Quality & Quantity The impact of education on fertility in Italy. Changes across cohorts and south-north differences. --Manuscript Draft-Manuscript Number:
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The impact of education on fertility in Italy. Changes across cohorts and south-north differences.
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Fertility; education; hazard models; selection bias; unobserved heterogeneity
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Roberto Impicciatore, PhD University of Bologna ITALY
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University of Bologna
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Roberto Impicciatore, PhD
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Roberto Impicciatore, PhD Gianpiero Dalla Zuanna, Phd
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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 multiprocess models (one hazard equation for the first three birth order 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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THE IMPACT OF EDUCATION ON FERTILITY IN ITALY. CHANGES ACROSS COHORTS AND SOUTH-NORTH DIFFERENCES.
Roberto Impicciatore (University of Bologna) Department of Statistical Sciences University of Bologna Via delle belle arti, 41 40126 Bologna Tel. +39 051 2088631 E-mail:
[email protected]
Gianpiero Dalla Zuanna (University of Padova) Department of Statistical Sciences University of Padova Via Cesare Battisti, 241/243 35121 Padova
Tel. +39 049 8274190 E-mail:
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The impact of education on fertility in Italy. Changes across cohorts and south-north differences.
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 multiprocess models (one hazard equation for the first three birth order 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.
Keywords: Fertility, education, hazard models, selection bias, unobserved heterogeneity. JEL: J13 Fertility; Family Planning; Child Care; Children; Youth
1. Introduction In Italy fertility levels fell below the replacement level in the mid-1970s and reached the lower peak of 1.2 twenty years later. The persistent low fertility has become an increasing concern with serious long-term consequences such as ageing process and the decline in the active working population. Generally speaking, low fertility is a result of a postponement of maternity that begun with the generations born at the end of the 1950s and became increasingly common and widespread among the cohorts born in the following decades (Caltabiano et al 2009). One driving force behind this change in the last fifty years is the increasing number of women with a high investment in human capital (Samir et al. 2010). This is a crucial element both in the New Home Economics theory (Becker 1991) and in the Second Demographic Transition (van de Kaa 1987; Lesthaeghe 1995) both predicting a negative association between fertility and education. The Italian case is particularly interesting because of the lowest low level of fertility (Kohler et al 2002; Billari and Kohler 2004) and the familistic context. In Italy the welfare support is weaker compared to other European countries, it does not offer a good setting for reconciling paid work and family life and it is based on the family as the locus of support (Ferrera 1996). In such a context, women who choose to form a new family can be polarized between career-oriented and family-oriented (Matysiak and Vignoli, 2013). In Italy, as well as in the other European countries, the wealthiest and the most-educated couples led the change being the first to control marital fertility (Festy 1979; Livi Bacci 1977). Nevertheless, some scholars have suggested that these differences, still strong following the Second World War, have attenuated over the last decades (for Italy, see De Sandre 1982; De Sandre et al. 1999; Barbagli et al. 2003). A closer look, however, reveals that this closing gap is less obvious from a longitudinal perspective. In Italy, among the cohorts born during 1940s and 1950s, fertility differences by education did not diminish (Caltabiano et al. 2009) as they did, for example, in Norway (Kravdal and Rindfuss 2008)1. These results suggest that in Italy the birth or absence of a birth may continue to have different meanings and consequences for couples according to their education and social class. An additional issue in the explanation of Italian fertility is related to territorial differences. Fertility levels have followed different trajectories in the north and in the south of the country and many of the elements closely intertwined with education and fertility, such as contraception, welfare for families with children, labour market, etc., are significantly different in the ‘two Italy’s’, thus making references to national averages potentially misleading.
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, chp. 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 mideighties, 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 20 th century, see Caltabiano et al. (2009).
The aim of this article is to evaluate the effect of education on the fertility choices — separately by birth order — of Italian women born between 1940 and 1990, net of confounding factors, underlying changes across cohorts and distinguishing between south and centre-north of Italy. The analysis of the effect of education on subsequent fertility requires specific measures and expedients. First, through the event history approach we can evaluate time dependent dynamics for first, second and third birth order, also for the cohorts of women who have not yet completed their reproductive lives. Second, we develop multiprocess models with correlated residuals across parities in order to account for unobserved variables usually interpreted as preferences towards having children, values and attitudes that may simultaneously affect both fertility tempo and fertility quantum and generate spurious effects between the two (Bratti and Tatsiramos 2011; Kreyenfeld 2002; Kravdal 2001, 2002, 2007). Third, differently from the vast majority of previous research in the Italian context, in our multiprocess model we also take into account the potential endogeneity of educational choices. Most previous analyses considered educational attainment as exogenous factors. Nevertheless, educational trajectories and the transition to the i-th child may compete in time and resources during the early adulthood (Martín-García and Baizán 2006; Liefbroer, 1999; Upchurch et al., 2002) and they may have common determinants (Billari and Philipov 2004; Buchmann 1989; Liefbroer 1999). In other words, a woman with strong family proneness may prefer to interrupt her educational career and devote herself to childbearing and childrearing. Unobserved characteristics may, therefore, influence both educational career and fertility decisions. Without taking into account this potential endogeneity, the estimated coefficients for educational variables could also proxy for an individual’s preference towards childbearing (Bratti and Tatsiramos 2011; Meroni 2010). The remainder of the article is organised as follows. In section 2 we introduce and discuss the theoretical background relating to the education-fertility nexus in the existing literature with a specific focus on the Italian context and we formulate our research hypotheses. In section 3 we describe data and methods used in our analysis. In section 4 we show descriptive findings and, in particular, total fertility rate and parity progression ratios according to educational attainment, birth cohort and geographical area. In section 5, we extend our analysis using simultaneous hazard models. Finally, section 6 provides some concluding remarks and discusses potential directions for future research.
2. The education-fertility nexus. Theoretical background and hypotheses.
The debate on the links between higher education and fertility is very rich and the literature provides explanations for both positive and negative associations. Considering educational attainment as a proxy for
social status and income2, the positive association — also known as ‘income effect’ — can be interpreted following a Malthusian and/or an evolutionary perspective: couples at the top of the ladder would have more children because they have better chances of raising them. On the one hand, the lower fertility among lower classes would reduce the burden of maintaining offspring and increase the chances of survival for themselves and their children. On the other hand, higher fertility would strengthen the elites, increasing the probability of offspring survival and cohort replacement (see Skirbekk 2008 for a review). The meta-analysis carried out by Skirbekk (2008) shows that prior to the fertility transition there was a clear positive relation between social status and number of children. With the demographic transition, i.e., with the decrease in infant mortality — which began almost everywhere in the higher social classes — and the general decline in fertility, a negative or neutral status-fertility relation emerged. The negative association between social status and fertility would be induced by the increasing opportunity costs with income and social status. The New Home Economics theory (Becker 1965) underlines the strong difference in gender’s role in the post-war nuclear family in Western societies, with the male as the breadwinner and the female as the homemaker and caretaker. This situation depicted by Gary Becker as the most efficient and rational organization of gender roles, has come under pressure when women life course has become increasingly masculinized mainly in terms of improved educational attainments and increasing labour market attachment. The most educated women who have access to better paid jobs would find it costly to be absent from the labour market (Becker and Lewis 1973). At the same time, the richest couples would find it more difficult to achieve opportunities for their many children to gain at least the same social and economic status as themselves (Dumont 1890; Dalla Zuanna 2007). A different perspective is given by the Second Demographic Transition theory (Van de Kaa 2004, Lesthaeghe 1995) which emphasizes the role of a cultural shift occurred in Western societies towards a more individualistic lifestyle and the spread of post-materialist value orientation. According to this approach, having children is one among different possible choices and the preference to have an (additional) child becomes weaker as education increases. This leads to the expectation that the highly educated are more likely to be in the vanguard of the demographic change (Lesthaeghe and Surkyn 1998). In order to disentangle the complex link between education and fertility, a distinction according to the birth order should be considered. On the one hand, there is a general consensus in the literature about the positive effect of higher education on the postponement and reduction in the propensity to experience the first birth event both at macro-level (Kohler et al. 2002; Rindfuss et al. 1996; Wilkie 1981) and micro-level (Billari and Philipov 2004; Bloemen and Kalwij 2001; Ermisch and Ogawa 1994; Nicoletti and Tanturri 2005). On the other hand, a less clear picture emerges for higher parities, revealing a stronger heterogeneity among countries. Previous studies, based on individual-level data and generally accounting for self-selection, shows contradictory results in Norway
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).
