Emigration during the French Revolution: Consequences in the Short and Longue Durée

NBER WORKING PAPER SERIES

EMIGRATION DURING THE FRENCH REVOLUTION:

CONSEQUENCES IN THE SHORT AND LONGUE DURÉE

Raphaël Franck

Stelios Michalopoulos

Working Paper 23936

http://www.nber.org/papers/w23936

NATIONAL BUREAU OF ECONOMIC RESEARCH

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October 2017

We would like to thank Sascha Becker, Davide Cantoni, Guillaume Daudin, Melissa Dell, Oded

Galor, Paola Giuliano, Moshe Hazan, Ruixue Jia, Oren Levintal, Omer Moav, Ben Olken, Elias

Papaioannou, Gerard Roland, Nico Voigtlaender, David Weil, and Ekaterina Zhuravskaya as well

as seminar participants at Brown, Harvard, Harvard Kennedy School of Government, Hebrew

University of Jerusalem, NBER Summer Institute Political Economy & Income Distribution and

Macroeconomics Workshop, Northwestern Kellogg, Paris-1, Princeton, Insead, NUS, Hong Kong

University of Science and Technology, Sciences-Po, Tel Aviv, IDC Herzliya, Toronto, Warwick,

and conference participants at the European Public Choice Society Meeting, the Israeli Economic

Association conference, and the Warwick/Princeton conference for valuable suggestions. We

thank Bernard Bodinier, Martin Fiszbein, and Nico Voigtlaender for sharing their data. We would

also like to thank Nicholas Reynolds for superlative research assistance. All errors are our own

responsibility. Stelios Michalopoulos and Raphael Franck have no relevant financial support to

disclose in relationship to this project. The views expressed herein are those of the authors and do

not necessarily reflect the views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been

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Emigration during the French Revolution: Consequences in the Short and Longue Durée

Raphaël Franck and Stelios Michalopoulos

NBER Working Paper No. 23936

October 2017

JEL No. N10,O10,O15

ABSTRACT

During the French Revolution, more than 100,000 individuals, predominantly supporters of the

Old Regime, fled France. As a result, some areas experienced a significant change in the

composition of the local elites whereas in others the pre-revolutionary social structure remained

virtually intact. In this study, we trace the consequences of the émigrés' flight on economic

performance at the local level. We instrument emigration intensity with local temperature shocks

during an inflection point of the Revolution, the summer of 1792, marked by the abolition of the

constitutional monarchy and bouts of local violence. Our findings suggest that émigrés have a

non monotonic effect on comparative development. During the 19th century, there is a significant

negative impact on income per capita, which becomes positive from the second half of the 20th

century onward. This pattern can be partially attributed to the reduction in the share of the landed

elites in high-emigration regions. We show that the resulting fragmentation of agricultural

holdings reduced labor productivity, depressing overall income levels in the short run; however, it

facilitated the rise in human capital investments, eventually leading to a reversal in the pattern of

regional comparative development.

Raphaël Franck

Hebrew University of Jerusalem

Department of Economics

Mount Scopus

Jerusalem 91905

Israel

[email protected]

Stelios Michalopoulos

Brown University

Department of Economics

64 Waterman Street

Providence, RI 02912

and NBER

[email protected]

1 Introduction

Tracing the origins and consequences of major political upheavals occupies an increasing part of

the research agenda among economists and political scientists. The Age of Revolution in Europe

and the Americas, in particular, has received much attention as these major political disruptions

are thought to have shaped the economic and political trajectories of the Western world toward

industrialization and democracy. This broad consensus concerning their paramount importance ,

nevertheless, goes in tandem with a lively debate regarding the exact nature of their consequences.

The voluminous literature on the economic legacy of the French Revolution attests to this.

On the one hand, there is a line of research that highlights its pivotal role in ushering the

French economy into the modern era. This perspective, which begins with 19th century thinkers

of di¤erent persuasions such as Thiers (1823–1827), Guizot (1829-1832), and Marx (1843 [1970])

and is continued during the 20th and 21st centuries by broadly left-leaning scholars (e.g., Jaurès

(1901-1903), Mathiez (1922-1924), Soboul (1962), Hobsbawm (1990), Garrioch (2002), Jones

(2002), and Heller (2006)), views the 1789 French Revolution as the outcome of the long rise

of the bourgeoisie, whose industrial and commercial interests prevailed over those of the landed

aristocracy. These authors, in making their case, stress the bene…ts from the weakening of

the Old Regime as manifested in the abolition of the feudal system, the consolidation of private

property, the simpli…cation of the legal system, and the reduction of traditional controls and …scal

hindrances to commerce and industry. However, the scholars, who argue that the reforms brought

about by the French Revolution were conducive to economic growth (e.g., Crouzet (2003)), are

aware of France’s lackluster e conomic performance during the 19th century vis-à-vis England

and Germany, and attribute it to the political upheavals that characterized the country and the

violence of the Revolution and Napoleonic Wars.

On the other hand, mostly liberal or conservative intellectuals (e.g., Taine (1876-1893),

Cobban (1962), Furet (1978), Schama (1989)) emphasize that France remained largely agricul-

tural vis-à-vis England and Germany until 1914. They argue that the French Revolution was

not motivated by di¤erences of economic interests between the nobility and the bourgeoisie, but

was rather a political revolution with social and economic repercussions (Taylor (1967), Aftalion

(1990)).

They consider that the French Revolution was actually “anticapitalist”contributing to

the persistent agricultural character of France during the 19th century. Besides the cost of war

and civil con‡ict, these studies emphasize the development of an ine¢ cient bureaucracy and the

adverse impact of changes in land holdings on agriculture.

In this study we attempt to shed some light on the short- and long-run economic conse-

Maza (2003) in fact argues that there was no genuine French bourgeoisie in 1789 as none of the politicians

deemed to represent the bourgeoisie expressed any consciousness of belonging to such a group.

quences of the French Revolution across départements (the administrative divisions of the French

territory). Speci…cally, we exploit local variation in the weakening of the Old Regime, re‡ected in

the di¤erent emigration rates across départements. During the Revolution, more than 100; 000 in-

dividuals emigrated to various European countries and the United States (Greer (1951)). Among

the émigrés, nobles, clergy membe rs, and wealthy landowners were disproportionately repre-

sented.

While the …rst émigrés left as early as 1789, the majority actually ‡ed France, during an d

after the summer of 1792 (Taine (1876-1893), Duc de Castries (1966), Bouloiseau (1972), Boisnard

(1992), Tackett (2015)), when the Revolution to ok a radical turn which French historian Georges

Lefebvre has called the “Second Revolution”(Lefebvre (1962)). During that summer, following

the arrest of King Louis XVI on August 10 and the “September Massacres” in Paris (Caron

(1935), Bluche (1992)), the hitherto uneasy coexistence of the monarchy and the revolutionaries

came to an abrupt end with the proclamation of the Republic on September 21, 1792. Four

months later, King Louis XVI was guillotined.

Our identi…cation strategy exploits local variation in temperature shocks at this in‡ection

point of the French Revolution (i.e., the summer of 1792) to get plausibly exogenous variation in

the rate of emigration across départements. The logic of our instrument rests on a well-developed

argument in the literature on the outbreak of con‡ict that links variations in economic conditions

to the opportunity cost of engaging in violence. To the extent that temperature shocks decrease

agricultural output (which we show to be the case in our historical context), an increase in the

price of wheat (the main staple for the Fren ch in the 18th century)

would intensify unrest among

the poorer strata of the population, thereby magnifying emigration among the wealthy supporters

of the moribund monarchy. Consistent with this argument, we show that, in August and Sep-

tember 1792, there were more peasant riots in départements that experienced larger temperature

shocks.

It is worth pointing out that the temperature shocks in the summer of 1792 are mild

compared to other years during the Revolution, thereby suggesting that ordinary income ‡u ctua-

tions at critical junctures may have a persistent e¤ect on subsequent development. Importantly,

temperature sho cks during the other years of th e Revolution predict neither emigration rates nor

subsequent economic performance.

Our …ndings suggest that émigrés have a nonmonotonic impact on comparative economic

performance unfolding over the subsequent 200 years. Namely, high-emigration départements

have signi…cantly lower GDP per capita during the 19th century but the pattern reverses over

the 20th century. Regarding magnitudes, an increase of half a percentage point in the s hare of

On the i mportance of wheat and bread in France in the 18th century, see, for example, Kaplan (1984) and

Kapl an (1996). See also Persson (1999) on grain m arkets during this period.

Al ong the same lines, Grosfeld, Sakalli, and Zhuravskaya (2017 ) …nd that anti-Jewis h pogroms in eas tern

Europe between 1800 and 1927 occurred w hen poor h arvests coincided wit h institutional and political uncertainty.

émigrés in the population of a département (which is the mean emigration rate) decreased GDP

per capita by 12:7% in 1860 but increased it by 8:8% in 2010.

Pinning down the exact mechanism(s) via which emigration shaped local economic per-

formance is challenging. Thanks to the detailed French historical census es, we attempt to shed

some light on this issue. A signi…cant fraction of the émigrés were landowners so their exodus is

likely to have in‡uenced the composition of local landholdings. Using th e agricultural census of

1862, we show that high-emigration départements have fewer large landowners and more small

ones. Indeed, the size of the average farm in France in 1862 was 23:12 acres, smaller than the

average farm of 115 acres in England in 1851 and the average farm of 336:17 acres in the United

States in 1860 (Shaw-Taylor (2005), Fiszbein (2016)).

This legacy of fragmented landholdings

has remained largely in place in France to this day. Furthermore we show that, during the 19th

century, this reduction in the preponderance of large private estates and the development of a

small peasantry had a negative impact on agricultural p roductivity by limiting the adoption of

scale-intensive mechanization methods. Moreover, we …nd that the share of rich individuals in

the population of high-emigration départements during the 19th century was signi…cantly smaller

compared to regions where few émigrés left. This absence of a critical mass of su¢ ciently wealthy

individuals in the era of capital-intensive modes of production may also explain the slow pace of

industrialization in the h igh-emigration départements during the 19th century.

Interestingly, as early as the midd le of the 19th century, these agriculturally lagging dé-

partements register slightly higher literacy rates than their richer, agriculturally more produc tive

peers. This modest educational edge widens during the early 20th century, after the French state

instituted free and mandatory schooling, eventually translating to higher inc omes per capita in

the later part of the 20th century. This …nding h ighlights that historical legacies may crucially

interact with state-level policies and is consistent with recent studies in developing c ountries

which show that increases in agricultural productivity red uce school attendance by increasing

the opp ortunity cost of schooling (see, e.g., Shah and Steinberg (2015)). By establishing a causal

link between the rate of structural transformation across regions in France and the inte nsity of

emigration, we shed new light on an intensely debated topic, that is, the economic legacy of the

1789 Revolution within France.

In Appe ndix Table D.1, we dis tinguish between French départements and US counties which were above and

below the median value of grain production in 1862 and in 1860, respectively. We a lso provide descriptive statistics

excludi ng French farms below …ve hectares an d US farms below ni ne acres so as to focus on farmers who were

presumably above subsistence levels. This robustness check is motivated by the fact that the 1860 US census does

not record plots less than t hree acres. Acro ss all di ¤erent metrics, French farms are signi…cantly smaller than the

US ones.

To be sure, violence du ring the French Revolution was rampant and multifaceted. Besides the violence of the

crow ds wh ich our identi…cation strategy leverages, where g roups of people vand alized shops and killed civilians and

po liticians (e.g., Jacques de Fles selle, Jean-Bertra nd Fér au d), Gueni¤ey (2011) discusses the top-down planned

annihilation of local populations exempli…ed by the civil war in the Vendée département, the use of the judi cial

Related Literature. Our study relates to the literature on the economic consequences

of revolutions and con‡ict. The latter is voluminous (see, e.g., Blattman and Miguel (2010)

for a thorough review) and usually focuses on the impact of these events on the cumulable

factors of production. Recent studies have shifted their attention to the institutional legacies of

con‡ict. In this respect, our work is closely related to Acemoglu, Cantoni, Joh nson , and Robinson

(2011). The latter explores the impact of institutional reform caused by the French occup ation

of German territories. Consistent with the view that barriers to labor mobility, trade and entry

restrictions were limiting growth in Europe, they …nd that French-occupied territories within

Germany eventually experienced faster urbanization rates during the 19th century. In our case, by

focusing on départements within France where the de-jure institutional discontinuities exploited

by Acemoglu, Cantoni, Johnson, and Robinson (2011) are largely absent,

we examine whether,

conditional on the nationwide consequences of the radical institutional framework brought forward

by the French Revolution, the local weakening of the Old Regime, re‡ected in the di¤erential

rates of emigration across départements, in‡uenced local development over a signi…cantly longer

horizon. Thus, our study is also closely related to Dell (2012) on the Mexican Revolution. She

…nds that land redistribution was more intense across municipalities where insurgent activity was

higher as a result of droughts on the eve of the Revolution, leading to lower economic performance

today. The latter was due to the fact that the Mexican state maintained ultimate control over

the redistributed land known as ejidos.

By looking at the impact of emigration across départements, our study also contributes to

a growing literature that investigates the economic consequences of disruptions in the societal

makeup of a region. Nunn (2008) and Nunn and Wantchekon (2011), for example, explore the

consequences of the slave trade for African countries and groups, whereas Acemoglu, Hassan, and

Robinson (2011) focus on the impact of the mass execution of Jews during the Holocaust on the

subsequent development of Russian cities.

Finally, our res earch is related to studies by Galor and Zeira (1993) and Galor and Moav

(2004), which argue for a nonmonotonic role of equality in the process of development. When

growth is driven by physical capital accumulation, a larger sh are of su¢ ciently wealthy families

would be bene…cial to local growth during the 19th century. However, areas with more evenly

distributed wealth would experience faster human capital accumulation, translating into better

economic outcomes during the 20th and 21st centuries. Consistent with this argument, we show

that the preponderance of small landowners in the high-emigration départements goes in tandem

system to a ssass inate political opponents durin g the Rei gn of Terror, and the war launched against foreign countries.

Unlike the violence of the crowds, these other types of violence do not seem to have responded to climate-induced

temporary income shocks.

See, for example, Soboul (1968) for a discussion regarding the application of the Code Civil and t he persistence

of local institut ions within France during the 19th century.

with an earlier takeo¤ in human capital accumulation in these regions.

The rest of the paper is organized as follows. In Section 2 we describe the historical

background on e migration and land redistribution during the French Revolution. In Section 3

we describe the data and our empirical methodology. In Section 4 we present our main …ndings

and in Section 5 we discuss some of the potential mechanisms that can account for the observed

pattern. In Section 6 we conclude.

2 Historical Background

In 1789, on the eve of the Revolution, France was the largest economy in Europe, with approxi-

mately 25 million inhabitants and lower wages compared to England (see Labrousse (1933) and

Toutain (1987)). Politically, it was a monarchy where King Louis XVI’s subjects were divided

into three orders: the nobility comprising between 150; 000 and 300; 000 members, the clergy

around 100; 000 members, and the Third Estate (artisans, bankers, lawyers, salesmen, peasants,

etc.) made up the rest. This political structure was to end with the Revolution. In Appendix

A:1 we brie‡y discuss its proximate and ultimate causes.

2.1 Emigration during the French Revolution

The April 8, 1792, law de…ned as émigrés all the individuals absent from the département in

which they possessed property, and, as a result of the July 27, 1792, law, their property could be

seized by the French state. The share of émigrés in the population of each département is our key

independent variable. The data were compiled by Greer (1951) from several original governmental

accounts. The sources are mostly o¢ cial publications such as the Liste Générale, par Ordre

Alphabétique, des Emigrés de toute la République (1792-1800) (General List in Alphabetical Order

of Emigrés throughout the Republic), local lists of émigrés, as well as the list of individuals who

received compensation af ter 1825 for the property they lost during the Revolution.

Greer (1951)

lists a total of 129; 091 individuals as émigrés.

The revolutionaries were quick to portray all the émigrés as members of the aristocracy who

had prospered on the poverty of French peasants and described them as the living manifestation

of the hostility to the Revolution. Emigrés were both chastised for abandoning the fatherland

to avoid danger in times of political instability and condemned for joining forces with “f oreign

tyrants” against the nation to restore a hated political regime. Revolutionaries thus passed a

series of laws against émigrés, depriving them of their state-funded pensions in 1790, legislating

that emigration was a crime in 1791, and eventually con…scating their property in 1792. In doing

France. Ministère des Finances. Eta ts Detaillés des Liquidations faites par la Co mmission d’Indem nité, a

l’époque du 31 décembre 1826 en Execut ion de la Loi du 27 avr il 1825, Paris, D e l’Imprimerie R oyale, 1827.

so, some of the revolutionaries were hoping to redistribute land and create a more egalitarian

society, but were disappointed not to see immediate consequences of their policies (e.g., Jones

(1988),Vivier (1998)).

The data collected by Greer (1951) on the émigrés during the Revolution paint a more

nuanced picture than the rhetoric of the revolutionaries, in terms of both the numbe r of émigrés

and their social compos ition. According to Greer (1951), the median département lost 0:31%

of its 1801 population (the …rst year for which we have reliable population data). Panel A

of Figure 1 displays the intensity of émigrés as a share of the population throughout France,

showing substantial spatial variation. Panel A of Table 1 lists the départements with the highest

and lowest emigration rates. Moreover, a substantial fraction of émigrés (but not all of the m)

belonged to the local elites, as can be seen in Panel B of Table 1 for the 69 départements for which

such information is available. They were mainly aristocrats and clergymen, as well as wealthy

urban dwellers and rural landowners from the Third Estate whose property was con…scated and

sold (some even lost the property of the Church that they had acquired in the early stage of

the Revolution).

As Panel C of Table 1 shows, the shares of the di¤erent types of émigrés are

strongly correlated. Some of the commoners who left France were servants of aristocrats and

followed their employers abroad. Others were landless peasants or artisans either ‡eeing for their

lives or searching for a better life (see Duc de Castries (1966)).

Revolutionary violence not only took several forms, but also its geographic and social

incidence was markedly di¤erent across French regions and social groups. The civil war was

mostly con…ned to the southeast and west of France, and was particularly intens e in the Vendée

département. The Reign of Terror, which entailed the use of the judicial system to assassinate

political opponents, was more intense in Paris, Lyon and Marseille (i.e., the three main French

cities), as well as in the west of France (Greer (1935), Gueni¤ey (2011)).

As such, unlike

the civil war and the judicial Terror, which were s patially concentrated, emigration was for

the contemporaries of the Revolution a spectacular consequence of revolutionary violence that,

at the time, seemed to a¤ect all of France. Moreover, while France under the monarchy had

experienced civil war in the 16th and 17th centuries and while public executions were common

during the 18th century (e.g., Bée (1983), Bastien (2006)), emigration was a speci…c consequence

of the Revolution b ec ause it implied the precipitous decline of a previously conspicuous social

On averag e, nobles were richer than peasants, and anecdotal evidence sugges ts that they possessed more land

prior to 1789. Of course, there were exceptions, and the living conditions of some nobles, for instance, those in

Britt any (Nassiet (1993)), were no t really di¤erent from tho se of the peasants. This can explain why before 1789,

po litical antagonism also existed within each of the three orde rs, for example, between min or and gre at nob les

(Furet (1978)). It may also help to ratio nalize why, d uring the Revoluti on, some commoners were favorable to

a c onstitutional monarchy (e.g ., Jean-Joseph Mouni er) while some aris tocrats support ed the radical turn of the

Revolution (e.g., Louis-Michel Le Peletie r de Saint-Fargeau).

Greer (1935) repo rts that there were less than 10 executions in 27 départements during the Terror.

and political group.

In this respect, emigration also di¤ered from the violence stemming from

the civil war and the judicial Terror, which disproportionately a¤ected peasants and workers.

2.2 The Intensi…cation of Emigration during the “Second Revolution”

During the summer of 1792, major political upheavals and widespread violence, starting with the

imprisonment of Louis XVI and his family in early August and culminating with the proclamation

of the republic a few weeks later, signi…ed the unraveling of the House of Bourbon and the

abolition of the monarchy. In Appendix A:2 we provide details on the unfolding of these events.

Many historical anecdotes describe how emigration accelerated during and immediately after the

summer of 1792 (e.g., Taine (1876-1893), Bou loiseau (1972), Tackett (2015)).

For instance,

reform-minded aristocrats who had played a political role in the …rst years of the Revolution,

such as the Marquis de Lafayette and the Duc de la Rochefoucauld-Liancourt, left France in

August 1792. In fact, Tackett (2015) (p. 215) writes that in September 1792, “conditions had

become so frightening that many wealthier families began ‡eeing Paris (...). Others, however,

seem to have concluded that the countryside was even more dangerous than Paris.”An additional

historical piece of evidence pointing to the intensi…cation of the emigration in the fall of 1792 is

the reaction of the British government: it introduced the Aliens Act in the House of Lords on

December 19, 1792, in an attempt to regulate the uncontrolled in‡ux of French nationals, which

created signi…cant anxiety in governmental circles that feared the presence of revolutionary spies

and saboteurs.

Several local historians (listed in Marko¤ (1996)) explicitly link emigration to local episodes

of violence during the su mmer of 1792. For instance, in Var, a high-emigration département, local

violence took the form of several days of rioting in Toulon, between July 28, 1792, and September

10th, 1792, where local revolutionaries targeted aristocrats, military o¢ cers, and wheat traders

whom they considered hostile to the Revolution (Havard (1911-1913)). Members of these groups

‡ed France for Italy. In Ariège a band of peasants led by a local revolutionary began to ransack

and burn castles in late August 1792 (Arnaud (1904)). As a result, many aristocrats, bourgeois,

and refractory priests sou ght refuge in Spain.

Many Protestants left France after the revocation of the Edit de Na ntes in 1685 by King Louis XIV (Scoville

(1953)) . However, Fren ch Protestants did not hold the political clout of the aris tocra ts who emigrated, an d their

exodus did not coincide wit h a massive political and economi c tran sformation akin to that of the French Revolution.

Greer (1935) estimates (Table 8, pp. 165- 166) that peasants and workers ma de up a combi ned 59:25% of the

total 16; 594 death sentences during the Terror while the nobles were only 8:25%, clergymen 6:5%, members from

the upper middle class 14% and members from the lower middle class 10:5% (no status was given to the remaining

1:5% of individuals sentenced to death). Note that the seemingly low o¢ cial numbe r of victims obscures th e fact

that many more people were kil led without a trial during the Terror .

Arguably, some émigrés had ‡ed France befo re the summer of 1792. For instanc e, the Count of Artois, who

would become Ki ng Charles X (r. 1824-1830), left in 1789, and Jean-Joseph Mounier , one of the royalist leaders

of the Amis de la Constitution Mona rchique (Friends of the Mo narchic Constitution ), ‡ed in 1790. A few also left

in the post-Thermidorian period in 1794-1795.

2.3 Emigration and Land Redistribution during the Revolution

The sale of the biens nationaux is con side red by some historians as “the most important event

of the French Revolution” (Lecarpentier (1908), Bodinier and Teyssier (2000)). Their claim is

based on the fact that a signi…cant amount of land was seized and sold by the government under

the name of biens nationaux (national goods) during this period. This land belonged to the

Church, the émigrés, and the counterrevolutionaries. The property of the Church was …rst seized

by the French revolutionaries to pay o¤ the debts of the French state on November 2, 1789. The

property of the émigrés and counterrevolutionaries was also con…scated for that purpose three

years later. It is not clear, however, whether the French state recovered much from those sales

due to its in‡ationary policies.

In addition, during the French Revolution, property rights were

granted on the villages’commons: some of the common land was sold to private individuals while

some of it was seized by the municipalities and, later on, leased to peasants (Vivier (1998)).

Land redistribution may have been consequential for the French départements for at least

two reasons. First, the amount of land which was seized and sold by the government during the

Revolution was signi…cant; Bodinier (1999) estimates that 10% of land changed hands. Second,

even though émigrés were invited to return to France in 1802 by Napoléon Bonaparte, he forbade

émigrés from reclaiming their landed property. The loss of their property was made permanent

in 1814 when it was rea¢ rmed by Louis XVIII (Louis XVI’s brother). Emigrés (and their

descendants) were to be compensated by the April 27, 1825, law, which came to be known as

the “milliard des émigrés” since these reparations amounted to nearly one billion French francs

(nearly 10% of the French GDP in 1825 (Maddison (2001))), but not all émigrés eventually

received comp ens ation for their losses. Overall, some of the émigrés were able to reconstitute

part of their landed estate, whereas others were only able to live a gentry life with modest means,

and some became destitute.

Nevertheless, there is no consensus as to who ultimately b ene …ted from the sale of the

biens nationaux. Schama (1989) suggests that the redistribution of land was not from the landed

elite to peasants, but rather a transfer of property within the landed classe s. The members of the

groups which were gaining economically before the Revolution and who managed to evade violence

For an overview of the successive laws pertaini ng to the sale of the biens nationaux, see Bodinier and Teyssier

(2000). For a speci…c analysis of the economic consequences of the sale of the Church property, see Finley, Franck,

and Johnson (20 17). On macroeco nomic policies during the French Re volution, see, for example, Sargent and Velde

(1995).

Aristocrats like the Marquis de Dr eux-Brézé in Sarth e and Barral de Montfer rat in Isère emerged …nancially

unscathed from the Revolutio n (Schama (1989 )). The Marquis de Lafayette seemed to have lost a large share of

his property and led a more modest life (Furet and Ozouf (1988)). Mme Lalanne, born Dudevant de Villeneuve,

solicited her admission to the poor house in Bordeaux (Gironde) that she had foun ded before the Revolution

(Boisnard (1992)). It must be noted that there is no evidence that the émi grés engaged in industrial and service

activities after their return; their ideological sta nce was certainly not conducive to such endeavors (Baldensperger

(1924)) .

by adopting a revolutionary stance (among them, many relatively wealthy urban bourgeois and

small farmers) emerged richer since they bought the landed properties of the Church and the

‡eeing landed gentry at a low price (see, e.g., Marion (1908), Cobb (1972), Sutherland (2003)).

Others argue that the sale of the biens nationaux was detrimental to the living conditions of

peasants during the 19th century because it created a small peasantry of su bsisten ce , thereby

consolidating the agrarian structure of France and delaying economic modernization (Loutchisky

(1897), Lefebvre (1924)). Finally, some contend that the redistribution of land was bene…cial to

French peasants: they became small-scale agrarian capitalists focused on market prod uc tion (Ado

(1987 [2012])). McPhee (1999), for example, provides anecdotal evidence on small landowners

who engaged in wine production in Herault.

Crucially, local monographs on the sale of the biens nationaux suggest that the eventual

extent of land redistribution and its bene…ciaries crucially depended on the extent of local em-

igration during the French Revolution. Th is is, in itself, partly to be expected since the biens

nationaux comprised the émigrés’ properties. Below, we provide examples revealing the inti-

mate relationship b etween the change in ownership structure, as a result of th e sale of the biens

nationaux in four départements, and the share of émigrés in the local population.

First, in Cher, which was the third lowest emigration département (0:11% of the popula-

tion), Marion (1908) documents that there was very little land parcelization and redistribution,

or if there was any, it bene…ted individuals who were already well o¤. For instance, in Ivoy-le-Pré

(9886 ha, 2; 438 inhabitants), not a single plot of land owned by an émigré was sold, while a

large domain was transferred from the abbey of Laurois to a major secular landowner, the local

fermier-général (a p rivate tax collector u nde r the Old Regime). Similarly, in Menetou-Râtel

(2; 801 ha, 1; 195 inhabitants), only 25 properties were sold, and 13 out of the 17 bu yers were

already major or medium-size landowners.

