Education, Other Socioeconomic Characteristics Across the Life Course, and Fertility Among Finnish Men
Education, Other Socioeconomic Characteristics Across the Life Course, and Fertility Among Finnish Men
Jessica Nise´n 0 1 2 3 4
Pekka Martikainen 0 1 2 3 4
Mikko Myrskyla¨ 0 1 2 3 4
Karri Silventoinen 0 1 2 3 4
Karolinska Institutet, Stockholm, Sweden
0 Centre for Health Equity Studies (CHESS), Stockholm University , Stockholm , Sweden
1 Max Planck Institute for Demographic Research , Konrad-Zuse-Straße 1, 18057 Rostock , Germany
2 Population Research Unit, Department of Social Research, University of Helsinki , P.O. Box 18 (Unioninkatu 35), 00014 Helsinki , Finland
3 School of Medicine, Osaka University , Suita , Japan
4 Department of Social Policy, London School of Economics , London , UK
The level of education and other adult socioeconomic characteristics of men are known to associate with their fertility, but early-life socioeconomic characteristics may also be related. We studied how men's adult and early-life socioeconomic characteristics are associated with their eventual fertility and whether the differences therein by educational level are explained or mediated by other socioeconomic characteristics. The data on men born in 1940-1950 (N = 37,082) were derived from the 1950 Finnish census, which is linked to later registers. Standard and sibling fixed-effects Poisson and logistic regression models were used. Education and other characteristics were positively associated with the number of
children, largely stemming from a higher likelihood of a first birth among the more
socioeconomically advantaged men. The educational gradient in the number of
children was not explained by early socioeconomic or other characteristics shared
by brothers, but occupational position and income in adulthood mediated
approximately half of the association. Parity-specific differences existed: education and
many other socioeconomic characteristics predicted the likelihood of a first birth
more strongly than that of a second birth, and the mediating role of occupational
position and income was also strongest for first births. Relatively small differences
were found in the likelihood of a third birth. In men, education is positively
associated with eventual fertility after controlling for early socioeconomic and other
characteristics shared by brothers. Selective entry into fatherhood based on
economic provider potential may contribute considerably to educational differentials in
the number of children among men.
Education may influence childbearing among men in various ways over the life
(Berrington and Pattaro 2014; Thomson et al. 2013)
. An economic
mechanism is among the most commonly discussed mechanisms linking achieved
educational level to fertility
(Huinink 1995; Kravdal and Rindfuss 2008)
educational attainment is expected to increase men’s fertility through higher income
levels and better labour market positions
. Economic potential may
also contribute to higher fertility among men through better chances in the marriage
(Becker 1993; Oppenheimer 1988; Oppenheimer et al. 1997)
other mechanisms linking education to childbearing among men are also likely to be
relevant, and the relative importance of different mechanisms may be sensitive to
parity and the societal context. The number of children can be viewed to result from
consecutive decisions in the life course
(Kreyenfeld and Konietzka 2008; Thomson
et al. 2013)
; fertility in this study comprises the eventual number of children and the
chances of a first, second, and third birth.
In life-course research on family and fertility, researchers have called for more
attention on potential early-life influences
(Huinink and Feldhaus 2009; Huinink
and Kohli 2014)
. Education plays a central role in the transmission of the
socioeconomic standing of the previous generation to the next (Breen and Jonsson,
2005) and strongly determines other socioeconomic characteristics in adulthood
(Elo 2009; Lynch and Kaplan 2000)
. Socioeconomic characteristics in early life
have been previously linked with men’s fertility
(Easterlin 1966; Thornton 1980)
This study aims to extend the previous literature by carefully analysing to what
extent educational differences in men’s fertility are explained by socioeconomic or
other characteristics in early life, or mediated by such characteristics in adulthood.
In addition to observed early characteristics, we control for unobserved
characteristics shared by brothers. Further, we describe the relationships of
socioeconomic characteristics in early life and adulthood with fertility.
Men’s fertility has traditionally attracted relatively little interest among
(Bledsoe et al. 2000; Forste 2002; Goldscheider and Kaufman
1996; Zhang 2011)
. Educational differentials in fertility are a widely studied topic,
but previous literature has mostly focused on women (Balbo et al. 2013). This study
aims at contributing to the understanding of the relations of education and other
socioeconomic characteristics across the life course with fertility among men by
utilizing longitudinal data on Finnish male cohorts born in the period 1940–1950.
The context of childbearing for these birth cohorts implied an increasing popularity
of the two-earner family model and government support for families, alongside
continuously gendered views of the breadwinner and caregiver roles in society
(Ellingsaeter and Leira 2006; Julkunen 1999)
2 Conceptual Framework
Several mechanisms may link education to fertility in men, and the relative
importance of different mechanisms may be sensitive to parity and the societal
context. The most commonly discussed mechanism builds on the economic approach
to fertility, assuming that individuals behave rationally and the demand for children
increases at higher income levels
(Becker 1993; Berk and Berk 1983; Pollak and
. This implies that higher acquired educational attainment leads to
better chances of providing for a family with a larger number of children through
accumulated human capital, higher income levels, and better labour market prospects.
This income effect thus suggests a positive influence of education on fertility that
operates through income and labour market position, regardless of parity. Yet at
higher income levels, the opportunity costs of children can increase through forgone
money and experience following reduced working hours
. In men, the
positive effect of income is expected to dominate any negative effect of opportunity
costs in contexts where men are considered main providers of the family income.
