Teenage Kicks: New (Old) Evidence on the Pill andTeenage Childbearing

Kelly S. Ragan∗

Stockholm School of Economics

March 4, 2015

Abstract

How did the introduction of oral contraception (‘the Pill’) alter teenage childbearingin Sweden? Teen fertility was halved in the decade following the Pill’s introduction.New data on oral contraceptive sales reveals that the largest declines occurred in com-munities with high take-up of the Pill. Differences-in-differences-in-differences (DDD)comparisons, exploiting time variation across localities and age groups, point to a strongnegative relationship between Pill use and fertility. Illegitimacy patterns from a centuryearlier are used as instruments to isolate that part of Pill use which is predetermined. IVestimates imply that the Pill’s diffusion could account for the entire decline in teenagechildbearing observed in the data. The estimated effect on non-marital childbearing islarge and negative; the data do not support the predictions of Akerlof et al (1996) butare consistent with the female empowerment model of Chiappori and Orrefice (2008).JEL Codes:Keywords: contraception, teenage childbearing, out-of-wedlock birthPreliminary and Incomplete

∗Address: Stockholm School of Economics, Box 6501, 113 83 Stockholm, Sweden. Kelly.Ragan@hhs.se.This work was supported by the Swedish Science Council (Grant 2012-643) and by the Swedish Royal Acad-emy of Science. I appreciate helpful comments from seminar participants at the NBER Summer Instituteand SOFI, Stockholm University.

1

1 Introduction

This article presents new Swedish data on the use of oral contraceptives (’the Pill’) and

develops an empirical approach to isolate a causal channel between Pill use and teen fertility.

I establish a strong first stage relationship between illegitimacy in 1860 and Pill take-up a

century later. I use these predetermined differences in Pill adoption to isolate the causal

effect of the Pill on teenage childbearing. The identifying assumption behind my choice of

instrument is consistent with the teen fertility data along many dimensions including placebo

policy reforms, the time pattern of reduced form effects, as well as overidentification tests

using alternative instruments. The Pill had a statistically and quantitatively significant

effect on teenage childbearing that could explain the halving of the teen birth rate after

the Pill was introduced. By identifying the effect of Pill use on teen fertility this paper

quantifies an empirical relationship that previous reduced form estimates have not been able

to characterize.

There are few more compelling examples of technology as liberator than the Pill. Yet,

the literature is divided regarding its importance. Declines in fertility were underway well

before the Pill was introduced.1 These long run trends, common across many countries, led

Becker (1991) and others to conclude that the Pill may have only represented a shift along

the technological frontier rather than a contraceptive innovation. Even if the Pill was an

improvement relative to existing contraceptive methods, the theoretical literature is ambigu-

ous regarding the Pill’s probable effects on nonmarital childbearing. Akerlof, Yellen, and

Katz (1996) provide a theoretical model of contraceptive innovation and female immisera-

tion, and argue that the Pill could increase the rate of out-of-wedlock childbearing among

young women.2 Chiappori and Oreffi ce (2008) develop a matching model with an explicit role

for contraception which clearly predicts that universal access to an inexpensive and highly

effective mode of contraception, such as the Pill, should reduce non-marital childbearing. My

IV approach, unlike previous quasi-experiments, is not limited to shifting Pill take-up among

either married or unmarried teens; hence I can decompose fertility effects by marital status.

1A prominent study of the Swedish case can be found in Schultz (1985).2Akerlof et al (1996) emphasize how the pill and liberalized abortion access altered ’shotgun marriage’

customs and reduced the bargaining power of women with preferences against contraception, inducing someto engage in premarital sex whom otherwise would not, a force leading to increased non-marital fertility.

2

Theory is ambiguous, but the data is clear. The Pill substantially reduced nonmarital child-

bearing among teens in both absolute and relative terms. The data do not support Akerlof

et al (1996), but are consistent with the predictions of Chiappori and Oreffi ce (2008).

Goldin and Katz (2002) use legal reforms that altered contraceptive access for unmarried

women between the ages of 18 and 21 as a source of exogenous variation in Pill use to

estimate the effect of Pill access on marital delay and career choice among young college

educated women in the U.S. Much of the ’power of the Pill’literature has focused on early

legal access to the Pill, not Pill use per se. Bailey (2010) uses the repeal of Comstock laws

which banned contraceptive sales to estimate the effect of Pill access on marital fertility.

Ananat and Hungerman (2012), Bailey (2006), and Bailey, Hershbein and Miller (2012)

are prominent examples which use family planning policy and legal reforms that altered

contraceptive access to estimate how Pill access affected women and the well-being of their

children. These studies generally find negative fertility effects. Yet, recent work by Myers

(2012) argues that once abortion access is taken into account, confidential access to the Pill

had little effect on women’s propensity to marry or have a child at a young age. Focusing on

teenagers does not resolve this ambiguity. Using a quasi-experimental design Guldi (2008)

finds small negative fertility effects of confidential early access to the Pill among white teens

but no effect for other teenagers.3

This paper empirically establishes a negative causal relationship between Pill use and teen

fertility in total, as well as among subpopulations of teens, using a new empirical strategy

which does not rely on variation in contemporary legal reforms or family planning policy.

The Swedish institutional setting mitigates confounding factors such as abortion legalization

emphasized by Myers (2012) and Joyce (2013).4 Concerns related to policy endogeneity in

the quasi-experimental literature and potential bias when relying on legal reforms as a source

of identification are moot since the Pill was available to all women over the age of fifteen

with no restrictions regarding marital status or parental consent. The instrumental variables

approach I use relies on a very different source of variation in Pill take-up than any previous

3This is not inconsistent with the literature on teen pregnancy prevention interventions. DiCenso et al(2002) survey 22 randomized studies and present a meta-analysis of interventions to reduce teen pregnancy.They find little effect of these interventions on contraceptive use or pregnancy among teens.

4Abortion was illegal in Sweden until 1975.

3

study. The reduced form estimates I report point to a sizable fertility reduction among teens

related to confidential Pill access. Moreover, I estimate first stage equations that clearly

establish the channel through which exogenous variation in Pill use operates, and complement

reduced form analysis with a full model where the causal chain is made explicit.5

Decomposing fertility effects by marital status not only allows us to discern which theory

match the data but also presents new puzzles with respect to the role the Pill played in the

secular declines in marital childbearing among teens observed in the data. Total teen fertility

declines match up well with the diffusion of the Pill. Consistent with Bailey (2010), I find

that marital fertility among married teens declines significantly. Yet, my estimates overstate

declines in non-marital fertility and predict an increase in marital fertility, consistent with

the positive correlation in the cross section data between the diffusion of the Pill and marital

fertility among teens. To better understand the observed and predicted increase in marital

fertility with respect to Pill diffusion I examine how the Pill altered marital behavior. My

estimates suggest that the Pill increased marriage among teens, consistent with increased

marital childbearing, but at odds with the marital delay mechanism emphasized in Goldin

and Katz (2002).

The empirical approach developed here could be used to evaluate the impact of the Pill on

a host of other behaviors. Establishing the impact of the Pill on fertility is a necessary first

step before one can establish any causal relationship between the Pill and other downstream

outcomes such as earnings or child outcomes. The paper proceeds with an overview of the

introduction of the Pill in Sweden with particular attention to the data used in this study

as well as important aspects of the institutional environment. A first look at the data on

the Pill and teenage childbearing is then presented. Using variation across time, across

localities, and across age groups with differential take-up rates I establish a strong negative

relationship between the Pill and teen fertility. Section 4 presents the empirical model and

the identifying assumptions behind the instrumental variables (IV) approach. Section 5

makes a case for the instrument, showing how illegitimacy from 1860 is highly correlated

with Pill use, but uncorrelated with changes in teen fertility before the Pill is introduced.

5Bailey, Hershbein and Miller (2012) provide evidence that early legal access increased pill use, but theirmain results, consistent with this literature focus on estimating the effect of early legal access on behavioraloutcomes. It should be noted that they do report first stage results for rural women.

4

Placebo policy estimates and intention to treat (ITT) effects implied by the empirical model

match up precisely with the timing of the Pill’s introduction. Section 6 presents the main

empirical results on teenage childbearing, while Section 7 decomposes the effect of the Pill

with regard to marital status. Section 8 concludes.

2 The Introduction of the Pill in Sweden

The Pill was approved for contraceptive use by the Swedish Board of Health and Welfare

in May of 1964. Pill use grew rapidly. Three percent of women between the ages of 15

and 44 were on the Pill in 1965, ten percent a year later, and by 1969 a quarter of fecund

females were on the Pill.6 Only condoms and coitus interruptus surpassed the Pill as a means

of preventing pregnancy among Swedish couples.7 By 1976, 30 percent of women in their

reproductive years were using the Pill annually, and more than half of teenage girls were on

the Pill by 1979.8 The unique data on Pill use and the institutional setting surrounding the

Pill’s introduction and rapid adoption are described below.

