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
lo4074co-s@student.lu.se
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.
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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
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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
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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).
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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
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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.
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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
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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
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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.
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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.
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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.
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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.
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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
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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.
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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).
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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.
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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.
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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”.
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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
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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
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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.
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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.
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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
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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
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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.
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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
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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.
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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.
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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.
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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
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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
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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.
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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-
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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
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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