Microsoft Word - Thesis_Cormack_June2020.docxEKHS01 Master’s Thesis
(15 credits ECTS) June 2020 Supervisor: Ingrid van Dijk Examiner:
Luciana Quaranta Word Count: 13,531
Master’s Programme in Economic Growth, Population and Development
(Demography track)
Intergenerational transmissions in reproductive behavior in the
context of the historical fertility transition
An analysis of a population in Southern Sweden 1813-1967
by
[email protected]
Abstract: The fertility decline that occurred in the industrialized
world between 1870-1930 has been extensively studied and a
considerable amount of theories exist to explain it. However, few
of them address past generations’ influence on reproductive
behavior. This thesis examines the presence and magnitude of
intergenerational transmission in childbearing from mothers to
daughters before, during and after the historical fertility decline
in a Southern Swedish population by using data from the Scanian
Economic Demographic Database from 1813-1967. Contrary to previous
research, transmission was not found during the fertility decline.
However, a small but statistically significant intergenerational
transmission in childbearing was detected after the fertility
decline in small families, living in an urban environment, from
both the highest and lowest social groups. This implies that some
families’ reproductive behavior was influenced by past generations
after the fertility transition. More granular analyses of this
population are recommended to deepen the understanding of the
influences detected in this study.
i
Acknowledgements
I would like to thank my supervisor Ingrid van Dijk for her
coaching, commitment and positivity throughout the process of this
research project. Your expertise has been incredibly valuable and
inspiring. Dank je wel! Further, I would like to thank the Centre
for Economic Demography at Lund University for letting me use their
data. Lastly, I would like to thank Robert for encouraging me to
pursue this master and cheering on me along the way, as well as
Alastair, Edward and Henry for being the ones who made me reflect
over intergenerational transmission in fertility behavior in the
first place.
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Table of Contents 1 Introduction
......................................................................................................................
1
1.1 Aim and scope
............................................................................................................
2 1.2 Outline of the thesis
...................................................................................................
2
2 Previous Research
............................................................................................................
4 2.1 Fertility in Sweden in the 19th and early 20th century
................................................ 4 2.2
Intergenerational transmission in fertility during the historical
fertility transition .... 5 2.3 Explanations of intergenerational
transmission in fertility behavior .........................
7
2.3.1 Biological explanations to intergenerational transmission in
fertility ............... 8 2.3.2 Societal explanations to
intergenerational transmission in fertility ................... 9
2.3.3 Family-specific explanations to intergenerational
transmission in fertility ....... 9 2.3.4 Individual explanations
to intergenerational transmission in fertility .............
10
3 Data
.................................................................................................................................
12 3.1 Source material
........................................................................................................
12 3.2 Data sample definitions
............................................................................................
13 3.3 Variables
..................................................................................................................
14
4 Methods
...........................................................................................................................
16 4.1 Empirical models
.....................................................................................................
16 4.2 Sensitivity analyses
..................................................................................................
18
5 Empirical Analysis
.........................................................................................................
19 5.1 Results
......................................................................................................................
19
5.1.1 Descriptive statistics
........................................................................................
19 5.1.2 Bivariate correlations
.......................................................................................
23 5.1.3 Multivariable regressions
.................................................................................
25 5.1.4 Sensitivity analysis
...........................................................................................
29
5.2 Discussion
................................................................................................................
31 5.3 Limitations
...............................................................................................................
35
6 Conclusions
.....................................................................................................................
37 References
...............................................................................................................................
39 Appendix A
.............................................................................................................................
44 Appendix B
.............................................................................................................................
45
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List of Tables Table 1. Bivariate correlation coefficients in
previous studies .................................................
7 Table 2. Descriptive statistics, all index women
.....................................................................
20 Table 3. Descriptive statistics, all index women and mothers’
fertility behavior over time ... 21 Table 4. Stratified descriptive
statistics, all index women and mothers’ completed fertility . 22
Table 5. Bivariate correlation coefficients over time
.............................................................. 24
Table 6. Stratified bivariate correlation coefficients, birth
cohorts 1900-1922 ...................... 25 Table 7. Multivariable
regression coefficients, birth cohorts 1900-1922
............................... 27 Table 8. Stratified
multivariable regressions, birth cohorts 1900-1922
.................................. 28 Table 9. Stratified
multivariable regressions, birth cohorts 1900-1922
.................................. 29 Table 10. Sensitivity
analysis. Multivariable regression coefficients, birth cohorts
1900-1922
..................................................................................................................................................
31
iii
List of Figures Figure 1. Influencing factors of intergenerational
transmission in completed fertility ............ 8 Figure 2. Index
women’s completed fertility by socioeconomic status
.................................. 23
1
1 Introduction
In the past two hundred years reproductive behavior has changed
dramatically in Sweden. In the beginning of the 19th century, women
would on average give birth to four or five children in a lifetime
and childbearing at this time was generally characterized by
“natural fertility”, which meant that couples did not intentionally
control their family size (Henry, 1961; Dribe, 2009). However, this
changed as a fast and considerable decline in childbearing started
around 1870-1890 in most industrialized countries and lasted into
the first decades of the 20th century, a period called the
fertility transition (Lee, 2003). Since then and up until
contemporary times, Swedish women have continued to give birth to,
on average, two children in a lifetime (Andersson et al., 2009).
The stable trend of lower childbearing has, together with an
increased life expectancy, contributed to the process of population
ageing in Sweden in the past century (Bengtsson, 2010).
The significant fertility decline and its causes have been widely
analyzed and discussed in the demographic literature. The fertility
transition occurred as large parts of Europe were going through
urbanization, secularization and industrialization, and while
scholars agree that reduced childbearing may have been related to
the societal transformation, theories on the specific mechanisms of
the lower fertility behavior are plenty (Friedlander, Okun &
Segal, 1999). Within an economic framework, Easterlin and Crimmins
(1985) suggested that the reduced fertility was a reaction to the
decline in infant and child mortality earlier in the 19th century,
i.e. a classic “supply and demand”-model. Other economic theory
claimed that the circumstances of modernization generated
preference for fewer children which the parents could invest more
in, i.e. a quantity-quality trade-off in family size (Becker,
1960). In addition, theory focusing on cultural aspects have aimed
to explain the appearance of the fertility transition as a
diffusion of new values, living circumstances and knowledge of
fertility control emerging in a more liberal climate (Carlsson,
1966; Lesthaeghe, 1980; 1983; Watkins, 1987). In recent Swedish
research, a diffusion of new fertility ideals has been suggested as
a plausible explanation to some of the decline (Dribe, 2009;
Bengtsson & Dribe, 2014).
Despite the fertility transition often being explained by the
emergence of new fertility attitudes, there is less research on the
role of the family in this diffusion and the magnitude of
intergenerational transmissions in reproductive behavior. The
existing literature covers geographically diverse, often smaller,
populations, where historical data has been available (see e.g.
Anderton, Tsuya, Bean & Mineau, 1987; Jennings, Sullivan &
Hacker, 2012; Reher, Ortega & Sanz-Gimeno, 2008; Rotering,
2017). It has been found that intergenerational transmissions
appeared at the same time as couples started deliberately
controlling their reproductive behavior at the end of the 19th
century, suggesting that the family played an increasingly
influential role in the fertility transition (Murphy, 1999).
However, the specific pathways of the transmission have been less
examined in a historical context (Murphy, 2013a).
2
Hence, by studying the presence of intergenerational transmission
in reproductive behavior in a population in the Southern Swedish
region of Scania born between 1800-1922 with individual data from
the Scanian Economic Demographic Database, this thesis contributes
with knowledge on the influence of the family on fertility
decisions during a period of new childbearing behavior. Due to the
existing research in this field being limited and representing
geographically diverse areas, this thesis also contributes to the
global literature with observations and analyses of an additional
historical population.
There is only one study that addresses the intergenerational
transmission in reproductive behavior during the historical Swedish
fertility transition and it covers a region in the Northern part of
the country (see Rotering, 2017). However, in contrast to the
Northern Swedish study, the sample included in this thesis reflects
a more socioeconomically diverse population with different
fertility patterns (Bengtsson & Dribe, 2009; Dribe et al.,
2017), which allows for further analyses.
1.1 Aim and scope
The aim of this thesis is to examine the presence and magnitude of
intergenerational transmission in fertility behavior, with a focus
on completed fertility, in a region of five rural parishes and one
town in Southern Sweden between women born 1800-1922 and their
mothers, in order to understand the family influence on
childbearing in the context of the historical fertility transition.
Further, specific factors potentially affecting this transmission
will be analyzed to identify variations in the behavior.
This thesis will analyze the following research questions:
1. What is the presence and magnitude of intergenerational
transmission in reproductive behavior between daughters and their
mothers in the Scanian population before, during and after the
fertility transition?
2. What factors influence the magnitude of the intergenerational
transmission in reproductive behavior during this time
period?
3. What does the presence of intergenerational transmission in
reproductive behavior reveal about the influence of the family
during this time period?
