Labor scarcity, technology adoption and innovation: evidence from the cholera pandemics in 19th century France

Franck, Raphaël Article — Published Version Labor scarcity, technology adoption and innovation: evidence from the cholera pandemics in 19th century France Journal of Economic Growth Provided in Cooperation with: Springer Nature Suggested Citation: Franck, Raphaël (2024) : Labor scarcity, technology adoption and innovation: evidence from the cholera pandemics in 19th century France, Journal of Economic Growth, ISSN 1573-7020, Springer US, New York, NY, Vol. 29, Iss. 4, pp. 543-583, https://doi.org/10.1007/s10887-024-09241-3 This Version is available at: https://hdl.handle.net/10419/315325 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Journal of Economic Growth (2024) 29:543–583 https://doi.org/10.1007/s10887-024-09241-3 1 3 Labor scarcity, technology adoption andinnovation: evidence fromthecholera pandemics in19th century France RaphaëlFranck1,2,3,4 Accepted: 14 January 2024 / Published online: 29 February 2024 © The Author(s) 2024 Abstract To analyze the impact of labor scarcity on technology adoption and innovation, this study uses the differential spread of cholera across France in 1832, 1849 and 1854, before the transmission mode of this disease was understood. The results suggest that a larger share of cholera deaths in the population, which can be causally linked to summer temperature levels, had a positive and significant short-run effect on technology adoption and innovation in agriculture but a negative and significant short-run impact on technology adoption in industry. These results can be explained by the positive impact of labor scarcity on human capital formation. Keywords Epidemics· Labor scarcity· Technology adoption· Technology-skill complementarity JEL Classification I15· N13· O33 I thank Yoshiaki Azuma, Graziella Bertocchi, Guillaume Blanc, Bruno Caprettini, Francesco Cinnirella, Cédric Chambru, Eve Colson-Sihra, Nicola Fuchs-Schündeln, Oded Galor, Véronique Gille, Tarek Harchaoui, Mariko Klasing, Petros Milionis, Masao Ogaki, Nuno Palma, Josep Pijoan-Mas, Niklas Potrafke, James Rockey, Carla Salvo, Shmuel San and Joseph Zeira, conference participants at the CEPR Macroeconomics and Economic Growth Meeting, Economic History Society, European Public Choice and Royal Economic Society, as well as seminar participants at Cemfi, CesIfo, Doshisha University, Graduate Institute of Policy Studies, Hebrew University of Jerusalem, HSE Moscow, IAST Toulouse, Keio University, Kyoto University, Paris-Dauphine University, University of Birmingham, University of Groningen, University of Manchester, University of Southern Denmark and University of Tokyo for helpful comments. Part of this article was written at the CesIfo Research Institute in Munich whom I thank for its hospitality. Idan Been provided excellent research assistance. I remain solely responsible for any mistakes. This article is dedicated to the memory of Yariv Welzman. * Raphaël Franck Raphael.Franc[email protected] 1 Department ofEconomics, The Hebrew University ofJerusalem, Mount Scopus, 91905Jerusalem, Israel 2 CEPR, London, UK 3 CesIfo, Munich, Germany 4 GLO, Essen, Germany 544 Journal of Economic Growth (2024) 29:543–583 1 3 1 Introduction To explain technology adoption, theoretical studies have developed the macroeconomic implications of production factors which can be either complementary or substitute (see, e.g., Aghion & Howitt, 1992; Zeira, 1998; Howitt, 1999; Acemoglu, 2007; 2010; Alesina etal., 2018). If labor and technology are complementary factors of production, then labor scarcity, whereby skilled and/or unskilled workers are needed to operate machinery, is detrimental to technology adoption.1 If they are substitute, then labor scarcity leads to high wages and is conducive to technology adoption. However, there are only a few empirical analyses for the effects of labor scarcity on technology adoption because obtaining a quasi-experimental framework that could provide causal evidence has turned out to be challenging. This study makes use of data about the cholera pandemics in 1832, 1849 and 1854 across France to provide reduced form estimates for the effect of labor scarcity on technology adoption and innovation.2 In so doing, it asks the following questions: (i) is labor scarcity conducive to technology adoption in agriculture and in industry or not, i.e., are production factors in agriculture and in industry complementary or substitute? (ii) is labor scarcity conducive to technological innovation? and (iii) is labor scarcity conducive to technology adoption and innovation in both the short-run and the mid-to long-run? 19th century France appears well suited for such an empirical analysis. First, the country was hit harshly by the cholera epidemics: it lost 102,739 individuals in 1832, 102,500 in 1849 and 142,749 in 1854, i.e., about 1% of the population died over 22 years.3 However some areas were hit more intensely than others. For instance, the department of Ariège in the South-West of France lost 4.2% of its population during the 1854 pandemic. Second, it was one of the first countries to experience the industrial revolution. Third, the French territory had been divided in small administrative divisions of nearly equal size in 1790 and thus, before the spread of cholera. During the period under study, there were 85 departments which were subdivided into 357 arrondissements: the average size of departments was 6,228km 2 while that of arrondissements was 1966km 2 . In the course of the 19th century, scientists offered competing theories on the spread of cholera and its cure. Although English physician John Snow had already published his first findings in 1849, it was only in 1855 with the second edition of his book that he conclusively demonstrated the role of contaminated water in the spread of the disease (Snow, 1855). And while Italian scientist Filippo Pacini had isolated the Vibrio Cholerae Bacterium in 1854, it was only in 1884 that German scientist Robert Koch would identify the Vibrio Cholerae Bacterium as the source of the disease and subsequently provide a treatment (Koch, 1884). Scientists have, by now, identified the different modes of transmission of cholera (Glass & Black, 1992). In particular, for a country like France whose weather 1 Several studies (e.g., Kremer, 1993; Ashraf & Galor, 2011) noted that historically, technological innovation occurred in densely-populated areas. 2 This paper thus differs from studies which use CES and/or Cobb–Douglas production functions to assess the rate of substitution between labor and technology. In this literature (e.g., Knoblach & Stöckl, 2019, for a recent survey), specific assumptions on estimation equations and technology dynamics have a substantial impact on the estimated parameters. We do not attempt to reproduce our main reduced form regression results with a CES production function given the specificities of our data as we discuss below. 3 To put these figures in perspective, estimates suggest that the Spanish flu in France killed about 0.61% of the population after WWI (238,000 out of 39,108,000 inhabitants) while the Covid-19 pandemic had killed 0.19% by 31 December 2021 (123,805 out of 66,314,842 inhabitants) (Ansart etal., 2009) 545 Journal of Economic Growth (2024) 29:543–583 1 3 is not warm throughout the year, cholera is particularly prone to transmission in the summer and specifically, in regions which are humid. In such an environment, transmission is often possible because the Vibrio cholerae bacterium can survive for six to seven weeks on dry clothes which were previously damp and sweaty. In fact, because the basic rules of microbe transmission and social distancing were unknown at the time, cholera was often spread during funeral wakes when mourners would touch the body of the dead and his/her dry clothes, thereby leading to the mistaken belief that the disease spread through airborne “miasmas”. But even if the spread of cholera before 1855 was not understood and could not be prevented, it is possible to conjecture in hindsight that the diffusion of the pandemics was correlated with local characteristics. While our empirical strategy controls for time-invariant characteristics with fixed effects, it might be the case that cholera spread more easily in areas near rivers where population density increased between 1832 and 1854. Moreover, the relationship between labor scarcity and technology adoption may ultimately reflect the potential effect of institutional, geographical, and cultural characteristics on the joint evolution of the labor supply and technological progress. Given the potential endogeneity in the relationship