Second, in Gironde, which was a close-to-median intensity emigration département (0:24%

of the population), Marion (1908) shows that the prope rties owned by the Church and the

émigrés were parcelized into several smaller land lots in many rural communes, thereby enabling

individuals who were previously landless to acquire some property. For instance, in Lu gon-et-

l’Île-du-Carnay (1094 ha, 947 inhabitants), some well-known merchants and notaries bought land,

but most of the buyers of biens nationaux were landless farmers and artisans (i.e., blacksmiths,

carpenters, coopers, masons, and shoemakers), who acquired small land plots.

Third, in Nord, an above-median intensity emigration département ( 0:35% of the popu-

lation), Lefebvre (1924) provides information for 15 villages in the district of Avesnes, which we

report in Table 2. The statistics reveal that large properties were parcelized, and there was a

substantial transfer of property from nobles to peasants and urban bourgeois. Moreover, part of

the land, often commons, whose property was in dispute was acquired by the state, that is, either

the central government or the local towns.

Finally, in Ille-et-Vilaine, a relatively high-emigration area (0:42% of the département’s

population), many aristocrats lost a signi…cant part of their properties. The castle and the

domain of the Vaurouault family near Saint-Malo, for example, were sold as biens nationaux in

1793. The family bought back the castle at the beginning of the 19th century but pe rmanently

lost the domain to small peas ants (Boisnard (1992)). Another famous local aristocratic family

that lost some of its land was that of François-René de Chateaubriand, the romantic writer and

heir to one of the oldest baronies in Britanny. This unfortunate turn of events for François-René

de Chateaubriand’s family might explain why he was adamant later in his political career that

émigrés should b e compen sated (Chateaubriand (1847), pp. 517-533).

It is against this background that we interpret the share of émigrés in each département

as a p roxy f or the weakening of the local landed elites of the Old Regime and the extent of land

redistribution. Below, we establish the empirical validity of these claims and trace the economic

consequences of land parcelization over time.

3 Data and Empirical Methodology

3.1 Measures of Income, Workforce, and Human Capital

To capture the short- and medium-run e¤ects of emigration on income per capita at the départe-

ment level prior to World War II, we use data on GDP per capita as reconstructed by Comb es ,

Lafourcade, Thisse, and Toutain (2011) and Caruana-Galizia (2013) for 1860, 1901 and 1930.

For the post-World War II period, data on income per capita at the département level are not

available before 1995, so we use data from the French National Institute of Statistics (INSEE,

Institut National de la Statistique et des Etudes Economiques) for 1995, 2000, and 2010. We also

construct the value added per worker in the agricultural, industrial, and service sec tors combining

the data of Combes, Lafourcade, Thisse, and Toutain (2011), who assess the value added in each

of thes e three sectors in 1860, 1930, 1982, and 1990, with the occupational data from the gov-

ernmental surveys carried out from the 19th century onward (Statistique Générale de la France

and INSEE). The descriptive statistics in Table D.2 indicate that the shares of the workforce in

the industrial and service sectors grew, respectively, from 21:6% and 15:3% in 1860 to 30:1% and

24:8% in 1930, indicating that slightly less than h alf of the working French population was still

engaged in agriculture before WWII. However, by 1990, the share of the agricultural workforce

had declined considerably, with the industrial and service sectors employing 30:7% and 60:0% of

the workforce, respectively.

We also explore the e¤ect of emigration during the French Revolution on the evolution of

human capital from the 19th century until today. For the period before World War II, we take

advantage of the data on the literacy of French army conscripts (France - Ministère de la Guerre

(1839-1937)).

The data enable us to compute the average share of illiterate conscripts, that is,

those who could neither read nor write, by decade between the 1840s and 1930s. Our statistics

in Table D.3 show the overall relatively high levels of literacy in France. Speci…cally, 26:7% of

French army conscripts in the 1840s, 16:0% in the 1870s, and 5:1% in the 1930s could neither read

nor write. Our post Word War II measures of human capital rely on the successive population

censuses carried out in 1968, 1975, 1982, 1990, 1999, and 2010. They allow us to compu te the

‡ow of men between the ages of 16 and 24 in each département who completed high school or

had a college degree or both.

3.2 Emigrés and Temp erature Shocks in the Summer of 1792

The observed relationship between em igration and regional development may re‡ect omitted vari-

ables which could explain both emigration and subsequent economic performance. For instance,

if emigration was proportional to the pool of “potential”émigrés, then high-emigration départe-

ments would be those with initially many nobles and many wealthy landowners. In other words,

since we do not have département-level data before and after the Revolution on the relative size

of each order (i.e., the nobility, the clergy, and the Third Estate) observed emigration rates may

be mechanically linked to the initial regional stock of the old elite and the extent of land con-

centration prior to 1789, thereby biasing ou r estimates. Moreover, despite the thorough e¤orts

to accurately reconstruct the numbers, (Greer (1951), p.17) acknowledges th at his “statistics,

cannot pretend to absolute exactitude. They include an irregular margin of error. In a few places

it may infringe as much as …fty per cent (e.g., in Var), in others it narrows to insigni…cance

(e.g., in Basses-Alpes).”

Another limitation of Greer (1951)’s data is that they do not provide

a yearly breakdown on the timing of emigration for each département but only for the 1789-1799

period as a whole.

To overcome these important measurement issues, we leverage the spatial variation in the

temperature shocks in the summer of 1792 as a source of variation for the share of émigrés in the

population of each département. Our identi…cation strategy is motivated by a strand of literature

documenting the e¤ect of climate on human activity and the outbreak of violence. The logic

is that abnormal weather conditions cause a tem porary decline in agricultural output, that is,

a transitory negative income shock for farming-based economies. Such a shock decreases the

These da ta are not subject to selection bias because every Frenchm an had to report for military service.

However, changes in consc ription rules meant that not every man eventually s erved during the 19th century

(Crépin (2009)).

Hi gonnet (1981) suggests, for example, that there were about 25; 000 noble émigrés inste ad of 16; 431, as

estimated by Gr eer (1951).

opportunity cost of violence which in our historical context can be meas ured by the intensity

of emigration rates across départements. For instance, in Orne in the west of France, a high-

emigration and high-temperature shock département in the summer of 1792, the villagers of Rai

and Corsei ransacked the Castle of Rai on Se ptemb er 23, 1792, demanding that the lord of the

manor abandon his feudal rights.

It is not clear when the emigration ‡ows, triggered by the events of the summer of 1792,

stopped. It is possible that emigration in some départements took place over several months

because violence continued after the summer of 1792. In this respect, two groups of regions stand

out. First, the départements of Deux-Sèvres, Loire-Inférieure, Maine-et-Loire, Morbihan, and

Vendée were the locus of the civil war in the west of France (e.g., Tilly (1964), Martin (1987)),

and second, the départements of Bouches-du-Rhône, Calvados, Gironde, and Var participated in

the Federalist Revolt in 1793 (see, e.g., Johnson (1986)). The common characteristic of these

territories was that they experienced high-temperature shocks in the summer of 1792 which

triggered a period of prolonged emigration and unrest.

In what follows, we explore the e¤ects of the di¤erential pattern of emigration during the

Revolution, which we show to be partly shaped by transitory local weather shocks in the summer

of 1792, on the long-term process of development across French départements. Our conjecture is

that emigration is likely to have had both medium- and long-run repercussions via the channels

of land redistribution and the curtailing of the upper tail of the local wealth distribution. In

this respect, it stands to reason that any direct economic impact of the summer shocks of 1792

beyond their e¤ect on emigration rates is unlikely to be quantitatively relevant several decades

after the event.

Note. In Appendix B, we o¤er two complementary pieces of evidence regarding the impact

of temperature shocks on economic conditions and lo c al violence. First, in Appendix B:1 we show

that larger temperature shocks translate into spikes in local wheat prices using data collected by

Labrousse, Romano, and Dreyfus (1970) for the 1797-1800 period which covers the latter part

of the Revolution (see Figure A.3 and columns (1)-(5) of Table D.5). Second, in Appendix B:2

we use the dataset on peasant revolts assembled by Marko¤ (1996) to quantitatively establish

that abnormal tem peratures in the summer of 1792 are systematically related to the incidence

of p eas ant revolts during the “Second Revolution”(see Figure A.1 and columns (6)-(7) of Table

D.5).

The lord of the m an or was Louis-Sébastien Des douits du Ray, a commoner who had been ennobled thanks to

the fortune he had m ad e when working in the Compagnie des Indes (du Mote y (1893), pp. 108-109). His children

emig rated, and years later, in 1826, he and his wife were compensated as ascendants of émigrés under the April 27,

1825, law for the property losses incur red during the French Revolutio n (France - Ministère des Finances. Etats

Déta illés des Liquidations f aites par la Commis sion d’Indemnité, à l’époque du 1er avril 1826 en Exécution de la

Loi du 27 avril 1825, Paris, De l’Imprim erie Royale, 1826. Vo l. 2, pp.2-3).

3.3 Temperature Shocks Construction

Our tempe rature data come from the Europ e an Seasonal Temperature and Precipitation Re-

construction Project, which was developed by paleoclimatologists at the University of Berne

(Luterbacher, Dietrich, Xoplaki, Grosjean, and Wanner (2004), Luterbacher, Dietrich, Xoplaki,

Grosjean, and Wanner (2006), Pauling, Luterbacher, Casty, and Wanner (2006)). These are

season-speci…c reconstructions for the 1500-1900 period, at a resolution of 0:5 by 0:5 decimal

degrees. These data are assembled using a multiplicity of indirect proxies such as tree rings,

ice cores, corals, ocean and lake sediments, as well as historical doc ume ntary records. As such,

measurement error may be nontrivial. Moreover, climatic records are interpolated over relatively

large areas, resulting in two cells per département on average.

According to the authors, the

quality and breadth of the underlying sources improve over time, particularly from the end of

the 18th century onward.

We follow Hidalgo, Naidu, Nichter, and Richardson (2010) and Franck (2016) and employ

two alternative measures of temperature shocks for the summer of 1792. First, we us e the squared

deviation of temperature:

d;t;s



d;t;s

 x

d;s



d;s



;

where the temperature x

d;t;s

in département d in year t of season s is standardized by the mean

d;s

and the standard deviation 

d;s

of temperature in each département d in season s, where

both the mean and standard deviation are computed over a baseline period. The baseline p eriod

which we use to compute x

d;s

and 

d;s

comprises all the s umme r temperatures in the 25 years

before 1792 (i.e., from 1767 until 1791). As we discuss below, we consider several robustne ss

checks to this baseline speci…cation.

Second, we de…ne the absolute deviation of temperature as:

d;t;s



d;t;s

 x

d;s



d;s



;

Panel B of Figure 1 maps the spatial distribution of the mean temperature in the summer

of 1792, while Panel C of Figure 1 portrays the squared deviation of temperature. In Panel D

we present these temperature shocks after partialing out the time-invariant geographic controls

described below. The observed spatial variation in temperature shocks of Panel D is our source

of identi…cation.

Départements were designed in 1790 to be o f relatively small size so that it would take at most one day of

horse travel to reach the département ’s administr ative center from any location in the département. On aver age,

the d épartement ’s area is 6; 000 km

, which is approxi mate ly the size of the US state of Delaware.

It is important to note that the summer of 1792 was comparable to the other summers

during the Revolution. The descriptive statistics in Table D.4 indeed show that the summer of

1792 is at the median of the summer temperature distribution for the 1788-1799 period, with an

average temperature of 17:97, standard deviation 1:36, and a minimum (maximum) temperature

of 13:69 (21:82). The temperature in the summer of 1792 was therefore less unusual than the

summers of 1788 and 1789 which led to the outbreak of the Revolution. In fact, the descriptive

statistics in Table D.4 show that the average temperature shock in the summer of 1792 was milder

than any other summer temperature shock during the 1788-1799 period.

3.4 Confounding Characteristics of Each Département

3.4.1 Geographic Characteristics

In the e mpirical analysis below, we control for the département’s area, land suitability for agri-

culture, elevation, longitude and latitude. These geographic characteristics may in‡uence both a

region’s emigration rate as well as its agricultural comparative advantage and hence the pace of

industrialization and, ultimately, economic growth. Controlling for longitude and latitude also

enables us to account for the location of industries before (and after) the Revolution that were

mostly situated in the east and north of France. Moreover, given the importance of temperature

shocks in 1792 for our identi…cation strategy (see below), we control for the average temperature

in the summer of 1792. In addition, we take into account the distance from each département’s

main administrative center (chef-lieu) to the coast, the border, and the three largest urban cen-

ters (before the French Revolution and to this day), Paris, Lyon, and Marseille. These variables

capture the potential confounding e¤ects of the geographic location of the départements, which

may have a¤ected emigration intensity and local development via the proximity to trade routes.

3.4.2 Prerevolutionary Characteristics

Di¤erences in loc al pre-1789 development outcomes may have jointly a¤ected emigration during

the Revolution and the subsequent evolution of income per capita. To account for these poten-

tially confounding factors, we add the following proxies. First, to capture prerevolutionary levels

of human capital, particularly the upper end of the distribution, we use an indicator for the

presence of a university in 1700 in the département (Bosker, Buringh, and van Zanden (2013)).

Second, we compute the share of the population that subscribed to the Quarto edition of the En-

cyclopédie in the mid -18th century (Darnton (1973), Squicciarini and Voigtländer (2015)) which

also captures the di¤usion of the ideas of the Enlightenment within France. Third, we construct

the number of mechanical mills in 1789 used in textile production (Bonin and Langlois (1997)).

This variable not only accounts for early industrialization but also for prerevolutionary agita-

tion as a substantial number of riots in France in 1788 and 1789 occurred in textile-producing

regions that su¤ered from the increased competition from English manufacturers after the sig-

nature of the Eden Treaty in 1786 (which lowered tari¤s between England and France (Mathiez

(1922-1924))).

Finally, we add a dummy for the départements which Vivier (1998) singles out

as having few commons just before the outbreak of the Revolution and hence more established

private prop erty rights over land.

3.5 Empirical Model

The e¤ect of emigration during the French Revolution on economic development is estimated

using 2SLS. The second stage provides a cross-sectional estimate of the relationship between the

share of émigrés in the population in each département du ring the Revolution and measures of

GDP p er capita, human capital, and additional economic outcomes at di¤erent points in time:

d;t

=  + E

+ X

:! + "

d;t

;

where Y

d;t

represents some proxy of economic performance in département d in year t, E

is the

log of the share of émigrés in the population of département d, X

is a vector of geographical and

prerevolutionary characteristics of département d, and "

d;t

is an i.i.d. error term for département

d in year t.

In the …rst stage, E

; the log of the share of émigrés in the population of département d

during the French Revolution is instrumented by Z

d;1792

, the squared (or absolute) deviation of

temperature in the summer of 1792:

= 

+ 

d;1792

+ X

:! + 

;

where X

is a vector of geographical and prerevolutionary traits of département d described

above.

4 Results

4.1 First Stage: Temperature Shocks in the Summer of 1792 and Emigration

The …rst-stage results are reported in Table 3 where the instrument is the squared (absolute) stan-

dardized deviation from average temperature in the summer of 1792 in columns (1)-(3) (columns

(4)-(6)). In all speci…cations and irrespective of the inclusion of geographic and historical con-

trols, the estimates reveal that the squared and absolute temperature deviations in the summer

On the Eden Treaty, see, for example, Henderson (1957), and on the consequences of t he disruption to interna-

tional trade caused by the revolutionary and Napoleonic Wars, see, for example, Heckscher (1922), Crouzet (1964)

and Juhász (2015).

of 1792 are positively and signi…cantly correlated at the 1% level with variations in the share of

émigrés across French départements. Th is e¤ect is also quantitatively large. In column (3) of

Table 3, the beta coe¢ cient equals to 0:549. Put di¤erently, a one-standard-deviation increase

in the squared deviation from temperature in the summer of 1792 (0:067) increases the share of

émigrés in the population by 0:42% (relative to a sample mean of 0:47% and a standard deviation

of 0:64%). Moreover, the F-statistic of the …rst stage is equal to 16:88 in the speci…cation where

the instrumental variable is the squared deviation of temperature in 1792 (column (3)) and 11:32

in the speci…cation where the instrumental variable is the absolute deviation of temperature in

1792 (column (6)), su ggesting that these instruments are not weak. Figure 2 graphs the …rst-

stage relationship between the squared deviation from average temperature in the summer of 1792

and the share of émigrés, conditional on geographic characteristics (Panel A) and conditional on

geographic and pre-1789 historical characteristics (Panel B).

Note. In Appendix B:3, we provide a series of robustness checks on the uncovered link

between temperature shocks in the summe r of 1792 and variation in the share of émigrés. These

robustness checks have a dual goal. The …rst is to highlight that consistent with the historical

narrative, the temperature shock of the summer of 1792 is the only signi…cant determinant of

emigration among all the temp erature shocks during the revolutionary period. Speci…cally, we

show that emigration rates are not explained by (i) temperature shocks in the other three seasons

of 1792 (Table D.6); (ii) summer temperature deviations between 1788 and 1800 (Table D.7); or

(iii) rainfall shocks in the summer of 1792 (Table D.8). We also show that (iv) Conley-corrected

standard errors at various distance thresholds provide similar …rst-stage results (Table D.9); (v)

and alternative time windows to standardize the temperature shocks (Table D.10) do not change

the patterns found.

Second, in an attempt to strengthen our identi…cation assumption, namely that the weather

shock in the su mmer of 1792 is uncorrelated with preexisting social and economic traits, we

gathered salient pre-revolutionary covariates at the département level and tested whe ther these

features predict the 1792 temperature deviation. S uch c ovariates include (i) episo d es of violence

immediately before (and after) the Revolution; (ii) complaints of the French population in 1789

as expressed in the cahiers de doléances; (iii) human capital before the Revolution proxied by

the share of brides and grooms that were able to sign their wedding contracts; (iv) the share of

the clergy that was hostile to the Revolution, and (v) the number of famous aristocratic families.

All in all, the results in Table D.11 are reassuring. None of these potentially important variables

correlates with our instrument, thus suggesting that it is a plausible source of identi…cation for

the impact of emigration on regional economic performance in the short and longue durée.

4.2 The E¤ect of the Emigrés on the Economy in the Medium and Long Run

In this subsection, we explore the impact of emigration during the Revolution on several economic

outcomes over time, namely income per capita, sectoral labor productivity, and the composition

of the workforce.

4.2.1 Emigrés and the Evolution of Income per Capita

The relationship between emigration and income per capita up to World War II is presented in

Table 4, where the instrument is the squared deviation from standardized temperature in the

summer of 1792. Table D.12 in Append ix D replicates Table 4 using the absolute deviation

from standardized temperature in the su mmer of 1792: As shown in columns (1), (5), and (9) in

Panel A of Table 4, the unconditional OLS relationship between emigration and GDP per capita

is negative in 1860 and 1901, and turns positive in 1930 but is insigni…cant. The relationship

between emigration and income per capita in 1860 strengthens and becomes signi…cant when

we account for geographical factors in column (2). The 2SLS estimates in columns (3)-(4), (7)-

(8), and (11)-(12) in Panel A of Table 4 reveal that there is a negative and signi…cant e¤ect

of emigration on income per c apita in 1860 and 1901 as well as a negative but insigni…cant

e¤ect in 1930, whether we only account for geographic controls or include both geographic and

prehistorical controls. A half-percentage-point increase in the share of émigrés in a département

decreases GDP per capita by 12:8% in 1860 and 18:8% in 1901.

In both Tables 4 and D.12, the

coe¢ cient estimates associated with the share of émigrés in the 2SLS regress ions are signi…cantly

larger than the corresponding OLS ones. Besides measurement error in the share of émigrés

resulting in attenuation bias in the OLS coe¢ cients, an additional and p erhap s more pertinent

explanation for the downward bias of the OLS coe¢ cient arises from the fact that the unobserved

initial presence of wealthy landowners and priests in the p op ulation of a given département (the

stock) and their measured share in the département’s population (the ‡ow) are mechanically

linked.

An alternative way to assess the negative but eventually vanishing impact of emigration

on local economic development during the 19th and early 20th centuries can be seen in Figure

D.4 where we take advantage of the data from Bonneuil (1997) on fertility and infant mortality

between 1811 and 1901. The fertility rate is computed as the Coale fertility index (Coale (1969))

for each département, while the infant mortality rate is computed as the share of children who

Few of our geographic and historical contr ols a re signi…cant in the 2SLS regressions reported in columns

(8) and (12). Longitude is positively correlated with in come per capita in 1860 and 1901, probably re‡ecting

the fact that dépar tements in the east o f France were more industrialized. A lack of c ommons in the 1780s

is also positi vely correlated with income per capita, which could be expected since commons were detrimental to

ag ricultur al produ cti vity. Finally, distance to the coast has a negative impact on income, as landlocked départeme nts

could not pro…t from maritime trade.

died before their …rst birthday. In Figure D.4 we report the coe¢ cients associated with the share

of émigrés in 2SLS regressions (available upon request) wh ere the dependent variable is the Coale

fertility index (Panel A) and infant mortality (Panel B). A high share of émigrés has a positive

and signi…cant e¤ect on fertility an d infant mortality until the 1880s, and no signi…cant impact

afterward.

The relationship between emigration and income per capita in the long run is presented in

Panel B of Table 4. As shown in columns (1), (5) and (9) unconditionally, emigration during the

Revolution has an insigni…cant positive association with income per capita across départements in

1995, 2000, and 2010. This relationship becomes signi…cantly positive once geographical features

are accounted for in columns (2), (6), and (10). Finally, the 2SLS estimates in columns (3)-(4),

(7)-(8), and (11)-(12) in Panel B of Table 4 suggest that emigration had a positive e¤ect in the

long run. A half-percentage-point increase in emigration increases GDP per capita in 1995 by

8:7%, in 2000 by 9:8%, and in 2010 by 8:8%.

Similar results are reported in Table D.12 in

Appendix D.

Our 2SLS estimates in Tables 4 and D.12 indicate that there was a reversal of the e¤ect of

emigration on income per capita: départements with more emigration were poorer until World

War I but became richer by the turn of the 21st century. We illustrate this reversal by plotting

in Figure 3 the coe¢ cients associated with the share of émigrés in the 2SLS regressions reported

in columns (4), (8), and (12) of Panels A and B in Tables 4 and D.12.

Robustness checks. This reversal in the impact of emigration on economic performance

is driven neither by a speci…c group of départements nor by outlier départements with “too few”

or “too many”émigrés. In Figure 4, we plot the coe¢ cients from 2SLS regressions on GDP per

capita in 1860 and 2010 where we remove one “nuts 1”region at a time.

In Figure 5, we plot

the coe¢ cients from 2SLS regressions on GDP per capita in 1860 and 2010, where we remove the

top and bottom 1%, 5%, 10% and 20% départements in the distribution of the share of émigrés.

Under all these alternative permutations, the coe¢ cient associated with the share of émigrés in

the 2SLS regressions remains consistently signi…cant: negative in 1860 and positive in 2010.

This pattern is also evident in the reduced-form estimates reported in Table D.7 in Appen-

dix D. Panels A and B of Figure 6 graph the reduced-form relationships between the temperature

shock in the summer of 1792 and GDP per capita in 1860 and 2010, respectively. Moreover, the

reduced-form regressions in Table D.7 in Appendix D show that no temperature shock in the

In the 2SLS regre ssion s, three covariate s have a systematic signi…cant e¤ect on GDP per capita in 1995, 2000,

and 2010. Speci…cally, the di stance of each département from Paris and Lyon is negatively correlated with income,

indicating the importance of these two major urb an centers on spatial d evelopment. Furth ermore we …nd that the

département ’s area is positively correla ted with income, suggesting the presence o f scale e¤ects.

The nomenc lature o f territorial units for statistics (or “nuts”) i s a standard fo r referencing administrative

divisions within European Union c ountries. Here we use the …rst level of “nuts” for France.

summers between 1788 and 1800, other than that of 1792, can explain this reversal. We also

show that the sign and statistical s igni…cance in the reduced-form relationship between tempera-

ture shocks in 1792 and GDP per capita in 1860 and 2010 is robust to using baselines other than

the 25 years preceding 1792, that is, using the 50 years before 1792 (1743-1791) or the 1751-1800,

1751-1775, and 1776-1800 periods in Table D.10 in Appendix D.

Finally, in Table D.13, we examine the impact of the social status of émigrés on GDP

per capita in 1860 and 2010 by distinguishing between rich émigrés (aristocrats, priests, and

upper middle class) and poor émigrés (lower middle class, workers, and peasants). Even though

statistics on these social groups of émigrés are only available for 69 out of 86 départements, the

2SLS regression results in Table D.13 are qualitatively similar to those in Table 4 insofar as the

shares of rich and poor émigrés have a negative and signi…cant e¤ect on GDP per capita in 1860

and a positive and signi…cant impact on GDP per capita in 2010.

4.2.2 Emigrés, Labor Productivity, and the Workforce

This subsection explores the e¤ect of emigration on labor productivity in the di¤erent sectors of

the economy. In Panel A of Table 5, we examine the impact of emigration on the value added

per worker in the agricultural, industrial, and service sectors in 1860, 1930, 1982, and 1990,

respectively. The 2SLS regressions in colum ns (1)-(3) show that emigration had a signi…cantly

negative impact on productivity in all three sectors in 1860. The estimates in columns (4)-(6)

reveal that there was still a negative e¤ect of emigration on agricultural productivity in 1930.

However, in columns (7)-(12), the e¤ect of the share of émigrés on pro du ctivity in each sector in

1982 and 1990 is pos itive and signi…cant.

The negative e¤ect of the share of émigrés on agricultural productivity in the mid-19th

century can be partially accounted for by the limited mechanization in agriculture in 1862 in high-

emigration départements. Speci…cally, in Table 6 we …nd th at, out of the 15 di¤erent categories of

agricultural instruments per worker in the agricultural sector, emigration is negatively correlated

with 13 of these inputs, and this e¤ect is signi…cant for the quantity of fertilizer and the number

of scari…ers, grubbers, searchers, seeders, and tedders. It is also signi…cantly and negatively

correlated with the …rst principal component of all these agricultural tools per worker in the

agricultural sector. These results are in line with the view that French agriculture remained

relatively backward as a result of the French Revolution.

In Panel B of Table 5, we examine the impact of emigration on the share of the workforce

employed in the agricultural, industrial, and service sectors. The 2SLS regressions in columns

In regressions available upon request, which are moti vated by the study of Rosenthal (1988) on irrigation in

the aft erma th of the Revolution, we analyze the impact of emigration during the Revolution on the area drained

in each department as wel l as the number of pipe factorie s in each département in 1856 using the information in

Barral (18 58). We …nd that emigration had an insig ni…cant impact on bo th variables.

(1)-(3) show that emigration had a positive but insigni…cant impact on the share of the workforce

in the agricultural sector in 1860, a positive and signi…cant e¤ect at the 10% level on the share

of the workforce in the service sector, but a negative and signi…cant e¤ect at the 1% level on the

share of the workforce in the industrial sector. This last result suggests that emigration during

the French Revolution delayed the structural transformation of France toward the industrial era,

in line with the analysis of Cobban (1962). Moreover, the regressions in columns (4)-(6) show

that in 1930, emigration still had an insigni…cant e¤ect on the share of the workforce in the

agricultural sector, a negative and signi…cant e¤ect at the 10% level on the share of the workforce

in the industrial sector, and a positive and signi…cant e¤ect at the 5% level on the share of the

workforce in the service sector. Finally, the regressions in columns (7)-(9) show that in 2010,

emigration had a negative and signi…cant e¤ect at the 1% level on the share of the workforce in

the agricultural sector as well as a positive and signi…cant e¤ect on the sh ares of the workforce

in the industrial s ector at the 5% level and in the service sector at the 1% level.