(Kravdal 2001, 2007) and Western Germany (Köppen 2006; Kreyenfeld 2002) and a substantial lack of association in Austria (Hoem et al. 2001). Nevertheless a positive educational gradient has been found in different contexts as Sweden (Berinde 1999; Hoem 1993; Hoem and Hoem 1989), Estonia (Klesment and Puur 2010) and France (Köppen 2006). This positive effect is not in line with the Becker’s theory and the Second Demographic Transitions. How can a positive educational gradient on second and third order fertility be explained? According to the New Household Economics perspective, a delayed motherhood is less costly for a woman’s working career. Thus, for higher educated women the first childbirth may occur after having accumulated a substantial amount of work experience; an additional child may not penalise their careers given that the combination of paid work and family responsibilities may be less stressful for college-educated women. Other authors argue that high educated women are not particularly career-oriented (Mott and Shapiro 1983; Sobotka and Testa 2006; Wilkie 1981). Nevertheless, the transition rate to the second (and third) child may simply be pushed up by a “time-squeeze” effect (Kreyenfeld 2002). Following a later entry to motherhood (Ní Bhrolcháin and Beaujouan 2012), women with high education have less time at their disposal before reaching the biologically determined age limit of fertility and this might induce them to accelerate their subsequent childbearing. This kind of effect has been noticed in Southern Europe (Klesment et al 2014) whereas it is not supported in Germany (Kreyenfeld 2002), in Denmark (Gerster et al 2007), and in the Eastern European countries (Klesment et al 2014; Klesment and Puur 2010). An emerging interpretation takes into account that low gender equity is evidenced in the lack of support for women to combine paid employment and childbearing and an increase in gender equity can be considered as a precondition of a rise in fertility from very low levels (McDonald 2000). Indeed, gender equity between the couple and the father’s involvement in child care are more common among better educated people and have meaningful effects on second and higher parities (Brodmann, Esping-Andersen and Güell 2007; Duvander and Andersson 2006; Duvander, Lappegård and Andersson 2010). This perspective has been developed in the Incomplete Revolution proposed by Esping-Andersen (2009). This theoretical approach suggests that together with the increase in educational attainment of women, an unstable situation emerged in which couples are involved in suboptimal outcomes like a lower actual than desired fertility. However, the consequently decline in fertility in the first stage of the transition may turn into a reversal in the second stage with the emergence and the consolidation of a more gender-balanced system. The striking aspect is that both decline and the turnaround in the level of fertility are driven by the very same social group, higher educated couples who more readily adopt egalitarian norms (Esping-Andersen and Billari 2015). In the unstable situation, the gap between desired and actual fertility tend to be stronger among more educated women which have higher aspirations in their career and more difficulties to combine work and family. Say differently, in this intermediate stage women education plays as predicted by the New Household Economics: gender-convergence in market productivities implies rising opportunity costs of motherhood, in particular among higher educated, career women with a consequently postponement of motherhood and decline in fertility. In a gender-equality system, which characterizes a
subsequent stage, more educated women have higher possibilities to reconcile work and family and couples, which are similar in terms of human capital, follow a model of dual careers and shared home production. Focusing on Italy, previous analyses highlighted the relevant role of women’s education on fertility proneness. Highly educated women tend to postpone family formation and childbearing (Salvini 2004; Rosina 2004) but women with more human capital and economic resources do show, ceteris paribus, a higher propensity to have children (Dalla Zuanna and Tanturri 2007; Mills et al. 2008; Rosina and Testa 2009). The positive effect of higher education on second child birth is confirmed in a comparative perspective (Klesment et al 2014) and when unobserved heterogeneity is included in the hazard models (Rosina and Caltabiano 2010). Nevertheless, Gottard et al (2015), using a time-dependent frailty found that higher educated women tend to postpone the birth of a possible second child with respect to lower educated women, at least until they complete their education. The vast literature on the relation between education and fertility mainly focuses on national territory as a whole without taking into account differences within countries. In some cases, sub-national differences are so profound to make reference to national average potentially misleading. The Italian case is a strong candidate in this sense. Southern regions show very low female employment rate compared to the rest of the country (31.6% compared to 52.3% in the centre and 57% in the north in 2012) and a lower level of gender-equity. Both women and men in the southern regions have more conservative attitudes toward the mother’s and wives’ roles in comparison with the rest of the country and women in the southern regions of the country do not modify their share of chores according to their educational status as women in the north (Dotti Sani 2012). The usual interpretation of the south-north demographic difference is that the former area simply lags behind the latter (Livi Bacci 1977) because of the delayed development in the southern regions of many aspects of modernisation, such as education, industrialisation and secularisation. Nevertheless, this perspective fails to capture some recent and historical differences between the regions of southern and central-northern Italy. This view is supported by the lower diffusion of fertility outside marriage in the South (Castiglioni and Dalla-Zuanna 2009) even though dissimilarities between the two macro areas have been found in the trend of fertility rates by birth order (Santini 1995 and 2006) and in the timing of the decrease in fertility under 30 and recovery over 30 (Caltabiano et al. 2009). More generally, the idea that the South is simply a laggard of the North is likely to be too simplistic hiding several recent and historical differences. Some authors (most recently Micheli 2012) have noted several family peculiarities in southern Italy suggesting that we should not refer to a single family model for Italy. Moreover, historians of the family already revealed the existence of different rules of family formation in the different areas (see e.g., Barbagli and Kertzer 1990). Finally, other analyses suggest a clear difference as regards level of general trust in the northern and southern regions (Banfield 1958; Putnam 1993), a factor that is positively related to fertility (Aasve et al 2012). In contemporary Western societies the change in women’s role is still incomplete and the new gender-equality model is far from being dominant (Esping-Andersen 2009). In the Italian context, the limited support for reconciling employment career and parenthood, as well as relatively traditional attitudes towards gender roles,
imply considerable opportunity costs among highly educated women (Salvini 2004; Mencarini and Tanturri 2006). This would entail, ceteris paribus, a negative rather than positive educational gradient not only for the first child birth but also for the subsequent birth orders. Nevertheless, women with high education, who enter parenthood at a later age, may accelerate the progression to second and higher order births producing a timesqueeze effect that increases the transition rates. Thus, as a first hypothesis we expect that the negative educational gradient on the first birth is softened or even disappear in the subsequent orders. The difficulties experienced by women in the labour market may strengthen the constraints given by the combination of paid work and family responsibilities. This can be true in particular in the southern regions. Thus, we expect a stronger negative educational gradient in the south of Italy compared to the rest of the country (second hypothesis). Finally, we expect that younger cohorts lead the change and, consequently, that a positive effect of higher education emerge among younger cohorts (third hypothesis).
3. Data and methods The multipurpose survey, ‘Famiglia e Soggetti Sociali’ (FSS 2009), conducted at the end of 2009 by the Italian National Institute of Statistics (ISTAT), contains broad retrospective information on life course trajectories, including data on education, job career, family formation and fertility for a large sample of the resident population. In particular, we use a subsample of 14,860 women born between 1940 and 1990. As the first step of our analysis we trace a description of fertility differences by level of education for the cohorts of Italian women who had children in the last decades of the 20th century. In this section we restrict our subsample to 7,484 women born between 1940 and 1964 (aged 45-69 years at the interview) in order to show the completed fertility estimates for these cohorts. Our relatively large sub-sample allows us to calculate the total fertility rate (or completed fertility rate) and the parity progression ratios at the first, second, and third child (PPR1, PPR2, PPR3), i.e., the proportion of women who progress from one parity to the next, distinguishing by cohort, education, and area of birth (north or south). In the second step of our analysis we develop a multivariate analysis where the effect of education on the propensity to have a jth-order child is evaluated through hazard models. This kind of regression model allows us not only to consider women with a complete fertility history but also those interviewed before the end of their reproductive age (i.e., right-censored). Therefore, the multivariate analysis can be extended to cohorts born between 1940 and 1990. Furthermore, we are able to include the effect of time-varying covariate. For the transition to the first parity, episodes begin at the 15th birthday and end with the birth of the first child (event occurred) or at the interview (event is right-censored). The baseline is the woman’s current age. For the transition to the second and third parity, episodes begin at the birth of the first (second) child and end with the
birth of the second (third) child or at the interview. In this case the baseline is the duration since the birth of the first (second) child. Two critical points emerge in the application of this kind of model. The first is linked to the different timing of the first birth according to education that may act on the second and higher birth order. We try to explain this mechanism following Kravdal’s (2007) suggestions. More educated women tend to have their first child at older ages (we assume, for example, that the average age at first birth is 30 for more educated, and 25 years for less educated women). Let us consider the hazard of second birth among women with at least one child: when the age of the mother and the duration of time since the birth of the preceding child are taken into account, the effect of education is evaluated by comparing the transition rates of women with different levels of education — taking the mother’s and the child’s age as a constant. Within the group of women, say, aged 32 years with a 2 year old child, we compare the subgroup of higher educated women (who, having had their first child at 30 years of age fall perfectly within the average age at first birth) with the subgroup of lower educated women (who are ‘deviant’ in the sense that their age at first birth is later than the average of the corresponding subgroup). Suppose that there is a woman-specific unobserved factor (say, Z) that is constant throughout the reproductive life, for the latter subgroup, the deviant behaviour ‘hides’ a low Z value. Therefore, if Z is not taken into account the propensity to have the second child at 32 years of age for more educated women would be overestimated. This distortion is due to the influence of unobserved factors capable of influencing the preceding choices, in this specific case, the intensity and the tempo of first order births. One approach to avoiding this bias has been proposed by Kravdal (2001, 2002, 2007) and consists of the simultaneous estimation of hazard equations (one for each birth order) containing an identical residual expressing the total deviation of each woman from the rest of the sample with regard to the unobserved characteristics (e.g., a greater propensity towards building a career as opposed to a family, primary infertility problems, etc.). However, in this approach, Z is hypothesised as uncorrelated with education at the beginning of the reproductive period, i.e., education is an exogenous factor in the model. This leads us to the second critical point: there may be some unobserved factors that lie behind both education and fertility choices, such as ‘preference’ for a greater or lesser number of children that plays an important role in determining actual fertility (Hakim 2000, 2003; Vitali et al. 2009) In this example, it would seem that the preference is negatively correlated with level of education as those more oriented toward having a large family would tend to invest less in building up their own human capital (Becker 1991). Hakim (2000) argues that in developed countries — where fertility has now been controlled for several generations — this preference forms during infancy and adolescence and varies little over the course of a woman’s reproductive life. This interpretation does not conflict with the results of several other studies that hypothesise an influence of genetic factors — that do not change throughout life — on the propensity for low or high fertility (Kohler et al. 1999; Kohler and Rodgers 2003). In order to disentangle the effect of education on fertility behaviours from the potential influence of unobserved confounding factors that may affect both education and fertility (e.g., preferences linked to education, contextual characteristics,
contraceptive behaviour, etc.), we simultaneously estimate three hazard equations, one for each birth order, together with one ordered probit equation with multiple categorical outcomes (low, medium and high level of education)3. The three hazard models are functions of education, a set of covariate X (both time-fixed and timevariables), and an unobserved factor potentially correlated with the residual included in the fertility equations. Multiprocess model accounting for potential endogeneity of education on fertility behaviour represent a research strategy that has been rarely adopted (one exception is Meroni 2010) given that most previous analyses based on multiprocess approach considered education as exogenous factor (e.g. Bratti and Tatsiramos 2011; Caltabiano and Rosina 2010; Gottard et al. 2015; Hoem and Hoem, 1989; Hoem et al., 2001; Kravdal 2001; Kreyenfeld 2002). Nevertheless, this research strategy has been adopted in some studies where fertility, partnership formation and partnership dissolution are modelled jointly — see, among others, Lillard 1993; Upchurch et al. 2002, Steele et al. 2005. More formally we have developed four equations (subscript i stands for i-th woman):
ln i(1) t (1) t (1) Edui '(1) X i(1) ln i(2) t (2) t (2) Edui '(2) X i(2)
ln i(3) t (3) t (3) Edui '(3) X i(3) Edui * '
(4) 2
X
(4) i
(1)
if Edui * 1 ' low ' where Edui ' medium ' if 1 Edui * 2 ' high ' if 2 Edui *
where t is the duration of the episode; ln i( j ) t is the logarithm of the risk of having a jth child at time t; ( j ) (t ) is the baseline function; Edui is the level of education and ( j ) is the relative regression parameter; X i( j ) is the vector of exogenous covariate for the j-th equation and '( j ) is the relative regression parameters vector. We assume that ε and λ, respectively, reflect the woman’s propensity (constant over time) to have a higher fertility and to reach a medium level of education, and are normally distributed and potentially correlated. Therefore, we hypothesise the following variance-covariance structure of unobserved heterogeneity in the simultaneous equations model:
0 2 N , 0
1
(2)
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 1 , 2 ,... 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.
