Abstract
Unified growth theory suggests the fertility decline was crucial for achieving long-term growth, yet the causes behind that decline are still not entirely clear from an empirical point of view. In particular for France, the first country to experience this demographic transition in the European context, the reasons why some areas of the country had lower fertility than others are poorly understood. Using département level data for the last quarter of the nineteenth century, this paper exploits the French regional variation to study the correlates of fertility, estimating various fixed-effects models. The findings confirm the importance of some of the forces suggested by standard fertility choice models. Education in general, female education in particular, for example, seems to be crucial. Results also highlight the relevance of non-economic factors (such as secularisation), for which I provide new measurements. The presence of spatial dependence also suggests that diffusion of fertility played a particular role.
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Notes
Due to various technical matters, dating the beginning of the fertility decline has been a hotly debated issue. See Guinnane et al. (1994) for a discussion. Fifty years is, however, a rather conservative estimate. Most scholars agree the fertility transition in Western Europe began around the 1880s (e.g., Coale and Watkins 1986), yet the first signs in France appeared in the last decade of the XVIII century (see e.g., Weir 1983).
The administrative unit of département was adopted during the French Revolution to replace the old ‘provinces,’ which showed considerable heterogeneity among them. The 83 départements that were created in 1790 were, in contrast, under a common central administration and had a more-or-less regular size (it was chosen so that any person within its borders could go and return from its capital by horse in two days). Over time the number increased to 87 in the period I study, and eventually to 101 today.
For example, Weir (1983) proposed that the institutional changes brought by the Revolution gave tools to reallocate land and labour, by making bourgeois children superfluous as labourers and costly as consumers. Cummins (2013), after studying the few individual level data available between 1750 and 1850, emphasised the key role of the Revolution in changing inequality, thus leading to the decrease in family size. González-Bailón and Murphy (2013), using an agent-based simulation to model French demographic behaviour, hint that certain socio-cultural aspects associated with the dismantlement of the Church led by the revolutionaries are at least partly to blame for the decline.
It is probably fair to say that many economic historians can associate most of our understanding of the French fertility decline over the last 30 years with these two scholars. The late Etienne van de Walle was attached to the Princeton Project and researched different aspects of the French experience (Van de Walle 1974, 1976, 1980, 1992; Van de Walle and Muhsam 1995; Lesthaeghe and van de Walle 1976). David Weir, on the other hand, produced an unfortunately never fully published dissertation at Stanford (Weir 1983), and then a series of papers until the mid-1990s (Weir 1984a, b, 1992, 1993a, b, 1994, 1995; Mroz and Weir 1990).
Van de Walle (1976), for example, suggested that low mortality and high income played a role in decreasing fertility, while urbanisation or industrialisation did not. McQuillan (1984), however, found that different modes of production were indeed associated with family size, with areas of capitalist production having higher fertility indicators. Watkins (1991), for her part, concluded that variation across départements could largely be explained—as did van de Walle—by variations in disposable income, but also by migration, size of the State, and whether French was or not the main language spoken.
To my knowledge, the only exceptions being a working paper that expands the analysis of Galloway et al. (1994) on Prussia to incorporate spatial dependence (Goldstein and Klüsener 2014), and a recent article on the quality–quantity trade-off, also for Prussia (Becker et al. 2012). Another (preliminary) manuscript by Guillaume Daudin and co-authors (Daudin et al. 2012) also addresses the issue of diffusion in France quantitatively, yet in a different way—by estimating bilateral flows of migrations. An even more recent working paper by Spolaore and Wacziarg (2014) builds on the idea of barriers to innovation and adoption, and study how cultural and ancestral distance affected the pattern of fertility decline in Europe. I relate my results to theirs later in the paper.
The literature on this stems from the seminal contribution of Becker (1960), where, in essence, children are alternatively seen as consumption or investment goods: They can be a source of satisfaction for the parents or assets that yield some return over time (a source of future services, especially labour and old age security).
As is often the case, a Scandinavian country provides an exception. A study of Sweden suggests that indeed increases in the relative wages of women contributed to the fertility decline (Schultz 1985).