All in all, the evidence in Tables 5 and 6 sheds some light on the sources of the nega-

tive impact of emigration on incomes during the 19th century shown in Table 4. It suggests

that emigration during the French Revolution disp roportionately and inverse ly a¤ected agricul-

tural productivity up until World War II and slowe d down the structural transformation toward

industry during the 19th century. Nevertheless, since the second half of the 20th century, high-

emigration départements have been hosting a more productive workforce in the industrial and

service sectors.

5 Mechanisms

In this section we explore some potential channels which may account for the ne gative e¤ect of

emigration during the Revolution on the standards of living in the 19th century and its positive

e¤ect toward the end of the 20th century. First, we investigate how the absence of émigrés seems

to have had an impact on the size and the composition of the local elites during the 19th century.

Second, we analyze the impact of émigrés on the landownership structure. Finally, we examine

their e¤ect on the evolution of human capital across départements over time.

5.1 Emigration during the Revolution and the Economic Elites of the 19th

Century

Here we investigate how emigration during the Revolution in‡uenced the size and composition of

local elites during the 19th century. The 2SLS estimates in Table 7 focus on electors in the 1839

In Tab le D.14, we examine the impact of emigration during the Revolution on t he population in each départe-

ment (Panel A) as well as in the chef -lieu (i.e., administrati ve ce nter) of each département (Panel B). We …nd that

emig ration during the Re volution has no impact on population density until World War II.

elections under the regime of the July monarchy (1830-1848). At that time, the voting franchise

was restricted to men above the age of 25 who could pay 200 francs worth of direct annual taxes.

This was a signi…cant amount cons idering that th e average daily wage of bakers in Paris in 1840

was equal to four francs (Chevallier (1887), p.46).

The 2SLS estimates in column (1) of Table 7 show that émigrés had a negative e¤ect

on the share of electors in the popu lation in 1839. The presence of a smaller economic elite in

high-émigrés areas suggests that the local elites were severely weakened by emigration during

the Revolution, leaving these départements with fewer wealthy individuals who could potentially

fund the costly investments of industrialization. This …nding is in line with the evidence in Table

5, that départements with a large share of émigrés were characterized by both lower productivity

and lower employment in the industrial sector.

Moreover, the estimates in Table 7 suggest that emigration had a negative e¤ect on the

share of landowners among the electors (column (2)), a positive but insigni…cant e¤ect on the

share of businessmen and professionals (i.e., doctors and lawyers) (columns (3)-(4)), as well as a

positive and signi…cant e¤ect on the share of civil servants (column (5)). The …nding in column

(2) highlights the relative paucity of su¢ ciently wealthy landowners that may explain the lower

agricultural productivity in 1860 in high-emigration départements. We come back to this issue

in the next section where we d iscu ss in detail how the composition of agricultural landholdings

shaped local development.

The estimate in column (5) of Table 7 shows that in 1839, electors in high-emigration

départements were disproportionately drawn from the pool of civil servants. At …rst, this pat-

tern may seem puzzling, but it is in line with the analysis of Tocqueville (1856) on how the

French Revolution contributed to the growth of the French adminis tration and the central state.

The increased presence of civil servants in high-emigration départements is corroborated by the

estimates in Table D.16, where we show that emigration had a positive and signi…cant e¤ect

on the workforce share of civil servants in 1851 and 1866 as well as a positive but insigni…cant

one in 1881. All in all, the evidence suggests that there were relatively more civil servants, and

presumably, a more powerful administrative machine, in the départements where the Revolution

had been more intense, as proxied by the share of émigrés in the population.

In Tab le D.15, we examine the impact of emigrati on on local …nancial development. We proxy the latter by

the total value of loans (in Fre nch francs) granted by local savings banks and by the number of contr acts sealed

by notaries in each département, keeping in mind that notaries had, by the second half of the 19th century, lost

their central role as …nancial intermediari es which they ha d held prior to the Revolution (Ho¤man, Postel-Vinay,

and Rosenth al ( 2000)). We …nd that emigration is negatively correlated with both measures during the 19th

century ( the e¤ect is, however, only signi…cant on the number of co ntracts sealed by notaries in 1861). Overall,

the results sugge st t hat the negative e¤ect of émigrés on GDP per capita only weakly stemmed from …nancial

underdevelopment.

5.2 Emigration during the Revolution and the Composition of Agricultural

Holdings

We have already established that in départements with a higher share of émigrés, labor agricul-

tural productivity was signi…cantly lower and fewer rich landowners voted in the elections held

in 1839. In this section, we further examine the impact of emigration on the size of agricultural

landholdings.

In the agricultural census of 1862, landholdings are categorized in brackets according to

their size. The largest landholdings are those in the category above 40 hectares. Given the

historical account and the evidence on the composition of the elites, one would expect to …nd

that high-emigration départements have a dearth of large holdings. This is shown to be the

case in column (1) in Panel A of Table 8 where the dependent variable is the share of farms

above 40 hectares: a one-percentage-point increase in the share of émigrés in the population

decreases the share of farms above 40 hectares in 1862 by 1:54%. It is instructive to link this

…nding with the work of David (1975) (pp.221-231) on the adoption of the mechanical reaper

for harvesting wheat in 1854-1857 in the United States. He …nds that the mechanical reaper

was only economically viable for farms larger than roughly 20 hectares. In 1862 only 13% of

farms were above 20 hectares in the median French département, while 52:9% and 58:5% of farms

were above that threshold in the United States in 1860 and England in 1851 (Grigg (1992)),

respectively. Moreover, as we show in column (2), French départements that experienced a

larger exodus during the Revolution had systematically fewer farms above this scale-e¢ cient size.

Namely, we …nd that a one-percentage-point inc rease in the share of émigrés in the population

decreased the share of farms above 20 hectares in 1862 by 0:87%. This absence of su¢ ciently

large landholdings echoes the …ndings in Table 6 regarding th e delayed mechanization of French

agriculture in high-emigration départements.

In columns (3)-(5) in Panel A of Table 8, our de pendent variables are the ratio of the number

of farms of 40 hectares and above to the number of farms below 10 hectares in 1862 and the ratio

of the number of farms of 50 hectares and above to the number of farms below 10 hectares in 1929

and 2000. These variables are meant to capture the relative abundance of large- to small-sized

farms within a département. Over the last 150 years, regions in France where emigration was

more intense during the 1789 Revolution consistently feature an agricultural landscape dominated

by small- to medium-sized farmers and a scarcity of large ones.

The demise of large landed

elites and the creation of a small peasantry mainly working for subsistence, at least until World

War II, was part of the legacy of the émigrés’ ‡ight during the French Revolution. Panels C

and D of Figure 6 plot th e residuals of the reduced-form regressions between the summer of 1792

Additional results available upon request show that the share of émigrés had a positive but insigni…cant e¤ect

on the total number of farms and to tal number of farms per inhabitant in 1862.

temperature shock and the ratio of farms above 40 hectares to farms below 10 hectares in 1862

and between the summer of 1792 temperature shock and the share of farms above 20 hectares in

1862.

It is interesting to compare the results in Panel A of Table 8 to those of Finley, Franck,

and Johnson (2017), who …nd that the auctions of Church land during the Revolution are pos-

itively correlated with land concentration during the mid-19th century (and hence, with higher

investments in agriculture). Their rationale is that the auctions of Church property, which took

place in the early stages of the Revolution before the summer of 1792, mainly entailed a transfer

of land from the Church to members of the wealthier sections of the local society. In our context,

the extent to which the local elite might eventually have been able to bene…t from the Church

property would depend on the extent of emigration during the Revolution. In other words, if

our conjecture is right, one would expect to …nd that the negative impact of emigration on land

concentration to be magni…ed in areas where more Church land was auctioned. This is what we

…nd in Panel B of Table 8, where we run reduced-form regressions on the 67 départements for

which we have information on the share of the Church property sold during the Revolution (Bo-

dinier and Teyssier (2000)). Speci…cally, we control for the latter and add the interaction term

between the share of Church land sold in each département and the temperature s hocks in the

summer of 1792. In all the regressions, we …nd, in line with Finley, Franck, and Johnson (2017),

that the share of the Church property sold in each département is positively correlated with the

presence of large estates, and more importantly for our analysis, that the interaction term is

negative and highly signi…cant. The direct e¤ect of temperature shocks also remains precisely

estimated, suggesting that emigration did lead to a decline in the share of large landowners even

in the absence of Church land redistribution. Its impact, ne vertheless, was signi…cantly stronger

precisely where more Church land was sold.

One may naturally wonder why market forces did not “correct”this ine¢ cient size of small

landholdings over time. In other words, why did this lopsided ownership structure in agriculture

survive when one would expect consolidation to take place? Although a thorough exploration

of this subject would take us beyond the con…nes of the current study, we venture a tentative

explanation below.

First of all, it must be noted that there was no deliberate, o¢ cial policy designed speci…cally

to perpetuate the fragmentation of landownership status quo during the 19th century (Agulhon,

Désert, and Specklin (1976)). Nevertheless, the existence of the octrois might help to explain why

the tendency toward consolidation might have been less pronounced. The octrois were the local

taxes levied on almost all goods entering towns (e.g., meat, wine, fruits, vegetables, coal, etc.)

and, de facto, functioned as internal trade barriers within France (before and after 1789, as they

were only …nally abolished in 1943). These octrois favored small local farmers whos e production

would be exempt from paying them. Throughout the 19th century, the central government

progressively reined in the ability of towns to levy octrois, and on December 29, 1897, the French

Parliament passed a law which came into e¤ect on January 1, 1901, dictating a substantial

decrease in octrois rates. This law, which was the outcome of the lobbying from “progressives”

who sought to improve citizens’ health by promoting the consumption of wine as opposed to

liquor, b e ne…ted large wine producers in the south, who were able to produce cheap wine in large

quantities. The law thus crowded out small wine producers who successfully lobbied for costly

anti-competitive legislation which was adopted in 1905 to reduce fraud and adulteration in the

wine m arket and which, de facto, protected small producers of local wine (Franck, Johnson, and

Nye (2014)). This example suggests that local demand for barriers to entry would be stronger in

regions dominated by small landowners since competition from large farmers would be damaging

to their revenues. In fact this is what we …nd in Table D.17: départements with a larger share

of émigrés had in 1875 more French towns (“communes”) which were protected by octrois taxes,

and the magnitude of these taxes for various pro du cts were also likely to be signi…cantly higher.

Another potential explanation for the negative impact of emigration on agricultural pro-

ductivity may stem from the positive e¤ect of emigration on the share of commons in e ach

département in 1863, as can be seen in column (6) in Panel A of Table 8. A one-percentage-point

increase in the share of émigrés in the population increases by 1:72% the share of commons in

1863 . As discu sse d by Vivier (1998), there is ample anecdotal evidence that the central state

and the local governments seized the commons during the Revolution in places where there were

more émigrés. In turn, the local governments leased those lands to farmers for a limited number

of years. Such leases in agriculture may have had a negative e¤e ct on agricultural productivity

by limiting investments in machinery and promoting intensive production methods which would

be damaging for land productivity in the long run.

The evidence in this section provides a possible f oray into u nd erstand ing why local in comes

were depressed during the 19th century in regions that émigrés left in large numbers. Can the

same economic forces, re‡ecte d in the distribution of agricultural landholdings, help to explain

the takeo¤ of these initially lagging regions? This is what we ask below.

French towns (“communes”) could lend their land under ordinary lease s or gr ant longtime leases. The “ordi-

nary” leases were l im ited to 9 years in 1791 for all communes, but exceptions could be granted by the national

administr ati on . The 9-year limit was soon extended to 18 years. Moreover, in 1859, the law was changed so that

the ordinary leases of the communes were a minimum of 9 years and a maximum of 27. Furthermore, c ommunes

had the right to deliver lifelong leases on commons.

5.3 Emigration during the Revolution and Human Capital Accumulation

This section examines whether the positive e¤ect of emigration on the standards of living in the

long run can be explained by its impact on the evolution of human capital accumulation of each

département before and after World War II.

5.3.1 The E¤ect of Emigrés on Human Capital Accumulation

For the period before World War II, our empirical analysis focuses on the decadal averages between

the 1840s and the 1930s of the share of illiterate French army conscripts, that is, 20-year-old men

who reported for military service in the département where their father lived and who could

neither read nor write. In the 2SLS regressions reported in Table 9, we …nd that emigration has a

negative e¤ect on the share of illiterate conscripts throughout the period. This e¤ect is signi…cant

at the 10% level in the 1840s and 1870s, and barely insigni…cant during the 1850s (p-value=0.12),

1860s (p-value=0.23), 1880s (p-value=0.17), and 1890s (p-value=0.27). The negative e¤ect of

emigration on illiteracy is consistently signi…cant for the generations of c onsc ripts in the 1900s,

1910s, and 1930s, that is, for the 20-year-old men who would have bene…ted from the adoption

of the 1881-1882 laws on free and mandatory schooling until age 13. This pattern suggests that

in high-emigration areas throughout the 19th century, and despite their limited means, parents

attempted to invest relatively more in th e human capital of their children compared to the more

a- uent, low emigration départements. This tendency toward higher literacy rates becomes more

evident after 1881-1882 when schooling becomes free and man datory until the age of 13. Below

we discuss how this pattern may be attributed to the relatively low returns to agricultural labor

compared to the other sectors of the economy.

Since World War II, départements which experienced large emigration waves during the

French Revolution have maintained their human capital advantage already apparent at the turn

of the 20th century. This can be seen in Panels A and B of Figure D.5, where we plot the

coe¢ cients associated with the share of émigrés in 2SLS regressions. In Panel A the depend ent

variable is the share of men ages 16-24 with only a high-school degree in 1968, 1975, 1982, 1990,

1999 and 2010, whereas in Panel B the dependent variable is the share of men ages 16-24 with

a college degree in the respective years. The share of émigrés has a p os itive and signi…cant

e¤ect on the share of males age 16-24 with only a high school degree for 1975, 1982, and 1990.

Nevertheless, the share of émigrés has a positive and signi…cant e¤ect on the share of men aged

16-24 with a college degree consistently between 1968 and 2010. These results suggest that since

World War II, human capital accumulation has been more intense overall in high-emigration

regions; although it seems as though there has been a slow convergence over the last two decades

in terms of high school completion rates, the relatively earlier transition to widespread literacy

in high-émigrés areas has conferred upon them an educational edge re‡ected in a greater ‡ow of

college graduates to this day.

5.3.2 The Opportunity Cost of Education and Child Labor

Naturally, to understand why literacy rates di¤er across regions over time, one needs to tease

out the forces that shape the demand and supply of schooling locally. This is not an easy task.

However, one element that makes the case of France easier to analyze is the fact that primary

schooling became free and mandatory until the age of 13 after the adoption of the 1881-1882 laws.

Although this would imply that th e supply of schooling over time should become more uniform

across regions, we …nd that high-emigration départements expe rience systematic under provision

of primary schools per school-aged (5-15 years of age) population until WWI. This is shown in

Panel A of Table D.18. A similar pattern is found in Panel B where the dependent variable

is the total public spending per pupil between 1876 and 1901. Panel C of Table D.18 actually

suggests that the limited supply of schooling re‡ected an overall und er provision of public goods

in high-emigration départements which also had a less dense transportation network up until at

least World War I. Moreover, given the role of the Church in the provision of schooling in France

before and after the Revolution (see Appendix A:3 for a discussion), it is worth pointing out

that Table D.19 shows that the temperature shocks in the summer of 1792 are not correlated

with variables that Franck and Johnson (2016) show to be good proxies for religiosity in France

before World War I. This lack of correlation between our instrument and the number of religious

communities in each département devoted to education, charity, and solely to religious purposes

in 1856 (from the 1856 French census), as well as the share of representatives in the lower house

of Parliament who voted against the separation of Church and State in 1905 (Franck (2010)),

suggests that the Church’s ability to provide education in the 19th century was uncorrelated with

emigration during the Revolution.

In light of these observations, the fact that literacy became more widespread in the high-

emigration regions which received overall fewer public goods (including public primary schools)

is all the more striking. But what may rationalize this pattern? We o¤er two complementary

pieces of evidence.

First, a potential explanation for the rise in literacy across high-emigration areas from the

late 19th century onward may be partly attributed to the observation that one of the factors that

in‡uence human capital investments is the relative return of working in agriculture versus services

and industry. To the extent that literacy is arguably more complementary to non agricultural

occupations, a larger gap in labor productivity in favor of services and industry might act as

a catalyst for human capital accumulation. We already noted in Panel A of Table 5 that the

emigration wave during the Revolution had a negative impact on labor productivity in all sectors

of the economy until World War II. But what happened to the relative sectoral returns? In Panel

C of Table 5, we run 2SLS regressions where the depen de nt variable is the ratio of the value added

per worker in the non agricultural sector (i.e., industry and services) vis-à-vis the agricultural one

in 1860, 1930, 1982, and 1990. Emigration had a positive and signi…cant e¤ect on this ratio at the

1% level in 1860 and 1930 in columns (1) and (2), suggesting the relative desirability of the non-

agricultural sector of the local economy in high-emigration départements. In columns (3) and (4),

the e¤ect of emigration on the relative lab or productivities in 1982 and 1990 is insigni…cant. This

would be expected, as a supply response regarding human capital accumulation would eventually

lower the di¤erential wages between the agricultural and non agricultural sectors.

A second explanation for the early rise of literacy in the high-emigration areas can be traced

to the opportunity cost of ac quiring education. Besides the direct monetary cost of attending

school, a relevant but often underappreciated part of the decision on whether to acquire schooling

would be the forgone wages that a child would bring home. In the case of 19th-c entury France,

this outside option would be tightly linked to productivity in agriculture.

Taking into account

both the depressed labor productivity in the agricultural sector of high-émigrés areas until World

War II and the decline in the monetary costs of primary schooling after 1881, it is plausible to

expect individuals in high-émigrés départements to eventually accumulate human capital at a

faster pace instead of working in the agricultural sector.

We examine the conjecture that children and teenagers would be less likely to work in

the agricultural sector by using data from the 1929 agricultural survey. This survey provide s

information at the département level on the number of individuals below the age of 15 working

in agriculture. The 2SLS regression results reported in Table 10 show that in high-emigration

départements in 1929, individuals below the age of 15 were systematically less likely to work in

the agricultural sector, and presumably more likely to stay in school. The pattern is the same

whether the baseline is the overall workforce in agriculture, the number of daily agricultural

workers, the total number of daily agricultural workers (including foreign workers) below the age

of 15, or the total number of French and foreign daily agricultural workers above the age of 15.

In an e¤ort to provide some historical background to the uncovered relationship between

returns to agriculture and the relative delay in human capital accumu lation in low-emigration

areas during the 19th century, we turn to the parliamentary deb ates which preceded the adoption

of mandatory schooling laws in France. In this respect, it is interesting to examine the debates

held in 1881-1882 when laws on mandatory schooling until age 13 were …rst adopted, but also

The adverse e¤ect of higher agricult ural productivity on human cap ita l accumulation has be en recently docu-

mented by Shah and Steinberg (2015) in the context of India.

in 1936 when mandatory schooling was extended until the age of 14.

Politicians who voiced

concerns regarding the implementation of the mandatory schooling laws in 1881-1882, such as

Jean-Edmond Laroche-Joubert who supported them or Ferdinand Boyer who opposed them,

thought that parents would refuse to send their children to school because they would be deprived

of the wages that their children would earn by working on nearby large farms (Journal O¢ ciel,

Debats, Chambre des députés 18-21 décembre 1880, pp. 36-75).

Laroche-Joubert represented

in the lower house of Parliament the Charente département, a low-e migration area with a high

ratio of large to small farms, while Boyer represented in the lower house of Parliament the

Gard département, also a low-emigration place with a median ratio of large to small farms. In

1936, the same type of argument was made by Henri Connevot, who represented Creuse, a low-

emigration département with a high ratio of large to small farms in the upper house of Parliament.

Speci…cally, Connevot was in favor of the law but expressed h is concerns that "in our rural areas,

very often, thirteen-year old children, as soon as they pass the primary school certi…cate, are sent

away to work by their parents, both boys and girls, to earn 100 f rancs per month the …rst year

and 150 francs per month the second year. Th ese are therefore three thousands francs which are

lost by needy families. It will therefore be very di¢ cult to enforce the law, or it will be necessary

to grant allowances to those families." (Journal O¢ ciel, Débats Parlementaires, Sénat, 28 ju illet

1936, p 903. Translation is ours.)

Emigration, Landownership and C omparative Development Weaving together

the evidence so far, one may wonder whether the time-varying impact of émigrés on comparative

development may be quantitatively explained by the persistent di¤erences in the composition of

agricultural landholdings brought about by the emigration during the Revolution. In other words,

can the relative increase in the numbe r of small landowners account for the inverse relationship

between emigration rates and agricultural productivity in the medium run, as well as higher

human capital accumulation and b e tter economic performance after World War II?

We examine this hypothesis in Table 11 where we assess the change in the magnitudes

of our baseline …ndings regarding the value added per worker in agriculture in 1860 (Table 5)

and GDP per capita in 2010 (Table 4) when we account for the ratio of farms ab ove 40 ha to

farms below 10 ha in 1862, which re‡ects the degree of concentration in landownership. First,

the association between the ratio of large to small farms and economic performance changes sign

In 1881-1882, as well as in 1936, most of the polit icians who supported mandatory schooling laws did so because

they thought that t he development of a state-fu nded secular sys tem woul d consolidate the Repu bli can regime by

weakening the Catholic Church. Conversely, the opposition t o the mand atory schooling laws was motivated by the

defense of the Catholic school system (see Franck and Johnson (2016) and the reference s therein).

Jules Ferry, who was the prime minister when the June 16, 1881, law was adopted a nd minister of education

when the Ma rch 28, 1882, law was pass ed, conceded that the implementation of mandatory schooling might be

problematic. (Journal O¢ ciel, Chambre des Députés, 20 décembre 1880, p. 112).

over time, similar to the e¤ect of emigration during the Revolution. A département dominated by

large farms in mid-19th-century Franc e was signi…cantly more productive in agriculture in 1862;

however, départements where the agricultural sector was populated by small- and medium-sized

farmers in 1862 have higher income per capita in 2000. Moreover, accounting for the composition

of agricultural holdings decreases the estimated coe¢ cient on the s hare of émigrés by roughly

half when the dependent variable is the value added per worker in agriculture in 1860, and by

approximately 40% when the variable of interest is GDP per capita in 2010. This implies that a

sizeable fraction of the obs erved reversal in the relationship between emigration rates during the

Revolution and subsequent economic performance is indeed driven by the nonmonotonic impact

of the concentration in landownership on comparative development.

The uncovered evidence is complementary to the mechanism p roposed by Franck and Galor

(2015), who argue that the early industrialization across French départements led to underinvest-

ment in education and lower employment in skilled-intensive occupations.

6 Conclusion

It is widely debated whether the 1789 Revolution enabled economic growth and industrialization

in France or stalled French development by consolidating an agrarian structure of small near-

subsistence farmers. In this study, we focus on the economic consequences of the local weakening

of the Old Regime, as proxied by the share of émigrés, mostly aristocrats, wealthy landowners and

clergymen, who ‡ed France during the 1789-1799 period and whose property was con…scated and

sold by the revolutionaries. Our identi…cation strategy exploits local variation in temperature

shocks during the summer of 1792 to obtain plausibly exogenous variation in the share of émigrés

across French départements. Emigration intensi…ed in August and Se ptemb er of 1792 when the

Revolution took a radical turn. King Louis XVI was imprisoned, and a few weeks later, the …rst

French Republic was proclaimed. At this critical juncture of the French Revolution, we show

that local shocks in the economic environment (captured by temperature shocks) are a strong

predictor of local emigration rates.

The study establishes that emigration during the French Revolution has had a nonmonotonic

impact on regional income per capita over the subsequent 200 years. While emigration had a

negative impact on income during the 19th ce ntury, it had a positive and signi…cant e¤ect in the

long run. We suggest several mechanisms that may rationalize this pattern. First, in départe-

ments with more émigrés, there was more land redistribution. Large estates were fragmented into

smaller ones. This pattern may explain the archaic means of agricultural production in France

and its delayed industrialization during the 19th century. Second, the size and composition of the

local elites were shaped by emigration during the Revolution. High-emigration areas had f ewer

wealthy individuals as well as fewer large landowne rs.

We conjecture that the changes in the economic environment due to emigration during the

Revolution shaped the incentives for human capital accumulation over time. Speci…cally, we …nd

that high-emigration départements have system atically higher literacy rates among the conscripts

even before the adoption of the laws regarding free and mandatory schooling in 1881-1882, and

this relationship further strengthens with respect to the generations born thereafter.

This early rise in literacy rates may be linked to two elements in‡uencing human capital

accumulation. First, départements with a higher share of émigrés in the population during

the Revolution are shown to have a larger gap in labor productivity in favor of the industrial

and service sectors up until World War II. To the extent that human capital is complementary

to non agricultural activities, raising the relative productivity of the latter would incentivize the

accumulation of basic literacy in high-emigration areas. Second, the opportunity cost of acquiring

education re‡ected in agricultural productivity was lower in the high-e migration départements.

Indeed, using data from 1929, we show that child labor in agriculture was lower in départements

with low-emigration rates and high-agricultural productivity, underlying the adverse dynamic

impact of high opportunity cost on school attendance. Since World War II, these départements

have kept their edge in education, as re‡ected in higher rates of college graduates today. As such,

the reduc tion in the share of wealthy individuals in the local population and the fragmentation

of agricultural property in the wake of the Revolution are consistent with studies predicting a

nonmonotonic role of equality in the process of development (Galor and Zeira (1993), Galor and

Moav (2004)).

Our study suggests several potential avenues for future research. For example, political

upheavals at di¤erent stages of development may shape economic trajectories and social pref-

erences across generations, and lead to the emergence of new political institutions over time.

Second, our study suggests that radical policies of land redistribution in agrarian societies can

have economic consequences that have a time- varying impact. Further research could explore

how policies speci…c to the industrial or service sector may in‡uence the long-term evolution of

human capital.

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A. Emigrés as a Share of the

Dé partement’s Population

B. Average Temperature in

Summer 1792

C. Squared Devi ation from Tempera ture

in Summer 1792 (baseline 1767-1791)

D. Squared Deviation from Temperature

in Summer 1792 (baseline 1767-1791)

Partialing Out Ge ographic Controls

Figure 1: Share of Emigrés in Population and Summer Temperature in 1792 in French Départe-

ments

Source: Greer (1951),Luterbacher, Dietrich, Xoplaki, Grosjean, and Wanner (2004),Lut erbacher, Dietrich,

Xoplaki, Grosjean, and Wanner (2006 ); Pauling, Luterbacher, Casty, and Wanner (2006).