2.1 Data on Pill Use

I quantify the specific channel of the Pill with data on oral contraceptive (OC) sales. Data

on OC sales by locality are constructed from 1970 onward using the quarterly Swedish Drug

Market publication from Läkemedelstatistik, AB. This publication presents complete infor-

mation on OC sales across 70 local markets that constitute the entire universe of OC sales in

Sweden. Although the Pill was introduced in 1964, local sales data is first available in 1970.

Previous studies have relied on retrospective surveys to determine whether and which types

of contraceptives women used at different points in time. Individual level surveys are useful

in eliciting information on which types of birth control methods women had experience with,

but this information on the extensive margin is an incomplete picture of women’s exposure

to the Pill. I take a different approach. I use actual sales data. Pill sales capture both the

6Swedish Board of Health and Welfare (1984).7See Lewin (2000). There are no recurring surveys on contraceptive use during this period so it is not

possible to see how pill use evolved relative to other contraceptives.8See Table 2 of Swedish Board of Health and Welfare (1984).

5

extensive margin, more and more women opt to try the Pill, as well as the intensive margin

that women continue to take the Pill. The direct quantification of Pill use proximate to the

Pill’s introduction is an important contribution of this study.

Pill use is quantified in terms of expenditures per woman or teen. Geographically disag-

gregated data does not distinguish between different types of OC and only reports sales in

Swedish Krona (SEK). Converting sales into doses requires a price index, and the additional

assumption that the composition of OC sales across communities is uniform. Converting sales

data into doses introduces measurement error, to avoid this the main results are presented

in terms of sales. Table 1 presents data on OC sales and price data on the most popular

brand of OC by year. Price data can be used to transform sales into doses. For example,

Follinyl was the most popular OC accounting for over 27 percent of sales in 1970. A 21 day

regimen of Follinyl was priced at 2.94 SEK, less than the price of a movie ticket. Annual OC

sales correspond to over 400,000 women using the leading brand annually. According to the

leading brand price index, roughly a quarter of fecund females were using the Pill in 1970,

consistent with the data reported by the Swedish Board of Health and Welfare (1984).

2.2 Swedish Institutional Setting

Use of the Pill was widespread, especially among young women. Swedish law provided women

as young as 15 access to contraceptive services without regard to parental knowledge or

consent. Maternal health clinics provided information about contraceptives and supplied di-

aphragms to women regardless of their marital status, and had since the 1950s. The Riksför-

bundet för Sexuell Upplysning (RFSU) kept a list of doctors known to provide contraceptives

but by the late 1960s they deemed this unnecessary as doctors were universally willing to

provide contraceptive services to women.9

The assumption of uniform institutions may be diffi cult to support in some settings, but in

the context of Sweden this assumption is accurate in both the de jure and de facto sense. The

laws regulating the sales of contraceptives do not differ by jurisdiction and are set by national

regulatory bodies. In addition, the medical and retail pharmaceutical sectors in Sweden are

highly regulated, and almost entirely operated by public entities that are subject to central

9See Linner (1967).

6

administration.10 Prescription drugs could only be distributed by a publicly administrated

network of pharmacies whose assortment of drugs, staffi ng, and hours of operation were

subject to public supervision. Drug prices were fixed and did not vary across markets. In

this unique setting where the supply curve for the Pill was flat, fixed and identical across

markets, differences in take-up are arguably demand driven. The importance of the demand

side of the market in determining Pill use will inform the IV approach.

3 A First Look at the Data

Using data on OC sales as a prism to shed light on the fertility decisions of teenagers helps

build the case that the Pill’s diffusion played a causal role in the steep decline in teenage

childbearing observed in Sweden. Pill use in Sweden was heavily skewed toward young women.

By the late 1970s more than half of teenaged girls were using the Pill. Given that such a

large share of teens came to use the Pill it is natural to ask whether there is any evidence in

the aggregate that the Pill altered fertility patterns among teenagers.

Time series and cross-section data illustrate how the introduction of the Pill coincided

with a steep drop in teenage fertility that was largest in communities where take-up of the Pill

was greatest. This analysis is combined with data on the fertility patterns of older women

who used the Pill at much lower rates to construct a differences-in-differences-in-differences

(DDD) estimator of the Pill’s impact on teen childbearing. The descriptive statistics point

to a strong negative relationship between the Pill and teen fertility.

3.1 Time Series Data on Teen Fertility

Figure 1 shows how teenage childbearing fell by nearly 2 births per 100 women, a 50 percent

decline, in the decade following the Pill’s introduction.11 Teenage marital fertility essentially

disappeared after the Pill, declining from 1.5 births per hundred teens in 1965 to less than

0.3 births in 1975. Non-marital fertility also declined.

10In addition to these features of the legal and medical environment, it should also be noted that sexeducation has been compulsory in Sweden since 1956, and that the guidelines for the sex education curriculumare set at the national level.11Oral contraceptives were first approved for use in May of 1964.

7

Figure 1: Teenage Fertility 1961-1975

The distribution of teen fertility rates across markets is plotted in Figure 2. The sharp

decline in teenage childbearing is reflected in the leftward shift in the distribution from 1965

to 1969. In 1965, 20 percent of teens lived in communities with three or fewer births per

100 teens. By 1969 80 percent of teens lived in communities with less than three births per

100 teens, and by 1973 almost all teens lived in areas with less than three births.

0.2

.4.6

.81

Pro

babi

lity 

<= B

irth 

Rat

e

0 2 4 6Births Per 100 Women Aged 15­19

1961 1965 1969 1973

Note: Fertility per 100 women. Population weighted

Distribution of Teenage Fertility Rates

Figure 2: Distribution of Teen Fertility

8

­1­.5

0.5

1

0 5 10 15 20 25Pill Use Per Woman 15­19

Log Differences Fitted values­4

­20

2

0 5 10 15 20 25Pill Use Per Woman 15­19

Level Differences Fitted values

Note: Pill use measured in SEK per 15­19 year old women. Fertility is per 100 women aged 15­19.

10 Year Log (Left Panel) and Level (Right Panel) DifferencesPre/Post Pill Change in Fertility vs. Pill Use

Figure 3: Change in Teen Fertility vs. Pill Use

3.2 Time Variation Across Communities: Differences-in-Differences

Fertility declines were not uniform, but occurred to a larger extent in communities with high

take-up of the Pill. Figure 3 plots 10 year changes in teen fertility from before until after the

Pill against Pill use by market.12 Although teen births fell in most communities, declines

were largest where take-up of the Pill was greatest. Time differences net out fixed factors

across markets. The negative slope in Figure 3 indicates that permanent fertility differences

do not explain the negative relationship between teen fertility and Pill use.

3.3 Time Variation Across Communities and Age Groups: Differences-

in-Differences-in-Differences

Time variation across communities and age groups can be used to construct a differences-

in-differences-in-differences (DDD) comparison. Table 2 uses data on the age distribution of

12Log differences in the left panel, level differences in the right panel. The sizes of the circles in Figure 3indicate market population.

9

­1­.5

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Log 

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0 5 10 15 20 25

­.8­.6

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0 2 4 6 8 10

­1.5

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0 5 10 15 20 25Pill Per Woman Aged 15­19

­6­4

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2

0 2 4 6 8 10Pill Per Woman Aged 30­34

­4­3

­2­1

01

0 2 4 6 8 10Pill Per Woman Aged 35­39

Note: Fertility expressed per 100 women of each age group.

10 Year ChangesPre/Post Pil l  Differences in Ferti l i ty vs. Pi l l  Use By Age

Figure 4: Differences in Fertility from Before and After the Pill vs. Pill Use by Age

Pill prescriptions to compare the fertility of low/high Pill use age groups in low/high Pill

use markets before and after the introduction of the Pill.13 I use data on the age distribution

of Pill use to define a high use age group, women aged 15-19, and two low use age groups,

women between 35-39 and 30-34. Figure 4 presents 10 year log differences (top panel) and

level differences (bottom panel) in births per 100 women by age before/after the Pill relative

to Pill expenditures. The negative correlation between fertility changes and Pill take-up seen

among teens is reversed among older women where relatively high Pill use communities have

smaller fertility declines.

Comparing fertility patterns of women who use the Pill at much lower rates than teens

allows us to account for the role of location specific trends in driving teen fertility changes.

