Abstract: Abortion is an important aspect of the fertility decision and the impact of economic conditions on the wellbeing of individuals and families. This study investigates the relationship between business cycle indicators in the context of women’s growing importance in Sweden’s labor market and economy. It finds that female relative wages have had the strongest impact on increasing abortion demand. The influence of GDP per capita and relative cohort size have also been strong. Theses effects have been strongest in the period when abortion was available on a limited basis before 1975. The explanatory value on age-specific abortion demand after 1975 is mixed.
Key words: Abortion, abortion rate, abortion ratio, Sweden, business cycle, OLS regression, women’s relative wages

1.
Introduction…………………………………………………………………………...5
2.
Background…………………………………………………………………………...6
2.1.
Abortion and contraception in Sweden…………………………………………..6
2.2.
An overview of Sweden’s economic history……………………………………12
3.
Previous literature and theory………………………………………………………..15
3.1.
Previous models of abortion demand……………………………….…………..15
3.2.
Theory.………………….…………..…………………………………………...17
3.2.1.
Cost-benefit analysis in fertility models………………….…………..….17
3.2.2.
The costs and benefits of contraception and unwanted pregnancies…….19
4.
Model, methodology, and data……………………………………………………….22
4.1.
Model……………………………………………………………………………22
4.2.
Methodology…………………………………………………………………….23
4.3.
Data.……………………………………………………………………………..24
5.
Theoretical considerations of the variables…………………………………………..26
5.1.
The abortion rate and abortion ratio…………………………………………….27
5.2.
Vacancies and unemployment…………………………………………………..28
5.3.
Per Capita GDP………………………………………………………………….20
5.4.
Female relative wages…………………………………………………………...31
5.5.
Relative cohort size………………………………………………………….......34
5.6.
Services………………………………………………………….........................35
5.7.
Education…………………………………………………………......................36
6.
Results…………………………………………………………..................................38
6.1.
Abortion demand in Sweden 1939-2010…………………..................................38
6.2.
Abortion demand in Sweden 1960-2010…………………..................................43
6.3.
Age-specific models………………….................................................................46
6.3.1.
Age-specific abortion rate…………………..............................................46
6.3.2.
Age-specific abortion ratio…………………............................................53
7.
Discussion and concluding remarks …………………................................................57
8.
Works Cited………………….....................................................................................61
9.
Appendix…………………..........................................................................................66
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List of Figures
2.1. The number of births and abortions in Sweden 1920-2010…………………………..7
2.2. Growth rate of births and abortions in Sweden 1920-2010…………………………..7
2.3. The percentage of abortions granted by Socialstyrelsen (SOS) vs two physicians in
Sweden 1939-1974…………………………..…………………..……………….10
2.4. Percent of abortion applications to the National Board of Health and Welfare
(Socialstyrelsen) granted…………………………..…………………..…………11
5.1. The abortion rate and abortion ratio in Sweden 1939-2010……..………………….27
5.2.
Growth rates of the abortion rate and vacancy rate in Sweden 1939-2010…………30
5.3. Growth rates of the abortion rate and GDP per capita………………………………31
5.4. Female relative wages and the abortion rate in Sweden 1939-2010………………...32
5.5. Growth rates of the abortion rate, female relative wages in Sweden 1939-2010…...33
5.6. Growth rate of the abortion rate and percent change of cohort size in Sweden 1939-
2010…………………………..…………………………..………………………35
5.7. Growth rates of the abortion rate and services in Sweden 1939-2010……………...36
5.8. Growth rates of the abortion rate and female entrants into higher education in
Sweden 1939-2010………………………..………………………...……………37
6.1. Abortions per 1000 women in Sweden - distribution by age 1975-2010…..……….47
6.2. Growth rates of abortion rate per 1000 women by age 1975-2010- five year moving averages………………………..…………………………..…………………......48
6.3. Percent of known pregnancies ending in abortion in Sweden by age 1975-2010…..53
6.4. Growth rates of age-specific abortion ratio in Sweden 1975-2010 - five year moving averages………………………..…………………………..…………………......54
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List of Tables
1. Abortion demand in Sweden 1939-2010……………………………………...………39
2. Abortion demand in Sweden 1939-1974……………………………………...………40
3. Abortion demand in Sweden 1975-2010……………………………………...………42
4. Abortion demand in Sweden 1960-2010……………………………………...………44
5. Age-specific abortion demand in Sweden 1975-2010 – Age-specific abortion rate….50
6. Age-specific abortion demand in Sweden 1975-2010 – Age-specific abortion ratio…56
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The relationship between births and the economy has long been the subject of economic inquiry. However, birth rates are not the only indicator of changing fertility patterns or the effect of economic conditions on personal fertility decisions. The association between abortion and the business cycle can also reveal important information about the connections between pregnancy behavior and the economy.
Sweden has long had an exceptionally high abortion rate, with almost one out of every four pregnancies ending in abortion since the 1960s (Statistics Sweden). Clearly, abortion represents an important form of birth control, and is a major part of fertility decisions in
Sweden. This paper will investigate the relationship between abortion demand in Sweden and the economic factors that determine the business cycle and historical economic development in Sweden.
Since abortion was legalized in the United States and several other industrialized nations in the 1970s, the economic factors that influence abortion demand have been increasingly studied. Many of these studies have been conducted on abortion trends in the
United States, and many of these American studies have focused on the changes in teen abortion and birth rates and the effect of abortion restrictions on abortion and birth rates, both political issues that are pertinent in the United States, but that are less pressing elsewhere (see, for example, Medoff 1988; 197; 2007, Levine, Trainor and Zimmerman
1996). This has led to substantial research about these younger age groups to the exclusion of others, and to the exclusion of a more complete understanding of abortion as a fertility decision. There have been fewer studies that investigate the relationship between fertility and abortion in a context where abortion is unrestricted, as in Sweden.
Furthermore, there has been almost no investigation of the direct relationship of abortion to economic conditions over time. While many studies have utilized economic variables like unemployment or female labor force participation, these have typically been secondary to measurements of abortion restriction or abortion cost. Additionally, these studies have almost always focused on conditions in a specific year, without extending analysis over a longer period (for example, Rothstein 1992). As such, many studies have lost sight of the essential role of abortion as a fertility outcome.
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The goal of this paper is to investigate the relationship of abortion demand in
Sweden to the business cycle over the course of two periods; the first from the beginning of legal abortion access in Sweden, in 1939 to 2010; and the second from 1960, when
Sweden emerged as a modern economy with its emphasis on integrating women’s labor with family through strong social welfare policies (see Schön 2010). Additionally, it will investigate the differences in the determinants of age-specific abortion demand after
1975. This thesis will endeavor to determine what economic influences have had the greatest impact on abortion demand and whether the developing economic role of women has changed these economic relationships.
The following section of this paper will present historical context of Sweden’s abortion legislation and development of abortion in Sweden, the history of the use of contraceptives and other birth control, and Sweden’s recent economic history. Section three will cover previous research and theory. The second half of the study will be a multivariate OLS regression of abortion demand. The model, methodology, and data are presented in section four. Section five explores the theory behind the selected variables and their expected contributions to abortion demand. Section six presents the results of the OLS regressions for abortion demand. The final section will present overall conclusions and possibilities for further research.
Unrestricted abortion and contraception have been available and subsidized in
Sweden since 1975. Before 1975, abortion was available legally in Sweden on a restricted basis from 1939 (Grönqvist 2012). Abortion between 1939 and 1974 required a woman to receive permission from either two physicians or from the National Board of health and Welfare, or Socialstyrelsen (SOS). An abortion could only be granted for one of a few specified reasons: sickness, weakness, humanitarian reasons, and eugenic reasons. The case of expected weakness was introduced in 1946, and fetal damage in
1963. Social needs, such as poverty or inadequate housing, were not officially included.
The definition of what exactly constituted any of these categories was vague, and left to physicians to interpret. As such the number of abortions granted and the justifications
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used for them changed significantly over the period of limited abortion; the number of abortions increased dramatically from about 1960 to the change in laws in 1975. The increasing number of abortions permitted can be seen as indicative of the developing social acceptance of abortion leading up to the change in laws in 1975 (Cassel 2009).
There are some developments within the provision of abortion during the restricted period that hint at underlying social changes. Notably, there was a clear shift in the condition diagnosed as a predicate to abortion during the 1960s. Beginning in the middle of the decade, ‘expected weakness’, previously less frequent than the other two main reasons, ‘weakness’ and ‘sickness’, spiked as the main reason for abortion. This increase corresponds to the general increase in abortion occurring at this time. The proportion of abortions granted by two physicians, as opposed to the Socialstryrelsen , also rapidly increased beginning in 1964; Cassel (2009) proposes that the increase in
‘expected weakness’, the role of physicians, and the increase in abortions are connected, suggesting that the vague ‘expected weakness’ was used as an easy diagnosis by doctors who were willing to grant more abortions as public sentiment became less critical.
Conversely, he proposes that women seeking an abortion may have found this condition the most likely to succeed in their petition.
It is worthwhile to examine the long-term development of both abortions and births in Sweden in order to understand changes in Swedish fertility. To get a fuller
Figure 2.1. The number of births and abortions in Sweden 1920-2010
160000
140000
120000
100000
80000
60000
40000
20000
0
Abortions Births
Source: SCB, Socialstyrelsen, Johnston
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Figure 2.2. Growth rates of births and abortions in Sweden, 1920-2010
0,45
0,35
0,25
0,15
0,05
-0,05
-0,15
-0,25
Births Abortions
Source: Author’s calculations based on data from SCB, Socialstyrelsen, and Johnston picture, births are pictured from 1920, almost twenty years before abortion was legalized
(figure 2.1). From the beginning of the period until 1932, births in Sweden were in sharp decline, during the post World War I recession that occurred in Sweden and much of the rest of the world. Baby booms peak in 1944, 1964, 1990, and increase though the end of the period. Birth rates hit low points in 1933, 1960, 1982, and 1999; these periods correlate to poor economic climates in Sweden, especially through the long slump from the mid-1970s and 1980s and economic crisis in 1993 (Edvinsson 2005).
The number of abortions started out low, with only 439 performed in Sweden in
1939. However, by 1951 the number of abortions had increased to 6328. Though the number is not large, this is a huge percentage increase (Fig. 2.2; see also Cassel 2009).
The number of abortions grew by more than ten percent every year from 1942 through
1950, peaking at over 35 percent in 1944. Thereafter the number of abortions generally decreased until 1961, declining by almost twenty percent in 1952, 1956, and 1958. Cassel
(2009) attributes this decline to negative reactions both by officials and the general public to the drastically increasing number of abortions in the decade before.
From 1961 until the mid-1970s the number of abortions grew enormously, accelerating through the first part of the 1960s, and increasing at a decelerating rate until
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1975, the year when abortion on demand was legalized. For the next decade the increase in abortions stalled, and in many years the number declined. The lack of a significant increase in the number of abortions in 1975 or 1976, and the smooth transition into the new policy, indicates that there was little unmet demand for abortion services in Sweden.
This supports Cassel’s (2009) assertions that attitudes toward abortion had been steadily changing over the previous years. Posner (1992) further connects the small decrease in the number of abortions the year after access was expended to strong contraceptive and sexual education promotions programs on the part of the Swedish government that led to fewer unwanted pregnancies. This is also the same time as oral contraception was introduced in Sweden, which might have decreased unwanted pregnancies (Grönqvist
2012).
After 1975 abortions and births develop similarly. Both peak in the late 1970s and the late 1980s, and hit low points in the mid-1980s and late 1990s, increasing again through the end of the period. Similar movements in births and abortions can also be seen in the period of restricted abortion, especially as both figures decline at the end of the
1950s after having reached high points in the previous fifteen years. The growth rates of both births and aboritons are especially similar after 1975. This comparable development indicates that they are likely to respond to similar factors.