Somme

Pas de Calais

Seine Inferieure

Herault

No rd

Oise

Lo zere

Cantal

Vaucluse

Pyrennees Orientales

Aude

Creuse

Gard

Loire (Haute)

Eure

Vienne (Haute)

Ta rn

Calvados

Correze

Lo ire

Lot

Ind re

Aisne

Lot-et-Garonne

Aube

Ardennes

Seine et Marne

Ardeche

Gironde

Alpes (Hautes)

Manche

Landes

Charente

Aveyron

Cher

Marne

Meuse

Loiret

Seine et Oise

Puy de Dome

Drome

Yonne

Marne (Haute)

Eure-et-Loir

Ariege

Loir-et-Cher

Nie vre

Vienne

Allier

Charente-inferieure

Dordogne

Moselle

Gers

Indre-et-Loire

Garonne (Haute-)

Rhone

Pyrenees (Hautes)

Orne

Bouches du Rhone

Vosges

Isere

Sevres (Deux)

Saone et Loire

Pyrenees (Basses)

Savoie

Saone (Haute)

Ain

Cote d'Or

Seine

Sarthe

Rhin (Haut)

J u ra

Vendee

Loire-Inferieure

Mayenne

Meurthe-et-Moselle

Cotes du Nord

Rhin (Bas)

Doubs

Alpes (Basses)

Maine-et-Loire

Ille-et-Vilaine

Morbihan

Var

Fi n i s t e re

Alpes-Maritimes

-2 -1 0 1 2

Share of Emigres in Department Population

-.1 -.05 0 .05 .1

Squared Dev iation f rom Temperature in Summer 1792 (1767-1791)

Conditional on Geographic Characteristics

Temperature Shocks in 1792 and Emigration across French Departments

Somme

Pas de Calais

Seine Inferieure

Herault

Lo zere

No rd

Cantal

Ga rd

Aude

Loire (Haute)

Vaucluse

Ta rn

Calvados

Creuse

Pyrennees Orientales

Vienne (Haute)

Aube

Oise

Aisne

Lo ire

Lot-et-Garonne

Gironde

Alpes (Hautes)

Ind re

Manche

Eu re

Correze

Ardennes

Ardeche

Lot

Seine et Marne

Charente-inferieure

Marne

Charente

Drome

Ari ege

Marne (Haute)

Aveyron

Meuse

Puy de Dome

Yonne

Allier

Landes

Eure-et-Loir

Nie v re

Ge rs

Loir-et-Cher

Vienne

Cher

Indre-et-Loire

Garonne (Haute-)

Dordogne

Rhone

Vosges

Moselle

Savoie

Bouches du Rhone

Pyrenees (Hautes)

Orne

Seine et Oise

Sevres (Deux)

Saone (Haute)

Cote d'Or

Seine

Isere

Saone et Loire

Pyrenees (Basses)

Ain

Loiret

Doubs

Sarthe

J ura

Rhin (Haut)

Mayenne

Vendee

Loire-Inferieure

Meurthe-et-Moselle

Ille-et-Vilaine

Cotes du Nord

Alpes (Basses)

Morbihan

Rhin (Bas)

Maine-et-Loire

Var

Fi n i st e re

Alpes-Maritimes

-2 -1 0 1 2

Share of Emigres in Department Population

-.1 -.05 0 .05 .1

Squared Deviation from Temperature in Summer 1792 (1767-1791)

Conditional on Geographic and Pre-1789 Historical Characteristics

Temperature Shocks in 1792 and Emigration across French Departments

A. IV is the Squared Deviat ion from Temperature in

Summer 1792, Condi tional on Geographic Controls

B. IV is the Squared Deviation from Temperature in

Summer 1792, Condi tional on Geographic and Historical Controls

Figure 2: Temperature Deviation in the Summer of 1792 and the Share of Emigrés, Controlling for Geographic and p re-1789 Historical

Characteristics

Note: These …gures depict the part ial scatterplots of the e¤ect of temperature shocks in the summer of 1792 on the share of émigrés in the population of

each Fr ench département . Pane l A presents the relationship with the squared deviati on from temperature in the summer of 1 792 (1767-1791) , while Panel B

reports the relationship with the absolute deviation from temperature in the summer of 179 2 (1767-1 791). Thus, the x- and y-axes in Panels A and B plot

the residuals obtained from regressing the share of émigrés in the populati on against the squared and absolute deviations from temperature in the summer of

1792, conditional on geographic and historical controls.

-1 -.5 0 .5

1860

1901

1930

1995

2000

2010

-.6 -.4 -.2 0 .2 .4

1860

1901

1930

1995

2000

2010

IV is the Squared Deviation from Temperature in

Summer 1792, Condi tional on Geographic and Historical Controls

IV is the Abso lute Deviatio n from Temperature in

Summer 1792, Condi tional on Geographic and Historical Controls

Figure 3: The E¤ect of Share of the Emigrés on GDP per Capita in 1860, 1901, 1930, 1995, 2000 and 2010

Note: These …gures. display the estimated coe¢ cients of the share of émigrés in the populat ion on GDP per capita 1860, 1901, 1930, 1995, 2000, and

2010 in the 2SLS regressions in Table 4, conditional on all the geographic and historical controls. Intervals re‡ect 90% con…dence leve ls.

-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5

Excluding Nuts2

Excluding Nuts3

Excluding Nuts4

Excluding Nuts5

Excluding Nuts6

All Nuts

Excluding Nuts1

Excluding Nuts7

Excluding Nuts8

-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5

Excluding Nuts2

Excluding Nuts3

Excluding Nuts4

Excluding Nuts5

Excluding Nuts6

All Nuts

Excluding Nuts1

Excluding Nuts7

Excluding Nuts8

GDP per Capita in 186 0, Removing One "Nuts" at a Time.

IV is the Squared Deviati on from Temperature in Summ er 1792,

Conditional on Geographic an d Historical Controls

GDP per Capita in 201 0, Removing One "Nuts" at aTime.

IV is the Squared Deviati on from Temperature in Summ er 1792,

Conditional on Geographic an d Historical Controls

Figure 4: The E¤ect of the Share of Emigrés on GDP per Capita in 1860 and 2010, Removing one "nuts" at a Time

Note: These …gures display the estimated coe¢ cients of the share of émigrés in the populatio n on GDP per capita i n 1860 and 2010 in the 2SLS regressions,

conditional on all the geographic and historical controls, where w e remove one "nuts" at a time. The nomenclature of territorial units f or statistics (or “nuts”)

is a s tandard for referencing administrative divisions within European Unio n countries. In this study, we use the …rst level of “nuts”for France. The complete

2SLS regressions are available upon request. Interval s re‡ect 90% con…dence leve ls.

-.8 -.6 -.4 -.2 0 .2 .4

Excluding Top 20%

Share of Emigres

Excluding Top 1%

Excluding Top 5%

Excluding Top 10%

Excluding Bottom 20%

Excluding Bottom 10%

Excluding Bottom 5%

Excluding Bottom 1%

-.8 -.6 -.4 -.2 0 .2 .4

Excluding Top 20%

Share of Emigres

Excluding Top 1%

Excluding Top 5%

Excluding Top 10%

Excluding Bottom 20%

Excluding Bottom 10%

Excluding Bottom 1%

GDP per Capita in 186 0, and the Di stributi on of Emigration

IV is the Squared Deviation from Temperature in Summer 1792

Condi tional on Geographic and Historical Controls

GDP per Capita in 201 0, and the Di stributi on of Emigration

IV is the Squared Deviation from Temperature in Summer 1792

Condi tional on Geographic and Historical Controls

Figure 5: The E¤ect of the Share of Emigrés on GDP per capita in 1860 and 2010, Removing one the top and bottom 1%, 5% , 10%

and 20% in the Distribution of the Share of Emigrés

Note: These …gures display the estimated coe¢ cients of the share of émigrés in the populatio n on GDP per capita i n 1860 and 2010 in the 2SLS regressions,

conditional on all the geogra phic and his torical controls, where we re move the top and bottom 1%, 5%, 10%, and 20% départeme nts in the distribution of the

share of émigrés. The complete 2SLS regressions are availa ble upon request. Intervals re‡ect 90% con…dence le vels.

Ain

Ai sne

Al li er

Alpes (Basses)

Al pes (Hautes)

Al pes-Maritimes

Ardeche

Ardennes

Ariege

Aube

Aude

Aveyron

Bouches du Rhone

Calvados

Cantal

Charente

Charente-inferieure

Cher

Correze

Cote d'Or

Cotes du Nord

Creuse

Dordogne

Doubs

Drome

Eure

Eure-et-Loir

Finistere

Gard

Garonne (Haute-)

Gers

Gironde

Herault

Ille-et-Vilaine

Indre

Indre-et-Loire

Isere

Jura

Landes

Loir-et-Cher

Loi re

Loire (Haute)

Loire-Inferieure

Loiret

Lot

Lot-et-Garonne

Lozere

Maine-et-Loire

Manche

Marne

Marne (Haute)

Mayenne

Meurthe-et-Moselle

Meuse

Morbihan

Nievre

Nord

Oise

Orne

Pas de Cal ais

Puy de Dome

Pyrenees (Basses)

Pyrenees (Hautes)

Pyrennees Orientales

Rhin (Bas)

Rhin (Haut)

Rhone

Saone (Haute)

Saone et Loire

Sarthe

Savoie

Seine

Seine Inferieure

Seine et Marne

Seine et Oise

Sevres (Deux)

So mme

Tarn

V ar

Vaucluse

Vendee

Vi enne

Vi enne (Haute)

V osges

Yonne

-.4 -.2 0 .2 .4 .6

Share of Farms above 20 Hectares

-.1 -.05 0 .05 .1

Log GDP per Capita in 1860

Conditional on Geographic and Pre-1789 Historical Characteristics

Temperature Shocks in 1792 and Departemental-Level Income per Capita 1860

Ain

Ai sne

Al li er

Alpes (Basses)

Al pes (Hautes)

Al pes-Maritimes

Ardeche

Ardennes

Ariege

Aube

Aude

Aveyron

Bouches du Rhone

Calvados

Cantal

Charente

Charente-inferieure

Cher

Correze

Cote d'Or

Cotes du Nord

Creuse

Dordogne

Doubs

Drome

Eure

Eure-et-Loir

Finistere

Gard

Garonne (Haute-)

Gers

Gironde

Herault

Ille-et-Vilaine

Indre

Indre-et-Loire

Isere

Jura

Landes

Loir-et-Cher

Loi re

Loire (Haute)

Loire-Inferieure

Loiret

Lot

Lot-et-Garonne

Lozere

Maine-et-Loire

Manche

Marne

Marne (Haute)

Mayenne

Meurthe-et-Moselle

Meuse

Morbihan

Moselle

Nievre

Nord

Oise

Orne

Pas de Cal ais

Puy de Dome

Pyrenees (Basses)

Pyrenees (Hautes)

Pyrennees Orientales

Rhin (Bas)

Rhin (Haut)

Rhone

Saone (Haute)

Saone et Loire

Sarthe

Savoie

Seine

Seine Inferieure

Seine et Marne

Seine et Oise

Sevres (Deux)

So mme

Tarn

V ar

Vaucluse

Vendee

Vi enne

Vi enne (Haute)

V osges

Yonne

-.2 -.1 0 .1 .2 .3

Share of Farms above 20 Hectares

-.1 -.05 0 .05 .1

Log GDP per Capita in 2010

Conditional on Geographic and Pre-1789 Historical Characteristics

Temperature Shocks in 1792 and Departemental-Level Income per Capita 2010

A. IV is th e Squared Devi ation from

Temperature in Su mme r 1792

B. IV is the Squared Devi ation from

Temperature in Su mme r 1792

Ain

Ai sne

Al li er

Alpes (Basses)

Al pes (Hautes)

Al pes-Maritimes

Ardeche

Ardennes

Ariege

Aube

Aude

Aveyron

Bouches du Rhone

Calvados

Cantal

Charente

Charente-inferieure

Cher

Correze

Cote d'Or

Cotes du Nord

Creuse

Dordogne

Doubs

Drome

Eure

Eure-et-Loir

Finistere

Gard

Garonne (Haute-)

Gers

Gironde

Herault

Ille-et-Vilaine

Indre

Indre-et-Loire

Isere

Jura

Landes

Loir-et-Cher

Loi re

Loire (Haute)

Loire-Inferieure

Loiret

Lot

Lot-et-Garonne

Lozere

Maine-et-Loire

Manche

Marne

Marne (Haute)

Mayenne

Meurthe-et-Moselle

Meuse

Morbihan

Moselle

Nievre

Nord

Oise

Orne

Pas de Cal ais

Puy de Dome

Pyrenees (Basses)

Pyrenees (Hautes)

Pyrennees Orientales

Rhin (Bas)

Rhin (Haut)

Rhone

Saone (Haute)

Saone et Loire

Sarthe

Savoie

Seine

Seine Inferieure

Seine et Marne

Seine et Oise

Sevres (Deux)

So mme

Tarn

V ar

Vaucluse

Vendee

Vi enne

Vi enne (Haute)

V osges

Yonne

-2 -1 0 1 2

Ratio of 40ha Farms to 10ha Farms

-.1 -.05 0 .05 .1

Temperature Deviations in the Summer of 1792

Conditional on Geographic and Pre-1789 Historical Characteristics

Temperature Shocks in 1792 and Ratio of 40ha Farms to 10ha Farms in 1862

Ain

Ai sne

Al li er

Alpes (Basses)

Al pes (Hautes)

Al pes-Maritimes

Ardeche

Ardennes

Ariege

Aube

Aude

Aveyron

Bouches du Rhone

Calvados

Cantal

Charente

Charente-inferieure

Cher

Correze

Cote d'Or

Cotes du Nord

Creuse

Dordogne

Doubs

Drome

Eure

Eure-et-Loir

Finistere

Gard

Garonne (Haute-)

Gers

Gironde

Herault

Ille-et-Vilaine

Indre

Indre-et-Loire

Isere

Jura

Landes

Loir-et-Cher

Loi re

Loire (Haute)

Loire-Inferieure

Loiret

Lot

Lot-et-Garonne

Lozere

Maine-et-Loire

Manche

Marne

Marne (Haute)

Mayenne

Meurthe-et-Moselle

Meuse

Morbihan

Moselle

Nievre

Nord

Oise

Orne

Pas de Cal ais

Puy de Dome

Pyrenees (Basses)

Pyrenees (Hautes)

Pyrennees Orientales

Rhin (Bas)

Rhin (Haut)

Rhone

Saone (Haute)

Saone et Loire

Sarthe

Savoie

Seine

Seine Inferieure

Seine et Marne

Seine et Oise

Sevres (Deux)

So mme

Tarn

V ar

Vaucluse

Vendee

Vi enne

Vi enne (Haute)

V osges

Yonne

-1.5 -1 -.5 0 .5 1

Share of Farms above 20 Hectares

-.1 -.05 0 .05 .1

Temperature Deviations in the Summer of 1792

Conditional on Geographic and Pre-1789 Historical Characteristics

Temperature Shocks in 1792 and Share of Farms Above 20 Hectares in 1862

C. IV is th e Squared Deviation from

Temperature in Su mme r 1792

D. IV is th e Squared Devi ation from

Temperature in Su mme r 1792

Figure 6: Temperature Deviation in the Summer of 1792 and GDP per Capita in 1860 and 2010,

Controlling for Geographic Traits

Note: These …gure s depict the partial sca tterplots of the associati on between the sq uared deviation of

temperature in the summer of 1792 ( 1767-1791) on GDP per capita in 1860 (Panel A), GDP per capita in

2010 (Panel B), the ratio of farms above 40 ha to farms below 10 ha in 1862 (Panel C), as well as the ratio of

farms above 20 ha in 1862 (Panel D). Thus, the x- and y-axes plot the residuals obtained from r egressing the

share of émigrés i n the population against the squared deviations from temperature in the s ummer of 1792 ,

conditional on the geographic and historical set of covariates.

Table 1: Emigrés during the Revolution

Panel A. Départements with High and Low Emigration

Five départements with largest Five départements with smallest

Number of émigrés Share of émigrés Number of émigrés Share of émigrés

Moselle 3827 Alpes-Maritimes 1.26% Loire 105 Loire 0.04%

Pyrenees Orientales 3854 Bouches-du-Rhone 1.80% Hautes-Alpes 105 Hautes-Alpes 0.09%

Bouches-du-Rhone 5125 Var 1.96% Cher 239 Cher 0.11%

Var 5331 Pyrenees Orientales 3.48% Haute-Loire 271 Rhone 0.11%

Bas-Rhin 20510 Bas-Rhin 4.56% Indre 278 Haute-Loire 0.12%

Panel B. Social Groups

Nobles 23% Priests 34%

Upper Middle Class 10% Lower Middle Class 3%

Working Class 6% Peasants 7%

Unidenti…ed 17%

Panel C. Correlations between Social Groups

Priests Nobles Upper Middle Class Lower Middle Class Working Class

Nobles 0.62

Upper Middle Class 0.46 0.56

Lower Middle Class 0.54 0.50 0.80

Working Class 0.58 0.52 0.71 0.89

Peasants 0.53 0.35 0.43 0.76 0.86

Note : The data on the social categories of émigrés report ed in Panels B and C are only availab le

for 69 out of the 86 départeme nts in mainland France. In Panel C, the correlations a re between the

natural logarithm of the variables.

Source: Greer (1951).

Table 2: Property Ownership before and after the French Revolution in 15 Villages in the District

of Avesnes in the Nord Département

Ownership

Before After

the Revolution (%)

Peasants 33.52 44.18

Bourgeois 4.73 25.68

Nobility 37.08 14.35

Church 18.80 0.03

Poor Institutions and Hospitals 0.69 0.58

Commons* 5.18 15.80

Note: * Before the Revo lution, there was no clear ownership of the commons.

Source: Lefebvre (1924, Tableau II, pp.892-893).

Table 3: First-Stage Regressions: Squared and Absolute Deviations from Temp e rature in Summer 1792

(1) (2) (3) (4) (5) (6)

First stage: the instrumented variable is the Share of Em igres

Squared Devation from Temperature in Summer 1792 (1767-1791) 4.450*** 5.929*** 6.159***

[1.052] [1.393] [1.499]

Absolute Devation from Temperature in Summer 1792 (1767-1791) 2.365*** 2.612*** 2.590***

[0.497] [0.708] [0.770]

Geographic Controls No Yes Yes No Yes Yes

Historical Controls No No Yes No No Yes

F-stat (1st stage) 17.89 18.11 16.88 22.61 13.62 11.32

Observations 85 85 85 85 85 85

Note: This table reports the …rst stage of the 2SLS regressions where the IV is the squared deviatio n

of standardized summer temperature i n 1792 (columns (1)-(3)) or the absolute deviation of standardized

summer temperature in 1792 (columns (4)-(6)) and where the instrumented variable is the share of émigrés in

the population (the dependent variable in the second st age of the 2SLS regression is GDP per capita in 1860

as shown in Tables 4 and D.12). The speci…cations in columns (1) and (4) do not include controls, those in

columns (2) and (5) only include geogra phic controls, while those in columns (3) and (6) include all co ntrol s.

The dependent variable is in logarithm. Robust standard errors are reported in brackets. *** signi…cant at

the 1% level, ** at the 5% level, * at the 10% level.

Table 4: Emigrés on GDP per Capita (IV: Squared Deviation of Temp e rature in Summer 1792)

Panel A. GDP per capita 1860-1930

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

OLS OLS 2SLS 2SLS OLS OLS 2SLS 2SLS OLS OLS 2SLS 2SLS

GDP per capita 1860 GDP per capita 1901 GDP per capita 1930

Share of Emigres -0.0109 -0.0811*** -0.257*** -0.255*** -0.00861 -0.0681 -0.376** -0.376** 0.0340 -0.00614 -0.0532 -0.0505

[0.0322] [0.0304] [0.0853] [0.0749] [0.0388] [0.0534] [0.184] [0.181] [0.0289] [0.0288] [0.0542] [0.0443]

Adjusted R2 -0.011 0.585 -0.012 0.278 0.002 0.608

Geographical Controls No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes

Historical Controls No No No Yes No No No Yes No No No Yes

Observations 85 85 85 85 83 83 83 83 85 85 85 85

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 5.929*** 6.159*** 4.967*** 4.895*** 5.929*** 6.159***

in Summer 1792 (1767-1791) [1.393] [1.499] [1.267] [1.209] [1.393] [1.499]

F-stat (1st stage) 18.113 16.881 15.359 16.378 18.113 16.881

Panel B. GDP per capita 1995-2010

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

OLS OLS 2SLS 2SLS OLS OLS 2SLS 2SLS OLS OLS 2SLS 2SLS

GDP per capita 1995 GDP per capita 2000 GDP per capita 2010

Share of Emigres 0.0237 0.0478** 0.174*** 0.174*** 0.0238 0.0553** 0.201*** 0.196*** 0.0201 0.0493* 0.171*** 0.176***

[0.0195] [0.0212] [0.0525] [0.0541] [0.0199] [0.0222] [0.0600] [0.0617] [0.0225] [0.0254] [0.0602] [0.0607]

Adjusted R2 0.003 0.472 0.001 0.470 -0.005 0.466

Geographical Controls No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes

Historical Controls No No No Yes No No No Yes No No No Yes

Observations 86 86 86 86 86 86 86 86 86 86 86 86

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 5.950*** 6.216*** 5.950*** 6.216*** 5.950*** 6.216***

in Summer 1792 (1767-1791) [1.378] [1.487] [1.378] [1.487] [1.378] [1.487]

F-stat (1st stage) 18.647 17.476 18.647 17.476 18.647 17.476

Note: This table reports the e¤ec t of the s hare of émigrés in the population on GDP per capita in

1860, 1901, and 1930 (Panel A) and in 1995, 2000, and 2010 (Panel B) in OLS and 2SLS regressions. All

the de pendent variables are in logarithm. The IV in the …rst stage of the 2SLS regressions is the squared

standardized deviation from temper ature in the summer of 1792. Robust s tandard errors are reported in

brackets. *** signi…cant at the 1% level, ** at the 5% level, * at the 10% level.

Table 5: The E¤ect of Emigrés on the Value Added Per Capita and the Workforce in Agriculture, Industry

and Services, 1860-1990

Panel A. Value Added per Worker in Agriculture, Industry and Services

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

1860 Value Added per Worker in 1930 Value Added per Worker in 1982 Value Added per Worker in 1990 Value Added per Worker in

Agriculture Industry Services Agriculture Industry Services Agriculture Industry Services Agriculture Industry Services

Share of Emigres -0.444*** -0.178* -0.193*** -0.478*** -0.0272 -0.0434 0.531*** 0.603** 0.517** 0.694*** 0.628*** 0.521**

[0.129] [0.0965] [0.0630] [0.144] [0.0523] [0.0443] [0.185] [0.250] [0.224] [0.227] [0.240] [0.223]

Geographic controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 85 85 85 85 85 85 86 86 86 86 86 86

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.216*** 6.216*** 6.216***

in Summer 1792 (1767-1791) [1.499] [1.499] [1.499] [1.499] [1.499] [1.499] [1.499] [1.499] [1.499] [1.487] [1.487] [1.487]

F-stat (1st stage) 16.881 16.881 16.881 16.881 16.881 16.881 17.476 17.476 17.476 17.476 17.476 17.476

Panel B. Share of Workforce in Agriculture, Industry and Services

(1) (2) (3) (4) (5) (6) (7) (8) (9)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Share of Workforce in

Agriculture 1860 Industry 1860 Services 1860 Agriculture 1930 Industry 1930 Services 1930 Agriculture 2010 Industry 2010 Services 2010

Share of Emigres 0.0514 -0.321*** 0.201* -0.103 -0.130* 0.139** -0.787*** 0.168** 0.151***

[0.0669] [0.115] [0.104] [0.0968] [0.0743] [0.0641] [0.215] [0.0684] [0.0501]

Geographic controls Yes Yes Yes Ye s Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 85 85 85 85 85 85 86 86 86

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.216*** 6.216*** 6.216***

in Summer 1792 (1767-1791) [1.499] [1.499] [1.499] [1.499] [1.499] [1.499] [1.487] [1.487] [1.487]

F-stat (1st stage) 16.881 16.881 16.881 16.881 16.881 16.881 17.476 17.476 17.476

Panel C. Ratio of Value Added per Worker in Industry and Services to Agriculture

(1) (2) (3) (4)

2SLS 2SLS 2SLS 2SLS

Ratio of Value Added per Worker in Industry and Services to Agriculture

1860 1930 1982 1990

Share of Emigres 0.243* 0.440*** -0.000431 -0.147

[0.126] [0.147] [0.135] [0.136]

Geographic controls Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes

Observations 85 85 86 86

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.159*** 6.159*** 6.216*** 6.216***

in Summer 1792 (1767-1791) [1.499] [1.499] [1.487] [1.487]

F-stat (1st stage) 16.881 16.881 17.476 17.476

Note: This table reports the e ¤ec t of the share of émigrés i n the population on the value added per worker

in agriculture, industry, and services in 1860, 1930, and 1990 (Panel A) and the shares of the workforce in

agriculture, indus try, and services in 186 0, 1930, and 2010 (P anel B) in 2SLS regressions. All the depende nt

variables are in logarithm. The IV in the …rst stage of the 2SLS r egressions is the squared standardized

deviation from temperature in summer 1792. Robust standard errors are r eported in bracket s. *** signi…cant

at the 1% l evel, ** at the 5% level, * at the 10% level.

Table 6: Emigrés and the Mechanization of Agriculture, 1862

(1) (2) (3) (4) (5) (6) (7) (8)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Fe rtilizer Ploughs Scari…ers Grubbers Searchers Horse Hoes Harrows Ridgers

per Worker in Agricultural Sector, 1862

Share of Emigres -0.413*** -0.199 -1.893*** -2.568*** -1.229** -0.746 0.003 -0.535

[0.147] [0.131] [0.560] [0.766] [0.551] [0.467] [0.301] [0.466]

Geographic controls Yes Yes Yes Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes Yes Yes

Observations 85 85 85 85 85 85 85 85

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159***

in Summer 1792 (1767-1791) [1.499] [1.499] [1.499] [1.499] [1.499] [1.499] [1.499] [1.499]

F-stat (1st stage) 16.881 16.881 16.881 16.881 16.881 16.881 16.881 16.881

(9) (10) (11) (12) (13) (14) (15) (16)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Seeders Root Cutters Tedders Reapers Croppers Steam-Powered Threshers Animal-Powered Threshers First Principal Component of Agricultural Tools

per Worker in Agricultural Sector, 1862

Share of Emigres -1.268** -0.366 -1.873*** -0.997 -0.695 0.394 -0.454 -1.799**

[0.545] [0.434] [0.698] [0.872] [0.754] [0.368] [0.514] [0.748]

Geographic controls Ye s Yes Yes Yes Ye s Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes Yes Yes

Observations 85 85 85 85 85 85 85 85

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159*** 6.159***

in Summer 1792 (1767-1791) [1.499] [1.499] [1.499] [1.499] [1.499] [1.499] [1.499] [1.499]

F-stat (1st stage) 16.881 16.881 16.881 16.881 16.881 16.881 16.881 16.881

Note: This table reports the e¤ect of the share of émigrés in the population on t he number of agricultural instruments per agricultural worker in the

agricultural sector in 1862 in 2SLS regressions. All the dependent variables are in logarithm. The IV in the …rst stage of the 2SLS regressions is the squared

standardized deviation from temperature in summ er 179 2. Robust standard errors are reported in bracke ts. *** signi…cant at the 1% level, ** at the 5% level,

* at the 10% level.

Table 7: Emigrés and Electors in 1839 under the Censitory Regime of the July Monarchy

(1) (2) (3) (4) (5)

2SLS 2SLS 2SLS 2SLS 2SLS

Share of Electors Share of Landowners Share of Businessmen Share of Professionals Share of Civil Servants

in Department Population among Electors among Electors among Electors among Electors

Share of Emigres -0.546*** -0.101** 0.0917 0.147 0.425**

[0.168] [0.048] [0.098] [0.112] [0.172]

Geographic controls Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes

Observations 81 67 67 67 67

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 7.733*** 7.872*** 7.872*** 7.872*** 7.872***

in Summer 1792 (1767-1791) [1.514] [1.600] [1.600] [1.600] [1.600]

F-stat (1st stage) 26.093 24.195 24.195 24.195 24.195

Note: This table reports the e¤ect of the share of émigrés in the population on the share of voters in

the population and the shares of landowners, businessmen, professionals (i.e., lawyers and doctors), and civil

servants among those voters in 1839, under the censitory regime of King Louis Philippe (1830-1848), in 2SLS

regre ssi ons. All the dependent variables are in logarithm. The IV in the …rst st age of the 2SLS regressions is

the squared standardized deviation from temperature in summer 1792. Robust standard er rors are reported

in brackets. *** signi…cant at the 1% level, ** at the 5% level, * at the 10% l evel.

