Industrial activities and primary schooling in early nineteenth-century France


This article investigates the impact of industrial activities on primary instruction in early nineteenth-century France. To do so, I use a newly constituted database on the location and characteristics of primary schools at the level of municipalities. This database is extracted from the Guizot survey conducted in 1833, before the implementation of the first national law making the opening of a school mandatory in any municipality more than 500 inhabitants. By using mineral deposits as an instrument, I first show that the presence of industrial activities in a given municipality was positively influencing the presence of primary schools. An increase in the supply of schools by municipalities explains this association. Additional resources transferred to them by manufactures favoured this increase through an income effect. However, I find no significant link between industry and the accumulation of human capital. On the contrary, I provide indications that industrial activities were associated with lower enrolment rates. If they had a positive impact on the demand for schooling, it was only for a very restricted part of the population.

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Fig. 1

SourceStatistique générale de la France, Guizot survey—report to the King (colour figure online)

Fig. 2

Source: Industrial survey


  1. 1.

    See Katz (2016) for an illustration in the American context.

  2. 2.

    This level corresponds to what is called “secondary education” nowadays.

  3. 3.

    This last case in which parents could benefit freely from a schooling service was extremely rare at the beginning of the nineteenth century.

  4. 4.

    A precise description of the daily life of schools and teachers in the eighteenth and early nineteenth centuries can be found in Duveau (1957). Analyses or testimonies on the state of primary schooling by teachers from the early nineteenth century are available in Lorain (1837) and Meunier (1981). In order to have an analysis of local schooling development in the eighteenth century, see, for example, Allain (1981), Julia (1970), Vovelle (1975) or Laget (1971). See Gildea (1983) for a local study from 1800 onwards for the departments of Ille-et-Vilaine, Gard and Nord.

  5. 5.

    In order to have a full political analysis and a description of the laws, projects and debates about primary instruction during this period of time, one can refer to Gontard (1959) and the second and third chapters in Furet and Ozouf (1977a).

  6. 6.

    See Nique (1990) to have a description of educational state measures from 1815 onwards.

  7. 7.

    This analysis has been partly contested as it did not take into account the age structure of the population (Fleury and Valmary 1957).

  8. 8.

    This is done in Fig. B1 in Appendix for online. Educational characteristics are displayed at the department level in these maps, while the analysis is at the municipal level. This is done for the sake of simplicity and clarity in the presentation of the data on education, or industry.

  9. 9.

    This idea, along with the level of industrial performance of the French economy, has been greatly debated. They have been deemed low and stagnating compared to Great-Britain in the 1940s and 1950s, before a revisionist literature insisted on the relatively good economic performance of France during the nineteenth century and on the distinctive path of growth this country followed (Crouzet 1966; O’Brien and Keyder 1978). This point of view has subsequently been qualified by authors amending the figures on French productivity growth and insisting anew on the difficulties known by the agricultural and industrial sectors compared to the British ones (Crafts 1977, 1984). See Lévy-Leboyer (1978), Mathias and Postan (1978) for an analysis of French industrial investment and Lévy-Leboyer and Bourguignon (1985) for a macroeconomic analysis of French economy along the century. See Crouzet (2003) for a historiography of French economic growth during the nineteenth century, from the “retardation-stagnation” thesis to the “moderate revisionism”. To have an economic analysis and description of the industrialisation period in France and other European countries over the century, see Braudel and Labrousse (1976), Verley (1997, 1999). For an analysis of the French case under the Ancien Régime, see, for example, Sée (1925) or Woronoff (1998).

  10. 10.

    A lot of information on this survey is available here:

  11. 11.

    These departments are: Ardèche, Ardennes, Cher, Corrèze, Côtes-du-Nord (Côtes D’Armor), Finistère, Gard, Gers, Indre, Indre-et-Loire, Loire-Inférieure (Loire Atlantique), Loiret, Lozère, Marne, Morbihan, Nièvre, Oise, Bas-Rhin, Saone-et-Loire, Seine-et-Marne, Deux-Sèvres and Vaucluse. At that time, there were 86 departments existing and 26 academies. Current denominations of departments are specified in brackets when a change occurred.

  12. 12.

    Excluding the three departments located on this very line.

  13. 13.

    See, for example, on this point the criticisms made in Luc (1986) and Luc and Gavoille (1987). See Grew and Harrigan (1986) for a reply and Furet and Sachs (1974) for a use of these data.

  14. 14.

    These data can be found here

  15. 15.

    See Table A1 in Appendix for online.

  16. 16.

    It is possible, for industrial and demographic characteristics, to directly compare data at the level of municipalities and factories. This is what is done in Table A2 in Appendix for online. This does not modify the global picture about the representativeness of the data used. Demographic characteristics exhibit the same differences than at the district level. It is true that the percentage of municipalities with large factories now differ significantly in the sample. However, the magnitude of the difference does not indicate that municipalities in the sample were characterised by highly more concentrated industrial activities. Industrial wages and taxes on industrial activities remain comparable between factories in the sample compared to France, which indicates that the factories present in the data are comparable to the “average factory” at the national level.