Whether the dramatic increase of abortions in the 1960s and early 1970s was due primarily to increases in demand or increases in supply is an important distinction. Rising abortion demand can be seen through the huge increase in the number of women who had abortions. Abortion supply was controlled by legal restrictions and the authorities’ willingness to grant abortions, and can be approximated by the proportion of applications approved. However, access to the number of petitions both submitted and granted is limited, since only applications to the Socialstyrelsen are available for analysis, and the
Public Health in Sweden reports issued by Socialstyrelsen only report the number of applications received and granted from 1949. This means that data is unavailable from
1939 through 1948 and that it is not possible to know the percentage of petitions to physicians that were granted. While this makes it difficult to measure the full number of applications for abortion, especially as increasing numbers of applications were made to physicians, it is still worth investigating.
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Figure 2.3. The percentage of abortions granted by Socialstyrelsen (SOS) vs two physicians in Sweden 1939-1974.
10
Source: Cassel 2009
On the supply side, the change in laws indicates that restrictions were becoming looser. This can be seen through the introduction of new categories as justification for abortion: ‘expected weakness’ in 1946 and fetal damage in 1963 (Cassel 2009). The percentage of abortions granted by the Socialstyrelsen was decreasing significantly through much of the 1960s and into the 1970s (fig. 2.3). As mentioned, Cassel (2009) connects the rising prevalence of abortions granted by two doctors to a looser supply of abortion.
The number of petitions to Socialstyrelsen that were approved was also increasing. Figure 2.4 shows of the percentage of the applications to Socialstyrelsen that were approved between 1949 and 1974. The strong decline in the percent granted through the later part of the 1950s supports Cassel’s (2009) assertion that after the rapid rise of abortions between 1939 and 1951, there was a negative response by the authorities and a subsequent attempt to lower the abortion rate. However, from 1960 until the end of the period of restricted access, the percentage of abortion applications granted increased from under 63 percent to 97 percent in 1974, indicating a clear loosening of supply by the official governing body. At least from the standpoint of the Socialstyrelsen, the restrictions on abortion were diminishing.
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Figure 2.4. Percent of abortion applications to the National Board of Health and
Welfare (Socialstyrelsen) granted
100
95
90
85
80
75
70
65
60
55
50
Source: Public Health in Sweden (Allmän hälaso-och sjukvård) – Socialstyrelsen.
Various years
In the late 1980 and early 1990s, several studies were prompted by concerns over the rising abortion rate and AIDS to examine how Swedish women use birth control and contraception, and provide a general overview of the how Swedish women have used birth control over the past few decades. Because of the impact of the proper use of contraception on abortion, the development of contraceptive access in Sweden provides useful insights into changes in abortion demand. Contraceptive use in Sweden has not been consistent. The cost of birth control and the public’s comfort with oral contraceptives have had significant impacts on sales on several occasions, with corresponding changes in abortion rates. Additionally, the introduction of emergency contraception in the mid 1990s has provided new contraceptive options for Swedish women.
Oral contraception was legalized the same year as the abortion legislation changed. Since at least the 1980s, it has been the most frequently reported form of birth control for Swedish women under 30 (Andersch and Milsom 1982, Brännström,
Josefsson, and Liljestrand 1991, Milsom, Sundell, and Andersch 1991, Ingelhammar et al
1994, Larsson et al 1997). When the pill was first introduced it was heavily subsidized for all women, but subsidies were cut in 1984, increasing prices and driving down sales.
Many youth clinics reported inconsistent pill use and rising abortion rates, especially
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among younger women. Subsidies for young women were reintroduced in the late 1980s and early 1990s, and have remained in place since (Larsson et al 1997, Grönqvist 2012).
Many Swedish women have discontinued their use of oral contraceptives due to side effects or fears of side effects. Swedish women of all ages have frequently stopped taking the pill because of both experienced side effects and fear over possible side effects in the future. (Andersch and Milsom 1982, Milsom, Sundell, and Andersch et al 1991,
Helström et al 2003). Negative news stories in the early 1970s, the late 1970s, and the mid 1980s are connected to declining pill sales (Larsson et al 1997).
Emergency contraception made a relatively late entry into the Swedish market and was not widely available until after 1993, later than many other countries (Tydén,
Wetterholm, and Odlind 1998, Falk et al 2001). Initially available on a prescription-only basis, access was expanded to non-prescription, but behind the counter access in 2001, and became available over the counter by 2003 (Gainer et al 2003). In Tydén,
Wetterholm, and Odlind’s (1998) study, the majority of emergency contraception users were teens and students at either the high school or university level.
In general, contraceptive use among sexually active women in Sweden seems to be poorest among teenage women, but improves with age. Milsom, Sundell, and
Andersch 1991 compare their survey results to results obtained from the same women five years previously (Andersch and Milsom 1982) and find substantial improvement.
Brännström, Josefsson and Liljestrand (1991) in their study of contraceptive usage in a suburban and rural Swedish population, find that up to twenty-five percent of women did not use contraception during their first sexual encounter. This is congruent with Falk et al’s (2001) report that up to twenty percent of sexually active adolescent women do not use contraception, and that up to two thirds of adolescents wait over a year from their first sexual encounter to obtain information about contraception.
A basic overview of Swedne’s economic history, especially with regards to women’s labor force participation and periods of recession, is an important tool for understanding the changes in fertility and abortion demand. Sweden began its transition from an agrarian to an industrial economy between 1870 and 1910, during the Second
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Industrial Revolution (Edvinsson 2005). In the 1890s Sweden moved to the global forefront of economic and industrial development, and remained a leading industrial power until the end of the 1960s (Schön 2010), when Sweden’s classic industrial period came to an end (Edvinsson 2005).
There are several economic waves in twentieth century Sweden. The first of these waves occurred between 1890 and 1930, during which wages increased, demand and domestic production rose, and living standards improved. Sweden emerged relatively unscathed from World War One. However, the 1920s were more difficult, and a rationalization period led to high unemployment and decreased prices throughout the decade. Like many periods of rationalization, the 1920s were a time of social change.
Women were making their way into the urban paid labor force, especially in the developing services, and agricultural employment continued to decline. Women frequently worked shorter periods than men, for less pay, and so were attractive in times of expansion when cheap, temporary labor was in demand. As a result of increased female labor demand, female relative wages increased through the 1920s. As has often been the case during similar periods of rationalization and increased demand for female labor, the birth rate declined significantly. However, the period was not one solely of gains, and traditionally female professions – such as nursing and teaching, social work and secretarial positions – remained systematically underpaid (Schön 2010).
Sweden’s second economic wave took place between 1930 and 1975, again cumulating in a period of rationalization and economic and social change. Sweden’s economy was the world’s fastest growing between 1930 and 1950, as much of the rest of the developed world floundered in a recession and economic crisis. Though Sweden was not as badly harmed as much of Europe and America, the 1930s were still a period of high unemployment, decreased price and lower investment, dwindling exports, and financial crisis. World War II decreased the role of international trade globally, and strengthened self-dependence in Sweden as in much of Europe. Postwar growth throughout Europe was enormous; the period from 1950 to 1975 in Western Europe was the golden age of growth, and much of the region was able to make up for slow growth in the previous decades. Sweden in the 1950s and 1960s was very prosperous. However,
Swedish growth fell behind in the middle of the 1970s, and Sweden would not recover its
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former position for several decades (Edvinsson 2005). The culmination of growth in the late 1960s led to another period of rationalization, during which many of the trends that had occurred in the 1920s – rising female labor force participation, increased female wages, and lower birth rates – occurred again. Women’s employment increased at a time when government policies made work life and home life more compatible (Schön 2010).
While women had systematically worked in the paid economy before it had been seen as less desirable than a woman’s role as wife and mother. Increasingly from the 1960s there was a shift in public discourse and female employment became more normative (Stanfors
2003). The Swedish welfare state developed considerably after the war with many programs that aided working families with young children. Public childcare became available and also served as a large source of employment for women. Child allowances expanded to cover more families, and parental insurance was introduced to make up for time lost from work directly after a child was born (Schön 2010).
The growing supply of female labor was further made possible by an increase in semi-skilled positions in the service sector that allowed women without previous working experience to enter the market, and the lower relative wages that women still earned made them attractive to employers, as had been the case in the 1920s (Schön 2010).
Though the 1980s, women were narrowing the education gap as they entered higher education in growing numbers. The employment gap and the wage gap were also contracting, as women were more consistently employed in positions where they had greater responsibilities than they had previously (Schön 2010).
Sweden suffered a severe crisis in the early 1990s leading to spikes in bankruptcy and unemployment for all age groups through the rest of the decade (Schön 2010). The withdrawal from the labor market of younger age groups was especially notable as many people between 16 and 24 chose to pursue higher education as an alternative to a bleak labor market; throughout the decade enrollment increased by as much as 100% in some instances (Stanfors 2003). Though GDP recovered and the economy grew after the 1993 crisis, employment did not follow. (Davis and Henerkson 2006). Unemployment was especially hard on young people (Verick 2009). One of the labor market changes that accompanied the rise of unemployment in the 1990s was a shift in the nature of employment. Permanent full-time jobs became less common, and temporary work and
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fixed-term contracts became increasingly prevalent, especially among women, youth, and immigrants (Stanfors 2003). Unemployment rose again in the early 2000s as the economy underwent rationalization in the private sector (Schön 2010), but the impact by the end of the 2000s was not as severe on young people as was the depression of the 1990s (Verick
2009).
A large proportion of the research about abortion demand has taken place in an
American context. As a result, there has been a strong emphasis on the cost of abortion, the effects of different legal restrictions, and the relationships between abortion restrictions and teen pregnancy, factors that are not as important in Sweden. Typically,
American studies have found that Medicaid restrictions cause women to cross boarders to obtain abortions (see Blank, George, and London 1996) and teen abortion restrictions do not have noticeable impacts on the aggregate abortion rate (American abortion statistics are not available on an age-specific level). See, for example, Medoff (1988, 1997, 2007),
Rothstein (1992), and Blank, George, and London (1996)
Later modeling of abortion demand has shifted from modeling the likelihood of abortion after pregnancy has already occurred to modeling the costs of abortion on the likelihood of pregnancy occurring in the first place. This later approach takes into account changes in women’s investment into more effective contraceptive techniques as the cost of terminating a pregnancy increases, and is a fuller representation of the entire fertility process and the relationship of contraception to economic factors. In many ways, the empirical evidence supports the theory that pregnancy rates are highly impacted by abortion costs, as long as the costs are not prohibitive. Legalization of abortion has led to a permanent decrease in fertility and increase in abortion rates (Levine and Staiger, 2002;
Levine, 2004; Ananat, Gruber, and Levine 2006). However, Levine and Staiger (2002) find that a shift from moderate restriction to no or almost no restriction does not greatly affect the birth rate, but does lead to an overall increase in abortions, indicating an increase in the pregnancy rate. In studies investigating the effect of American statespecific abortion costs and restrictions, Medoff (2008; 2009) finds that some restrictive
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measures decreased the pregnancy rate while others had less effect. Medoff (2011) extended this model to investigate the impact of abortion costs on women’s choice of contraceptive techniques, but found no clear relationship.
Blank, George, and London (1996) find that when Medicaid restrictions have been enacted, and then enjoined, there is still a notable decrease in abortion rates. Since the law has been nullified, there should in theory be no effect on abortion rates, since no direct restriction was enacted . However, there is clearly some force acting to deter women from obtaining an abortion. While Blank et al suggest that the passage of the bill in the first place must correlate with other factors that act to limit abortion access, it also seems plausible that the decrease in abortion could be read as in line with Levine and
Staiger’s (2002) approach, where the decrease in abortion is a contraceptive reaction by women, wherein they increase their contraceptive intensity and decrease pregnancy as the price of an abortion increases. Though these studies focus on specifically American phenomena, the underlying premise – that women change their pregnancy behavior when faced with rising costs – is important in an analysis of abortion demand.