between labor scarcity and technology, and in light of the historical evidence linking summer temperature levels and humidity to the spread of cholera in France (Delaporte, 1986; Bourdelais & Raulot, 1987), this paper uses the historical weather data of Luterbacher etal. (2004), Luterbacher etal. (2006) and Pauling etal. (2006) to establish the causal impact of the cholera on technology adoption. The empirical analysis shows that summer temperatures in 1832, 1849 and 1854 have a causal impact in the local intensity of cholera deaths in the population of each department. This finding is robust to using Acemoglu etal. (2020)’s maximum likelihood strategy that accounts for interpolation concerns in the measurement of temperature across geographic units. More generally, our results are robust to falsification tests showing that the share of cholera deaths cannot be explained by other seasonal temperature and rainfall levels in other years as well as to pre-trends tests for observable demographic and economic characteristics. The results establish that in the short-run, a larger share of cholera deaths in the population had a positive and significant effect on technology adoption and innovation in agriculture but a negative and significant impact on technology adoption in industry. As such, our results suggest that labor and capital are substitute factors of production in agriculture and complementary in the industrial sector, in line with recent studies on the impact of labor scarcity that rely on policy variations in migration (e.g., Abramitzky etal., 2023; San, 2023). However our findings indicate that the effects of the cholera pandemics on technology adoption and innovation were quantitatively limited.4 A department experiencing a median loss in population because of the cholera epidemics (0.057%) would have adopted 0.28 additional mechanized ploughs per day laborer over the following years but would have had 3.68 fewer steam-powered machines per worker in the year after each epidemic. These results are robust to accounting for spatial autocorrelation using Colella etal. (2020)’s approach as well as for heterogeneous treatment effects using the two-way fixed effects estimators of de Chaisemartin and D’Haultfoeuille (2020). 4 It is possible that pandemics only have a major economic effect on economies when the death toll reaches a high threshold, e.g., when one third of the population died during the Black Death in the Middle Ages. However, since the 19th century, no pandemic in countries out of the Malthusian trap has killed that many people. The public policy implications of our results therefore call for a careful approach as the economic consequences of pandemics may not be as disruptive as one would think. 546 Journal of Economic Growth (2024) 29:543–583 1 3 Moreover, our study suggests that the positive impact of labor scarcity on human capital accumulation can explain our main results. As population loss increased the expected returns to literacy and literate workers were sought out in industrial work (e.g., Katz & Margo, 2014; Atack etal., 2019; Franck & Galor, 2022), the rise in the share of literacy workers in the population offset the immediate negative effect of the population losses on technology adoption in industry. In parallel, this increase in literate workers, who would most likely avoid low-paying work in agriculture, fostered agricultural mechanization. Additional regressions show that this human capital channel for our results is robust to accounting for migration, urbanization, a cultural shift as proxied by a change in religiosity, fertility and nuptiality patterns as well as local financial intermediation. This study is related to three strands of the economics literature but seeks to provide a different perspective. First, it is related to research on pandemics, income shocks and economic growth (e.g., Chakraborty etal., 2010; Adda, 2016; Rasul, 2020; Albanesi & Kim, 2021). Pandemics could spur growth by increasing available resources to surviving individuals, especially for economies at the Malthusian stage of development (Lagerlöf, 2003; Young, 2005; Siuda & Sunde, 2021).5 However, it is difficult to ascertain the impact of pandemics for countries out of the Malthusian trap: while Ambrus etal. (2020) find a long-term impact of the 1854 cholera pandemic on poverty within London, studies on the 1918–1920 Spanish flu (e.g., Barro etal., 2020; Jordà etal., 2020; Lin & Meissner, 2020) concur that it had short-term negative effects but differ as to its actual long-run persistence. Second, this paper is related to research seeking to explain technology adoption during the industrial revolution in the 19th century (e.g., Mokyr, 2009; Akcigit etal., 2017; Juhász, 2018; Caprettini & Voth, 2020; Franck & Galor, 2022). Research starting with Habakukk (1962) has argued that labor scarcity, and the ensuing high wages, led to the adoption of machinery. It is however unclear whether high wages in England and the USA actually stemmed from the relative abundance of coal or land, or from the presence of skilled workers with high levels of productivity(see, e.g., Kelly etal., 2014; Stephenson, 2018). Relatedly, the recent study of Voth etal. (2022) uses exogeneous local variation in gender imbalance triggered by mass conscription in England during the Revolutionary and Napoleonic wars, and finds that this type of labor scarcity fostered technology adoption in the early phase of the industrial revolution. Third, this study is related to research assessing the impact of labor market conditions on the adoption of labor-saving technology: these include Acemoglu and Finkelstein (2008) on healthcare, Manuelli and Seshadri (2014) and Hornbeck and Naidu (2014) on agriculture, Lewis (2011) on manufacturing, Acemoglu and Restrepo (2022) on the link between demographic factors and technology adoption as well as Dechezleprêtre etal. (2019) on the effects of labor costs on automation.6 In this respect, most of the recent literature on labor scarcity takes advantage of changes in migratory policies in the shortand mid-run (e.g., Moser etal., 2014; Clemens etal., 2018; Abramitzky etal., 2023; San, 2023). This study however seeks to give a different perspective by providing causal evidence over a 50-year period for the effects of labor scarcity caused by a disease whose transmission mode was then not understood and which had no cure. 5 The Black Death in Western Europe seems to have been conducive to growth in the long-run but its effects were different in Eastern Europe (e.g., Voigtländer & Voth, 2013; Jedwab etal., 2019). 6 Other studies dealing with the relative scarcity of production factors on technological adoption include Newell etal. (1999) and Hassler etal. (2021) on the rise of energy prices and scarce natural resources as well as Hanlon (2015) on cotton. 547 Journal of Economic Growth (2024) 29:543–583 1 3 The remainder of this article is as follows. Section2 presents the data and Sect.3 the empirical strategy. Section4 discusses the main results. Section5 shows that the increase in human capital explains our main results and establishes that alternative mechanisms do not provide convincing explanations. Section6 concludes. 2 Data The dataset comprises information on the 85 departments and 357 arrondissements in mainland France, as well as on individuals living across the country, during and after the 1832, 1849 and 1854 cholera pandemics.7 As we note below, information is sometimes missing for some outcome variables immediately after 1832 and in those instances, we are therefore compelled to restrict the sample to the aftermath of the 1849 and 1854 pandemics. Table A.1 reports the descriptive statistics for the variables in the empirical analysis across the departments and arrondissements as well as for the variables used in the individual-level analysis. Tables A.2 and A.3 provide descriptive statistics for the additional variables employed in falsification tests and robustness analyses. 