Generally speaking, a strong correlation between pairs of residuals means that some common unobserved factors (at individual level) simultaneously influence the two processes (fertility and education). The estimate of the parameters of the model via maximum likelihood can be obtained using aML (Lillard and Panis 2003). The variable of interest is education. Starting from the level specified by the International Standard Classification of Education, education is coded as follows: low (pre-primary, primary or lower secondary level); medium (upper secondary level); high (tertiary level). Taking advantage of the complete educational histories collected in FSS 2009, educational attainment is operationalised as a time-varying variable that takes account of the current level of education throughout individual biography. An alternative approach considers the level of education as a time-fixed variable under the assumption that those who achieve higher levels of education are, from a very early age, oriented towards accomplishing the latter (Bratti 2011 and 2003; Kravdal 2000). However, in this case the estimates may be confounded by reverse causality given that childbearing may have affected a woman’s interest in and opportunities for taking further education, thus producing an underestimation of the true causal effect (Kravdal 2004, 2007; Hoem and Kreyenfeld 2006). For example, the original education goals can be hindered by an unplanned birth and revised upwards in case of unexpected childlessness (Kravdal 2001). Together with the current educational attainment, we also consider a dummy for being engaged in full-time education (student: yes/no) based on the age of leaving school. The relevance of educational enrolment is widely emphasised in the literature (Billari and Philipov 2004; Blossfeld and Huinink 1991; Golscheider and Waite 1986; Hoem 1986; Kravdal 2007). The effects of education may in fact reflect differences in the labour force participation of women, as already suggested in the text. This can be accomplished by including a time-varying indicator that takes account of women currently working in a paid job. The exogenous time-fixed covariates included in the hazard models are: area of birth (with two levels, north and south4); woman’s birth cohort (1940–49, 1950–59, 1960–69, 1970–79, 1980–90); number of siblings (0–1, 2, 3+) and, in the transition to the second or higher child birth, the woman’s age at previous birth (15–24, 25–29, –34, 35+), a variable that can capture the potential catch-up effect for women with a postponed fertility. Finally, in the probit model we also consider mother’s and father’s educational level (primary, lower secondary, upper secondary) and socioeconomic status (social class) — a variable based on highest job category of the two parents when the woman was 14 years of age and with three levels: low (workers), middle (employees, teachers, self-employed and dealers), and high (managers, entrepreneurs and professionals)5. 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
4. Descriptive findings. Indicators of fertility levels We start our descriptive analysis by looking at the temporal evolution of the association between education and fertility in Europe over the last century. For this purpose, we use the meta-dataset made available by Skirbekk (2008). We have ranked the 202 European populations included in the data-set according to the period of data collection (from 1910 to 2002) to show the relative percentage difference between the fertility of the highest and the lowest level of education (Figure 1). There is no temporal trend, and the negative association between fertility and education prevails throughout the 20th century in Europe, with a mean difference of 20% between the fertility of the least and the most educated people. Looking at this figure it is easy to share Skirbekk’s conclusion that education has become an increasingly important determinant of status during the 20th century and, since the education-fertility relation is more negative, this implies that the overall status-fertility relation is more negative. [FIGURE 1]
We can now move to the analysis of the FSS 2009 data for the Italian context aiming at tracing a description of fertility differences by level of education in the last decades of the 20th century. Table 1 shows that only 13% of Italian women born between 1940 and 1964 and interviewed in 2009 are childless, 23% have only one child, and almost two out of three women have two or more children. In general, TFR of these women (i.e. total fertility of the cohorts born between 1940 and 1964) is 1.79. This is the result of the combination between relatively high probabilities of having a first and second child (PPR1=0.87 and PPR2=0.73) and low probabilities of having a third (PPR3=0.32), confirming that Italian low fertility in the recent past has mainly been due to a lack of higher parities. This result contrasts sharply with other countries such as France where the parity progression ratios for the cohorts born between 1953 and 1964 (TFR=2.06) were, respectively, PPR1=0.90, PPR2=0.79 and PPR3=0.44 (Toulemon et al. 2008).
[TABLE 1]
Fertility levels by cohort and education (Figure 2) show a large and roughly constant difference between low educated women (13 years), regardless of the rapid decrease in the share of low educated women over cohorts, passing from 74% among all women born
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.
in 1940-49 to 41% among those born in 1960-64 (Table 2). Fertility differences in terms of education are more pronounced in the southern regions. Despite the fact that the analysis of PPRs (Table 2) suggests a generalised inverse association between education and fertility for any order and cohort both in the north and the south, for the second and the third child differences according to education tend to reduce among younger cohorts in the north whereas they remain more evident and persistent in the southern area. Moreover, as distinct from the north, where PPR2 are generally lower than PPR1, in the south the chances of having the second child are very high, similar to those related to the first child, showing that, until the middle of the 1970s, the first tract of marriage life was slightly influenced by fertility control in each social stratum. For the third child, the high level is maintained among less educated women but it suddenly decreases among younger cohorts born during the 1950s and with higher education that approaches the levels observed in the north. Briefly, cohort data show that differences in fertility by birth order and education in Italy are far from disappearing. They are strong everywhere for PPR1 and in the south for PPR2 and PPR3 following the ‘traditional’ inverse association. In the next section we will see if these differences are maintained, cancelled or become more pronounced after controlling for potentially confounding factors.
[TABLE 2]
5. Multiprocess hazard models Table 3 contains estimates from both the independent hazard model (separately for each parity transition) and the simultaneous equations model as in (1). We start considering the results coming from independent models. Focusing on the propensity to have the first child birth, estimates roughly confirm the negative educational gradient already highlighted in the previous section and, in particular, the higher propensity to become mother among the lower educated women compared to the other groups. This negative effect persists even after having controlled for the condition of being a student, showing that the pure mechanical postponement effect of being a student is not the only relevant dimension in delaying fertility produced by education, as already underlined in Bratti and Tatsiramos (2011). Nevertheless, unlike previous descriptive results, the multivariate approach highlights a positive and highly significant educational gradient on the propensity to have a second and a third child. This results stresses the relevance of taking into account the age at the previous child in the calculation of the risk of having an additional child and the time-varying variables accounting for the working condition and being a student. Indeed, the relative risk for the more educated women turns positive after the introduction of these three variables. Simultaneous equation model basically confirm the sign of the relation found in the independent equation models even though some differences emerge according to the magnitude of the effect. The negative educational gradient found for the first child is emphasized by simultaneous equation models whereas the positive gradient for higher order is softened.
The standard deviation of the common residual in the three fertility equations is significantly different from zero (0.92), suggesting the presence of a certain level of selectivity with regard to these individual characteristics in the second and third birth order. Failing to take selectivity into account would lead to an overestimation of the positive relation between fertility and higher education in the second and third child birth. In our model we also consider the potential effect of common unobserved factors on both fertility and education. The correlation among residuals is negative (-0.06) but close to zero and not supported by an adequate significance level. Although we believe that this result needs further investigation and testing, it suggests that the hypothesis of negatively correlated unobserved factors linked both to fertility and education choices cannot be easily supported.
[TABLE 3]
For the other covariates included in the fertility equations we did not observe substantial changes between independent and simultaneous equations models. Generally speaking, younger cohorts show a lesser propensity towards maternity for every birth order, especially the first; to be a student is negatively correlated with maternity; and southern women from large families, and not currently working, are more prone to become mothers and to have a second and a third child. Moreover, we noticed a negative effect of age at first birth for parity higher than one, a result that indicates the prevalence of biological constraints over a cultural catch-up effect later in the reproductive life course. The probit equation for education (table 3 last column) shows that the propensity to achieve a higher level of education is higher among women with few siblings and with a strong family background, i.e., highly educated parents belonging to the higher social classes, and it increases over birth cohorts with an apparent reversal for the younger group (born during 1980s), mainly due to the fact that they have not yet completed their educational career before the interview6. The positive educational gradient for the second and third birth may be explained in terms of a time-squeeze among high educated women. In order to test this mechanism, we include in our model the interaction between the current level of education and the age at previous child birth. The resulting estimates (Table 4) show that women with a tertiary degree tend to have the second child more rapidly than lower educated counterparts for any groups but the educational gap widens when the age at first is higher than 30 years. A similar time-squeeze effect is not confirmed for the third child birth, also because of a lack of an adequate statistical significance. As further evidence, in Figure 3 we show the log-hazard for the transition to the second and third birth as a function of the duration since the previous birth according to the level of education7. Tertiary educated women show a
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 2002). Thus, we are forced to consider the level
higher risk of having a second child in the four years after the first birth and a lower propensity thereafter, thus suggesting an acceleration in the second childbearing. This time-squeeze effect is less evident for the third childbirth. The interaction between level of education and birth cohort (Table 5, columns 1–3) clearly shows an increasing relevance of education in the propensity to have a child among younger cohorts. Highly educated women born in the 1970s or later show a lower propensity to become mothers but a higher chance of having an additional child after having had their first child. Among older cohorts, the effect of education is less clear and often lacks an adequate statistical significance. Nevertheless, the estimates for the whole country hide some relevant differences between geographical areas. The interaction between education and area of residence (Table 5, columns 4 and 5) highlights two main results. First, the negative effect on the transition to the first birth, although confirmed in both areas, emerges in a different way: in the north we found differences between each couple of levels; in the south we found no differences in the hazard for women with an upper secondary level of education and those with a tertiary degree. Second, and most important, we found an opposite effect of higher education on the propensity to have a third child, i.e., a positive effect in the north and a negative effect in the south. More precisely, if in the northern regions the higher propensity to have a third child can be observed among women with a tertiary education, in the southern regions the difference is between women with a low level (up to lower secondary) of education and those with a higher level (at least upper secondary level). Thus, the positive effect observed for Italy as a whole is mainly provided by the strong positive effect reported in the north that includes more than 60% of the women in the sample.