Other studies present more universal results. In response to economic indicators, abortion is a normal good; Medoff (2007), for example, estimates that 20 percent of the decline in abortion demand in the United Sates between 1982 and 2000 was due to increases in the price alone. Abortion demand is strongly positively related to female labor force participation, as well as the proportion of women who are unmarried.
Unemployment has had mixed relationships with abortion demand; Rothstein (1992) and
Medoff (1997) found a significant negative influence of unemployment on abortion demand, implying a pro-cyclical relationship or abortion demand. Other studies (for example Medoff 2007) have found unemployment to have an insignificant impact.
Opportunity cost, especially for young women, has been an important component of abortion research. Several studies have focused on the relationship between heightened employment opportunities for teenagers in America and the teen birth rate, with the prediction that these increased opportunity costs would lessen teenager birth rates. Colen,
Geronimus, and Phipps (2006) find that during the economic boom in America during the
1990s, ten birth rates decreased significantly, and identify a significant relationship between higher employment rates and decreased teen pregnancies, especially for young
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Black women. Arkes and Klerman (2007) also find a counter-cyclical relationship between teenage fertility in America and female labor-force participation, though procyclical fertility was found among 18-20 year old black males.
Beyond the investigations of whether or not abortion costs impact pregnancy behavior, there have not been many studies that explicitly link abortion and fertility trends, especially as they relate to economic conditions. One of the few to discuss all three is Rahmqvist (2006), in his investigation of the correlations between Swedish abortion, fertility, and unemployment rates between 1980 and 2004. Rahmqvist finds a close relationship between the birth rate and abortion in Sweden from 1980 until 2004, with a correlation of 0.76 with a P value under 0.001 for the period 1980-2004. This relationship has been fairly constant, with approximately one in four pregnancies leading to abortion between 1980 and 2004. Although Rahmqvist investigated the correlation between both abortion and births and births and unemployment, he did not measure the correlation of abortion and unemployment. Additionally, because he used only correlation coefficients, there is no measurement of the directionality of the relationship or which variable is acting in which direction
Methodologically, few authors have used a time series approach when examining abortion demand. Most studies have focused on the abortion demand in a single year, except for Blank, George, and London (1996) which uses panel data from 1974 to 1988, and Medoff 1997, which uses pooled time series for 1982 and 1992. This study will be different in the time frame examined, from 1939 to 2010, its time-series approach, and its examination of medium-term change, as opposed to year-to-year differences.
The theoretical bases of abortion demand models are fertility demand theories and the cost-benefit analysis of a marginal child. Each family will maximize the utility of each child by controlling, as much as possible, the number of children; the expenditures made on each child, which indicates the quality of each child; and expenditures on other goods. The decision of whether or not to have an abortion is in essence the same decision as whether or not to have a child. However, the abortion decision is different from other
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fertility cost-benefit analyses because it takes place when a woman has already become pregnant; while other decisions about fertility control are made before sexual activity, abortion occurs after. This allows the decision to be made with more complete information about the implications of the pregnancy or a potential birth. Thus, in its basic framework, while the decisions surrounding abortion are no different than other fertility decisions, the context in which decisions take place are unique (Levine 2004).
The demand for children refers to the optimal number of children that a couple desires given no impediment either to having children or preventing births (Becker 1991).
Households attempt to maximize the utility function
U = U(n, q, Z) in which n is the number of children; q is the quality of each child, assumed to be the same for all children of the same household; and Z is household consumption of all other commodities. The household budget constraint is represented by
P n n + π z
Z = I in which P n
represents the costs of bearing and rearing children and π z
is the cost of goods
(Z), and I is total income. Parents will maximize the budget constraint so that the marginal utility of an additional child is equal to the marginal utility of additional consumption of other goods, balancing the cost and benefit of the marginal child. An increase in the cost of children would thus decrease the demand for children, while at the same time increasing demand for other goods and services, Z. Long-term declines in fertility are thus a function of lowered demands for children. This itself is largely tied to increasing costs of raising children, and an increasing demand for children of higher quality (Becker 1991).
This demand for children of higher quality has disrupted the long-standing relationship of income and fertility, as demand for quality children increases with income, driving down the quantity of children demanded. There are many factors that have contributed to increased costs of childbearing over the last century. According to
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Becker (1991), the most influential has been the rising value of women’s time and growing influence of female opportunity cost on the fertility decision.
Thus at the heart of the abortion demand model is the role of female opportunity cost. Opportunity cost in fertility models assumes that women invest more time into child-related activities than men. The value of a woman’s time increases as her earning power rises or she invests time in education or the work force; as the value of time increases, so does the cost of spending time on raising children. This leads to the expectation that fertility will fall when women have higher relative earning power or are heavily engaged in careers or education. Lower relative wages, decreased income or withdrawal from education or the work force lessen the cost of having children, and so fertility is expected to be higher when women earn and work less. Because they lower the demand for children, high opportunity costs are expected to have a strong positive impact on abortion demand. Likewise, abortion demand can be expected to fall when opportunity costs are lower.
Opportunity cost is commonly measured through several different variables.
Perhaps the clearest are women’s relative wages and female labor force participation.
These directly measure women’s earning power in relation to men, women’s participation in the labor market, and how much potential income must be sacrificed when time must be taken off to care for a child. GDP, as a measure of income, also has an opportunity cost component. This is essentially the same effect as women’s relative wages; the higher per capita GDP the more must be sacrificed in order to devote time to a child. However, this effect is not as pure in measurements of GDP because GDP also measures the impact of pure income. Education, as developed by Michael (1973), also has an opportunity cost component. Women engaged in higher education are likely to be highly inconvenienced by a pregnancy and childbirth, and the subsequent care of children.
While the cost-benefit analysis of fertility is certainly important to understand in the context of the abortion decision, it is divorced from the context of unwanted pregnancies. Fertility theory typically assumes that all fertility decisions are planned, conscious, and executed with access to perfect information and perfect contraceptive
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technology. It tends to ignore the set of decision-making processes that precede a birth: the cost-benefit analysis and decision-making process surrounding the use of contraceptives and unwanted pregnancies. An understanding of the costs and benefits that lead couples to not contracept provides a greater understanding of why women choose to have abortions. This section will rely on Luker’s (1978) study to outline many of the potential costs of contraception and possible benefits of an unwanted or aborted pregnancy. Luker, through a series of in-depth interviews at a single abortion clinic in
California, develops theories surrounding both the costs of contraception and the potential benefits that a pregnancy, even a pregnancy that ends in abortion, hold for a woman. In her framework, the decision to use less than effective contraceptive techniques is still a decision reached through rational decision-making processes. An end result of unwanted pregnancy or abortion does not necessarily mean that a woman or couple failed in their goals, as the sole goal of sexual intercourse or contraceptive use is not always to avoid becoming pregnant.
One of the essential components in the cost-benefit decision-making analysis is time. Outcomes have different costs or benefits at varying distances from the time at which decisions are made. Typically the likelihoods of future outcomes, such as the probability of pregnancy or the necessity of an abortion, are discounted, lowering the perceived cost. This was especially evident in participants’ tendencies to stress current costs and downplay future costs, especially regarding the costs of contraceptives and the possibility of becoming pregnant.
Many of the costs of contraception are fairly straightforward. One of the most obvious is the price, especially over an extended period of time. The costs of obtaining the method can also be a barrier to use, including time spent visiting doctors, embarrassment or unpleasant situations while buying contraception, or the costs associated with obtaining knowledge on contraception in the first place.
Many obstructions to contraceptive use come from prevailing social attitudes surrounding sex, contraception, and a woman’s expected relationship to sex. For many women these costs were prohibitive. These social costs are likely to vary both across time and by location, as well as individually. Planning for contraception was incompatible with many women’s feelings of propriety regaring how they, as a woman, should behave.
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Women also were hesitant to use methods such as the pill or an IUD outside of a stable relationship. Luker described this ‘advertisement of availability’ as a loss of female bargaining power.
The physical and biological side effects was another set of costs that frequently turned women away from effective contraceptive methods. Many women in Luker’s study expressed that they had stopped using the pill because of experienced or anticipated side effects. This result is consistent with the Swedish experience; Ingelhammar et al
(1994) and Helström et al (2003) both find that a large proportion of Swedish women in relationships who were seeking abortions had discontinued hormonal contraception or
IUDs because of discomfort, side effects, or concerns about possible side effects.
Many couples found the actual implementation of contraceptive devices to be a barrier to use. Interrupting intercourse to access a birth control method ruined the sexual experience, and many felt the ‘cold’ planning and lack of spontaneity was an unnatural component of intimate relations. Partners’ resistance also presented significant costs for several women who’s partner would not accommodate their request for contraception, whether by refusing to use a condom or being unwilling to wait for the protection of the pill.
While not immediately obvious, there are many potential benefits to an unwanted or unplanned pregnancy. Though many of these potential benefits vanish when the decision to have an abortion is reached there are still significant reasons why an aborted pregnancy may have value to a woman. On a personal level, a pregnancy can affirm selfidentity by ‘proving’ that one is a woman, especially valuable to some in periods when gender roles are changing or becoming less distinct. Similarly, it can be proof of fertility, which was important to many women who feared infertility either as a side effect of the pill or because of a failure to conceive after extended periods of non-protected sex.
A pregnancy can also have strong implications on a woman’s relationship to others in her life, especially with her immediate family or her romantic partner. In the context of a family, a pregnancy can force parents to acknowledge a pregnant daughter as an adult and an independent and sexual person. For single women, a pregnancy may be the impetus that leads to marriage. For women who are married, it might be the jumpstart to beginning a family with a husband who is not openly committed to having
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children. If pregnancies in this situation end in abortion, then the pregnancy allows a woman to gather more information about her relationship and the commitment of her partner. These benefits of pregnancy are not often explicitly part of the decision making process, but their presence can act to tip the scales away from contraception if the initial costs of contraception are high enough.
A woman’s ability to ‘undo’ an accidental or unwanted pregnancy – and her knowledge that such a method or option exists – can also lead to higher risk-taking and discounted future costs. When the highest potential cost of preventing an unwanted birth is legal abortion it is easier to justify risk-taking. Of course, there are costs to an abortion.
According to Luker (1978), the physical and medical costs of abortion are hardly different than contraception; abortions are safe and result in fatalities less frequently than completed pregnancy. The costs associated with an abortion are primarily socially construed, through high prices, invasion of personal privacy, and social stigma.
This model follows established abortion demand models by including measures for income and female opportunity cost. It is augmented by Easterlin’s measure of population distribution (see Easterlin 1980) and measures of the Swedish business cycle and structural change. The model is adapted from those used by Medoff (1988, 1997) and
Rothstein (1992). These previous models involve variables that measure opportunity cost effects such as income and women’s labor force participation, as well as factors that are likely to impact personal preferences, such as religion or region of residence. They also include American-specific measures such as Medicaid funding, legal status of abortion and the price of an abortion. A measure of the number of women who are single is also frequently included. Further variables such as education or unemployment are typically included as secondary variables in extended models.
Many of these factors are not appropriate in a Swedish context, and so are not included in this model. Obviously, Sweden does not have any difference in the monetary coverage of abortion by region, and so a variable such as those that relate to Medicaid funding is not useful. Neither does Sweden have any difference in laws pertaining to
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abortion either by region or age, so these measures are also excluded. Additionally, abortion in Sweden is not costly; medical abortion as of 2002 was available at a cost of about $30 USD (Jones and Henshaw 2002). Finally, marriage and childbearing are not as closely connected in Sweden in recent decades, and childbearing often precedes marriage, if marriage occurs at all (Ohlsson 2009). Therefore a woman’s singleness is not a very strong indicator of whether or not she has a partner, or whether or not she is likely to abort or give birth. Therefore, no variable regarding marriage status is included in this analysis.