The strengthening of women’s labour market position is suggested to have been
followed by men’s increasing involvement at home and a continuing change
towards more symmetrical gender roles
(Goldscheider et al. 2015; Hook 2006)
Nordic countries, including Finland, have been forerunners in this respect, with such
changes occurring earlier than elsewhere
(Esping-Andersen 2009; Goldscheider
et al. 2014)
. Despite increased expectations towards men’s involvement at home
from the 1950s onwards, gender roles still remain somewhat asymmetrical, even in
(Joshi 1998; Prince Cooke and Baxter 2010)
. Findings regarding
time trends in socioeconomic differences of fertility do not necessarily indicate
weakening expectations for men as economic providers
(Hart 2015; Kravdal and
Rindfuss 2008; Lappega˚rd et al. 2011; Ravanera and Beaujot 2014;
WinklerDworak and Toulemon 2007)
The traditional economic approach to family has been criticized for its
underlying assumption of a gendered division of labour in the household
(EspingAndersen 2009; McDonald 2000; Oppenheimer 1997)
. Gender specialization in the
strict sense of the term has not been the reality of industrialized countries in recent
decades, but the concepts of income effect and opportunity cost are still relevant.
The expected positive influence of men’s education on their fertility operating
through an income effect could be counterbalanced particularly at higher parities if
more highly educated parents decided to invest more in each of their children,
thereby increasing the costs per child
(Becker 1993; Becker and Lewis 1973)
phenomenon refers to the trade-off between the quantity and quality of children,
with higher-income parents potentially preferring to have fewer children with higher
quality regarding aspects such as children’s education and well-being.
Education may also relate to fertility among men through non-economic
mechanisms, that is, mechanisms that do not operate via income and position in the
labour market. For example, during educational enrolment, the incompatibility of
student and parent roles is likely to constrain childbearing
(Corijn and Klijzing 2001;
. Yet given that men are less constrained by declines in fecundity
(Billari et al. 2011; Schmidt et al. 2012)
, men have time to catch up on their
childbearing after completing their studies, and the effect of enrolment is likely to be
less decisive on their eventual fertility. Any negative effect of long-term enrolment
could, however, be expected to be strongest for the first parity
Educational level may also reflect life values: post-materialist values more
common among the more highly educated may be linked to weaker preferences for
a large number of children and the seeking of fulfilment in life in alternative ways
. Further, the strength of the two-child norm may vary according to
educational group, as, for example, stronger intentions towards having at least two
children were witnessed among highly educated British men
. Moreover, men at different educational levels may differ in their
knowledge and practice of contraceptive behaviour, which may affect fertility,
particularly at younger ages and lower parities
Finally, given the scarce evidence
(Baiza´n and Mart´ın-Garc´ıa 2006;
, it remains possible that the education–fertility association in men
would be confounded by certain early influences that directly influence both
education and fertility. Characteristics of the family of origin could confound the
association if they influenced preferences and opportunities regarding education and
(Axinn et al. 1994; Miller 1992, 1994; Thornton 1980)
. According to
economic reasoning, material resources in the family of origin may discourage
fertility because of the relatively high consumption aspirations adopted in childhood
(Easterlin 1966; Thornton 1980)
. These could be reflected in
acquiring education at the cost of childbearing or in limiting the number of children
to ensure the children’s sufficient quality. Additionally, life goals other than family
building might be emphasized more in families of higher socioeconomic status
(Rijken and Liefbroer 2009; Scott 2004)
, and the potential influence may extend to
behavioural outcomes in the next generation.
The arguments introduced thus far have primarily concerned fertility
decisionmaking among couples. Fertility is closely related to union formation and stability
such that differentiating between cause and effect is ambiguous
Pattaro 2014; Huinink 1995; Van Bavel et al. 2012)
. In the context of this study,
marital unions could be primarily considered potential mediators of the education–
fertility association. Economic approaches predict that men with better standing in
the labour market are more successful in the marriage market
Oppenheimer 1988; Oppenheimer et al. 1997)
. To some extent, this may relate to
prospective childbearing: if economic resources are required for establishing an
independent household and having children, and men are considered important
family-income providers, then the more highly educated men with actual or
prospective higher incomes and better labour market prospects may be viewed as
more attractive partners and potential fathers by women.
Accordingly, a man’s higher education usually predicts both higher chances of
marrying and marital stability
(Lyngstad and Jalovaara 2010; Prince Cooke and
. In Finland, men educated to lower levels have been shown to be
disadvantaged in both the formation
(Finna¨s 1995; Jalovaara 2012)
(Finna¨s 1997; Jalovaara 2003)
of marital unions. The experience of divorce is
usually associated with lower fertility, but remarrying may increase the fertility of
men (Van Bavel et al. 2012). Further, given the tendency of socioeconomic
(Domanski and Przybysz 2007; Ma¨enpa¨a¨ 2015)
, the effect of female
partner’s characteristics on educational differences in men’s fertility remains an
important area of research
(Begall 2013; Jalovaara and Miettinen 2013)
3 Previous Findings
In Nordic countries, men educated to higher levels, at least in younger birth cohorts,
less often remain childless and have higher numbers of children on average
and Huber 2007; Goodman and Koupil 2009; Kravdal and Rindfuss 2008;
Lappega˚rd et al. 2011; Nikander 1995; Nise´n et al. 2014a; Rønsen and Skrede
. In other Western countries, the corresponding associations vary from positive
to flat to negative
(Barthold et al. 2012; Hopcroft 2015; Keizer et al. 2008; Kiernan
1989; Kneale and Joshi 2008; Nettle and Pollet 2008; Parr 2010; Ravanera and
Beaujot 2014; Skirbekk 2008; Thomson et al. 2013; Toulemon and
LapierreAdamcyk 2000; Toulemon et al. 2008; Tragaki and Bagavos 2014; Weeden et al.