1.2 Outline of the thesis
The thesis is structured as follows. Section 1 contains an
introduction to the topic and its contribution to existing
research. It explains the research scope and aim, as well as
articulates the main research questions. Section 2 presents
previous research, describing fertility behavior in Sweden during
the 19th and into the start of the 20th century, outlining the
previous studies of intergenerational transmission during the
fertility transition and lastly, in four sub-sections, reviews the
main factors identified to influence the intergenerational
transmission of fertility in
3
the literature. In Section 3, the Scanian Economic Demographic
Database used in the thesis is introduced, the sample restrictions
are described, and the variables of the analysis are defined. In
Section 4, the methodology of the analysis, using bivariate
correlations and multivariable regressions, is explained. The
sensitivity analysis is also presented. In Section 5, the results
of the empirical analysis are shown, analyzed and discussed.
Section 6 concludes the findings of the thesis, discusses their
implications and closes with suggestions of further research in the
field.
4
2 Previous Research
This chapter discusses earlier conducted research and is structured
into three subsections. The first section describes the fertility
behavior in Sweden in the 19th and at the start of the 20th
century. The second section reviews the literature of
intergenerational transmissions in fertility during the fertility
transition. The third section addresses previously identified
factors influencing the transmission in childbearing between
mothers and daughters.
2.1 Fertility in Sweden in the 19th and early 20th century
Before the fertility transition had commenced in Sweden, most
couples were not yet deliberately controlling childbearing to
achieve a specific family size and women had four to five children
in a lifetime (Dribe, 2009). Childbearing and marriage were highly
connected and 93% of births occurred within marriage (Statistics
Sweden, 1969). Marriage in the early 1800s was reliant on stable
economic circumstances as the couple needed both a secure income
and a house (Dribe & Lundh, 2005). Although fertility was not
consciously controlled at this time, frequent births were prevented
through longer breastfeeding periods or postponement at tough
economic times (Watkins, 1987; Bengtsson & Dribe, 2006).
It was not until the 1880s that fertility started dropping in
Sweden and ten years later in the Southern Swedish region of
Scania, a fall that lasted until the 1930s (Dribe, 2009). In their
microlevel analysis of the fertility transition in Scania,
Bengtsson and Dribe (2014) observed that the decline occurred for
all age groups of women through both increased birth intervals
(spacing) and a reduction in parity progression (stopping), but
with different timings for different socioeconomic groups. By the
1930s, the percentage of births that occurred within marriage had
fallen to 86% (Statistics Sweden, 1969). This happened despite
lower barriers to enter marriage, due to improved living standards
and job opportunities for individuals of lower socioeconomic status
during the industrialization process (Dribe & Lundh, 2005).
Hence, while childbearing was strongly associated with marriage and
not yet deliberately controlled at the start of the 19th century,
reproductive behavior was less connected to civil status and more
consciously determined a hundred years later.
There is evidence of several factors contributing to the fertility
decline in Europe at the end of the 19th and into the 20th century.
Firstly, in the case of Sweden, Dribe (2009) observed that the
urbanization process led to women increasing their participation in
the paid labor market and more children attending school, which
created circumstances that benefitted those with smaller families.
Secondly, he observed that the gradually more liberal environment
changed attitudes and norms towards childbearing. Thirdly, the
decline has been explained as a necessity for individuals to
improve their living standards and social status in Sweden as well
as other
5
historical populations (Van Bavel, 2006; Van Bavel, Moreels, Van de
Putte & Matthijs, 2011; Bengtsson & Dribe, 2014; Dribe,
Hacker & Scalone, 2014; Dribe at al., 2017). In the town of
Leuven, Belgium, it was observed that individuals with few siblings
were most likely to improve their social status during the
fertility transition (Van Bavel, 2006). In another Belgian town,
Antwerp, it was noted that especially the middle class limited
their childbearing to improve their living standards (Van Bavel,
Moreels, Van de Putte & Matthijs, 2011). Yet, studies of
multiple populations in Scandinavia, North America and Italy have
shown that although individuals with the highest social status were
first to reduce their fertility, by the end of the transitional
period the number of children born per woman had converged for most
social groups, except for the farmers and unskilled workers who
still had large families (Dribe, Hacker & Scalone, 2014;
Bengtsson & Dribe, 2014; Dribe et al., 2017). Hence, while the
fertility decline at a macro level can be explained by factors such
as urbanization, modernization and a diffusion of new family
ideals, the effect varied at the micro level by socioeconomic
status and over time.
2.2 Intergenerational transmission in fertility during the
historical fertility transition
Intergenerational transmission in fertility has been studied for at
least the past hundred years, but with different hypotheses and
findings over time (Murphy, 1999). Despite limited data, there is
evidence of childbearing behavior not being transmitted
intergenerationally in the pre- transitional period, which was
characterized by natural fertility (for the case of England see
e.g. Langford & Wilson, 1985). Instead, family-influenced
fertility patterns started appearing when reproductive behavior
became deliberate, the societal influences diminished and
individual family size ideals emerged in the late 19th century (see
e.g. Kohler, Rodgers & Christensen, 1999; Bras, Van Bavel &
Mandemakers, 2013; Anderton, Tsuya, Bean & Mineau, 1987;
Jennings, Sullivan & Hacker, 2012; Reher, Ortega &
Sanz-Gimeno, 2008; Rotering, 2017).
Anderton, Tsuya, Bean and Mineau (1987) analyzed a population in
Utah in the mid 1800s and its following generation during the
fertility transition. The authors uncovered a growing, positive
relationship between women and their mothers when it came to family
size. Their results implied that the fertility transition had
increased the influence of the family on individual childbearing
and that it was mediated through similarities in the mothers’ and
daughters’ age at marriage. Nonetheless, the authors welcomed
future research on the role of the socioeconomic environment in the
intergenerational transmission, as their study did not include this
aspect.
In fact, Jennings, Sullivan and Hacker (2012) expanded the study by
Anderton, Tsuya, Bean and Mineau (1987) and found that the high
correlation in age at first marriage previously detected was most
likely the result of transmissions in socioeconomic status. They
argued that there were differences in the ability to enter marriage
between socioeconomic groups at the time and therefore they
proposed that age at last birth would be better suited for
analyses, as it would indicate a stopping behavior in parity
progression. The authors identified small but positive correlations
over generations in age at last birth. Similar conclusions about
the correlation in age at last birth between mothers and daughters
arrived from Reher, Ortega and
6
Sanz-Gimeno (2008) who analyzed a sample of women in the Spanish
town of Aranjuez during the fertility transition. When it came to
the correlation in completed fertility, they found it to be twice
as strong as Jennings, Sullivan and Hacker (2012), but based their
study on a dataset approximately 2% the size and a population in an
urban area, which needs to be considered when interpreting their
findings.
Furthermore, regardless of representing different geographically
located populations, Jennings, Sullivan and Hacker (2012) and
Reher, Ortega and Sanz-Gimeno (2008) found a growing presence of
intergenerational transmission in reproductive behavior from the
father’s family. Both these studies concluded that women as well as
men became increasingly involved in the decision-making of family
size during the fertility transition. They argued that the previous
generations’ attitudes on childbearing started influencing couples
at this time of reduced pressure from society. Equally, Kohler,
Rodgers and Christensen (1999) and Bras, Van Bavel and Mandemakers
(2013) who studied twins and siblings during the fertility
transition in Denmark and a region in the Netherlands, claimed that
the observed increase in the intergenerational transmission of
fertility between mothers and daughters was due to inherited
childbearing motivations that emerged as the societal expectations
on family size diminished.
Similar to these two studies, but in the Swedish context, Rotering
(2017) analyzed the presence of intergenerational transmission in
reproductive behavior during the fertility transition in an area in
Northern Sweden and found “evidence of weak, but positive” (p. 196)
transmissions in completed fertility both from mothers and
mothers-in-law. He observed little variation in the fertility
behavior within the studied population and flagged for this
potentially being due to 70% of the sample being farmers. In
addition, he noted that the strength of the transmission diminished
over time and towards the end of the transition. He suggested that
the resulting fertility behavior was transferred through the
socioeconomic environment during the transition, similarly to what
Anderton, Tsuya, Bean and Mineau (1987) had addressed in their
study.
See Table 1 for the results of the intergenerational transmissions
in fertility from mothers to index women (their daughters) during
the fertility transition for the four studies who analyzed
bivariate correlation coefficients.
7
Relative number of children
Reher, Ortega & Sanz-Ginmeno (2008) 0.154 *** 409
Jennings, Sullivan & Hacker (2012) 0.092 *** 19,938
Rotering (2017) 0.085 *** 5,008
***p<0.001 **p<0.01 *p<0.05. The time periods are slightly
differently defined in the literature due to different starts to
the fertility decline. Anderton et al. (1987): birth cohorts
1860-1869 (derived from calculations by Murphy, 1999. p-value not
available). Reher et al. (2008): childbearing period 1891-1945.