Of the five départements with the lowest level of marital fertility in 1831, only Gironde had a major city (10,000 inhabitants or more) in 1801. At the same time, départements with a considerable population in large urban centres like Bouches-du-Rhône or Loire Inférieure still had \(\hbox {I}_{\mathrm{g}}\) indices above 0.60 as late as 1851. Assessing industrial development is more complicated. Some figures for the late eighteenth century show clusters of textile production in the Bassin Parisien, but also in Brittany and the départements north of Paris, whereas metallurgic activity was concentrated in the east of the country, and here the fertility behaviour was close to average (Léon 1970, pp. 228, 234, 238). The fact that areas leading the decline were located in major valleys is suggestive, but it is difficult to go beyond facile interpretations when thinking about the relevance of physical geography: both areas had large rural sectors, but agricultural activity was concentrated on wheat in one and vines in the other (Rémond 1966, pp. 55–58). And while both had large rivers, integration appears to have differed considerably between those regions (Daudin 2008).
For example, revolutionary laws lowered the age before which parental consent was needed, authorised divorce, and, by making civil contracts independent of the Church, it avoided the prohibitions of marriage in certain periods such as Advent and Lent (Bergeron 1981, p. 110). Most notably, the Jourdan–Delbrel law in 1798 exempted married men from conscription, which created a clear incentive to marry, orthogonal to the decision of having children. Further, a recent body of literature also suggests (more as an empirical regularity than in terms of a theory) that social upheavals have a profound effect on the evolution of birth rates (Binion 2001; Caldwell 2004; Bailey 2009).
According to this literature, it was the spread of new ideas—and not the change in material conditions—that accounted for the decline (Cleland and Wilson 1987, p. 27). Lesthaeghe, for example, suggests that the intellectuals of the Enlightenment provided the raw material that, in the hands of the French revolutionaries, led to the legitimisation of individual freedom of choice in different aspects of life, including fertility (Lesthaeghe 1983, p. 413).
Up to the late eighteenth century, Catholicism dominated social life (see e.g., Le Bras 1955), conveyed much of the normative framework in France, and had strong attitudes regarding family behaviour and against contraception. In particular, it condemned heavily the ‘sin of Onan,’ the main technique couples had to control fertility at the time (Flandrin 1979, pp. 194–196).
Already during the eighteenth century there were signs of de-Christianisation. Attendance at mass became less frequent, the number of people joining the clergy diminished, and the proportion of religious books owned by those rich enough to buy them fell considerably (Gibson 1989, p. 3).
Arguments not directly related to Catholic views on sexual behaviour are consistent with this story. Given the extent of the influence of the Church, it is likely that weakly religious areas could have been more sensitive to the institutional changes brought by the Revolution and these changes could have had an impact on fertility. Inheritance laws provide a clear example. Although supposedly affecting the whole nation simultaneously, it has been suggested these laws were unequally enforced (Brandt 1901), and that, in this, the influence of the Church (by promoting or opposing implementation) could have been instrumental.
There are various reasons for this, the most straightforward being social pressure. Another is uncertainty. The decision to reproduce is an important yet relatively infrequent choice in a lifetime; this makes people rely on the experience and judgment of others to make their own assessment, which introduces a particular form of social interaction effect by means of learning. See discussion in, e.g., Kohler (2001) or González-Bailón and Murphy (2013).
Although van de Walle long ago pointed out that the use département-level data had been largely neglected (Van de Walle 1974, p. 8), little has been done since then, and the handful of papers that have done so used only a few covariates in a simple cross-sectional framework (e.g., McQuillan 1984; Watkins 1991, Chap. 7).
At least so far the use of household-level data has proved limited. Some studies (e.g., Weir 1995; Hadeishi 2003; Cummins 2013) have used this sort of information but had to constrain themselves to relatively small, and most likely special, areas of France. Also, they could study the potential influence of only a small number of covariates that are unlikely to grow in the future. Weir took advantage of one of those rare examples where some information could be cross-referenced from other sources for a town (Weir 1995, p. 2), and recently Cummins (2013) has studied another three, but it is uncertain whether much can be done with others due to lack of records that can be potentially linked to the demographic data (Cummins 2013, p. 453).
