /><figcaption>Figure A12: Violent Death and Random Connections in June or July</figcaption></figure>

Note: Effect size and confidence intervals of randomly connecting to each Constituent within a random network for the nobility, within random networks until the end of July. The dependent variable is violent death. Effects for Robespierre and Lafayette are highlighted by vertical lines; effects of similar global brokers (top 2.5% of betweenness scores by December, like Robespierre) are coloured black.

<figure><img src="image_12.png" /><figcaption>Figure A13: Violent Death and Random Connections in June, July or August</figcaption></figure>

Note: Effect size and confidence intervals of randomly connecting to each Constituent within a random network for the nobility, within random networks until the end of August. The dependent variable is violent death. Effects for Robespierre and Lafayette are highlighted by vertical lines; effects of similar global brokers (top 2.5% of betweenness scores by December, like Robespierre) are coloured black.
<figure><img src="image_13.png" /><figcaption>Figure A14: Violent Death and Random Connections in June, July, August or September</figcaption></figure>

Note: Effect size and confidence intervals of randomly connecting to each Constituent within a random network for the nobility, within random networks until the end of September. The dependent variable is violent death. Effects for Robespierre and Lafayette are highlighted by vertical lines; effects of similar global brokers (top 2.5% of betweenness scores by December, like Robespierre) are coloured black.

<figure><img src="image_14.png" /><figcaption>Figure A15: Violent Death and Random Connections in June, July, August, September or December</figcaption></figure>

Note: Effect size and confidence intervals of randomly connecting to each Constituent within a random network for the nobility, within random networks until the end of December. The dependent variable is violent death. Effects for Robespierre and Lafayette are highlighted by vertical lines; effects of similar global brokers (top 2.5% of betweenness scores by December, like Robespierre) are coloured black.
<table><caption>Table A15: Moderate Revolutionaries & Exposure to Robespierre</caption><thead><tr><th></th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td><strong>Moderate Revolutionaries</strong></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Exposure to Robespierre</strong></td><td>-0.238**<br>[0.087]</td><td>0.067<br>[0.173]</td><td>0.022<br>[0.104]</td><td>-0.049<br>[0.072]</td></tr><tr><td>×Clergy</td><td>0.440***<br>[0.149]</td><td>-0.032<br>[0.264]</td><td>-0.048<br>[0.146]</td><td>0.061<br>[0.118]</td></tr><tr><td>×Third</td><td>0.171*<br>[0.098]</td><td>-0.170<br>[0.177]</td><td>-0.104<br>[0.126]</td><td>0.008<br>[0.084]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,079</td></tr></tbody></table>

Note: Dependent variable is whether the Constituent became a moderate revolutionary, with the treatment exposure to Robespierre in his random network. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.

<table><caption>Table A16: Army of Princes & Robespierre Exposure</caption><thead><tr><th></th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td><strong>Exposure to Robespierre</strong></td><td>0.048<br>[0.119]</td><td>0.138*<br>[0.080]</td><td>0.151**<br>[0.068]</td><td>0.184**<br>[0.087]</td></tr><tr><td>×Clergy</td><td>-0.101<br>[0.137]</td><td>-0.195**<br>[0.092]</td><td>-0.182***<br>[0.065]</td><td>-0.158**<br>[0.077]</td></tr><tr><td>×Third</td><td>-0.051<br>[0.119]</td><td>-0.157*<br>[0.092]</td><td>-0.167**<br>[0.076]</td><td>-0.199**<br>[0.096]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,079</td><td>1,079</td><td>1,079</td><td>1,079</td></tr></tbody></table>

Note: Dependent variable is whether the Constituent enlisted in an army opposed to the republic, with the treatment exposure to Robespierre in his random network. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.
<table><caption>Table A17: Lafayette exposure effect on violent death, unbalanced controls</caption><thead><tr><th>Violent Death</th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td>Exposure to Lafayette</td><td>-0.237***<br>[0.059]</td><td>-0.181***<br>[0.046]</td><td>-0.184***<br>[0.033]</td><td>-0.229***<br>[0.058]</td></tr><tr><td>×Clergy</td><td>0.102<br>[0.062]</td><td>0.179**<br>[0.078]</td><td>0.247***<br>[0.059]</td><td>0.263***<br>[0.051]</td></tr><tr><td>×Third</td><td>0.449***<br>[0.080]</td><td>0.319***<br>[0.099]</td><td>0.289***<br>[0.085]</td><td>0.294***<br>[0.080]</td></tr><tr><td>Combat exp.</td><td>0.023<br>[0.084]</td><td></td><td></td><td>0.006<br>[0.057]</td></tr><tr><td>Law</td><td>0.018<br>[0.025]</td><td></td><td></td><td></td></tr><tr><td>Valois club</td><td>0.047<br>[0.080]</td><td></td><td></td><td></td></tr><tr><td>Assemb. Exp.</td><td></td><td>0.018<br>[0.023]</td><td></td><td></td></tr><tr><td>Society of 30</td><td></td><td>0.312***<br>[0.066]</td><td></td><td></td></tr><tr><td>Army background</td><td></td><td></td><td>0.059<br>[0.058]</td><td>0.064<br>[0.053]</td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>1,065</td><td>1,065</td><td>1,079</td><td>1,079</td></tr></tbody></table>

Note: Dependent variable is whether the Constituant died a violent death, with the treatment exposure to Lafayette in his random network, controlling for unbalanced pre-treatment characteristics. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.
<table><caption>Table A18: Robespierre exposure effect on violent death, unbalanced controls</caption><thead><tr><th>Violent Death</th><th>(1)<br>June-July</th><th>(2)<br>June-Aug</th><th>(3)<br>June-Sept</th><th>(4)<br>June-Dec</th></tr></thead><tbody><tr><td><strong>Exposure to Robespierre</strong></td><td>-0.342**<br>[0.167]</td><td>-0.223**<br>[0.093]</td><td>-0.015<br>[0.135]</td><td>-0.023<br>[0.100]</td></tr><tr><td>×Clergy</td><td>0.344**<br>[0.149]</td><td>0.300***<br>[0.083]</td><td>0.002<br>[0.180]</td><td>0.010<br>[0.147]</td></tr><tr><td>×Third</td><td>0.330*<br>[0.171]</td><td>0.219**<br>[0.095]</td><td>0.004<br>[0.151]</td><td>0.025<br>[0.113]</td></tr><tr><td>Age</td><td>-0.001<br>[0.001]</td><td>-0.001<br>[0.001]</td><td>-0.001<br>[0.001]</td><td></td></tr><tr><td>Arist. Title</td><td>0.019<br>[0.071]</td><td></td><td></td><td></td></tr><tr><td>Law</td><td>0.012<br>[0.022]</td><td></td><td></td><td></td></tr><tr><td>Assemb. Exp.</td><td></td><td></td><td>0.020<br>[0.020]</td><td></td></tr><tr><td>Freemason</td><td></td><td></td><td>0.002<br>[0.030]</td><td></td></tr><tr><td>Fixed effects:</td><td></td><td></td><td></td><td></td></tr><tr><td>Order, department and all interactions</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>996</td><td>996</td><td>985</td><td>1,079</td></tr></tbody></table>

Note: Dependent variable is whether the Constituant died a violent death, with the treatment exposure to Robespierre in his random network, controlling for unbalanced pre-treatment characteristics. Different columns consider cumulative networks up to July, August, September and December respectively. All models include fixed effects by Order, department and their interaction. Standard errors are clustered by relevant past and current bureau(s) of July, August, September and December. *< 0.1, **< 0.05 and ***< 0.01.