Table 8: Size Distribution of Private Landholdings over Time and Share of Commons in 1863

Panel A. Emigres and Land Distribution

(1) (2) (3) (4) (5) (6)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Share of Farms above Ratio of the Number of Farms Share of Commons

40ha, 1862 20ha, 1862 40 ha to 10 ha, 1862 50 ha to 10 ha, 1929 50 ha to 10 ha, 2000 1863

Share of Emigres -1.535*** -0.873*** -1.603*** -1.755*** -0.768*** 1.720**

[0.453] [0.290] [0.481] [0.494] [0.266] [0.811]

Geographic controls Yes Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes

Observations 86 86 86 86 86 84

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.216*** 6.216*** 6.216*** 6.216*** 6.216*** 5.131***

in Summer 1792 (1767-1791) [1.487] [1.487] [1.487] [1.487] [1.487] [1.221]

F-stat (1st stage) 17.476 17.476 17.476 17.476 17.476 17.657

Panel B. Temperature Shock in Summer 1792, Farms in 1862, 1929 and 2000, and Commons in 1863

(1) (2) (3) (4) (5) (6)

Reduced Form Regressions

Share of Farms above Ratio of the Number of Farms Share of Commons

40ha, 1862 20ha, 1862 40 ha to 10 ha, 1862 50 ha to 10 ha, 1929 50 ha to 10 ha, 2000 1863

Squared Deviation of Temperature -6.599** -3.172* -6.724** -10.19*** -4.419*** 11.91**

in Summer 1792 (1767-1791) [2.676] [1.830] [2.903] [3.254] [1.570] [5.677]

Share of Church Property Sold in Department 536.9* 351.8* 598.8* 405.4 345.3 -631.0

[280.0] [203.0] [320.0] [408.7] [259.3] [563.4]

Squared Deviation of Temperature -11,099*** -5,562* -12,117*** -8,537** -9,867*** -1,781

in Summer 1792 (1767-1791) * Share of Church Property Sold in Department [3,875] [2,969] [4,516] [4,191] [3,640] [6,594]

Adjusted R2 0.396 0.282 0.365 0.513 0.667 0.557

Observations 67 67 67 67 67 66

Note: Panel A of this table reports the e¤ect of the share of émigrés in the population on the share of farms above 40 ha and 20 ha in 1862 (col umns

(1)-(2)), on the ratio of farms above 40 ha to farms below 10 ha in 1862 (column (3)), on the ratio of farms above 50 ha to 10 ha in 1929 (column (4)) , on the

ratio of farms above 40 ha to 10 ha in 2000 (col umn (5)), and on the share of the commons w ithin the département in 1863 (column (6)) in 2SLS regressions.

All the dependent variables are in logarithm. The IV in the …rst stage of t he 2SLS regressions is the squared standardized deviation from temperature in

summer 1792. Panel B of this table reports reduced-form regressions that include the share of Church land sold during the Revolution i n the 67 départements

where this information is avai lable (Bodinier and Teyssier, 2000) and the int eraction between this variable and the IV, i.e., the squared standardized d eviat ion

from tempe rature in the summer of 179 2. Robust standard erro rs are reported in brackets. *** si gni…cant at the 1% level, ** at the 5% lev el, * at the 10 %

level.

Table 9: Emigrés and the Share of Illiterate Army Conscripts

(1) (2) (3) (4) (5) (6) (7) (8) (9)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Share of Illiterate Conscripts by Decade

1840s 1850s 1860s 1870s 1880s 1890s 1900s 1910s 1930s

Share of Emigres -0.330* -0.285 -0.260 -0.460* -0.342 -0.318 -0.543** -0.605** -0.343***

[0.185] [0.183] [0.214] [0.241] [0.250] [0.290] [0.254] [0.246] [0.109]

Geographic controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 84 84 86 86 84 84 84 84 86

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.834*** 6.834*** 6.216*** 6.216*** 5.131*** 5.131*** 5.131*** 5.131*** 6.216***

in Summer 1792 (1767-1791) [1.547] [1.547] [1.487] [1.487] [1.221] [1.221] [1.221] [1.221] [1.487]

F-stat (1st stage) 19.515 19.515 17.476 17.476 17.657 17.657 17.657 17.657 17.476

Note: This table reports the e¤ect of the shar e of émigrés in the populatio n on t he share of illiterate

French army cons cri pts, i.e., 20-year-old men who reported for military service in the d éparteme nt where their

father lived, in 2SLS regressi ons. All the dependent variables are in log arithm. The IV in the …rst stage of

the 2SLS regressions is the squared standardized deviation from temperature in the summer of 1792. Robust

standard errors are reported in brackets. *** signi…cant at the 1% level, ** at the 5% leve l, * at the 10% lev el.

Table 10: Emigrés and French Workers below Age 15 in the Agricultural Sector, 1929

(1) (2) (3) (4)

2SLS 2SLS 2SLS 2SLS

Share of Share of French agricultural workers below age 15 among Ratio of

the agricultural workforce agricultural workers agricultural workers below age 15 agricultural workers above age 15

Share of Emigres -1.087** -0.794** -0.833** -0.804**

[0.444] [0.394] [0.344] [0.403]

Geographic controls Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes

Observations 85 86 86 86

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.877*** 6.863*** 6.863*** 6.863***

in Summer 1792 (1767-1791) [1.685] [1.678] [1.678] [1.678]

F-stat (1st stage) 16.656 16.724 16.724 16.724

Note: This ta ble repo rts the e¤ect of the share of émigrés in the population on the share of French agricultural workers below age 15 among the agricultural

workforce (column (1)), agric ultural workers (column (2)), a gricultural workers below age 15 (column (3)), and agricultural work ers above age 15 (column (4))

in 2SLS regressions. Al l the dependent variables are in l ogarithm. The IV in the …rst stage of the 2SLS regressions is the squared standardized deviation from

temperature in the summer of 1792. Robust standard errors are reported in brackets. *** signi…cant at the 1% level, ** at the 5% level, * at the 10% l evel.

Table 11: Can Land Redistribution Explain the Impact of Emigrés?

(1) (2) (3) (4)

2SLS 2SLS 2SLS 2SLS

1860 Value Added per Worker in Agriculture GDP per capita 2010

Share of Emigres -0.444*** -0.224** 0.176*** 0.105**

[0.129] [0.0988] [0.0607] [0.0413]

Ratio of 40ha Farms to 10ha Farms, 1862 0.134*** -0.0441***

[0.0367] [0.0160]

Geographic controls Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes

Observations 85 85 86 86

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.159*** 7.309*** 6.216*** 7.446***

in Summer 1792 (1767-1791) [1.499] [1.405] [1.487] [1.403]

F-stat (1st stage) 16.881 27.071 17.476 28.147

Note: This table reports the e¤ect of the share of émigrés in the population on the value added per worker in a griculture in 1860 (as in Table 5) and on

GDP per ca pita in 2010 (as in Table 4), ac coun ting for the ratio of farms above 40 ha to farms below 10 ha in 1862 in columns (2 ) and (4) in 2SLS regressions.

All the dependent variables are in logarithm. The IV in the …rst stage of the 2SLS regre ssi ons is the square d standardized d eviat ion from temperatur e in the

summer of 1792. Robust standard errors are re ported in brackets. *** signi…cant at the 1% level, ** at the 5% leve l, * at the 10% level.

Appendix for Online Publication

A. Historical Background

A.1 The Origins of the French Revolution

Most historians now agree on the immediate causes of the French Revolution. The Old

Regime experienced a …scal crisis in the late 1780s, resulting mainly from the French support to

the American War of Independence and by an ine¢ cient tax system in need of reform. The c risis

was exacerbated by two consecutive years of bad harvests and peasant revolts in 1788 and 1789

(see, e.g., Aftalion (1990), Balla and Johnson (2009), Waldinger (2014), and Tackett (2015) for a

discussion).

However, the structural causes of the French Revolution are still debated. Some historians

emphasize the rise of the bourgeoisie, while others stress the con‡icts within the nobility and

the Third Estate (Furet (1978)). Such a debate is keenly related to the importance of ideas in

the unfolding of events and, in particular, to the violence of the French Revolution, leading to a

declaration of war against foreign countries and to internal con‡ict. As noted by Israel (2014),

there were revolts before and after the French Revolution which did not have major political and

economic consequences: it is therefore di¢ cult to argue that ideas would not p lay a role in the

deeper roots of the French Revolution and the outbreak of revolutionary violence. These ideas

include the development of a French national identity encouraged by the monarchy in the wake

of the defeat in the Seven Years’War (1756-1763) as well as the development over two centuries

of a national state with a centralized administration which gradually rendered local aristocrats,

who used to serve as local justice o¢ cers, costly and redundant (Tocqueville (1856)). These ideas

also relate to the Enlightenment philosophers and their revolutionary disciples. Enlightenment

philosophers dismissed revealed religions and criticized existing social and political hierarchies,

but they were oblivious to their optimistic faith in reason, nature and people.

When every

revolutionary thought that he represented the “people”, and that his actions were guided by the

“will of the people,”he then felt legitimized in using violence so that his revolutionary ideas would

prevail.

According to Furet (1978) this also explains the obsession of revolutionaries with trea-

sons and conspiracies: the revolution was inherently good, seen as freeing the entire population

from tyranny, and therefore, only hidden an d evil forces would oppose it. This “revolutionary

On the philosophy of Enlightenment, see, f or example, Cassirer (1932 [2009]) as well as Gay (1966 ) and Gay

(1969). On the relationship between Enlightenment philosophy and the revolution, see notably Mornet (1 933) and

Martin (2006), and speci…cally Koyré (1948) on Condorcet, the only Enlightenment p hilosopher who took an active

part in the Revolution.

For instance, in 1782, future revolutionary leader Jean-Louis Carra published a book where he advoc ated

violence to overthrow “superstition” and “tyranny” (Carra (1782)). Another telling example can be found in the

Instruction written to the s oldiers o n 26 Brumaire Year II ( November 16, 1793) by Comité du Salut Public m ember

Jean-Mar ie Collot d’Herbois as they quelled the revolt in Lyon: “Everything is permissible for those who act in

service of the revolution”(Tout est permis pour ceux qui agissent dans le sens de la révolution). On the repression

carried ou t by Collot d’Herbois in Lyon , see, for example, Palmer (1941) an d Biard (1995).

mentality” (Vovelle (1985)) may rationalize the revolutionaries’obsession with …nding culprits

and conspirators among their royalist opponents but also amidst the most devoted in their own

ranks.

A.2 The “Second Revolution”

During the summer of 1792, France experienced political turmoil and widespread agitation

that would lead to the collapse of the House of Bourbon. The Legislative Assembly had declared

war on April 20, 1792, against Austria. France attacked th e Aus trian Netherlands, but Prussia

joined forces with Austria and, at …rst, the French army su¤ered losses. These foreign armies were

thought to be preparing to invade France, and rumors spread among the Parisian population that

nobles and priests were plotting with the leaders of the foreign armies. Th e Brunswick Manifesto,

issued on July 25, 1792, by Charles William Ferdinand, Duke of Brunswick and commander of the

armies allied against France, heightened the tensions as it threatened that Parisian civilians would

be held personally respon sible and tried in a military court if the members of the French royal

family were harmed. While this measure was intended to intimidate the French revolutionaries,

it on ly galvanized them. On August 10; 1792, the radical Parisian sans-culottes, supported by

volunteers from Brittany and the South of France, attacked the King’s castle and jailed Louis

XVI and his family. As rumors of foreign invasion intensi…ed, aristocrats and priests who were

thought to be a part of the conspiracy against the revolution became targets of violence.

On September 2-6, 1792, the radical sans-culottes, who were mostly of bourgeois back-

ground, slaughtered aristocrats and clergy members who were imprisoned in the Parisian jails,

along with petty thieves and prostitutes (Soboul (1958)). Similar episodes of violence occurred

in various parts of France (Caron (1935), Bluche (1992), M arko¤ (1996)). Some of this violence

was caused by peasant revolts, which were exacerbated by the July 22, 1792, decree pertaining

to mass conscription, as well as by the June 18, 1792, and August 25, 1792, laws, which subor-

dinated the payments of feudal dues to the presentation of the primal titles (Peyrard (1996), pp.

107-114, Ado (1987 [2012]), pp. 311-322). The war took a di¤erent turn with the victory of the

French revolutionary army on September 20; 1792, at Valmy. T he following day, the monarchy

was abolished and the republic proclaimed. The trial of King Louis XVI began on December 11,

1792. On January 20, 1793, the members of the National Convention voted 380 to 310 in favor

of his execution, and he was guillotined the next day.

A.3 Pri mary School Provision during the 19th Century

Under the Old Regime, the French state barely intervened in primary schooling and let the

Church organize its own network of primary schools (Lebrun, Quéniart, and Venard (2003)). The

As revolutio nary leader Jacques -Pierre Br iss ot exclaimed in a 1791 speech: “We need great treasons” (Nous

avons besoin de grandes trahiso ns) (Brissot (1792)).

French Revolution harmed the Catholic school system, but the successive French rulers between

1799 and 1830 (Napoléon Bonaparte, 1799-1815, Louis XVIII, 1815-1824, Charles X, 1824-1830)

enabled the Church to (re-)develop its educational network. After 1830, the French political

regimes (the July Monarchy under King Louis-Philippe I, 1830-1848, the Second Republic, 1848-

1852, and the Second Empire, 1852-1870) were less favorable to the Church, but education laws

which were passed under those regimes fostered the development of Catholic schools. Thus,

François Guizot, who was King Louis-Philippe I’s prime minister, reshaped the organization of

schooling in France with the June 28, 1833, law that compelled all French communes to host

a primary school in their jurisdiction. This law enabled the Church to organize its own private

education system, but also to retain its control over public schooling. In particular, monks

and nuns could be employed as teachers in public schools while religious instruction remained

mandatory During the Second Republic, Education Minister Alfred de Falloux passed the March

15, 1850 law and the August 27, 1851, regulation that favored the Church since towns would

not have to fund a public school if a private (i.e., Catholic) school already operated in their

jurisdiction. Besides, all teachers had to ful…ll the duties prescribed by the Church. Finally,

Catholic secondary schools could compete with public secondary schools and could still receive

subsidies from the State and from the local governments.

Nonetheless, the political stance of the Catholic Church led to a con‡ict on education

against the French state which reached its apex after the establishment of the Third Republic in

1875. The Republicans, who opposed the Catholic Church for its support for the Royalist politi-

cians, …rst weakened the Catholic educational system in the 1880s and 1890s before separating

Church and State in 1905. See, for example, Mayeur (2003), Franck and Johnson (2016), and

Franck and Galor (2017) for recent studies on this issue.

B. Temperature Shocks, Wheat Prices, Local Violence, and E migrat ion

B.1. The Impact of Temperature Shocks on Wheat Prices

In late 18th-century France, there is ample anecdotal evidence suggesting that abnormal

weather conditions would negatively impact crops and in particular wheat production, which was

the main crop cultivated and consumed in most French départements (Kaplan (1984), Kaplan

(1996)). Late spring and summer climatic conditions are important determinants of the winter

wheat yields (Triticum aestivum), which is planted in the fall and harvested in the summer or

early autumn of th e following year.

When local markets are not perfectly integrated, local wheat prices are likely to respond to

local yield ‡uctuations, increasing the probability of social agitation when prices rise.

Anecdotal

On the growth and developme ntal st ages of wheat and the impact of weather conditions, see, for example,

Haun (1973) and Zadoks, Chang, and Konzak (1974).

On market integration (and la ck thereof) during the Revolut ion, see, for exa mple, Daudin (2010).

evidence from historians such as Soboul (1962) (pp.342-346) and Johnson (1986) (p.256) are

consistent with this reality.

Unfortunately, there are no comprehensive data on wheat prices

for 1792, but such data do exist for 1797-1800, that is, for the later p art of the Revolution

(Labrousse, Romano, and Dreyfus (1970)). This allows us to run panel-level regressions where

the price of wheat in each département is linked to the temperature shocks in the summer in that

département over the 1797-1800 period:

d;t

= 

+ 

d;t

+ u

d;t

;

where P

d;t

is the price of wheat in département d in year t, Z

d;t

is the temperature deviation

in département d in the summer of year t, 

and 

are département and year …xed e¤ec ts, and

d;t

is an error term for département d in year t. We consider several speci…cations for Z

d;t

including the squared and absolute deviation de …ned in the main text. For completeness, we also

constructed sep arate measures for positive (and negative) weather shocks to investigate whether

wheat prices di¤erentially respond to abnormally warm or cold summer temperatures.

We report the regression results in columns (1)-(5) of Table D.5. In the …rst column, our

explanatory variable is the squared deviation from s tandardize d temperature; this speci…cation

does not include département …xed e¤ects so as to highlight the source of variation in our identi…-

cation strategy. In columns (2)-(5) we include département-spec i…c constants to account for any

time-invariant département-level characteristics: the main explanatory variable is the squared

deviation from standardized temperature in column (2), the absolute deviation from standard-

ized temperature in column (3), the positive and negative squared deviations in column (4) and

the positive and negative absolute deviations in column (5).

Reassuringly, increas es in temperature shocks at th e département level led system atically

to higher wheat prices during the 1797-1800 period, consistent with an economy composed of

relatively fragmented markets where local weather ‡uctuations have material local economic

consequences. In Figure D.3 in Appendix D we plot the percentage change in yearly wheat prices

between 1797 and 1798 and the di¤erence in summer temperature shocks for the same period.

There is a c learly positive relationship.

B.2. The “Second Revolution”: Violence and Emigration during the Summer

of 1792

To provide some support to the narrative that emigration in a département was p artly

driven by local violence resulting from abnormal weather conditions, we test whether the tem-

perature shocks in the summer of 1792 are signi…cantly related to local riots during the “Second

In a study of th e Revolution in the South of France between 1789 and 1793, John son (1986 ) writes ( p.256):

“The great concentration of violent episodes occurred in March 1789, July and August 1789, July 1791, March and

April 1792, and July and August 1792. All occu rred in either the sp ring or summer and were for the most part

the results of poor ha rvest s and food shortages.”

Revolution.”For this purpose, we use the data from Marko¤ (1996), who provides information on

local riots in August and September 1792, which we aggregate at the level of the département. We

have information on 82 departements. The average département has 53:30 riots with a standard

deviation of 193:65, a minimum of 0, and a maximum of 1; 489. In the OLS regressions, R

the log of the number of riots in August and September 1792 in département d, and Z

d;1792

the squared (or abs olute) deviation of temperature in the summer of 1792:

= 

+ 

d;1792

+ X

: + v

where X

is a vector of economic, geographical, and institutional characteristics of département

d, and v

is an error term for département d.

We report the regression results in columns (6) and (7) of Table D.5 in Appendix D.

Larger temperature shocks at the département level in the summer of 1792 lead systematically to

more riots. Figure D.1 provides a graphical representation of the statistical association implied

by column (6) in Table D.5. The evidence uncovered regarding the robust impact of abn ormal

temperatures on wheat prices and peasant revolts during the in‡ection point of the French Rev-

olution, namely the summer of 1792, increases our con…dence regarding the plausibility of our

identi…cation strategy.

B.3 First-Stage Robustness Checks: Temperature Shocks in the Summer of

1792 and Emigration

Given our reliance on the credibility of temperature shocks as a plausible source of varia-

tion for emigration during the Revolution, we have performed a comprehens ive set of robustness

checks. First, we show that the weather conditions in the su mmer of 1792 are the critical tem-

perature shocks during the Revolution for understanding emigration. Second, in an attempt to

mitigate concerns that our instrument correlates mechanically with preexisting measures of de-

velopment (or other large-scale events after the end of the Revolution), we amass ed a multitude of

alternative indexes of social and economic signi…cance, failing to …nd any systematic association.

Speci…cally, emigration rates are explained neither by deviations from temperatures in the

spring, fall, or winter of 1792 in Table D.6, nor by deviations from temperatures in all the other

summers between 1788 and 1800 in Table D.7 and Figure D.2. Also, we show in Table D.8 that

squared and absolute deviations from standardized rainfall in the summer of 1792 do not explain

variations in the share of émigrés. In Table D.9 we report the …rst-stage relationship between

the squared temperature deviation in the s umm er of 1792 and the share of émigrés accounting

for spatial dependence in the error structure (Conley (1999)). Moreover, in Table D.10, we show

that our …rst-stage regression results are robust to using other baselines, such as a 50-year rolling

window based on summer temperatures between 1747 and 1791, a couple of …xed 25-year windows

(1751-1775 and 1776-1800), or a …xed 50-year window (1751-1800). Furthermore, in regressions

available upon request, we show that deviations from temperature in the summers from 1788 to

1800 do not systematically map into variations in the number of death sentences across France

during the 1793-1794 Reign of Terror (Greer (1935)).

We also test in regressions available u pon

request add itional speci…cations for the …rst-stage regression, …nding that measures of abnormal

temperatures other than the squared and absolute deviation of temperature in the summer of 1792

are less strongly correlated with the sh are of émigrés. In particular, we …nd that the one- sided

deviation of temperature is only weakly correlated with the share of émigrés, thus suggesting

that both higher and lower than average temperatures in the summer of 1792 contributed to the

‡ight of the émigrés.

Moreover, we provide in Tables D.11, D.19, and D.20 several tests in support of the plausi-

bility of the exclusion restriction. These tests are meant to show that our instrumental variable,

the summer of 1792 temperature shock, is not correlated with variables which may potentially

be correlated with emigration rates and the evolution of income per capita in the medium and

long run. In Panel A of Table D.11, we focus on violence before 1789 and after 1815, as proxied

by the “‡our war” of 1775, which is viewed as the last major s eries of riots triggered by bad

harvests and hunger before 1789 (Bouton (1993)), and by the post-1815 “white terror”when the

royalist regime of Lou is XVIII arrested and se ntenced to death some of their revolutionary and

Bonapartist opp one nts (Resnick (1966)). In Panel B of Table D.11, we examine the demands of

the French population in 1789 as expresse d in the cahiers de doléances (Hyslop (1934), Shapiro

and Marko¤ (1998)). We aggregate at the département level the numb er of times major political

and economic issues were mentioned in the cahiers de doléances.

Such issues include the ap-

proval of vote by head (a …rst step toward democratic voting which was in opposition to the vote

by order as was the case under the Old Regime), state intervention in education, tendency to

socialism, as well as the abolition of guilds, feudal dues, and serfdom. In Panel C of Table D.11,

we measure human capital before the Revolution proxied by the share of brides and grooms who

could sign their wedding contracts over the 1686-1690 and 1786-1790 periods (Furet and Ozouf

(1977)). Lastly, in Panel D of Table D.11, we assess the p resen ce of the clergy that was hostile

to the Revolution, and the numb er of famous aristocratic families. We use the data from Tackett

(1986) on the share of clergymen who refused to take the oath in support of the Constitution

Civile du Clergé in 1791. As Tackett (1986) shows, this piece of legislation, which was hostile to

the Catholic Church (Godechot (1951)), re‡ected not only the views of the local priests at the

We …nd that the unconditional relationship be tween tempera ture deviation in the summer of 1792 is signi…-

cantly and positively correlated at the 10% level with the share of death s entences d uring the Reign of Terror, but

that this is driven by the number of deat h sentences in one département, Loire-Inférieure.

Cahiers de doléances were redacted at the level of the baillage, which was an administra tive division of France

under the Ancien Régime.

start of the Revolution but also those of the laypeople who pressured priests to accept or reject

the oath, thereby providing a measure of the religiosity of the local population. In addition, we

use information on the most prestigious noble families, as listed in the Almanach de Saxe Gotha,

in 1750, which can be viewed as proxying for the higher ends of the stock of regional political

and economic power (Squicciarini and Voigtländer (2015)).

In Table D.19, we show that temperature sho cks in the summer of 1792 are not correlated

with variables that proxy religiosity during the long 19th century (see Franck and Johnson (2016)

for a discussion): these are the number of religious communities in each département devoted to

education, charity, and solely to religious purposes in 1856 (from the 1856 French census), as well

as the s hare of representatives in the lower house of Parliament who voted against the separation

of Church and State in 1905 (Franck (2010)). Finally, we examine in Table D.20 whether our

instrument is correlated with the spread of the phylloxera in 1875 and 1890, a disease which was

harmful to vine roots but also to the health of the people living in the regions hit more harshly

(Banerjee, Du‡o, Postel-Vinay, and Watts (2010)).

All in all, while information prior to 1789 at the département level on the number of

priests, large landown ers, and land distribution is missing,

the results reported in Tables D.11,

A.19, and D.20 are reassuring since none of the potentially important variables is correlated with

our instrume nt. Indeed, if our instrument was systematically correlated with an economic and

political factor related to the land distribution or the composition of the population before the

Revolution, such a correlation would likely have been re‡ected in these observed traits including a

culture of violence before, during, and after the Revolution, complaints in the cahiers de doléances,

prerevolutionary human capital, local religiosity, and proxies for the presence of local elites.

The data of Fu ret and Ozouf (1977 ) and Squicciarini a nd Voigtländer (2015) do not cover all the French

départements and cannot therefore be included as part of the historica l contro ls in our baseline regressions.

W hil e some attempts were made to survey the French population under the Old Regime, it was only un-

der Napoleon Bonaparte’s rule in 1801 that t he …rst systematic count of the French population was undertaken

(Dupâquier and Dupâquier (1985)). S till, it was only in 1851 that a survey o¤ered for the …rst time systematic

information on the professions of the inhabitants at the local level. Moreover, t he cadastre, which registered prop-

erty ownership at the local level, was also given an impulse under Napoleon Bonaparte’s rule in 1807 but was only

completed in 1850 (Bloch (1929)).

D. Figures and Tables

Ain

Aisne

Allier

Alpes (Basses)

Alpes-Mar itimes

Ardec he

Ardennes

Ariege

Aube

Av eyron

Bouches du Rhone

Calvados

Cantal

Charente

Charente-inferieure

Cher

Correze

Cote d'Or

Cotes du Nord

Creuse

Dordogne

Doubs

Eure

Eure-et-Loir

Finistere

Gard

Garonne (Haute-)

Gers

Gironde

Herault

Ille-et-Vilaine

Indre

Indre-et-Loire

Isere

Jura

Landes

Loir-et-Cher

Loire

Loire (Haute)

Loire-Inferieure

Loiret

Lot

Lot-et-Garonne

Lozere

Maine-et-Loire

Manche

Marne

Marne (Haute)

Mayenne

Meurthe-et-Moselle

Meuse

Morbihan

Moselle

Nievre

Nord

Oise

Orne

Pas de Calais

Puy de Dome

Pyrenees (Basses)

Pyrenees (Hautes)

Pyrennees Orientales

Rhin (Bas)

Rhin (Haut)

Rhone

Saone (Haute)

Saone et Loire

Sarthe

Seine

Seine Inferieure

Seine et Marne

Seine et Oise

Sevres (Deux)

So m me

Tarn

Var

Vaucluse

Vendee

Vienne

Vienne (Haute)

Vosges

Yonne

-4 -2 0 2 4

Temperature Shock in the Summer of 1792

-.1 -.05 0 .05 .1

Riots in August & September 1792

Conditional on Geographic and Historical Characteristics

Temperature Shocks in Summer 1792 and Department-Level Violence in Summer 1792

IV is the Squared Deviati on from Temperature in Summ er 1792

Figure D.1: Temperature Deviation in Summer 1792 and Local Violence in Summer 1792, Con-

trolling for Geographic and Historical Characteristics

Note: This …gure depi cts the partial scatterplot of the e¤ect of temperature shocks in the summer of 1792

on the logarithm of the number of riots in August and September 1792 in each French département. Thus,

the x- and y- axes plot the residuals obtained from regressing the logarithm of the number of riots in August

and Septembe r 1792 against the square d deviation from temperature in the summer of 1792, conditional on

geographic and historical controls.

