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The impact of education on fertility in Italy. Changes across cohorts and south–north differences

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Abstract

Several studies suggest that over the last decades in Italy the negative effects of women’s education on fertility have attenuated. However, recent analyses developed in other countries highlight that selection bias and potential endogeneity of education should be taken into account. Using data from the ISTAT multipurpose survey ‘Famiglia e Soggetti Sociali’, conducted in 2009, we apply a multiprocess model (one hazard equation for the first three birth orders and one ordered probit equation for the probability to achieve a specific level of education) with potentially correlated unobserved heterogeneity components at the individual level. Our results show that the role of education on fertility behaviours not only remains important but also tends to have an increasing relevance among younger cohorts. On the one hand, a higher proportion of highly educated women postpone first childbirth or remain childless; on the other hand, among those who decide to become mothers, we found a positive effect of higher education on the propensity to have a second child, a result that can be interpreted in terms of a time-squeeze effect among tertiary educated women. Relevant territorial differences emerge relating to the effect of higher education on the third child birth, being positive in the north of the country and negative in the south.

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Fig. 1

Source Our elaboration on the data-set collected by Skirbekk (2008)

Fig. 2

Source Istat FSS 2009

Fig. 3

Source Istat FSS 2009

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Notes

  1. The discrepancy between period and cohort data is due to the fact that the level of education strongly influences the timing of reproduction. Dalla Zuanna and Tanturri (2007, Chap. 5) demonstrate—by employing an own-children method to 1971, 1981, 1991, and 2001 Veneto census data (the region located in northeast Italy, home to approximately 5 million inhabitants)—that whilst differences by level of education for fertility intensity were practically disappeared by the mid-80s, the average age at delivery has increasingly differentiated by level of education. Among the Veneto generations born in the 1950s, fertility differences by level of education remain significant, contrary to what one observes when looking only at period data. For a general discussion of fertility by period and cohort in Italy during the second half of the twentieth century, see Caltabiano et al. (2009).

  2. For example, in Italy, education and income are strongly related. In 2000, the mean income of a man aged 30–44 with less than an upper secondary education was 72 % of that of his age-mates with an upper secondary education (the same proportion was 86–87 % in France, Sweden, the Netherlands, and Germany, OECD 2004, Table A11.1a).

  3. The idea in an ordered probit equation is that there is a latent continuous metric (y) underlying the ordinal responses observed (y*) and specific thresholds \(\tau_{1} ,\tau_{2},\ldots \tau_{k - 1}\) partition the real line into a series of regions corresponding to the k ordinal categories. Given that there are three possible categories (low, medium and high level of education), we need two thresholds.

  4. Northern regions are Piedmont, Val d'Aosta, Lombardy, Liguria, Veneto, Trentino Alto Adige, Friuli-Venezia Giulia, Emilia Romagna, Tuscany, Marche, Umbria and Lazio; southern regions are Abruzzo, Molise, Basilicata, Apulia, Campania, Calabria, Sicily and Sardinia.

  5. This analysis represents a substantial development of the analysis presented in Dalla Zuanna and Impicciatore (2010). The main difference is linked to the different data used for the analysis. Dalla Zuanna and Impicciatore (2010) was based on Istat Multipurpose “Aspects of Daily Life”, a survey that does not include questions regarding fertility behaviour, forcing to indirectly reconstruct fertility histories using the own-children method through information available only for cohabiting children. Moreover, these data do not have retrospective information relating to life trajectories that are potentially interrelated with fertility behaviour as the educational career and the employment condition. This is a crucial point, given that the main bias in the results obtained in this previous analysis is linked to the lack of time-varying variables in the models.

  6. Estimates shown in Table 3 do not change substantially if we consider a probit equation relating to the probability to achieve a tertiary degree instead of an upper secondary level (results here not shown).

  7. The aML software used for this analysis does not support the interaction between a duration spline (in our case, the time since previous birth) and a time-dependent variable (see Lillard and Panis 2003). Thus, we are forced to consider the level of education at the interview, i.e. as a time-fixed variable instead of the level of education as a variable that can change over the life course as we did in the previous analyses.

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  1. Department of Statistical Sciences, University of Bologna, Via delle belle arti, 41, 40126, Bologna, Italy

    Roberto Impicciatore

  2. Department of Statistical Sciences, University of Padova, Via Cesare Battisti, 241/243, 35121, Padua, Italy

    Gianpiero Dalla Zuanna

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Impicciatore, R., Zuanna, G.D. The impact of education on fertility in Italy. Changes across cohorts and south–north differences. Qual Quant 51, 2293–2317 (2017). https://doi.org/10.1007/s11135-016-0388-0

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