. A recent comparative study reported childlessness at the ages of 40–44 to be
more common among men educated to lower levels in 13 out of 19 European
(Miettinen et al. 2015)
Previous studies show that men enrolled in education have low chances of
experiencing a childbirth
(e.g. Dribe and Stanfors 2009; Kravdal 2007; Thalberg
. A higher level of education in turn, as estimated often after controlling for
educational enrolment and a few other socioeconomic characteristics in adulthood,
has been found to predict both higher
(Hart 2015; Lappega˚rd and Rønsen 2013;
Winkler-Dworak and Toulemon 2007)
(Guzzo and Furstenberg 2007;
Liefbroer and Corijn 1999; Mart´ın-Garc´ıa 2009; O¨ zcan et al. 2010)
entry rates into
fatherhood, and some studies document no differences
(Dribe and Stanfors 2009;
Huinink 1995; O¨ zcan et al. 2010)
or a U-shaped pattern (To¨lke and Diewald 2003).1
1 Apart from varying control variables, these studies differ in the follow-up, which may affect the results,
i.e. the effect of educational level may be more positive if men are followed up to a higher age.
Apart from education, higher income and often a stronger attachment to the labour
market tend to associate with higher entry rates into fatherhood
(Hart 2015; Huinink
1995; Kravdal 2002; Kreyenfeld and Andersson 2014; Lappega˚rd and Rønsen 2013;
Liefbroer and Corijn 1999; O¨ zcan et al. 2010; Pailhe´ and Solaz 2012; Schmitt 2012;
To¨lke and Diewald 2003; Winkler-Dworak and Toulemon 2007)
Studies on higher-order birth rates among men suggest mainly positive
associations with educational level in Nordic countries
(Duvander and Andersson
2006; Duvander et al. 2010; Kravdal 2007; Kravdal and Rindfuss 2008; Lappega˚rd
and Rønsen 2013; Thomson et al. 2013)
, but not necessarily elsewhere
BronteTinkew et al. 2009
; Guzzo and Furstenberg 2007; Ola´h 2003). For example, in
Norway, educational level stimulated higher-order births, after controlling for
enrolment and some background characteristics
, or for enrolment,
income, and parental education
(Lappega˚rd and Rønsen 2013)
. Moreover, a
welleducated male partner tends to increase second birth rates among women
et al. 2013; Gerster et al. 2007; Kreyenfeld 2002)
. With respect to male income and
labour market attachment, there is evidence of a positive effect on second but not
necessarily third births
(Andersson and Scott, 2007; Kravdal 2002; Kreyenfeld and
Andersson 2014; Lappega˚rd and Rønsen 2013; Pailhe´ and Solaz 2012)
Studies analysing educational differences in fertility often control for some
family-of-origin characteristics, such as parental education, family type, and level of
urbanization, with little attention paid to them
(e.g. Huinink 1995; Kravdal and
Rindfuss 2008; Liefbroer and Corijn 1999; Winkler-Dworak and Toulemon 2007)
The literature on early-life predictors of childbearing associates a higher
socioeconomic position of a parent with later entry into parenthood
(e.g. Dahlberg 2015;
Dribe and Stanfors 2009; Hynes et al. 2008; Thornton 1980)
, but the respective
findings regarding the eventual number of children of men vary
Koupil 2009; Murphy and Wang 2001; Parr 2010; Rijken and Liefbroer 2009)
Further, associations with men’s fertility have been found for other than
socioeconomic characteristics of the family of origin, such as number of siblings
(Kolk 2014; Murphy and Wang 2001; Rijken and Liefbroer 2009)
Some previous studies indicate differences between parities with respect to
(Guzzo and Furstenberg 2007; Kravdal and Rindfuss 2008)
; in Norway, a
stronger effect on first than higher-order births appeared
(Lappega˚rd and Rønsen
. Also the association of other socioeconomic characteristics with fertility in
men may depend on the parity in question
(Kravdal 2002; Kreyenfeld and
Andersson 2014; Lappega˚rd and Rønsen 2013; Pailhe´ and Solaz 2012)
. Little is
known on potential parity differences regarding early-life socioeconomic
2 Furthermore, studies motivated by evolutionary theory and measuring both socioeconomic
characteristics and the number of children at late reproductive ages do not find education after accounting for
income to predict higher fertility, whereas income, respectively, predicts higher fertility in men
et al. 2012; Fieder and Huber 2007; Goodman and Koupil 2010; Hopcroft 2015; Nettle and Pollet 2008;
Weeden et al. 2006)
4 Aims and Context of the Study
The role of education and other socioeconomic characteristics in men’s fertility has
been addressed in the previous literature, but the mechanisms behind educational
differences are still not entirely clear. Economic mechanisms related to income and
position in the labour market are often discussed, but alternative mechanisms remain
plausible with potentially varying importance depending on parity and the societal
context. We conceptualize occupational position as a more proximate indicator of
earning potential and attachment to the labour market than education, whereas income
measures actual earnings and is a strong indicator of economic resources overall
2009; Lynch and Kaplan 2000)
. To gain both overall and parity-specific
understanding, fertility in this study comprises the eventual number of children and chances of a
first, second, and third birth. The research questions are:
How do the level of education, occupational position, income, and
earlylife socioeconomic characteristics associate with fertility in men?
Are educational differences in men’s fertility explained by early-life
socioeconomic or other characteristics shared by brothers?
Are educational differences in men’s fertility mediated by occupational
position and income in adulthood?