  17. 17.

    More information on them can be found in Chanut et al. (2000) for the industrial one, in Marin and Marraud (2011b) and Demonet (1990) for the agricultural one.

  18. 18.

    More information on this survey is available in Marin and Marraud (2011a).

  19. 19.

    Descriptive statistics on agricultural controls are present in Table A3 in Appendix for online.

  20. 20.

    Taking 30 workers or 40 workers as a threshold would not change the results of the paper. This would amount to select 44% and 39% of the municipalities with industrial activities as characterised by the presence of large factories. However, above 40 workers, the restriction applied to industrial variables makes the analysis unreliable using the instrumental variable strategy presented in Sect. 4.1.

  21. 21.

    The memoirs of Louis-Arsène Meunier were not published until 1981. He lived between 1801 and 1887.

  22. 22.

    See also de Pleijt and van Zanden (2016), Mokyr (2005), Baten and van Zanden (2008) and Allen (2003) for a global analysis at the European level, Mitch (1993) for the British case. This latter estimated that, in 1841, only 4.9% of men and 2.2% of women in England were employed in jobs where literacy was absolutely required. Around half of men were employed in jobs where it had nearly no chance of being useful.

  23. 23.

    See Fig. B2 in Appendix for online.

  24. 24.

    I will also introduce fixed effects at the district level in the estimations instead of agricultural controls and department fixed effects. Since this work is cross-sectional, I do not introduce time-varying factors at these levels.

  25. 25.

    See, for example, Laget (1971) on the Protestant influence in the Bas-Languedoc and (Furet and Ozouf 1977a) on the influence of the Breton patois in the Morbihan.

  26. 26.

    The demographic growth of municipalities with factories was 7% higher than the one of their counterparts between these 2 years. The difference remained significant even after controlling by other municipal characteristics and agricultural variables.

  27. 27.

    On this point see also (Galor et al. 2009). See pages 5 and 6 in Appendix for online to have a description of the variables used in the estimations.

  28. 28.

    I tried to use the distance to the nearest deposit as an instrument too. However, it remained weak in the first stage. This is due to several factors. First, the exploitation of mineral deposits really began to surge after the 1840s in France, which reduces its potential influence on the concentration of metallurgic factories in the surrounding municipalities. Secondly, this sector has for a long time been relying on the use of charcoal, not coal itself, and water-powered engines (Woronoff 1998). The distance between factories and the main mines was also a factor which favoured the use of these alternative resources (Crouzet 1996). Finally, even if textile and food sectors were more intensively relying on the use of steam engines powered by coal, they were still using far more water-powered engines at that time (around six times more in the food sector for example). Their location was therefore not greatly dependent on the presence of deposits in a given municipality.

  29. 29.

    There were less than seven factories per municipality in 90% of the municipalities with industrial activities. This makes the use of the number of factories difficult and increases the probability to find an effect driven by outliers or only by a small number of big towns.

  30. 30.

    These data, analysed in Aron et al. (1972), rely on the evaluation of 489,610 conscripts’ height.

  31. 31.

    See Steckel (1995) for a review of the literature on this point.

  32. 32.

    This is in line with descriptive statistics showing that schools were located in municipalities with a higher population, in 1793 and 1833, and a lower population dispersion. See Table A4 in Appendix for online.

  33. 33.

    Descriptively, there is a strong association between schools and factories since, in 79% of the municipalities with a factory, a school was also present. See Table A5 in Appendix for online.

  34. 34.

    Ideally, one would like to decompose the effect of manufactures on primary schooling by taking other thresholds on the number of workers. However, this would often amount to restricting data too much for any econometric analysis. This is also why I stick to factories with more than 20 workers to measure the impact of large manufactures on primary instruction.

  35. 35.

    Logit estimations lead to similar results. Odds ratio indicates that the presence of a primary school in a given municipality was around 1.7 times more likely when a (large) factory was located in the same area. The increase was of, respectively, 1.2 for any additional factory. However, the outcomes are only significant when agricultural controls are introduced without department fixed effects, except for the number of factories. See Table A6 in Appendix for online.

  36. 36.

    It relies on a test robust to clustering developed by Wooldridge.

  37. 37.

    The stock interpretation is less interesting here as mining activities were present in only 1.21% of municipalities.

  38. 38.

    The difference between the two being significant at a 1-per cent level. This is coherent with what was found in other studies on the effect of mining activities, and especially coal, on the development of cities (Fernihough and O’Rourke 2014).

  39. 39.

    See Table A7 in Appendix for online.

  40. 40.

    See Table A8 in Appendix for online.

  41. 41.

    See Tables A9 and A10 in Appendix for online.

  42. 42.

    Even if the evaluation of these returns by families is far from obvious (Jensen 2010).

  43. 43.