Guinnane et al. (1994) warn us to be cautious when drawing conclusions from Princeton indexes, arguing that they do not describe perfectly parity dependence/independence, especially when there is substantial cultural heterogeneity or in the early stages of the transition. Since I am not making statements about parity dependence and looking into a period when the transition is ongoing, those caveats are not particularly relevant in this case.
That is, through the different channels discussed in the previous section. Clearly, it is sometimes difficult to determine whether a particular factor is affecting fertility through one or more of these channels. For example, women’s education could be influencing natural fertility (by making healthier, more fecund mothers), children’s supply (by reducing the chances of child mortality), children’s demand (by increasing the mother’s opportunity costs), and regulation costs (by affecting moral costs). Although it is plausible to imagine cases in which the mechanisms could be disentangled, in practice we must satisfy ourselves with assessing the overall effect of a particular variable.
Other studies of the fertility decline have considered it important to incorporate migration, yet none found it to be of any relevance for explaining fertility (e.g., Brown and Guinnane 2002, p. 43; Dribe 2009, pp. 80–81). This negative effect I find of net inflow of women in their mid-twenties probably works by increasing the denominator of the \(I_{g}\) index, as mean age of marriage in this period for most départements is in the low twenties. It is difficult to draw strong conclusions based upon this outcome, but one way of reading it is that women—at least those migrating—were delaying births rather than spacing them. They could also be learning. It has been suggested lately (Daudin et al. 2012) that migrants went to départements with low fertility (which Table 1 above supports), where they probably adopted the social norm and eventually transmitted it back to their départements of origin, fostering the diffusion effect.
The random effects approach does not have these problems, but it assumes that individual effects are uncorrelated with the other regressors. This assumption is too strong for my model, as some of the variables we are unable to observe are likely correlated with those we do observe. This can generate inconsistent estimates due to omitted-variable bias, so in this context, fixed effects are generally preferred.
Many potential elements could be the source of this. For example, one plausible story is that the result is partly driven by differences in breastfeeding practices. Those were largely unobserved, likely time invariant, probably showed a certain degree of variation across départements, and could affect both infant mortality and fertility.
Murtin (2013), using a panel of countries in the long run (1870–2000), and controlling for mortality and education, finds the same positive effect of income on fertility. Other works that have looked into income-related measures have instead relied upon wage data, which arguably are closely connected to the idea of opportunity cost of time rather than that of wealth and, as expected, they suggest either a negative correlation with fertility (Brown and Guinnane 2002, p. 44; Dribe 2009, p. 85) or no correlation at all (Galloway et al. 1994, p. 152).
Of course, the instrument not only needs to be exogenous in the sense of being stochastically determined, but also to affect fertility only through its effect on child mortality. It is indeed plausible to think that shocks to temperature could, for example, affect agricultural output, indirectly affecting fertility. Since I am including as a regressor the proportion of people working on agriculture, this mechanism is at least partly taken into account. It is also reasonable to think that temperature in itself could affect human reproduction. There is in fact some suggestion in the biological literature that high temperatures have a negative impact on fertility in tropical places, but there seems to be nothing conclusive for more temperate regions like France (Bronson 1995). In fact, in within the dataset I am using, there is no correlation whatsoever between fertility and deviations in summer temperatures in the current or previous year.
Although the peak varies from year to year and place to place (Cheney 1984, p. 563), the effect seems to concentrate in July, so in the estimations I use this as a robustness check.
Appendix 2 includes details on the first stage corresponding to each of the models.
Using lagged mortality instead of current mortality (not reported) does not change these results.
Becker et al. (2010, 2012), following Galor and Moav (2009), have suggested landownership inequality is a reasonable instrument in this context (see e.g., Becker et al. 2010, p. 186) but no such a variable exists for France during the period of study for the five years of the panel. Avenel (1894) does report certain cross-sectional estimates on the size of land plots, which under certain assumptions could be connected to landownership, but when I assessed the association with educational investment (different measures of enrolment), none was found.