Jennings et al. (2012): birth cohorts 1840-1899. Rotering (2017)
birth cohorts 1850-1899. This thesis defines 1850-1899 as
transitional birth cohorts and 1900-1922 as post-transitional birth
cohorts.
Further, there is a vast amount of research analyzing the presence
of intergenerational transmissions in fertility after the
transition and up until contemporary times. Many of these studies
will be discussed in section 2.3 as they assess different
mechanisms of the transmission, rather than just the presence of
it. However, generally, the correlation between generations’
fertility behavior appears to have increased over time (Murphy,
1999). In fact, in a later study, Murphy (2013b) concluded that the
influence of the family on reproductive behavior still exists in
the present day, while other parameters that explained the
fertility transition, such as urban living or liberal religious
views, have lost their importance.
2.3 Explanations of intergenerational transmission in fertility
behavior
Although there is evidence of intergenerational transmission in
reproductive behavior since the start of the fertility decline,
there is not the same kind of clarity concerning the factors that
explain its magnitude. The historical and contemporary literature
discuss multiple explanations of intergenerational transmission in
completed fertility, which can be clustered into four areas:
biological, societal, family-specific and individual-specific. Each
of these factors affect the intergenerational transmission in
either the age at the start of the reproductive life, the length of
the birth intervals or the age at the end of the reproductive life.
For the intergenerational transmission in completed fertility to
change, any or all of these three determinants need to change. See
Figure 1 for an overview of the mechanisms of intergenerational
transmission in completed fertility, which the following four
sections will review.
8
2.3.1 Biological explanations to intergenerational transmission in
fertility
One of the earliest studies that detected similarities in
reproductive behavior across generations appeared in 1899 as
Pearson, Lee and Bramley-Moore mathematically modelled and analyzed
childbearing behavior in 19th century England. They examined
correlations between mothers and daughters, fathers and sons, as
well as paternal grandmothers and granddaughters, and identified
intergenerational transmissions in completed fertility in all
relationships, but it was especially strong on the maternal side.
The authors drew the conclusion that not only childbearing itself,
but also fecundity, which is the physiological ability to have
children, was transmitted over generations. The biological
explanation to intergenerational transmission in reproductive
behavior has since then been questioned and claimed to be
non-existent as different aspects of socialization have become more
prominent (Murphy, 1999; Langford & Wilson, 1985).
In fact, more recently, twin and sibling studies have shown that it
was not biological factors, but new attitudes and preferences of a
specific family size which were passed on to the next generation,
as the intergenerational transmission appeared in a period of
macroeconomic and social change (see e.g. Kohler, Rodgers &
Christensen, 1999; Bras, Van Bavel & Mandemakers, 2013; Tropf
et al, 2015). Murphy (2013a) argued that the introduction of
population surveys on family size preferences contributed to this
conclusion as insights on family ideals were detected. Hence, the
focus in most of the literature on intergenerational transmissions
in reproductive behavior lies in explaining the correlations with
societal, family- specific and individual variables.
9
2.3.2 Societal explanations to intergenerational transmission in
fertility
The influence of societal circumstances on intergenerational
transmission in reproductive behavior has been widely discussed.
Studies have found a stronger relationship between mothers’ and
daughters’ childbearing amongst populations in urban areas, which
has been explained by an increased decision-making power for women
in the household and consciousness of fertility choices due to the
modernization process that occurred in towns (Kohler, Rodgers &
Christensen, 1999; Reher, Ortega & Sanz-Gimeno, 2008; Bras, Van
Bavel & Mandemakers, 2013). The Swedish literature has also
addressed that the increased female labor force participation
influenced the fertility decline (Dribe et al., 2014), but there
are not any studies specifically of the influence of women entering
the paid labor market on intergenerational transmissions in
reproductive behavior.
It has been suggested that the more liberal environment with
smaller families at the start of the 20th century shaped individual
ideals of smaller family size and hence encouraged similarities in
childbearing patterns over generations (Udry, 1996). Likewise,
there is empirical evidence of stronger intergenerational
transmission in stable economies and societies, which has been
explained by an increased freedom to make individual decisions when
the macro-environment has not included threats or uncertainty
(Kohler, Rodgers & Christensen, 1999; Bras, Van Bavel &
Mandemakers, 2013). If this holds true, one could argue that
similar mechanisms would equally apply on a family-level for those
with the highest socioeconomic status and most financial
stability.
2.3.3 Family-specific explanations to intergenerational
transmission in fertility
The family-specific drivers of the intergenerational transmission
in reproductive behavior include a broad spectrum of variables,
where the household’s socioeconomic status is often brought up as a
dimension of interest, due to its explanatory role in the fertility
decline in multiple geographically diverse populations, including
Scania (e.g. Dribe, Hacker & Scalone, 2014; Dribe et al.,
2017). Yet, there are few historical studies who go into depth in
the area, possibly due to lack of data. Bras, Van Bavel and
Mandemakers (2013) identified that women from the highest
socioeconomic strata as well as the unskilled workers were most
influenced by their mothers’ family size during the Dutch fertility
transition. They argued that it was a result of the women from the
highest social groups being “forerunners” (p. 129) in the
transition and the latter taking on new types of employment that
emerged in the industrialization process, which encouraged smaller
families. Later in time, Duncan et al. (1965) also identified that
higher level of education significantly increased the
intergenerational transmission when studying two different samples
of individuals born in the mid-1900s in the USA. More recently,
studies of Swedish women born in the second half of the 20th
century have shown that there is an intergenerational transmission
of young motherhood, which is mediated through a lower maternal
education level (Stanfors & Scott, 2013; Högnäs & Grotta;
2019). Hence, generally, the family influence on fertility behavior
appears to vary by social status, both historically and more
recently.
10
The second factor influencing the magnitude of the
intergenerational transmission is the index women’s number of
siblings. During the fertility transition, Anderton, Tsuya, Bean
and Mineau (1987) saw that women from larger families were less
likely to copy their mothers’ childbearing behavior and argued that
this was due to them having cared for their siblings, experienced
the work that went into having a large family and hence were less
keen to have one themselves. Equally, in contemporary Norway, Cools
and Hart (2017) observed that especially girls growing up in
families with two siblings, which was considered a larger family,
were less prone to have many children themselves. Likewise, Zimmer
and Fulton (1980) studied Scottish women in the 1950s and observed
that those from smaller families were more likely to replicate
their mother’s fertility pattern. The authors claimed that this was
due to having benefited from opportunities of social mobility when
growing up and wanting to pass that advantage on to their children.
However, contrasting results from the early 21st century in the
United Kingdom suggested that the intergenerational transmission in
completed fertility was especially strong for women with many
children (Booth & Kee, 2009). Further studies are needed in
present times, but it may be that the influence of the number of
siblings on fertility behavior has shifted over time.
Lastly, although researchers have aimed to determine the impact of
socioeconomic status and number of siblings on intergenerational
transmissions in reproductive behavior, the results are often
either weak or mixed. In lieu of clear evidence of one
family-specific mediator, Kolk (2014) suggested that “childbearing
preferences and family formation norms” (p. 101) explained the
similarities over generations, a conclusion which has been shared
with several scholars analyzing different time periods (Kohler,
Rodgers & Christensen, 1999; Bras, Van Bavel & Mandemakers,
2013; Morosow & Trappe, 2018; Dahlberg & Kolk, 2018). There
is, however, little empirical evidence proving attitudes to be
intergenerationally transmitted. Axinn et al. (1994) aimed to
investigate childbearing preferences through a survey for American
mothers in the 1960s and daughters in the 1980s and found them to
be similar for both generations. In fact, just as the fertility
transition has been explained by a diffusion of family ideals and
attitudes, it appears reasonable that similarities in reproductive
behavior over generations would be described with these same
factors.
2.3.4 Individual explanations to intergenerational transmission in
fertility
There are individual factors which have shown to significantly
influence intergenerational transmissions in fertility patterns.
Firstly, a large amount of studies have found that the correlation
between mother and daughter is especially strong if the daughter is
first-born (Johnson & Stokes, 1976; Zimmer & Fulton, 1980;
Reher, Ortega & Sanz-Gimeno, 2008; Sullivan, Jennings &
Hacker, 2013; Morosow & Kolk, 2019). The correlation with the
previous generation has been observed to be twice as large for
first-born women versus those born later (Johnson & Stokes,
1976; Reher, Ortega & Sanz-Gimeno, 2008). It has been argued
that first- borns are more exposed to the social values of the
family and hence more likely to take them on (Jennings, Sullivan
& Hacker, 2012). There is one exception in the historical
literature, which found a negative correlation in completed
fertility between mothers and first-born daughters, and it is the
study of the Utah population during the fertility transition by
Anderton, Tsuya, Bean and Mineau (1987). They studied a population
with generally large families and
11
argued that being a first-born daughter in a larger family meant
participating in caretaking of younger siblings, which influenced
these women to reduce their own family size. Furthermore, Murphy
(1999) observed that birth order became less important as families
got smaller and, indeed, in contemporary Denmark, Murphy and
Knudsen (2002) did not find any significant relationship between
birth order and intergenerational transmissions in fertility.