[TABLE 4] To conclude, our main results can be summarised as follows. (1) The lower fertility that still shows up among the better educated in the descriptive analysis is strongly associated with their later entry into motherhood. (2) When using a simultaneous equations regression model, the educational gradient is negative for the first child and positive for the second and third child, roughly confirming the results obtained using the independent equations model. (3) Highly educated women are under a time-squeeze which accelerates the transition rate to the second child. (4) Education tends to have an increasing effect among younger cohorts both as a higher negative effect for the first child and for a stronger positive effect for subsequent birth orders. (5) The positive effect on the third child birth among higher educated women is not confirmed in the southern regions.
5. Discussion 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.
Using survey data, this article attempted to isolate the connection between education and fertility in Italy for the female cohorts born between 1940 and 1990. We applied an event-history approach that controls for two possible biases: the first is linked to selectivity and, in particular, to the different timing of the first birth according to education which may affect the second and higher birth order; the second relates to the potential endogeneity of education since there may be common unobserved factors that influence both education and fertility choices. Our results suggest that the impact of education levels on fertility behaviours has not lessened over time. As larger numbers of higher educated women postpone first childbirth or remain childless in order to achieve higher positions in the labour market, it is possible that the conflict between a career and a family is more strongly felt among educated woman. This emerges clearly in a country like Italy where there is a lack of publicly available childcare and the persistent dominance of strict gender roles. This result is in line with the new household economics perspective proposed by Becker (1981) considering that a delayed motherhood is less costly for a woman’s working career. However, among mothers, we found, ceteris paribus, a positive educational gradient on the propensity to have an additional child, in particular among women born during the 1970s and 1980s. According to Hoem and colleagues (2001), the higher propensity to have a child among more educated women can be explained according to their better position in the labour market: it may be easier for women graduates to combine work and parenthood since they have more protective labour contracts. Indeed, the public sector is the most important employer of better-educated women. For example, many women with a tertiary level of education are teachers, a category with at least the same family orientation as other women but with jobs that have more flexible work schedules. Higher family income may also have a role to play in being better able to afford an additional child. However, the evidence is not so clear. For example, in Norway a higher income for husbands was found to reduce third birth probabilities (Kravdal 1992). An alternative explanation refers to the assumption made by Kravdal (2001) that ‘cultural elites’ take the lead in the movement towards more childfriendly preferences, and the better educated have started to more strongly appreciate the emotional returns of parenthood. Our results also highlight relevant cohort and territorial differences. Firstly, variations by cohort supports the idea that in the Italian framework both the decline and the turnaround in the level of fertility are driven by the same social group, i.e., higher educated couples (Esping-Andersen 2009; Esping-Andersen and Billari 2015). Secondly, in the southern regions the negative impact of education on the first birth transition is stronger than in the rest of the country and there is no evidence of a higher propensity among graduate women to have a third child, as can be observed in the north. This latter result suggest that in the richer and more developed areas the increasing number of graduate women does not represent an inevitable decrease in the third birth order fertility. This is not the case in the southern regions.
Recalling our first hypothesis, we found that the negative educational gradient on the first birth is not only softened but even reversed for the second and third child birth thus not confirming our expectation. We also gave evidence that this positive effect is linked to a time-squeeze among more educated women who accelerates the transition rate to the second child. As suggested by Klesment et al (2014), the time-squeeze effect in Italy may more than compensate for the lowest progression ratio to second birth among the highly educated. As far as the second hypothesis is concerned, the peculiarities of the southern regions of Italy are also reflected in the fertility behaviour confirming the expected results of a different educational gradient compared to what emerge for the rest of the country. Finally, it is fully confirmed the third hypothesis that younger cohorts led the change in the relation between education and fertility. From a methodological point of view, our analysis reveals that the potential endogeneity of education does not emerge as a relevant feature given that estimates does not change after the introduction of an additional equation for the educational attainments. Furthermore, the development of simultaneous equations in order to account for unobserved variables that may simultaneously affect both fertility tempo and fertility quantum, modify also the magnitude but not the sign of coefficients obtained using independent equations. This is in line with Kravdal (2007) who suggests that other factors arise as more relevant than the control for selection through joint modelling, but who, however, underlines that simultaneous modelling of all transitions represent important checks allowing for more robust results. Specifically, our analysis reveals the importance of the dynamic measures of educational and job career within the models confirming the strong incompatibility between school enrolment, being employed and fertility proneness. Without taking theses time-varying factors into account estimates may be biased, in particular when we analyse cohorts of women who have not finished their reproductive period. Conversely, our estimates are not sensitive to the approach used in the definition of education giving basically the same effect both for time-fixed and time-variable definition of education, thus excluding the possibility of reverse causality. This is mainly due to the fact that in Italy it is quite rare to take more education after first birth (see, for example, Sironi et al 2015). We want to conclude with three reflections, directed above all to those who wish to further investigate these issues with, hopefully, richer and more detailed data. First, focusing on the propensity to become a mother, the strong and negative effect of education is not only confirmed but reinforced among the younger cohorts considered in our sample. Indeed, for the youngest women the influence of education on fertility is increasingly linked to the continual postponement of events that ‘traditionally’ precede the birth of a child: access to relatively stable employment, partnership formation, and departure from the family of origin (Billari and Rosina 2004). Recently, among those with higher levels of education (and children of higher-educated parents) there has been a slight acceleration of important life course events due to a rapid increase in extra marital cohabitation (Rosina and Fraboni 2004). However, this has been counterbalanced by the increasing number of young high school and university graduates employed on flexible (precarious) contracts; a strong cause of the postponement of conjugal and reproductive choices in Italy (Salvini and Ferro 2006). These trends could be changed by recent
legislative changes which since 2015 make it easier for companies to propose permanent contracts. Obviously, it is too early to draw conclusions. Second, we want to suggest two possible explanations for the south-north differences. The first explanation takes into account the idea of ‘incomplete revolution’, i.e., the revolutionary change in women’s role proposed by Esping-Andersen (2009). Among the younger cohorts, the increased involvement of fathers in childcare may result in a positive effect on higher parities by providing women with a greater potential to reconcile work and family. In Italy, the new gender-equality model is far from being dominant and the ‘female revolution’ is still incomplete and unstable where couples are involved in suboptimal outcomes such as a lower actual fertility than desired (Esping-Andersen 2009). This is why the negative effect of education on fertility continues to be strong, for the first birth order in particular. However, the higher propensity for a second and third child birth among more educated women in the younger cohorts suggests that the north of Italy is moving towards a more genderequal system where couples, who are similar in terms of human capital, follow a model of dual careers and shared home production. Conversely, in the south the negative effect of education on the third child birth may mirror the lower level of gender-equity and the more conservative attitudes toward the mothers’ and wives’ roles in comparison with the rest of the country (Dotti Sani 2012). The second explanation for the south-north differences focuses on contraception and the higher propensity among the lower educated (up to 8 years of schooling) on the third child birth that emerges in the south. It has been underlined that the use of technological contraception in Italy has spread more widely among higher educated women (Dalla Zuanna et al. 2005; De Sandre 1982), resulting in a higher risk of having an additional unexpected child among lower educated women (Castiglioni et al. 2001). Therefore, for a considerable proportion of women with low levels of education, the birth of a third child seems to be the result of a ‘constraint’ rather than a ‘choice’, as it appears to be for more educated women. If the link between education and contraception is stronger in the south, then we have more unexpected children in this area among less educated women. Further research is needed. Third, we want to stress some limitations of our analysis. As far as the model is concerned, it is important to underline the assumptions made for the unobserved factors. These factors are considered as normally distributed and invariant over time, i.e., any change in preferences that leads to a shift in the hazard of conception is due to observed circumstances (Steele et al. 2005). Following a usual demographic interpretation (Kravdal 2001, Kreyenfeld 2002), this implies that individual orientation towards family life and fertility is constant over time. We agree with Upchurch et al. (2002) that more research is needed to test these assumptions and to suggest fruitful ways to extend to a time-varying unobserved heterogeneity term and a non-Normal distribution. A possible solution has been proposed by Gottard et al (2015) who investigated the relationship between fertility and women's education in Italy by allowing the frailty component to vary over time as a way of acknowledging that women's orientation towards childbearing may change over the life course. The results showed that either disregarding the unobserved component or assuming a time-constant unobserved heterogeneity can lead to misleading results. This a very interesting and promising solution even though they assume that the time varying
frailty component is piecewise constant jumping at two fixed points in time (at age 28 and 35 years) regardless of individual characteristics. Focusing on data limitations, it should be stressed that the finding of a positive gradient of education on second and third birth risk may just be the result of unavailable information on variables found to be important by other researchers in this context such as partner’s characteristics. Previous analysis (Kreyenfeld 2002) show that in West Germany partner’s education is more strongly associated with transition to second birth than women’s education. Having a birth is a dyadic decision (Beckman 1983) and the key role of partners’ education on fertility behaviour has been underlined also for the Italian context (Régnier-Loilier and Vignoli 2011; Rosina and Testa 2009). Unfortunately, retrospective life course information about previous partners was not included in the FSS survey so that we cannot account for this information in our model. According to Gottard et al (2015), the unavailable information on partner’s education can be viewed as an important unobserved characteristic that can be best captured by an individual time-dependent random effect. There is also a lack on other potentially relevant information such as income, educational career pattern and the type of job qualification. In addition, another interesting line of research relates to the fertility differences according to the area of education. As Hoem and colleagues (2006) have suggested for Sweden, on average, women educated for jobs in the teaching or health sectors have much higher fertility than women in other areas of education. Indeed, the vast majority of empirical studies on the impact of education on fertility have paid little attention to the qualitative dimension of education, focusing mainly on the level of education and on educational enrolment (Martín-García and Baizán 2006). Further analyses taking into account this additional information are required for a better understanding of the potential biases given by observed and unobserved factors. Despite these limitations, our study shed light on the relation between fertility behaviour and education in the Italian context giving evidence that are robust both to age selection bias and the potential endogeneity of educational choices and suggesting a separate analysis for the southern regions of the country.