Table 2 presents the data as a DDD comparison. Average changes in births before/after

the Pill in both low and high Pill use areas (defined as above or below median Pill use per

13Women between 30 and 34 accounted for 12 percent of scripts for the pill and women aged 35-39 togetheraccounted for 9 percent of pill sales. According to the Drug Information Committee of the Swedish Boardof Health and Welfare (1984) a quarter of OC prescriptions were written to teenagers. This is more thandouble the share of prescriptions written to women in their early thirties, and three times that of women intheir late thirties. (insert: relative to their population shares.)

10

woman 15-44) for high Pill use teens and low Pill use populations are presented. Differencing

between high and low Pill use areas approximates the slopes for each age group plotted in

Figure 4. Differencing again across age groups gives an estimate of the total effect of the

Pill, net of both age and community specific time effects. Dividing through by differences in

average Pill use across groups we have an estimate of the coeffi cient on Pill use in a linear

fertility model that includes both age and community specific time effects. The DDD analysis

suggests that community specific trends, such as a general decline in fertility in urban areas,

cannot explain the strong negative association between Pill take-up and teen fertility seen in

Figure 3.

3.4 Looking at the Data: A Summary

A negative relationship between the diffusion of the Pill and teenage childbearing is seen in the

data. The steep decline in teen fertility that coincides with the introduction of the Pill and the

strong negative relationship between fertility declines and Pill use are reinforced by the DDD

analysis which nets out the effect of market level trends. The strong negative relationship

holds regardless of whether fertility changes are measured in log or level differences. The

negative relationship is robust to the inclusion of age and community time effects, but there

remain a host of confounding factors that could explain the negative association seen in the

data. For example, trends in local marriage markets specific to teenage girls may drive fertility

changes, not the Pill.14 In the next section I write down a model that allows for unobserved

location specific time effects in teen fertility and outline an IV approach aimed at identifying

the causal effect of the Pill by identifying a part of Pill use that is predetermined by historical

factors from a century earlier and arguably unrelated to contemporaneous trends in labor

and marriage markets.

4 The Empirical Model

Consider a model where teen fertility in community i and time t, denoted Birthi,t, is a

function of Pill use in community i, a community specific fixed characteristic, γi, teen specific

14More generally, the concern is that there are omitted fertility trends specific to teens by each location.

11

local time effects, denoted by φi,t, and an idiosycratic fertility shock denoted by εi,t:

Birthi,t = βPilli,t−1 + γi + φi,t + εi,t.

Rewrite the model in differences that span the introduction of the Pill:

Birthi,t −Birthi,t−j = βPilli,,t−1 + φi,t − φi,t−j + εi,t − εi,t−j.

Initial Pill use is zero everywhere and hence omitted. The persistent community character-

istic, γi, is differenced out.

The challenge is to estimate β when the local trends in fertility related to other factors,

captured by the φi,t’s, are unobserved. This is addressed by using an instrumental variables

approach, where out-of-wedlock births (OWB) from a century earlier are used to instrument

for the diffusion of the Pill. The instrument for Pill use is defined the following way:

Zi,t =

0 t < 1964

OWBi t ≥ 1964

Time variation comes from the legalization of the Pill, common across communities. Cross

section variation in latent demand comes from historical illegitimacy patterns. The inter-

action of the Pill’s legalization and historical patterns that determine latent demand define

the instrument, Zi,t = OWBi × I(1 if t ≥ 1964, else 0).

Illegitimacy from a century earlier is highly correlated with take-up of the Pill, as shown in

the next section. I make the additional assumption that historical illegitimacy is uncorrelated

with contemporaneous fertility trends across communities, except through the influence on

Pill use. This means Zit − Zit−j is uncorrelated with both φi,t − φi,t−j and εi,t − εi,t−j, or

cov(φi,t − φi,t−j, Zit) = 0.

In words, this choice of instruments assumes that changes in teen motherhood during the

late 1960s are not affected by the level of illegitimacy from a century earlier except through

the take-up of the Pill.

By using historical variation in illegitimacy I isolate that part of Pill use which is pre-

determined. The strong relationship between illegitimacy in 1860 and Pill use provides a

12

channel through which exogenous differences in Pill demand can be identified. The empiri-

cal model explicitly takes into account the direct effect that persistent factors may have on

teenage fertility across communities through γi. This provides some plausibility to the ex-

clusion restriction. Although the identifying assumption cannot be verified, the next section

will show how illegitimacy in 1860 is uncorrelated with trends in teen fertility in the period

prior to the Pill’s legalization, consistent with the identifying assumption that underlies the

empirical model.

5 Historical Illegitimacy and the Pill

Since the 17th Century, large and persistent differences in out-of-wedlock births (OWB)

divided Sweden into distinct demographic regions.15 OWB patterns are interesting not only

as a demographic regularity invariant to mass migration and industrialization, but also as

a strong predictor of latent demand for the Pill.16 Illegitimacy rates from 1860 predict a

quarter of the variation in Pill use a century later. This section documents the correlation

between Pill take-up and OWB in 1860, paying particular attention to how OWB is related

to fertility trends before and after the Pill’s introduction.

5.1 The Geography of Historical Illegitimacy and Pill Use

Sundbärg (1910) compiled data through the 17th Century to illustrate how differences in

illegitimacy behavior persisted over hundreds of years. A single characteristic that defines

illegitimacy patterns is diffi cult to discern; high rates of illegitimacy are seen north and

south of the limes norrlandicus, along coasts and plains. Mining and industrial regions have

relatively high occurrence of unwed birth, but many areas with high levels of illegitimacy were

primarily agrarian. Heckscher (1949) argued that urbanization led to the differential pattern

of out-of-wedlock fertility, but Frykman (1975) shows how areas with high illegitimacy had

population densities no different from central Småland, an area of low illegitimacy.

15See Sundbärg (1910).16Frykman (1975) presents a detailed analysis of non-marital fertility trends and the ethnological back-

ground regarding their social roots. Sklar (1977) also discusses the importance legal and economic develop-ments in the 19th century with regard to illegitimacy in Sweden.

13

Figure 5: Maps of Out-of-Wedlock Births in 1860 and Pill Use per Woman in 1970

14

Figure 5 maps illegitimate births per 100 live births across markets in 1860 and demand

for the Pill a century later. These maps illustrate the positive correlation that underlies the

strong first stage result presented in the next section. The maps illustrate how the correla-

tion between unwed births and Pill use a century later is not solely driven by North/South

differences. Even within regions, illegitimacy and Pill use are closely related. Similarly, urban-

ization does not seem to be the defining factor driving illegitimacy and Pill take-up. Although

Stockholm has the highest rates of Pill use and illegitimacy, Gothenburg, the second largest

city, is not one of the top ten Pill demand markets nor is it an area of high illegitimacy in

1860.

5.2 Illegitimacy in 1860 and Teen Fertility: Before the Pill

If teen specific time effects are correlated with OWB in 1860 this would violate the identifying

assumption. I can’t test this directly, but I can test whether changes in teen fertility before

the Pill are correlated with OWB in 1860. Table 3 summarizes results from regressions of

changes in teen fertility on historical illegitimacy, year fixed effects, and regional time trends.

The top panel uses measures of teen fertility in logs and levels, the middle panel looks at

younger and older teens, and the bottom panel breaks out teen fertility by marital status.

There is no significant correlation between fertility changes before the Pill and OWB in 1860

regardless of the measure used. The lack of a significant correlation between teen fertility

before the Pill’s introduction and illegitimacy from a century earlier is reassuring, as it is

consistent with the identifying assumption behind the IV model.

5.3 Illegitimacy in 1860 and Teen Fertility: Placebo Treatments

Placebo experiments are another way to test whether OWB in 1860 is correlated with local

time effects before the Pill. I define a placebo instrument identical to the instrument defined

in Section 4 except for the timing of the Pill’s introduction:

Z62i,t =

0 t < 1962

OWBi t ≥ 1962

15

Specification 1 in Table 4 reports the intention to treat (ITT) coeffi cients on OWB in 1860

for a fixed effects regression on a panel of teen fertility from 1961-1963. The coeffi cient

on the placebo instrument is not significantly different from zero. The second specification

extends the data window by one year, conducting the same placebo treatment. Specification

3 estimates the effect of a placebo treatment corresponding to the Pill being introduced

in 1963. The coeffi cients are not significantly different from zero.17 Placebo tests provide

further reassurance that the instrument is not correlated with trends in fertility before the

Pill’s introduction.