Hence, it appears that the effect of being first-born is more
relevant in the transitional context where families were starting
to become smaller, but less so in a post-transitional period.
Secondly, there is research suggesting that a stable and happy
childhood increases the influence of the family on fertility
decisions as individuals who are content with their own upbringing
aim to achieve the same family size as they have been exposed to
(Duncan et al., 1965; Johnson & Stokes, 1976). However, there
are few studies empirically examining childhood circumstances and
intergenerational transmissions in fertility and none that apply to
the fertility transition, which is most likely due to this type of
data not existing historically.
Summarizing, the collective literature suggests that during the
fertility transition the societal changes, the family socioeconomic
status, the family attitudes towards childbearing, the number of
siblings as well as the birth order were factors influencing the
magnitude of the correlation in fertility behavior between two
generations of women. These aspects, with the exception of family
attitudes, due to lack of data specifically on attitudes, will be
examined in the empirical part of this thesis to understand if any
or all can be found in this thesis’ population.
12
3 Data
This section describes the data used in the analysis of
intergenerational transmissions in fertility behavior, including
restrictions that have been made to the original dataset. It
discusses the relevancy and representativity of the studied
population and goes through the key variables of the
analysis.
3.1 Source material
The data used in this paper comes from the Scanian Economic
Demographic Database (SEDD), administered by the Centre for
Economic Demography, Lund University, Sweden. The version used is
5.1. The database is longitudinal and consists of individuals
residing in Western Scania in Southern Sweden from 1813-1967. It
contains demographic information such as births, deaths and
migration on an individual and family level for all residents in
five rural parishes (Hög, Kävlinge, Sireköpinge, Kågeröd and
Halmstad) from 1813-1967 and one town (Landskrona) from 1922-1967.
The information in the database originates from church records,
catechetical examination registers, as well as tax and income
registers, which means that the individual socioeconomic status can
be determined based on the occupation of the household head, in
addition to the demographic variables (Centre for Economic
Demography, 2020).
In this analysis, the Historical International Standard of
Classification of Occupations, HISCO (Van Leeuwen, Maas &
Miles, 2002) is determined on an individual level based on the
occupation of the household head. In a second step, the Historical
International Social Class Scheme, HISCLASS (Van Leeuwen &
Maas, 2011) is estimated according to skill level, also based on
information about the household head. The latter results in the
following classification of socioeconomic status for this study: 1.
Higher managers and professionals, 2. Lower managers and
professionals, 3. Foremen and medium skilled workers, 4. Farmers
and fishermen, 5. Lower skilled and farm workers, 6. Unskilled and
farm workers and 7. Unknown. Due to few individuals in the first
group, strata 1 and 2 are combined in this thesis.
Several studies who use data from the Scanian Economic Demographic
Database describe the area. Bengtsson and Lindström (2003) explain
that the rural area of this study provided diversity in terms of
socioeconomic circumstances, where Halmstad and Sireköpinge were
considered most “noble” (p. 288) and although the area as a whole
remained rural for the complete 19th century, Kävlinge grew into a
minor town due to the industrial development towards the end of the
century. In fact, almost all of the population growth in the rural
area occurred in Kävlinge (Bengtsson & Dribe, 2014).
13
Likewise, the town of Landskrona went through a process of doubling
its population between 1900-1960 from 14,399 to 28,286 inhabitants
(Statistics Sweden, 1969). Sjöcrona (1933) explained that from the
last decades of the 19th and into the 20th century, the town was
characterized by an industrial evolution, where the harbor and the
construction of an integrated railway in the mid-1800s contributed
to its development. He outlaid that the main industries and
employers included the sugar refinery, production of artificial
fertilizers, iron and steel industries and later motor
vehicles.
The expansive growth of Landskrona corresponded to the patterns of
Sweden as a whole. Statistics Sweden (1969) reported that the share
of the population living in towns at the turn of the 20th century
had grown to 21.5%, which was twice as much as 40 years earlier.
Additionally, they registered that by 1965, just over half of the
Swedish population lived in urban areas. In other words, over a
hundred years, the country went from being mostly rural into a mix
of rural and urban living environments. However, migration took
place in the rural area of this thesis already in the first half of
the 19th century as families moved shorter distances, within or to
nearby parishes, possibly due to necessity of larger housing or
improved job prospects (Dribe, 2003). Hence, this area was dynamic
long before the modernization process had begun.
The database contains highly reliable data and has been used
frequently and widely in renowned historical demographic studies
covering living standards and life course analyses at Lund
University (e.g. Bengtsson & Lindström, 2000 & 2003;
Bengtsson & Dribe, 2006). Nonetheless, despite the studied
region’s socioeconomic diversity, development and migration
patterns corresponding to the Swedish macro environment, it needs
to be considered in the reading of the results that the analysis is
based on a population in a specific region. In addition, due to the
size and the restrictions implemented to the specific sample of
this thesis, some analyses need to be interpreted with
caution.
3.2 Data sample definitions
The original, longitudinal data in this analysis is transformed
into cross-sectional data to study the completed fertility
behavior. The final data sample includes demographic variables and
fertility behavior for unique index women and their mothers. This
means that each index woman only appears once in the dataset, but
mothers with more than one daughter are present as many times as
they have daughters. Hence, while the sample is illustrative of
index women’s behavior, the mothers’ generation will be biased
towards larger families.
The index women are classified into three different birth cohorts,
where birth cohorts 1800- 1849 represent women who were in
childbearing ages before the fertility transition, birth cohorts
1850-1899 at the start of and during the fertility transition and
birth cohorts 1900-1922 at the end and after the fertility
transition. This is based on findings by Dribe (2009) who observed
that the first women to reduce their fertility were born circa 1850
as well Bengtsson and Dribe (2014) who found that childbearing
declined in the 1870s for women from the highest socioeconomic
groups and by 1900-1909, all groups were having fewer
children.
14
Further, the sample containing index women and their mothers is
reduced to fit the requirements of this study. Firstly, all women
without children are dropped from the study. Booth and Kee (2009)
argued that since childlessness could occur voluntarily and
involuntarily, childless women should be dropped in order to remove
any involuntary effect. However, Tropf et al. (2019) claimed that
childless women should be included as valuable insights are lost
through exclusion of this group. The direction of this thesis to
remove childless women is aligned with most research in the field
and especially the main studies that address the fertility
transition.
Secondly, the dataset is restricted to include only index women and
mothers who have completed their reproductive life in the studied
region, meaning that anyone who has out- migrated or passed away
before the age of 45 is excluded. Similar restrictions were made by
Reher, Ortega and Sanz-Gimeno (2008), Jennings, Sullivan and Hacker
(2012) and Rotering (2017). The studied region has a large presence
of migration and by removing women out- migrating, the sample is
reduced significantly. In addition, index women and mothers who in-
migrated to the area after the age of 40 are excluded as there is a
risk that they had children before entering the studied region who
did not accompany them on the move, and hence their existence would
not be known.
Thirdly, and similar to the case of in-migrating women, the
addition of urban index women and mothers to the original rural
database in 1922 comes with the risk of including incomplete
fertility behavior for urban mothers, as their childbearing could
have occurred as early as the 1880s and some of their children were
not present in the area 1922 anymore. To minimize this risk of
underestimating mothers’ completed fertility, only urban index
women born starting 1907 are included in this analysis. See
Appendix A for the completed fertility of mothers of the 1900-1919
birth cohorts, where it is clear that the completed fertility was
lower for mothers of birth cohorts 1900-1906 than 1907-1919.
Lastly, and contrary to most of the literature (e.g. Zimmer &
Fulton, 1980; Jennings, Sullivan & Hacker, 2012; Rotering,
2017), the main analysis in this thesis does not only include
married index women and mothers, but all women with children. This
was done in order to understand the holistic fertility behavior,
independent of marital status, and to maintain a substantial sample
size. In fact, Statistics Sweden (1969) reported that 6-15% of all
childbirths in Sweden occurred outside of marriage between
1801-1967. However, this thesis also includes a sensitivity
analysis of the intergenerational transmissions between married
index women and mothers, to test the robustness of the main
analysis. See sections 4.2 and 5.1.4.
3.3 Variables
There are three main outcome variables in this thesis that measure
fertility behavior: completed fertility, age at first marriage and
age at last birth. The completed fertility is the main outcome
variable and used in all models, while age at first marriage and
age at last birth are only used in the initial, basic correlation
model.
15
The completed fertility is defined as the total number of children
associated with each mother in the region. This includes any live
childbirth that takes place in the region and any child in-
migrating with their mother.
The age at first marriage is calculated for all married women based
on the marriage date and the birth date and represent the start of
the reproductive life.
The age at last birth is calculated based on the date of the
women’s last registered birth and represents the end of the
reproductive life for the index women. Due to lack of complete
information for a larger proportion of the mothers’ generation, the
age at last birth is calculated based on the birth date of the
youngest child registered in the area and the mother’s own birth
date.