We expect to find positive associations between men’s socioeconomic
characteristics in adulthood and fertility, but do not hypothesize about the respective
associations with early socioeconomic characteristics (i). Regarding educational
differences, we expect any explanatory role of early characteristics to be weaker (ii)
than any mediating role of adult characteristics (iii). Parity differences, if any, are
expected to show as weaker associations of socioeconomic characteristics with
higher-order births. We build on the current literature by studying the role of
education in men’s childbearing more thoroughly in relation to other socioeconomic
characteristics across the life course. The study is based on Finnish
populationbased register data on birth cohorts 1940–1950 with detailed non-retrospective
measurements of early-life characteristics. The data uniquely allow follow-up of the
early-life stages up until late reproductive ages, a rich non-retrospective
measurement of early-life socioeconomic characteristics, and sibling comparison.
The context of this study implied low living standards in the mid-twentieth century,
but rising levels thereafter
(Ja¨ntti et al. 2006)
. When the men studied here were born,
Finland was a poor country that was at war or recovering from it. Later, in the second
half of the century, the overall living standards rose rapidly due to economic growth
and structural change. Concurrently, the publicly provided welfare support for
families increased as part of the welfa
re state expansion (Rønsen 2004
), and women
and men born between 1940 and 1950 witnessed this increasingly during their prime
childbearing years. The Finnish society is often described as having relatively low
social inequality in terms of income
(Ja¨ntti et al. 2006)
, and the Finnish educational
system is considered flexible and socially inclusive
(Orr et al. 2011)
Finland is characterized by a relatively strong dual-earner family model (e.g.
separate taxation of the husband and wife since 1976)
(Aarnio and Eriksson 1987)
In 1970, 39% of married women aged 24–54 were housewives, whereas in 1980
correspondingly only 10%
. The labour force participation of
women with preschool children was already high in the 1950s and 1960s, and the
share of employed mothers working part-time low even in Nordic comparisons
(Rønsen and Sundstrom 2002)
. The heavier burden of breadwinning still continued
to fall on men’s shoulders, as exemplified in 1982 when men’s earnings comprised
over 60% of total household earnings among married dual-earner couples (Aarnio
and Eriksson 1987). In the studied birth cohorts, men were still educated to higher
levels than women
Despite the high share of dual-earner families in Finland in the 1970s, the role of
men in housework and childrearing remained limited. In the 1970s, a more equal
division of labour between mothers and fathers was facilitated by legislation, and
Finnish fathers have been eligible to take parental leave since 1978
and Leira 2006; Haataja 2004)
. Yet by then over half of the male cohort under study
had already become fathers
(Nise´n et al. 2014a)
. The initial 2-week leave that
fathers were entitled to was later extended
. Fathers’ role in childcare
still continued to be weak compared to women: in 1990 fathers took only 2–3% of
all parental leave days in Finland
(Ellingsaeter and Leira 2006; Haataja 2004)
5.1 Data and Variables
The data were obtained from a 10% sample of households drawn from the 1950
Finnish census (permission TK-53-704-10)
(Statistics Finland 1997)
. Information on
members of the sampled households was subsequently linked to sociodemographic
information from quinquennial censuses in 1970–1995 and to the Finnish Population
Register for fertility histories. We restricted the data to the 1940–1950 birth cohorts.
The original sample consisted of 411,628, of whom 91,452 were born between 1940
and 1950 and lived in a one- or two-parent family at the time of the census in 1950
(46,782 men). Respondents were excluded with missing information on childhood
variables or absent from the census at the ages of 30–34 (n = 7417), lost to follow-up
at the ages of 45–49 (n = 2281), or with an unrealistic age at first birth (n = 2). Loss
to follow-up was attributable to emigration, mainly to Sweden in the late 1960s and
the early 1970s, and to a lesser extent to mortality between 1950 and 1990/1995. The
final study sample included 37,082 men. Brothers were identified based on
information on place of residence, household, and family collected in 1950. The
analysed men came from 27,305 families, of which at least two male siblings were
identified in 7671 families. This identification procedure did not distinguish between
biological and non-biological siblings. Siblings who had died, moved out of the
parental home, or were not yet born at the time of the census were not covered.3
3 A sensitivity analysis conducted on a sample of men born in 1943–1947 indicated that the main
interpretations were not sensitive to this measurement issue.
Monthly information on live-born biological children was linked to the data from
birth records from 1970 to 2009. Children born before 1970 were included, except
in cases where they did not live with their fathers around the year 1970 when
personal identification codes were introduced. The study participants were
59–69 years old at the end of the follow-up in 2009. In these data, the fertility of
men (M 1.81, SD 1.45) was close to that of women in a corresponding sample
(M 1.85, SD 1.38)
(Nise´n et al. 2014b)
. Thus, we expect bias from unknown
paternity to be small. In addition to the total number of children, the likelihood of a
first, second, and third birth was analysed.
The socioeconomic characteristics in adulthood comprised level of education,
occupational position, and income. These variables were measured at one point in
time at the age of 30–34 based on census information from the years 1970, 1975, or
1980. The main explanatory variable, the level of education, was categorized into four
classes: basic, lower secondary, upper secondary, and tertiary (Table 1). The basic
level refers to a maximum of 9 years of mandatory education. The lower-secondary
level refers to brief vocational training (\3 years) undertaken in addition to basic
education. Upper-secondary education refers to either academic education
(matriculation) or vocational training (C3 years) undertaken in addition to basic education.
The tertiary level refers either to a university degree or to vocational training at the
highest level (C4 years after general education). Occupational position was classified
as manual worker, lower white collar, upper white collar, farmer/self-employed (64%
farmers), or other/unknown. For income, the values from different years of taxable
income reported in the census were first converted into income in 2012
and then divided into quintiles. In the sample, 3% had no income.