5.4 Illegitimacy in 1860 and Teen Fertility: The Reduced Form

The Pill’s legalization in 1964 should induce fertility responses well before disaggregated

sales data become available in 1970.18 I test whether the instrument is working through the

hypothesized channel, legal access to the Pill, by estimating a reduced form model in the

period following legalization. The model estimated in Table 5 parallels the model in Section

4 where Pilli,t has been replaced with the instrument Zi,t.19 Table 5 reports the coeffi cients

on OWB in 1860 as the data window is extended from 1965 through 1974. The coeffi cient

estimates reported in Table 5 suggest that the Pill had a negative and significant effect

on fertility, corresponding to a drop of almost 17 percent in the decade after the Pill was

approved for contraceptive use.20 Reduced form estimates provide further support for the

identifying assumption, namely that legalization of the Pill in 1964 drives the importance

of OWB in 1860 as a predictor of teen fertility declines, not trends coinciding with data

availability in 1970.

A similar reduced form approach can be used to trace out how the Pill altered teen

fertility at specific points in time relative to the pre-legalization period. Figure 6 plots ITT

17The results are the same when placebo tests are run on specifications in levels.18See Section 2.1 for further details on the data.19The equation estimated in Table 5 is Birthi,t = βZi,t−1 + γi + δt + η

∗j t+ εi,t where Zi,t is defined as

Zi,t =

{0 t < 1964

OWBi t ≥ 1964

and ηj denotes region specific time trends. Robust standard errors are clustered at the market level. All ofthe ITT estimates reported in Table 5 are weighted by the teen population.20The total effect is computed by multiplying β̂ by the population weighted average value of OWB in 1860

16

effects and confidence bands from rolling one year response estimates. The first two points

correspond to the placebo estimates reported in specifications 1 and 3 of Table 4 scaled

by the teen population weighted illegitimacy rate in 1860. The vertical line indicates the

introduction of the Pill. There is an immediate reduction in teen fertility of almost 7 percent

in 1965, a year of partial treatment, relative to the pre-legalization period. The effect grows

rapidly through 1969 when the estimated reduction reaches 27 percent. The estimated effects

gradually decline during the 1970s, reaching 15 percent in 1974. The estimated magnitudes of

the ITT estimates mirror the dynamics of aggregate Pill use reported by the Swedish Board

of Health (1984). The share of women aged 15-44 using the Pill on an annual basis is plotted

by the dashed line, and illustrates a rapid take up of the Pill that peaks in the late 1960s and

then declines gradually through 1974. The reduced form estimates capture the increasing

intensity of treatment and subsequent decline observed in the aggregate data.

17

Reduced Form Fertility Effect Estimates and the Share of Women Using the Pill by Year

Teen fertility is strongly predicted by OWB in 1860 in the years following the Pill’s in-

troduction, but not in the years immediately preceding legalization. Reduced form estimates

from placebo treatments and the actual legalization of the Pill make a strong case for the

instrument shifting Pill take-up, and in turn fertility, while being uncorrelated with contem-

poraneous fertility trends. The timing corresponds precisely to the introduction of the Pill,

matching the relative magnitudes of the time pattern of initial partial treatment as well as

the data on the Pill’s diffusion in the aggregate.

18

6 The Pill’s Impact on Teenage Childbearing

The reduced form estimates discussed in the previous section not only make a strong case for

the instrument working through the hypothesized channel but also suggest that confidential

access to the Pill reduced teenage childbearing for the average teen by 17 percent in the

decade following the Pill’s approval for contraceptive use.21 With data on Pill use across

markets we can take the analysis a step further, characterizing the relationship between Pill

use, not just Pill access, and teen fertility. By estimating the full model presented in Section

4 we can quantify the causal channel between Pill use and teen fertility.

Table 6 present OLS and IV estimates of the Pill’s effect on teen fertility based on a log

linear version of the empirical model presented in Section 4, while Table 7 reports results for

a linear in levels version of the model.22 Coeffi cient estimates on Pill use are negative and

highly significant regardless of whether the results are weighted by the population of teen

women (Tables 6 and 7 columns 1-2) or whether region trends are included.23 Specification 2

in Table 6 includes both population weights and region trends, and is the baseline specification

used throughout the paper. OLS estimates of this baseline specification (Table 6, column 2,

top panel) suggest that every Krona increase in average Pill use reduced teen fertility by 2.4

percent.

IV estimates are roughly twice the magnitude of OLS estimates (second panel, Tables

6 and 7). The IV coeffi cient from the baseline log linear model, β̂IVlog = −0.053, implies a

predicted fertility decline of 69 percent if all teens behaved like the marginal teen identified

by the IV estimates, somewhat greater than the observed decline.24 The coeffi cient from

the comparable regression in levels, β̂IVlevel = −0.109, implies a predicted fertility decline of

1.42 births per 100 teens, less than the actual decline seen in Figure 1. Both OLS and IV

21See Table 5 for the ITT estimates and predicted fertility effects. Since treatment intensities vary wecan compute an interval of fertility responses ranging from a reduction of 6 percent in the community withthe lowest illegitimacy in 1860 to a 40 percent reduction in Stockholm, the highest historical illegitimacycommunity.22The equations estimated in Tables 6 and 7 are Birthi,t = βPilli,t−1+γi+δt+η

∗j t+εi,t where ηj denotes

region specific time trends. Robust standard errors are clustered at the market level.23Regions are defined as in Sundbärg (1910).24See Table 6, column 2, middle panel. The bottom panel of Table 6 reports the first stage of the IV

regression. Coeffi cient estimates on historical illegitimacy are highly significant, and the F-statistics reportedin the middle panel are well above standard thresholds for weak instruments.

19

estimates point to a significant negative relationship between Pill use and the teen birth rate.

First-stage results are reported in the bottom panel of Tables 6, and OWB in 1860 is highly

significant in all first stage regressions.

These results provide further evidence that the strong negative relationship between Pill

use and teen fertility is causal. The larger magnitude of IV estimates, relative to OLS, may

be driven by several factors. Attenuation bias, introduced by measurement error in teen

Pill use, may contribute to the difference in magnitudes between the estimates. Larger IV

estimates may also be related to the instrument shifting the behavior of a highly responsive

teen population. Predicted fertility responses based on the IV estimates are larger than

the reduced form estimates in Table 5 and Figure 6. This is consistent with the fertility

reduction for the marginal Pill user being greater than the fertility effect of Pill access for

the average teen. The IV coeffi cients provide a consistent estimate of the fertility reduction

among teenage Pill users and suggest that the Pill was a significant contraceptive innovation

for Swedish teens.

6.1 Alternative Instruments

Other historical instruments plausibly satisfy the exclusion restriction as well. Table 8

presents first and second stage results for alternative historical instruments.25 The first col-

umn of Table 8 presents the baseline results using OWB in 1860, as reported in column 2 of

Table 6. This specification includes regional trends and is weighted by the teen population.

The second specification presents results when nonmarital fertility is measured in 1910. The

first stage is strong and the coeffi cient on Pill use is negative and highly significant regardless

of when OWB is measured. Specifications 3 and 4 use butter prices from the 19th Century

to instrument for Pill take-up. The use of butter prices follows Schultz (1985) who showed

how terms of trade shocks affecting the dairy sector, the primary employer of women in 19th

Century Sweden, altered women’s wages and in turn their marital fertility decisions. Butter

prices provide a very different source of variation in Pill take-up that arguably satisfies the

identifying assumption. The first stage is strong when using butter prices alone, or in combi-

25Note that regional time trends are included in all of the specifications in Table 8. Results are similarwhen region trends are excluded.

20

nation with OWB in 1860. Specification 4 reports overidentification test results (J-statistic).

The test does not reject the null hypothesis that the instruments are uncorrelated with the

residuals from the estimation equation. This provides another piece of evidence consistent

with the exclusion restriction.

6.2 Alternative Age Groups

My focus on teen fertility parallels Guldi (2008). Yet, much of the previous literature has

focused on 18-21 year old women. Although age specific Pill use measures do not exist, I can

measure fertility for these subpopulations before and after the Pill. Table 9 presents results

from OLS and IV regressions of fertility among 15-17 year old women, 18-19 year olds, 18-21

year olds, and 15-21 year old women on teen Pill use, year and market fixed effects, region

trends, and population weights. Point estimates are largest for the youngest age group, but

generally similar to the baseline results in Table 6. IV estimates are twice the magnitude of

OLS estimates across all age groups. Reduced form estimates, reported in the lower panel of

Table 9, are highly significant. For the case of 15-21 year old women, the predicted fertility

reduction from Pill access is over 12 percent. This is larger than estimates reported in Guldi

(2008) who found that confidential access to the Pill reduced fertility for a similar population

by 8.5 percent.