2.1 Cholera outbreaks 2.1.1 Cholera transmission channels Cholera is a waterborne disease which is most vibrant between 15 and 25 Celsius degrees. But if drinking contaminated water remains the most well-known mode of catching the disease because of Snow (1855)’s seminal study, modern research (Glass & Black, 1992) has demonstrated that there are several transmission channels for the cholera. The bacterium can indeed survive and adapt to different environments so that it is not only observed in populated located maritime areas, rivers and lakes, but also in dry areas, notably in Africa (Stock, 1976; Cliff etal., 1986). Transmission modes of the disease other than contaminated water include contaminated food, fomites (inanimate objects such as clothes that have been exposed to the infection) as well as person-to-person transmission. All these transmission channels interact with one another under various weather conditions to spread the disease. In fact, in the 19th century, transmission was very common along travel routes as well as during funeral wakes when mourners touched the body and the clothes of the dead. This is possible because the cholera bacterium can remain alive during six to seven weeks on dry clothes which were contaminated when they were damp and sweaty. Furthermore, the recurrent seasonal pattern has also been shown to differ across various areas of the world. For instance, in the 320km-long Cheaseapeake Bay on the US east coast, it has been observed that warmer summer temperatures entail a resurgence of the cholera and that the bacterium is more prevalent in the north of the Bay (where temperatures are slightly lower than in the south) because of the difference in the salinity and 7 The analysis is restricted to mainland France and excludes Corsica where no death from cholera was recorded in 1832 and 1849, and where there were only 220 cholera deaths out of 236,251 inhabitants in 1854 (0.09% of the population). Moreover, three new departments (Alpes-Maritimes, Haute-Savoie and Savoie) were added to France in 1860. Since they were not part of France during the 1832, 1849 and 1854 pandemics, they are excluded from the analysis. 548 Journal of Economic Growth (2024) 29:543–583 1 3 humidity levels (Colwell, 2004; St.Laurent etal., 2021). As we discuss in the next section, this is a similar pattern to the incidence of cholera in 19 th c. France, whereby cholera was more prevalent in the north than in the south during the summer. 2.1.2 Cholera in19th century France To build the main explanatory variable on the intensity of cholera outbreaks in 1832, 1849 and 1854, the study uses the official statistics provided by the French government on the share of cholera deaths within the population of each department (France, 1862). As can be seen in Fig.1, the three cholera pandemics mainly affected the north of France and the Atlantic Coast. The south of France was only hit harshly in 1854.8 Only 11 departments located in the hinterland south-west of the French territory were spared in the three cholera outbreaks (Cantal, Corrèze, Creuse, Dordogne, Gers, Landes, Lot, Lozère, Hautes-Pyrénées, Vienne and Haute-Vienne). Here two remarks are important. First it must be noted that before 1855, the transmission mode of the cholera had not been conclusively established. At a time where basic knowledge about microbes was just being discovered, some scientists were mistakenly arguing that there were airborne “miasmas”which explained the diffusion of the disease. As such, avoiding polluted water sources, as well as proper hygiene and social distancing, did not play a role in the behavior of individuals: since no-one knew how the disease spread, it was not even clear that running away from areas affected by the cholera could offer any protection.9 Second, the disease was a problem for the central State, the local governmental authorities, the Church as well as the local associations. However there was no health policy which any government or organization could implement to stop the disease. As can be seen in Table1, the distribution of cholera deaths within the population of each department is skewed: the 25th percentile is equal to 0, the median 0.057%, the 75th percentile 0.30% and the 99th percentile 2.84%. This reflects the fact that the disease reached most departments at least once in either 1832, 1849 and 1854, but only a few were hit harshly. Nonetheless, 20 departments lost more than 1% of their population in at least one of the three outbreaks. Tables B.1 and B.2 provide additional descriptive statistics and tests regarding the share of cholera deaths in the population. Table B.1 distinguishes between the gender and age of the victims during the 1854 pandemic while Table B.2 focuses on the share of victims by distinguishing departments by their mean and median population in each of the three pandemics. The tests of means reported in both Tables B.1 and B.2 are never significant, thereby alleviating concerns that some sections of the population would be more (or less) likely to die from exposure to the cholera. In particular, the tests in Table B.1 suggest that our results cannot be driven by the gender and/or age of the cholera victims within the population of the departments hit by the cholera while those in Table B.2 indicate that they cannot be driven by the size of the departmental population and hence by the propensity of the 8 Anecdotal evidence suggests that each time, the cholera came by boat from England. It only spread to the south-east of France in 1854 because of the French soldiers who embarked from the southern harbors of Toulon and Marseille to fight the war in Crimea. 9 The French population soon came to refer to the cholera as the “blue fear”(peur bleue) because of the blue coloration that the faces of sick individuals would take just before dying. The expression “peur bleue”is still commonly used in French and refers to something which is terrifying. 549 Journal of Economic Growth (2024) 29:543–583 1 3 victims to inhabit urban or rural departments. The results are not surprising and in line with the discussion in the previous sub-section that person-to-person transmission was a major cause of the spread of the cholera in the 19th century, and was as common as contamination through polluted water. It is therefore not surprising that the prevalence of the cholera is not related to population density and social status. It is worth noting that there were additional cholera outbreaks in 19th century France, i.e., in 1884 and 1892. However, they occurred after 1855, when the transmission mode of the cholera had been finally established by Snow (1855). As a result, it is preferable to restrict the main analysis to the pre-1855 cholera outbreaks: this avoids endogeneity concerns that specific areas might become more efficient than others in preventing the spread of the disease once the mode of contagion was known. In this respect, we show in Table B.3 that the spread of cholera before 1855 was not correlated with its spread in 1884 and 1892 whose consequences were more limited because local authorities then understood and could prevent the diffusion of the disease. Table B.4 further shows that the 1832, 1849 and 1854 cholera pandemics were not correlated with the various causes of deaths in each department in 1855. Moreover, Table B.5 shows that the cholera pandemics in 1832, 1849 and 1854 are not correlated with the spread of illnesses before the 19th century insofar as there is no correlation with the number of towns hit by the spread of the plague in the 18th c. in each department. 2.2 Summer temperature in19th century France As established by modern research (e.g., Glass & Black, 1992), the Vibrio Cholerae Bacterium quickly spreads in humid environments where temperatures are above 15 degrees Celsius. This implies two predictions for the diffusion of cholera in France. First, cholera mainly spreads during the summer because this is the season when temperatures in France are above 15 degrees Celsius for a long time period. Second, cholera is more likely to spread in the North than in the South of France because relative humidity is always higher in northern areas where temperatures are always relatively lower. While this second point might seem slightly counter-intuitive to the reader because humans feel humidity more accurately (and hence experience more discomfort) at higher levels of temperature, it is actually the case that relatively lower temperatures entail more relative humidity because they enable for less water evaporation (Wallace & Hobbs, 1977; Lutgens & Tarbuck, 2015). In the case of France, the regression results in Table C.1 use modern weather data from 42 weather stations in 2018 and establish that lower temperatures are indeed associated with higher relative humidity, accounting for weather station fixed effects as well as month-, dayand hourfixed effects.10 Given the properties of the Vibrio Cholerae Bacterium and the historical context, our identification strategy predicts that (1) temperatures in the summer of 1832, 1849 and 1854, and not in any other season or in any other year, are significantly correlated with the spread of cholera because this is the only time period where temperatures remain above 10 The negative correlation between temperature and relative humidity is not specific to France. For instance, (2019, Table1) report that in China, where temperatures in the North are lower than in the South, there is a negative correlation between mean temperature and relative humidity throughout the year that is only significant at the 5% level during the summer. For the sake of the argument, it should also be noted that the Sahara desert is located to the South of the Mediterranean sea and that this desertic area is dryer than the coastal Mediterranean areas of North Africa. 