The Effect of Hormonal Contraception on Antidepressant Use Evidence

Home , ATC code G02, Injectable birth control

The effect of hormonal contraception on antidepressant use

Evidence from a free hormonal birth control program

Liselotte Seljom

Master of Economic Theory and Econometrics

UNIVERSITETET I OSLO

May 2018

The effect of hormonal contraception on antidepressant use:

Evidence from a free hormonal birth control program

Master’s Thesis written by Liselotte Seljom to obtain the degree of Master of Economic Theory and Econometrics

Supervised by Professor Edwin Leuven at the Department of Economics at the University of Oslo

© Liselotte Seljom 2018 The effect of hormonal contraception on antidepressant use: Evidence from a free hormonal birth control program Liselotte Seljom http://www.duo.uio.no/ Trykk: Reprosentralen, Universitetet i Oslo

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Abstract

The prevalence of depression among young people in Norway varies greatly between the genders. Adolescent girls report a three times higher experience of depressive symptoms as their peer boys. At the same time hormonal birth control is found to be associated with higher rates of depression. While suggesting it is unclear whether this relationship is causal. This thesis aims to analyze the effect of a subsidy on hormonal contraception consumption and the subsequent use of antidepressants. To do so, we will exploit a policy experiment that took place May 2008 until May 2009 where women in the age group 20-24-years old and living in a geographic limited part of Norway were offered free hormonal contraceptives. The analysis will use prescription data collected from the National Prescription Register “Reseptregisteret” and investigate the impact of hormonal contraceptive use on antidepressants, using a difference-in-differences framework.

Preface

First and foremost, I would like to thank my supervisor Edwin Leuven for his excellent guidance through engaging knowledge and insightful comments. I am also very grateful to Menon for supporting this thesis by covering the cost of the data set applied and a special thanks to Marcus Gjems Theie in that regard. I am also grateful for the help I have received from the National Prescription Register in the process of creating and applying for the data set.

I would like to thank Jørgen Larsen, Vidar Rugset and Kim Dennis Seljom for comments on earlier versions and I would also like to thank my friends, family and colleagues for their various contributions and encouragement. All errors are my own.

Table of contents

1 Introduction ...... 1 2 Literature ...... 3 3 Data ...... 5 4 Empirical approach ...... 12 5 Results ...... 18 6 Conclusion ...... 32 References ...... 33 A Appendix ...... 34 B Analysis code ...... 38

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1 Introduction

Hormonal contraception is a popular method of fertility control used by women in Norway. Statistics collected from the National Prescription Register’s website show that 57% of Norwegian women in the age group 20-24-years old redeemed at least one prescription of hormonal contraceptives in Norway in 20171 (Reseptregisteret, 2018). Combined oral contraception2, also commonly known as “the pill” are the most chosen method of hormonal contraception. About 100 million women worldwide used the combination pill as their birth control in 2003 (Diana B. Petitti, 2003).

For most hormonal contraceptives the occurrence of mood swings and depressive symptoms can be found among the potential side effects listed in the package insert (Felleskatalogen, 2018). In Norway, and other countries, the occurrence of reported depression is equal between girls and boys before puberty (Bakken, 2017). As of puberty and out a gender disparity occurs and during the adolescent years, girls experiences depressive symptoms twice as frequent as their peer boys (Reneflot, Aarø, Aase, Reichborn-Kjennerud, & Tambs, 2018). The gender difference in depression seems to puzzle researchers as a clear reason for the presence of it is yet to be found. (Nolen-Hoeksema & Girgus, 1994) (Weissman & Klerman, 1977).

Angold et al. (1999) suggest that the causal explanation of the increase in depression among women needs to focus on changes in hormone levels. Recent studies have linked the synthetic hormones found hormonal contraception to subsequent adolescent depression (Skovlund et al. (2016), Zettermark et al. (2018).

From May 2008 until May 2009, the women aged 20-24-years living in either Tromsø or Hamar were subsidized with free hormonal birth control. This “free program” was a part of a study on young women’s contraception habits and the correlated effect on abortion rates (Øren, Leistad, & Haugan, 2010).

This thesis uses prescription data collected from The National Prescription Register to analyze the effect of subsidy on hormonal contraceptives consumption and on the subsequent use of antidepressants. The collected data set covers the prescription taken out in Tromsø, Hamar,

1 94 344 women in age group 20-24 took in 2017 out at least one type of hormonal contraceptive from ATC- group G03A (Reseptregisteret, 2018). 2 Combined oral contraceptives contains two kinds of hormones: estrogen and progestin. There is also progestin- only pill, called mini-pill, that only contains progestin without estrogen. (Felleskatalogen, 2018) 1

Bodø or Porsgrunn since 2004 and is analyzed with a difference-in-difference framework. Bodø and Porsgrunn are used as control municipalities. Tromsø is compared to Bodø and Hamar is compared to Porsgrunn.

The women living in the participating municipalities were also surveyed twice, in the beginning of the free program in 2008 and after it had ended in 2009. 93% in 2008 and 92% in 2009 reported that they had used hormonal contraception in their life time (Øren, Leistad, & Haugan, 2010). One of the main reasons for not utilizing hormonal contraception or discontinuing was “fear of side effects” (Øren, Leistad, & Haugan, 2010), and where women reported that the type of information regarding hormonal contraception they wanted more of, 67% in 2008 and 68% in 2009 answered “side effects”, where hormonal contraceptives association with mood swings and depression were frequently mentioned.

The results in this thesis show that the subsidy in 2008/2009 increased the consumption of hormonal contraception. There are larger increases of some methods of hormonal contraception than other, and this also varies across the treated municipalities. There does not seem to be association of the increased hormonal contraception during the free program with the use of antidepressants during the free program.

2 Literature

Although there has been research on the association of hormonal contraceptives and depression for almost as long as hormonal contraception has been available3, the question of whether there is a causality between hormonal contraception and depression, is still inadequately addressed.

Böttcher et al. (2012) concluded that the effects of hormonal contraceptives on the onset or severity of depressive disorders were still unclear. Böttcher et al. found two main confounders, in the existing analysis and available data, that made it hard to draw a conclusion on the overall effect of hormonal contraceptives on depression. This was due to inconsistent use of the term “depression” and a large number of different compositions/methods of hormonal contraception.

Skovlund et al. (2016) is a Danish cohort study that combined data from the National Prescription Register and the Patient Register from the Danish population of adolescent girls and young women. Skovlund et al. find that hormonal contraception is associated with subsequent use of antidepressants, especially among adolescents. They compared the probability of women on hormonal contraceptives to start on antidepressants or get a depression diagnosis compared to women who did not use hormonal contraceptives.