In addition to quantifying the fertility effects of Pill access, I can also compute the pre-

dicted fertility decline associated with using the Pill. For 15-21 year old women the OLS

coeffi cient is -0.017; instrumenting yields a coeffi cient of -0.036.26 Given average Pill ex-

penditures, this implies a predicted fertility reduction of 22 to 47 percent. The estimated

fertility responses to Pill use are much greater than reduced form estimates. This suggests

that reduced form estimates of the fertility effects of confidential access for the average teen

significantly understate the behavioral effects of Pill use relative to OLS and IV estimates.

26Both coeffi cients are significant at the 0.1 percent level.

21

6.3 Robustness Checks

6.3.1 Alternative Clustering

One may be concerned that shocks are correlated across geographically proximate markets.

One way to take this into account is to cluster at a more aggregated level to allow for

correlation of residuals across markets. Standard errors are little changed when clustering at

the county level instead of the more disaggregated market level.

6.3.2 Alternative Pill Use Measures

To be written.

6.4 Estimate Comparisons

The previous literature on the Pill has relied on reduced form estimation based on natural

experiments which shifted access to the Pill for different populations at different points in

time. Guldi (2008) focuses on teenagers confidential access to the Pill. As seen in Table 5 and

Figure 6, reduced form estimates of the impact of Pill access on teen fertility are generally

larger for the Swedish case.

The main contribution of this paper, though, is to estimate the impact of Pill use on teen

fertility. Although reduced form estimates such as Guldi (2008) and Bailey (2010), for the

case of married teens, are suggestive of Pill use fertility effects the causal channel is not well

understood. Bailey, Hershbein and Miller (2012) report first stage results for rural women

using changes in family planning policy as an instrument to shift Pill use, but their full model

estimation is the exception in this literature. By making a case for historical illegitimacy as a

valid instrument for Pill take-up, I can quantify the causal channel between Pill use and teen

fertility in the wake of the Pill’s introduction, a relationship that relatively little is known

about. The predicted fertility reduction associated with Pill use is 69 percent in the baseline

specification (Table 6, column 2). This suggests that reduced form estimates understate the

fertility effects of the Pill for the marginal Pill user identified by IV.

Are the estimates reported in Tables 6-9 comparable to previous research on Pill use

among Swedish teens? The Pill’s influence on teenage childbearing in Sweden has been

22

studied by Grönqvist (2009). He uses the introduction of subsidies on teenagers purchases

of certain types of OC in a subset of Swedish municipalities beginning in 1989, 25 years after

the Pill’s introduction. Grönqvist (2009) estimates the effect of exposure to a subsidy on

the log teen fertility rate, the dependent variable in the baseline specification and extensions

presented here. Grönqvist estimates a coeffi cient of -0.076 with a standard error of 0.054,

and concludes that exposure to OC subsidies reduced teen fertility by 7.5 percent. The

subsidy exposure effect is not directly comparable to the ITT or full model estimates I

present. One way to compare the fertility effects induced by the Pill’s introduction to the

subsidies considered by Grönqvist is to compute the implied subsidy rate that would have

led to a fertility reduction similar to those predicted by the baseline estimates reported in

Table 6. Grönqvist (2009) reports an average subsidy of 75 percent. Assuming linearity, a

10 percentage point increase in the subsidy rate translated into a one percent reduction in

teen fertility. In comparison, the IV estimates in Table 6 (column 2) imply that confidential

access to the Pill, although unsubsidized, led to a 69 percent reduction in teen fertility. To

generate a similar behavioral response using subsidies would require a subsidy rate of nearly

700 percent. Even in comparison to the ITT estimates reported in Table 5 and Figure 6,

the subsidy exposure effects reported by Grönqvist (2009) are a half or a third the size of

estimates based on the Pill’s introduction. The behavioral responses brought about by the

introduction of the Pill are larger than those brought about by later subsidy policies.

7 Marital and Nonmarital Childbearing and the Pill

Theory has focused on how contraceptive innovations alter nonmarital childbearing. Figure 7

plots log differences before/after the Pill for both marital and nonmarital childbearing versus

Pill use. The strong negative relationship between total fertility and Pill use is mirrored in

the nonmarital fertility patterns plotted in the right panel. Yet, marital childbearing behaves

very differently; marital births per teen and Pill use appear to be positively correlated, as

seen in the left panel of Figure 7. Did the Pill both increased marital fertility and reduce

nonmarital fertility among teens? In the following sections I report estimates of the Pill’s

effect on childbearing among teenagers by marital status and relate these estimates to the

23

­4­3

­2­1

0

0 5 10 15 20 25Pill Use per Woman 15­19

Change in Marital Births Fitted values

­.5

0.5

11.

5

0 5 10 15 20 25Pill Use per Woman 15­19

Change in Nonmarital Births Fitted values

Note: Fertility is measured by mother's marital status but expressed per 100 of the total teen female population.

10 Year Log Differences Before and After the PillChange in Fertility vs. Pill Use by Marital Status

Figure 6: Changes in Teen Fertility by Marital Status

theoretical and empirical literature. In order to understand the forces that drive the fertility

patterns seen in Figure 7 an analysis of teenage marriage behavior is also presented.

7.1 Nonmarital Childbearing

Akerlof et al (1996) emphasize how the diffusion of improved contraceptives and the use of

abortion may contribute to increased nonmarital childbearing as norms enforcing marriage

in the case of pregnancy are eroded. Column 1 report results for a regression of the log of

nonmarital births per single teens on Pill use, year and market fixed effects, and regional time

trends. Column 2 reports results for a similar model where nonmarital fertility is expressed

per the total teen population. Estimates by OLS and IV are large, negative and highly

significant regardless of how nonmarital fertility is measured. Given average Pill use in 1974,

the OLS estimates imply that nonmarital fertility dropped by 48 to 50 percent as a result of

the Pill’s diffusion. IV estimates are even larger.

The Pill may have led to a decline in nonmarital fertility relative to the teen population,

but the share of nonmarital births among teens may have increased. In 1964, 59 percent of

teen births occurred outside of marriage. By 1974 this rate had increased to 87 percent.

Swedish time series data is consistent with Akerlof et al (1996) in the sense that the OWB

24

share increased after the Pill was introduced. Yet, a positive correlation between the share of

births occurring outside of marriage and the Pill is not seen in the panel regressions presented

in Table 10 (column 3). Regressing the share of teen births that occur outside of marriage

on Pill use per teen, year and market fixed effects, and regional time trends yields a negative

and significant coeffi cient on Pill use. The diffusion of the Pill led to a decline in nonmarital

fertility both relative to the teen population and as a fraction of teen births. Although a

general increase in the non-marital fertility share coincided with the introduction of the Pill

there is little evidence to support the view that the diffusion of the Pill led to increased

nonmarital fertility.

7.2 Marital Childbearing

Table 11 estimates the impact of the Pill on births per the married teen population in both

logs (column 1) and levels (column 3). Column 1 reports OLS (top panel) and IV (bottom

panel) estimates of the Pill’s impact on log births per married woman. The IV estimates,

β̂IVlog = −0.022 suggest that a one SEK increase in average Pill use translated into an decline

in teen marital fertility of 2.2 percent. Given average expenditures of 13 SEK this translates

into a marital fertility reduction of 29 percent. Column 3 reports estimates of a similar

specification where marital fertility is measured in levels. The IV estimate in column 3,

β̂IVlevel = −1.681 implies that every Krona increase in average Pill use led to a decline of 1.68

births per 100 married women. This implies a reduction of 22 births per 100 married teens,

half of the 1964 level.

The significant and large negative relationship between marital fertility per the population

of married women is consistent with the findings of Bailey (2010), though larger in both

absolute and relative terms than the reduced form effects she estimates. Bailey (2010) focuses

on marital fertility relative to the population of married women, using sales bans as a way

to identify the marital fertility effects of Pill access. Bailey estimates the impact of sales

bans for every year from 1951 through 1980. The largest fertility effect among teenagers

occurs in 1964, with sales ban states having almost one more birth per 100 married teens

than states without sales bans. Marital births per married teen were very similar in the U.S.

and Sweden during this period. In 1963 there were 49 live births per 100 married teens in

25

the U.S. Hence, percentage increases in teen marital fertility induced by sales bans were on

the order of 2 percent in 1964, an order of magnitude less than the estimated effect of Pill

use reported in column 1 of Table 11.