There are also a few elements that are missing from these models that have been added in this analysis. A measurement of cohort size, based on Easterlin (1980) has been included to measure the effect of the size of a cohort and competition on the likelihood of abortion. Additionally, a variable to measure change in the structural composition of the
Swedish economy is included.
There are some variations of the models used for different periods, as the quality of statistics and the appropriateness of indicators change over time. While the models are written with the abortion rate as the dependent variable, the abortion ratio will also be tested. The main model for the period 1939-2010 will be
𝐷𝑙𝑛𝐴𝑅𝐴𝑇𝐸 = 𝛼 + 𝛽1𝐷𝑙𝑛𝑉𝐴𝐶𝑅𝐴𝑇𝐸 + 𝛽2𝐷𝑙𝑛𝐺𝐷𝑃 + 𝛽3𝐷𝑙𝑛𝑅𝐸𝐿𝑊𝐴𝐺𝐸 + 𝛽4𝑙𝑛𝐶𝑂𝐻
+ 𝛽5𝐷𝑙𝑛𝑆𝐸𝑅𝑉 + 𝜀
This study will utilize a basic OLS regression model. The ordinary least-squares regression model is one of the most often used and most accessible methods of estimating regression relationships. However, it is dependent upon several assumptions. Many of these assumptions are that the model and variables are specified correctly and are valid approximates of what is to be estimated. One that is harder to prepare for is noncorrelation between the explanatory variables and the error term. It is also likely to have some inconsistencies due to the role of tastes and preferences influencing the variables.
Further, the OLS method assumes a linear relationship (Feinstein and Thomas 2002).
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Data is drawn primarily from official Swedish statistics from both databanks and printed sources. Obviously, there are some limitations to the data in the periods available, the frequency with which it is collected, and the level at which it is recorded.
This study uses two dependent variables to measure abortion demand. The first, the abortion rate, measures abortions per 1000 women aged 15-44. The second, the abortion ratio, is the number of abortions for every 100 pregnancies for women 15-44.
Because women of different ages are likely to have very different fertility responses to economic or other conditions, age-specific abortion demand will also be tested after
1975, when these figures become available. Using both measurements of abortion demand in Sweden is valuable, because each provides a different insight. The abortion rate per 1000 women indicates the changing levels of abortions for a constant population, and will allow for analysis of increasing or decreasing numbers of abortions over time.
Abortions per 100 conceptions controls for changes in contraceptive technology and accounts for total conceptions, juxtaposing abortions against births as a pregnancy outcome. Comparing these two measures during similar time frames also offers some insight into the movement of conception and births in general. If the abortion rate increases but the abortion percent is relatively constant or declines, as happened in the early 1990s, it can be inferred that the growth in the abortion rate is the function of greatly increasing conception rates, rather than a substitution of abortions for births. The relationship of abortion to conceptions is important in the analysis of many variables, where a decreased abortion rate might mean that the variable is acting to repress conceptions in general or that it is acting to encourage births over abortions as pregnancy outcomes. Since these two reactions are essentially opposite mechanisms, it is important to be able to assess the true effects. These data are available from Sweden’s National
Board of Health and Welfare, or Socialstyrelsen , and have been augmented by Robert
Johnston’s historical Statistics.
Income will be measured by per capita GDP. This measurement incorporates the entire population of Sweden, as opposed to workers in just one sector. It is also a way to take into account the shifts of the economy as a whole. Because abortion is a normal good (Medoff 2007), abortion demand is expected to increase with rising GDP. Children
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are also a normal good that increases with income, and so it could be the case that conceptions as a whole will increase in periods of high and growing GDP. The income effect of GDP is offset by an opportunity cost effect, as higher GDP also leads to a higher cost of having children.
Female opportunity cost will be directly measured through women’s relative wages. Female labor force participation figures are available, but only from 1963 onwards, and so they are not used. Additionally, the growth of services will capture some of the same effect as female labor force participation (Schön 2010).
The business cycle will be measured through employment, GDP, and the growth of the services sector in the Swedish economy. Employment and the cyclical changes in the Swedish labor market are measured in two forms: vacancies measured by the number of vacancies divided by the entire population aged 16 to 65, and are used for the full period from 1939 to 2010. Because there are no figures for the first half of the period for labor force participation by gender, the entire population, rather than one gender, is an appropriate measure. The unemployment rate is used for the regressions from 1960 to
2010 and 1975 to 2010. This is due to changes in the way each variable has been recorded in Sweden over time that make each more appropriate to different time periods
(Stanfors 2003). The change in the composition of the Swedish economy is measured by the share of services. This variable is particularly useful in measuring the growing labormarket opportunity for women.
Relative cohort size, derived from Easterlin (1980), estimates the amount of intergenerational competition encountered by young people that may impact their decisions regarding future family formation, and thus abortion. This is measured by the ratio of Swedish men ages 35 to 64 to men ages 20 to 34. While there are several different cohort ratios that could be used, Stanfors (2003) argues that the age range 20-34 for young workers is appropriate in a Swedish context, where a long tradition of compulsorily education have led to later entries into the labor market, marriage, and age at first birth. Female entrants into higher education measures the number of women entering higher education for the first time per 1000 women ages 18-34. This age range is selected to coincide with the measure of cohort size discussed above, but is slightly expanded to include younger women entering higher education.
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Abortion data come from two places: the Swedish National Board of Health and
Welfare, or Socialstyrelsen , and Robert Johnston’s historical abortions statistics.
Abortion data from Socialstyrelsen comes from three sources: the published abortion reports from 1997 and from 2011, and from the online downloadable database. All statistics for the years after 1970 come from the National Board of Health and Welfare.
Data from before 1970 comes from Robert Johnston’s historical abortion statistics.
Aggregate rates per 1000 women ages 15-44 are calculated by the author using population data from Statistics Sweden. Age-specific rates in five year groupings are the official age specific rates generated by the National Board of Health and Welfare. Birth figures also come from the Statistics Sweden online database. Female relative wages are calculated by Maria Stanfors. Female entrants into higher education are likewise provided by Maria Stanfors and updated from Statistics Sweden database by the author. Vacancies are obtained from the Swedish Statistical Yearbooks from various years. GDP, unemployment, and sector change come from Schön and Krantz Swedish Historical
National Accounts.
This study will focus on medium-term variation. To account for the relative changes taking place, all variables are logged. Variables were then tested for stationarity at logs and at first differences. All variables were tested using both Dickey-Fuller and
Phillips-Perron unit root tests. Only relative cohort size was stationary at the logged value, but the first difference was not stationary. Therefore, the log of the cohort size has been used, and the first difference for all other variables. This presents some inconsistency in the interpretation of the results, and is one of the shortcomings of this particular model.
In the next section the theoretical implications of the variables are discussed. Only the form of the variables used in the regression analysis is displayed, unless the development of the variable is of particular importance historically or in the analysis.
Graphs of all the variables at levels can be found in the appendix. For the sake of convenience, the abortion rate is used in graphical comparisons of abortion and other
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variables through this study. However, both measures are discussed in the regression analysis.
Figure 5.1. The abortion rate and abortion ratio in Sweden 1939-2010
30
25
20
15
10
5
0
Abortion ratio Abortion rate
Source: Socialstyrelsen and Johnston
The two measures of abortion demand used in this study are the abortion rate
(DlnARATE), measured by abortions per 1000 women; and the abortion ratio, or abortion percent (DlnAPER) measured by abortions per 100 pregnancies.
Before 1975, the development of both the abortion rate and the abortion ratio was virtually identical due to the rapid increase in the number of abortions performed. After
1975, changes in the abortion rate are similar to changes in the raw number of abortions; this indicates that the changes in the number of abortions was dependent not on changes in the demographic makeup of Sweden, for which the abortion rate compensates, but were instead due to other influences. The abortion ratio is significantly different from the abortion rate after 1975, especially from 1988 to 1996. This period was a large baby boom (see fig. 2.1), when birth rates increased significantly but abortions declined. The abortion ratio after 1996 is almost flat and the proportion of pregnancies ending in abortion is just under 26 percent. Given the huge swings in conception and fertility
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during this period, especially in the late 1980s and early 1990s, this indicates that births and abortions in this period have moved in more or less the same ways at the same times.
Unemployment in this study is both an indicator of the business cycle and a measure of opportunity cost. It has been selected because of the likelihood that the close household- and individual-level experiences of unemployment are more likely to have an impact on fertility decisions than other business cycle measures that are less tangible to individuals (see Sobotka, Skirbekk, and Philipov 2011). Thus, unemployment is a strong proxy of the business cycle for household level decisions such as fertility.
Unemployment has a mixed theoretical relationship with childbearing and captures two contradicting aspects of the fertility decision. First, it represents an income effect; higher unemployment is assumed to lead to diminished income, which in turn makes couples less able to afford a child. This follows from Becker (1991), in which he establishes a relationship between increased income and child demand. This effect is expected to produce a pro-cyclical relationship between the business cycle and fertility, and a lowered demand for children during periods of unemployment would then be expected to increase the demand for abortion.
Alternatively, Becker (1991) also proposes that employment creates a high opportunity cost of having a child, especially for women. An individual who is employed must take time away from work in order to raise a child and surrender potential earnings or other opportunities. This would make those who are gainfully employed potentially less likely to have a child. In this situation, unemployment represents a lowered opportunity cost to childbearing and counter-cyclical fertility, and abortion demand would be expected to decrease when unemployment rises.
These effects are further complicated when it comes to gender. While male employment has usually been tied to pro-cyclical fertility trends, female unemployment is less straightforward. This is because men have not traditionally been the main childcaretakers, and so their participation in the labor market is not as affected by the birth of a child. Instead, men’s primary-earner status has meant that high levels of male employment are associated with higher income, and greater probability of having
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children. Women have traditionally not been the primary household income-earner; female employment has often been considered a source of supplementary income, and fertility and women’s employment have historically been counter-cyclical (Becker 1991).
In this framework, periods of unemployment for women present a window in which opportunity costs are lower (Rothstein 1992, Medoff 1997). Giving birth while employed would mean taking time away from potential earnings to spend at home with a child; giving birth when already unemployed mitigates this opportunity cost. Thus, a traditional view of women’s employment would suggest that fertility should increase as women’s unemployment decreases, in turn suggesting lower abortion rates.
However, the relationship between abortion and unemployment is even less direct than the relationship between fertility and unemployment. Apart from the conflicting theoretical implications of unemployment on fertility, the unemployment rate has several theoretical effects on abortion demand. On one hand, abortion itself is a normal good, and so demand would be expected to decline as income decreases due to unemployment. On the other hand, giving birth presents a much greater expense than an abortion, and so abortion could be expected to rise as couples decide to avoid the high costs of a child. A third alternative is that abortions and births together would decrease due to couples being more careful to avoid conception during financially difficult times (Rothstein 1992). This final prediction is in line with the theory proposed in Kane and Staiger (1996) and several follow-up studies (Levine and Staiger 2002, Levine 2004) in which abortion acts as a form of insurance against unwanted births, and increased costs of pregnancy outcomes cause both births and abortions to decrease. This situation would imply that it is conceptions that move with the business cycle, as opposed to births or abortions individually.
In this study, general vacancies (DlnVACRATE) and unemployment
(DlnUNEMP), as opposed to female unemployment, will be used as a business cycle indicator. Rothstein (1992) uses general unemployment on the basis that the general unemployment rate is a better indicator of the state of the economy, and thus a more appropriate influence on abortion demand. The unique female opportunity cost factor will be estimated through outer variables such female relative wages. This approach assumes a largely causal effect of employment on fertility, and less effect of fertility on
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Figure 5.2. Growth rates of the abortion rate and vacancy rate in Sweden 1939-2010
0,1
0
-0,1
-0,2
-0,3
0,4
0,3
0,2
0,4
0,2
0
-0,2
-0,4
-0,6
-0,8
GR ARATE GR VACRATE
Source: Socialstyrelsen,
Swedish statistical yearbook (various years), author’s calculations employment. Though Becker (1991) suggests that there is some causation in the opposite direction, he finds that the majority is from employment to fertility.