Enrolment rates such defined refer really to gross enrolment, and particularly high values can indicate low educational attainment (e.g. associated with repetition). Since data availability impedes the estimation of net enrolment rates, by introducing the square of the variable I can account for the implied non-linearity.
Moreover, I evaluated the alternative of also dropping 1886, but the results did not change substantially.
If this reading is indeed correct, an interesting political economy corollary stems from this argument. Since the revolutionary government had the typical pro-natalistic interest of modern states (they need people to pay taxes and fight wars), its success in dismantling (at least partly) the Church’s structure might have been Pyrrhic, as it hampered the institution that was helping to sustain high levels of fertility.
The choice of W is indeed arbitrary, but not more arbitrary than assuming no spatial dependence (i.e., a matrix of zeros), as is normally done. Of the many different matrices that can be chosen, the distance matrix (which has as elements the inverse of the distance between the centroid of each unit, in this case, département) is among the most typical. I explored using another common choice, the neighbouring matrix (that is, one that assigns the value of 1 to adjacent départements and 0 to the rest), but the results did not change.
For 1876, series V33 to V40 from REC_T28, and V197 to V204, V212 to V219, V227 to V234, and V242 to V249 from REC_T30. For 1881, series V149 to V154, V164 to V169, V179 to V184, V194 to V199, V209 to V214, and V224 to V229 from REC_T41, and V7 to V12 from REC_T42. For 1886, series V177 to V182, V190 to V195, V203 to V208, V216 to V221 from REC_T46, and V215 to V220 from REC_T47_II. For 1891, V289 from REC_T63_II, V18, V30, V102, and V126 from REC_T64_I.
Series V70 to V73, V99 to V102 and V128 to V131.
For 1876, series V11 to V18 from REC_T28, and V55 to V63 from REC_T30. For 1881, series V229 to V237 from REC_T30, and V233 to V241 from REC_T40. For 1886, series V188 to V196 from REC_T44, and V106 to V114 from REC_T45. For 1891, series V220 to V230 from REC_T61, and V169 to V179 from REC_T62_I. For 1896, series V69 to V79 from REC_T71_I, and V255 to V265 from REC_T71_II.
Series V77, V79, V80, V82, V83, V85, V86, V88, V89, V91, V92, V112, V113, V115, V116, V118, V119, V121, V122, V124, V125, V127, and V128 from ENSP_T38.
Series V21, V187 and V210 from MVTPOP_T126_II.
Frédéric Salmon has worked extensively on electoral statistics in France. See, for example, Salmon (2001) and http://geoelections.free.fr.
I also explored the possibility of using the plausible alternative of linking the average of 1889 and 1893 to 1891, and the one of 1893 and 1898 to 1896 instead, but the general results were largely unchanged.
I have to thank Roman Studer here for letting me know about this study and putting me in contact with the researchers in charge of it.
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Acknowledgments
This paper stems from research I carried out for my DPhil at Oxford University’s Economics Department, while being a member of Nuffield College and a Student Research Associate of TARGET (Canada), and continued as a Research Fellow at Università Bocconi. It has benefited greatly from discussions with my supervisor, Bob Allen, with my thesis examiners, Knick Harley and Cormac Ó Gráda, and with various colleagues at Nuffield, specially Natàlia Mora-Sitja and Roman Studer. Steve Bond, Mauricio Drelichman, Timothy Guinnane, Nicola Pavoni, Michelle Pellizzari, Martín Rossi, Nicolas Serrano-Velarde, and Agnese Vitali also provided very useful feedback at different stages of the project. Earlier versions of this article were presented at various seminars and conferences, and I want to thank their participants for helpful comments. In particular, Marina Adshade, Guido Alfani, Jean-Pascal Bassino, Francesco Cinnirella, Neil Cummins, Claude Diebolt, Jean-Pierre Dormois, Guillaume Daudin, Price Fishback, Regina Grafe, David Greene, Phil Hoffman, Michael Huberman, Jane Humphries, Avner Offer, Gilles Postel-Vinay, and Jacob Weisdorf. The support and advice I received from all these people, four anonymous referees, and the editor of this journal has been very valuable to me, but the responsibility for all remaining errors and omissions is, of course, entirely mine.