Having established that the Pill led to fewer births per married teen, and that the mag-

nitude of this effect was large, we turn the effect of the Pill on marital fertility per the total

teen population. If the share of teens that marry is inelastic to contraceptive innovations,

or if the Pill led to a decline in the share of teens entering marriage, we would expect the

share of marital births per the teen population to decline. A negative relationship is not

born out in the data. Instead, we find a positive correlation between Pill use and marital

births, as seen in Figure 7.27This positive correlation stands in sharp contrast to the negative

relationship between Pill use and both total and nonmarital teen fertility. Estimating the

impact of the Pill on marital births per the population of married teens misses the larger

trend toward increased marital fertility brought about by increased entry into marriage, an

effect undocumented in the previous literature and discussed in the next section.

The Pill increased fertility within marriage among Swedish teens. This effect is seen in

columns 2 and 4 of Table 11 where the impact of the Pill on marital births per the total teen

population is estimated. IV estimates of the log specification, reported in the bottom panel

of column 2, suggest that for every Krona increase in average Pill use marital births per

the teen population increased by 5.5 percent. Column 3 reports estimates for a specification

in levels, and the IV estimates in the bottom panel point to a similar relationship where a

one Krona increase in Pill use increased marital fertility by over 0.03 births per 100 teens,

or an increase in marital fertility per teen of 29 percent relative to 1964 levels. The steady

decline in marital childbearing per teen seen in the time series data does not appear to be

driven by the diffusion of the Pill. The next section presents evidence on how the Pill altered

teen marriage behavior and contrasts these result to studies such as Goldin and Katz (2002)

which have emphasized how the Pill led to marital delay among college educated women in

the U.S.27The positive relationship between Pill use and teen marital fertility is not mechanically driven by a

decline teen marriage rates, as both marital and nonmarital birth rates are measured relative to the totalteen population in Figure 7.

26

­.06

­.04

­.02

0.0

2

0 5 10 15 20 25Pill per Woman 15­19

Women 15­19 Fitted values

­.3­.2

­.10

.1

0 5 10 15 20Pill per Woman 20­24

Women 20­24 Fitted values

­.01

­.005

0.0

05

0 5 10 15 20 25Pill per Woman 15­19

Women 15­17 Fitted values­.1

5­.1

­.05

0.0

5

0 5 10 15 20 25Pill per Woman 15­19

Women 18­19 Fitted values

Note: Proportion married is the ratio of married women to al l  women in each age group.

Change in Proportion Married vs. Pill Use

Figure 7: Female Married Population by Age

7.3 Teen Marriage and the Pill

Marriage among teens was on the rise in Sweden from 1960 when 2.7 percent of women aged

15-19 were married through 1968 when 5.8 percent married. Marriage rates declined sharply

thereafter, only 2.5 percent of teens were married in 1969 and 1.3 percent by 1975. Despite

this aggregate decline, Figure 7 depicts a weak positive relationship between Pill take-up and

teenage marriage. Figure 7 plots log differences in the share of married females relative to Pill

use for several age groups. The top panel contrasts teen marital behavior to that of women

aged 20-24, a population that more closely parallels the college graduates studied by Goldin

and Katz (2002). Teen marriage appears to be positively related to Pill use, while marriage

among women in their 20s shows a negative relationship. The bottom panel decomposes teen

marriage for younger and older teens; a positive correlation is seen among both populations.

Although a thorough treatment of the Pill’s effect on marriage is beyond the scope of the

current study, the empirical model in Section 4 can be used to estimate the impact of the

Pill on teen marriage. Table 12 reports results from OLS and IV regressions of the share of

27

teens that are married on current and lagged Pill use, as well as year and market fixed effects

and region time trends.28 OLS and IV estimates of the Pill’s effect on marriage are reported

for both women and men. For women, both OLS and IV results point to a positive and

significant relationship between adoption of the Pill and the decision to marry during their

teens. The results are similar for men, though not significant when estimated by OLS. The

estimates in Table 12 suggest that a one SEK increase in Pill use among teens led to a 10

percent increase in the teen marriage rate. Goldin and Katz (2002) emphasize how the Pill

enabled young college educated women in the U.S. to delay marriage. Although the Pill may

have been important for fertility delay among women in their 20s, or women who completed

a college degree, I do not find evidence that the Pill contributed to marital delay among

Swedish teenagers in the decade after the Pill. To the contrary, the Pill appears to have

increased teenagers likelihood to marry, and in turn increased marital birth among teens.

8 Conclusion

This paper investigates teenage childbearing in Sweden and estimates how Pill use altered

fertility in the decade after its introduction. A novel IV strategy is presented which uses

variation in nonmarital childbearing from a century earlier to identify exogenous variation

in take-up of the Pill. IV estimates provide evidence that the diffusion of the Pill led to a

statistically and economically significant reduction in teenage childbearing of 50 percent or

greater. The fertility effect of Pill access for the average teen substantially understates the

impact of the Pill for the marginal Pill user identified by the IV estimates. By identifying

the effect of Pill use on teen fertility this paper quantifies an empirical relationship that the

reduced form literature has not been able to characterize.

Full model estimates are diffi cult to compare directly to the previous literature as most

studies have reported reduced form estimates of the impact of Pill access. For example, Guldi

(2008) estimates that Pill access reduced fertility for 15-21 year old women by 8.5 percent.

Table 9 presents reduced form estimates of the effect of Pill access on births for a similar

population which implies that Pill access reduced fertility by over 12 percent. Although

28Fertility results use lagged pill use, but it could be argued that current pill use is the relevant variablefor marriage decisions.

28

the reduced form estimates I present in Section 5.4 are larger than many in the previous

literature, their striking feature is how small the implied behavioral effects are relative to the

full model estimates discussed in Section 6.

Theory is ambiguous as to how women’s use of highly effective contraceptive methods

such as the Pill changed the composition of fertility across marital groups. The data is

clear; the diffusion of the Pill led to a decline in nonmarital fertility both relative to the teen

population and as a fraction of teen births. The data do not support Akerlof et al (1996),

but instead the female empowerment model of Chiappori and Orrefice (2008).

I find evidence that the Pill reduced marital fertility among married teens. Point estimates

are an order of magnitude larger than the estimates reported in Bailey (2010). Yet, the

negative fertility effects of the Pill among married teens does not translate into a decline in

marital fertility in the aggregate. To the contrary, I estimate that marital fertility per the

total teenage population increased in Pill use. Increased selection into marriage among young

women is the driving force behind this rise in marital fertility. This facet of teen fertility

has not been discussed in the previous literature and provides a point of comparison to the

marital delay mechanisms emphasized by Goldin and Katz (2002) in their study of college

educated women in the U.S.

Although the Pill may have influenced a variety of decisions over women’s life course,

its primary effect must be seen with regard to fertility. In the absence of a measureable

fertility response, ancillary effects on marriage, education and income become tenuous. Using

the introduction of the Pill in Sweden to estimate the effect of Pill use on teen fertility

and documenting a large and significant effect with regard to delay of fertility during the

teenage years is an important contribution to the literature. Moreover, this paper presents

an empirical design which opens the door to future studies regarding the long run impact of

the Pill on women’s schooling, career choices, and child outcomes.

References

[1] Akerlof, G., J. Yellen, andM. Katz (1996), "An Analysis of Out-of-Wedlock Childbearingin the United States." The Quarterly Journal of Economics, 111(2): 277-317.

29

[2] Ananat, E. and D. Hungerman (2012), "The Power of the Pill for the Next Generation:Oral Contraception’s Effects on Fertility, Abortion, and Maternal and Child Character-istics." The Review of Economics and Statistics. 94(1):37-51.

[3] Bailey, Martha J. (2006) “More Power to the Pill: The Impact of Contraceptive FreedomonWomen’s Life Cycle Labor Supply.”The Quarterly Journal of Economics, 121(1):289—320.

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[10] Frykman, J. (1975) "Sexual Intercourse and Social Norms: A Study of Illegitimate Birthsin Sweden 1831-1933." Ethnologia Scandinavica, 1975: 110-150.

[11] Frykman, J. (1977) Horan i Bonde Samhället. LiborLäromedel, Lund.

[12] Goldin, C. and L. Katz (2002), "The Power of the Pill: Oral Contraceptives andWomen’sCareer and Marriage Decisions."Journal of Political Economy,110(4):730-770.

[13] Grönqvist, Hans (2009), "Putting teenagers on the Pill: the consequences of subsidizedcontraception." Institute for Labor Market Policy Evaluation Working Paper No. 2009:8.

[14] Guldi, M. (2008), "Fertility Effects of Abortion and Birth Control Pill Access for Mi-nors." Demography, 45(4):817-827.