The visual relationship between the growth rate of the vacancy rate and the abortion rate is not particularly strong. This lack of an obvious relationship may be because of the complicated and often conflicting theoretical relationships between employment and abortion. It could also be that employment has differential effect by age that are not visible in the aggregate abortion rate. See the appendix for graphs of the unemployment rate and abortion rate.
Per capita GDP (DlnGDP) is used as an estimate of income, following from
Becker (1991). This represents a general income effect and is indicative of changing living standards and wellbeing. Many abortion studies use actual income; Rothstein
(1992) for example uses per capita disposable income for all persons. Other studies prefer to focus on female income specifically; Medoff (1988; 1997; 2007) uses average female income. However, with historical statistics, often some sectors of income are unavailable;
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Figure 5.3. Growth rates of the abortion rate and GDP per capita
0,4
0,3
0,2
0,1
0
-0,1
-0,2
-0,3
GR ARATE GR GDP
Source: Socialstyrelsen, Schön and Krantz, author’s calculations per capita GDP incorporates all sectors and also provides a useful business cycle component.
Income and abortion demand are typically positively related (Medoff 1988; 1997;
2007, Rothstein 1992). Graphically, it can be seen that the growth rate of GDP has a positive relationship with the growth rate of the abortion rate, especially before 1950 and after 1975. Thus, we can expect a strong positive influence of GDP on abortion demand.
The role of female opportunity cost in fertility decisions was introduced by
Becker, and its inclusion changes fertility models from pro-cyclical model, reliant on male income, to counter-cyclical model, determined by female opportunity costs
(Stanfors 2003). Female relative wages measures (DlnRELWAGE) the ratio of women’s income to men’s income, or the relative financial value of women’s employment. Higher relative wages represent a high opportunity cost to having children and starting a family, as a woman who is making more money will face greater lost income or decreased work opportunities from taking time off to give birth and raise a child. Becker (1991) proposes that a majority of fertility change over the last century has been due to increases in women’s earning power, and the increased opportunity cost that childbearing represents.
While abortion rates are not the same as these fertility shifts, they also likely to
0,25
0,2
0,15
0,1
0,05
0
-0,05
-0,1
-0,15
-0,2
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experience a strong influence from changing relative income as women elect to defer and reduce births.
The role of relative wages in Easterlin’s (1980) models also predict that increased relative wages will lead to lowered demand for children. In this model, women’s labor force participation and relative wages increase when male income decreases; essentially women’s relative income rises when households are less financially secure. Thus, it is not increased job prospects for women that draw them into the labor market, but diminished job prospects for men that force women to work. Therefore, in Easterlin’s model, the impact of high female relative wages on fertility decisions is not one of the opportunity cost from time taken away from employment, but an income effect, in which women only participate in the labor market in times of lowered male income. This, too, is likely to lead to greater abortion demand, as those with lowered means are more likely to defer childbearing.
Both the effect of opportunity cost and the marginal role of female labor are less likely to be as strong in Sweden in recent decades, but may have played a strong part in the fertility decision before the middle of the 1960s. In the first instance, Sweden has developed many programs which lessen the costs of giving birth and raising children through generous paid parental leave time and free childcare, and better enable women to
Figure 5.4. Female relative wages and the abortion rate in Sweden 1939-2010
25
20
15
10
5
0
0,95
0,9
0,85
0,8
0,75
0,7
0,65
0,6
Abortion Rate
Source: Socialstyrelsen, Maria Stanfors.
Relative Wages
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maintain their labor market attachment (Schön 2010). In the second instance, female labor market participation in Sweden over the later part of the twentieth century is different than what Easterlin assumes in his model. In Sweden women have been a regular part of the labor force in increasing numbers since the 1960s, and two-earner households have become standard (Stanfors 2003). Thus, female wages are not an auxiliary but an expected part of a family’s income.
It is important to understand the huge increase in female relative wages through the 1960s, at the time that both the service sector was growing and the abortion rate was increasing. Figure 5.4 illustrates this huge growth, and the very similar movements of the two variables from the beginning of the period through the early 1970s. This concurrent increase is the clear dominant feature of the relationship, and persists in the growth rates of the two variables (figure 5.5). The strongest growth rates, though not the highest levels, occurred in the late-1940s and 1950s, when labor shortages following the second world war drew many women into the labor force (Stanfors 2003, Schön 2010). At the same time, abortion had recently been legalized, and the abortion rate was rapidly increasing. After the mid 1970s, the relationship becomes less closely linked. The strong association between the two variables over the entirety of the period predicts that there will be a strong influence of women’s relative wages on abortion demand.
Figure 5.5. Growth rates of the abortion rate and female relative wages in Sweden
1939-2010
0,4 0,04
0,3
0,03
0,2
0,1
0
0,02
0,01
0
-0,1
-0,2
-0,3
-0,01
-0,02
GR ARATE GR RELWAGE
Source: Socialstyrelsen, Maria Stanfors, author’s calculations
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While the growth of female relative wages and the abortion rate are incredibly similar before the 1970s, it is important to remember the greater social changes that were occurring in Sweden during this time and the influences these changes might have had on both of these variables. Additionally, changes in the abortion rate were not a ‘free market’ response, as abortion was restricted until 1975.
Easterlin (1980) proposes that the size of the cohort in which a person is born has profound impacts on a person’s prospects and choices throughout life, especially in young adulthood. Easterlin’s cohort hypothesis states that members of larger cohorts will face more competition gaining access to opportunities and resources, such as jobs and professional advancement. This leads to lower relative income and a postponement of life plans such as marriage and child bearing. Conversely, members of small cohorts will have an easier time getting better-paying jobs and will tend to marry and have children earlier. Much of this effect is due to young couples’ aspirations to reach the same degree of affluence as their parents, using the standard of living they experienced growing up as a baseline for what is necessary to begin their own family. Thus, it is not absolute income that influences a young couple’s feeling of stability, but income relative to that in their parental home. A failure to attain the standards of living that a couple experienced growing up will cause them to delay childbirth and will be more likely to raise abortion demand.
Easterlin measures the cohort effect (lnCOH) through the ratio of the older cohort to the younger cohort, with larger older cohorts representing increased barriers to younger people rising in the work force. This measurement assumes that older workers are preferred to younger workers, and that the two are not perfect substitutes (Stanfors
2003). An increase in the ratio of men aged 35-64 over those aged 20-34 signals an increase in the competition among those entering the labor market and potentially starting a family. Thus, a higher value is expected to lead to higher abortion demand.
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Figure 5.6. Growth rate of the abortion rate and percent change of cohort size in Sweden
1939-2010
0,4 0,04
0,3
0,03
0,2
0,1
0
-0,1
-0,2
-0,3
0,02
0,01
0
-0,01
-0,02
-0,03
-0,04
-0,05
GR ARATE Percent change COH
Source: Socialstyrelsen,
Statistics Sweden, author’s calculations
There is a strong visual relationship between the growth rate of the abortion rate and the cohort ratio (figure 5.6), especially from the middle of the 1950s to the late
1980s, although it is less strong in the beginning and the end of the period. T he visual relationship predicts a strong influence of relative cohort size on abortion demand.
The growth of the service sector (DlnSERV) led to increased opportunities for women’s employment. This variable will measure the growth of the services sector as a proportion of the total Swedish economy. The service sector grew especially during the
1960s, and provided a large number of semi-skilled jobs that helped propel women’s entry into the labor market (Schön 2010). Because services were such a strong factor in increasing female employment and women’s opportunity cost the growth of services is expected to have a positive influence on abortion demand.
Both the abortion rate and services have strong growth through the 1960s, with even closer development in the 1990s and later, though the relationship is very weak before the 1960s (fig. 5.7). Because of this, the growth of services is likely to have the strongest influence on abortion demand in the 1960s, rather than earlier in the period.
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Figure 5.7. Growth rates of the abortion rate and services in Sweden 1939-2010
0,4 0,1
0,3
0,2
0,1
0,08
0,06
0,04
0,02
0
-0,1
-0,2
-0,3
0
-0,02
-0,04
-0,06
GR ARATE GR SERV
Source: Socialstyrelsen , Schön and Krantz, (calculations by Maria Stanfors), author’s calculations
Entry into higher education (DlnHIED) is a strong indicator of both opportunity costs and women’s preferences. Michael (1973) proposes that time-intensive activities that make relatively little use of an individuals acquired human capital, such as raising children, represent a high opportunity-cost to the individual. Both childrearing and education are highly time-consuming activities, which makes engaging in both simultaneously difficult.
Michael (1973) finds that in general a woman’s education level is negatively associated with the probability of her giving birth and that individuals with higher levels of education, especially women, tend to postpone both marriage and children. It is typically assumed that the factors that lead a woman to want to postpone births will lead her to seek an abortion in the event of an unintended pregnancy (e.g., Medoff 1988; 2008,
Rothstein 1992).
Michael (1973) presents two different ways in which a person’s education impacts their fertility outcomes and a couple’s use of contraceptives. First, he proposes that those with higher education are more likely to use contraceptives successfully, which
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would theoretically lower a couple’s demand for abortion. Second, he highlights the opportunity cost effect for women in education, which would imply increased abortion in the event of pregnancy. Michael also connects the relationships of income, education, and fertility; couples that are more highly educated are more likely to make more money; those with higher incomes typically demand fewer, but higher quality children, which impacts a couple’s demand for contraception and abortion as education increases.
Medoff (1997), building off of Michael (1973), proposes that education can have differential impacts on a woman’s abortion demand throughout her life. According to
Medoff, it is likely that education will increase abortion demand for younger women, who face higher opportunity costs of giving birth. For older women, Medoff proposes that education is likely to lead to more efficient contraceptive use, thus leading to fewer unwanted pregnancies, and a lower abortion demand. However, since this variable measures entrants into higher education, rather than average years of education, it is unlikely to reveal the influence of education on abortion at higher ages.
Several studies have used completed high school education as a variable to measure abortion demand in the United States (e.g, Medoff 1988; 1997; 2008, Rothstein
1992). These studies have typically found non-significant negative influences of
Figure 5.8. Growth rates of the abortion rate and female entrants into higher education in
Sweden 1939-2010
0,6
0,4
0,2
0
-0,2
-0,4
-0,6
-0,8
GR HIED GR ARATE
Source: Socialstyrelsen, Högskoleverket
, author’s calculations
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education on abortion demand. However, it is possible that entry into higher education has a different effect than completing high school, as this is arguably a stronger indicator of preferences than more standard compulsory education.
The visual relationship between the growth rate of the abortion rate and the growth rate of female entrants into higher education is not strong, and so it is not clear whether there will be a strong statistical relationship between the two variables (fig. 5.8).
As mentioned before, the model for the period 1939-2010 for both the abortion rate and the abortion ratio is
𝐷𝑙𝑛𝐴𝑅𝐴𝑇𝐸 = 𝛼 + 𝛽1𝐷𝑙𝑛𝑉𝐴𝐶𝑅𝐴𝑇𝐸 + 𝛽2𝐷𝑙𝑛𝐺𝐷𝑃 + 𝛽3𝐷𝑙𝑛𝑅𝐸𝐿𝑊𝐴𝐺𝐸 + 𝛽4𝑙𝑛𝐶𝑂𝐻
+ 𝛽5𝐷𝑙𝑛𝑆𝐸𝑅𝑉 + 𝜀
Each abortion demand variable was tested with and without a time trend. The time trend was insignificant in both models, and did not greatly impact the significance or coefficients of the other variables, and so was not included in this model. Durban-Watson tests were run on all regressions, and some serial correlation was found. Robust error checks were performed on all regressions, and as these had only minor impacts on the results, the original regressions are presented here.