Finist er e

Cot es du Nor d

M orbihan

I lle - e t - V ila in e

M anche

Loir e- I nf er ieur e

M ayenne

Vendee

M aine- et - Loir e

Calvados

Sevres ( Deux)

O rne

Sart he

Char ent e- inf er ieur e

Vienne

I ndr e- et -Loir e

Char ent e

Eure

Seine I nfer ieur e

G ir onde

Eure- et -Loir

Loir - et -Cher

Vienne ( Haut e)

Dor dogne

Seine et O ise

Indre

Pas de Calais

Somme

O ise

Nord

Seine

Ais ne

Lot - et -G aronne

Loir et

Seine et M arne

Landes

Creuse

M arne

Cher

Cor r ez e

Yonne

Ardennes

Aube

Nievre

G ers

M euse

Lot

M o s e lle

M eurt he- et -M oselle

A llie r

M arne ( Haut e)

Cot e d'O r

Rhone

Saone et Loir e

G aronne ( Haut e- )

Vosges

Pyrenees (Basses)

Puy de Dom e

Cant al

Loir e

Rhin ( Bas)

Saone ( Haut e)

Pyrenees ( Haut es)

Isere

Ariege

Rhin (Haut)

A in

Jura

Tarn

Loir e ( Haut e)

Doubs

Aveyron

Alpes ( Haut es)

Drome

Lozer e

Ardeche

Aude

Savoie

Pyrennees O rient ales

Alpes ( Bas ses)

Her ault

Vaucluse

G ard

Bouches du Rhone

Var

Alpes- M ar it im es

-2 -1 0 1 2 3

Share of Emigres in Department Population

-1 -.5 0 .5 1 1.5

Summer Temperature Shock, 1788

coef = .15870935, (robust) se = .185636, t = .85

Nord

Pas de Calais

Ais ne

Ardennes

Somme

O ise

M arne

Seine I nfer ieur e

M anche

M o s e lle

Calvados

Seine et M arne

Seine

Seine et O ise

M euse

Eure

O rne

Cot es du Nor d

M eurt he- et -M oselle

Rhin ( Bas)

Eure- et -Loir

Aube

Finist ere

I lle - e t - Vila in e

M ayenne

Sart he

Loir et

M orbihan

Yonne

M arne ( Haut e)

VosgesLoir - et -Cher

M aine- et - Loir e

Loir e- I nf er ieur e

Rhin (Haut)

I ndre- et- Loir e

Cot e d'O r

Vendee

Saone ( Haut e)

Cher

Sevres ( Deux)

Nievre

Vienne

Indre

Char ent e- inf er ieur e

Doubs

A llie r

Char ent e

Vienne ( Haut e)

Saone et Loir e

Creuse

Puy de Dom e

Jura

G ir onde

Dor dogne

Cor r ez e

Loir e ( Haut e)

Loir e

Cant al

Lot - et -G aronne

G ard

Landes

Lozer e

Ardeche

Lot

Rhone

Bouches du Rhone

Alpes- M ar it im es

G ers

Var

A in

G aronne ( Haut e- )

Aveyron

Vaucluse

Her ault

Tarn

Ariege

Savoie

Pyrenees ( Haut es)

Pyrenees (Basses)

Aude

Alpes ( Bas ses)

Pyrennees O rient ales

Drome

Isere

Alpes ( Haut es)

-2 -1 0 1 2 3

Share of Emigres in Department Population

-1 0 1 2 3

Summer Temperature Shock, 1789

coef = -.12435885, (robust) se = .0859038, t = -1.45

Vendee

I ndre- et- Loir e

Rhin ( Bas)

M eurt he- et -M oselle

Yonne

M arne ( Haut e)

Sevres ( Deux)

Loir - et -Cher

Loir e- I nf er ieur e

M aine- et - Loir e

Vosges

Cher

Char ent e- inf er ieur e

Vienne

Aube

Loir et

Cot e d'O r

Indre

M euse

Sart he

M o s e lle

M orbihan

Char ent e

Nievre

Rhin (Haut)

M ayenne

I lle - e t - V ila in e

Eure- et- Loir

G ir onde

Saone ( Haut e)

Cot es du Nor d

Vienne ( Haut e)Finist ere

A llie r

Seine et M arne

M arne

O rne

Seine et O ise

Creuse

Dor dogne

Seine

Eure

Lot - et -G aronne

Cor r ez e

Landes

Puy de Dom e

Seine I nfer ieur e

Calvados

Saone et Loir e

M anche

Doubs

O ise

Ardennes

G ers

Ais ne

Cant al

Somme

Lot

Loir e ( Haut e)

Loir e

Jura

Pas de Calais

G aronne ( Haut e- )

Lozer e

Nord

Rhone

Pyrenees (Basses)

G ard

Ardeche

Pyrenees ( Haut es)

Aveyron

A in

Ariege

Tarn

Bouches du Rhone

Her ault

Aude

Vaucluse

Pyrennees O rient ales

Drome

Savoie

Var

Alpes- M ar it im es

Isere

Alpes ( Bas ses)

Alpes ( Haut es)

-2 -1 0 1 2 3

Share of Emigres in Department Population

-.5 0 .5 1

Summer Temperature Shock, 1790

coef = .05017074, (robust) se = .36909154, t = .14

Seine et O ise

I ndr e- et -Loir e

Char ent e- inf erieur e

Eure- et -Loir

Seine I nfer ieur e

Seine

Sevres ( Deux)

Eure

Loir - et -Cher

O ise

Somme

G ir onde

Pas de Calais

Loir et

Char ent e

Seine et M arne

Vienne

Sart he

O rne

Nord

M aine- et - Loir e

Vendee

Ais ne

M ayenne

Calvados

Landes

Dor dogne

Loir e- I nf er ieur e

Lot - et -G aronne

M arne

I lle - e t - V ila in e

Yonne

M anche

Ardennes

Aube

Pyrenees (Basses)

Vienne ( Haut e)

M orbihan

Indre

G ers

Pyrenees ( Haut es)

Cot es du Nor d

M euse

Cor r ez e

Cher

Finist ere

Lot

Creuse

G aronne ( Haut e- )

M o s e lle

M arne ( Haut e)

Nievre

Cant al

Cot e d'O r

M eurt he- et -M oselle

Puy de Dom e

A llie r

Ariege

Tarn

Vosges

Aveyron

Lozer e

Loir e ( Haut e)

G ard

Saone et Loir e

Saone ( Haut e)

Aude

Her ault

Pyrennees O rient ales

Doubs

Loir e

Ardeche

Jura

Rhin (Haut)

Rhin ( Bas)

Bouches du Rhone

Vaucluse

A in

Rhone

Drome

Savoie

Alpes- M ar it im es

Var

Isere

Alpes ( Haut es)

Alpes ( Bas ses)

-2 -1 0 1 2 3

Share of Emigres in Department Population

-.2 0 .2 .4

Summer Temperature Shock, 1791

coef = .43920668, (robust) se = .66653364, t = .66

Cant al

Aube

Tarn

Nievre

M ar ne

Pas de Calais

Ariege

Ais ne

A llie r

Aveyron

Puy de Dom e

Cher

Nord

Somme

Yonne

Aude

Creuse

Rhone

G aronne ( Haut e- )

O ise

Loir e

Cor r ez e

M euse

Saone et Loir e

Pyrennees O rient ales

Cot e d'O r

Ardennes

Seine et M arne

Isere

M arne ( Haut e)

Lot

Indre

Loir e ( Haut e)

Lozer e

Seine

Drome

Her ault

Alpes ( Haut es)

Pyrenees ( Haut es)

Vienne ( Haut e)

M o s e lle

Seine et O ise

Ardeche

Loir et

A in

G ers

M eurt he- et -M oselle

Vosges

Saone ( Haut e)

Pyrenees (Basses)

Jura

Dor dogne

Savoie

Seine I nfer ieur e

Lot - et -G aronne

G ard

Loir - et -Cher

Doubs

Vaucluse

Eure- et -Loir

Eure

Landes

Vienne

Rhin (Haut)

I ndre- et- Loir e

Char ente

Rhin ( Bas)

Alpes ( Bas ses)

G ir onde

Bouches du Rhone

O rne

Sart he

Calvados

Sevres ( Deux)

Char ent e- inf er ieur e

M ayenne

M aine- et - Loir e

Var

M anche

Alpes- M ar it im es

Vendee

I lle - e t - V ila in e

Loir e- I nf er ieur e

Cot es du Nor d

M orbihan

Finist ere

-2 -1 0 1 2 3

Share of Emigres in Department Population

-.1 0 .1 .2 .3

Summer Temperature Shock, 1792

coef = 4.3363774, (robust) se = 1.050517, t = 4.13

Nord

M aine- et - Loir e

Vendee

M ayenne

Calvados

Pas de Calais

O rneLoir e- I nf er ieur e

Sevres ( Deux)

Ais ne

Sart heSomme

M orbihan

Char ent e- inf er ieur e

I lle - e t - V ila in e

Seine I nfer ieur e

Finist ere

Vienne

O ise

M anche

Eure

Cot es du Nor d

Char ent e

I ndr e- et -Loir e

Ardennes

G ir onde

Vienne ( Haut e)

Eure- et -Loir

Seine et M arne

Seine et O ise

Seine

M arne

Loir - et -Cher

Dor dogne

Indre

M euse

Lot - et -G aronne

Creuse

M o s e lle

Loir et

Cor r ez e

Landes

M eurt he- et -M oselle

Aube

Cher

G ers

Yonne

Rhin ( Bas)

M arne ( Haut e)

Lot

Vosges

Puy de Dom e

Cant al

A llie r

Nievre

Cot e d'O r

Saone ( Haut e)

G aronne ( Haut e- )

Rhin (Haut)

Loir e ( Haut e)

Saone et Loir e

Pyrenees ( Haut es)

Pyrenees (Basses)

Doubs

Ariege

Loir e

Tarn

Aveyron

Jura

Lozer e

Rhone

Aude

Ardeche

G ard

A in

Pyrennees O rient ales

Her ault

Bouches du Rhone

Var

Vaucluse

Drome

Alpes ( Bas ses)

Isere

Alpes- M ar it im es

Savoie

Alpes ( Haut es)

-2 -1 0 1 2 3

Share of Emigres in Department Population

-2 0 2 4

Summer Temperature Shock, 1793

coef = -.00209229, (robust) se = .08628495, t = -.02

M aine- et - Loir e

G ers

Char ente

I lle - e t - V ila in e

Lot -et -G ar onne

Sevres ( Deux)

Cot es du Nor d

M ayenne

Vienne

Dor dogne

Char ent e- inf erieur e

M anche

Loir e- I nf er ieur e

G ir onde

M orbihan

Ariege

Pyrenees ( Haut es)

G aronne ( Haut e- )

Pyrennees O rient ales

Vendee

Finist ereVienne ( Haut e)

Landes

Aude

I ndr e- et -Loir e

Lot

Sart he

Tarn

Calvados

Cor r ez e

Pyrenees (Basses)

Indre

O rne

Aveyron

Creuse

Her ault

Loir - et -Cher

Cant al

Eure

Seine I nfer ieur e

Lozer e

Eure- et -Loir

Cher

Puy de Dom e

G ard

Seine et O ise

Loir et

A llie r

Loir e ( Haut e)

Seine

Somme

O ise

Pas de Calais

Ardeche

Bouches du Rhone

Vaucluse

Alpes ( Bas ses)

Alpes ( Haut es)

Seine et M arne

Nievre

Drome

Var

Alpes- M ar it im es

Nord

Ais ne

Yonne

Savoie

Isere

Loir e

A in

Jura

Saone et Loir e

Cot e d'O r

Rhone

Aube

M arne

Doubs

M arne ( Haut e)

Saone ( Haut e)

Ardennes

M euse

Vosges

M eurt he- et -M oselle

M o s e lle

Rhin (Haut)

Rhin ( Bas)

-2 -1 0 1 2 3

Share of Emigres in Department Population

-.5 0 .5 1 1.5

Summer Temperature Shock, 1794

coef = .29518282, (robust) se = .22905571, t = 1.29

Alpes- M ar it im es

Var

Alpes ( Bas ses)

Alpes ( Haut es)

Bouches du Rhone

Isere

Savoie

Vaucluse

Drome

A in

Jura

Doubs

Her ault

Pyrennees O rient ales

G ard

Ardeche

Rhin (Haut)

Rhone

Aude

Rhin ( Bas)

Saone ( Haut e)

Lozer e

Aveyron

Loir e ( Haut e)

Loir e

Tarn

Vosges

Saone et Loir e

Ariege

Cant al

Puy de Dom e

M eurt he- et -M oselle

G aronne ( Haut e- )

Creuse

A llie r

Cor r ez e

Lot

Cot e d'O r

Vienne ( Haut e)

Pyrenees ( Haut es)

M o s e lle

M arne ( Haut e)

G ers

Dor dogne

Pyrenees (Basses)

Indre

Nievre

Lot - et -G aronne

Char ent e

Vienne

M euse

Cher

Sevres ( Deux)

Landes

G ir onde

Char ent e- inf er ieur e

Nord

Vendee

Loir e- I nf er ieur e

Ais ne

I lle - e t - V ila in e

Cot es du Nor d

Ardennes

M aine- et - Loir e

M arne

M orbihan

I ndr e- et -Loir e

M anche

Aube

Yonne

M ayenne

Pas de Calais

Finist ere

Somme

Loir - et -Cher

O ise

Sart he

Seine et M arne

Calvados

Loir et

O rne

Seine I nfer ieur e

Seine

Seine et O ise

Eure

Eure- et -Loir

-2 -1 0 1 2 3

Share of Emigres in Department Population

-1 -.5 0 .5 1

Summer Temperature Shock, 1795

coef = -.18221759, (robust) se = .194968, t = -.93

Alpes- M ar it im es

Var

Alpes ( Bas ses)

Alpes ( Haut es)

Bouches du Rhone

Rhin ( Bas)

Savoie

Vaucluse

Rhin (Haut)

Doubs

Isere

Drome

Jura

Nord

M eur t he-et -M oselle

M o s e lle

Vosges

Saone ( Haut e)

G ard

A in

Ardennes

Her ault

Ais ne

Ardeche

Pas de Calais

M euse

Pyrennees O rient ales

Lozer e

M arne ( Haut e)

Loir e ( Haut e)

Cot e d'O r

Somme

M arne

Saone et Loir e

Aude

Rhone

Aveyron

Loir e

Puy de Dom e

O ise

A llie r

Tarn

Aube

Cant al

Nievre

Cher

Seine et M arne

Creuse

Ariege

Yonne

Indre

Seine

Cor r ez e

Seine I nfer ieur e

Seine et O ise

Vienne ( Haut e)

G aronne ( Haut e- )

Lot

Loir et

Pyrenees ( Haut es)

Pyrenees (Basses)

Eure

Vienne

Loir - et -Cher

Dor dogne

G ers

Eure- et -Loir

M anche

Lot - et -G aronne

I ndr e- et -Loir e

Calvados

Char ent e

O rne

M ayenne

Landes

Sevres ( Deux)

I lle - e t - V ila in e

Sart he

M aine- et - Loir e

Loir e- I nf er ieur e

Cot es du Nor d

Char ent e- inf erieur e

G ir onde

Vendee

M orbihan

Finist ere

-2 -1 0 1 2 3

Share of Emigres in Department Population

-1 -.5 0 .5 1

Summer Temperature Shock, 1796

coef = -.33015176, (robust) se = .19397246, t = -1.7

Jura

Her ault

Rhin (Haut)

Vaucluse

Doubs

Rhin ( Bas)

Lozer e

Pyrennees O rient ales

Saone ( Haut e)

Ardeche

Loir e ( Haut e)

Aude

G ard

A in

Aveyron

Vosges

Alpes ( Bas ses)

Cant al

Puy de Dom e

M eurt he- et -M oselle

Tarn

Savoie

Ariege

Drome

M o s e lle

Bouches du Rhone

Creuse

Nord

Loir e

Cor r ez e

Saone et Loir e

A llie r

M arne ( Haut e)

Cot e d'O r

G aronne ( Haut e- )

Lot

Alpes ( Haut es)

Ais ne

M euse

Rhone

Alpes- M ar it im es

Pyrenees ( Haut es)

Ardennes

Vienne ( Haut e)

Isere

Indre

Pas de Calais

G ers

Cher

Pyrenees (Basses)

Nievre

Dor dogne

M arne

Var

Lot - et -G aronne

Somme

O ise

Landes

Vienne

Aube

Char ent e

Yonne

Seine et M arne

G ir onde

Seine

Char ent e- inf erieur e

I ndr e- et -Loir e

Sevres ( Deux)

Seine et O ise

Loir - et -Cher

Seine I nfer ieur e

Loir et

Vendee

Eure

M aine- et - Loir e

Eure- et -Loir

Loir e- I nf er ieur e

Sart he

M ayenne

I lle - e t - V ila in e

O rne

Calvados

M anche

M orbihan

Cot es du Nor d

Finist ere

-2 -1 0 1 2 3

Share of Emigres in Department Population

-.5 0 .5 1

Summer Temperature Shock, 1797

coef = .16653201, (robust) se = .19541229, t = .85

Alpes ( Haut es)

Isere

Drome

Savoie

Alpes ( Bas ses)

Rhone

A in

Saone et Loir e

Loir e

Vaucluse

Jura

Nievre

Cot es du Nor d

Alpes- M ar it im es

Finist ere

Rhin (Haut)

Doubs

I lle - e t - V ila in e

Saone ( Haut e)

Loir e- I nf er ieur e

Rhin ( Bas)

M orbihan

Var

Vosges

Cot e d'O r

A llie r

M anche

Ardeche

M aine- et - Loir e

Sevres ( Deux)

Vendee

M ayenne

Vienne

M eurt he- et -M oselle

Indre

Char ent e

M arne ( Haut e)

Vienne ( Haut e)

I ndr e- et -Loir e

Char ent e- inf erieur e

Cher

Sart he

Calvados

Creuse

M o s e lle

Dor dogne

O rne

Yonne

Loir - et -Cher

Lot

Cor r ez e

Bouches du Rhone

Tarn

Puy de Dom e

Aube

Lot - et -G aronne

Loir e ( Haut e)

Aveyron

M euse

G ir onde

Loir et

Aude

Eure- et -Loir

G aronne ( Haut e- )

Cant al

Ariege

Pyrennees O rient ales

Eure

G ers

Her ault

Lozer e

M arne

Landes

Seine I nfer ieur e

Seine et M arne

Seine et O ise

Seine

G ard

Ardennes

Pyrenees ( Haut es)

Pyrenees (Basses)

O ise

Ais ne

Somme

Pas de Calais

Nord

-2 -1 0 1 2 3

Share of Emigres in Department Population

-.5 0 .5

Summer Temperature Shock, 1798

coef = .11270558, (robust) se = .38155305, t = .3

Alpes ( Haut es)

Isere

Savoie

Alpes- M ar it im es

Alpes ( Bas ses)

Rhin ( Bas)

Drome

Rhone

M o s e lle

Rhin (Haut)

M eur t he-et -M oselle

Ardennes

M euse

Vosges

Var

Nord

M ar ne

M anche

Saone ( Haut e)

A in

M arne ( Haut e)

Doubs

Aube

Ais ne

Vaucluse

Cot es du Nor d

Finist ere

Calvados

Saone et Loir e

Cot e d'O r

Loir e

Pas de Calais

Seine et M arne

Jura

Somme

I lle - e t - V ila in e

O rne

Yonne

O ise

Eure

M orbihan

Seine I nfer ieur e

Nievre

M ayenne

Seine

Seine et O ise

Eure- et -Loir

Bouches du Rhone

Loir et

Sart he

Ardeche

Loir e- I nf er ieur e

M aine- et - Loir e

A llie r

Loir - et -Cher

I ndr e- et -Loir e

Vendee

Loir e ( Haut e)

Cher

Sevres ( Deux)

Puy de Dom e

G ard

Vienne

Indre

Char ent e- inf erieur e

Lozer e

Char ent e

Creuse

Vienne ( Haut e)

Cant al

Her ault

Cor r ez e

Dor dogne

Aveyron

Tarn

Lot

Aude

G ir onde

Pyrennees O rient ales

Lot - et -G aronne

G aronne ( Haut e- )

Ariege

G ers

Landes

Pyrenees ( Haut es)

Pyrenees (Basses)

-2 -1 0 1 2 3

Share of Emigres in Department Population

-4 -2 0 2 4

Summer Temperature Shock, 1799

coef = -.07915441, (robust) se = .05597598, t = -1.41

Figure D.2: Unconditional Correlation between the Squared Deviation from Tempe rature in

Summers 1788-1799 and the Share of Emigrés in the Population

Note: This …gure graphs the rel ationship between the squared deviatio n f rom sta ndardized temperature in

all the summers between 1788 and 1799 and the share of émigrés in the population. It shows that the ne gative

and signi…cant relationship between the squared deviation from standardized temperature in the summer of

1792 and the share of émigrés does not hold for any other summer between 1788 and 1799.

AIN

AISNE

ALLIER

BASSES-ALPES

HAUTES-ALPES

ARDECHE

ARDENNES

ARIEGE

AUBE

AUDE

AVEYRON

BOUCHES-DU-RHONE

CALVADOS

CANTAL

CHARENTE

CHARENTE-INFERIEURE

CHER

CORREZE

COTE-D'OR

COTES-DU-NORD

CREUSE

DORDOGNE

DOUBS

DROME

EURE

EURE-ET-LOIR

FINISTERE

GARD

HAUTE-GARONNE

GERS

GI RONDE

HERAULT

ILLE-ET-VILAINE

INDRE

INDRE-ET-LOIRE

ISERE

JURA

LANDES

LOIR-ET-CHER

LOIRE

HAUTE-LOIRE

LOIRE-INFERIEURE

LOIRET

LOT

LOT-ET-GARONNE

LOZERE

MAINE-ET-LOIRE

MANCHE

MARNE

HAUTE-MARNE

MAYENNE

MEURTHE

MEUSE

MORBIHAN

MOSELLE

NIEVRE

NO RD

OISE

ORNE

PAS-DE-CALAIS

PUY-DE-DOME

BASSES-PYRENEES

HAUTES-PYRENEES

PYRENEES-ORIENTALES

BAS-RHIN

HAUT-RHIN

RHONE

HAUTE-SAONE

SAONE-ET-LOIRE

SARTHE

SEINE

SEINE-INFERIEURE

SEINE-ET-MARNE

SEINE-ET-OISE

DEUX-SEVRES

SOMME

TARN

VAR (SAUF GRASSE 39-47)

VAUCLUSE

VENDEE

VIENNE

HAUTE-VIENNE

VOSGES

YONNE

-.4 -.2 0 .2

Percentage Change in Wheat Prices Between 1797 and 1798

-1 -.5 0 .5 1

Change in Summer Temperature Shocks Between 1797 and 1798

Unconditional Relationship

Wheat Prices Changes and Differences in Summer Temp. Shocks 1797-1798

W heat Price Changes, 1797-1798

Figure D.3: Wheat Price Changes and Di¤erences in Summer Temperature Shocks, 1797-1798

Note: This …gure gra phs the relationship between the change in the summe r t emperature shocks between

1797 and 1800 and the percent change in wheat prices between 1797 and 1798.

-.05 0 .05 .1 .15 .2

1841

1851

1861

1811

1821

1831

1871

1881

1891

1901

-.05 0 .05 .1 .15

1841

1851

1861

1871

1811

1821

1831

1881

1891

1901

A. Fertility B. In fant Mortality

Figure D.4: Emigres, Fertility, and Infant Mortality, 1811-1936

Note: This graph displays the es timated coe¢ cients of t he share of émigrés on f ertility and infant mortality between 1811 and 1901 in 2SLS regressions

where the IV is the squared deviation from te mperature in the summer o f 1792. All the dependent variables are in logarithms. Intervals re‡ect 95% con…dence

levels.

-.1 0 .1 .2 .3 .4

1968

1975

1982

1990

1999

2010

0 .2 .4 .6

1968

1975

1982

1990

1999

2010

A. Share of High-School Graduates among

Men Ages 16-24, 1968- 2010

B. Shar e of College Graduates among

Men Ages 16-24, 1968- 2010

Figure D.5: Emigres and the Human Capital of Frenchmen Ages 16-24, 1968-2010

Note: This graph displays the estimated coe¢ cients of the share of émigrés on the share of high school graduate s among men ages 16-24 and on the share

of college graduates among men ages 16-24, 1968-2010 in 2SLS regressi ons. The IV is the squared deviation from temperature in the summer of 1792. All the

depende nt variables are in logarithms. Int ervals re‡ect 95% con…d ence levels.

Table D.1: Average Farm Size in France in 1862 and in the USA in 1860

Observations Mean Median Std.Dev. Min. Max.

Average Farm Size, France, 1862

Average Farm Size 88 23.12 18.12 13.14 4.57 62.83

Average Farm Size, Above Median Temperature Shock in Summer 1792 43 27.35 25.98 14.39 7.97 62.83

Average Farm Size, Below Median Temperature Shock in Summer 1792 45 17.02 19.08 10.46 4.57 49.80

Average Farm Size, Above Median Wheat Production 1862 44 29.86 28.51 13.20 8.56 62.83

Average Farm Size, Below Median Wheat Production 1862 44 16.38 14.47 9.05 4.57 49.27

Average Farm Size, USA, 1860

Average Farm Size 1944 336.17 562.54 218.64 10.78 15172.6

Average Farm Size, Above Median Wheat Production 1860 979 248.49 189.38 301.30 10.78 5610.0

Average Farm Size, Below Median Wheat Production 1860 964 425.42 291.56 728.33 11.71 15172.6

Average Farm Size, France 1862, Excluding Farms below 5 ha (=12.36 acres)

Average Farm Size, Excluding Farms below 5 ha (=12.36 acres) 88 102.99 78.59 91.33 36.32 705.58

Average Farm Size, Excluding Farms below 5 ha (=12.36 acres), Above Median Temperature Shock in Summer 1792 43 107.01 92.09 81.61 46.33 484.77

Average Farm Size, Excluding Farms below 5 ha (=12.36 acres), Below Median Temperature Shock in Summer 1792 45 99.16 75.48 100.51 36.32 705.58

Average Farm Size, Excluding Farms below 5 ha (=12.36 acres), Above Median Wheat Production 1862 44 108.74 78.98 107.87 42.29 705.58

Average Farm Size, Excluding Farms below 5 ha (=12.36 acres), Below Median Wheat Production 1862 44 97.25 77.91 71.91 36.32 484.77

Average Farm Size, USA 1860, Excluding Farms Below 9 acres

Average Farm Size Excluding Farms Below 9 acres 1944 354.74 231.11 639.89 12.14 17403.0

Average Farm Size, Excluding Farms Below 9 acres, Above Median Wheat Production 1860 979 256.89 194.18 310.37 12.14 5610.0

Average Farm Size, Excluding Farms Below 9 acres, Below Median Wheat Production 1860 965 454.00 309.44 841.41 26.00 17403.0

Note: Farm size is measured in acres.

Table D.2: Descriptive Statistics

Obs. Mean Std.Dev Min. M ax.