[15] Heckscher, Eli (1949), Sveriges Ekonomiska Historia Från Gustav Vasa: Andra DelenDet Moderna Sveriges Grundlägning Fösta Halvbandet. Albert Bonniers, Stockholm.

[16] Joyce, T. (2013), "If Only Policy Analysis Were So Easy." Journal of Policy Analysisand Management, 32(4):897-99.

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[17] Lewin, Bo (2000), Sex in Sweden : on the Swedish sexual life 1996. Stockholm : NationalInstitute of Public Health.

[18] Liljeström, Rita (1974), A Study of Abortion in Sweden: A Contribution to the UnitedNations World Population Conference. Royal Ministry of Foreign Affairs. Stockholm:Norstedt.

[19] Linner, Birgitta (1967), Sex and Society in Sweden. New York: Pantheon Books.

[20] Läkemedelstatistik, A.B. "Swedish Drug Market: Statistical Survey of Registered Phar-maceutical Specialties in Sweden." Stockholm. 1970(I)-1974(IV).

[21] Myers, C. K. (2012), "Power of the pill or power of abortion? Re-examining the effects ofyoung women’s access to reproductive control." IZA Discussion Paper No. 6661. Bonn,Germany: Institute for the Study of Labor.

[22] Schultz, T. Paul (1985), "Changing World Prices, Women’s Wages, and the FertilityTransition: Sweden, 1860-1910." Journal of Political Economy, 93(6): 1126-1154.

[23] Statistics Central Byrån (1864), "Befolkning Statistisk Årsbok 1860." SOS, Stockholm.

[24] Statistics Central Byrån (1904), "Befolkning Statistisk Årsbok 1900." SOS, Stockholm.

[25] Statistics Central Byrån (1914), "Befolkning Statistisk Årsbok 1910." SOS, Stockholm.

[26] Sundbärg, Gustav (1907), Bevölkerungsstatistik Schwedens 1750-1900. Reprinted in Ur-val 3, Statistika Central Byrån.

[27] Sundbärg, Gustav (1910), Emigrationsutredningen Bilaga V: Ekonomisk-statistiskbeskrifning öfver Sveriges olika landsdelar. Royal Publisher, P. A. Norstedt and Sons,Stockholm.

[28] Swedish Board of Health and Welfare (1984), "Oral Contraceptives." National Board ofHealth and Welfare Workshop, Drug Information Committee, 1984:3, Stockholm.

31

Table 1: Pill Sales in Detail

Annual Sale and DosageYear Total OC Sales Annual Doses Female Population OC Use

1000 SEK Woman Years Aged 15-44 Percent of Women1970 14,948 423,452 1,585,915 26.71971 13,785 391,270 1,590,503 24.61972 14,697 374,973 1,591,942 23.61973 12,986 329,548 1,592,887 20.71974 14,079 334,983 1,600,290 20.9

Leading Brand Price IndexYear Leading Introduced Share of Price per

Brand OC Sales Month1970 Follinyl 1968 Q4 27.3 2.941971 Follinyl 1968 Q4 33.0 2.941972 Follinyl 1968 Q4 36.9 3.271973 Follinett 1971 Q3 25.6 3.281974 Follinett 1971 Q3 31.9 3.50Note: Price based on Q4 prices and sales data. Follinyl and Follinett produced by Recip.

32

Table 2: A Differences-in-Differences-in-Differences Analysis

Log Differences in Births Before-After the PillLow Pill Use Area High Pill Use Area Difference

Low Pill Users (30-34) -0.26 -0.21 0.04(0.14) (0.13)

Low Pill Users (35-39) -0.49 -0.45 0.04(0.22) (0.18)

High Pill Users (15-19) -0.21 -0.26 -0.05(0.27) (0.26)

Difference (Teens-30-34) 0.05 -0.05 -0.09Slope of Log Fertility in Pill Use per Woman (Age 30-34 Control Group): -0.04Difference (Teens-35-39) 0.28 0.19 -0.10Slope of Log Fertility in Pill Use per Woman (Age 35-39 Control Group): -0.04

Level Differences in Births Before-After the PillLow Pill Use Areas High Pill Use Area Difference

Low Pill Users (30-34) -2.10 -1.75 0.36(1.16) (1.04)

Low Pill Users (35-39) -1.76 -1.49 0.27(0.76) (0.57)

High Pill Users (15-19) -0.69 -0.80 -0.10(0.87) (0.81)

Difference (Teens-30-34) 1.41 0.95 -0.46Slope of Log Fertility in Pill Use per Woman (Age 30-34 Control Group): -0.20Difference (Teens-35-39) 1.06 0.69 -0.37Slope of Log Fertility in Pill Use per Woman (Age 35-39 Control Group): -0.16Note: Births are measured per 100 women aged 15-19.

33

Table 3: Changes in Teen Fertility Before the Pill

Teen Fertility in Logs and LevelsLog Level Log Level

OWB 1860 -0.0008 -0.0029 -0.0034 -0.0097(0.0009) (0.0026) (0.0030) (0.0083)

Teen Fertility by AgeAge 15-17 Age 18-19 Age 15-17 Age 18-19

OWB 1860 -0.0010 -0.0010 -0.0002 -0.0045(0.0013) (0.0011) (0.0033) (0.0036)

Teen Fertility by Marital StatusNonmarital Marital Nonmarital Marital

OWB 1860 -0.0001 -0.0009 -0.0014 -0.0056(0.0010) (0.0011) (0.0026) (0.0043)

Additional Controls:Year FE Yes Yes Yes YesRegion Trend Yes Yes Yes YesWeighted Yes Yes No NoClusters 70 70 70 70N 210 210 210 210Note: All teens refers to women aged 15-19. Births are expressedper 100 teens. Unless otherwise specified fertility is measured in logs.* p<.05, ** p<.01, *** p<.001

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Table 4: Teen Fertility Placebo Tests

(1) (2) (3)

OWB 1860 X 0.0004 -0.0011I(1 if t=1962, else 0) (0.0015) (0.0016)

OWB 1860 X -0.0027I(1 if t=1963, else 0) (0.0016)

Additional Controls:Market FE Yes Yes YesYear FE Yes Yes YesRegion Trend Yes Yes YesClusters 70 70 70N 210 280 280Weighted Yes Yes YesNote: Log births per 100 teens is the dependent variable.* p<.05, ** p<.01, *** p<.001

35

Table 5: Historical Illegitimacy and Teen Fertility: Reduced Form Estimates Over ExtendedData HorizonsTerminal Period 1965 1966 1967 1968 1969

OWB 1860 X -0.006*** -0.009*** -0.010*** -0.012*** -0.013***I(1 if t<=1964, else 0) (0.001) (0.001) (0.001) (0.001) (0.002)

N 350 420 490 560 630R-squared 0.891 0.886 0.875 0.860 0.863

Predicted Fertility Effect -0.068 -0.101 -0.108 -0.129 -0.144

Terminal Period 1970 1971 1972 1973 1974

OWB 1860 X -0.014*** -0.014*** -0.015*** -0.015*** -0.015***I(1 if t<=1964, else 0) (0.002) (0.002) (0.001) (0.001) (0.001)

N 700 770 840 910 980R-squared 0.863 0.856 0.854 0.859 0.858

Predicted Fertility Effect -0.150 -0.155 -0.162 -0.167 -0.168

Additional Information:Clusters 70 70 70 70 70Note: Log births per 100 teen women is the dependent variable. All specifications includeyear and market fixed effects, as well as regional time trends. Robust standard errors,clustered at the market level, are reported in brackets. All estimates are weighted bythe female teen population. See Section 4 for more details.* p<.05, ** p<.01, *** p<.001

36

Table 6: Log Births per 100 Women Aged 15-19 and Pill Use the Previous YearOLS

Pill Use in Previous Year -0.029** -0.024*** -0.019** -0.014*(0.009) (0.006) (0.007) (0.007)

R-squared 0.855 0.861 0.798 0.803

IV:1860 OWB X Pill Legal

Pill Use in Previous Year -0.058*** -0.053*** -0.045*** -0.034**(0.006) (0.009) (0.011) (0.011)

F-statistic 51.44 55.30 18.46 16.44

First Stage: Pill Use on OWB 1860 X Pill Legal

OWB 1860 X I(1 if year>=64, else 0) 0.280*** 0.301*** 0.460*** 0.437***(0.04) (0.04) (0.11) (0.11)