The results for the abortion rate (DlnARATE) and the abortion ratio (DlnAPER) from 1939 to 2010 in Sweden are very similar (table 1). In both models, GDP (DlnGDP), relative wages (DlnRELWAGE), and the cohort ratio (lnCOH) are significant at the one percent level, along with the constant. The explanatory powers of the models indicate that approximately forty nine percent of the changes in the abortion rate and fifty percent of the changes in the abortion ratio can be explained by these factors, and the F value indicates that both models are highly significant. Because the models are similar, they will be discussed together.
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DlnVACRATE
DlnGDP
DlnRELWAGE lnCOH
DlnSERV
_cons
DlnARATE DlnAPER
-0.005
(0.06)
1.506
(3.27)***
6.446
(5.08)***
0.445
(4.16)***
-0.139
(0.17)
0.316
(3.99)***
-0.055
(0.72)
1.522
(3.50)***
6.258
(5.22)***
0.431
(4.27)***
-0.214
(0.27)
0.303
(4.05)***
R squared adjusted R squared
N
F stat
Prob > F
0.52
0.49
71
14.27
0.00
* p <0.1; ** p <0.05; *** p <0.01
Source: Author’s calculations
0.53
0.50
71
14.91
0.00
In each model three of the effects are highly significant; GDP per capita, female relative wages, and cohort size. Vacancies and the services sector are not significant in either model. The strongest effect is female relative wages; an increase in the growth rate of relative wages increases the growth rate of both abortion measurements by over six percent. This is in accordance to theory, where women’s earning potential creates a strong opportunity cost in the event of pregnancy and can influence the decision to have an abortion. Per capita GDP is the next most influential variable, with a one percent increase in the growth rate raising the growth rate of both abortion measurements by over one and a half percent. This indicates a similar opportunity cost effect, overriding the effect of income. The impact of cohort size is in line with the theoretical predictions: increased competition for younger age groups increases the general abortion rate.
However, this effect is comparatively not very large, increasing the growth rate of the abortion rate less than half a percent as the cohort ratio increases by one percent. The
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DlnVACRATE
DlnGDP
DlnRELWAGE lnCOH
DlnSERV
_cons
R squared adjusted R squared
N
F-Stat
Prob (F-Stat)
DlnARATE
0.224
(1.24)
1.643
(2.70)**
9.932
(6.37)***
0.622
(4.33)***
0.234
(0.25)
0.421
(4.17)***
0.76
0.72
35
18.85
0.000
*p <0.1; ** p <0.05; *** p <0.01
Source: author’s calculations
DlnAPER
0.135
(0.65)
1.655
(2.38)**
9.051
(5.08)***
0.554
(3.37)***
0.158
(0.15)
0.372
(3.22)***
0.67
0.62
35
11.88
0.000 significant effect of the cohort ratio can also be assessed in conjunction with female relative wages; one of Easterlin’s proposed effects of larger cohorts was decreased male wages, leading to higher relative female wages. Thus, the strong impact of the growth of relative wages discussed above could also be due to women choosing to abort when poor economic conditions force them to enter the labor force in greater numbers. That the cohort effect and relative wages both significantly influence abortion demand is thus unsurprising.
While vacancies have no statistically significant impact on the abortion rate, this, combined with the small coefficient, could be a result of the conflicting theoretical influences of vacancies on abortion. Unfortunately, it is not possible to draw any clear conclusions about this from the model directly. The impact of services is also not significant.
Because of the changes in the legal status of abortion and the social development in the period from 1939 to 2010, it is prudent to look at this extended period in two parts to assess whether the results from this long period are obscuring changes in abortion
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demand before and after the law change in 1975. Thus the periods from 1939 to 1974 and from 1975 to 2010 will be assessed with the same model as was used for the long period.
The results for the first half of the long period (table 2) are stronger than the results for the entire period. One of the clearest indicators of this is the increase of the adjusted R-squared from 0.4866 to 0.7241 for the abortion rate, and from 0.498 to 0.6155 for the abortion ratio, indicating that the model explains a greater proportion of the variation in abortion demand during this first part of the period than during the full period. Furthermore, while the same variables are significant in this set of regressions as in the longer period, the coefficient is stronger for each one. Relative wages increased dramatically from 6.45 to almost ten for the abortion rate and from 6.25 to over nine for the abortion ratio, meaning that when the growth rate of relative wages increases by one percent, the growth rate of the abortion rate and the abortion ratio increase by almost ten percent and over nine percent, respectively. The coefficients for GDP and cohort size also increased, though not as dramatically as female relative wages.
The results for the second part of the period are much weaker (table 3). In these models the adjusted R-squared values have fallen significantly, and neither model is significant at the ten percent level. The model for the abortion rate from 1975 to 2010 is especially poor, with a negative adjusted R-squared and no significant variables. Clearly this is not a good model of abortion demand in the second half of the period, and so it would not serve to draw strong conclusions from it. The results from the model of the abortion ratio in this later period are also much weaker than the results in the full model and the first half, with an adjusted R-squared value of 0.11 and an F-statistic of 0.129.
However, these results are not quite so poor as to disallow some inferences, if not concrete results.
In the model of the abortion ratio from 1975-2010 both cohort size and the growth of services have significant results. Cohort size is the only variable that continues to have an effect in the second half of the period. The effect in this model, while according to predicted theory, is smaller than it has been in other periods, with a coefficient of only
0.1251.
The growth of services becomes significant for the first time, decreasing the growth rate of abortion by 1.5 percent as its growth rate increases by one percent. This is
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DlnVACRATE
DlnGDP
DlnRELWAGE lnCOH
DlnSERV
_cons
DlnARATE DlnAPER
0.003
(0.06)
0.272
(0.80)
-0.288
(0.34)
-0.010
(0.14)
-0.555
(0.51)
-0.003
(0.06)
-0.040
(1.12)
0.354
(1.29)
0.818
(1.20)
0.125
(2.21)**
-1.559
(1.76)*
0.101
(2.18)**
R squared adjusted R squared
N
F-Stat
Prob (F-Stat)
0.11
-0.04
36
0.75
0.589
*p <0.1; ** p <0.05; *** p <0.01
Source: author’s calculations
0.24
0.11
36
1.87
0.129 not in line with theory, which proposed that the effect of services is largely to increase women’s employment and wage-earning potential, and thus contribute to rising opportunity costs. The negative effect of services could indicate a changing relationship of abortion to the business cycle, from pro-cyclical to counter-cyclical. It could also be reflective of the growth of Swedish welfare programs that provided compensation for new parents, which tended to grow with services (Schön 2010) and perhaps decreased the appeal of abortion. However, with a rather weak model this is a tenuous hypothesis. The effect of relative wages, which was the strongest variable in both the previous period and the long model, is no longer significant. This is possibly due to decelerating increase of real wages as they leveled off after about 1980 and the increasing variations in abortion demand after 1975.
The clearest trend in comparing the effects on the abortion rate and abortion ratio over the period 1939 to 2010 is the very different strength of the model over the entire period, the first half of the period, and the second half of the period. Given the weaker
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results in the second half, the strong results for the entire period 1939-2010 are dependent on the explanatory power of the models in the first half of the period. The disparate results from the two halves of the period indicate that there are varying influences on abortion demand in the second half of the period than in the first. This is slightly surprising, given that abortion was less restricted after 1975 than it was previously. Thus, abortion could be more easily relied upon in the second part of the period, and so would be more likely to be a factor in couples’ decision-making processes. This would lead to the expectation that abortion would be more susceptible to economic conditions.
Abortion demand was clearly heavily influenced by economic conditions between
1939 and 1974. The variable with the strongest influence on abortion demand in the first half of the period is female relative wages. This reaffirms the strong role of opportunity costs effects that was fond in the long period. This is not an unreasonable result, as the increase of women in the labor force and their growing wages was one of the largest social trends during this sub-period, especially in the later portion. Women postponing or foregoing births in order to take advantage of economic opportunities is strongly in accordance with theory. This is even more so, because many of the welfare programs that allowed women to work and have children more easily were not introduced until the last part of this period or later, and so they would have little influence in decreasing the opportunity cost in this half of the period.
Also of note is the striking similarity of the results for both the abortion rate and the abortion percent. The results are almost identical in the earlier period especially. This is likely due to the incredible growth in the number of abortions that took place from about 1961 to 1975, which masked any smaller differences in the two figures.
In the context of the changing social conditions in Sweden, especially with respect to women, their labor market status, and abortion rights, the 1960s are a watershed decade. It was at this point that abortion became significantly more common and women’s wages increased substantially. Therefore, an analysis of abortion demand from 1960-2010 is of interest to this study. This model will rely on the same model as has been used previously, except for the substitution of unemployment for vacancies. As
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discussed above, vacancies and unemployment are essentially inverted measurements of the same phenomenon. Thus the model is:
𝐷𝑙𝑛𝐴𝑅𝐴𝑇𝐸 = 𝛼 + 𝛽1𝐷𝑙𝑛𝑈𝑁𝐸𝑀𝑃 + 𝛽2𝐷𝑙𝑛𝐺𝐷𝑃 + 𝛽3𝐷𝑙𝑛𝑅𝐸𝐿𝑊𝐴𝐺𝐸 + 𝛽4𝑙𝑛𝐶𝑂𝐻
+ 𝛽5𝐷𝑙𝑛𝑆𝐸𝑅𝑉 + 𝜀
Despite a difference of only fifteen years, the abortion demand models from
1960-2010 (table 4) are substantially stronger than the models for 1975-2010. This is probably because the time period from 1960 to 1975 was so important in the development of abortion access, as well as the development of many of the variables that were influential on abortion demand. Given the differences between the models from 1975-
2010 and 1960-2010, we can assume that the results from these models are largely influenced by the period from 1960 to 1975.
DlnUNEMP
DlnGDP
DlnRELWAGE lnCOH
DlnSERV
DlnHIED
_cons
DlnARATE DlnAPER DlnARATE DlnAPER
-0.063
(1.10)
-0.056
(1.03)
-0.068
(1.12)
-0.050
(0.88)
0.928
(1.97)*
2.829
(1.98)*
0.049
(0.46)
3.444
(2.74)***
0.004
(0.04)
0.748
(1.70)*
2.854
(2.13)**
0.145
(1.44)
2.844
(2.41)**
0.083
(1.07)
0.884
(1.76)*
2.899
(1.98)*
0.047
(0.43)
3.483
(2.73)***
-0.031
(0.29)
0.003
(0.04)
0.810
(1.73)*
2.758
(2.01)*
0.149
(1.46)
2.789
(2.33)**
0.042
(0.42)
0.083
(1.06)
R squared adjusted R squared
N
F-Stat
Prob (F-Stat)
Source: author’s calculations
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0.46
0.40
51
7.73
0.000
0.46
0.40
51
7.53
0.000
* p <0.1; ** p <0.05; *** p <0.01
0.46
0.39
51
6.33
0.000
0.46
0.38
51
6.19
0.000
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Three of the five variables are significant in the models of both the abortion rate and the abortion ratio. GDP is significant at the ten percent level for both models.
Relative wages are significant at the ten percent level for the abortion rate and at the five percent level for the abortion ratio, and services is significant at the one percent level for the abortion rate and the five percent level for the abortion ratio. The models are both significant at the one percent level, and each explains about forty percent of the variation in abortion demand.
In the model of abortion rate the variable with the strongest influence is the growth rate of services, increasing the growth rate of the abortion rate by 3.4 percent when it increases by one percent. Relative wages have the second largest coefficient, increasing the growth rate of the abortion rate by 2.83 percent, and GDP is less influential, increasing the abortion rate’s growth rate by a little less than one percent. The coefficients in the model of the abortion ratio are similar; the growth of services increases the growth rate of the abortion ratio by 2.84 percent, women’s relative wages by 2.85 percent, and GDP by 0.748 percent. With all these similarities, we can again assume that the influences on the abortion rate and abortion ratio were more or less the same in these models.