    The greater investment of businessmen into the schooling system was also due to the will to bypass factory laws aiming at regulating child work. This was especially the case in France after the passing of the 1841 law, the first one defining an upper bound on day-work hours for children depending on their age.

  44. 44.

    See Table A11 in Appendix for online.

  45. 45.

    Logit estimations also lead to the identification of a positive and significant association between the presence of large factories and the probability for a teacher to be paid. The coefficients are significant at a five- or 1-per cent level. Odds ratio indicates that teachers were around 1.5 times more likely to be paid regularly when a large factory was located in the same municipality. Results are available upon request.

  46. 46.

    See Table A12 in Appendix for online.

  47. 47.

    See Tables A13 and  A14 in Appendix for online.

  48. 48.

    One may consider municipal investment as a potential residue to parents’ willingness to pay for education. However, municipalities invested more in instruction in richer areas where enrolment rates were on average higher (Montalbo 2019). As a consequence, industry is not likely to have influenced the public supply of education because parents were less willing to school their children in the municipalities at stake.

  49. 49.

    One of the four direct taxes, the quatre vieilles, implemented by the Assemblée Constituante in 1791, along with the land tax, the personal property tax and the tax on doors and windows (implemented in 1798).

  50. 50.

    See Table A15 in Appendix for online.

  51. 51.

    See Table A16 in Appendix for online.

  52. 52.

    See Table A17 in Appendix for online.

  53. 53.

    This rise went hand in hand with a fall in the age at which children were starting to work.

  54. 54.

    The employment of children during the industrial revolution has been questioned in the English case. The reliability of quantitative data and assumptions made about the work of farmers’ children were central in this debate. On this point, see Cunningham (1990) and the replies Kirby (2005) and Cunningham (2005).

  55. 55.

    Around 145,000 children under 16 years old were working in the industry according to the industrial survey, two-third of them in textile manufactures.

  56. 56.

    Cotton industry was rapidly expanding at the beginning of the nineteenth century in France, especially in the Haut-Rhin department, in towns like Mulhouse or villages like Thann and Dornach and, for the department of Seine-Inférieure (named Seine-Maritime nowadays), in towns like Elbeuf. The negative effect of industrial activities on child work also seems to have been reinforced by the poor enforcement of factory laws in the time period following the one under scrutiny in this study. See, for example, Saito (2006), Weissbach (1977) and Pierrard (1974, 1987) in the case of the 1841 law regulating child work in French manufactures.

  57. 57.

    See Table A18 in Appendix for online.

  58. 58.

    These differences are significant at a 1-per cent level for both t tests depending on the presence of factories or large factories. The variation in enrolment rate levels between big and small municipalities can be explained by the age structure of the population and by the migration of young workers towards bigger towns.

  59. 59.

    Taking winter enrolment makes more sense than taking summer enrolment. Indeed, as many pupils had to assist adults in agricultural tasks, they attended school only between October and April months.

  60. 60.

    See Table A19 in Appendix for online.

  61. 61.

    See Table A19 in Appendix for online to have a decomposition by the 16 sectors reported in the industrial survey. These percentages are computed for France and are not restricted to the 22 departments in my database.

  62. 62.

    Textile factories were located in 308 municipalities in the data, building manufactures in 157.

  63. 63.

    See Table A20 in Appendix for online to have the average industrial wages by sector.

  64. 64.

    See Table A21 in Appendix for online.

  65. 65.

    In 80.4% of the cases, there was only one school in the municipality. When several schools were present, I took the average value of schooling years and number of subjects taught between them.

  66. 66.

    They were so in, respectively, 99.4, 98.4 and 92.1% of the primary schools for which this information is available. Arithmetic was taught in 62% of the schools, grammar in 44%, spelling in 49%, geography and linear drawing in 7%, land surveying in 10%, history and music in around 3%.

  67. 67.

    This is also true for the volume of industrial production.

  68. 68.

    Moreover, I find no impact in the OLS estimations of the mere presence of factories on the number of subjects. This makes the interpretation of the positive association with large factories and the industrial production as an industry-specific effect even more doubtful.

  69. 69.

    See Table A22 in Appendix for online.

  70. 70.

    These other types were water, wind and animal-traction engines.

  71. 71.

    There was 0.9 of them on average in municipalities with large factories against 0.01 in municipalities with manufactures with less than 20 workers and 1.8 in municipalities with mines.


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Correspondence to Adrien Montalbo.

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I am grateful to Jerôme Bourdieu, Lionel Kesztenbaum, Eric Maurin and Quentin Lippman for helpful comments. I would like to thank Anne-Marie Chartier and André Oliva for helping me access the archives and sharing data on the Guizot survey. I am also grateful to participants at the seminars in the Paris School of Economics, 67th Annual Meeting of the French Economic Association and 35èmes Journées de Microéconomie Appliquée.

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Montalbo, A. Industrial activities and primary schooling in early nineteenth-century France. Cliometrica 14, 325–365 (2020).

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  • Primary instruction
  • Industrial activities
  • Nineteenth-century France

JEL Classification

  • N33
  • N63