550 Journal of Economic Growth (2024) 29:543–583 1 3 15 degrees Celsius and that (2) summer temperature levels in 1832, 1849 and 1854 would be negatively correlated with the spread of cholera because northern French departments experienced relatively lower temperatures, and hence more relative humidity, than southern departments. Anecdotal evidence on the monthly spread of cholera in 1854 seems to support this prediction: Fig.2 shows that the disease spread from the north of the country and claimed the highest number of victims in July, August and September.11 Our study relies on the historical weather data of Luterbacher etal. (2004), Luterbacher etal. (2006) and Pauling etal. (2006). These data were reconstructed using various sources such as lake sediments and tree rings as well as historical records for every season over the 1500–1900 period at a resolution of 0.5 by 0.5 decimal degrees. There are therefore concerns about measurement error and the interpolation of climatic data over departments, i.e., two cells per department on average. Still Luterbacher etal. (2004), Luterbacher etal. (2006) and Pauling etal. (2006) show that the quality of the data improve over time, especially from the end of the 18 th c. onward. Figure3 maps those data for the summers of Fig. 1 Share of cholera deaths out of departmental population, 1832, 1849 and 1854. Note The source of the map layer is Daudin etal. (2019). Table 1 The distribution of the percentage of cholera deaths in the population across French departments in 1832, 1849 and 1854 This table reports descriptive statistics for the percentage of cholera deaths in the population across the 85 French departments in 1832, 1849 and 1854. The total French population amounted to 32,443,430 inhabitants in 1832, 36,910,360 in 1849 and 35,782,708 in 1854. Mean 25 th 50 th 75 th 90 th 99 th 1832 0.26 0 0.01 0.26 0.86 2.35 1849 0.20 0 0.02 0.22 0.88 1.70 1854 0.46 0.009 0.16 0.61 1.36 4.20 All Years Combined 0.31 0 0.06 0.30 0.90 2.84 11 More generally, it must be that acknowledged that water bodies could have increased the transmission of the cholera. However, since the empirical strategy which we discuss in Sect.3 uses department fixed effects, it is unlikely that water bodies can systematically explain variations in the spread of the cholera in 1832, 1849 and 1854. 557 Journal of Economic Growth (2024) 29:543–583 1 3 3.2 Summer temperatures andcholera deaths inthepopulation: first‑stage regression results andtests forpre‑trends 3.2.1 First‑stage regression results In line with the historical evidence on the spread of cholera in 19th century France, where the disease mainly hit northern departments during the summers of 1832, 1849 and 1854, Table 3 shows that the summer temperature instrument has a negative and significant effect on the share of cholera deaths in the population (the complete specifications with the control variables are shown in Table D.1). In all the specifications using robust clustered standard errors at the department level, this negative effect is significant at the 1% level. To ensure the robustness of our results, we also compute the standard errors with the maximum likelihood estimation strategy of Acemoglu etal. (2020) that corrects for measurement error and geographic correlation in rainfall measurement. These standard errors are reported in curly brackets in Table3: they confirm the significant and negative effect of summer temperature on the share of cholera deaths in the population. The estimate in Column 1 of Table3 suggests that a 1% decrease in summer temperature levels increased the share of cholera deaths in the population by 11.8%. Hence, for a department experiencing a decrease in temperature from the 75th percentile of summer temperature (18.10 degrees Celsius) to the 50th percentile (i.e., 17.38 degree Celsius), this 4.03% decrease in temperature would entail 0.6% more in the share of cholera deaths in the population, i.e., a decline equal to one standard deviation. Thus, in line with the historical evidence, these computations suggest that the successive cholera pandemics entailed a substantial loss of population. 3.2.2 Falsification tests androbustness checks forpre‑trends To enhance the credibility of our identification strategy, we present several falsification tests and robustness checks for pre-trends. They show that neither summer temperatures nor cholera deaths are correlated with potentially omitted variables pertaining to the preexisting characteristics of the departments that could drive their vulnerability to the cholera epidemics and their subsequent adoption of technology. Note that we already discussed the following robustness checks in Sect.2: (i) Tables B.1 and B.2 show that all population groups (distinguished by age or gender, urban or rural) were equally affected by the cholera; (ii) Tables B.3 and B.4 show that the numbers of victims in the 1832, 1849 and 1854 cholera pandemics were not correlated with the numbers of victims from various causes of death in each department in 1855 or with the numbers of victims in the minor cholera outbreaks in 1884 and 1892 (which occurred after the transmission mode of the disease was understood); (iii) Table B.5 shows that the diffusion of cholera pandemics in 1832, 1849 and 1854 is not correlated with the number of towns hit in each department by the spread of the plague in the 18 th century and (iv) Table B.7 shows that there are no significant differences in the prices of imported machinery in agriculture and industry that could potentially drive the results. 558 Journal of Economic Growth (2024) 29:543–583 1 3 In what follows, we summarize the additional falsification tests which we carry out in support of our identification strategy. In the Appendix, we present the data sources and report the regression results. Cholera, temperatures and rainfall. Because weather data are correlated over time, a potential concern regarding the identification strategy is that the significant effect of summer temperature levels on cholera deaths in the year of each pandemic can be attributed to the general effect of summer temperatures in other years, and is correlated with temperatures in other seasons and with rainfall. Reassuringly, the share of cholera deaths is not correlated with summer temperatures in the years just before or after the cholera outbreaks in Table D.2. Moreover, in the years of cholera outbreaks, the share of cholera deaths in the population is not correlated with temperatures in spring, fall and winter in Table D.3,19 with summer temperature shocks in Table D.4 and with rainfall in spring, fall and winter in Table D.5.20 In Table D.5 however, summer rainfall is significantly correlated with the Table 3 Summer temperature levels and share of cholera deaths in the population This table reports the first stage estimates relating summer temperature levels to the share of cholera deaths in the population in 1832, 1849 and 1854. Geographic controls for departments, which are interacted with year-fixed effects, include their land suitability, their share of carboniferous area and dummies for border and maritime departments. Constant not reported. All variables are in logarithm. Robust standard errors clustered at the department level are reported in brackets. Robust standard errors clustered at the departement level using the Maximum Likelihood approach of Acemoglu etal. (2020) are reported in curly brackets. ∗∗∗ p < 0.01,∗∗ p < 0.05,∗p < 0.1 (1) (2) (3) First stage: the instrumented variable is Share of Cholera Deaths in Population Summer temperature − 0.118*** − 0.141*** − 0.140*** [0.0271] [0.0303] [0.0308] {0.044}∗∗∗ {0.058}∗∗ {0.061}∗∗ Mean dep.var 0.0031 0.0031 0.0031 1st stage F-stat 19.012 21.652 20.788 Moran I − 0.008 − 0.008 − 0.008 Moran I p-value 0.212 0.209 0.210 Department and year fixed effects Yes Yes Yes Deviation from summer rainfall No Yes Yes Geographic controls * year fixed effects No Yes Yes GDP per capita No No Yes Clusters 85 85 85 Observations 255 255 255 19 As can be seen in Table D.3, there are specifications where temperatures in other seasons are sometimes significantly, but not systematically, correlated with the spread of the cholera. In other words, they may not indicate a significant impact on the spread of the cholera so much as a temporal correlation with summer temperatures. Thus these regressions suggest that it is best to use summer temperature as the sole instrument, which is significant throughout, instead of cherry-picking the data and use different seasonal temperatures in different regressions. 