The importance of the service sector is especially clear in the model of the abortion rate, where it has the strongest explanatory influence of any variable. This is a departure from previous models, in which services has not typically had any significant impact on abortion demand. This growth in services represents the change in the Swedish economy and an employment market increasingly amenable to high female employment.
A positive influence of the growth of services on abortion here indicates that women are facing an increasing opportunity cost between employment and giving birth, which leads to increased abortion demand. The second strongest influence on abortion demand in this period is the growth of relative wages. This result reinforces the earlier conclusions that opportunity cost has a major influence on a woman’s decision to procure an abortion.
GDP per capita also increases abortion demand. Assuming that higher GDP means higher income, this leads to the implication that abortion acts as a normal good, and that demand increases as income rises. It also lends support to the already-discussed opportunity-cost
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effect on abortion demand; as economic prosperity increases and GDP rises, so does abortion demand.
A secondary model of abortion demand from 1960 to 2010 (table 4) includes entrants into higher education as an additional variable. There are many theoretical relationships between education and abortion demand, as discussed above. However, when entrants into higher education are included in the model there is almost no effect on the models as a whole, and higher education is not significant. The impact that education does have on the overall model is generally negative, and lowers the adjusted R-squared for both models. Higher education’s insignificance could imply that the differential effects of higher education act in opposing directions, leading to its small coefficients and high insignificance, or that entry into higher education has no strong relationship to abortion demand.
Since 1975, the Swedish government has kept track of age-specific abortion rates.
This allows us to investigate if there are any differential effects on abortion demand by age in Sweden. The next section will be a discussion of the visual trends in the agespecific abortion rate, followed by regression models to further investigate the changes in each age-specific rate. The same will then be repeated for the age-specific abortion ratio.
The model specifications of age-specific abortion rate and abortion ratio are slightly altered from the models used previously. Because the huge development of services was largely a phenomenon of the 1960s and earlier twentieth century, it has been eliminated from these regressions. Instead, female entrants into higher education, which as an increasing phenomenon in the later part of the twentieth century, will be used to represent women’s opportunity costs and changing preferences. Additionally, a time trend will be introduced to account for any change that is occurring independent of the economic factors being measured. This is reasonable because of the significant cultural changes that have occurred between 1975 and 2010, especially regarding the age at which childbearing is most common. The time trend estimates these cultural shifts.
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Figure 6.1. Abortions per 1000 women in Sweden - distribution by age 1975-2010
40
35
30
25
20
15
10
5
0
15-19 20–24 25–29 30–34 35–39 40-44
Source: Socialstyrelsen
One of the most obvious trends of the age-specific abortion rates is the difference in variation between age groups (figure 6.1). While younger women have significant variation in their abortion rates, the older age groups are less likely to undergo large swings. However, all age groups are subject to similar patterns in abortion rate trends.
The other difference is the distinct rates for different age groups: younger women consistently obtain abortions at a higher rate than older women. The separation is so consistent that the rates are ‘stacked’ according to age, and, apart from teens, do not intersect at any point. Women 20-24 have the most abortions of any age group, and women 40-44 have the lowest abortion rate by a substantial margin; women in the 20-24 age group procure abortions at a rate of almost three to four times that of women in the
40-44 age group, and at about two to two and a half times women in the 35-39 age group.
Overall, abortion rates increase from the middle of the 1980s until the end of that decade, and then decrease by an almost equal amount through the later part of the 1990s.
Abortion rates again climb through the 2000s, but show signs of beginning to fall at the end of the period. These changes are more pronounced for younger age groups; the abortion rates for teens and women 20-24 increase almost ten percent from 1985 to 1989,
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and that of women 25-29 almost seven percent. The change for older age groups ranges from a slight decline to an increase of about four percent.
Teen abortions are the ‘wild card’ of the age groups, and deviate from the general trend at several points. The abortion rate for women 15-19 is the only rate to cross over another rate at any point. Teen abortions typically vary from fewer than those in the 30-
34 age group, to more than those in the 25-29 age group, though begin at the highest level when abortion restrictions were first removed. The subsequent steep decline in teen abortions may be due to increased sex education efforts on the part of the Swedish government at the same time as abortion was liberalized (Posner 1992). Teen abortion rates deviate in the final decade, declining before other age groups, and increase faster and earlier than those of older women in the middle of the 1990s. However this is perhaps better interpreted as an earlier demonstration of a trend than a divergence from a trend.
As mentioned in section 2, there are many similarities between the abortion rate and the birth rate. These similarities are clearest in these younger abortion rates, especially the peak in the late 1980s, decline and low rates through the 1990s, and rising rates again through the 2000s. The trough in the abortion rate for women in this age
Figure 6.2. Growth rates of abortion rate per 1000 women by age 1975-2010- five year moving average
0,06
0,04
0,02
0
-0,02
-0,04
-0,06
-0,08
15-19 20–24 25–29 30–34 35–39
Source: Socialstyrelsen, author’s calculations
Abortions per 1000 women by age group 1975-2011
Growth rate- Five year moving average
K. E. Gary
0,06
0,04
40-44
48

group through the 1990s also occurs at the same time as unemployment was highest and
Sweden was experiencing a financial crisis. This lends support to the idea that conceptions will decrease during difficult economic times, leading both to fewer births and lower abortion rates.
The trends for the growth rates of abortion rates reveal the similarities in abortion rates for all age groups (figure 6.2). Unsurprisingly, the most volatile age category is again teens, with wider swings than other age groups. The most distinct trend for all agespecific abortion rate growth rates is the increasing homogeneity of movement as the period progresses. While the abortion rates for all age groups are typically moving in the same way, there is a much greater spread in the 1980s than there is in the 2000s, when the growth rates (apart from teens) are all fairly clustered. At the beginning of the period, all abortion rates have positive but decelerating growth rates, and all growth rates become negative by about 1981. There is a turn around in 1982-1983, and all abortion rates begin to grow significantly, peaking in 1987-1988. Slower growth continued in all age groups until about 1990-1992, when abortion rates begin to decline again. Abortion rates are generally in decline, though decelerating, through the 1990s, apart from the age group 35-
39, for whom they are slightly positive.
As with the abortion rates themselves, the growth rates of the abortion rates are highest in younger age groups. The growth rate of the teen abortion rate have a similar shape of movement as the other age groups, but is more extreme and more rapid than others. In addition, the year-to-year change in the teen rate differentiates itself by its decline from the beginning of the series until 1982, and from 2005 until the end of the series, both periods in which growth rates in all other age groups were generally positive.
The changes in abortion rates of older women were not as distinct as those for younger women (see figure 6.1), but the similar movements are observable in the growth rates. While the abortion rate for women 40-44 was typically in decline, the decline slowed through the 1980s and early 1990s and became significantly less negative as the rates for other age groups increased. The same is true for the abortion rate of the 35-39 age group, which responds similarly to other growth rates in the series, with the potential exception of the second half of the 1990s.
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One of the clearest components in the models of the age-specific abortion rate is the lack of significance in models of higher age groups (table 5). This makes theoretical sense: older women are less likely to abort in order to be flexible about birth timing and their decisions are more likely to be based on things that are not influenced by economic conditions. Those who want to have a child are less able to postpone childbearing than younger women, and many older women will have already reached their desired parity and will not want to have an additional child. Additionally, older women are more likely to be more economically stable than their younger counterparts, and so variables like cohort size, which model marketplace competition, are less likely to have a strong impact. Only the models for the abortion rates for women aged 15-19, 20-24, and 35-39 have significant results, and thus only these models are discussed in detail here.
There are two significant variables in the model for the abortion rate for women age 15-19: unemployment, significant at the one percent level, and relative wages, significant at the ten percent level. The model is highly significant and the adjusted Rsquared explains approximately 47 percent of variation. The effects of both unemployment and relative wages on the teen abortion rate are negative. This is contrary to what previous results have been. In other models, especially in the earlier period from
1939-1974, relative wages have typically been the strongest positive influence on abortion demand. It could be that in this later period when relative wages are increasing less than in previous decades that relative wages have a stronger income effect than an opportunity cost effect and promote childbearing. Unemployment is highly significant, and lowers the abortion rate for teens by 0.19 percent when its growth rate increases by one percent. The negative influence of unemployment could indicate that women are taking advantage of periods of lower opportunity cost to have children, while they do not have to worry about losing work time. It is also potentially linked to lower rates of conception during periods of economic decline, as couples attempt to avoid childbearing in uncertain periods.
The model for the abortion rate of women ages 20-24 has three significant variables; unemployment, cohort size, and the time trend. Cohort and the time trend are significant at the five percent level, unemployment at ten percent. The entire model is
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significant at the five percent level and the adjusted R-squared explains approximately 26 percent of variation. The factors that impact the abortion rate for women 20-24 mostly part different from those that impact the abortion rate for teens. The negative impact of unemployment is a common significant variable between the two, but it is much stronger for teens; in the model for women 20-24 the impact is fairly small, causing the growth rate of the abortion rate to decline by only 0.075 percent when the growth rate of unemployment increases by one percent. The implications for unemployment on the abortion rate for women 20-24 are the same as they are for the abortion rate for teens.
The significant variable with the highest coefficient in this model is cohort size, decreasing the growth rate of the abortion rate by less than half of a percent when it increases by one percent. Cohort size has a negative effect on the abortion rate for women
20-24, which is counter to theory. Again, it is possible that this negative influence on abortions is due to a negative influence on conceptions in general, and that couples are more careful with their fertility when it is not advantageous to have a child.
The significance of the time trend indicates that there is a general shift in the abortion rate occurring apart from the influences exerted by these variables. This shift could be do to non-measurable factors, such as changes in cultural preferences, or it could be due to changes in the use of contraceptives or contraceptive technology. However, the effect of the time trend is very small, decreasing the growth rate of the abortion rate by only 0.0041 percent each year.
While the model for the abortion rate for women ages 35-39 itself is not significant, the variable for the growth rate of entrants into higher education is. In this model, an increase of the growth rate of entrants into higher education by one percent decreases the growth rate of the abortion rate for women 35-39 years old by 0.15 percent.
This result is a bit difficult to interpret, because this age group is not included in the measurement of education, and is not likely to have large numbers of first-time entrants into higher education. This result could be consistent with the hypothesis that higher education will decrease the abortion rate for older women, who might have better access to and better usage of contraceptive methods, as proposed by Michael (1973). However, a single result in an insignificant model is not conclusive.
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The percentage of pregnancies ending in abortion, or the abortion ratio, is in general more consistent than the abortion rates. This indicates that on a short-term basis, it is the number of women who become pregnant, not the proportion of pregnancies that end in abortion, which is shifting. The strongest message from this graph is that the abortion ratio for all age groups since 1975 has been changing on a secular basis; it appears that much of the developments are long-term cultural changes rather than influenced by short-term economic conditions. The appearance of a relatively flat aggregate abortion percent for all women after 1975 is essentially an illusion, and obscures significant changes in pregnancy outcomes between different age groups of women.
There are three different trends for the abortion ratio, clustered by age groups. For the youngest age groups the abortion ratio has increased, while for older women the abortion ratio has decreased. For the middle two age groups, it has remained more constant. The youngest two age groups, teens 15-19 and women 20-24, have drastically increased the percent of pregnancies ending in termination, each by about twenty
Figure 6.3. Percent of known pregnancies ending in abortion in Sweden by age
1975-2010
100
90
80
70
60
50
40
30
20
10
0
35–39 40–44 15–19 20–24 25–29
Data source: Socialstyrelsen, author’s calculations
30–34
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Figure 6.4. Growth rates in age-specific abortion ratio in Sweden 1975-2010 - five year moving averages
0,06
0,04
0,02
0
-0,02
-0,04
-0,06
-0,08
15–19 20–24 25–29 30–34 35–39 40–44
Data source: Socialstyrelsen; author’s calculations percentage points from 1975 to 2010. This increase has been more or less steady, except for a slight decline in the abortion percent during the beginning of the 1990s at the peak of the baby boom, and continuing through the first part of that decade, when unemployment in Sweden was at its height. The decrease in the abortion percent during the 1990s recession implies a trade-off for teens and younger women who might have decided to give birth when the opportunity cost was lower due to decreased employment.