Zettermark et al. (2018) is a pharmacoepidemiological study on 800 000 Swedish women. In Sweden depression rates for girls/women are twice as high compared to boys/men after puberty, with negligible disparities between the genders before puberty. In their research they apply a combined data set of prescription register and patient register data and they find that there is strong association from hormonal contraception on subsequent depression among adolescents, though not among adults.

In 2007 SINTEF was asked by the Norwegian Directorate of Health to research women in the age group 20-24-years’ habits regarding contraception and abortion. To test this, SINTEF did a case study with two treatment municipalities Tromsø and Hamar where women in the age group 20-24 were offered free hormonal contraceptives. There were also two comparable

3 In 1969, nine years after the pill was approved by the FDA in USA, journalist Barbara Seaman wrote The Doctors' Case Against the Pill. Among the risk Ms. Seaman lists is heart attack, blood clot, cancer and suicidal depression some of them. 3 control municipalities, Bodø and Porsgrunn, where the women in the same age group 20-24 were required to pay regular prices for their hormonal contraceptives. In the SINTEF report it is reasoned for the selected municipalities as follow “Based on number of women in the selection, better educational opportunities and student health services at the student cites, along abortion rates, became the municipalities Bodø and Porsgrunn chosen as control municipalities and Tromsø and Hamar as treatment municipalities” (Øren, Leistad, & Haugan, 2010). Tromsø and Bodø are larger municipalities and have established wide student health services, while Hamar and Porsgrunn are smaller municipalities with less established student health services. None of the chosen municipalities are located close or in the same area of Norway, which reduces the chance of contamination between the treated and the control group. The women in the eligible group for free hormonal contraceptives in 2008/2009 are assumed not to have travelled from Bodø or Porsgrunn to Hamar or Tromsø based on the incentive to get free hormonal contraception.

3 Data

The analysis uses data from the National Prescription Register. The women in the original data set are from age 14-32-years old, born in or between 1978 and 2002. They must have at least one observed out-take of the prescription remedies in the ATC-groups4 G02 or G03 regarding hormonal contraceptives or N06A regarding antidepressants of the type selective serotonin reuptake inhibitor (SSRI) class. The out-take must be in or between year 2004 and year 2016 at a pharmacy in one of the selected municipalities, Tromsø, Hamar, Bodø or Porsgrunn. Or they must have been registered living in one of the selected municipalities when they redeemed a prescription from the listed ATC-groups. The unit of observations is the redeem of a prescription on the individual level for each month. If a woman is at least observed one time in one of the municipalities, Tromsø, Hamar, Bodø or Porsgrunn, either by the location of pharmacy or registered living, then all her observed out-takes on hormonal contraceptives and antidepressants, also those not located to Tromsø, Hamar, Bodø or Porsgrunn, are in the original data set.

The original data set is an unbalanced panel data set. The women in the data set are only observed the months they redeem prescriptions. The women are identified by a project specific identification number. Some of the women are only observed before, under or after the treatment. The women are assumed to live in the municipalities where they redeem their current prescriptions regardless of whether they have registered residence in the same municipality or not. If either the pharmacy was in municipality Bodø, Hamar, Porsgrunn or Tromsø the information is given and if the prescription was redeemed in another municipality a collective term “other” is registered. The out-takes from category “other” is dropped as the women are assumed not to live in the treatment or control municipalities.

The different types of hormonal contraceptives have been divided into four subgroups based on their composition and method of use. The most common method is a so-called combination pill; which contains the hormones estrogen and progestin. Pills that fit this description are captured in the variable pill. Another popular type of hormonal contraceptive are the pills which only contain progestin. These pills are captured in the variable “mini” as

4 The Anatomical Therapeutic Chemical (ATC) Classification System is a system that is used to classify medicines based on the composition of the drug (WHO Collaborating Centre for Drug Statistics Methodology, 2012). The ATC-codes in the data set were G02BA03, G02BB01, G03A, G03HB01 and N06A. 5 they commonly are referred to as “mini-pills”. Patches, injections and vaginal rings (v-rings) are collected in the variable “ring” based on the method of use: patches and v-rings comes in packages of three and are changed monthly while each injection lasts for 3 months. The variable capturing long-active reversible contraceptives (LARC) such as intrauterine device (IUD) and implants are named “spiral”. For detailed information on which ATC-codes that are programmed to each variable, see appendix table A1.

The original data consisted in 1,544,779 out-takes by 74,109 different women. Two observations of the hormonal contraceptive group “pill” were deleted because they were “extreme outliers”. They reported an outtake of 93 and 101 prescriptions that ended up being a total of 6,363 and 7,812 pills per redeem. It is unlikely that a woman redeemed 6,363 or 7,812 hormonal contraceptive pills in one redeem. There was also another redeem the same month for both individuals carrying a reasonable number of pills. The reasonable out-takes were kept in the data set. The highest out-take of pills left in the data set is then 1,008, which two separate women was observed with. This is approximately equivalent to two and a half years of contraception supply5.

The free program started on May 13th in 2008 and ended on May 12th in 2009. The data are at the monthly level. The analysis therefore assumes that the program ran from June 2008 to May 2009. It is implemented years that are centered relative to the free program (that run from June to May), and in the empirical analysis the implemented years are used to control for year (and month).

For the analysis there is constructed two panel data sets. The first is municipality levelled where the data on the take-outs of a hormonal contraceptive are summed by municipality, year and age group. The women are divided into five age groups. These are 14-16 years, 16- 19 years, 20-24 years, 25-29 years and 30-32 years. This leaves us with a quasi-panel data set on the municipality level with yearly observations for each age group. This is the data set that is used to investigate the changes in consumption of the different hormonal contraceptive methods during the free program. The other data set created is a panel at the individual level. The panel structured data is balanced by filling in the missing observations for the months we do not observe the individuals. To accomplish this, we must assume on location for the month

5 The yearly user dosages are calculated to be 440 oral hormonal contraceptive pills. The number is justified in the appendix. 6 where the woman is not observed. The months they do redeem prescriptions, the women are located by the variable of “pharmacy location”. If a woman is observed taking out prescriptions in two different municipalities we assume she moved in the middle of the period. The months we do not observe any redeem of prescriptions becomes filled in with “outtake of 0”-observations.