All three outcome variables are converted into relative fertility
measurements by indexing their fertility behavior to the mean of
all women born in the same decade. This allows for more robust
analysis of intergenerational correlations in fertility behavior
during a time period of significant macro fluctuations in the
number of children born per woman. The approach is aligned with
Reher, Ortega and Sanz-Gimeno (2008), Jennings, Sullivan and Hacker
(2012) and Rotering (2017).
Additionally, the relative completed fertility is analyzed for
specific groups of women which have been identified as magnifying
the intergenerational transmission in reproductive behaviors in the
literature. They include the size of the family of origin (see e.g.
Anderton, Tsuya, Bean & Mineau, 1987; Reher, Ortega &
Sanz-Gimeno, 2008), birth order (see e.g. Johnson & Stokes,
1976; Booth & Kee, 2009; Jennings, Sullivan & Hacker, 2012;
Morosow & Kolk, 2019), socioeconomic status (see e.g. Duncan et
al., 1965; Bras, Van Bavel & Mandelmakers, 2013; Stanfors &
Scott, 2013) and urban residency (see e.g. Reher, Ortega &
Sanz-Gimeno, 2008; Bras, Van Bavel & Mandelmakers, 2013).
The first-born women are defined as women born in the region
without older siblings at the time of birth.
The later-born women are defined as women born in the region with
older siblings at the time of birth. Women not born in the region
are hence excluded all together from the birth-order
analysis.
The size of the family of origin is determined by each index
woman’s number of siblings relative to the mean number of siblings
of the decade birth cohort, where anyone with a number of siblings
above the mean is categorized as a large family and likewise those
with a number of siblings below the mean are categorized as
originating from a small family.
The socioeconomic status is defined as outlaid in section 3.1 by
HISCO and HISCLASS of the household head.
The urban and rural residency is specified based on the parish of
the household, where the five rural parishes are “rural” and the
town Landskrona is “urban”.
16
4 Methods
In this section, the methodology of the study is presented. The two
empirical models, bivariate correlations and multivariable
regressions, are describes in section 4.1. The sensitivity analysis
is explained in section 4.2.
4.1 Empirical models
This thesis is based on two main empirical models, a bivariate
correlation and a multivariable regression, which are run numerous
times with different modifications for analysis purposes. The
empirical analysis of this thesis is performed in the software
STATA.
Bivariate correlations, !,#, measure the relationship between two
variables, x and y, and are mathematically defined as the
covariance of the two variables divided by the square root of the
product of the two variables’ variance (Körner & Wahlgren,
2015). See equation (1).
!,# = $%&(!,#)
)*!" × *#" (1)
This thesis examines the bivariate correlations between the
fertility measurements of index women and their mothers. Hence in
equation (1), x represents the fertility behavior of index women
and y that of their mothers. As explained in section 3.3, there are
three main fertility measurements, which will be analyzed both in
terms of their absolute values and relative to their birth decade
cohort: completed fertility, age at first marriage and age at last
birth. The analysis is done separately for each index woman’s birth
cohort group in order to identify time- varying differences
throughout the fertility transition. The resulting correlation
coefficients are the first evidence of a relationship between index
women and their mothers’ reproductive behavior.
Most studies of intergenerational transmissions in fertility start
their analysis with bivariate correlations as they facilitate easy
and quick identification of a relationship between the two
generations (see e.g. Jennings, Sullivan & Hacker, 2012; Reher,
Ortega & Sanz-Gimeno, 2008; Rotering, 2017). However, the
bivariate correlations do not describe the identified relationship
further, so in addition, stratification of the dataset is done to
allow for correlation analyses of the specific groups of women
introduced in section 3.3. In contrast to the main literature,
there are two main explanatory factors that this analysis does not
cover: the husband’s and mother- in-law’s influence on fertility
(see Pearson, Lee & Bramley-Moore, 1899; Reher, Ortgea &
Sanz-Gimeno, 2008; Jennings, Sullivan & Hacker, 2012; Rotering,
2017) and childhood circumstances other than family size (see
Duncan et al., 1965; Johnson & Stokes, 1976). These
17
two aspects have been excluded due to the limited scope of this
thesis and lack of data, respectively.
Next, a multivariable regression is used to assess the
intergenerational transmission in fertility behavior. This model
measures the effect of mothers’ completed fertility on that of
their daughters while controlling for other variables that
potentially influence the childbearing of the second generation of
women. See equation (2).
- = . + /0 + 1 + - (2)
- is the completed fertility of the index woman, 0 is the completed
fertility of her mother, / is the relationship between the two
generations’ completed fertility, represent all control variables
and 1 is its coefficient , . is the constant and - the error term.
Equation (2) is the core model of this thesis, which is modified
into several different versions to test the relationship between
index women and their mothers’ fertility behavior.
In model (1), equation (2) is run without the control variables.
Similar to the bivariate correlation coefficients, model (1)
represents the basic relationship between the two generations’
fertility, but with an additional constant term. In model (2),
equation (2) is run controlling for index women’s birth order.
Model (3) also includes urban residency, while model (4) instead
controls for the socioeconomic status in addition to the existing
variables. Models (5) – (7) include interaction terms for first
birth order, urban residency and high social status, which analyze
the combined effect of these variables as well as providing further
robustness to the regression. See equation (3) for an example of
how model (5) is constructed.
- = . + /0 + 1 + 2- + 3- + 4(- × -) + - (3)
- is the completed fertility of the index woman, 0 is the completed
fertility of her mother, / is the relationship between the two
generations’ completed fertility, - takes on the value 1 if the
index woman is first born, - is the residency and takes on the
value of 1 if it is urban, - represents all control variables, . is
the constant and - is the error term. The interaction term - × - is
a combination of urban and first-birth order women and the effect
is measured through 4.
However, as the control variables and the interaction terms in the
regression model only reveal their impact on index women’s
fertility, not their impact on the intergenerational transmission
in reproductive behavior, stratification of regression model (2) is
done. This allows for interpretations of intergenerational behavior
for specific groups of women, e.g. first-borns or urban
residents.
The models used in this thesis are straightforward and identify the
relationship between mothers and index women’s completed fertility,
but their main weakness is that they do not allow for causal
interpretations. By controlling for potential explanatory variables
of fertility in the multivariable regression, some omitted variable
bias is reduced, but without knowing all underlying influences, a
causal conclusion cannot be drawn (Angrist & Pischke, 2009).
Equally, with stratification the strength of the intergenerational
transmission can be identified for specific groups, but little can
be concluded about the causality. However, I have only come across
one study of intergenerational transmissions in fertility that
takes a quasi-experimental
18
approach through instrumental variables (Cools & Hart, 2017).
The majority of previous studies in this field focus on analyzing
bivariate correlations and multivariable Cox proportional hazard
regressions estimating the “risk” of a specific behavior. Murphy
(2013b) and Reher, Ortega and Sanz-Gimeno (2008) use multivariable
regression models somewhat similar to the ones in this
thesis.
Lastly, it is essential to address that the potential selection
biases arising from e.g. migration, mothers with many daughters or
marital status in the analysis cannot be adjusted with this thesis’
chosen methodology. This will be considered and discussed in the
analysis.
4.2 Sensitivity analyses
While the expansion of the core model with various independent
variables and interaction terms facilitates assessing the
robustness of the main model, the analysis is still followed by
three additional sensitivity analyses which analyze the sample
restrictions made.
In the first sensitivity analysis all women in-migrating to the
studied region after the age of 30 instead of age 40 are excluded.
This reduces the sample size but diminishes the risk of including
women who move into the area without all their children. If the
main model has been too generous in its restrictions regarding age
at in-migration, the sensitivity analysis should show a stronger
intergenerational relationship in fertility. In the second
sensitivity analysis the dataset is restricted to only include all
mother-daughter connections where the mothers were married or
cohabiting at the age of 45. In the third sensitivity analysis the
sample is reduced to include all mother-daughter connections where
both index women and mothers were married or cohabiting at the age
of 45. If married index women are more prone to replicate their
mothers’ fertility behavior, the sensitivity analysis will generate
larger intergeneration transmissions than the main model.
The three sensitivity analyses will reveal if the results from the
main analysis remain robust, when the restrictions of the data
sample become stricter.
19
5 Empirical Analysis
The analysis that follows is based on the fertility behavior of
women with children and their mothers. In section 5.1 descriptive
statistics, bivariate correlation coefficients, multivariable
regression coefficients and sensitivity analyses are presented. In
section 5.2 the results are discussed and in 5.3 limitations
addressed.
5.1 Results
5.1.1 Descriptive statistics
The descriptive statistics of the index women born between
1800-1922 are presented in Table 2. As outlined in section 3.2, the
data sample consist of women residing in the studied area up until
at least age 45. Amongst these, 81% of the index women and 87% of
their mothers were married or cohabiting at the of age 45, a
percentage that drops to 72% when taking both factors into account.