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Figure
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Figure 1. Percentage difference between fertility of the most and the least educated people. 202 European population ranked by the year of survey or census (from 1910 to 2002) 1911 60
1971
1977
1993
1999
1999
40
20
0
-20
-40
-60
-80
Source: our elaboration on the data-set collected by Skirbekk (2008).
1999
1999
1999
1999
2002
Figure 2. Total fertility rates (TFR) by cohort and level of education. Italian women born between 1940
and 1964. Italy 2.60 2.10 1.60 1.10 0.60 1940-44
1945-49 Low
1950-54
1955-59
Medium
1960-64 High
North 2.60 2.10 1.60 1.10 0.60 1940-44
1945-49 Low
1950-54
1955-59
Medium
1960-64 High
South 2.60 2.10 1.60 1.10 0.60 1940-44
1945-49 Low
Source: Istat FSS 2009.
1950-54 Medium
1955-59
1960-64 High
Figure 3. Log-Hazard for the transition to the second and the third child birth according to the time elapsed since previous birth and level of education at the interview. Estimates from simultaneous hazard models for the second and third child birth. Italian women born between 1940 and 1990 Second child
Third child
-1.00
-2.00 0
2
4
9
15
-2.00
-3.00
0
4
9
15
-4.00
-3.00
-5.00 -6.00
-4.00
-7.00
-5.00
-8.00 Low
Medium
High
Low
Hazard models also include all the other covariates presented in table 3.
Source: Istat FSS 2009.
Medium
High
Table
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Table 1. Fertility of Italian women born between 1940 and 1964. Women by number of children (N) Women by number of children (%) Women with at least j children (%) Children by birth order (number) Children by birth order (row %) Children by number of siblings (number) Children by number of siblings (row %) Probability of having a child of order j (Parity Progression Ratio to j = PPRj) Source: Istat FSS 2009.
0 973 13.0 100
1 1,736 23.2 87.0
2 3,233 43.2 63.8
3+ 1,542 20.6 20.6
Total 7,484 100 ---
-----
6,511 48.5
4,777 35.6
2,126 15.9
13,414 100
1,734 12.9
6,466 48.2
3,442 25.7
1772 13.2
13,414 100
---
0.87
0.73
0.32
1.79 (TFR)
Table 2. Distribution of women and Parity Progression Ratios (PPR) by cohort, area and education. Italian
women born between 1940 and 1964. Distribution of women (column %) Italy
North
South
1940-49
1950-59
1960-64
1940-49
1950-59
1960-64
1940-49
1950-59
1960-64
Low
74.3
55.5
41.4
72.6
50.8
41.7
77.9
64.2
51.8
Medium
17.9
32.6
43.3
19.9
36.6
44.8
13.8
25.0
35.6
High
7.7
11.9
15.2
7.5
12.6
13.4
8.3
10.7
12.6
2698
3112
1674
1649
1850
1005
1041
1269
670
N
Parity Progression Ratios (PPR) First child 1940-49
1950-59
Second child 1960-64
1940-49
1950-59
Third child 1960-64
1940-49
1950-59
1960-64
Italy Low
0.90
0.92
0.86
0.78
0.76
0.72
0.41
0.37
0.35
Medium
0.82
0.85
0.83
0.72
0.69
0.69
0.21
0.21
0.20
High
0.82
0.81
0.76
0.68
0.67
0.68
0.25
0.22
0.18
North Low
0.91
0.92
0.84
0.73
0.68
0.63
0.32
0.28
0.24
Medium
0.82
0.86
0.83
0.67
0.63
0.64
0.15
0.17
0.19
High
0.81
0.80
0.73
0.62
0.59
0.63
0.20
0.29
0.16
Low
0.87
0.91
0.89
0.87
0.88
0.86
0.58
0.47
0.47
Medium
0.84
0.81
0.84
0.87
0.86
0.81
0.36
0.31
0.21
High
0.84
0.83
0.82
0.79
0.86
0.77
0.32
0.13
0.21
South
Source: Istat FSS 2009.
Table 3. Hazard models for the first, second and third child birth and ordered probit model for the highest level of education at the interview. Estimates from independent and simultaneous equations model. Italian
women born between 1940 and 1990. First child Indep. Simult. β Education (ref. Medium) Low 0.25 High 0.07 Currently working (ref. No) Yes -0.53 Currently student (ref. No) Yes -1.17 Area of residence (ref. Center-north) South -0.16 Birth Cohort (ref. 1960-69) 1940-49 0.20 1950-59 0.32 1970-79 -0.27 1980-90
-0.64
Age at previous child birth (ref. 25-29) 15-24 years 30-34 years 35+ years Number of siblings (ref. 0) 1 0.21 2 0.30 3 or more 0.45 Mother's educ. (ref. Primary) Lower sec. Upper sec./tert. Father's educ. (ref. Primary) Lower sec. Upper sec./tert. Social class (ref. Low) Middle High Missing
Second child Indep. Simult.
Third child Indep. Simult.
sig.
β
sig.
β
sig.
β
sig.
β
sig.
β
sig.
*** *
0.45 -0.12
*** **
-0.11 0.25
*** ***
-0.04 0.22
***
0.04 0.45
***
0.11 0.36
***
***
-0.74
***
-0.25
***
-0.42
***
-0.29
***
-0.45
***
-1.28
***
-0.11
*
-0.35
***
0.32
***
0.02
***
-0.18
***
0.37
*** *** ***
0.29 0.49 -0.40
*** *** ***
0.13 0.00 0.08
***
***
-0.71
***
-0.22
*** *** ***
0.24 0.37 0.56
*** *** ***
0.45
0.34
0.46
*** ***
**
0.31 0.14 -0.01
0.22 -0.06 0.14
***
**
-0.43
***
0.13
0.19 -0.24 -0.85
*** *** ***
-0.24 0.08 -0.32
*** * ***
0.59 -0.70 -1.13
*** *** ***
0.17 -0.32 -0.45
0.04 0.15 0.31
0.10 0.28 0.57
* *** ***
-0.20 -0.09 0.34
**
*** ***
-0.14 0.06 0.66
*
0.40 0.09 0.12
β
***
-0.84 -0.20 0.06
*** ***
-0.20
***
-0.01 -0.18 -0.59
*** ***
0.31 0.58
*** ***
0.45 0.78
*** ***
-0.20 -0.53 -0.58
*** *** ***
*** *** ***
***
Standard deviation of residual in the fertility equations 0.92*** Correlation between the residuals (fertility-education) in the simultaneous models -0.06 Number of cases
14846
Note: Statistical significance: * > 90%
Source: Istat FSS 2009.
9951
** > 95%
*** > 99%.
6843
sig.
-0.02
-0.05
***
Education Simult.
14846
Table 4. Interaction between level of education and age at previous child birth. Estimates from simultaneous hazard models for the second and third child birth. Italian women born between 1940 and 1990. Age at previous child birth 15-29 years β
30-34 years sig.
β
-0.27
***
-0.13
0.24
***
0.34
0.24 *** -0.15 High 0.45 ** 0.11 Note: Statistical significance: * > 90% ** > 95% *** > 99%. Hazard models also include all the other covariates presented in table 3.
-0.34
sig.
β
35+ years sig.
SECOND CHILD Education (ref. Medium) Low
-0.07
High
0.20
**
**
THIRD CHILD Education (ref. Medium) Low
0.28
Source: Istat FSS 2009.
Table 5. Interactions between level of education, birth cohort and area of residence. Estimates from simultaneous hazard models for the first, second and third child birth. Italian women born between 1940 and 1990. Cohorts
Area
1940-1954 (1)
1955-1969 (2)
1970-1990 (3)
β
β
β
sig.
sig.
sig.
North (4) β
South (5) sig.
β
sig.
FIRST CHILD Education (ref. Medium) Low
0.41
High
***
0.46
***
0.58
-0.08
-0.20
**
Low
0.08
-0.03
High
0.10
0.16
Low
0.44
***
0.42
***
0.52
-0.11
-0.21
***
0.00
-0.11
-0.09
***
SECOND CHILD Education (ref. Medium) *
0.39
***
0.24
0.05 ***
0.18
**
0.31
***
THIRD CHILD Education (ref. Medium) ***
-0.02
High 0.36 * 0.24 Note: Statistical significance: * > 90% ** > 95% *** > 99%. Hazard models also include all the other covariates presented in table 3.
Source: Istat FSS 2009.