In the following figure 1, figure 2, figure 3 and figure 4 show the average out-take, in the subpopulation of women 20-24-years-old living in either Tromsø, Hamar, Bodø or Porsgrunn. The hormonal contraceptives are categorized into the four aforementioned groups combination pills, mini-pills, patches, v-ring and injections and IUD and implants. The treated municipalities are plotted in red, while the control municipalities are plotted in blue. The y- axis measures the average yearly out-take per women per method in each municipality. On the x-axis it is the “skewed” year that is graphed and labelled.

Figure 1 Average out-take of pills, women 20-24-years, in the selected municipalities

There seem to be a significant increase in the consumption of combination pills in the treated municipalities during the free program in year 2008. This will be explored later by difference- in-difference.

Figure 2 average out-take of mini-pills for women 20-24-years

In figure 2 and in figure 3 the changes in treatment municipalities and control municipalities seem to be less substantial during year 2008.

Figure 3 average out-take of v-ring, patches and injections for women 20-24-years

Figure 4 average out-take of IUD and implants for women 20-24-years

From figure 4 Tromsø seems to react very active to the free program in terms of out-takes of IUD and implants. This also seems to be unique for Tromsø as none of the other

9 municipalities, treated or not, seems to report a particular increase in outtake of IUD and implants during 2008.

Figure 5 show the average outtake of antidepressants for women in the 20-24-year age group in the different municipalities.

Figure 5 average out-take of antidepressants for women 20-24-years

Hormonal contraception has changed over time. And not only have the composition of hormonal contraceptives changed, but so has the methods of consuming them. There seem to be a trend where women change from the combination pills, that require daily user action, to the spirals and implants that last for up to 5 years after insertion or to the mini-pills, which also requires daily user action, but has a different hormone composition. Figure 6 shows the average redeem of each method per woman per year. All the women in the original data set, age 14-32-years, are presented in this figure. The different scaling on the y-axis come from the different user dosages each method provides. One IUD or implant used for 5 years and corresponds approximately to 2200 birth control pills6.

6 The estimated yearly user dosage of hormonal contraceptive pills is approximately 440. The number is justified in the appendix. 10

Figure 6 Average take out per woman, 14-32-year-old

To try to deal with the issue of switching methods of hormonal contraception, I will add a measure of whether a woman is covered by hormonal contraception in a given month. The coverage is calculated based on the estimated daily user dosages of each of the contraceptive methods and an estimated stock. The first observed out-take of hormonal contraception is programmed as the initiated stock, and then for each month the monthly user dosage is subtracted, and any new out-take of hormonal contraceptives are added to the shrinking stock. If there are enough contraceptives in the stock to cover at least half a monthly use, the women are considered covered for the given month. It is assumed that a patient consumes the full amount of her out-takes. In the appendix table A2 there is more detailed information of how the daily user dosages for each method is defined.

To avoid any problems with identification there were not applied for more detailed information on the prescription out-takes than the monthly sum. This is not considered a problem for the main part of the analysis, but it would have been advantageous to separate at least May 2008 and May 2009 into two categories, one before and one after the implement of the free program.

4 Empirical approach

The free program in 2008 resulted in lower cost and more attention7 to hormonal contraceptives in Tromsø and Hamar. According to economic theory this should lead to an increase in the consumption of a normal good. To estimate the effect from the free program in year 2008 on hormonal contraception consumption and potentially subsequent antidepressants usage, a Difference-in-Difference (diff-in-diff) framework is applied.

Figure 7 and figure 8 show the average outtake of the different categories between the treatment and the control municipalities. The time trends seem to evolve similarly between treatment and control prior and post the free program and we see deviations during the free program. Considering this, the method diff-in-diff seems appropriate to use. Figure 7 show Tromsø, plotted in red, and Bodø, plotted in blue. Especially in category pill and spiral (IUD and implants) we see a jump in consumption during 2008 in Tromsø while not in Bodø.

Figure 7 Average outtake between Tromsø (red) and Bodø (blue)

Figure 8 show Hamar (red) and Porsgrunn (blue). Here the average consumption of pills increases substantial during 2008 in Hamar, while not in Porsgrunn.

7 Sintef handed out pamphlets and information about the free program at school and by post. 12

Figure 8 Average outtake between Hamar (red) and Porsgrunn (blue)

Since January 2002 adolescents in the age group 16-19 years have had subsidized consumption of hormonal contraceptives. The subsidy for 16-19-year-olds applies to all the municipalities in Norway (Ministry of Health and Care Services, 2005) which in our case implies that the 16-19-year-olds are treated every year in both the treatment and the control municipalities. There was not observed any significant difference between the control and the treatment municipalities regarding if the women had utilized the 16-19-year-olds subsidy or not (Øren, Leistad, & Haugan, 2010). Table 1 and table 2 gives an overview over the women who are subsidized in the different municipalities in the different time-periods. The difference in 2008 for the 20-24-year-olds, written in bold, is due to the free program.

Table 1 Overview over the subsidized age groups in the treatment municipalities: Tromsø and Hamar

Age group Subsidized Before 2008 Subsidized In 2008 16-19 yes yes 20-24 no yes 25-29 no no

Table 2 Overview over the subsidized age group in the control municipalities: Bodø and Porsgrunn.

Age group Subsidized Before 2008 Subsidized In 2008 16-19 yes yes 20-24 no no 25-29 no no

Ideally, we would like to observe the exact same individuals both treated with the free program in the year 2008 and not treated with the free program in the year 2008. Unfortunately, this is impossible as an individual is either treated or not. That is why we need a comparison group which is not treated.

The analysis compares treated 20-24-year-olds to untreated 20-24-year-olds in different years in the same municipalities. If these groups are comparable this will give us effect and confounding time effects. To deal with the confounding time effect in the treatment municipality, we estimate the time effect from the 20-24-year-olds in the control municipalities and subtract it from the total effect found in the treatment municipalities.

Prescription out-take in the treated municipalities is compared to the out-take in the control municipalities. For the diff-in-diff analysis to be valid, the time trend affecting the treatment group and the control group should be identical if there would be no policy reform implemented. That means we need to be able to assume that Bodø and Tromsø would have developed similarly over time without the intervention from the free program. The same must hold for Porsgrunn and Hamar, where Hamar is the treated municipality. This is called the “common trend assumption” and to test the assumption of a common trend between the treatment and the control we implement placebo reforms prior and post year 2008.

The municipalities that are chosen as treatment and control municipalities were chosen because they are comparable with respect to relevant characteristics for the research SINTEF aimed to answer. That means they are comparable with respect to characteristics that seem to be important for the use of hormonal contraceptives and abortion rates. It is assumed in this thesis that the treatment and control municipalities are also comparable with respect to characteristics that influence the usage of antidepressants.