The marital percentages in this thesis’ sample are slightly lower
than in Sweden as a whole (see section 2.1), which could be
explained by this thesis defining the marital status at the age of
45, when a proportion of women have been, but are no longer,
married.
The birth cohorts of the last 23 years represent a majority of the
sample (67%) as the database was expanded in 1922 to include one
town in addition to the rural parishes. This means that while 100%
of birth cohorts 1800-1899 were rural, only 13% of birth cohorts
1900-1922 lived in the rural and 87% in the urban area.
The Scanian index women included in this study represent all
socioeconomic groups and the heterogeneity is larger than observed
in the analysis on Northern Sweden where 70% were farmers
(Rotering, 2017). The highest status group is smallest representing
7% of the total sample and the remaining strata range between
21-24% each.
20
N
Mothers married or cohabiting at age 45 1,322 (87%)
Married or cohabiting at age 45 and mothers married or cohabiting
at age 45 1,084 (72%)
Birth cohorts
Urban birth cohorts 1800-1899 -- (0%)
Rural birth cohorts 1900-1922 132 (13%)
Urban birth cohorts 1900-1922 890 (87%)
Socioeconomic status
Foremen & medium skilled workers 312 (21%)
Farmers & fishermen 358 (24%)
Unskilled & farmworkers 314 (21%)
Source: Scanian Economic Demographic Database. Version 5.1.
(Bengtsson, Dribe, Quaranta & Svensson, 2017).
The descriptive statistics of fertility behavior of index women
born between 1800-1922 are presented in Table 3. Overall, they had
just over half as many children (2.93) as their mothers (5.11),
which reflects the generational differences in family size during
the fertility transition. This is also apparent when observing the
completed fertility for the three different birth cohorts. Index
women reduced their fertility from, on average, over five children
per woman born in the first half of the 19th century to two
children per woman born in the first two decades of the 20th
century. In addition, the standard deviation in this study declined
with time, which is a sign that women during the transition
eventually converged towards a more similar number of
children
21
than previously and aligns with the observations by Bengtsson &
Dribe (2014). The changing reproductive pattern over the course of
the 1800s and into the 1900s is also reflected in the completed
fertility in the mothers’ generation, although there is a
generational delay as well as a slight over-representation of
mothers with many children, as addressed in section 3.2.
The age of the index women at their first marriage is known for
large parts of the sample. For all birth cohorts, it remained
stable at 25-26 years, meaning that the average start of the
reproductive life did not differ remarkably in this long time
period. Instead, it is the age at last birth that declined from 38
years at the start of the 19th century to just below 31 years a
hundred years later, which is expected as the Scanian fertility
study by Bengtsson & Dribe (2014) concluded that the fertility
transition was defined, in part, by a stopping behavior at older
ages.
Table 3. Descriptive statistics, all index women and mothers’
fertility behavior over time
Index women Mothers
Index women’s variables Mean Std Dev N Mean Std Dev N
Completed fertility 2.93 2.28 1,516 5.11 2.65 1,516
1800-1849 5.38 2.63 331 6.20 2.34 331
1850-1899 3.83 2.63 163 5.92 2.66 163
1900-1922 2.00 1.20 1,022 4.64 2.61 1,022
Age at first marriage 25.2 4.33 1,407 25.2 5.22 1,271
1800-1849 25.8 5.51 281 25.7 5.11 242
1850-1899 26.2 4.86 134 25.1 4.19 103
1900-1922 24.9 3.81 992 25.1 5.34 926
Age at last birth 33.0 6.50 1,357 35.0 6.45 1,516
1800-1849 38.2 5.45 323 39.7 3.60 331
1850-1899 36.1 6.32 137 39.5 4.14 163
1900-1922 30.7 5.53 897 32.9 6.34 1,022
Source: Scanian Economic Demographic Database. Version 5.1.
(Bengtsson, Dribe, Quaranta & Svensson, 2017).
Further, in Table 4, it is observed that the completed fertility
for index women from larger compared to smaller families did not
differ significantly (3.00 vs. 2.88), despite the fact that these
two groups grew up with diverse number of siblings (7.41 vs. 3.21),
which is evidence of the convergence in childbearing just
discussed. Additionally, Table 4 reveals that 20% of women are
identified as first-born in their families of origin and they had
0.3 more children than later-born women (2.83 vs. 2.52). The
mothers of first-born daughters had fewer children than the mothers
of later-born daughters, which could be explained by a family with
later-born daughters must consist of at least two children or more,
while a family with a first-born daughter may consist of just one
child. However, the fact that first-born daughters came from
smaller
22
families, but had more children than the later-born women make them
interesting to analyze further, which will be done in section 5.2.
It is observed that the fertility behavior also differed in urban
versus rural areas, where women in birth cohorts 1900-1922 in the
urban region had fewer children than those in the rural
community.
Table 4. Stratified descriptive statistics, all index women and
mothers’ completed fertility
Index women Mothers All
Index women’s variables Mean Std Dev Mean Std Dev N
Completed fertility 2.93 2.28 5.11 2.65 1,516
Size of family of origin above cohort mean 3.00 2.31 7.41 1.89 688
(45%)
Size of family of origin below cohort mean 2.88 2.26 3.21 1.37 828
(55%)
First birth order 2.83 2.22 3.64 2.56 307 (20%)
Later birth order 2.52 1.94 5.67 2.44 700 (46%)
Rural (only birth cohorts 1900-1922) 2.37 1.62 5.18 3.14 132
(13%)
Urban (only birth cohorts 1900-1922) 1.94 1.11 4.55 2.51 890
(87%)
Source: Scanian Economic Demographic Database. Version 5.1.
(Bengtsson, Dribe, Quaranta & Svensson, 2017).
Lastly, the completed fertility differed by socioeconomic group and
over time, which is seen in Figure 2. It needs to be considered
that the data samples are small for groups 1-2, 3 and 6 in the
first birth cohort (N < 15). However, Figure 2 reveals that all
socioeconomic groups went through the fertility transition at this
time and although they started at different levels, all groups had
around two children per woman in the last birth cohort. Equally, it
can be observed in birth cohorts 1850-1899 that the decline in
fertility started in the highest socioeconomic stratum. Despite
similar numbers of children for the birth cohort 1900-1922 by
socioeconomic status, it is noted that at the end of the fertility
decline the farmers and fishermen had the largest families. This
corresponds to findings presented in the micro-level analysis by
Bengtsson and Dribe (2014) where a differently selected sample of
the Scanian Economic Demographic Database was used.
23
Figure 2. Index women’s completed fertility by socioeconomic
status
Source: Scanian Economic Demographic Database. Version 5.1.
(Bengtsson, Dribe, Quaranta & Svensson, 2017). N.B. Few
observations (N<15), in birth cohorts 1800-1849 for SES 1-2, SES
3 and SES 6. SES 1: higher managers & professionals; SES 2:
lower managers & professionals; SES 3: foremen & medium
skilled workers; SES 4: farmers & fishermen; SES 5: lower
skilled & farmworkers; SES 6: unskilled &
farmworkers.
5.1.2 Bivariate correlations
The first analysis of intergenerational transmission in fertility
behavior between index women and their mothers is done using
bivariate correlations. The resulting correlation coefficients are
presented in Table 5 for each of the three main variables of this
analysis: completed fertility, age at first marriage and age at
last birth, in their absolute and their cohort-relative form for
all women. Table 5 reveals that the magnitude and direction of the
absolute and relative variables do not, overall, differ largely
within each birth cohort. This suggests that fertility behavior
within families remained similar over time, relative to the average
behavior of the birth cohort. Still, the analysis will continue to
focus on the relative numbers to ensure robustness as per previous
literature.
The correlation in relative completed fertility between the two
generations in Table 5 increases and becomes significant for the
last group of women who were born 1900-1922 and had children
towards the end and after the fertility transition. This
coefficient is 0.0718. In Appendix B it is further shown that the
bivariate correlation appeared specifically for index women born
1910-1919. The earlier birth cohorts 1800-1899 display a small,
insignificant and negative correlation, and will not be analyzed in
depth, but the negative sign suggests a reversed behavior between
index women and mothers.
Age at first marriage and age at last birth do not show the same
trend. In fact, the coefficients are generally weaker and there is
not any statistical significance at a confidence interval of 95% in
this thesis’ sample. The intergenerational transmission in age at
first marriage for the first and the last birth cohorts is
approximately zero. The second birth cohort shows a higher
correlation, but which is still not significant, possibly due to
the small sample size (N=86). The
0
1
2
3
4
5
6
7
8
SES 1-2 SES 3 SES 4 SES 5 SES 6
24
same pattern is observed for age at last birth. All in all, there
are not any signs of intergenerational transmission in the start
and end of the reproductive life over the studied period and the
rest of the analysis will focus on completed fertility as the
measurement of fertility behavior.