The early-life socioeconomic characteristics included parental education, parental
occupational position, and measures of overall living conditions. These were
measured at one point in time in the 1950 census when the men were between the ages
of zero and 10. The parental level of education measures the highest qualification
achieved by either parent (74% of parents possessed the same level), categorized as
less than primary school, primary school, and more than primary school. The parental
occupational position was categorized as manual worker, professional or
administrative, farmer with\10 hectares of land, farmer with C10 hectares of land, and
selfemployed/other. The variables measuring overall living conditions included parental
home ownership (owner, renter, other, or unknown), crowding (number of persons per
heated room: \2, 2 \ 3, C3), and standard of living (poor, modest, good) in
childhood. In this approximate measure, the category poor referred to households
with no modern facilities such as electric lights, modest to households with one item,
and good to those with at least two items.
The control variables (year of birth, sibship size, and family type and living area
in childhood) were also measured at one point in time in the 1950 census when the
men were between the ages of zero and 10. Family type was categorized as two
parents with children, mother and children, and father and children; sibship size was
divided into three categories (0, 1–2, 3–).4 The living area covered five geographical
4 Siblings who died or moved out before the 1950 census or those who were born thereafter were not
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areas: the Helsinki (capital) region, the rest of Uusimaa (the area surrounding the
capital region in the south part of Finland), western Finland, and eastern and
northern Finland, which were both mainly agricultural areas in 1950.
Marital history was categorized as never married, intact married (first marriage
not dissolved due to divorce or the partner’s death), divorced/widowed (87%
divorced), or remarried. This classification was based on longitudinal information
on the formation and dissolution of marital unions until 2009. Data on marriages
formed and dissolved before 1970 were unavailable. Longitudinal information on
cohabitation was not available, but it was still very uncommon in the birth cohort
under study, becoming more common in Finland from the 1970s
5.2 Statistical Analyses
Standard Poisson regression was used to assess the associations of education and
other socioeconomic characteristics with the number of children. Standard logistic
regression was used to study the associations of these explanatory characteristics
with the likelihood of a first, second, and third birth. The full sample of men
(N = 37,082) was used in the analysis of the number of children and the likelihood
of a first birth. The likelihood of a second birth was analysed among fathers
(n = 29,943), and the likelihood of a third birth among fathers with at least two
children (n = 23,152).
We estimated nested standard regression models of all four fertility outcomes
using the following strategy (Tables 2, 3; for full models of Table 3 see
Supplementary material 1, 2, 3). In Model 0, the year-of-birth-adjusted associations
of socioeconomic characteristics with fertility are estimated (a separate model for
each socioeconomic characteristic). In Model 1, the education–fertility association
was additionally adjusted for other control variables than year of birth: living area
and family type in childhood, and sibship size. In Model 2, the association was
adjusted additionally for socioeconomic characteristics in early life: parental
education, occupational position and home ownership, and crowding and standard
of living in childhood. Models 3 and 4 add occupational position and income in
adulthood, respectively. Finally, marital history was added to Model 5.
An alternative to the standard Poisson model specification for analysing the
number of children is the negative binomial model, which would have been
preferable had there been overdispersion. However, evidence of overdispersion was
not found: in the full (corresponding to Model 5 in Table 2) negative binomial
model, the parameter indicating overdispersion did not differ from zero
(a = 2.7 9 10-8), and the model gave virtually the same results as the Poisson
model. We also considered the zero-inflated Poisson model as an alternative to the
standard Poisson model, but preferred the latter based on the very few differences in
predicted numbers of children between the models and the greater simplicity of the
standard Poisson model.
Conditional sibling fixed-effects (FE) versions of the Poisson and logistic
regression models were used to study whether the education–fertility association
was confounded by unobserved characteristics shared by brothers (Table 4). This
approach uses the family indicator included in the dataset to capture unobserved
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T M I M M M M M M M M S A
Model 0: explanatory variable ? year of birth
Model 1: level of education ? control variables
Model 2: Model 1 ? socioeconomic characteristics in early life
Model 3: Model 2 ? occupational position
Model 4: Model 3 ? income
Model 5: Model 4 ? marital history
Method: logistic regression analysis. In all models, year of birth is included as a continuous variable, but
the coefficient is not shown
SE: standard error
An asterisk indicates when the 95% confidence interval does not include 1
family characteristics and estimates the model parameters of education from
variation between brothers. Thus, the FE models account for the family environment
and genetic characteristics to the extent that these are shared by brothers, but at the
cost of restricting the sample because those without a brother are excluded. Further,
brother sets where all brothers had zero children are excluded in the Poisson FE
model, and those where all brothers had the same outcome are excluded in the
logistic FE models. The analysed brother sets include brothers born between 1940
and 1950, and alive and living in the same household in the 1950 census. These FE
models were constructed by conditional maximum likelihood estimation
. Table 4 also shows estimates from Models 0–2 run in the samples used in the
FE analysis to enhance comparability across models.
Throughout the analysis, we accounted for the clustering of brothers within
families in the calculation of variance-based measures. We used the bootstrap
Model 0: level of education ? year of birth
Model 1: level of education ? control variables
Model 2: Model 1 ? socioeconomic characteristics in early life
FE Model: level of education ? year of birth ? unobserved fixed family characteristics
SE: standard error
An asterisk indicates when the 95% confidence interval does not include 1
a Method: Poisson regression analysis
b Method: logistic regression analysis
c Estimates of other explanatory variables than education are not shown. In Models 0–2, explanatory
variables are included as in Tables 2 and 3: all variables except year of birth are included as categorical
procedure with cluster resampling in calculating the standard errors (SE) and 95%
confidence intervals (with significant findings indicated by asterisks) in Tables 2, 3,
4, with 1000 replications and sibling sets as the clusters. The estimates of the
Poisson regression models are reported as incidence rate ratios (IRR) and those of
the logistic regression models as odds ratios (OR). The Stata statistical package,
, was used for the statistical analysis.