20 The results in Table D.4 provide support for the validity of the exclusion restriction: different temperatures explain the differential impact of the pandemics across the French departments, but they were not outliers and therefore were unlikely to impact other variables, most notably agricultural yields. In Sect.4 559 Journal of Economic Growth (2024) 29:543–583 1 3 spread of the cholera in the first stage regressions, although its effect is quantitatively small. Since summer rainfall is not systematically significant in many 2nd stage and reduced form regressions, it seems that its limited impact is captured by summer temperature. Furthermore, Table D.6 shows that there is a statistically significant relationship between summer temperatures in 1832, 1849 and 1854 in log and level, and the share of cholera deaths in 1832, 1849 and 1854, but not between the latter and squared summer temperatures in level (which gives equal weight to abnormal rainfall and droughts) in those years. Finally Table D.7 shows that there is a statistically significant relationship between summer temperatures in 1832, 1849 and 1854 in log and level, and the share of cholera deaths in 1832, 1849 and 1854, but not a systematically significant relationship between the latter and seasonal rainfall, even when the interaction variables between summer temperatures and seasonal rainfall are included. Pre-pandemic trade and industry. A potential concern regarding the exogeneity of the relationship between summer temperature and cholera deaths pertains to trade and industry. In particular, it is possible that the transport of goods within France, and the associated circulation of people, would be correlated with weather conditions and would have an impact on the spread of the pandemic. Reassuringly, both Tables D.8 and D.9 show that there is no relationship between internal trade and temperature as well as between internal trade and the spread of cholera. In addition, Table D.10 shows that summer temperature and technology adoption in industry were not correlated before the first cholera pandemic in 1832. Namely, in 1789, 1811 and 1815, summer temperatures had no significant impact on the numbers of iron forges and mechanical mills in the cotton industry. Entry points and diffusion hubs It is possible that the local share of cholera deaths could be correlated with the initial point of entry of the disease or with a specific hub of diffusion such as a major city. Unfortunately, no-one knows with absolute certainty what the disease’s points of entry were. Each time, the cholera most likely came from England. At best, it can be said that the first cases of cholera were noticed in the Manche department in 1832, in the Aisne department in 1849 and in the Nord department in 1854. The Manche and Nord departments are located on the English Channel but the Aisne department is a landlocked area, making it even less likely to determine the point of entry in 1849 (Bourdelais & Raulot, 1987; France - Ministère de l’agriculture, 1862). Table D.11 tests the hypothesis that the intensity of the cholera pandemics would be correlated with London or with potential points of entry and diffusion hubs such as Paris, harbors like Rouen and Marseille, or Fresnes-sur-Escaut, the mining village in the Nord department where the first steam engine was used for industrial purposes in France in the 18th century. The results show that the distance between each of these hubs and the main administrative center of each department has no impact on the significant effect of summer temperature on the local share of cholera deaths in 1832, 1849 and 1854. Pre-pandemic characteristics of the population. Table D.12 shows that the first stage relationship is not influenced by omitted variables linking summer temperatures and the number of deaths in each department over time. Furthermore, Tables D.13 and D.14 show that summer temperatures and cholera deaths were not correlated with the number and below, we provide evidence for the lack of a persistent impact of the pandemics on agricultural yields and land rents. Footnote 20 (continued) 560 Journal of Economic Growth (2024) 29:543–583 1 3 density of inhabitants as well as with the age structure of each department prior to the 1832, 1849 and 1854 cholera pandemics.21 Pre-pandemic human capital and wealth. It could be conjectured that the share of cholera deaths in the population was correlated with the relative presence of poor/rich individuals or of educated/uneducated individuals. While there is no historical evidence suggesting that the cholera victims were characterized by specific social statuses or income levels, Tables D.15, D.16, D.17 are meant to assuage concerns regarding a possible link between cholera deaths, education and wealth. Thus, in line with the historical evidence, Table D.15 shows that the cholera claimed victims among different occupational groups, whether rich (e.g., shipowners), poor (e.g., tenant farmers) or educated (e.g., clergymen, professors & teachers).22 Furthermore, Table D.16 shows that there is no significant relationship between the share of cholera deaths in the population, the probability that the dead left an inheritance as well as the value of the inheritance. Finally, Table D.17 shows that the cholera pandemics were not correlated with human capital as proxied by the likelihood that individuals born one to 20 years before each pandemic could sign their wedding license (as opposed to mark it with a cross). 4 Results: Short‑term effects ontechnology adoption andinnovation This section explores the effect of the cholera pandemics on technology adoption and innovation in agriculture and industry. The regression results in Tables4, 5, 6 and 7 suggest that the cholera epidemics had short-term and quantitatively small effects on technology adoption and innovation (Appendix E reports the regression results with the full set of controls). These effects were conducive to technology adoption in agriculture but not in the industrial sector. In addition, the results suggest that the cholera epidemics entailed labor reallocation from the agricultural to the industrial sectors. They also indicate that the negative effects of labor scarcity lasted longer in the textile sector than in the industrial sector. The results are robust to the inclusion of control variables, including GDP per capita, thereby making it unlikely that they are driven by short-term negative income effects. In our results, our IV estimates for the effect of the cholera epidemics on technology adoption are two to three times larger than the OLS coefficients. A possible interpretation of these findings is that our regressions suffer from errors in variables and attenuation bias: while there is no evidence that the local civil servants who collected data on the number of cholera deaths sought to minimize or inflate the impact of the epidemics, some might have collected data more diligently than others. Another explanation is that our IV estimates reflect the expectations of individuals regarding the consequences of the cholera 22 The size of the coefficients in Table D.15 is not the same for all occupations. However, it is probably best not to provide an interpretation for the size of the coefficients. It is possible to speculate that textile workers were more negatively affected than blacksmiths or professors because their sector entailed more trade and interaction. However, the coefficient regressions also suggest that transport workers and members of the clergy were less affected by the cholera in 1854 than textile workers, even though they also interacted with many sections of the population. 21 The regression in Column 1 of Table D.13 has one fewer observation than the other regressions (254 instead of 255) because the Tarn-et-Garonne department was only created in 1808. Its future territory was split between neighbouring departments (mostly Lot and Haute-Garonne). We chose not to adjust the GDP per capita for these departments for this falsification test because it is unclear how we would account for the exact income differences between the various areas of Lot and Haute-Garonne. 