DID across municipalities (specification 1) Here women in the age range of 20-24-year-old are compared across the treatment municipality and the control municipality, over time. The idea is that similarly aged women have same trend across municipalities where Tromsø is compared with Bodø and Hamar is compared with Porsgrunn.

The DID is implemented using ordinary least square (OLS), where the regression equation below is estimated for each of contraceptive method categories, called y.

푦 = 훿퐷퐼퐷1푇푀 ⋅ 푌2008 + 푌 + 푀 + 휀

TM is the treatment dummy, Y2008 is the year 2008 dummy, Y is a year dummy added to capture the confounding time effects and M is a dummy on municipality.

We assume that the interaction is independent of 휀 conditional on 푌 + 푀:

퐸[휀|푇푀 ⋅ 푌2008, 푌 + 푀] = 퐸[휀|푌 + 푀]

The coefficient of interest is 훿퐷퐼퐷1 which is the estimated effect from the free program:

̂ 훿퐷퐼퐷1 = (푦̅푇푀=1,푌2008=1 − 푦̅푇푀=1,푌2008=0) − (푦̅푇푀=0,푌2008=1 − 푦̅푇푀=0,푌2008=0)

The estimated effect of the free program is the difference between the treatment municipality, in year 2008 and not in year 2008, subtracted by the difference between the untreated group, in year 2008 and not in year 2008. If the coefficient on the interaction term is significantly different from zero then there is a significant difference in the average redeem of hormonal contraceptives during the free program in the treated municipalities compared to the controls during the same time-period, for the same aged individuals.

To make sure that it is not just always the case that the 20-24-years old in the aforementioned groups act different across the municipalities we test year 2005, 2006, 2007, 2009, 2010 and 2011 with placebo reforms. In these years there was no known systematic difference between the treatment and the control group and we do not assume that there is an interfering from the free program prior and post year 2008. If the placebo reforms report significant differences across the treatment and the control group, then this implies a violation of the common trend assumption.

DID within municipality (specification 2) An alternative specification compares the 20-24-year-olds to the 25-29-year-olds within each municipality. The idea is that women located in the same municipality are subject to the same within municipality trend across age groups. The diff-in-diff regression is estimated separately for Tromsø, Bodø, Hamar and Porsgrunn. The interaction term consists of a dummy on age group and a dummy on 2008.

The coefficients are estimated by OLS. For each hormonal contraceptive method, called y, this equation is estimated:

푦 = 훿퐷퐼퐷2푇퐴 ∙ 푌2008 + 푌 + 퐴 + 휀

TA is the age group dummy that is equal to 1 if the woman is in the age group 20-24-years-old and equal 0 if she if not. Y2008 is equal to 1 in year 2008 and equal 0 if not. Y is a year dummy to control for confounding time effects and A is a dummy on age group to control for age group trends outside the free program.

We assume that the interaction is independent of 휀 conditional on 푌 + 퐴:

퐸[휀|푇퐴 ⋅ 푌2008, 푌 + 퐴] = 퐸[휀|푌 + 퐴]

The coefficient of interest is 훿퐷퐼퐷2 which is the effect of the free program:

̂ 훿퐷퐼퐷2 = (푦̅푇퐴=1,푌2008=1 − 푦̅푇퐴=1,푌2008=0) − (푦̅푇퐴=0,푌2008=1 − 푦̅푇퐴=0,푌2008=0)

This specification is also tested with placebo reforms the three years prior and post 2008.

Difference-in-Difference-in-Difference (DIDID) The previous two specifications assumed either common trend across municipalities or common trend across age groups. This can be relaxed. Here the purpose is to isolate the change in the consume of hormonal contraception that comes from the “genuine” effect of the free program by controlling for both the age groups differences and municipality differences in the same regression.

This is the equation that is estimated by OLS in the DIDID regression:

푦 = 훿퐷퐼퐷퐼퐷 ⋅ 푇퐴 ⋅ 푇푀 ⋅ 푌2008 + 훾 ⋅ 푇푀 ⋅ 푌2008 + 푌 ⋅ 퐴 + 푀 ⋅ 퐴 + 푌 + 푀 + 휀

In the equation above the coefficient 훿퐷퐼퐷퐼퐷 is the mean effect from switching the interaction term from 0 to 1. The interaction term of interest consists of three dummies, one for age group, one for treatment and one for time. The estimated effect from the interaction between treatment municipality (TM) and the year 2008 (Y2008) is controlled for. Each year is multiplied with each age group (Y ∙ A) to capture the age groups yearly changes across municipalities. Each municipality is multiplied with age group (M ∙ A) to capture the different age groups effect on consumption of hormonal contraception that does not vary over time but varies across municipalities. Lastly, dummies on year (Y) are added to control for the time trend and dummies on municipalities (M) are added to control for the within municipality characteristics.

The final part of the analysis investigates whether the overall use of hormonal contraceptives increases during the free program. To investigate the effect of the free program, on overall coverage, we perform specification 1, specification 2 and DIDID on the individual level panel-structured data. First, we estimate the effect of the free program on the average coverage and thereby on the consumption of antidepressants.

5 Results

Some of the regressions on hormonal contraception report significant changes during the free program. First, we present graphs from the diff-in-diffs specification 1 and specification 2 and then a combined output table, where the output from both the specifications and from the DIDID is reported. After this we present results from the individual panel regressions on overall hormonal contraception coverage and consumption of antidepressants.

DID across (specification 1) The graphs in figure 9 show the estimates from the DID across regression where Tromsø is compared to Bodø. Tromsø experiences a significantly larger use of “spiral” compared to Bodø in year 2008. None of the placebo years report a significant difference on spiral consumption. For pills, mini-pills and v-rings the effect from the free program on Tromsø compared to Bodø is less clear. The y-axis measures the effect on the average out-take of hormonal contraception per woman.

Figure 9 Effects on average birth control take-out, Tromsø - Bodø (DID across)

The DID across regression estimated for Hamar and Porsgrunn are displayed in figure 10 and reports a significant effect on consumption of combination pills in 2008. Before and after 2008 the consumption of combination pills is insignificantly different between the municipalities, which strengthens the assumption of a present common trend. Mini-pills, v- rings and spiral does not seem to change significantly between Hamar and Porsgrunn. For the placebo years there is no significant differences.