The men had on average 1.81 (SD 1.45) children in their lifetime, with 81% having
at least one child. The parity distribution by level of education and other descriptive
characteristics of the study population are shown in Table 1. Two was the most
common number of children across educational groups (37%), with higher shares
among men educated to upper-secondary and tertiary levels (42–43%). A large
share of the men (45%) were educated to the basic level in this cohort, and only 14%
had acquired tertiary education. Most men came from families with parental
education at most at the primary level (76%) and from households with poor or
modest living standards (75%). Manual worker was clearly the largest occupational
Table 2 presents the results on the number of children. A clear positive gradient
was apparent, with men educated at the tertiary level having 20% and at the
uppersecondary level 11% more children than those educated at the basic level (Model 0).
Other socioeconomic characteristics in adulthood also predicted fertility; men of
higher occupational positions, farmers or self-employed, and especially those with
higher incomes had a larger number of children on average. Additionally,
socioeconomic characteristics in early life predicted fertility, higher parental
education and occupational position, and less crowded and better-equipped
childhood housing associated with higher fertility. The associations with the early
characteristics were, however, modest and weaker than the associations with the
Including additional control variables (living area and family type in childhood
and sibship size) (Model 1) did not change the year-of-birth-adjusted association
between education and fertility. Likewise, adjusting for early socioeconomic
characteristics (Model 2) had no effect on these estimates. In turn, adjustments to
occupational position (Model 3) and income (Model 4) clearly attenuated the
differences by education by 41–68%.5 A weak positive association remained after
all adjustments, with the tertiary-educated men having 8% more children than those
with basic education. Accounting additionally for marital history (Model 5) further
reduced the estimates of education, with now only tertiary-educated men having 5%
more children compared to basic-educated men.
Results from the parity-specific analysis are shown in Table 3. Higher education,
higher occupational position, and especially higher income all clearly predicted a
higher likelihood of a first birth in the model adjusted for year of birth only (Model
5 Attenuation in percentages is calculated as (ORModel4 - ORModel2)/(ORModel2 - 1) 9 100.
0). Favourable socioeconomic characteristics in early life also predicted higher
chances of a first birth, even if less strongly than characteristics in adulthood. The
educational differences in the likelihood of a first birth remained after adjusting for
other control variables (Model 1) and early-life socioeconomic characteristics
(Model 2). The adjustment for occupational position (Model 3) and income (Model
4) again strongly, by 40–90%, attenuated the differences by education. When
marital-history differences had been adjusted for (Model 5), no significant
differences by education remained.6
Education and other socioeconomic characteristics in adulthood also predicted
higher chances of a second birth, even if less strongly in comparison with first
births, in the year-of-birth-adjusted models (Model 0). Some of the favourable
characteristics that predicted higher chances of a first birth had a similar effect on
second births. Adjustment for other control variables (Model 1) and socioeconomic
characteristics in early life (Model 2) had a negligible effect on the estimates of
education on second-birth likelihood, but adjustment for occupational position and
income in adulthood (Models 3–4) attenuated the differences between the
uppersecondary and tertiary-educated groups and the basic-educated group by 33–41%.
The adjustment for marital history had a marginal effect, and the highly educated
men were still more likely to experience a second birth (Model 5).
Differences by education in the likelihood of a third birth were small: in the
yearof-birth-adjusted model the fertility of the lowest and highest educated was at the
same level and that of men in the middle categories slightly lower (Model 0).
Occupational position had a weak positive association, whereas men in the lowest
income quintile were more likely to experience a third birth than two-child fathers
with higher incomes. Most of those favourable early-life socioeconomic
characteristics that predicted higher chances of a first, and to some extent a second birth, had
a null or weak negative association with third-birth likelihood. The adjustment for
controls and early-life socioeconomic characteristics had no effect on the education
estimates (Models 1–2), and the adjustment for occupational position and income
even slightly increased the estimate of the tertiary educated (Models 3–4). The
adjustment for marital history had a marginal effect, and after the adjustment, a
weak nonlinear association of education with the third-birth likelihood remained
Additionally, we note that while staying unmarried was associated with low
fertility, remarried men showed the highest numbers of children and the highest
chances of a first and especially a third birth (Table 2; Supplementary material 1, 2,
3). Divorced or widowed men had lower numbers of children and were less likely to
have a first or a second birth than men in intact marriages. An additional analysis
(not shown) indicated that when marital history was added to Model 2 before
adjustments for occupational position and income, the remaining differences by
educational level in the number of children and first-birth likelihood attenuated by
more than two-thirds, whereas the respective attenuation in subsequent births was
6 The interpretation was the same when a dichotomized indicator of marital history was used (ever vs
7.1 Main Interpretations
This study assessed whether the association between men’s education and their
fertility can be explained by early socioeconomic and other characteristics or
mediated by later socioeconomic characteristics. Another aim was to analyse how
the socioeconomic characteristics of men across the life course associated with
fertility among men. Fertility comprised the eventual number of children and
chances of a first, second, and third birth. The study takes the previous literature
further by showing that socioeconomic advantage across the life course associates
with male fertility: among Finnish men born between 1940 and 1950, lower
numbers of children were found among men from socioeconomically
lessadvantaged families and among those with a lower educational level, occupational
position, and income level. In addition, concerning parity-related differences,
education and several other characteristics in early life and adulthood related more
strongly to the likelihood of a first than a second and, particularly, a third birth.