561 Journal of Economic Growth (2024) 29:543–583 1 3 Table 4 The effects of the cholera in 1849 and 1854 on the Number and horse power of machines per worker in the mining industry one year after each pandemic (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Average number of steam-powered machines Average horse power of steam-powered machines per Worker Year t+1 per Worker Year t+1 Share of cholera deaths in population − 24.33*** − 30.79*** − 28.51*** − 75.52** − 64.49** − 32.98** − 37.52** − 34.61** − 104.7** − 91.55** [8.538] [9.632] [8.720] [31.71] [28.28] [13.66] [15.18] [14.38] [46.60] [43.80] Department and year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Deviation from summer rainfall No Yes Yes Yes Yes No Yes Yes Yes Yes Geographic controls No Yes Yes Yes Yes No Yes Yes Yes Yes GDP per capita No No Yes No Yes No No Yes No Yes Within R2 0.174 0.228 0.255 0.123 0.137 0.155 Mean dep.var 38.709 38.709 38.709 38.709 38.709 401.809 401.809 401.809 401.809 401.809 Clusters 85 85 85 85 85 85 85 85 85 85 Observations 170 170 170 170 170 170 170 170 170 170 First stage: the instrumented variable is share of cholera deaths in population Summer Temperature − 0.180*** − 0.179*** − 0.180*** − 0.179*** [0.0471] [0.0485] [0.0471] [0.0485] 1st stage F-stat 14.602 13.577 14.602 13.577 Reduced form: the dependent variable is Average number of steam-powered machines Average horse power of steam-powered machines Per Worker Year t+1 Per Worker Year t+1 Summer Temperature 13.60** 11.53** 18.86** 16.37** [5.977] [5.357] [8.808] [8.172] 562 Journal of Economic Growth (2024) 29:543–583 1 3 This table presents OLS and IV regressions relating the share of cholera deaths in each department to the number and horse power of steam-powered machines per worker as well as to the number of boilers and steam generators per worker in the mining sector in the year after each cholera outbreak. Geographic controls for departments, which are interacted with year-fixed effects, include their land suitability, their share of carboniferous area and dummies for border and maritime departments. Constant not reported. Robust standard errors clustered at the department level. ∗∗∗ p < 0.01,∗∗ p < 0.05,∗p < 0.1 Table 4 (continued) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Average number of steam generators Average number of boilers Per Worker Year t+1 Per Worker Year t+1 Share of Cholera Deaths in Population − 21.29** − 27.51*** − 24.89*** − 86.21** − 74.20** − 13.64 − 20.14* − 20.51* − 90.47** − 99.37** [8.887] [10.31] [9.427] [34.05] [30.86] [10.50] [10.35] [10.36] [38.90] [41.65] Department and year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Deviation from summer rainfall No Yes Yes Yes Yes No Yes Yes Yes Yes Geographic controls No Yes Yes Yes Yes No Yes Yes Yes Yes GDP per capita No No Yes No Yes No No Yes No Yes Within R2 0.131 0.182 0.215 0.211 0.329 0.330 Mean dep.var 40.091 40.091 40.091 40.091 40.091 45.667 45.667 45.667 45.667 45.667 Clusters 85 85 85 85 85 85 85 85 85 85 Observations 170 170 170 170 170 170 170 170 170 170 First stage: the instrumented variable is share of cholera deaths in population Summer temperature − 0.180*** − 0.179*** − 0.180*** − 0.179*** [0.0471] [0.0485] [0.0471] [0.0485] 1st stage F-stat 14.602 13.577 14.602 13.577 Reduced form: the dependent variable is Average Number of Steam Generators per Worker Year t+1 Average number of boilers per worker year t+1 Summer temperature 15.53** 13.26** 16.30** 17.76** [6.325] [5.718] [7.479] [7.237] 563 Journal of Economic Growth (2024) 29:543–583 1 3 Table 5 The effects of the cholera in 1849 and 1854 on employment, wages and production in the wake of each pandemic (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Average Number of Workers Year t+1 Average Wage per Worker Year t+1 Share of cholera deaths in population 3.960 9.768 8.128 38.65 30.41 − 24.46 − 11.48 − 12.78 99.52 101.0 [7.685] [9.736] [8.917] [33.55] [31.01] [22.63] [22.71] [22.23] [93.35] [92.91] Department and year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Deviation from summer rainfall No Yes Yes Yes Yes Yes Yes Yes Yes No Geographic controls No Yes Yes Yes Yes Yes Yes Yes Yes No GDP per capita No No Yes No Yes No Yes No Yes No Within R2 0.064 0.326 0.341 0.009 0.130 0.131 Mean dep.var 176.404 176.404 176.404 176.404 176.404 66.535 66.535 66.535 66.535 66.535 Clusters 85 85 85 85 85 85 85 85 85 85 Observations 170 170 170 170 170 170 170 170 170 170 First stage: the instrumented variable is share of cholera deaths in population Summer temperature − 0.180*** − 0.179*** − 0.180*** − 0.179*** [0.0471] [0.0485] [0.0471] [0.0485] 1st stage F-stat 14.602 13.577 14.602 13.577 Reduced form: the dependent variable is Average number of workers year t+1 Average wage per worker year t+1 Summer Temperature − 6.961 − 5.436 − 17.93 − 18.06 [6.389] [5.804] [16.01] [15.70] (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Average Value of Extracted Coal (t+2)-(t+3) Average Value of Extracted Peat (t+2)-(t+3) Share of Cholera Deaths in Population − 2.765 − 2.409 3.615 − 2.625 2.732 − 9.316** − 11.37** − 10.98** − 25.95** − 24.71* [2.242] [1.832] [6.429] [1.867] [6.724] [4.672] [5.090] [5.254] [12.06] [13.79] Departmentand year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Deviation from summer rainfall No Yes Yes Yes Yes No Yes Yes Yes Yes 564 Journal of Economic Growth (2024) 29:543–583 1 3 This table presents OLS and IV regressions relating the share of cholera deaths in each department to the number and wage of workers in the mining sector in the year after each cholera outbreak and to the values of extracted coal and peat two and three years after each cholera outbreak. Geographic controls for departments, which are interacted with year-fixed effects, include their land suitability, their share of carboniferous area and dummies for border and maritime departments. Constant not reported. Robust standard errors clustered at the department level. ∗∗∗ p < 0.01,∗∗ p < 0.05,∗p < 0.1 Table 5 (continued) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Average Value of Extracted Coal (t+2)-(t+3) Average Value of Extracted Peat (t+2)-(t+3) Geographic controls No Yes Yes Yes Yes No Yes Yes Yes Yes GDP per capita No No Yes Yes No No No Yes No Yes Within R2 0.091 0.157 0.162 0.367 0.464 0.467 Mean dep.var 0.527 0.527 0.527 0.527 0.527 0.234 0.234 0.234 0.234 0.234 Clusters 85 85 85 85 85 85 85 85 85 85 Observations 170 170 170 170 170 170 170 170 170 170 First stage: the instrumented variable is share of cholera deaths in population Summer temperature − 0.180*** − 0.179*** − 0.180*** − 0.179*** [0.0471] [0.0485] [0.0471] [0.0485] 1st stage F-stat 14.602 13.577 14.602 13.577 Reduced form: the dependent variable is Average value of extracted coal (t+2)-(t+3) Average value of extracted peat (t+2)-(t+3) Summer temperature − 0.651 − 0.488 4.675** 4.417 [1.157] [1.213] [2.330] [2.660] 565 Journal of Economic Growth (2024) 29:543–583 1 3 Table 6 The effects of the cholera in 1849 and 1854 on the number of mechanized ploughs and animal-powered threshing machines per day laborer and on the number and wage of agricultural day laborers in 1852 and 1862 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Mechanized ploughs per day laborer Animal-powered threshing machines per day laborer Share of cholera deaths in population 67.29** 55.09* 58.35* 323.6*** 369.9*** 18.90* 18.32** 18.58** 2.708 3.002 [28.36] [29.67] [29.65] [117.4] [135.1] [9.529] [8.501] [8.516] [7.922] [7.940] Departmentand year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Deviation from summer rainfall No Yes Yes Yes Yes No Yes Yes Yes Yes Geographic controls No Yes Yes Yes Yes No Yes Yes Yes Yes GDP per capita No No Yes No Yes No No Yes No Yes Within R2 0.615 0.672 0.674 0.354 0.488 0.49 Mean depvar 2.80 2.81 2.82 2.83 2.84 0.1 0.1 0.1 0.1 0.1 Clusters 85 85 85 85 85 85 85 85 85 85 Observations 170 170 170 170 170 170 170 170 170 170 First stage: the instrumented variable is share of cholera deaths in population Summer temperature − 0.180*** − 0.179*** − 0.180*** − 0.179*** [0.0471] [0.0485] [0.0471] [0.0485] 1st stage F-stat 14.602 13.577 14.602 13.577 Reduced form: the dependent variable is Mechanized ploughs per day laborer Animal-powered threshing machines per day laborer Summer temperature − 58.29*** − 66.12*** − 0.488 − 0.537 [16.49] [18.28] [1.531] [1.549] (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Number of day laborers Average wage of day laborers Share of cholera deaths in population − 15.56*** − 12.39** − 11.32** − 43.19*** − 38.86*** 0.0072 0.0051 0.0039 0.0353** 0.0304* [5.449] [5.467] [5.452] [14.68] [14.63] [0.005] [0.006] [0.006] [0.018] [0.018] Departmentand year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 566 Journal of Economic Growth (2024) 29:543–583 1 3 This table presents OLS and IV regressions relating the share of cholera deaths in each department to the number of mechanized ploughs and animal-powered