Explanatory variables

Share of Emigres in Population 86 0.0047 0.0064 0.00 0.05

Altitude 88 353.37 344.24 36.02 1729.22

Land Suitability 88 0.75 0.19 0.21 0.98

Latitude 88 46.54 2.11 42.60 50.49

Longitude 88 2.62 2.66 -4.06 7.55

Distance to Paris 88 357.07 178.66 0.00 693.86

Distance to Lyon 88 322.25 145.85 0.00 709.62

Distance to Marseille 88 448.50 210.44 0.00 879.23

Department Area 88 618807.00 148900.10 61087.20 1084890.00

Distance to Border 88 191.11 134.17 16.56 557.59

Distance to Coast 88 159.54 111.61 10.42 411.07

Temperature in Summer 1792 88 17.97 1.36 13.69 21.82

Lack of Commons in Department 88 0.32 0.47 0 1

Mechanical Mills 1789 88 0.08 0.31 0 2

Encyclop ed ie Subscribers 86 1.00 0.00 1 1.00

University in 1700 88 0.18 0.39 0 1

GDP per capita

GDP p er c apita 1860 87 498.18 144.20 273.00 1105.00

GDP p er c apita 1901 86 863.42 269.40 255.30 1816.40

GDP p er c apita 1930 87 6464.61 1500.21 4033.47 14109.90

GDP p er c apita 1995 88 17.64 3.17 13.23 38.83

GDP p er c apita 2000 88 20.37 3.99 15.49 47.72

GDP p er c apita 2010 88 24.65 5.60 18.36 63.22

Value added by wor kforce in each sector

1860 Value Added per Worker in Agriculture 87 0.00 0.00 0.00 0.00

1930 Value Added per Worker in Agriculture 87 0.01 0.00 0.00 0.02

1982 Value Added per Worker in Agriculture 88 3699.27 6510.40 225.52 55433.29

1990 Value Added per Worker in Agriculture 88 6069.24 6372.52 320.53 36589.30

1860 Value Added per Worker in Industry 87 0.00 0.00 0.00 0.00

1930 Value Added per Worker in Industry 87 0.02 0.00 0.01 0.03

1982 Value Added per Worker in Industry 88 5182.49 9865.68 304.84 88828.12

1990 Value Added per Worker in Industry 88 10524.74 23123.32 685.78 210220.80

1860 Value Added per Worker in Services 87 0.00 0.00 0.00 0.00

1930 Value Added per Worker in Services 87 0.01 0.00 0.01 0.02

1982 Value Added per Worker in Services 88 6716.78 12338.99 670.73 111846.40

1990 Value Added per Worker in Services 88 10455.12 20475.20 1034.12 186043.20

Workforce in agriculture, industry and services

Share of the Workforce in Agriculture 1860 87 0.63 0.16 0.01 0.89

Share of the Workforce in Agriculture 1930 87 0.45 0.16 0.00 0.73

Share of the Workforce in Agriculture 1982 88 0.13 0.07 0.00 0.34

Share of the Workforce in Agriculture 1990 88 0.09 0.05 0.00 0.26

Share of the Workforce in Agriculture 1999 88 0.07 0.04 0.00 0.19

Share of the Workforce in Agriculture 2010 88 0.22 0.09 0.00 0.47

Share of the Workforce in Industry 1860 87 0.22 0.11 0.06 0.52

Share of the Workforce in Industry 1930 87 0.30 0.11 0.13 0.63

Share of the Workforce in Industry 1982 88 0.34 0.07 0.20 0.49

Share of the Workforce in Industry 1990 88 0.31 0.06 0.15 0.44

Share of the Workforce in Industry 1999 88 0.26 0.05 0.14 0.36

Share of the Workforce in Industry 2010 88 0.23 0.03 0.14 0.33

Share of the Workforce in Services 1860 87 0.15 0.07 0.05 0.47

Share of the Workforce in Services 1930 87 0.25 0.08 0.13 0.54

Share of the Workforce in Services 1982 88 0.53 0.07 0.40 0.71

Share of the Workforce in Services 1990 88 0.60 0.06 0.47 0.76

Share of the Workforce in Services 1999 88 0.68 0.06 0.57 0.85

Share of the Workforce in Services 2010 88 0.53 0.09 0.37 0.86

Table D.3: Descriptive Statistics

Obs. Mean Std.Dev Min. Max.

Child Labor, Agricultural Survey, 1929

Share of French agricultural workers below age 15 in the agricultural sector 87 0.01 0.01 0.00 0.07

Share of French agricultural workers below age 15 among agricultural workers 89 0.01 0.01 0.00 0.06

Share of French agricultural workers below age 15 among agricultural workers below age 15 89 1.00 0.00 1.00 1

Share of French agricultural workers below age 15 among agricultural workers above age 15 89 0.07 0.05 0.01 0.26

Voters in 1839

Share of Electors in Departmental Population 82 0.01 0.00 0.00 0.01

Share of Landowners Among Electors 67 0.56 0.09 0.28 0.72

Share of Businessmen Among Electors 67 0.24 0.09 0.10 0.60

Share of Professionals Among Electors 67 0.11 0.04 0.04 0.24

Share of Civil Servants Among Electors 67 0.09 0.04 0.02 0.18

Share of Illiterate Conscripts

Share of Illiterate Consc ripts 1840s 85 0.37 0.18 0.03 0.71

Share of Illiterate Consc ripts 1850s 85 0.32 0.17 0.03 0.68

Share of Illiterate Consc ripts 1860s 88 0.23 0.14 0.02 0.54

Share of Illiterate Consc ripts 1870s 89 0.16 0.10 0.01 0.47

Share of Illiterate Consc ripts 1880s 86 0.11 0.08 0.01 0.38

Share of Illiterate Consc ripts 1890s 86 0.05 0.04 0.01 0.20

Share of Illiterate Consc ripts 1900s 86 0.03 0.03 0.00 0.15

Share of Illiterate Consc ripts 1910s 86 0.03 0.02 0.00 0.09

Share of Illiterate Consc ripts 1930s 89 0.05 0.01 0.03 0.08

Price of Wheat, 1797-1800

Wheat Price, 1797-1800 337 18.28 4.92 9.08 38.48

Share of Church Land Sold in Department

Share of Church Land Sold in Department 67 0.025 0.013 0.00 0.156

Table D.4: Descriptive Statistics

Obs. Mean Std.Dev Min. Max.

Average Temperature in Summers 1788-1800

Average Temperature in Summer 1788 88 18.48 1.38 14.18 22.31

Average Temperature in Summer 1789 88 17.37 1.3 12.66 20.87

Average Temperature in Summer 1790 88 18.09 1.43 14.03 22.04

Average Temperature in Summer 1791 88 18.16 1.37 13.93 21.95

Average Temperature in Summer 1792 88 17.97 1.36 13.69 21.82

Average Temperature in Summer 1793 88 18.49 1.44 14.72 22.53

Average Temperature in Summer 1794 88 18.38 1.33 14.16 22.13

Average Temperature in Summer 1795 88 17.39 1.38 13.23 21.34

Average Temperature in Summer 1796 88 17.37 1.37 13.21 21.34

Average Temperature in Summer 1797 88 17.84 1.41 13.58 21.93

Average Temperature in Summer 1798 88 18.48 1.37 13.83 22.13

Average Temperature in Summer 1799 88 16.82 1.32 12.88 20.77

Average Temperature in Summer 1800 88 17.86 1.42 13.39 21.57

Squared Standardized Deviation of Summer Temperature

Squared Standardized Deviation of Summer Temperature 1788 (1763-1787) 86 0.82 0.53 0.02 2.27

Squared Standardized Deviation of Summer Temperature 1789 (1764-1788) 86 1.34 1.00 0.00 3.73

Squared Standardized Deviation of Summer Temperature 1790 (1765-1789) 86 0.27 0.29 0.00 1.25

Squared Standardized Deviation of Summer Temperature 1791 (1766-1790) 86 0.15 0.15 0.00 0.51

Squared Standardized Deviation of Summer Temperature 1792 (1767-1791) 86 0.05 0.07 0.00 0.30

Squared Standardized Deviation of Summer Temperature 1793 (1768-1792) 86 0.97 1.17 0.00 5.45

Squared Standardized Deviation of Summer Temperature 1794 (1769-1793) 86 0.43 0.44 0.00 1.61

Squared Standardized Deviation of Summer Temperature 1795 (1770-1794) 86 1.35 0.47 0.32 2.17

Squared Standardized Deviation of Summer Temperature 1796 (1771-1795) 86 1.48 0.49 0.31 2.28

Squared Standardized Deviation of Summer Temperature 1797 (1772-1796) 86 0.32 0.34 0.00 1.35

Squared Standardized Deviation of Summer Temperature 1798 (1773-1797) 86 0.48 0.19 0.00 0.96

Squared Standardized Deviation of Summer Temperature 1799 (1774-1798) 86 5.25 1.64 1.68 9.31

Squared Standardized Deviation of Summer Temperature 1800 (1775-1799) 86 0.26 0.32 0.00 1.29

Absolute Standardized Deviation of Summer Temperature

Absolute Standardized Deviation of Summer Temperature 1788 (1763-1787) 86 0.85 0.32 0.13 1.51

Absolute Standardized Deviation of Summer Temperature 1789 (1764-1788) 86 1.05 0.48 0.01 1.93

Absolute Standardized Deviation of Summer Temperature 1790 (1765-1789) 86 0.44 0.28 0.00 1.12

Absolute Standardized Deviation of Summer Temperature 1791 (1766-1790) 86 0.33 0.21 0.02 0.72

Absolute Standardized Deviation of Summer Temperature 1792 (1767-1791) 86 0.19 0.14 0.01 0.55

Absolute Standardized Deviation of Summer Temperature 1793 (1768-1792) 86 0.81 0.56 0.01 2.33

Absolute Standardized Deviation of Summer Temperature 1794 (1769-1793) 86 0.54 0.37 0.01 1.27

Absolute Standardized Deviation of Summer Temperature 1795 (1770-1794) 86 1.14 0.22 0.56 1.47

Absolute Standardized Deviation of Summer Temperature 1796 (1771-1795) 86 1.20 0.22 0.56 1.51

Absolute Standardized Deviation of Summer Temperature 1797 (1772-1796) 86 0.47 0.31 0.01 1.16

Absolute Standardized Deviation of Summer Temperature 1798 (1773-1797) 86 0.67 0.17 0.02 0.98

Absolute Standardized Deviation of Summer Temperature 1799 (1774-1798) 86 2.26 0.36 1.30 3.05

Absolute Standardized Deviation of Summer Temperature 1800 (1775-1799) 86 0.41 0.31 0.01 1.13

Table D.5: Do Temperature Deviations In‡uence Loc al Food Prices and Local Violence?

(1) (2) (3) (4) (5) (6) (7)

OLS OLS OLS OLS OLS OLS OLS

Price of Wheat 1797-1800 Riots in Aug. & Sept. 1792

Squared Deviation from Temperature 0.030*** 0.028***

in Summer 1797-1800 [0.006] [0.006]

Absolute Deviation from Temperature 0.063***

in Summer 1797-1800 [0.020]

Negative Squared Deviation from Temperature 0.029***

in Summer 1797-1800 [0.006]

Positive Squared Deviation from Temperature 0.159**

in Summer 1797-1800 [0.077]

Negative Absolute Deviation from Temperature 0.065***

in Summer 1797-1800 [0.020]

Positive Absolute Deviation from Temperature 0.200***

in Summer 1797-1800 [0.064]

Squared Deviation from Temperature 6.077***

in Summer 1792 (1767-1791) [1.536]

Absolute Deviation from Temperature 2.553***

in Summer 1792 (1767-1791) [0.784]

Within R2 0.148 0.522 0.511 0.529 0.519

Adjusted R2 0.522 0.516 0.506 0.522 0.512

Department …xed e¤ects No Yes Yes Yes Yes

Year …xed e¤ects Yes Yes Yes Yes Yes

Clusters 85 85 85 85 85

Geographic controls Yes Yes

Historical controls Yes Yes

F-stat 15.654 10.592

Observations 337 337 337 337 337 82 82

Note: This tabl e reports the e¤ect of the absolute and squared deviat ion from standardized temperature

in summer 1797-1800 on the price of wheat in OLS regress ions with département - and ye ar-…xed e¤ects in

1797-1800 period (columns 1-4) and in the summer of 1792 on the number of riots in A ugust and September

1792 accounting for geographic and historical control s (columns 5- 6). All the dependent variables are in

logarithm. Robust standard errors are reported in brackets. *** signi…cant at the 1% level, ** at the 5% level,

* at the 10% level.

Table D.6: Robustness Checks on the First-Stage Regressions: Squared and Absolute Deviations from Temperature in Summer, Spring,

Fall and Winter 1792

Panel A: Squared Deviation from Temperature

(1) (2) (3) (4) (5) (6) (7) (8)

First stage: the instrumented variable is Share of Emigres in Population

Squared Devation from Temperature in Summer 1792 (1767-1791) 6.159*** 10.26*** 5.983*** 7.203*** 11.48***

[1.499] [2.151] [1.533] [1.715] [2.295]

Squared Devation from Temperature in Spring 1792 (1767-1791) -1.029 3.221** 3.225**

[1.063] [1.317] [1.409]

Squared Devation from Temperature in Autumn 1792 (1767-1791) 2.807 1.600 3.661

[2.780] [2.348] [2.331]

Squared Devation from Temperature in Winter 1792 (1767-1791) -0.200 0.721* 1.119**

[0.444] [0.412] [0.508]

Geographic Controls Yes Yes Yes Yes Yes Yes Yes Yes

Historical Controls Yes Yes Yes Yes Yes Yes Yes Yes

F-stat (1st stage) 16.88 0.94 1.02 0.20 12.98 8.73 9.17 7.69

Observations 85 85 85 85 85 85 85 85

Panel B: Absolute Deviation from Temperature

(1) (2) (3) (4) (5) (6) (7) (8)

First stage: the instrumented variable is Share of Emigres in Population

Absolute Devation from Temperature in Summer 1792 (1767-1791) 2.590*** 3.772*** 2.518*** 2.679*** 3.492***

[0.770] [0.974] [0.860] [0.777] [1.046]

Absolute Devation from Temperature in Spring 1792 (1767-1791) -2.647 5.326 4.817

[2.704] [3.215] [3.189]

Absolute Devation from Temperature in Autumn 1792 (1767-1791) 1.511 0.319 1.330

[1.500] [1.283] [1.386]

Absolute Devation from Temperature in Winter 1792 (1767-1791) 0.591 0.827 1.216

[0.589] [0.528] [0.756]

Geographic Controls Yes Yes Yes Yes Yes Yes Yes Yes

Historical Controls Yes Yes Yes Yes Yes Yes Yes Yes

F-stat (1st stage) 11.32 0.96 1.01 1.01 8.04 5.79 7.03 4.15

Observations 85 85 85 85 85 85 85 85

Note: This ta ble reports robus tness checks to our baseline …rst-stage speci …cation in the 2SLS regressions where the IV is the squared deviation of

standardized summer temperature in 1792 (Panel A) and the absolute deviation of standardized summer te mperature in 1792 (Panel B) and where the

instrumented variable is the shar e of émigrés in the population (the dependent variable in the second stage of the 2SLS regressio n is GDP per capita in 1860

as shown in Tables 4 and D.12) when all geographic and historical controls are included. The robustness checks consider the e¤ect of the squared and absolute

deviation f rom standardized temperature in spring, fall, and winter 1792. The dependent variable is in logar ithm. Robust standard errors are reported in

brackets. *** signi…cant at the 1% level, ** at the 5% level, * at the 10% level.

Table D.7: Robustness Checks: Deviations from Temperature in Summer 1792 on GDP per

Capita 1860: Summers 1788-1800

Panel A. GDP per capita 1860

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

Reduced Form

GDP per capita 1860

Squared Deviation from Temperature in Summer 1792 (1767-1791) -1.572*** -1.485*** -1.551*** -1.510*** -1.775*** -1.651*** -1.656*** -1.085** -1.578*** -2.356*** -1.245** -1.750*** -1.582***

[0.381] [0.373] [0.391] [0.400] [0.518] [0.395] [0.482] [0.450] [0.385] [0.508] [0.514] [0.460] [0.384]

Squared Deviation from Temperature in Summer 1788 (1763-1787) 0.225

[0.188]

Squared Deviation from Temperature in Summer 1789 (1764-1788) 0.0267

[0.0576]

Squared Deviation from Temperature in Summer 1790 (1765-1789) 0.142

[0.115]

Squared Deviation from Temperature in Summer 1791 (1766-1790) 0.259

[0.415]

Squared Deviation from Temperature in Summer 1793 (1768-1792) 0.0260

[0.0348]

Squared Deviation from Temperature in Summer 1794 (1769-1793) 0.0535

[0.146]

Squared Deviation from Temperature in Summer 1795 (1770-1794) 0.290**

[0.141]

Squared Deviation from Temperature in Summer 1796 (1771-1795) 0.0855

[0.152]

Squared Deviation from Temperature in Summer 1797 (1772-1796) 0.316**

[0.156]

Squared Deviation from Temperature in Summer 1798 (1773-1797) 0.141

[0.171]

Squared Deviation from Temperature in Summer 1799 (1774-1798) -0.0154

[0.0254]

Squared Deviation from Temperature in Summer 1800 (1775-1799) -0.0891

[0.210]

Adjusted R2 50.745 48.659 51.532 48.843 53.864 49.995 47.751 56.946 44.806 56.396 49.938 50.004 49.379

F-stat 0.643 0.646 0.638 0.642 0.639 0.640 0.638 0.655 0.639 0.654 0.641 0.639 0.639

Geographical Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Historical Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 85 85 85 85 85 85 85 85 85 85 85 85 85

Panel B. GDP per capita 2010

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

Reduced Form

GDP per capita 2010

Squared Deviation from Temperature in Summer 1792 (1767-1791) 1.093*** 1.077*** 1.151*** 1.141*** 1.123** 0.896*** 1.022** 1.217*** 1.088*** 0.839* 0.812* 0.702* 1.102***

[0.316] [0.328] [0.318] [0.336] [0.440] [0.320] [0.422] [0.365] [0.313] [0.438] [0.463] [0.421] [0.320]

Squared Deviation from Temperature in Summer 1788 (1763-1787) -0.0406

[0.113]

Squared Deviation from Temperature in Summer 1789 (1764-1788) 0.0739

[0.0453]

Squared Deviation from Temperature in Summer 1790 (1765-1789) 0.108

[0.103]

Squared Deviation from Temperature in Summer 1791 (1766-1790) -0.0386

[0.331]

Squared Deviation from Temperature in Summer 1793 (1768-1792) 0.0650**

[0.0260]

Squared Deviation from Temperature in Summer 1794 (1769-1793) 0.0452

[0.121]

Squared Deviation from Temperature in Summer 1795 (1770-1794) 0.0735

[0.107]

Squared Deviation from Temperature in Summer 1796 (1771-1795) 0.0709

[0.125]

Squared Deviation from Temperature in Summer 1797 (1772-1796) 0.102

[0.113]

Squared Deviation from Temperature in Summer 1798 (1773-1797) -0.120

[0.120]

Squared Deviation from Temperature in Summer 1799 (1774-1798) -0.0338

[0.0214]

Squared Deviation from Temperature in Summer 1800 (1775-1799) 0.0878

[0.112]

Adjusted R2 0.596 0.590 0.601 0.596 0.590 0.618 0.591 0.592 0.591 0.594 0.595 0.602 0.593

Geographical Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Historical Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

F-stat 69.199 64.722 63.743 72.482 68.747 81.521 62.906 68.585 64.497 73.169 62.912 73.857 63.111

Observations 86 86 86 86 86 86 86 86 86 86 86 86 86

Note: This table reports reduced-form re gressions that assess the e¤ect of the squared deviat ion from

standardized temper ature in the summer of 1792 on GDP per capita in 1860 (Panel A) and GDP per capita

in 2 010 (Panel B), account ing for the squared deviatio n standardized temperature in the summe rs over the

1788-1800 period. It shows t hat only the squared deviation fr om standardized temperature in 1792 has a

negative impact on GDP per capita in 1860 and a positive impact on GDP per capita in 2010. The dependent

variables are in l ogarithm. Robust standard erro rs are re ported in brackets. *** signi…cant at the 1% level,

** at the 5% level, * at the 10% level .

Table D.8: First-Stage Regressions: Squared and Absolute Deviations from Tempe rature and

Rainfall in Summer 1792

(1) (2) (3) (4)

First stage: the instrumented variable is the Share of E migres

Squared Deviation from Temperature in Summer 1792 (1767-1791) 6.159*** 6.458***

[1.499] [1.524]

Squared Deviation from Rainfall in Summer 1792 (1767-1791) 0.980*

[0.525]

Absolute Deviation from Temperature in Summer 1792 (1767-1791) 2.590*** 2.840***

[0.770] [0.828]

Absolute Deviation from Rainfall in Summer 1792 (1767-1791) 0.617

[0.420]

Geographic controls Yes Yes Ye s Yes

Historical controls Yes Yes Ye s Yes

F-stat (1st stage) 85 85 85 85

Observations 16.862 28.958 13.190 18.876

Note: This table reports robustness checks to our baseline …rst- stage speci …cation in the 2SLS re gressions

where the IV is the squared and absolute deviation of standardized summ er temperature in 1792 and where

the instrumented var iable is the share of émigrés in the population (the dependent vari able in the second

stage of the 2SLS regression is GDP per ca pita in 1860 as shown in Table 3). The robustne ss checks conside r

the e¤ect of the squared and absolute deviation from st andardized rainfall in the summer of 179 2. All the

depende nt variables are in logarithm. Robust standard errors are report ed in brackets. *** signi…cant at the

1% level, ** at the 5% level, * at the 10% level.

Table D.9: First-Stage Regressions: The Impact of Summer Deviations f rom Temperature in

Summer 1792 on Emigration, Accounting f rom Spatial Correlation

(1) (2) (3)

OLS OLS OLS

Share of Emigres

Squared Deviation from Temperature 4.336 5.950 6.216

in Summer 1792 (1767-1791)

White Robust Standard Errors [1.140]*** [1.445]*** [1.481]***

Spatial std. errors, 25 km [1.038]*** [1.278]*** [1.332]***

Spatial std. errors, 50 km [1.043]*** [1.279]*** [1.333]***

Spatial std. errors, 100 km [1.141]*** [1.278]*** [1.319]***

Spatial std. errors, 200 km [1.449]*** [1.185]*** [1.177]***

Spatial std. errors, 300 km [1.634]*** [1.154]*** [1.102]***

Spatial std. errors, 400 km [1.732]** [1.185]*** [1.071]***

Spatial std. errors, 500 km [1.761]** [1.229]*** [1.069]***

Geographic controls No Yes Yes

Historical controls No No Yes

Observations 86 86 86

Note: This table reports White robust standard errors and spatial Conley (1999) standard errors for the

…rst stage of our 2SLS regr essions between our IV, the square d deviation from standardized tempe rature in

the summe r of 1792, and the i nstrumented variable, the share of émigrés in the populat ion. The dependent

variable is in logarithm. Robust standard errors are reported in b rackets. *** signi…cant at the 1% level, **

at the 5% l evel, * at the 10% level.

Table D.10: Robustness Checks: Baseline Deviations from Temperature in S umm er 1792 and

GDP p er capita 1860 and 2010

Panel A. GDP per capita 1860

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Reduced Form

GDP per capita 1860

Squared Devation from Tem perature in Summer 1792 (1767-1791) -1.572***

[0.381]

Absolute Devation from Tem perature in Summer 1792 (1767-1791) -0.637***

[0.167]

Squared Devation from Tem perature in Summer 1792 (1742-1791) -1.050***

[0.282]

Absolute Devation from Tem perature in Summer 1792 (1742-1791) -0.740***

[0.177]

Squared Devation from Tem perature in Summer 1792 (1776-1800) -3.524***

[0.819]

Absolute Devation from Tem perature in Summer 1792 (1776-1800) -1.152***

[0.334]

Squared Devation from Tem perature in Summer 1792 (1751-1775) -0.614***

[0.183]

Absolute Devation from Tem perature in Summer 1792 (1751-1775) -0.618***

[0.153]

Squared Devation from Tem perature in Summer 1792 (1751-1800) -1.731***

[0.432]

Absolute Devation from Tem perature in Summer 1792 (1751-1800) -0.748***

[0.209]

Adjusted R2 0.643 0.627 0.635 0.638 0.654 0.639 0.623 0.628 0.641 0.629

F-stat 50.745 44.345 41.400 39.224 58.143 49.856 36.158 34.390 45.597 40.144

Geographical Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Historical Controls Yes Yes Ye s Yes Yes Yes Yes Yes Yes Yes

Observations 85 85 85 85 85 85 85 85 85 85

Panel B. GDP per capita 2010

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Reduced Form

GDP per capita 2010

Squared Devation from Tem perature in Summer 1792 (1767-1791) 1.093***

[0.316]

Absolute Devation from Tem perature in Summer 1792 (1767-1791) 0.516***

[0.140]

Squared Devation from Tem perature in Summer 1792 (1742-1791) 0.627***

[0.229]

Absolute Devation from Tem perature in Summer 1792 (1742-1791) 0.304**

[0.144]

Squared Devation from Tem perature in Summer 1792 (1776-1800) 2.439***

[0.632]

Absolute Devation from Tem perature in Summer 1792 (1776-1800) 0.951***

[0.209]

Squared Devation from Tem perature in Summer 1792 (1751-1775) 0.388***

[0.144]

Absolute Devation from Tem perature in Summer 1792 (1751-1775) 0.213*

[0.121]

Squared Devation from Tem perature in Summer 1792 (1751-1800) 1.168***

[0.366]

Absolute Devation from Tem perature in Summer 1792 (1751-1800) 0.471***

[0.167]

Adjusted R2 0.596 0.599 0.569 0.544 0.608 0.620 0.564 0.534 0.589 0.569

F-stat 69.199 70.393 74.461 81.776 69.595 74.409 72.049 90.221 72.318 69.639

Geographical Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Historical Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 86 86 86 86 86 86 86 86 86 86

Note: This table reports reduced-form regressions that assess the e¤ect of our IVs, the s quared and

abso lute deviations from standardized t emperature in the summer o f 1792, on GDP per capita in 1860 (Panel

A) and GDP per capita in 2010 (Panel B), where we consider ba seline periods other than the 25 ye ars preceding

1792. In all speci…cations, the squared devia tion from standardized temperature in 1792 has a neg ative impact

on GDP per capita in 1860 and a positive impact on GDP per capita in 2010. The dependent variables are

in logarithm. Robust standard errors are reported in bracket s. *** signi…cant at the 1% level, ** at the 5%

level, * at the 10% le vel.

Table D.11: Summer Temperature Shock 1792 and Emigration: Falsi…cation Tests

Panel A. Violence before and after 1789-1815.

(1) (2) (3) (4)

OLS OLS OSLS OSLS

Riots during Flour War White Terror - Convictions White Terror - Convictions White Terror

May - June 1775 in Ordinary Court 1815-1816 in Provost Courts 1816-1818 Arrests 1815-1816

Squared Deviation from Temperature -2.807 -6.521 0.870 0.347

in Summer 1792 (1767-1791) [1.954] [4.265] [1.883] [2.825]

Geographical Controls Yes Ye s Yes Yes

Historical Controls Yes Yes Yes Yes

Observations 86 84 84 84

Panel B. Cahiers de Doleances.

(1) (2) (3) (4) (5) (6) (7)

OLS OLS OLS OLS OLS OLS OLS

Approving Vote State Intervention Abolition of Mercantilist Reform or Abolition Abolition of Tendency Towards

by Head in Education Guilds Demands of Feudal Dues Serfdom Socialism

Squared Deviation from Temperature 0.764 0.575 0.113 -0.131 0.772 -0.115 -0.106

in Summer 1792 (1767-1791) [0.632] [0.507] [0.335] [0.346] [0.687] [0.144] [0.214]

Geographical Controls Yes Yes Yes Yes Yes Yes Yes

Historical Controls Yes Yes Yes Yes Yes Yes Yes

Observations 77 77 77 77 77 77 77

Panel C. Human Capital before the Revolution.