-0.03 0.71
-0.08 ***
0.54
***
0.17
Answers to reviewers
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Comments for the Author: Reviewer #1 This paper examines the potential effect of self-selection on the association between women's education and fertility in Italy using recent retrospective, nationally representative data. I very much liked the idea of challenging recent literature by modelling education and fertility in a common maximum likelihood framework. I believe the idea is strong enough to merit publication. Nonetheless, after reading it, I think the paper needs revisions in a number of areas. In particular, it needs to be better placed in contemporary research on the topic. 1) The paper misses references to a recent paper published on the same topic and on the same data: Gottard/Mattei/Vignoli, 2015, "The relationship between education and fertility in the presence of a time varying frailty component", Journal of the Royal Statistical Society A, Statistics in Society. This early paper investigated the relationship between fertility and women's education in Italy, using the same data (the 2009 Household Multipurpose Survey of Family and Social Subjects). In addition, the paper built on current literature by allowing the frailty component to vary over time as a way of acknowledging that women's orientation towards childbearing may change over the life course. The results showed that either disregarding the unobserved component or assuming a time-constant unobserved heterogeneity can lead to misleading results. As a corollary, I urge the authors to place their paper in line with the one of Gottard and colleagues. At the very last, the authors need to reflect on the limit of their methodological framework, which only accounts for a time-invariant unobserved heterogeneity component. We strongly agree with the reviewer. We add references to the cited paper and we tried to place the paper in line with Gottard et al 2015 as well as the other literature relating to the nexus fertility-education in Italy and Europe (see section 2 in the revised paper). Focusing on the methodological framework, a specific discussion about and the shortcomings of the research strategy (also referring to the approach used in Gottard et al 2015) and data limitation was added in the concluding section (section 6 in the revised paper). The methodological discussion presented in Section 3.3 (page 6) may represent the novelty of this paper compared to previous research, as early authors did not include an education equation in the multiprocess system. I suggest anticipating this innovative aspect of the paper already in the introduction. Thank you for noticing that. We really appreciate this suggestion and we tried to emphasize this aspect both in the introduction and in section 3 (Data and methods). 2) Following Kreyenfeld (2002): Is a time-squeeze effect the reason of the positive effect of education on second birth risks? Higher educated women are usually older when they have their first child than other women. Since participation in the educational system is not easily compatible with raising children, higher educated women generally postpone parenthood until they reach mature ages, and having a first child later in one's life involves having less time at one's disposal before reaching the biological limits of fertility. Such a time-squeeze could increase the transition rate to the second child. The author(s) need to address the potential role of time-squeeze by interacting age at first birth with education, or (better) by interacting the time elapsed since first birth and education. This is a very interesting and useful suggestion. A special effort was made in order to emphasize this point leading to one of the most important improvement in the revised paper. After having discussed the meaning of the time-squeeze effect and the evidence in support and against this hypothesis in the recent literature (see section 2 in the revised paper), we considered both interactions suggested by the reviewer (they are included in table 4 and figure 3 in the revised paper). We tested the time-squeeze effect both for second and third child birth (see section 4 for the presentation of the results and section 5 for a brief discussion).
Interestingly, we found evidence that Italian women are under a time-squeeze effect which accelerates the transition rate to the second child. This time-squeeze effect is less evident for the third childbirth. 3) Another concern is that the finding of a positive effect of education on second birth risk may just be the result of unavailable information on variables found to be important by other researchers in this context, in particular, partner's education. For example, Kreyenfeld (2002) found a negative association between higher education and transition to second child using a time-constant frailty; however, she adjusted for partner's education, which was found to be more strongly associated with transition to second birth than women's own education. Thus, I suggest the authors to include a more thorough discussion of limitations of the dataset, in particular that information on partner's education is not available and discuss how this may have affected your results. Yes, we agree with this suggestion. Following the results found in Kreyenfeld (2002) and the discussion made in Gottard et al (2015) we extended the discussion related to data limitations (at the end of section 5). As highlighted in the concluding section, we think that in addition to partner’s education other relevant information are missing such as income, educational career pattern, the type of job qualification and the area of education. 4)
I think the paper also misses important references to recent Italian and European literature:
The findings presented in this paper seem to contradict those presented in Dalla Zuanna/Impicciatore, 2008, "Bassa fecondità e istruzione nell'Italia di fine Novecento". Working Paper 2008-09. Dipartimento di Scienze Economiche, Aziendali e Statistiche, Università degli Studi di Milano. In fact, these early authors showed that the positive relationship between education and the transition to the second child significantly reverses once accounting for unobserved heterogeneity. Please elaborate on differences between your own results and those of Dalla Zuanna and Impicciatore. Actually, in Dalla Zuanna & Impicciatore 2008 (or in the enghlish version of 2010) the authors claimed that “The positive effect of education on second and third birth order observed in a descriptive analysis and in models with independent equations is revealed to be due simply to a spurious relation. Net of selectivity, a negative relation between education and fertility emerges for second and third birth order.” Results in the submitted paper go in a different direction. Even though the approach is very similar, there are several differences that may explain the different results. Firstly, Dalla Zuanna & Impicciatore (2008) working paper was based on “Aspetti della vita quotidiana (AVQ)” (aspects of daily life) 1993-2002. Despite the noticeable size of the sample used (almost 75 thousand individuals), this survey does not include questions regarding fertility behaviour, forcing the authors to indirectly reconstruct fertility histories using the own-children method through information available only for cohabiting children and limiting the observation to women up to 40 years of age at the interview. This strategy implies a systematic underestimation in the total number of children (estimated by authors as -5%) compared to national values from Population Registers. This underestimation would not be a problem if the exit from parental home is not associated with parents’ education. However, this is hardly be assumed that the timing of leaving parental home and parent’s education are independent. For example, Sironi, Barban and Impicciatore (Parental social class and the transition to adulthood in Italy and the United States, Advances in Life Course Research, 26, 2015) suggest that in Italy children of less educated parents, and women in particular, tend to leave home earlier compared to those with highly educated parents. In other words, the selection operating in the strategy adopted in Dalla Zuanna and Impicciatore cannot be considered as independent from the relation under studies (effect of education on fertility) and then ot can prodeces a bias in the results. Secondly, in Dalla Zuanna & Impicciatore (2008) education is considered as exogenous variables (no control for endogeneity was included in the model) and different cohorts were considered (1953-1977 compared to 1940-1990 in our article). Nevertheless, the main difference is that AVQ data do not contain retrospective information being a crosssectional survey. This implies that it is not possible to trace life trajectories that are potentially interrelated
with fertility behaviour. As highlighted by Dalla Zuanna and Impicciatore (2008), this aspect represents a considerable obstacle in terms of the number of time-dependent control variables included in our analysis. As a result, the explained variance in the multivariate model tend to be lower with a possible inflation of the standard error of the unobserved heterogeneity term. In particular it is not possible to include time-varying variables for educational attainment, currently working or not, being a student or not (as we did in our article) being the only variables considered the level of education (as time-fixed), place of residence (at the interview) and woman’s birth cohort. The relevance of the time-varying variables in the definition of our results emerge also for FSS 2009 data, i.e data used in our article. In the following table, we show the estimates considering as explanatory variable the highest level of education at the interview (i.e. as a time fixed variable) and without taking into account the time-varying variables for being a student and having a job and model. Model (1) consider the model with independent equations and model (2) the multiprocess model without the equation for education. In other words, we estimate models that are very similar to those estimated in Dalla Zuanna & Impicciatore 2008. Well, the comparison between (1) and (2) suggests that after controlling for age selection bias through the development of simultaneous equations, the educational gradient for the second birth changes from positive to negative. Thus, even with FSS data the results would show a reversal in the educational gradient for the second birth, similarly to what emerged in Dalla Zuanna and Impicciatore (2008).
Table. Hazard model for the first, second and third child birth. Education is operationalized as a time-fixed variable. No time-varying variables included in the models. Estimates from independent equation model without time-varying variable, multiprocess model for the three fertility equations without time-varying variables (2) and multiprocess models also considering time varying variables (3). Simultaneous equations Simultaneous equations independent equations (without time varying (with time varying (1) variables) (2) variables) (3) FIRST CHILD Low education 0.40 *** 0.67 *** 0.49 *** Medium 1.00 1.00 1.00 education High education -0.37 *** -0.57 *** -0.29 *** SECOND CHILD Low education -0.05 * 0.11 *** 0.02 Medium 1.00 1.00 1.00 education High education 0.13 *** 0.01 0.13 ** THIRD CHILD Low education 0.13 ** 0.30 *** 0.20 *** Medium 1 1 1 education High education 0.43 *** 0.26 ** 0.33 *** sigma rho
0.93
***
0.95 0.4
*** ***
In column (3) of the same table, we also estimated the same model as in (2) but with the time-varying variables for being a student and having a job. This model cannot be estimated in Dalla Zuanna and Impicciatore because of a lack of longitudinal data. Column 3 shows that the effect of higher education is positive (as in column 1 and differently from 2). We know (looking at the results of our submitted paper) that this positive effect remains after controlling for education endogeneity and considering education as a timevarying variable. Here the point is that the additional retrospective information that are available in FSS compared to AVS are crucial. In particular, we cannot evaluate correctly the effect education without taking into account the condition “to be enrolled” in education (and to a lesser extent ”being employed”) as highlighted in the existing literature (and also highlighted in section 3 of our revised paper).
In conclusion, our paper contains several relevant improvements compared to Dalla Zuanna & Impicciatore (2008) suggesting that the present paper contain more robust results. We tried to summarize these points in note 5 (section 3).
The first paragraph of the introduction is rather weak. The introduction could be better placed in the European demographic debate on education and fertility, especially in relation of partners' educational profiles. Among others: Grow/Van Bavel, 2015, "Assortative Mating and the Reversal of Gender Inequality in Education in Europe: An Agent-Based Model", PLOS One. The structure of the paper has been radically re-organised. The introduction is richer in its contents aiming at emphasize the relevance of the analysis within the current debate on the relationship between education and fertility in Western countries. Besides, several other changes has been made in the structure of the paper: - section 2 has been widely enlarged by including a richer discussion on theories considering the linkages between education and fertility; - a specific focus on previous results for the Italian case has also been added in section 2; - section 2 now includes specific hypotheses and expected results; - section 2 changed title “The fertility-education nexus in the Italian context” in order to highlight that the contents is related both to the theoretical debate and to the previous empirical findings and that a specific focus is devoted to the Italian context; - the description of the organization of the paper moved from section 2 to section 1; - the aim of the papers highlighted in section 2 moved to the beginning of the discussion (as a summary of what we did in the paper); - sub-sections were eliminated; - section 5 in the old version is now divided into two new sections (section 4 containing descriptive findings and section 5 for the multivariate and multiprocess analysis); - section 5 also contains results relating to the potential time-squeeze effect; - the discussion section was enlarged containing more information about shortcomings and data limitations and additional links with the theoretical section. The article written by Grow and Van Bavel is very interesting but it focuses on homogamy and partner’s characteristic. Given that we cannot include the couple’s characteristics in our model, according to us, the specification of such a perspective in the introduction may be confusing for the reader.