R-squared 0.317 0.384 0.237 0.272

Region Trend No Yes No YesWeighted Yes Yes No NoClusters 70 70 70 70N 560 560 560 560Note: The dependent variable in the top panels is log births per 100 women aged 15-19. Pilluse is the average expenditure (SEK) on oral contraceptives per teen. Year and market fixedeffects are included in all specifications. Robust standard errors are clustered at the marketlevel and reported in brackets. OWB 1860 is the ratio of unwed births to total live births in1860. The instrument is defined as the interaction of OWB 1860 and a dummy equal to 1if the pill is legal (1964 and later) and zero otherwise. The F-statistic reports the value of theF-test of excluded instruments.* p<0.05, ** p<0.01, *** p<0.001

37

Table 7: Births per 100 Women Aged 15-19 and Pill Use the Previous YearOLS

Pill Use Previous Year -0.066** -0.045** -0.052* -0.027(0.024) (0.016) (0.021) (0.021)

R-squared 0.838 0.852 0.778 0.793

IV:1860 OWB X Pill Legal

Pill Use Previous Year -0.153*** -0.109*** -0.165*** -0.097**(0.015) (0.030) (0.036) (0.033)

F-statistic 51.44 55.30 18.46 16.44

Region Trend No Yes No YesWeighted Yes Yes No NoClusters 70 70 70 70N 560 560 560 560Note: The dependent variable in the top panel is births per 100 women aged 15-19. Pill useis the average expenditure (SEK) on oral contraceptives per teen. Year and market fixedeffects are included in all specifications. Robust standard errors are clustered at the marketlevel and reported in brackets. OWB 1860 is the ratio of unwed births to total live births in1860. The instrument is defined as the interaction of OWB 1860 and a dummy equal to 1if the pill is legal (1964 and later) and zero otherwise. The F-statistic reports the value of theF-test of excluded instruments. See the bottom panel of Table 6 for first stage results.* p<0.05, ** p<0.01, *** p<0.001

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Table 8: Log Teen Fertility and Pill Use: Alternative InstrumentsOWB OWB Butter OWB in 1860

Instruments: in 1860 in 1910 Prices and Butterin 1862 Prices in 1862

Second Stage: Log Births per 100 Women 15-19 on Pill Use

Pill Use in Previous Year -0.053*** -0.051*** -0.047*** -0.050***(0.009) (0.011) (0.011) (0.009)

F-statistic 55.30 22.35 10.17 34.03

J-Statistic 0.233

First Stage: Pill Use on Historical Instruments

OWB in 1860 X 0.301*** 0.229***I(1 if year>=64, else 0) (0.04) (0.04)

OWB in 1910 X 0.427***I(1 if year>=64, else 0) (0.09)

Butter Prices in 1862 X 0 .111** .081*I(1 if year>=64, else 0) (0.03) (0.03)

R-squared 0.384 0.488 0.367 0.481

Additional Information:N 560 560 560 560Clusters 70 70 70 70Note: All specifications include year and market fixed effects as well as regiontime trends. Robust standard errors, clustered at the market level, are reportedin brackets. Results are weighted by the population of women aged 15-19. OWBrefers to out-of-wedlock births measured per 100 live births. TheJ-statistic has a p-value of 0.629. Hence we cannot reject the null hypothesis thatthe instruments are uncorrelated with the residuals of the estimation equation.* p<0.05, ** p<0.01, *** p<0.001

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Table 9: Log Fertility and the Pill: Alternative Age Groups

Dependent Age 15-17 Age 18-19 Age 18-21 Age 15-21Variable: Fertility Fertility Fertility Fertility

OLS

Pill Use in Previous Year -0.026* -0.021*** -0.015*** -0.017***(0.010) (0.006) (0.003) (0.004)

R-squared 0.648 0.849 0.874 0.867

IV: OWB 1860 X Pill Legal

Pill Use in Previous Year -0.063*** -0.045*** -0.029*** -0.036***(0.014) (0.008) (0.004) (0.005)

F-statistic 54.71 48.27 53.50 53.50

Reduced Form: OWB 1860 X Pill Legal

OWB 1860 -0.019*** -0.013*** -0.009*** -0.011***(0.003) (0.002) (0.001) (0.001)

R-squared 0.654 0.852 0.872 0.868

Additional Information:N 560 560 560 560Clusters 70 70 70 70Note: All specifications include year and market fixed effects, region trends,and teen population weights. Robust standard errors are clustered at themarket level and reported in brackets. (N=560,Cluster=70)* p<0.05, ** p<0.01, *** p<0.001

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Table 10: Pill Use and Teen Nonmarital Fertility

Log Nonmarital Log Nonmarital NonmaritalDependent Births per Births per Births ShareVariables: 100 Single 100 Teens of All

Teens Teen Births

OLS

Pill Use in Previous Year -0.036*** -0.037*** -0.008***(0.007) (0.007) (0.002)

R-squared 0.762 0.762 0.921

IV:1860 OWB X Pill Legal

Pill Use in Previous Year -0.064*** -0.066*** -0.010*(0.012) (0.012) (0.004)

F-statistic 55.38 55.38 55.30

Additional Information:Weights Single Teens Single Teens All TeensClusters 70 70 70N 560 560 560Note: All specifications include year and market fixed effects and regiontime trends. Robust standard errors are clustered at the market leveland reported in brackets.* p<0.05, ** p<0.01, *** p<0.001

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Table 11: Pill Use and Teen Marital Fertility

Log Marital Log Marital Marital MaritalDependent Births per Births per Births per Births perVariables: 100 Married 100 Teens 100 Married 100 Teens

Teens Teens

OLS

Pill Use in Previous Year -0.013** 0.019 -0.445 0.025***(0.005) (0.010) (0.287) (0.007)

R-squared 0.731 0.907 0.761 0.914

IV:1860 OWB X Pill Legal

Pill Use in Previous Year -0.022* 0.055*** -1.681*** 0.033**(0.009) (0.016) (0.433) (0.012)

F-statistic 44.01 44.01 43.99 43.99

Additional Information:Weights Married Teens Married Teens Married Teens Married TeensClusters 70 70 70 70N 560 560 560 560Note: All specifications include year and market fixed effects and regiontime trends.* p<0.05, ** p<0.01, *** p<0.001

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Table 12: Pill Use and Teen Marriage

Dependent Log Married Log Married Log Married Log MarriedVariables: per 100 Teen per 100 Teen per 100 Teen per 100 Teen

Women Women Men Men

OLS

Pill Use in Current Year 0.034** 0.017(0.011) (0.017)

Pill Use in Previous Year 0.044** 0.032(0.014) (0.020)

R-squared 0.887 0.892 0.714 0.711

IV:1860 OWB X Pill Legal

Pill Use in Current Year 0.075*** 0.102***(0.016) (0.026)

Pill Use in Previous Year 0.102*** 0.126***(0.017) (0.031)

F-statistic 56.03 55.38 49.47 46.66

Additional Information:N 560 560 560 560Clusters 70 70 70 70Note: All specifications include year and market fixed effects as well asregion time trends. Robust standard errors, clustered at the market level,are reported in brackets. All estimates are weighted by the teen population.* p<0.05, ** p<0.01, *** p<0.001

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Table 13: Teen Fertility Placebo Tests Using Alternative Butter Price Instrument

(1) (2) (3)

Butter Prices in 1862 X 0.0006 -0.0003I(1 if t=1962, else 0) (0.0010) (0.0009)

Butter Prices in 1862 X -0.0015I(1 if t=1963, else 0) (0.0010)

Additional Information:Clusters 70 70 70N 210 280 280Note: Log births per 100 teens is the dependent variable. Allspecifications include year and market fixed effects as well asregion trends. Robust standard errors, clustered at the marketlevel, are reported in brackets. All specifications are weightedby the teen population.* p<.05, ** p<.01, *** p<.001

Table 14: ITT Effects for the Alternative Butter Price InstrumentTerminal Period 1965 1966 1967 1968 1969Butter Prices in 1862 X -0.001 -0.002* -0.002* -0.003** -0.003**I(1 if t<=1964, else 0) (0.0009) (0.0009) (0.0009) (0.0010) (0.0011)N 350 420 490 560 630R-squared 0.888 0.877 0.864 0.845 0.847Terminal Period 1970 1971 1972 1973 1974Butter Prices in 1862 X -0.003** -0.003** -0.004** -0.004** -0.004**I(1 if t<=1964, else 0) (0.0011) (0.0011) (0.0012) (0.0012) (0.0012)N 700 770 840 910 980R-squared 0.849 0.841 0.839 0.845 0.844Additional Information:Clusters 70 70 70 70 70Note: Log births per 100 teen women is the dependent variable. All specifications includeyear and market fixed effects, as well as regional time trends. Robust standard errors,clustered at the market level, are reported in brackets. All estimates are weighted bythe female teen population.* p<.05, ** p<.01, *** p<.001

44