threshing machines per day laborer. Geographic controls for departments, which are interacted with year-fixed effects, include their land suitability, their share of carboniferous area and dummies for border and maritime departments. Constant not reported. Robust standard errors clustered at the department level. ∗∗∗ p < 0.01,∗∗ p < 0.05,∗p < 0.1 Table 6 (continued) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Number of day laborers Average wage of day laborers Deviation from summer rainfall No Yes Yes Yes Yes No Yes Yes Yes Yes Geographic controls No Yes Yes Yes Yes No Yes Yes Yes Yes GDP per capita No No Yes No Yes No No Yes No Yes Within R2 0.924 0.934 0.936 0.464 0.576 0.593 Mean dep.var 26661.65 26661.65 26661.65 26661.65 26661.65 0.02 0.02 0.02 0.02 0.02 Clusters 85 85 85 85 85 85 85 85 85 85 Observations 170 170 170 170 170 170 170 170 170 170 First stage: the instrumented variable is share of cholera deaths in population Summer temperature − 0.180*** − 0.179*** − 0.180*** − 0.179*** [0.0471] [0.0485] [0.0471] [0.0485] 1st stage F-stat 14.602 13.577 14.602 13.577 Reduced form: the dependent variable is Number of day laborers Average wage of day laborers Summer temperature 7.780*** 6.947*** − 0.00637** − 0.00543* [2.428] [2.596] [0.00315] [0.00324] 573 Journal of Economic Growth (2024) 29:543–583 1 3 Table 8 The effects of the cholera in 1832, 1849 & 1854 on the signatures of wedding licenses by spouses born one to 20 years after each cholera pandemic (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) OLS OLS OLS 2SLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS 2SLS Signature of wedding license for All individuals Individuals working in agriculture Born 1 to 20 years after each epidemic Share of cholera deaths in population 6.754*** 5.983*** 5.554*** 27.89*** 25.69*** 28.13*** 5.991* 4.962 3.454 39.37*** 42.48** 40.72** [1.324] [1.379] [1.685] [4.940] [5.950] [6.729] [3.329] [3.462] [4.002] [14.40] [17.21] [16.12] Male − 0.00912 − 0.00906 − 0.00905 − 0.00932 − 0.00905 − 0.00915 − 0.0126 − 0.0129 − 0.0130 − 0.0114 − 0.0117 − 0.0117 [0.00694] [0.00693] [0.00693] [0.00698] [0.00696] [0.00697] [0.0145] [0.0145] [0.0145] [0.0144] [0.0143] [0.0143] Departmentand year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Deviation from summer rainfall No Yes Yes No Yes Yes No Yes Yes No Yes Yes Geographic controls No Yes Yes No Yes Yes No Yes Yes No Yes Yes GDP per capita No No Yes No No Yes No No Yes No No Yes Mean dep.var 0.797 0.797 0.797 0.797 0.797 0.797 0.731 0.731 0.731 0.731 0.731 0.731 Moran I − 0.0002 − 0.0002 − 0.0002 − 0.0002 − 0.0002 − 0.0002 0.002 0.002 0.002 − 0.001 − 0.001 − 0.001 Moran I p-value 0.246 0.246 0.246 0.246 0.246 0.246 1.000 1.000 1.000 0.242 0.243 0.243 Clusters 3085 3085 3085 3085 3085 3085 1744 1744 1744 1744 1744 1744 Observations 11,953 11,953 11,953 11,953 11,953 11,953 3,224 3,224 3,224 3,224 3,224 3,224 First stage: the instrumented variable is Share of Cholera Deaths in Population Summer temperature − 0.0825*** − 0.0708*** − 0.0628*** − 0.0510*** − 0.0443*** − 0.0472*** [0.00571] [0.00545] [0.00460] [0.00531] [0.00516] [0.00497] 1st stage F-stat 208.975 168.945 186.706 92.480 73.729 89.878 Reduced form: the dependent variable is Signature of wedding license for 574 Journal of Economic Growth (2024) 29:543–583 1 3 Table 8 (continued) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) OLS OLS OLS 2SLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS 2SLS Signature of wedding license for All individuals Individuals working in agriculture Born 1 to 20 years after each epidemic All individuals Individuals working in agriculture Born 1 to 20 years after each epidemic Summer temperature − 2.301*** − 1.818*** − 1.766*** − 2.009*** − 1.883** − 1.920*** [0.400] [0.414] [0.416] [0.725] [0.745] [0.741] \This table presents OLS and IV regressions relating the share of cholera deaths to the ability of brides and grooms born one to 20 years after each outbreak to sign their wedding license. Geographic controls for departments, which are interacted with year-fixed effects, include their land suitability, their share of carboniferous area and dummies for border and maritime departments. Constant not reported. Robust standard errors clustered at the year-department level. ∗∗∗ p < 0.01,∗∗ p < 0.05,∗p < 0.1 575 Journal of Economic Growth (2024) 29:543–583 1 3 number of participants in courses for male adults and apprentices as well as public spending on these courses. However, labor scarcity neither had a significant effect on the number of courses for female adults and apprentices nor on the number of participants in these courses. A potential explanation for this result is that agricultural mechanization mainly reduced the demand for male labor, thereby leading men to immediately invest more in human capital and seek work in industry where literacy skills were necessary (e.g. Franck & Galor, 2022). Table I.5 shows that the impact of the cholera pandemics in 1832, 1849 and 1854 on the primary school attendance rate of boys and girls out of the population age 5–15 in 1837, 1851 and 1856 is positive but not significant in all the specifications. Moreover, Tables I.6 and I.7 assess the effect of the cholera on public spending by the three tiers of the French government (i.e., the central state, the departments and the communes) on primary schooling.29 Whether we consider total education spending or education spending per inhabitant, the results suggest that the pandemic had a negative impact on the departments’ spending but none on that of the communes and of the central state, and overall, no effect on total public spending on primary schooling. Those results should be put in the general context of 19th century French education. There were of course primary schools in France before the first cholera pandemic in 1832 (Mayeur, 2003). Moreover, after the 28 June 1833 law (known as the “Loi Guizot” after the then Minister of Education), all communes had to host a primary school in their jurisdiction. That school could be privately or publicly funded, and run by a secular teacher paid by municipality or by the local priest (or nun). Thus, we may not find any significant impact of the cholera on school spending because state intervention in education was already taking place in France at the time, independently of the pandemics. As such, in line with our analysis that views labor and technology as complementary factors of production in industry and substitute in agriculture, labor scarcity entailed a rise in human capital in the aftermath of the cholera pandemics. This increase did not stem from the rising importance of state-funded primary schooling. Instead it resulted from private investments made by parents in their own human capital as well as that of their children. 5.3 Alternative explanations Other than the increase in human capital, factors such as migration, urbanization, fertility, age at marriage, religiosity or local financial intermediation, could provide alternative explanations for our main results. In this section, we briefly present the tests which we carry out to assess the importance of such factors and provide more detailed explanations, including the data sources, in the Appendix. Reassuringly, our tests show that these factors were not correlated with the spread of cholera or with summer temperatures in 1832, 1849 and 1854. Migration and urbanization. 19th century France was characterized by a high rate of internal migration (Daudin etal., 2019) but no historical evidence connects migration and urbanization to the cholera epidemics. If anything, the potential effects of labor scarcity on migration and urbanization are not straightforward. Labor scarcity entails higher wages and may attract immigrants but the adoption of new technology may lower wages and hence 29 Because of data limitations, Tables I.6 and I.7 only focus on the impact of the 1854 cholera pandemic. 