Other Comments 5) The title is too general. I think that the illustrated changes across generations (and the focus on geographical differentials) could be reflected in the title in order to attract potential readers. Following this suggestion we changed the title as follows: “The impact of education on fertility in Italy. Changes across cohorts and south-north differences” 6) I found a bit odd presenting a Table in the introductory section. Can it be moved in another section (i.e. Section 2)? Actually, table 1 is not essential to the economy of the paper. Thus, we decided to drop it from the paper, leaving the necessary references. 7) At times, Section 2 mixes findings of micro-level studies and findings of macro-level studies. A systematization of the presentation in this respect will improve the text.
We changed the text accordingly trying to better identify macro and micro-level approaches. 8)
The paper needs content and English proof-reading. Some examples:
Sentences like "we found a positive effect of education on the propensity to have a second child" are unclear. It should be, either, "we found a positive educational gradient", or "a positive effect of higher education". We tried to avoid this ambiguous expressions throughout the whole text. The first sentence of the paper needs re-writing: "Several studies have demonstrated that in Italy — similar to the rest of Europe and the world — the first to control marital fertility were the wealthiest and the most-educated couples". -
Discussion. Reference to the sustainability of the welfare system (see first sentence) comes as a surprise.
We changed these sentences accordingly and we deleted the sentence about the sustainability of the welfare system. Reviewer #2 The manuscript "Fertility and education in Italy" is not without merit. The issue addressed covers an interesting area of research and the analysis is consistent with the research aims. However, the manuscript is not yet publishable because the organization of the different sections is not well structured and needs improvements. Beyond a clear structure, the manuscript would benefit from a stronger focus, a clear statement of the research questions (or hypotheses) and a (more extensive) development of the theoretical arguments. In addition, the clarity of writing needs to be refined and the novelty of the contribution has to be emphasized in both the introduction and the concluding section. The focus of the paper is not very clear. On one hand, it seems that the main focus is on methodological issues i.e., the paper aims to advance knowledge on the education-fertility link by using refinement in the standard statistical modeling by controlling for selection and unobserved heterogeneity. On the other hand, the main attention in the results and discussion sections is deserved to the description of geographical differences. The two perspectives can of course co-exist but they should be described in a more systematic manner. By explaining the main hypotheses the author(s) aim to validate with the analysis, it would become clear the link between methodological refinements and substantive results related to the differences between regions. A special effort was made to improve the structure of the paper and to better place the analysis within the scientific debate on education and fertility. The aims of the paper were restated and research hypotheses are now available in text (section 2). Finally, the discussion section was enlarged trying to further emphasize the contribution of the paper to the existing literature. More in details (as already specified in response to reviewer #1), the main changes made in the revised paper are the following: - The introduction is richer in its contents aiming at emphasize the relevance of the analysis within the current debate on the relationship between education and fertility in Western countries. - section 2 has been widely enlarged by including a richer discussion on theories considering the linkages between education and fertility; - a specific focus on previous results for the Italian case has also been added in section 2; - section 2 now includes specific hypotheses and expected results; - section 2 changed title “The fertility-education nexus in the Italian context” in order to highlight that the contents is related both to the theoretical debate and to the previous empirical findings and that a specific focus is devoted to the Italian context; - the description of the organization of the paper moved from section 2 to section 1;
- the aim of the papers highlighted in section 2 moved to the beginning of the discussion (as a summary of what we did in the paper); - sub-sections were eliminated; - section 5 in the old version is now divided into two new sections (section 4 containing descriptive findings and section 5 for the multivariate and multiprocess analysis); - section 5 also contains results relating to the potential time-squeeze effect; - the discussion section was enlarged containing more information about shortcomings and data limitations and additional links with the theoretical section.
In the introduction section the reader expects to learn about the additional contribution of the proposed analysis in respect to the existing literature, but the author(s) explain only the approach proposed in their analysis without emphasizing the potential novelty and the innovative aspects of their contribution (as well as the benefits stemming from the proposed 'new' approach) The introduction was widely rewritten. In the revised version of section 1 we tried to emphasize what is new in (or at least what characterizes) our approach. Basically, we refer to three aspects: 1. the model specification (accounting for the potential endogeneity of education); 2. the territorial differences and the south-north divide; 3. the cohort variation. The theoretical section does not make reference to any theoretical framework of fertility but contains just a literature review of studies addressing the education-fertility link. It is unusual to find a description of new elaborated empirical results in a theoretical section, as in Figure 1 providing results based on the extension of the meta-dataset used by Skirbekk. This elaboration could be usefully moved to the beginning of the results section (descriptive analysis). Surprisingly, the theoretical arguments developed by Becker, Hakim and Kohler and Rodgers are addressed/mentioned -- even though in a very synthetic manner -- only later in the paper, i.e., in the section on data and methods! The theoretical section was substantially enlarged referring to three main theories: 1. new household economics (Becker 1981); the second demographic transition (Van de Kaa 2004, Lesthaeghe 1995) and the incomplete revolution (Esping-Andersen 2009). Connected to these approaches, a large body of literature of studies addressing the nexus fertility-education were added also with a specific focus on Italy (see section 2). More than 20 new references were added in the revised papers. Figure 1, which provides results based on the extension of the meta-analysis used by Skirbekk, moved to the beginning of the results section. The other theoretical arguments mentioned in section 4, and in particular the arguments developed by Hakim, Kohler and Rodgers, are functional to the interpretation of the latent component in the simultaneous model but there are not, according to us, the main theoretical background for our analysis (as instead are the three theories aforementioned). In the concluding part of the theoretical section the author(s) argue that the aim of the research is to examine the link between education and fertility while explicitly addressing problems related to potential biases, i.e., selectivity of the sample due to the different time schedule of fertility in different educational groups and endogeneity related due to the fact that fertility and education choices might be driven by common unobserved factors. It would be important to emphasise the utility of such an approach by referring to those studies - some of them conducted in Italy (if any) -- addressing the education-fertility link in a more standard way, i.e., through a classical statistical analysis. This would allow the reader to grasp the relevance as well as the novelty and the methodological implications of the proposed study also in terms of implication for future research. Furthermore, the reasons for covering both sample selection and unobserved heterogeneity are not exhaustively explained in the paper and the relative weight of each of them in producing the bias in the empirical results is not discussed in the paper. Are previous statistical analyses not
taking this approach (not addressing selectivity or not accounting for unobserved heterogeneity) showing a negative correlation between education and fertility? It is not easy to answer to these questions because previous results in the literature suggests controversial results and the heterogeneity in the outcomes do not depends only on the kind of model but also to the control variables included in the model. Using the traditional approach, i.e. without multiprocess models, previous results show that highly educated women tend to postpone family formation and childbearing (Salvini 2004; Rosina 2004) but women with more human capital and economic resources do show, ceteris paribus, a higher propensity to have children (Dalla Zuanna and Tanturri 2007; Mills et al. 2008; Rosina and Testa 2009; Klesment et al 2014). Using multiprocess models (without taking into account the endogeneity of education), Rosina and Caltabiano 2010 found a positive effect of higher education on second child birth whereas Dalla Zuanna and Impicciatore (2010) found a negative educational gradient not only for the first child but also for the second and the third child. A negative effect of higher education was found by Meroni 2010 who adopted a model that almost the same as the one we adopted in our paper (three equations for each birth order and one probit for education). These two studies, however, are probably biased because they lack of longitudinal information on life trajectories that are potentially interrelated with fertility behaviour such as, in particular, the school enrolment (being enrolled or not at school or university) and having a job or not (see also point 4, reviewer #1). Finally, Gottard et al (2015), using a time-dependent frailty found that higher educated women tend to postpone the birth of a possible second child with respect to lower educated women, at least until they complete their education. We tried to summarize these results in section 2 and 3 in the revised paper. Generally speaking, the kind of model adopted is not totally new in literature. However, we tried to include several features at once, namely, the selection bias given by the age at previous birth, the potential endogeneity of education and, not less important, the time-varying variable related to employment condition, school enrolment and time-dependent educational level. In the section 1 of the revised paper we tried to emphasize the relevance of the analysis and, in particular, the fact that the potential endogeneity of education is almost never considered. Additional comments on the reasons for covering both sample selection and unobserved heterogeneity and the relative weight of each of them in producing the bias in the empirical results were added in the discussion section. Comparing the estimates of the statistical models in table 4 it seems that the difference concern only the magnitude but not the sign of the coefficients. What is the additional contribution of controlling for selection? How would the estimates look like without such a control? Actually, table 4 (table 3 in the revised paper) compare results from simultaneous models and results coming from models estimated separately (independent equations) that do not take into account neither age selection bias nor potential education endogeneity. In order to limit the output and then to improve readability, we decided to drop estimates coming from model with simultaneous equations but without the probit equation for education. Indeed, the effect of education is very similar to those obtained for the full model. In other words, results do not change after having controlled for potential endogeneity of education. Even though this does not emerge as a relevant feature, it represent an additional check (together with the check for age selection bias, parents’ characteristics, school enrolment, etc) that give to our results a higher level of robustness (see also section 5) In the results section the author(s) start to present results on parity progression ratios by level of education and parity to make the argument that they are negatively correlated. Afterwards, they basically discuss the results of the simultaneous equation models. The intermediate passage, the statistical modeling without unobserved heterogeneity and the control for bias, is not discussed in an exhaustive manner. It would extremely helpful to discuss the outcome of this intermediate passage allowing the reader to better assess the relevance of adopting a simultaneous equation models in the statistical analysis of the correlation between education and fertility.