Table 5. Bivariate correlation coefficients over time
Index women and mothers’ absolute fertility variables
Index women and mothers’ relative fertility variables
Coefficient p-value N Coefficient p-value N
Completed fertility
Age at first marriage
Age at last birth
1800-1849 0.0170 0.7606 323 0.0037 0.9544 323
1850-1899 0.1198 0.1632 137 0.0499 0.5824 137
1900-1922 0.0275 0.4109 897 -0.0225 0.6426 897
***p<0.001 **p<0.01 *p<0.05 Source: Scanian Economic
Demographic Database. Version 5.1. (Bengtsson, Dribe, Quaranta
& Svensson, 2017).
In order to determine variations in intergenerational transmissions
in fertility more specifically, the correlations are stratified by
the characteristics identified in the literature, namely
socioeconomic status, birth order, size of family of origin and
rural/urban residency for the index women born 1900-1922. In Table
6 it is observed that the correlations differ in all the
above-mentioned dimensions. The intergenerational transmission is
stronger for women from relatively larger than smaller families
(0.1011 vs. 0.0657). Additionally, first-born women have more
similar fertility behavior to their mothers, than their younger
sisters do (0.1110 vs. 0.0768).
There are differences in intergenerational transmission in
completed fertility by socioeconomic status, where the largest
correlation coefficient is found in the highest socioeconomic
groups (SES 1-3). However, there is not a clear gradient present as
the unskilled workers display a correlation coefficient of 0.1151,
with a p-value just above 5%. The only two strata without any
transmission are the farmers, fishermen and low skilled workers.
Lastly, the correlation
25
coefficient for urban women is both significant and larger than the
full sample, while there is not any evidence of the rural women
replicating their mothers’ family size for these birth
cohorts.
Table 6. Stratified bivariate correlation coefficients, birth
cohorts 1900-1922
Relative completed fertility
Origin family size above cohort mean 0.1011* 0.0313 454
Origin family size below cohort mean 0.0657 0.1178 568
First birth order 0.1110 0.0974 224
Later birth order 0.0768 0.0716 551
1-2. Higher managers & professionals, lower managers &
professionals 0.2424* 0.0186 94
3. Foremen & medium skilled workers 0.1500* 0.0121 279
4. Farmers & fishermen 0.0231 0.8347 84
5. Lower skilled & farmworkers -0.0577 0.3914 223
6. Unskilled & farmworkers 0.1151 0.0523 285
Rural 0.0027 0.9755 132
Urban 0.0843* 0.0118 890
***p<0.001 **p<0.01 *p<0.05 Source: Scanian Economic
Demographic Database. Version 5.1. (Bengtsson, Dribe, Quaranta
& Svensson, 2017).
5.1.3 Multivariable regressions
The bivariate correlation model is expanded into a multivariable
regression, where the index women’s relative completed fertility is
the outcome variable. This allows for analyses of the effect of
several variables on index women’s fertility, in addition to the
mothers’ fertility. By building out the regression model, the
robustness of the relationship between the index women and their
mothers’ fertility behavior is also tested.
In Table 7 seven different models are presented. Model (1) is the
basic regression of the index women and mothers’ relative completed
fertility. In model (2), first and later birth order are added as
control variables. In model (3) urban residency is added. In model
(4), the socioeconomic status is added, where the highest group is
the reference. In models (5) – (7), interaction terms representing
the combination of urban residency, first birth order and high
social status are added, allowing for analysis of combined effects
and providing additional robustness.
26
Table 7 shows that there is a significant relationship between
index women and their mothers’ relative completed fertility. The
bivariate regression model shows a relationship of 0.0761 between
index women and mothers, which is slightly higher than observed in
Table 5. When controlling for the index women’s birth order in
model (2), the relationship between the two generations of women
increases to 0.0928, suggesting that model (1) understated the
transmission. However, as variables are added in models (3) – (7)
to control for urban residency, socioeconomic status and the
interaction of these parameters, the intergenerational relationship
stabilizes between 0.0834-0.0872. This implies that by controlling
for additional variables in a multivariable regression, the
intergenerational effect is more specifically identified.
Overall it is observed that while there is an intergenerational
transmission in fertility, especially urban residency, but to some
degree also socioeconomic status, determines the index women’s
completed fertility to a larger extent. The interaction terms in
models (5) – (7) are not statistically significant at the 95%
confidence level, but they control for the effect of combinations
of the explanatory variables on completed fertility and contribute
with testing the robustness of the model.
Concluding, the intergenerational effect in the multivariable
regression ranges between 0.0834- 0.0928, where most models land
around 0.085. However, urban residency and socioeconomic status are
equally determinants of completed fertility during this time
period. Model (4) will be examined in the stratified regressions
and the sensitivity analysis, and will from now on be referred to
as the “main model” as it includes all control variables except for
the interaction terms.
27
Table 7. Multivariable regression coefficients, birth cohorts
1900-1922
Dependent variable: index women’s relative completed
fertility
Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model
(7)
Mothers’ relative completed fertility
First birth order 0.0030 0.0528 0.0467 0.3111 0.0509 0.0476
Later birth order -0.0630 -0.0078 -0.0222 -0.0351 -0.0103
-0.0210
Urban residency -0.2064*** -0.1961** -0.1569* -0.1997**
-0.2023**
--
--
--
--
First Birth Order × Urban
0.1031
R² 0.0052 0.0078 0.0196 0.0279 0.0309 0.0285 0.0280
***p<0.001 **p<0.01 *p<0.05. SES 1: higher managers &
professionals; SES 2: lower managers & professionals; SES 3:
foremen & medium skilled workers; SES 4: farmers &
fishermen; SES 5: lower skilled & farmworkers; SES 6: unskilled
& farmworkers. Source: Scanian Economic Demographic Database.
Version 5.1. (Bengtsson, Dribe, Quaranta & Svensson,
2017).
To further isolate where the influence of the family is strongest
and to assess the validity of the stratified bivariate correlation
coefficients in Table 6, the main multivariable regression model
(4) in Table 7 is presented in ten different stratified
multivariable models in Tables 8 and 9.
First, in Table 8, it is observed that the intergenerational
transmission is more than twice as high for women from smaller than
larger families (0.2166 vs. 0.1143). This is contrary to the
bivariate correlations in Table 6 and demonstrates the importance
of isolating the effect that the previous generation has on the
index women through a multivariable model. In this case, it is
especially the birth order and urban residency that changed the
relationship between mothers’ and daughter’s fertility compared to
the bivariate model, since first-borns from large families were
more likely to have many and urban women from small families were
more likely to have few children.
28
The stratification by birth order in the multivariable regression
renders higher intergenerational transmission for first-born than
later-born women, which was observed in the bivariate correlation
models in Table 6 as well. Nonetheless, the variables are not
statistically significant at the 5% significance level (p-values
between 6-8%) and need to be interpreted with some caution. On the
other hand, index women with urban, but not rural, residency remain
likely to take on their mothers’ childbearing pattern, as noted in
the bivariate correlation models in Table 6.
Table 8. Stratified multivariable regressions, birth cohorts
1900-1922
Dependent variable: index women’s relative completed
fertility
Model (4)
Mothers’ relative completed fertility
First birth order 0.0467 0.1910 -0.0187 0.0202 0.2695
Later birth order -0.0222 0.0590 -0.0835 -0.0341 0.0202
Urban residency -0.1961** -0.0912 -0.3127*** -0.4789**
-0.1613
--
--
--
--
--
--
--
Constant 1.0358*** 0.8592*** 1.0779*** 1.3229*** 0.9352***
0.8435*** 0.9561**
N 1,022 454 568 224 551 890 132
R² 0.0279 0.0468 0.0428 0.0576 0.0269 0.0186 0.0504
***p<0.001 **p<0.01 *p<0.05. SES 1: higher managers &
professionals; SES 2: lower managers & professionals; SES 3:
foremen & medium skilled workers; SES 4: farmers &
fishermen; SES 5: lower skilled & farmworkers; SES 6: unskilled
& farmworkers. Source: Scanian Economic Demographic Database.
Version 5.1. (Bengtsson, Dribe, Quaranta & Svensson,
2017).
In Table 9 it is observed that the most significant
intergenerational transmission in completed fertility occurred for
women from the highest strata. However, in families of foremen and
medium skilled workers as well as unskilled workers, the similarity
in childbearing over generations is almost as strong (0.1690 and
0.1598 respectively). The p-value of unskilled workers is 6%, so
just over the 5%-limit and this groups should not be disregarded in
the analysis. Hence, bivariate correlation and multivariable
regression models identified the same overall trends in
intergenerational transmissions per socioeconomic group, but the
magnitude of the medium and unskilled workers’ coefficients
increased in the multivariable model. Urban
29
residency lowered the fertility for all groups except for the
foremen and medium skilled workers where it did not make a large
difference. Thus, the different fertility behaviors in an urban
compared to a rural environment were also reflected in most social
groups.