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Appendices
Appendix 1: Details on data sources
1.1 Official French statistics and demographic data
The major part of the dataset was constructed using official statistics from the Service de la Statistique Général de la France between 1878 and 1903, and Table 7 details the references for most of the variables collected. The proportions of people in different economic activities were calculated by adding up the number of individuals working as chefs, patrons, employes, commis, ouvriers, or journaliers in the diverse sectors within either industry or agriculture, and dividing them by the total population. These figures were extracted from various series of the 1876, 1881, 1886, and 1891 recensements (ACRH 2011b),Footnote 40 and from the 1896 statistique industrielle (ACRH 2011a).Footnote 41 Number of school-aged children (4–12 years old) used to compute school attendance also comes from the recensements (ACRH 2011b).Footnote 42 The retrospective data on literacy I use to run robustness checks in Section 3.4 come from the Statistique de l’enseignement Primaire, 1829–1877 (ACRH 2011c),Footnote 43 and infant mortality rates corresponding to 1871 (used to create the 5-year lag in Section 3.3) were built from the 1972 figures of the Mouvement de la Population (ACRH 2011d).Footnote 44 Fertility data were available from studies that are part of the European Fertility Project. In particular, marital fertility \((I_{g})\) was obtained from the main publication of the project (Coale and Watkins 1986, pp. 94–107). Migration came from Bonneuil (1997).
1.2 Information on elections
Political variables were obtained from different sources. Frédéric Salmon kindly shared with me his estimations of turnout at the polls for all legislative elections between 1876 and 1898, as well as the votes received by different parties in 1877, 1885, and 1898, which I used to estimate the proportion of votes received by republican groups (as opposed to monarchist parties).Footnote 45 I constructed this latter measure for 1889 in the same way using the data estimated by Rudelle (1982, pp. 264–267), and for the three other elections (1876, 1881, and 1893) I relied upon (Avenel (1894), p. 65). For the purpose of the panel I applied the elections of 1876, 1881, 1885, 1889, and 1893 for 1876, 1881, 1886, 1891, and 1996 respectively.Footnote 46
1.3 Temperature data
Climatologic data was kindly given by a group of researchers at the University of Bern, who calculated a monthly series spanning five centuries of European climatologic data (Luterbacher et al. 2004).Footnote 47 They reconstructed the climatic history of Europe using a large number of homogenised instrumental data series, as well as additional information coming from sea-ice, tree rings, and documentary records (Luterbacher et al. 2004, p. 1500), thus obtaining a grid with a resolution of 0.5\(^{\circ }\) \(\times \) 0.5\(^{\circ }\) (which in the case of France is equivalent to having a measure each 38 km in the east–west spectrum, and approximately 55 km in north–south direction) where the value at each point represents the monthly average temperature in the 0.5\(^{\circ }\) radius. About 250 of these data-points lay on French territory. To obtain estimates of the temperature in each département, I averaged the values corresponding to the points laying on that department. Following this procedure, I calculated for the years of the panel the deviations of summer temperature (July to September) from the corresponding 1850–1900 mean.
Appendix 2: Additional econometric output
The tables below detail the econometric output used to construct the elasticity estimates given in the text. Table 8 shows the results of the pooled-OLS and fixed-effects regressions that appear in Table 2. Tables 9 and 10 present the estimates of the first and second stages in the 2SLS regressions corresponding to Table 3 in the text, Tables 11 and 12 those corresponding to Table 4, and Tables 13 and 14 those of Table 5. Table 15 gives the estimates of the spatial regressions discussed in Sect. 3.6.
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Murphy, T.E. Old habits die hard (sometimes). J Econ Growth 20, 177–222 (2015). https://doi.org/10.1007/s10887-015-9111-6
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DOI: https://doi.org/10.1007/s10887-015-9111-6