The reading of the results of multivariate analysis was re-organized in order to highlight similarities and differences with descriptive findings (see section 5 in the revised paper). In particular, it is highlighted that: 1. Focusing on the propensity to have the first child birth, estimates roughly confirm the negative educational gradient for the first child birth already highlighted in the descriptive analysis and, in particular, the higher propensity to become mother among the lower educated women compared to the other groups. 2. unlike previous descriptive results, the multivariate approach highlights a positive and highly significant educational gradient on the propensity to have a second and a third child. This results stresses the relevance of taking into account the age at the previous child in the calculation of the risk of having an additional child and the time-varying variable relating to working condition and being a student. Indeed, the relative risk for the more educated women turns positive after the introduction of these three variables. In order to justify this last sentence, six nested models (using simply independent equations) were estimated (see next table). In column (1) we have a very basic model with only three covariates (education as a timefixed variable, birth cohort, area) and in column (2)-(5) we progressively include an additional variables up to the full model in column (6) (that is the same as the model with independent equations in table 3 of the paper). We can see that in model (1) a negative educational gradient emerges for each birth order, similarly to the analysis of PPRs. Then, for the second and third child birth, the effect turns into positive as additional covariates were progressively introduced. Particularly important is the contribution given by the age at previous birth. Nested hazard model (independent equations) for the first, second and third child birth. Italian women born between 1940 and 1990. baseline, cohort, area, education (time fixed) (1)
+ siblings
+ having a job
+ being a student
(2)
(3)
(4)
+ time at previous birth (5)
FULL MODEL (education as a time variable as in table 3 (6)
FIRST CHILD Low education Medium education High education
0.44 1.00 -0.38
***
0.08 1.00 0.00
***
0.46 1.00 0.07
***
***
0.40 1.00 -0.36
*** ***
0.34 1.00 -0.42
*** ***
0.25 1.00 -0.04
***
0.25 1.00 -0.04
***
0.25 1.00 0.07
***
-0.10 1.00 0.21
***
***
***
-0.11 1.00 0.25
***
0.04 1.00 0.45
*
SECOND CHILD Low education Medium education High education
0.03 1.00 0.01
-0.03 1.00 0.06
-0.03 1.00 0.08
*
***
THIRD CHILD Low education Medium education High education
0.35 1.00 0.11
***
0.26 1.00 0.22
*** **
0.27 1.00 0.15
***
0.08 1.00 0.47
***
3. Simultaneous equation model basically confirm the sign of the relation found in the independent equation models even though some differences emerge according to the magnitude of the effect. The negative educational gradient found for the first child is emphasized by simultaneous equation models whereas the positive gradient for higher order is softened. Going from parity progression analysis to statistical modeling the units of analysis are also different: cohorts of women in the descriptive analysis of parity progression ratios and women in the statistical modeling analysis. There is not a discussion of this issue in the paper. On the one hand, descriptive finding expressing the quantum of fertility as the parity progression ratios and the resulting (cohort) total fertility rates, can be applied only to cohorts of women who have already finished their reproductive behaviour. Otherwise, the resulting measures can be biased by differences in the timing of
the phenomenon. This is why we limit the descriptive analysis only to women born between 1940 and 1964 (aged 45-69 years at the interview). This is now better specified at the beginning of section 3. On the other hand, event history analysis can evaluate time dependent dynamics for first, second and third birth order, also for the cohorts of women who have not yet completed their reproductive lives (because they are interviewed at a younger ages, i.e. they are right-censored). This allows us to extend the analysis to cohorts born between 1940 and 1990 (this is now better specified in section 3, as well as in the introduction)
Furthermore, given that the approach is based on, or inspired by, a similar approach adopted by Kravdal (2001, 2002, 2007), the reader expects to find a discussion of the differences between this research and Kravdal's previous research in the concluding section of the paper. The differences between the two analyses/approaches have not been emphasized in a proper manner: for example, the different way of treating the education variable (as time-varying covariate rather than fixed-effect variable) is described only in the footnote without explaining the reasons (and the implications) behind the different methodological choice. We drop the cited footnote and we included its content in section 3 (data and methods) in a more extensive explanation of why we consider time-varying education instead of time fixed. Generally speaking, this point (but also most of previous suggestions made by reviewer #2) arises a very interesting issue that is not well highlighted in the initial version of the paper. The comparison between Kravdal (2001) and Kravdal (2007) makes aware of the sensitivity to the specification of education. If in Kravdal 2001, the author found a negative gradient of education on fertility when simultaneous equations are considered. However, in the following article (Kravdal 2007) he found that a high educational level for a woman tend to stimulate her second- and third-birth rates. Differences in the outcome are mainly due to the specification of level of education as a time-varying variable, instead of a time-fixed as in Kravdal (2001). Interestingly, results in Kravdal (2007) - suggesting that in the Norwegian context other factors emerges as more relevant than the control for selection through joint modelling - are very close to those obtained in our research. Generally speaking, our analysis reveals that the potential endogeneity of education does not emerge as a relevant feature given that estimates does not change after the introduction of an additional equation for the educational attainments. Furthermore, the development of simultaneous equations in order to account for unobserved variables that may simultaneously affect both fertility tempo and fertility quantum, modify also the magnitude but not the sign of coefficients obtained using independent equations. Nevertheless, simultaneous modelling of all transitions and education attainments represent important checks allowing for a higher level of robustness for our results. In details, our analysis reveals the importance of the dynamic measures of educational and job career within the models confirming the strong incompatibility between school enrolment, being employed and fertility proneness. Without taking into account theses time-varying factors when we analyse cohorts of women who have not finished their reproductive period may lead to biased results. Conversely, our estimates are not sensitive to the approach used in the definition of education giving basically the same effect both for timefixed and time-variable definition of education thus excluding the possibility of reverse causality. This is mainly due to the fact that in Italy it is quite rare to take more education after first birth. This notes were added in section 5 (discussion)
Finally, it would be helpful to find in the result section a (more) detailed description of all coefficients with statistical significant level in Table 4. For example, the description on page 10 (Finally, the propensity to achieve at the least an upper secondary level of education increases over birth cohorts …..) is too short and does not match always the (sign of the) coefficients reported in Table 4. Thank you for noting it. We corrected a couple of inaccuracies in the text (propensity to achieve an higher level instead of “at the least an upper secondary level of education”; no differences according to the area of residence ). The non-linear cohort effect is mainly due to the fact that younger women (born during 1980s)
have not yet completed their educational career before the interview, thus showing an apparent reversal in the increasing trend. This is now explained in section 5.
On page 9, the second sentence in the second paragraph immediately below TABLE 2 is not clear ("This descriptive result might suggest the existence of a progressive 'marginalization' …."), it needs to be reformulated. We deleted this sentence. In the revised paper we simply stressed that the share of low educated women have decreased over cohort (as highlighted in table 2)
The 'fertility gap' is normally used for denoting differences between intended and actual fertility. Here it is used to mean something different and this can generate confusion. This point should be clarified. We changed into the following sentence: “Fertility differences in terms of education are more pronounced in the southern regions” On page, 11, second paragraph; a summary of the main results is fine but here (after the description of all statistical results) the author(s) need to be more explicit. For example, in point 3) what does it mean that education tends to have 'an increasing relevance' among younger cohorts? That it is more strongly positively/negatively correlated to fertility? Or, 'the positive effect of education on third birth is not confirmed'? The effect is, indeed, positive but not statistically significant (Table 5). We tried to better explain this point. The new version is (this is point 4 in the revised version) : “Education tends to have an increasing effect among younger cohorts both as a higher negative effect for the first child and for a stronger positive effect for subsequent birth orders.” Point 2) seems to say that the results of the modeling using and not using a system of simultaneous equations are similar; this statement downsize the relevance of this latter approach, is this what the author(s) really want to say? If yes, why did they keep using this approach also in the estimation of the interaction effects (Table 5)? Would the results of these interactions (especially the North-South differences) be the same in a modeling using independent equations? We think that we have already answered to this point. However, even though results are very similar, we think that considering simultaneous models (also considering the education probit equation) is a relevant check in or analysis. As suggested by Kravdal (2007): “If the intention is to get as clean measure as possible of how education is related to the parity transition in focus, a simultaneous modelling of all transitions should make good sense.” This is why we prefer to show the estimates from multiprocess modelling also for the interactions. Point 1) argues that there are differences between the results based on parity progression approach and those based on the statistical model but the reasons behind these differences are not explained in the paper (not even in another section of the paper). The comparison between results based on PPRs and those with multivariate approach was added at the beginning of section 5 in the revised version of the paper. The concluding section needs to reconcile the findings related to the methodological advancement proposed in the paper with the substantive findings related to differences North South by explaining the extent to which such differences would have not been emphasized in a proper manner by using a genuine conventional approach. Moreover, a clear distinction between the results using just the simultaneous equations and the results using both the simultaneous equations and the control for selection has to be
added in the concluding section. This would help the reader to better understand the extent of the limits of a simultaneous equations approach addressed in the final paragraph of the concluding section. Discussion section was substantially enriched in order to emphasize if simultaneous equation really matters and what the research strategy can (or cannot) add to the research questions (See also previous answers) Figure 1 is this association controlling for confounding factors? No. Considering the information provided in Skirbekk 2008 Fertility trends by social status. Demographic Research 18(5): 145-180: “To be included in this dataset, each sample must have a measure of fertility by social status (education, occupation/social class/rank in a social hierarchy, income/wealth). Data on minimum two social status groups are required for the sample to be included.[…] Observed correlations between social status and fertility levels are recalculated in a standardised way to produce comparable indicators Table 2: to which year is the TFR (1.79) referred to? Here we consider the (longitudinal) total fertility rate of the cohorts born between 1940 and 1964. This is specified in the revised text. References: Kravdal (Demograhic Research) and Wilkie (J Marriage) are missing the year of publication. Ok. We added the year of publication in the revised text.