Table 9. Stratified multivariable regressions, birth cohorts
1900-1922
Dependent variable: index women’s relative completed
fertility
Model (4) SES 1-2 SES 3 SES 4 SES 5 SES 6
Mothers’ relative completed fertility
First birth order 0.0467 0.0237 0.0777 0.1412 0.1591 -0.0016
Later birth order -0.0222 -0.1667 0.0141 -0.0891 0.1654
-0.0430
Urban residency -0.1961** -0.1510 0.0231 -0.2993 -0.6007***
-0.1320
--
Constant 1.0358*** 0.9735*** 0.7497*** 1.2133*** 1.4624***
1.0511***
N 1,022 94 279 84 223 285
R² 0.0279 0.1022 0.0254 0.0617 0.0634 0.0169
***p<0.001 **p<0.01 *p<0.05. SES 1: higher managers &
professionals; SES 2: lower managers & professionals; SES 3:
foremen & medium skilled workers; SES 4: farmers &
fishermen; SES 5: lower skilled & farmworkers; SES 6: unskilled
& farmworkers. Source: Scanian Economic Demographic Database.
Version 5.1. (Bengtsson, Dribe, Quaranta & Svensson,
2017).
5.1.4 Sensitivity analysis
This thesis’ data sample has been defined by a number of
restrictions to allow for correct interpretations. Women
in-migrating after the age of 40 were excluded to limit the risk of
under- reporting the completed fertility, due to older children not
necessarily migrating into the region with their mothers. Likewise,
all women with children were included in the analysis,
independently of their civil status, in order to assess the
holistic fertility behavior and maintain a larger sample size.
Hence, despite section 5.1.3 providing robustness checks through
the extended models and stratification, the selection of
in-migrating and married women is analyzed in more detail as a
sensitivity analysis. Table 9 presents the results of the main
model (4) in Table 7 adjusted for exclusion of: 1) all women
in-migrating after age 30, 2) mothers not
30
married or cohabiting at the age of 45 and 3) index women and
mothers not married or cohabiting at the age of 45.
Firstly, by excluding all women who in-migrated to the region after
the age of 30, the sample drops by 13% (128 observations). However,
the intergenerational transmission increases from 0.0839 to 0.1023.
Secondly, when excluding all unmarried mothers and their daughters,
the sample drops by 12% (120 observations) but the correlation
between the generations strengthens compared to the main model
(0.0873). Thirdly, when excluding all unmarried index women and
mothers, the sample drops by 26% (265 observations), the
intergenerational transmission reaches its lowest level so far
(0.0558) and the statistical significance disappears. Generally, in
all three sensitivity models, urban residency and socioeconomic
status impacted index women’s fertility the most. The difference
between these variables in the main model compared to the
sensitivity analysis is small, with the exception of the last
analysis where all women are married. Hence, while the same
variables influence index women’s completed fertility in the main
model and the models in the sensitivity analysis, the
intergenerational effect on fertility starts differing when
modifying the sample restrictions in the three sensitivity
analyses. The interpretation of this will be discussed in more
detail in section 5.2.
31
Model (4)
45
cohabiting at age 45
Mothers’ relative completed fertility
0.0839* 0.1023** 0.0873* 0.0558
Urban residency -0.1961** -0.1968* -0.1639** -0.1921*
--
--
--
--
R² 0.0279 0.0258 0.0277 0.0308
***p<0.001 **p<0.01 *p<0.05. SES 1: higher managers &
professionals; SES 2: lower managers & professionals; SES 3:
foremen & medium skilled workers; SES 4: farmers &
fishermen; SES 5: lower skilled & farmworkers; SES 6: unskilled
& farmworkers. Source: Scanian Economic Demographic Database.
Version 5.1. (Bengtsson, Dribe, Quaranta & Svensson,
2017).
5.2 Discussion
This thesis aimed to analyze the presence and magnitude of
intergenerational transmission in reproductive behavior, before,
during and after the fertility transition in a Southern Swedish
population of women born between 1800-1922 and their mothers, in
order to understand the influence of the family on fertility
behavior. Additionally, previously identified factors magnifying
the transmission were examined.
Observing the fertility behavior of the sampled population, the
patterns align with Bengtsson and Dribe (2014) who studied another
subset of this database. The overall trends also correspond to the
existing knowledge of the fertility transition in Sweden (see e.g.
Dribe, 2009). A clear decline in the number of children per woman
is detected throughout the transition due to a reduction in the
reproductive life through a lower age at last birth. It is noted
that the highest socioeconomic groups were first to reduce their
fertility during the transition. In the post-
32
transitional period, all strata had converged towards two children
per woman. Smaller differences were observed where the farmers and
fishermen had slightly larger and the highest social groups had
slightly smallest families. The convergence towards two children
per woman corresponds to the existing literature (see e.g. Dribe,
Hacker & Scalone, 2014; Bengtsson & Dribe, 2014; Dribe et
al., 2017). Additionally, women in the urban area had almost 0.5
fewer children than women in the rural areas (1.94 vs. 2.37), which
is likely explained by the changed living conditions that came with
the urbanization (Dribe, 2009). It is also observed that, despite
originating from much smaller families, the first-born women had
0.3 more children than women of later birth order (2.83 vs. 2.52).
Although the difference is small, in the historical context it
could be explained by different socialization and resource
allocation from the parents (Zimmer & Fulton, 1980; Jennings,
Sullivan & Hacker, 2012). Thus, the main fertility patterns of
this thesis’ sample correspond to other Swedish studies during the
same time period and although there was a convergence in
childbearing for all social groups, there were still different
behaviors within the population, e.g. from a rural-urban or
first-later birth order perspective.
This study did not find a statistically significant presence of
intergenerational transmission in completed fertility before or
during the fertility transition. This is contrary to the main
literature treating intergenerational transmission in fertility in
this time period (see e.g. Anderton, Tsuya, Bean & Mineau,
1987; Reher, Ortega & Sanz-Gimeno, 2008; Jennings, Sullivan
& Hacker, 2012; Bras, Van Bavel & Mandemakers, 2013;
Rotering, 2017). Instead, the correlation in family size between
mothers and daughters is detected towards the end and after the
historical fertility transition. The delayed presence of
intergenerational transmission in fertility behavior would suggest
that the influence of the family was not a driving force of the
fertility transition in this sample of women, but its importance
emerged once families were already smaller. This, in turn, would
imply that the diffusion of values that has been claimed to explain
parts of the fertility transition (see e.g. Dribe, 2009; Bengtsson
& Dribe, 2014; Dribe, Hacker & Scalone, 2014), did not
spread from past generations within the family, but rather from
other social channels in this population.
However, it needs to be considered that while a delay in the
appearance of intergenerational transmission in reproductive
behavior has been observed, there are also a couple of
characteristics specific to this data sample that could explain it.
Firstly, due to high rates of migration in especially the 1800s,
there are relatively few observations of women of childbearing ages
before and during the fertility transition, which may hinder the
detection of intergenerational transmission. Secondly, in this
sample, there is an uneven split of rural and urban residency over
time. This is problematic as the most historical birth cohorts were
mainly rural women and the more recent birth cohorts were mainly
urban women. Hence, it is plausible that the non-existing
intergenerational transmission during the fertility transition in
this analysis was due to lack of data on urban behavior, where the
influence of the family on reproductive behavior could have been
significant. Thus, these circumstances need to be considered when
drawing conclusions.
Further, the identified intergenerational transmission in completed
fertility for index women born towards the end and after the
fertility transition, i.e. birth cohorts 1900-1922, was of
relatively small magnitude. It was estimated with two
methodologies: a bivariate correlation and a multivariable
regression, where the former resulted in a coefficient of 0.0718.
This is lower than previously conducted research of populations
during the fertility decline, whose
33
coefficients have ranged from 0.0853 in Northern Sweden (Rotering,
2017) to 0.154 in a Spanish town (Reher, Ortega & Sanz-Gimeno,
2008). The multivariable regression models, which controlled for
other determinants of index women’s fertility and contributed to
isolating the intergenerational effect generated slightly larger
estimates ranging between 0.0834-0.0872 for the most extensive
models. The fact that this interval is rather small, and the
multivariable models generated quite similar results, speak in
favor of their validity and robustness.
However, the intergenerational transmission in fertility in this
population was still smaller than observed in other studies. Three
aspects of the sample definitions were examined further to identify
if the lower correlation was a result of selection bias arising.
Firstly, when excluding women based on a lower age of in-migration,
the correlation in childbearing between mothers and daughters
increased by 22% from 0.0839 to 0.1023. This indicates that the
sample restriction on in-migration in the main analysis may have
been too relaxed and has introduced a bias underestimating the
influence of the family in reproductive behavior. In addition,
differences in unobserved attitudes towards family size between the
migrants and the non- migrants could equally have contributed to
this bias (van Bavel, 2006). Secondly, most of the previous
research examined marital fertility behavior (see e.g. Jennings,
Sullivan & Hacker, 2012; Rotering, 2017), while this study did
not make a distinction on civil status. The sensitivity analysis of
this study did not find any signs of increased correlation in
childbearing between generations when analyzing only married women,
suggesting that marital status did not create any selection bias
underestimating the main model. Thirdly, the current analysis takes
the perspective of index women, meaning that each studied index
woman was only present in the sample once, but the mothers in the p