As for the number of children overall and for first births, the results are in line
with previous studies from Nordic countries
(e.g. Kravdal and Rindfuss 2008;
Lappega˚rd et al. 2011; Lappega˚rd and Rønsen 2013)
, showing that a higher number
of children and first birth rates are associated with educational and other
socioeconomic advantages in adulthood in men. Similarity between the two fertility
outcomes is expected: in a previous Finnish study, the educational gradient in the
number of children was mostly due to different first-birth chances
(Nise´n et al.
. Varying results outside Nordic countries
(e.g. Miettinen et al. 2015)
reflect measurement issues but also true contextual influences. Our results regarding
early socioeconomic characteristics differ from a recent Swedish study (Dahlberg
2015), which found no differences in first-birth likelihood by parental education
among men, with the discrepancy potentially attributable to different study periods.
Second births were also predicted by higher educational levels and by other
indicators of socioeconomic advantage, but to a smaller extent than first births.
These findings coincide with earlier studies in similar institutional settings, which
mainly find positive effects of the father’s education on higher-order births
(Duvander and Andersson 2006; Duvander et al. 2010; Kravdal 2007; Kravdal and
Rindfuss 2008; Lappega˚rd and Rønsen 2013; Ola´h 2003; Thomson et al. 2013)
Similar to the present study, in some previous studies weaker effects of income
(Lappega˚rd and Rønsen 2013)
births than on first births among men also appeared. In Denmark, unemployment
depressed second birth rates overall less than first birth rates at older ages
(Kreyenfeld and Andersson 2014)
. Besides economic factors, normative reasons,
such as following the two-child norm, may be relatively important determinants of
the transition to the second child
(Bacci 2001; Goldstein et al. 2003)
, at least in the
More favourable socioeconomic characteristics did not consistently associate
with higher third-birth chances. This is similar to earlier Nordic studies showing that
two-child fathers with weak labour market attachment
(Andersson and Scott 2007;
Kravdal 2002; Kreyenfeld and Andersson 2014)
and low-earning couples were more
likely to have a third child
(Duvander and Andersson 2006; Duvander et al. 2010)
After controlling for other socioeconomic characteristics (and marital history), a
small positive effect of tertiary education on the chances of a third birth emerged in
this study. Apart from economic reasons, two-child parents with high education may
have relatively strong preferences for a larger number of children (Ruokolainen and
Notkola 2002), which may show in their realized third-birth chances.
A novel finding was that the educational gradient in fertility in men was neither
explained by early-life socioeconomic nor by other characteristics shared by
brothers. These results reinforce the prevailing understanding, according to which
mechanisms in later phases of the life course are more relevant in explaining the
educational differences in men’s fertility. Indeed, men’s occupational position and
income mediated approximately half of the association between education and the
number of children. Differences in first births were strongly mediated by such
factors. The corresponding mediation was more modest for second births, and only
relatively small differences by education were found in the third-birth chances.
Education is a major determinant of long-term labour market position and
income, whereas income and occupational position are proximate measures of
economic standing, earning potential, and labour market attachment
Lynch and Kaplan 2000)
. Given the current results, a plausible interpretation is that
men’s economic potential creates differences between educational groups more
strongly in the chance of having a first birth than in the chances of subsequent births.
Men’s role as financial providers of the family may be more central for the process
of entering parenthood than for subsequent childbearing. A similar interpretation
may apply for results from another Nordic country, Norway
(Lappega˚rd and Rønsen
We view union formation and stability primarily as potential mediators of the
association between education and fertility. Union experiences were measured by
marital history; in the studied birth cohorts, marriage was still the normative context
. Remarried men had the highest numbers of children
(see also Van Bavel et al. 2012), but the largest fertility differences lay between the
never married and other men. Marital history strongly mediated the chance of a first
birth, but its inclusion as a model covariate had only a marginal effect for
educational differences in subsequent births. Given the differences in other
socioeconomic characteristics and marital history, those with tertiary education had
5% more children relative to those with basic education, but were not more likely to
have a first child. This means that after accounting for such characteristics, the
slightly higher fertility of the tertiary-educated group resulted from higher chances
of second and third births.
Union formation selective on men’s potential as economic providers is thus likely
to contribute to the educational differences in the number of children fathered by
men through entry into parenthood. Along the lines of economic reasoning, men’s
potential as economic providers is an important precondition for setting up an
(Oppenheimer 1988; Oppenheimer et al. 1997)
. Therefore, women are likely to view men with actual or
prospective higher incomes and better positions in the labour market as more
attractive partners and potential fathers of their future children. Those educational
differences in the second- and third-birth chances of men that were witnessed after
accounting for their other observed socioeconomic characteristics, and marital
history could be attributable to other factors. In the Finnish context, these could
include varying strength of the two-child norm across educational groups, stronger
preference-based selection into parenthood among more highly educated female
(Kravdal 2001; Kreyenfeld 2002)
, and other such characteristics of female
partners likely to vary between educational groups of men.7
The division of labour in families was less decisive in Finland than in many other
Western countries in the 1960s and 1970s during the prime childbearing years of the
studied men. Dual-earner families were increasingly common in Finland after the
1950s, and the wife’s income, often from full-time work, contributed an important
share to the family income
. Still, a larger share of family income
was earned by men in most dual-earner families
(Aarnio and Eriksson 1987)
Gender roles also remained asymmetrical in household and caring responsibilities;
for instance, in the 1980s, less than 5% of Finnish men used their right to parental
(Ellingsaeter and Leira 2006)
. Among Finnish women born between 1940 and
1950, educational level associated negatively with their eventual number of children
(Nise´n et al. 2014b)
. Besides gendered union patterns, fertility in educationally
heterogamous unions may contribute to the gender difference. This point deserves
more attention in future studies. Overall, the different associations among men and
women may indicate gendered views of the breadwinner and caregiver roles in
society, but also other issues such as a stronger effect of educational enrolment
(Dribe and Stanfors 2009; Kravdal 2007)
An important issue regarding the interpretation of the parity-specific analysis is
the selection of men into the risk group of a subsequent birth. Given that entering
the first and, to a lesser extent, the second parity is selective on socioeconomic
characteristics, the population at risk of a second or third birth is non-representative
of the whole male population. For example, low-income men enter parenthood at a
7 Overall, female partners’ characteristics may contribute to observed differences, but analysing them
requires a different analytical approach, as typically those without partners are excluded
Jalovaara and Miettinen 2013)
lower rate than high-income men, but those low-income men who do so may be a
select group with additional characteristics that make them particularly attractive as
partners and suitable as fathers. This may affect the results concerning subsequent
parities, for example, by making some gradients less positive. A previous study used
simultaneous modelling of first, second, and third birth rates to tackle this selection
problem but concluded that this only marginally affected the estimates of education
on men’s second and third births
. It is thus unlikely that such
selection strongly drives the current results.