576 Journal of Economic Growth (2024) 29:543–583 1 3 Table 9 Cholera in 1832, 1849 and 1854: number of participants in courses for male and female adults and apprentices This table presents OLS and IV regressions relating the share of cholera deaths in each department to the number of participants in courses for male and female adults and apprentices. Geographic controls for departments, which are interacted with year-fixed effects, include their land suitability, their share of carboniferous area and dummies for border and maritime departments. Constant not reported. Robust standard errors clustered at the department level. ∗∗∗ p < 0.01,∗∗ p < 0.05,∗p < 0.1 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Number of participants in courses for Male adults and apprentices 1837–1850-1863 Female adults and apprentices 1850–1863 Share of cholera deaths in population 26.49 19.20 21.42 126.0** 130.6** 29.80 34.15 32.28 − 3.720 − 19.63 [23.36] [24.45] [24.46] [57.31] [59.70] [27.62] [29.86] [28.32] [88.75] [89.00] Departmentand year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Deviation from summer rainfall No Yes Yes Yes Yes No Yes Yes Yes Yes Geographic controls No Yes Yes Yes Yes No Yes Yes Yes Yes GDP per capita No No Yes No Yes No No Yes No Yes Within R2 0.404 0.430 0.438 0.023 0.057 0.059 Clusters 85 85 85 85 85 85 85 85 85 85 Observations 255 255 255 255 255 170 170 170 170 170 First stage: the instrumented variable is Share of Cholera Deaths in Population Summer temperature − 0.141*** − 0.140*** − 0.180*** − 0.179*** [0.0303] [0.0308] [0.0471] [0.0485] 1st stage F-stat 21.652 20.788 14.602 13.577 Reduced form: the dependent variable is Number of participants in courses for Male adults and apprentices 1837–1850-1863 Female adults and apprentices 1850–1863 Summer Temperature − 17.75** − 18.32** 0.670 3.509 [7.848] [7.826] [16.31] [15.97] 577 Journal of Economic Growth (2024) 29:543–583 1 3 Table 10 The effects of the cholera in 1832, 1849 and 1854 on Spending on courses for male adults and apprentices and the number of courses for male and female adults and apprentices in 1837, 1850 sand 1863 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Spending on courses for Number of courses for Number of courses for Male adults and apprentices Male adults and apprentices Female adults and apprentices 1837–1850–1863 1837–1850–1863 1850–1863 Share of cholera deaths in population 57.53*** 53.71** 54.65** 148.6 150.9 0.765 − 5.898 − 5.073 42.42 44.19 9.979 13.94 13.12 7.052 0.957 [20.98] [22.16] [21.69] [98.38] [98.31] [15.05] [15.24] [15.29] [40.81] [41.06] [11.81] [12.02] [11.27] [29.05] [30.16] Departmentand year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Deviation from summer rainfall No Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes Yes Yes Geographic controls No Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes Yes Yes GDP per capita No No Yes No Yes No No Yes No Yes No No Yes No Yes Within R2 0.607 0.615 0.616 0.416 0.438 0.441 0.009 0.054 0.058 Mean dep. var 41.2 41.2 41.2 41.2 41.2 6669.5 6669.5 6669.5 6669.5 6669.5 2.04 2.04 2.04 2.04 2.04 Moran I − 0.008 − 0.008 − 0.008 − 0.008 − 0.008 − 0.007 − 0.007 − 0.007 − 0.007 − 0.007 − 0.012 − 0.012 − 0.012 − 0.012 − 0.0122 − 0.012 Moran I p-value 0.239 0.236 0.235 0.237 0.237 0.259 0.257 0.257 0.253 0.255 0.207 0.210 0.209 0.210 0.209 578 Journal of Economic Growth (2024) 29:543–583 1 3 Table 10 (continued) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS OLS OLS OLS 2SLS 2SLS Spending on courses for Number of courses for Number of courses for Male adults and apprentices Male adults and apprentices Female adults and apprentices 1837–1850–1863 1837–1850–1863 1850–1863 Clusters 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 Observations 255 255 255 255 255 255 255 255 255 255 170 170 170 170 170 First stage: the instrumented variable is Share of Cholera Deaths in Population Summer temperature − 0.141*** − 0.140*** − 0.141*** − 0.140*** − 0.180*** − 0.179*** [0.0303] [0.0308] [0.0303] [0.0308] [0.0471] [0.0485] 1st stage F-stat 21.652 20.788 21.652 20.788 14.602 13.577 Reduced form: the dependent variable is Spending on courses for Number of courses for Number of courses for Male adults and apprentices Male adults and apprentices Female adults and apprentices 1837–1850–1863 1837–1850–1863 1850–1863 Summer temperature − 20.94 − 21.16 − 5.976 − 6.198 − 1.270 − 0.171 [14.69] [14.60] [5.988] [5.957] [5.454] [5.553] This table presents OLS and IV regressions relating the share of cholera deaths in each department to spending on courses for male adults and apprentices and the number of courses for male and female adults and apprentices. Geographic controls for departments, which are interacted with year-fixed effects, include their land suitability, their share of carboniferous area and dummies for border and maritime departments. Constant not reported. Robust standard errors clustered at the department level. ∗∗∗ p < 0.01,∗∗ p < 0.05,∗p < 0.1 579 Journal of Economic Growth (2024) 29:543–583 1 3 trigger emigration (e.g., Fadinger & Mayr, 2014). It may also be the case that individuals would leave areas hit by the cholera to escape death and would not come back. Tables J.1 and J.2 show that migration and urbanization were not correlated with the spread of cholera and cannot therefore drive our main results (it nonetheless bears pointing out that both Tables do not rule out that migration and urbanization could have played a role in technology adoption and innovation). Religiosity. To account for research highlighting the link between natural disasters (such as pandemics) and religiosity (e.g., Bentzen, 2019), we explore whether the cholera outbreaks could be correlated with changes in religiosity and potentially with a deeper cultural shift that could delay or accelerate technology adoption and innovation. Table J.3 shows that the pandemics had a positive and significant but quantitatively small effect on the share of seminarians in the population, and no significant impact on the share of religious community members in the population. Overall, these results suggest that religiosity was not affected by the cholera pandemics and cannot therefore explain their impact on technology adoption. Fertility and nuptiality. Mortality shocks triggered by pandemics could have an impact on optimal fertility behavior (Boucekkine etal., 2009; Siuda & Sunde, 2021). However, given that the fertility decline in France had begun in the late 18th century (e.g., Galor, 2011; Daudin etal., 2019; Blanc & Wacziarg, 2020), it is not clear whether the spread of cholera could have an impact on fertility rates and on the age at marriage. Tables J.4 and J.5 show that indeed, the cholera epidemics had no systematic significant effect on fertility and nuptiality patterns, thereby suggesting that those channels did not affect our results. Local financial intermediation. Because of the relationship between financial intermediation, economic growth and innovation (e.g., Gorodnichenko & Schnitzer, 2013; Gennaioli etal., 2014), we examine whether labor scarcity fostered technological adoption through the presence of local banks. Table J.6 reports the impact of the cholera pandemics on the amount of deposits per capita in the savings banks of each department averaged over the five-year period which followed each pandemic. The effect is insignificant in all the specifications. These results thus suggest that local financial development was not correlated with the cholera outbreaks and cannot therefore drive our results pertaining to technology adoption and innovation. 6 Conclusion This paper examines the impact of labor scarcity entailed by the cholera epidemics in 1832, 1849 and 1854 in France on subsequent technology adoption and innovation. The results show that in the short-run, labor scarcity had a positive and significant impact on technology adoption and innovation in agriculture while it had a negative impact on technology adoption in industry. This negative impact lasted longer in the textile industry than in the mining sector. As labor scarcity increased the expected returns to human capital, individuals invested more in their own literacy: this increase in the share of literate individuals in the population canceled out the negative effect of the population loss on technology adoption. Moreover, menial agricultural work became less appealing to literate workers, thereby leading to more technology adoption and innovation in agriculture. 580 Journal of Economic Growth (2024) 29:543–583 1 3 There are three main implications of this study. First, it suggests that in the 19th century, labor and technology were substitute factors of production in agriculture but complementary in industry. Second, it provides some support for the notion that agricultural mechanization in 19th century France was partly fostered by labor scarcity. Third, it provides a moderate view on the effects of repeated pandemics on economic growth. Notwithstanding the human losses, the economic consequences of pandemics in societies that escaped the Malthusian trap appear quantitatively limited in the short-run and disappear in the midto long-run. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1088702409241-3. Funding Open access funding provided by Hebrew University of Jerusalem. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References Abramitzky, R., Delavande, A., & Vasconcelos, L. (2011). 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