Figure 10 Effects on average birth control take-out, Hamar - Porsgrunn (across DID – specification1)

DID within (specification 2) The within municipality regression in Tromsø does also report a significant increase for the consumption of spirals, like seen in DID across. The 20-24-year-olds in Tromsø also seem to increase the consumption of combination pills and rings compared to the 25-29-year-olds.

Figure 11 Effects on average birth control take-out within Tromsø (specification 2) where the 20-24-year-olds are compared to the 25-29-year-olds

The DID within municipality regression in Hamar shows a significant increase of the consumption of combination pills like the DID across also reported. Also, similar to DID across, DID within does not report any significant increase in the consumption of the other methods.

Figure 12 Effects on average birth control take-out within Hamar (specification 2) where the 20-24-year-olds are compared to the 25-29-year-olds

Looking at the graphs it seems that both DID across and DID within pick up at the same effects. This strengthens the hypothesis of the effect being caused by the free program, which is a common and known change in both specifications, and that the reported effects do not simply come from a difference in municipality trend and/or age group trend that happen to change at the same time as the free program was implemented.

The figures from within Bodø and within Porsgrunn report no significant differences between the 20-24-year old age group and the 25-29-year old age groups consumption of hormonal contraception. The graphs over Porsgrunn and Bodø can be found in the appendix.

The tables below report the estimated coefficients and standard errors on the interaction terms in the different regression specifications. The outputs are reported for each of the hormonal contraception categories and for each of the specifications.

Output table 3, from Tromsø and Bodø, reports the coefficients on the interaction terms from DID across, DID within and the DIDID. The coefficients on the placebo reforms are not reported in the table.

Table 3 Diff-in-diff Tromsø and Bodø pille mini spiral ring

2008 DID across 9.451 5.926 0.0277** 0.0957 (5.667) (2.793) (0.00774) (0.179)

2008 DID within Tromsø 14.24* -0.552 0.0218* 0.248* (5.573) (3.236) (0.00716) (0.102)

2008 DID2 within Bodø -0.818 -4.668 -0.000594 -0.0885 (5.562) (3.237) (0.00697) (0.162)

N 24 24 24 24

DIDID Tromsø Bodø 15.06 4.116 0.0224* 0.337 (9.400) (15.65) (0.0151) (0.405)

N 48 48 48 48 Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

The women in Tromsø are measured with an increase in consumption of spirals to be 0.0218 – 0.0277 yearly, depending on the specification. This is comparable to a little less than 1 month of consumption or 36 combination pills8. Within Bodø the regression shows no significant change in either of the methods. The effect from DID across has the highest significance level at 99% significance and also reports the highest increase of 0.0277 spirals. DID within also report significant increase of combination pills and v-rings during the free program.

Spirals are more expensive because spirals are a one-time purchase you utilize for 3-5 years9, in contrast to the combination pills, mini-pills and patches, injections and v-rings which usually are bought every three months. All the hormonal contraceptives were subsidized for exactly one year, so the most cost-effective choice is to choose the one-time purchase of the expensive spiral, that lasts for 3-5 years, rather than start or continuing the methods that requires a new purchase every three month. The free period will run out after a year and then you must pay for the upcoming next years out of pocket if you choose the short-lasting methods. It is therefore reasonable and expected to find the increase in the spiral, as the regression shows.

8 The monthly user dosage of combination pills is measured to yield 37.3 pills. See appendix. 9 Long-active reversible contraceptives such as spirals and implants can be kept for 3-5 years. In this thesis it is assumed that a woman is covered for 3 years from the month she redeems a remedy in the category spiral. 22

On the other hand, the diff-in-diff regressions over Hamar and Porsgrunn does not tell the same story. Women in Hamar received the same offer, as women in Tromsø, of free hormonal contraceptives for exactly one year. The same theory of spiral being the most cost-effective choice and therefore the most rational choice applies for women in Hamar as well as for women in Tromsø. Therefore, it should be expected women in Hamar to also take the rational choice and choose the most cost-effective method. From the regression it does not seem to be the case as they only significant difference in the consumption is the increase of combination pills.

Output table 4, from Hamar and Porsgrunn, reports the coefficients on the interaction terms from DID across, DID within and the DIDID estimates.

Table 4 DID Hamar and Porsgrunn

pille mini spiral ring 2008DID across 30.53** -2.597 0.00213 0.0726 (8.661) (5.985) (0.00392) (0.237)

2008DID within Hamar 35.00** -4.131 0.00363 0.134 (8.064) (7.968) (0.00572) (0.121)

2008DID within Porsgrunn -3.129 -4.919 0.00607 0.0136 (6.203) (2.994) (0.0754) (0.164)

N 24 24 24 24

DIDID Hamar Porsgrunn 38.13* 0.787 -0.00244 0.12 (18.05) (14.92) (0.0160) (0.415)

N 48 48 48 48 Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

The average woman in Hamar consume significantly more of the combination pills. Between 30.53 and 38.13 more pills are on average consume during the year 2008. This is comparable to a monthly usage or a little less. The increase of combination pills captured by DID across and DID within are significant at a 99% significance level, while the DIDID estimate is significant at a 95% level. The within Porsgrunn regression does not report any significant difference.

One possible explanation for why the women in the municipalities Tromsø and Hamar reacts differently to the same policy change could be that the more developed municipalities, in terms of student health care services, have more knowledge of the different methods. It is relatively new that young women who have not born any children yet are offered to insert a IUD (spiral). It is possible that women in Tromsø are more comfortable with the method and that they have easier access in terms of a broader supply of doctor’s offices that offer information about the IUD and to insert them. Tromsø simply has a more developed student health care service than Hamar (Øren, Leistad, & Haugan, 2010). The free program was promoted in schools and it is not unthinkable that these different municipalities, Tromsø and Hamar, react differently to a free program. Nevertheless, there are clear results that the women 20-24-years old that are offered free hormonal contraception increase the consumption of at least some methods.

Birth control coverage and antidepressant use To investigate a possible causality between the consumption of hormonal contraception and antidepressant use I now turn to individual data. I first verify that the overall use of hormonal contraception increases during the free program (not only certain method of hormonal contraception) and by what amount.

The regression from the overall coverage from hormonal contraceptives shows that the cover increases during the free program in the treated municipalities. This increase can come from new users of hormonal contraceptives and/or from women using the medicine more actively and continuously or stocking up. Either way there is substantial evidence of women consuming more of the hormonal contraceptives when they are for treated by the free program.

Figure 13 is from the regression of Tromsø compared to Bodø, DID across:

Figure 13 Effects on average birth control take-out DID across Tromsø and Bodø (specification1)

For Tromsø and Bodø the results of the coverage seem to increase significantly during the free program in year 2008. The individual level regression shows the same as the one on the municipality level. The graph imply that the common trend assumption holds as the placebo reforms report little significant difference.