The abortion ratio for women aged 35-39 and 40-44, the oldest two age groups, moved in the opposite direction of younger women, decreasing steadily after 1980, though slowing during the baby boom in the 1990s. These decreases in the abortion ratio at older ages, combined with the more-or-less steady abortion rate for women 35-39 and only slightly decreasing abortion rate for woman 40-44, demonstrates a cultural shift toward childbearing at older ages from the 1980s onward. Abortion ratios for women 25-
29 and 30-34 have been relatively stable, but follow the same age trends as other groups; the abortion ratio for women 30-34 decreases slightly, and that of women 25-29 increases, mirroring the trends of younger women.
While the steady transitions of the abortion ratios indicates the impact of social trends rather than other exogenous forces, there are clear influences of some economic
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trends on the abortion ratios, especially through the late 1980s and 1990s. There is a general decrease in all abortion ratios in the years around 1990, when births were high in
Sweden. Similarly, almost all abortion ratios increase quickly or slow their decline in the mid 1990s and peak around 1995-1997, the height of Sweden’s economic crisis and record unemployment. This is especially clear in a graph of the growth rates of the agespecific abortion ratio, shown as five-year moving averages (figure 6.4). The oppositional movements of age-specific abortion ratios also indicates that there are likely to be different factors that impact fertility decisions for women between age groups.
The growth rates of the age-specific abortion ratios underscore these trends
(figure 6.4). The growth rates for younger women are almost entirely positive, only dropping below zero in the early 1990s and the end of the period, while those for older women only grow in the mid-1990s. The mid-1990s increase in the abortion ratio for all age groups, except perhaps for women 40-44, is clear.
None of the models of the age-specific abortion ratio are significant overall, but three models have variables that are significant (table 6). These models are for women in middle age groups; 25-29, 30-34, and 35-39. There are two variables that are significant in all three of these models; the time trend and the cohort ratio. The time trend is significant for women 25-29 and 35-39 at the ten percent level and for women 30-34 at the five percent level. However, the coefficients on these trends are small, the largest only 0.0051. Additionally, these are all positive. This fits the expectations from looking at the graph of the abortion ratio for women 25-29, who have a small increase in their abortion ratio between 1975-2010. However, this is unexpected for older women, especially those 35-39, who have a clear decline in their abortion ratio. The cohort ratio acts in accordance to theory in all three models, increasing abortion demand by around half a percent in each model.
The only model to have a significant variable other than the time trend or the cohort ratio is that for women 25-29, for which relative wages are significant. Relative wages are a powerful factor in decreasing the abortion ratio for women 25-29, as they were for decreasing the abortion rate for the youngest two age groups. As before, this could indicate that the income effect of relative wages is increasing over the opportunity cost effect, and this makes it more appealing for women to give birth rather than abort.
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The objective of this thesis was to determine whether certain economic factors have had an impact on abortion demand in Sweden from the time abortion was legalized in
Sweden in 1939, and to determine whether evolving labor conditions for women and shifting economic climate from the 1960s onward influenced the relationship of abortion demand to the business cycle. It also examined the different impact of economic factors on age-specific abortion demand from 1975. The results indicate that there has been substantial economic influence on abortion demand in Sweden. The most important of these factors has been from female relative wages and opportunity cost effects, especially in the 1960s and 1970s when Sweden was undergoing significant social change.
The impact of economic factors on abortion demand varies significantly through the history of legal abortion in Sweden. The strongest models of abortion demand are the aggregate models from the period before abortion was legal on demand, from 1939 to
1975. The strength of these models, in combination with the weakness of the models from 1975 to 2010, indicate that the models for the entire period from 1939 to 2010 were largely determined by the effects measured from 1939 to 1975. The strength of the models of abortion demand from 1960 to 2010 further indicate that it is the relationships of the variables in the period form 1960 to 1975 that have the strongest impact on overall abortion demand.
The variable with by far the strongest relationship to abortion demand in these aggregate models has been the growth of female relative wages. That rising relative wages increased abortion demand highlights the overall importance of opportunity cost on women’s fertility decisions, especially earlier in the twentieth century. The importance of women’s relative income and opportunity cost in the determination of abortion demand is highly in line with the literature surrounding fertility demand and Becker’s
(1991) discussion. Interestingly, the abortion rate of teenage women has a negative relationship with female relative wages, which could indicate a shift form pro-cyclical abortion demand to counter-cyclical abortion demand, and a change of the role of relative wages from an opportunity cost to an income effect. This could also be due to teenage women being more cautious about pregnancy when it carries a higher opportunity cost.
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The positive effect of GDP per capita on abortion demand is also consistent with an opportunity cost effect as abortions rise during times of economic prosperity. This measure typically has more influence on the abortion rate than the abortion ratio. The positive influence of GDP on abortion demand could also be related to higher overall conception rates when GDP is higher.
Cohort size also plays an important role in abortion demand, indicating that the negative pressure of competition is highly connected to fertility decisions. Cohort size can be seen as a mirror effect to relative wages, increasing abortion demand when men face increased competition for work. Instead of reflecting improved conditions for women, it reflects poorer conditions for men. Cohort size has repeatedly been significant, but its influence was strongest in earlier time periods.
Despite the close historical relationship between services and female wages, the growth of services does not appear to have a strong overall impact on abortion demand, with significant impacts on abortion demand only in the 1960-2010 models. That this variable is only strong during these years is reasonable, since it was during the 1960s and
1970s that the growth in the service sector was so great. It is also not surprising that the power of the growth of services is similar to that of relative wages in these models, since it was largely the increase in services that propelled the increase in women’s relative wages.
The impacts of unemployment and education, two variables with very mixed and contradictory effects on abortion demand, are not very much clarified by this study.
Unemployment is significant in only a few models; the revised model of the abortion rate from 1975 to 2010, and the models of the two youngest age groups’ age-specific abortion rates. Unemployment’s theoretical inverse, vacancies, is never significant. When unemployment is significant, it always exerts a negative impact on the abortion rate, meaning that abortion is pro-cyclical with the business cycle. This is congruent with abortion’s positive relationship with GDP. It is difficult to determine a clear relationship between unemployment and age-specific abortion rates because only three age-specific abortion rate models are significant, and of those, only tow have significant results for unemployment. However, that these two results are for the youngest two age groups indicates that the unemployment rate has more impact on younger women than older
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women. Additionally, the fact that unemployment is only ever significant for the abortion rate, and never the abortion percent, indicates that unemployment does little to influence a woman’s decision to abort once she becomes pregnant, and so must instead act on the likelihood of becoming pregnant in the first place. Thus, it is likely that in Sweden, times of high unemployment have caused younger couples to be more careful in their use of fertility control measures. It is reasonable that this would have more impact on younger women, who might have more to lose in terms of long-term employment from an extended absence from the labor market. Additionally, older women will be less inclined to delay their fertility than the very young, decreasing unemployment’s impact on older age groups.
Education has even less of a clear impact on abortion demand. Entries into education are significantly negative in only one model, in the age-specific abortion rate for women 35 to 39. Though the lack of significant models makes it difficult to compare these results to any other situation, this does lend some support to Michael’s (1973) hypothesis that at older ages, higher education will reduce abortion rate in older women.
The relationship between the abortion rate and the abortion percent also changes over time. In all models that contain the impact of the 1960s, when the growth in both the abortion percent and the abortion rate was so strong, the economic impacts on both measurements were more or less the same. However, in the models that begin in 1975, there are some differences. Since many of these models are not significant, it is not possible to make a full comparison, but there are some consistent patterns. Cohort size has a significant effect on all models of the abortion rate with significant results from
1975-2010, while it is never significant for the later abortion rate models. This indicates that while competition from larger cohorts has a strong impact on the pregnancy outcomes once women become pregnant, it may not have as much influence on whether or not women become pregnant. The time trend is also significant in all three age-specific abortion rate models that have significant results. However these results are less conclusive because the time trend is always positive, whereas the abortion percentage is decreasing for women in older age groups.
Overall, opportunity costs effects have been the strongest influence on Swedish abortion demand. However, the changing limitations of the supply of abortion before
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1975 and the overwhelming increases in both abortion supply and demand in the 1960s complicate many of these results. Additionally, there are still many relationships that have not been investigated. Importantly, this study does not thoroughly investigate the relationships between fertility and abortion demand, especially at an age-specific level.
Looking more closely into these relationships could help further untangle some of the theoretical questions that cannot be answered by an examination of abortion demand alone. A more thorough investigation of the impacts on parental relief welfare programs would make the analysis stronger, as would the derivation of a total abortion rate, akin to the total fertility rate. Furthermore, investigating the different relationships between abortion and economic indicators at a county level, to understand any regional differences within Sweden, would lead to a fuller understanding of abortion in Sweden.
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Data
Johnston, Robert. Johnston’s Archive: Abortion statistics and other data. [online] available at: http://www.johnstonsarchive.net/policy/abortion [accessed on 13
January 2013].
Schön, L. and Krantz, O. Swedish Historical National Accounts 1560―2010.
Swedish National Board of Health and Welfare (Socialstyrelsen) Abortion statistics
(Abortstatistik). Online database.
Swedish National Board of Health and Welfare (Socialstyrelsen) (1999). Aborter 1997.
Stockholm: Socialstyrelsen.
Swedish National Board of Health and Welfare (Socialstyrelsen) (2012). Aborter 2011.
Stockholm: Socialstyrelsen.
Swedish National Board of Health and Welfare (Socialstyrelsen). Public Health in
Sweden (Allmän hälaso-och sjukvård) – Stockholm: Socialstyrelsen. Various years
Statistics Sweden (Statistiska centralbryån). Labor Force Surveys
(Arbetskraftsundersökningen), various years.
Statistics Sweden (Statistiska centralbryån). Populations Statistics (SOS
Befolkningsstatistik), online database.
Statistics Sweden (Statistiska centralbryån). Statistical Yearbook (Statistisk årsbok), various years.
Swedish National Agency for Higher Education (Högskoleverket). Higher education beginners per municipality (Högskolenybörjare per kommun), online database.
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Independent variables and the abortion rate
A.1. The abortion rate and vacancy rate in Sweden 1939-2010
25
10
5
0
20
15
20
15
10
5
0
Aboriton rate VACRATE
Source: Socialstyrelsen , Swedish statistical yearbook, various years
A.2. The abortion rate and unemployment rate in Swede 1939-2010
25
Aboriton rate UNEMP
Source: Socialstyrelsen , Swedish labor force survey.
0,35
0,3
0,25
0,2
0,15
0,1
0,05
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A.3. GDP per capita and the abortion rate in Sweden 1939-2010
25
20
15
10
5
0
Aboriton rate
Source: Socialstyrelsen,
Schön and Krantz.
GDP
A.4. Female relative wages and the abortion rate in Sweden 1939-2010
25
20
15
10
5
0
0,75
0,7
0,65
0,6
0,95
0,9
0,85
0,8
Relative Wages Abortion Rate
Source: Socialstyrelsen, Maria Stanfors.
6000
5000
4000
3000
2000
1000
0
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15
10
5
0
A.5. Relative cohort size and the abortion rate in Sweden 1939-2010
25
20
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
20
15
10
5
0
Aboriton rate COH
Source: Socialstyrelsen, Statistics Sweden, author’s calculations.
A.6. Abortion rate and the service sector in Sweden 1939-2010
25
0,65
0,55
0,45
0,35
0,25
0,15
Aboriton rate SERV
Source: Socialstyrelsen , Schön and Krantz, Maria Stanfors.
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A.7. Female entrants into higher education and the abortion rate in Sweden 1939-
2010
70
60
50
40
30
20
10
0
HIED Aboriton rate
Source: Socialstyrelsen, Högskoleverket , author’s calculations
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