Another consideration in the interpretation of our results is related to children’s
quality as opposed to their quantity
(Becker 1993; Becker and Lewis 1973)
with more resources may restrict their subsequent fertility in order to guarantee
sufficient resources for their earlier-born children. This could potentially contribute
to some of the current parity-specific results. We cannot rule out this possibility but
would consider the trade-off at least of less importance than direct considerations of
affording to set up an independent household and have children. Importantly, we do
not witness any strongly negative gradients by income or other socioeconomic
characteristics in any fertility outcome.
Finally, men’s socioeconomic characteristics may also reflect characteristics such
as health or problem-solving skills, which are correlated with education and may
directly affect fertility, but which could not be measured here. The comparison of
brothers was an attempt to more closely implement a causal research design:
brothers partly share their social environment and genetic make-up. We found that
neither observed nor unobserved characteristics shared by brothers explained the
association between education and fertility in men, but brothers may still differ in
relevant ways not captured here
(Holmlund 2005; Kohler et al. 2011)
to educational level and labour market success on the one hand and the chances of
marrying and childbearing on the other. More research is welcomed on the relative
importance of the different mechanisms behind educational differences in men’s
We consider the strengths of this study to include the large set of
nonretrospectively measured socioeconomic variables from early life and the
longitudinal measurement of men’s fertility based on administrative registers. Additionally,
we view the sibling comparison as an innovative approach in research on men’s
fertility. The limitations of this methodological approach should also be
acknowledged, however. The method does not capture genetic or environmental
endowments unshared by brothers, as, for example, siblings may be exposed differentially
to the family environment due to effects of birth order and birth interval length
(Kohler et al. 2011)
The study sample was limited in that men who died or emigrated between 1950
and 1970 or 1970 and 1985/1990/1995 were excluded. The men excluded from the
sample prior to 1970 were more often born before 1945 or came from lower
socioeconomic backgrounds, mother-only families, or Lapland
(Elo et al. 2014)
These differences were not large and thus unlikely to bias our main findings.
Children born before 1970 were registered only if co-residing with their fathers in
the 1970 census. This may introduce a selective bias in our sample
In our data, among women born in the early 1940s, living alone with children in
1970 was more common among those educated to lower levels. In the period
1966–1970, however, only 5% of children were reported to have been born out of
wedlock in Finland
Our measurement of socioeconomic characteristics in adulthood is compromised
by its non-time-varying nature. As a large share of fertility had occurred according
to the measurement at the ages of 30–34, we face the risks of anticipatory analysis
(Hoem and Kreyenfeld 2006)
. Although men’s later timing of and less intensive role
in childbearing make children less likely to interfere with their educational careers
compared to women
(Woodward et al. 2006)
, the reverse causality remains
plausible, particularly for income, due to incentives to support a family
(Gupta et al.
2007; Lundberg and Rose 2002)
. In a sensitivity analysis using income measured at
the ages of 40–44, the main results were changed only marginally, suggesting that
the variable used reflects long-term income.
Having higher education and other favourable socioeconomic characteristics across
the life course associated positively with the eventual number of children among
Finnish men born in 1940–1950 in a context where a dual-earner family model was
increasingly dominant but men’s role as breadwinners still remained relatively
strong. The findings further suggested that early-life socioeconomic or other
characteristics shared by brothers do not explain the association of education with
fertility in men. In turn, income and labour market position appear as substantial
mediators of the association of education with the chance of a first birth.
Educational differences regarding second births were smaller and the respective
mediating role of other characteristics weaker. A small differentiation by education
and other socioeconomic characteristics was present for third births overall. The
findings indicate that men’s potential as economic providers appears to be more
decisive for their entry into parenthood than for their subsequent childbearing and
that this potential is a major determinant of educational differentials in the number
of children among men. It appears that early-life socioeconomic circumstances are
not insignificant even for men’s eventual fertility, but socioeconomic characteristics
in adulthood are more important. To conclude, the entry into fatherhood selective on
men’s potential as economic providers contributes strongly to the positive
association between education and the number of children among men.
Acknowledgements The authors wish to express gratitude to the associate editor and two anonymous
reviewers for their valuable comments. The authors wish to thank Statistics Finland for the user
Funding This work was supported by the Finnish Cultural Foundation [to JN], the Finnish Academy
[264539, 250569 and 255388 to PM; 266592 to KS], the European Research Council [ERC-StG-336475
to MM], and the NordForsk Network on Register-Based Life Course Studies [to PM]. Open access
funding was provided by Max Planck Society.
Compliance with Ethical Standards
Conflict of interest The authors declare that they have no conflict of interest.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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distribution, and reproduction in any medium, provided you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were
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