In the DID across regression between Hamar and Porsgrunn we see a significant jump in cover in year 2008 and the common trend assumption seem to be “less violated” between Hamar and Porsgrunn. In figure 14 we see that there are less significant differences post and prior to the free program, than it was for the regression between Tromsø and Bodø. However, the level of increase in the cover-variable seems to be similar in Tromsø and Hamar during the free program. The cover-variable is a dummy-variable taking on the value 0 if the woman is not covered and value 1 if she is considered to be covered. The regression seems to pick up on a cover increase of approximately 0.03 for the average woman, meaning that during the free program the average woman living in the treated municipalities are 3% more likely to be covered by hormonal contraception.

Figure 14 estimated cover specification 1, comparing Hamar to Porsgrunn

The following graphs are from the diff-in-diff estimated from DID within shows the outcome variable “cover” during the free program and associated placebos.

Figure 15 Average change in cover DID within municipality (specification 2)

Antidepressants are a possible treatment for depression and depression is linked as a possible side effect of hormonal contraception. Here I present the graphs from the individual panel regressions on the antidepressants consumption. The consumption of the antidepressants is estimated for all the specifications; DID across, DID within and the DIDID. The average consumption of antidepressants does not seem to experience a significant change in the year 2008.

Figure 16 below shows the DID across regression between Tromsø and Bodø. And figure 17 report the results from the Hamar and Porsgrunn regressions.

Figure 16 Average change in outtake of antidepressants DID across Tromsø and Bodø (specification1)

Figure 11 Average change in outtake of antidepressants DID across Hamar and Porsgrunn (specification1)

Neither the DID within regression on the individual data seem to report a causality between the increase of hormonal contraception in year 2008 and the consecutive consumption of antidepressants. The graphs are presented beneath in figure 14.

Figure 18 Average change in outtake of antidepressants, within municipality (specification2)

Table 5 reports the estimated interaction terms from the free program on the consumption of hormonal contraception and on the consumption of the antidepressant.

Table 5 output table from the panel regression of Tromsø and Bodø

COVER ANTIDEPRESSANTS

Tromsø VS Bodø DID across 0.0230*** 0.124 (0.00358) (0.066)

N 779495 779495

Tromsø DID within 0.0180*** 0.0509 (0.00314) (0.0844)

N 941499 941499

Bodø DID within -0.00783 0.247 (0.00426) (0.153)

N 490043 490043

DIDID 0.0259*** -0.162 (0.00531) (0.16)

N 1431542 1431542

Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001

The cover increases significantly in Tromsø for all specifications at all conventional levels of significance, while in Bodø there is no significant increase. The regressions show no significant change in antidepressant consumption for neither of the municipalities.

Table 6 output table from the panel regression of Hamar and Porsgrunn

COVER ANTIDEPRESSANTS

Hamar VS Porsgrunn DID across 0.0402*** 0.218 (0.00528) (0.169)

N 311129 311129

Hamar DID within 0.0368*** 0.185 (0.00537) (0.184)

N 305990 305990

Porsgrunn DID within -0.0617 -0.272 (0.00567) (0.211)

N 284040 284040

DIDID 0.0451*** 0.529 (0.00780) (0.278)

N 590030 590030 Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001

The cover increases significantly in Hamar while in Porsgrunn there is no significant increase. The individual panel regressions show no significant change in antidepressant consumption.

Skovlund et al. (2016) showed that the subsequent depression rates peaked after 6 months use on hormonal contraception. This is tested and does not seem to have significant effect on antidepressants consumed correlated to the free program. If there was a lagged effect from the free program of hormonal contraceptives on the consumption of antidepressants, it could also be captured by the 2009 placebo reform, but as seen on the following graphs this does not seem to be the case.

6 Conclusion

This thesis provides supplementary evidence that the subsidy on hormonal contraception during the free program in year 2008 increased the consumption. In both the treated municipalities the overall use of hormonal contraception seemed to increase compared to the control municipalities. The use of antidepressants does not seem to change significantly during the free program period. However, the use of antidepressants is more spare and intricate compared to hormonal contraceptives and this data set based merely on information regarding prescription redeemed might not be complex enough to capture a correlation or causality even if it were to exist.

Skovlund et al. (2016) found the association of hormonal contraception and depression strongest among adolescents and Zettermark et al. (2018) only found association for adolescent, not for adults. This thesis analysis an older population than the adolescents. The adolescent years are a time where girls originally go through changes due to puberty. It could be that this group in particular is sensitive to exogenous hormones, like hormonal contraception provides.

It could also be possible that the older sample of women using hormonal contraceptives could be influenced with a “skepticism-bias” compared to the younger population. A 16-19-year-old might not ask or be as aware of the health risks associated with hormonal birth control as a 20-24-year-old might be. Also, older women might be more familiar with mental health problems which could increase consciousness of the possible issues regarding hormonal contraception.

Regardless, women suffer from depression, alongside the need for a liable contraception method is present. The lack of a clear answer on whether such a popular birth control causes depression is a problem either way, as the inadequate knowledge diminishes the absolute value of hormonal contraception which is, after all, connected to its overall acceptability and usage.