(1) (2) (3) (4)

OLS OSLS OLS OLS

Share of grooms who Share of brides who Share of grooms who Share of brides who

signed their wedding contract signed their wedding contract signed their wedding contract signed their wedding contract

1686-1690 1686-1690 1786-1790 1786-1790

Squared Deviation from Temperature -0.876 0.101 -0.273 -1.732

in Summer 1792 (1767-1791) [1.363] [1.425] [1.521] [1.390]

Geographical Controls Yes Yes Yes Yes

Historical Controls Yes Yes Yes Yes

Observations 75 75 78 78

Panel D. Clergy and Aristocracy at the Outbreak of the Revolution

(1) (2) (3)

OLS OLS OLS

Share of Clergymen Against the Number of Noble Families Share of Noble Families in Gotha

1791 Oath to the Civil Constitution of the Clergy in Gotha Almanach 1790 Almanach in Population 1790

Squared Deviation from Temperature -1.434 -19.92 0..00006

in Summer 1792 (1767-1791) [1.698] [19.22] [0.00007]

Geographical Controls Yes Yes Yes

Historical Controls Yes Yes Yes

Observations 76 83 83

Note: This table reports reduced-form r egressions between our IV, the squared deviation from stan-

dardize d temperature in the summer of 1792 and several var iabl es which could potent ially be endogenous to

economic g rowth, and which could bias our estimates if they were correlated with our IV. These are variables

pertaining to violence before 1789 and after 18 15, demands from the cahiers de doléances (Panel B), measures

of human capital before the Revolution (Pa nel C), and measures for the p resenc e of the local clergy and

aristocracy at the outbreak of the Revolution (Panel D). All the dependent variables are in logarithm. Robust

standard errors are reported in brackets. *** signi…cant at the 1% level, ** at the 5% leve l, * at the 10% lev el.

Table D.12: Emigrés and GDP per capita (IV: Absolute Deviation of Temp erature in Summer 1792)

Panel A. GDP per capita 1860-1930

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

OLS OLS 2SLS 2SLS OLS OLS 2SLS 2SLS OLS OLS 2SLS 2SLS

GDP per capita 1860 GDP per capita 1901 GDP per capita 1930

Share of Emigres -0.0109 -0.0811*** -0.186** -0.246*** -0.00861 -0.0681 -0.214 -0.278 0.0340 -0.00614 -0.0386 -0.0370

[0.0322] [0.0304] [0.0729] [0.0784] [0.0388] [0.0534] [0.158] [0.193] [0.0289] [0.0288] [0.0535] [0.0459]

Adjusted R2 -0.011 0.585 -0.012 0.278 0.002 0.608

Geographical Controls No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes

Historical Controls No No No Yes No No No Yes No No No Yes

Observations 85 85 85 85 83 83 83 83 85 85 85 85

First stage: the instrumented variable is Share of Emigres

Absolute Deviation from Temperature 2.612*** 2.590*** 2.163*** 1.937*** 2.612*** 2.590***

in Summer 1792 (1767-1791) [0.708] [0.770] [0.651] [0.641] [0.708] [0.770]

F-stat (1st stage) 13.616 11.320 11.050 9.139 13.616 11.320

Panel B. GDP per capita 1995-2010

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

OLS OLS 2SLS 2SLS OLS OLS 2SLS 2SLS OLS OLS 2SLS 2SLS

GDP per capita 1995 GDP per capita 2000 GDP per capita 2010

Share of Emigres 0.0237 0.0478** 0.205*** 0.204*** 0.0238 0.0553** 0.223*** 0.215*** 0.0201 0.0493* 0.195*** 0.197***

[0.0195] [0.0212] [0.0615] [0.0670] [0.0199] [0.0222] [0.0675] [0.0704] [0.0225] [0.0254] [0.0660] [0.0706]

Adjusted R2 0.003 0.472 0.001 0.470 -0.005 0.466

Geographical Controls No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes

Historical Controls No No No Yes No No No Yes No No No Yes

Observations 86 86 86 86 86 86 86 86 86 86 86 86

First stage: the instrumented variable is Share of Emigres

Absolute Deviation from Temperature 2.632*** 2.620*** 2.632*** 2.653*** 2.632*** 2.620***

in Summer 1792 (1767-1791) [0.701] [0.757] [0.701] [0.739] [0.701] [0.757]

F-stat (1st stage) 14.107 11.970 14.107 12.871 14.107 11.970

Note: This table reports the e¤ect of the share of émigrés in t he population on the logarithm of GDP per

capita in OLS and 2SLS regressions in 1860, 1901, and 1930 (Panel A) and in 1995, 2000, and 20 10 (Panel

B). The IV in the …rst sta ge of the 2SLS regressi ons is the absolute standardized deviation from t emperature

in the summ er of 1792. All the dependent variables are in loga rithm. Robust standard e rrors are reported in

brackets. *** signi…cant at the 1% level, ** at the 5% level, * at the 10% level.

Table D.13: The E¤ect of the Social Categories of Emigrés on GDP per capita in 1860 and 2010

(1) (2) (3) (4)

2SLS 2SLS 2SLS 2SLS

GDP per capita 1860 GDP per Capita 2010

Share of Rich Emigres -0.293** 0.205**

(Clergy, Nobility, Upper Middle Class) [0.116] [0.0885]

Share of Poor Emigres -0.0824*** 0.0605**

(Lower Middle Class, Workers, Peasants) [0.0313] [0.0244]

Geographic Controls Yes Yes Yes

Historical Controls Yes Yes Yes Yes

Observations 68 68 69 69

First stage: the instrumented variable is

Share of Rich Emigres Share of Poor Emigres Share of Rich Emigres Share of Poor Emigres

Squared Deviation from Temperature 4.342*** 15.46*** 4.638*** 15.70***

in Summer 1792 (1767-1791) [1.247] [3.840] [1.293] [3.760]

F-stat (1st stage) 12.130 16.207 12.862 17.422

Note: Thi s table reports the e¤ect of the di¤erent categories of émigrés in the population on GDP per

capita in 1860 and 2010 in 2SLS regressions. All the dependent variables are in logarithm. The IV in the …rst

stage of the 2SLS regressions is the squared standardized deviation from temperature in the summer of 1792.

Robust standard errors are reported in brackets. *** signi…cant at the 1% l evel, ** at the 5% level, * at the

10% level.

Table D.14: Emigrés and Population Size, 1801-2010

Panel A. Population of Département, 1801-2010

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Population of Département

1801 1821 1841 1861 1881 1901 1921 1968 1982 1999 2010

Share of Emigres 0.0600 0.0778 0.0975 0.0630 -0.139 -0.0447 0.202 0.398** 0.492** 0.554*** 0.594***

[0.0927] [0.0956] [0.0989] [0.107] [0.148] [0.165] [0.148] [0.182] [0.195] [0.204] [0.208]

Geographic controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 84 84 84 86 84 84 86 86 86 86 86

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.834*** 6.834*** 6.834*** 6.216*** 5.131*** 5.131*** 6.216*** 6.216*** 6.216*** 6.216*** 6.216***

in Summer 1792 (1767-1791) [1.547] [1.547] [1.547] [1.487] [1.221] [1.221] [1.487] [1.487] [1.487] [1.487] [1.487]

F-stat (1st stage) 19.515 19.515 19.515 17.476 17.657 17.657 17.476 17.476 17.476 17.476 17.476

Panel B. Population of Chef-Lieu of Département, 1806-2006

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Population of Chef-Lieu of Département

1806 1821 1841 1861 1881 1901 1921 1946 1968 1982 1999 2006

Share of Emigres -0.188 -0.0795 -0.186 0.0696 0.143 0.517 0.585 0.700 0.802 0.867* 0.942* 0.972**

[0.273] [0.240] [0.219] [0.270] [0.298] [0.475] [0.508] [0.518] [0.498] [0.491] [0.492] [0.482]

Geographic controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 86 86 84 85 86 86 86 86 86 86 86 86

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.216*** 6.216*** 6.834*** 6.209*** 6.216*** 6.216*** 6.216*** 6.216*** 6.216*** 6.216*** 6.216*** 6.216***

in Summer 1792 (1767-1791) [1.487] [1.487] [1.547] [1.484] [1.487] [1.487] [1.487] [1.487] [1.487] [1.487] [1.487] [1.487]

F-stat (1st stage) 17.476 17.476 19.515 17.514 17.476 17.476 17.476 17.476 17.476 17.476 17.476 17.476

Note: This table reports the e¤ect of the share of émigrés in the populat ion on the population in each

département (Panel A) and in the chef -lieu (i.e., main administr ative center) of each d épartement over the

1801-2010 period. All the dependent variables are in logarithm. The IV in the …rs t stage of the 2SLS regressi ons

is the squared standar dized deviat ion from temperature in the summer of 1792. Robust standard errors are

reported in brackets. *** signi…cant at the 1% leve l, ** at the 5% level, * at the 10% level.

Table D.15: Emigrés and Financial Development: Savings Banks’Loans and Contracts Sealed

by Notaries

(1) (2) (3) (4) (5) (6)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Total Value of Loans from Savings Banks Contracts Sealed by Notaries

1875 1881 1900 1861 1901 1931

Share of Emigres -0.122 -0.166 0.0108 -0.197* -0.141 0.167

[0.290] [0.256] [0.195] [0.112] [0.131] [0.133]

Geographic controls Yes Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes

Observations 83 83 83 86 83 86

First Stage: the Instrumented variable is Share of Emigres

Squared Deviation from Temperature 4.895*** 4.895*** 4.895*** 6.216*** 4.895*** 6.216***

in Summer 1792 (1767-1791) [1.209] [1.209] [1.209] [1.487] [1.209] [1.487]

F-stat (1st stage) 16.378 16.378 16.378 17.476 16.378 17.476

Reduced Form

Squared Deviation from Temperature -0.600 -0.813 0.0527 -1.225* -0.689 1.039

in Summer 1792 (1767-1791) [1.611] [1.430] [1.068] [0.692] [0.702] [0.853]

Note: This table reports the e¤ect of the share o f émigrés in the population on the amount o f loans giv en

by savings banks (columns 1-3) and the number of contracts sealed by notaries (columns 4-6) where the IV

is the squared standardized deviation from summer temperature in 1792. All the dependent variables are in

logarithm. Robust standard errors are reported in brackets. *** signi…cant at the 1% level, ** at the 5% level,

* at the 10% level.

Table D.16: Emigrés and Civil Servants in the Workforce in th e 19th century

(1) (2) (3)

2SLS 2SLS 2SLS

Share of Civil Servants in Workforce

1851 1866 1881

Share of Emigres 0.814*** 0.363** 0.150

[0.217] [0.180] [0.262]

Geographic controls Yes Yes Yes

Historical controls Yes Yes Yes

Observations 84 86 83

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 6.834*** 6.216*** 4.895***

in Summer 1792 (1767-1791) [1.547] [1.487] [1.209]

F-stat (1st stage) 19.515 17.476 16.378

Note: This table reports the e¤e ct of the share of émigrés in the population on the share of civil servants

in the workforce during the 19th century where the IV is the squared standardized deviation from summ er

temperature in 1792. All the dependent va riables are in logarithm. Robust standard errors are reported in

brackets. *** signi…cant at the 1% level, ** at the 5% level, * at the 10% level.

Table D.17: Emigrés and Octroi Tax Rates, 1875

(1) (2) (3) (4) (5)

2SLS 2SLS 2SLS 2SLS 2SLS

Share of Communes with Octroi in 1875 Octroi Tax Rates

Out of Total Number of Communes by Département in 1875 on

in Département) Pure Alcohol Beef Sheep Pork

Share of Emigres in Population 1.281*** 0.199 0.261** 0.319* 0.337**

[0.428] [0.248] [0.116] [0.174] [0.164]

Geographical controls Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes

Observations 83 83 83 83 83

First stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 4.895*** 4.895*** 4.895*** 4.895*** 4.895***

in Summer 1792 (1767-1791) [1.209] [1.209] [1.209] [1.209] [1.209]

F-stat (1st stage) 16.378 16.378 16.378 16.378 16.378

Reduced Form

Squared Deviation from Temperature 6.269*** 0.973 1.278** 1.559* 1.650**

in Summer 1792 (1767-1791) [2.140] [1.351] [0.579] [0.881] [0.812]

Note: This table reports the e¤e ct of the share of émigrés in the population on the share of communes

with an octroi in each département in 1875 as well as on the ta x rates on several goods in 1875 where the

IV is the squared standar dized de viation from summer temperatur e in 1792. All the dependent variables a re

in logarithm. Robust standard errors are reported in brackets. *** signi…cant at the 1% level, ** at the 5%

level, * at the 10% le vel.

Table D.18: Emigrés and Public Spending before World War I

Panel A. Primary schools and male & female population age 5-15

(1) (2) (3) (4) (5) (6)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Ratio of schools to male and female population age 5-15

1876 1881 1886 1891 1896 1901

Share of Emigres -0.387** -0.407** -0.389** -0.335* -0.277 -0.427***

[0.156] [0.167] [0.157] [0.183] [0.187] [0.156]

Geographic controls Yes Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes

Observations 83 83 82 82 83 83

First Stage: the instrumented variable is Share of Emigres

Squared Deviation from Temperature 4.895*** 4.895*** 4.893*** 4.811*** 4.895*** 4.895***

in Summer 1792 (1767-1791) [1.209] [1.209] [1.210] [1.239] [1.209] [1.209]

F-stat (1st stage) 16.378 16.378 16.359 15.065 16.378 16.378

Panel B. Total Public Spending on Education per Pupil in Primary Schools

(1) (2) (3) (4) (5) (6)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Total Public Spending per Pupil

1876 1881 1886 1891 1896 1901

Share of Emigres 0.0005 -0.184* -0.133 -0.393** -0.127 -0.358**

[0.0971] [0.102] [0.0908] [0.165] [0.103] [0.139]

Geographic controls Yes Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes

Observations 83 83 83 83 83 83

First Stage: the Instrumented variable is Share of Emigres

Squared Devation from Temperature 4.895*** 4.895*** 4.895*** 4.895*** 4.895*** 4.895***

in Summer 1792 (1767-1791) [1.209] [1.209] [1.209] [1.209] [1.209] [1.209]

F-stat (1st stage) 16.378 16.378 16.378 16.378 16.378 16.378

Panel C. Roads & Railroads

(1) (2) (3) (4) (5) (6) (7) (8) (9)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS

Area Covered by Roads Area Covered by Railroad Total Spending on

within Department’sTerritory within Department’sTerritory Road Maintenance

1881 1900 1913 1881 1900 1913 1881 1900 1913

Share of Emigres -0.526*** -0.447*** -0.671*** -0.443** -0.172 -0.155 -0.153 -0.587*** -0.417***

[0.160] [0.143] [0.225] [0.223] [0.130] [0.117] [0.179] [0.175] [0.134]

Geographic controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Historical controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 83 83 83 83 83 83 83 83 83

First Stage: the Instrumented variable is Share of Emigres

Squared Deviation from Temperature 4.895*** 4.895*** 4.895*** 4.895*** 4.895*** 4.895*** 4.895*** 4.895*** 4.895***

in Summer 1792 (1767-1791) [1.209] [1.209] [1.209] [1.209] [1.209] [1.209] [1.209] [1.209] [1.209]

F-stat (1st stage) 16.378 16.378 16.378 16.378 16.378 16.378 16.378 16.378 16.378

Note: This table repor ts the e¤e ct of the shar e of émigrés in the popula tion on measures per taining to

public spending on education per pupil (Panel A), the number of primary schools with respect to the male

and female population ages 5-15 (Panel C), and the infrastructur e of roads and railroads (Panel C) where the

IV is the squared standar dized de viation from summer temperatur e in 1792. All the dependent variables a re

in logarithm. Robust standard errors are reported in brackets. *** signi…cant at the 1% level, ** at the 5%

level, * at the 10% le vel.

Table D.19: Summer Temperature Shock 1792 and Religiosity before World War I: Falsi…cation

Tests

(1) (2) (3) (4)

OLS OLS OLS OLS

Share of Representatives in the lower House of Parliament Number of Religious Communities Devoted to

Against the Separation of Church & State Educational Purposes 1856 Charity Purposes 1856 Only Religious Purposes 1856

Squared Deviation from Temperature -0.671 -2.419 2.930 -0.254

in Summer 1792 (1767-1791) [0.534] [2.131] [1.903] [2.467]

Geographical Controls Yes Yes Yes Yes

Historical Controls Yes Yes Yes Yes

Observations 83 82 82 82

Note: This table reports reduced- form regr essions between our IV, the square d deviation from standard-

ized temperatur e in the summer of 1792 and variables which could potentially be endogenous to economic

growth, and which could bias our estimates if they we re correlated with our IV. These are variables pertaining

to r eligiosity befor e World War I. All the dependent variables are in logarithm. Robust standard errors are

reported in brackets. *** signi…cant at the 1% leve l, ** at the 5% level, * at the 10% level.

Table D.20: Summer Temperature Shock 1792 and the Phylloxera: Falsi…cation Tests

(1) (2)

OLS OLS

Departments hit by

the Phylloxera in

1875 1890

Squared Deviation from Temperature -0.347 -0.312

in Summer 1792 (1767-1791) [0.735] [1.005]

Geographical Controls Yes Yes

Historical Controls Yes Yes

Observations 86 86

Note: This table reports reduced- form regr essions between our IV, the square d deviation from standard-

ized temperatur e in the summer of 1792 and variables which could potentially be endogenous to economic

growth, and which could bias our estimates if they we re correlated with our IV. These are variables pertaining

to the d éparteme nts hit by the phylloxera in 1875 and 1890. All the dependent variables are in logarithm.

Robust standard errors are reported in brackets. *** signi…cant at the 1% l evel, ** at the 5% level, * at the

10% level.

Table D.21: Descriptive Statistics for Variables in Robustness Analysis

Obs. Mean Std.Dev Min. Max.

Infant Mor tali ty (Age 0-1)

Infant Mortality (Age 0-1) 1811 85 0.30 0.08 0.16 0.53

Infant Mortality (Age 0-1) 1821 85 0.29 0.10 0.14 0.60

Infant Mortality (Age 0-1) 1831 85 0.32 0.09 0.16 0.53

Infant Mortality (Age 0-1) 1841 85 0.27 0.08 0.14 0.46

Infant Mortality (Age 0-1) 1851 85 0.30 0.08 0.16 0.48

Infant Mortality (Age 0-1) 1861 88 0.29 0.10 0.12 0.63

Infant Mortality (Age 0-1) 1871 86 0.31 0.08 0 0.49

Infant Mortality (Age 0-1) 1881 86 0.25 0.08 0 0.48

Infant Mortality (Age 0-1) 1891 86 0.22 0.06 0 0.40

Infant Mortality (Age 0-1) 1901 86 0.19 0.04 0 0.29

Infant Mortality (Age 0-1) 1911 86 0.04 0.01 0.02 0.07

Infant Mortality (Age 0-1) 1931 89 0.07 0.01 0.01 0.10

Coale Fertility Index

Coale Fertility Index 1811 87 0.40 0.10 0.24 0.87

Coale Fertility Index 1821 87 0.39 0.11 0.24 0.82

Coale Fertility Index 1831 87 0.37 0.11 0.23 0.74

Coale Fertility Index 1841 87 0.34 0.08 0.21 0.61

Coale Fertility Index 1851 87 0.34 0.07 0.21 0.54

Coale Fertility Index 1861 90 0.31 0.06 0.21 0.48

Coale Fertility Index 1871 88 0.29 0.06 0.18 0.50

Coale Fertility Index 1881 88 0.29 0.06 0.20 0.57

Coale Fertility Index 1891 88 0.25 0.05 0.16 0.45

Coale Fertility Index 1901 88 0.25 0.04 0.18 0.42

Coale Fertility Index 1911 87 0.21 0.03 0.14 0.30

Coale Fertility Index 1931 90 0.19 0.03 0.12 0.25

Table D.22: Descriptive Statistics for Variables in Robustness Analysis

Obs. Mean Std.Dev. Min. Max

Octroi Tax Rates

Octroi Tax Rates Pure Alcohol 1875 86 13.07 7.12 3.8 45

Octroi Tax Rates Oil of First Quality 1875 86 9.50 6.12 0 42.65

Octroi Tax Rates Beef 1875 86 7.62 2.61 3 20

Octroi Tax Rates Veal 1875 86 8.21 3.91 0 20

Octroi Tax Rates Sheep 1875 86 8.27 3.04 0 20

Octroi Tax Rates Pork 1875 86 7.02 3.02 0 20

Octroi Tax Rates Charcoal 1875 86 0.71 1.14 0 10

Cahiers de Doleances

Approving Vote by Head 77 0.06 0.25 0 1

Etatisme in Education 77 0.05 0.28 0 2

Abolition in Guilds 77 0.03 0.16 0 1

Mercantilist Demands 77 0.04 0.19 0 1

Reform or Abolition of Feudal Dues 77 0.08 0.27 0 1

Abolition of Serfdom 77 0.01 0.11 0 1

Tendency towards Socialism 77 0.0 0.1 0 1

Noble Families

Number of Noble Families in Gotha Almanach 1790 85 13.67 7.66 1 41

Share of Noble Families in Gotha Almanach in 1790 Population 83 0.00005 0.000025 0.000003 0.0001

Total Public Spending per Pupil

Total Public Spending per Pupil 1876 86 4.12 10.29 0 93.28

Total Public Spending per Pupil 1881 86 8.35 4.52 0 22.88

Total Public Spending per Pupil 1886 86 18.43 4.97 3.06 37.10

Total Public Spending per Pupil 1891 86 26.70 5.81 16.05 50.17

Total Public Spending per Pupil 1896 86 32.39 7.06 18.92 53.67

Total Public Spending per Pupil 1901 86 39.25 29.79 16.97 302.18

Commune Public Spending per Pupil

Commune Public Spending per P up il 1876 86 12.36 3.76 4.04 29.68

Commune Public Spending per P up il 1881 86 10.27 5.60 2.47 43.19

Commune Public Spending per P up il 1886 86 9.78 12.36 1.57 111.28

Commune Public Spending per P up il 1891 86 8.43 14.31 1.01 128.01

Commune Public Spending per P up il 1896 86 7.12 10.07 1.52 82.45

Commune Public Spending per P up il 1901 86 12.28 15.31 1.16 127.04

Pre-revolutionary human capital

Share of grooms who signed their wedding contract 1686-1690 77 0.26 0.15 0.06 0.64

Share of brides who signed their wedding contract 1686-1690 77 0.12 0.07 0.01 0.33

Share of grooms who signed their wedding contract 1786-1790 80 0.42 0.24 0.05 0.92

Share of brides who signed their wedding contract 1786-1790 80 0.23 0.17 0.02 0.69

Violence before and after the Revolution

Riots during Flour May-June 1775 88 3.50 13.94 0 101

White Terror- Convictions in Ordinary Court 1815-1816 85 44.07 43.69 0 185

White Terror- Convictions in Provost Court 1815-1816 85 3.15 3.92 0 24

White Terror - Arrests 1815-1816 85 39.79 59.32 0 494

Départements hit by Phylloxera

Départements hit by Phylloxera 1875 89 0.18 0.39 0 1

Départements hit by Phylloxera 1890 89 0.29 0.46 0 1

Religiosity before WWI

Number of Religious Communities Devoted to Educational Purposes 1856 85 36.15 167.28 0 1547

Number of Religious Communities Devoted to Charitable Purposes 1856 85 16.69 77.463 0 712

Number of Religious Communities Devoted to Only ReligiousPurposes 1856 85 7.73 36.06 0 333

Share of Representatives in the lower House of Parliament Who Voted against Separation of Church & State 1905 86 0.68 0.30 0 1

Table D.23: Descriptive Statistics for Variables in Robustness Analysis

Obs. Mean Std.Dev. Min. Max

Population of Departement

Population of Departement 1801 85 641577.8 2933688 110732 27300000

Population of Departement 1821 86 706318.6 3249226 121418 30500000

Population of Departement 1841 86 793475.5 3651846 132584 34200000

Population of Departement 1861 89 837300.4 3925182 125100 37400000

Population of Departement 1881 87 862890.3 4005441 74244 37700000

Population of Departement 1901 87 892279.3 4150369 92304 3.90E+07

Population of Departement 1921 89 876884.7 4138580 89275 3.92E+07

Population of Departement 1968 88 593623.9 791113.2 80736 6648664

Population of Departement 1992 88 649898 821404.6 76948 6285496

Population of Departement 1999 88 698841.7 878124.3 75644 6340619

Population of Departement 2010 88 747640.3 942826 79096.9 6860285

Population of Chef-Lieu of Departement

Population of Chef-Lieu of Departement 1806 88 28030.7 70275.86 857 649412

Population of Chef-Lieu of Departement 1821 88 28839.17 71452.48 2792 657172

Population of Chef-Lieu of Departement 1841 85 38780.45 102935.3 4465 935261

Population of Chef-Lieu of Departement 1861 87 58251.8 184675.9 5139 1696141

Population of Chef-Lieu of Departement 1881 88 73552.09 245154.9 6749 2269023

Population of Chef-Lieu of Departement 1901 88 98459.64 311575.6 7065 2714068

Population of Chef-Lieu of Departement 1921 88 111380.4 353485.3 6109 2906472

Population of Chef-Lieu of Departement 1946 88 122694.7 367106 6010 2725374

Population of Chef-Lieu of Departement 1968 88 158219.7 441138.5 9331 3224442

Population of Chef-Lieu of Departement 1982 88 154265.8 427001.5 9282 3370085

Population of Chef-Lieu of Departement 1999 88 155334.1 428480.4 9109 3427738

Population of Chef-Lieu of Departement 2006 88 154276.4 435911.3 8681 3479900

Ratio of schools to male and female population age 5-15

Ratio of schools to male and female population age 5-15 1876 86 0.013 0.005 0.004 0.029

Ratio of schools to male and female population age 5-15 1881 86 0.013 0.006 0.004 0.054

Ratio of schools to male and female population age 5-15 1886 85 0.013 0.004 0.004 0.028

Ratio of schools to male and female population age 5-15 1891 84 0.011 0.004 0.003 0.021

Ratio of schools to male and female population age 5-15 1896 86 0.014 0.006 0.003 0.029

Ratio of schools to male and female population age 5-15 1901 86 0.016 0.006 0.004 0.033

Infrastructure and Spending on Infrastructure

Roads in Departement’s Territory 1881 (in percent) 86 12.53 3.46 5.00 21.20

Roads in Departement’s Territory 1900 (in percent) 86 5.47 1.86 2.34 12.86

Roads in Departement’s Territory 1913 (in percent) 86 12.70 3.53 1.81 20.65

Area Covered by Railroad withiin Departement’s Territory 1881 (in percent) 85 0.62 0.70 0.14 5.97

Area Covered by Railroad withiin Departement’s Territory 1901 (in percent) 85 0.84 0.53 0.25 4.55

Area Covered by Railroad withiin Departement’s Territory 1913 (in percent) 85 1.00 0.65 0.32 5.91

Total Spending on Road Maintenance 1881 86 3101386 1962050 335044 16200000

Total Spending on Road Maintenance 1900 86 1624075 1062873 218520 7595945

Total Spending on Road Maintenance 1912 86 2757364 1466609 353330 8948850

Contracts Sealed by Notaries

Contrats Sealed by Notaries 1861 88 40001.82 18805.45 8644 139690

Contrats Sealed by Notaries 1901 85 31436.32 22222.62 6157 179727

Contrats Sealed by Notaries 1931 88 33577.77 35862.64 4662 306451

Total Value of Loans from Savings Banks

Total Value of Loans from Savings Banks 1875 86 3132973 2964086 300374 18500000

Total Value of Loans from Savings Banks 1881 86 5864920 5311230 716117 37400000

Total Value of Loans from Savings Banks 1900 85 13200000 15800000 2360311 139000000