References

Angold, A., Costello, E. J., Erkaneli, A., & Worthman, C. M. (1999, September 1). Pubertal changes in hormone levels and depression in girls. Psychological Medicine, ss. Volume 29, Issue 5. Bakken, A. (2017). Ungdata. Nasjonale resultater 2017. Oslo: Nova. Böttcher, B., Radenbach, K., Wildt, L., & Hinney, B. (2012, July ). Hormonal contraception and depression: a survey of the present state of knowledge. Gynecology and Obstetrics, ss. 286(1), 231-236. doi:10.1007/s00404-012-2298-2 Diana B. Petitti. (2003, 10 9). Clinical practice. Combination estrogen-progestin oral contraceptives. NEJM Group, ss. 349:1443-1450. doi:10.1056/NEJMcp030751 Felleskatalogen. (2018, 04 25). Hentet fra Legemidler: https://www.felleskatalogen.no/medisin/yasmin-bayer-ab-565567 Helsedirektoratet. (2012). Lett tilgjengelig prevensjon til unge kvinner - Helsedirektoratets vurdering og anbefalinger. Oslo: Helsedirektoratet. Ministry of Health and Care Services. (2005, juni). Gratis prevensjon til unge kvinner 16 til og med 19 år. Gratis prevensjon til unge kvinner 16 til og med 19 år - Innføring av positiv liste for refusjon fra trygden for hormonelle prevensjonsmidler fra 01.09.05 - overgangsordning. Ministry of Health and Care Services. (2009). Forebygging av uønsket svangerskap og abort blant tenåringer. Helse- og omsorgsdepartementet. Nolen-Hoeksema, S., & Girgus, J. S. (1994). The emergence of gender differences in depression during adolescence. Psychological Bulletin. doi:10.1037/0033-2909.115.3.424 Reneflot, A., Aarø, L., Aase, H., Reichborn-Kjennerud, T., & Tambs, K. Ø. (2018). Psykisk helse i Norge. Folkehelseinstituttet. Reseptregisteret. (2018, 25 04). Søkeside. Hentet fra Reseptregisteret: http://www.reseptregisteret.no/Prevalens.aspx Skovlund, C., Mørch, L., Kessing, L., & Lidegaard, Ø. (2016). Association of Hormonal Contraception With Depression. JAMA Psychiatry, 73(11), ss. 1154–1162. doi:10.1001/jamapsychiatry.2016.2387 Weissman, M. M., & Klerman, G. L. (1977). Sex Differences and the Epidemiology of Depression. Arch Gen Psychiatry. doi:10.1001/archpsyc.1977.01770130100011 WHO Collaborating Centre for Drug Statistics Methodology. (2012). Guidelines for ATC classification and DDD assignment 2013. Oslo: WHO. Zettermark, S. (., Vicente, R. (., & Merlo, J. (. (2018, Mars 22). Hormonal contraception increases the risk of psychotropic drug use in adolescent girls but not in adults: A pharmacoepidemiological study on 800 000 Swedish women. PLoS ONE 13(3). Øren, A., Leistad, L., & Haugan, T. (2010). Endres prevensjonsvaner og abortrate hos kvinner 20-24 år ved tilbud om gratis hormonell prevensjon? Teknologi og samfunn. SINTEF.

A Appendix

Figure A 1 Effects on average birth control take-out within Bodø (specification 2)

Figure A 2 Effects on average birth control take-out within Bodø (specification 2) 34

Table A 1 The ATC-codes divided into the different hormonal contraception categories.

ATC code Type Out-take

Variable pille

G03AA07 pille 378,595

G03AA09 pille 157,107

G03AA12 pille 281,064

G03AA14* pille 1,014

G03AB03 pille 75,587

G03AB04 pille 43,333

G03AB08 pille 2,280

All combination pill out-take 1 030 917

Variable mini

G03AC01 mini 13,168

G03AC02 mini 299

G03AC09 mini 151,570

Microluton* mini

All mini-pill out-take 165 037

Variable ring

G02BB01 Ring 57,619

G03AA13 Patch 4507

G03AC06 Injection 33268

All ring out-take 95 394

Variable spiral

G02BA03 IUD 8,448

Jadelle* Implant 423

G03AC08 Implant 5,988

All spiral out-take 14 859

Total HC out-takes 1 306 207

G03AD** Emergency contraception 460

G03HB01** Anti-androgen 62,164

*Both Microluton and Jadelle var registered at the same ATCcode G03AC03 in different periods. ** Hormonal contraception that was not subsidized during the free program *** Prescription that have gone out of production. G03AB03 expired 2006 and G03AC02 expired in 2005.

To calculate the daily user dosage, it is assumed that half of the women each month skips their period. Each pill case contains 21 active pills and 7 non-active pills. That means they start directly on next pill case or insert the ring/patch before their period start to avoid the non-active pills and skip their periods.

Table A 2 The calculated monthly user dosages

Hormonal contraception Defined user dosage Monthly consume (1 month is category on average equal to 1.09. 1 average cycle is equal to 28 days)

Pille (combination pills) Consume 31 active pills (31+10) + (24+8)/2 = 36.5 pills (skip period) or 25 active monthly are the defined average pills. There are 21 active consume. pills per case and 28 pills

total per case. To obtain 32

active pills there is on

average 10 non-active pills excess, and to obtain 25 active pills there is on average 7.6 non-active pills excess.

Mini (progesterone only Consume 32 active pills (31+10) + (24+8)/2 = 36.5 pills pills) (skip period) or 25 active monthly are the defined average pills. There are 21 active consume. pills per case and 28 pills

total per case. To obtain 32 active pills there is on average 10 non-active pills excess, and to obtain 25 active pills there is on average 7.6 non-active pills excess.

Ring (vaginal rings, Consume 1.33 (skips ((1.33*1.09)+(1*1.09))/2=1.335 patches and injections) period) or 1 per cycle

Spiral (Implants and Consume 1 spiral for 3 3 years is approximately 36 intrauterine device (spiral)) years cycles, which again gives a monthly consume of spiral

equal 0.028

B Analysis code clear all capture cd C:\Users\lisel\Dropbox\Masteroppgave use "Raw data/2361_Reseptregisterdata_201801/2361_Reseptregisterdata_201801" label def kommune 403 "Hamar" 805 "Porsgrunn" 1804 "Bodø" 1902 "Tromsø" 9999 "Annen kommune" label val pasientbostedkommunenr_gen apotekkommunenr_gen kommune drop pasientbostedkommunenavn_gen apotekkommunenavn_gen rename pasientbostedkommunenr_gen komnr rename apotekkommunenr_gen apkomnr rename pasient* * rename utleverings* * gen date = ym(aar, mnd) format date %tm drop kjonn utenid generate pille = inlist(atckode,"G03AA07","G03AA09","G03AA12","G03AB04","G03AB03") generate mini = inlist(atckode,"G03AC01","G03AC09","G03AC02") //minipille gen ring = inlist(atckode,"G02BB01","G03AC06","G03AA13") gen spiral = inlist(atckode,"G03AC08","G02BA03") /// | (varenavn=="Jadelle implantat 75mg") gen unknown = inlist(atckode, "G03HB01","G03AB03","G03AA14","G03AB08")| (varenavn=="Microluton tab 30mcg") gen progesteron = inlist(atckode,"G03AD01","G03AD02") gen antidep = 0 replace antidep = substr(atckode,1,1)=="N" encode varenavn , gen(vare) drop varenavn label var antidep "Antidepressant" label var pille "combination pills progrestin and osterogen" label var mini "progrestin only" label var ring "combination prog and ost patch, v-ring" label var spiral "long-acting reversible contraceptives" egen id = group(lopenr) label var id "ID for women in the study" destring ord, dpcomma replace rename varepakningstr npack replace npack = subinstr(npack,"X","x",1) split npack, parse("x") replace npack2="1" if npack2=="" g npills = real(npack1) * real(npack2) gen total = npills * ord replace pille = pille*total replace mini = mini*total replace spiral = spiral*total replace ring = ring*total replace antidep = antidep*total drop if pille>=6000 compress save working_data, replace clear all set more off g year = year(dofm(date-5)) drop if year==2003 | year==2016 recode alder (0/15=0 "<16") (16/19=1 "16/19") (20/24=2 "20-24") (25/29=3 "25-29") /// (30/max=4 "30+"), gen(xage) drop if xage==0 bys lopenr year: gen npeople = 1 if _n==1 g treat = (apkomnr == 403 | apkomnr == 1902) g tgroup = xage==2 & treat==1 g xfree = (year==2008) * tgroup replace xfree = 1 if xage==1 keep if inrange(alder,20,24) collapse treat xfree (sum) pille mini ring spiral, by (lopenr ap year xage) collapse treat xfree pille mini ring spiral , by (ap year xage) keep if ap!=9999 global sample inlist(apk, 403, 805) qui foreach outcome in pille mini spiral ring { forv y=2005/2011 { cap drop free gen free = year==`y' & xage==2 & treat==1 replace free = 1 if xage==1 reg `outcome' free xfree i.xage i.y i.ap if $sample

est store m_`outcome'`y' } }

*DID2 //global sample inlist(apk, 1902) global sample inlist(apk, 403) //global sample inlist(apk, 1804) //global sample inlist(apk, 805) qui foreach outcome in pille mini spiral ring { forv y=2005/2011 { cap drop free gen free = year==`y' & xage==2 reg `outcome' free xfree i.xage i.y if $sample est store m_`outcome'`y' } }

*DIDID individual qui foreach outcome in pille mini spiral ring{ forv y=2005/2011 { xtreg `outcome' 1.treat#2008.year#2.xage 1.treat#2008.year xage##(year ap) if inlist(apk, 1804, 1902) & inlist(xage, 2, 3) } }

Creating individual panel data: clear all set more off use working_data set scheme mg collapse (sum) pille mini ring spiral antidep, by(date lopenr fodtar apkomnr) sort lopenr date by lopenr (date) : g lastobs = _n==_N by lopenr (date) : g xdate = date[_n+1] g pdate = date fillin lopenr date by lopenr (date) : replace lastobs = lastobs[_n-1] if lastobs==. by lopenr (date) : replace xdate = xdate[_n-1] if xdate==. by lopenr (date) : replace pdate = pdate[_n-1] if pdate==. g middate = pdate + int((xdate - pdate) / 2) by lopenr (date) : replace apkomnr = apkomnr[_n-1] if apkomnr==. & ((date < middate) | lastobs) gsort lopenr – date by lopenr : replace apkomnr = apkomnr[_n-1] if apkomnr==. sort lopenr date mvencode pille mini ring spiral antidep, mv(0) override bys lope : egen byear = mean(fodtar) g year = year(dofm(date)) gen alder = year – byear collapse (sum) pille mini ring spiral antidep, by(lopenr date alder apkomnr) g s = 0 g t = 0 g u = 0 g v = 0 bysort lope (date) : replace s = s[_n-1] - min(36.5, s[_n-1]) + pille if _n>1 bysort lope (date) : replace t = t[_n-1] - min(36.5, t[_n-1]) + mini if _n>1 bysort lope (date) : replace u = u[_n-1] - min(1.335, u[_n-1]) + ring if _n>1 bysort lope (date) : replace v = v[_n-1] - min(0.028, v[_n-1]) + spiral if _n>1 g cover = 1 if s > 18.25 | t > 18.25 | u > 0.65 | v > 0.014 replace cover = 0 if cover == . keep lope apkomnr date alder antidep cover collapse apkomnr (sum) cover antidep, by(lope date alder) xtset lopenr date compress save monday, replace

*DID1 individual clear all use Monday set scheme mg keep if inlist(apk, 1902, 1804, 805, 403) g year = year(dofm(date-5))

39 drop if year==2003 | year==2016 recode alder (0/15=0 "<16") (16/19=1 "16/19") (20/24=2 "20-24") (25/29=3 "25-29") /// (30/max=4 "30+"), gen(xage) keep if xage==2 | xage==3) g treat = (apkomnr == 403 | apkomnr == 1902) g tgroup = xage==2 & treat==1 g xfree = (year==2008) * tgroup global sample inlist(apk, 805, 403) //global sample inlist(apk, 1804, 1902) qui foreach outcome in cover { forv y=2005/2011 { cap drop free gen free = year==`y' & tgroup== xtreg òutcome' free xfree i.y i.xage if $sample est store òutcome'`y' } coefplot òutcome'*, keep(free) vert name(òutcome', replace) legend(off) /// xlabel(`=1-3/8' "2005" `=1-2/8' "2006" `=1-1/8' "2007" 1 "2008" `=1+1/8' "2009" `=1+2/8' "2010" `=1+3/8' "2011" ) /// title("") subtitle("") xtitle("Year") ytitle("") yline(0) ylabel(,form(%4.1f)) }

*DID2 Individual clear all use Monday set scheme mg keep if inlist(apk, 1902, 1804, 805, 403) g year = year(dofm(date-5)) drop if year==2003 | year==2016 recode alder (0/15=0 "<16") (16/19=1 "16/19") (20/24=2 "20-24") (25/29=3 "25-29") /// (30/max=4 "30+"), gen(xage) keep if xage==2 | xage==3) g tgroup = xage==2 g xfree = (year==2008) * tgroup global sample inlist(apk, 805) //run for each mun qui foreach outcome in cover { forv y=2005/2011 { cap drop free gen free = year==`y' & tgroup== xtreg òutcome' free xfree i.y i.xage if $sample est store òutcome'`y' } coefplot òutcome'*, keep(free) vert name(òutcome', replace) legend(off) /// xlabel(`=1-3/8' "2005" `=1-2/8' "2006" `=1-1/8' "2007" 1 "2008" `=1+1/8' "2009" `=1+2/8' "2010" `=1+3/8' "2011" ) /// title("") subtitle("") xtitle("Year") ytitle("") yline(0) ylabel(,form(%4.1f)) }

*DIDID individual qui foreach outcome in cover{ forv y=2005/2011 { xtreg `outcome' 1.treat#2008.year#2.xage 1.treat#2008.year xage##(year ap) if inlist(apk, 1804, 1902) & inlist(xage, 2, 3) } }