# Gendered Pathways: How do STEM Majors Fare in the Labor Market?

Rosa Weber, Camilla Härtull, Jan Saarela

<figure><img src="image_1.png" /></figure>

## To cite this version:

Rosa Weber, Camilla Härtull, Jan Saarela. Gendered Pathways: How do STEM Majors Fare in the Labor Market?. 2024. hal-04794142

HAL Id: hal-04794142

https://hal.science/hal-04794142v1

Preprint submitted on 20 Nov 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
# Gendered Pathways: How do STEM Majors Fare in the Labor Market?

Rosa Weber<fnref n="1" />,<fnref n="2" />,<fnref n="3" />, Camilla Härtull<fnref n="3" />, and Jan Saarela<fnref n="3" />

## Abstract

Although research in the STEM field has extensively examined its gendered characteristics, the vast majority of the literature has concentrated on educational transitions and young adults. More limited attention has been devoted to the longer-term work-family trajectories of STEM majors, and how these are linked to gender earnings gaps. In response, we exploit Finnish register data to identify the most common work-family trajectories followed by college educated men and women with STEM majors in ages 30–40 (N=150,796). Given marked differences in gender proportions across STEM fields, we distinguish computer science and engineering majors from natural science majors. In a second step, we assess gender differences in the returns to distinct work-family trajectories and within-gender differences. We report three main findings. First, women are able to combine a career in computer science and engineering and having children. Second, across occupations, mothers earn considerably less than fathers. This suggests that even though women can combine work and family, they do not benefit in terms of earnings. Third, beyond uncovering gender gaps, we show that a major mechanism underlying parental gender gaps is that men receive notable fatherhood premiums across work trajectories. For women, findings reveal more heterogeneous patterns. Among computer science and engineering majors, women have similar earnings across trajectories. Conversely, women with natural science majors gain from working in computer science and engineering.

**Keywords:** Inequality/Social Stratification; Gender; Life Course

<footnote n="1">¹ Stockholm University, Department of Sociology, 106 91 Stockholm, Sweden</footnote>

<footnote n="2">² Institut National d'Études Démographiques, 93300 Paris, France</footnote>

<footnote n="3">³ Åbo Akademi University, Demography Unit, Strandgatan 2, FIN-65100 Vasa, Finland</footnote>
# Introduction

In the United States and Europe, there has been growing demand for science, technology, engineering, and mathematics (STEM) skills in the labor market and a stark increase in STEM jobs (European Institute for Gender Equality 2017; Deming and Noray 2020; Atalay et al. 2020). Despite government and public initiatives to promote women in STEM, substantial gender disparities in the pursuit of STEM education and occupations persist (Prieto-Rodriguez, Sincock, and Blackmore 2020; Tekniikan akateemiset TEK 2024). However, it remains unclear what mechanisms underlie the persistent underrepresentation of women in STEM occupations and how they structure earnings disparities between men and women.

We address these questions by empirically investigating the longer-term work trajectories of men and women with STEM majors, and by linking these trajectories to observed gender earnings gaps. With the aim of gaining a more holistic understanding of individuals’ trajectories, we further assess competing life course events, and in particular family formation. STEM figures centrally in policy debates and typically pertains to three education fields of study: information and communication technology (hereinafter referred to as computer science); engineering, manufacturing, and construction (hereinafter engineering); and natural science, mathematics, and statistics (hereinafter natural science) (following the ISCED 2013 classification at the two-digit level). However, gender imbalances are consistently larger in computer science and engineering than in natural science (Cimpian, Kim, and McDermott 2020; Sassler, Michelmore, and Smith 2017; White and Smith 2022). For example, in Finland the share of women in computer science and engineering has fluctuated between 15% and 30% between the years 1987 and 2022 (Figure 1). Conversely, women comprise more than half of natural science majors since 2010.<fnref n="1" /> Assessing gender gaps

<footnote n="1">¹ The US literature shows that the proportion of women in physics is considerably lower than in other natural science fields, such as biology and chemistry. In Finnish register data, which are used here, the education field of study follows the ISCED 2013 classification (two-digit level). We are therefore unable to differentiate between</footnote>
in computer science and engineering versus natural science, as we do here, is consequently of
particular interest. We provide novel insights by addressing two research questions: (1) *How
do gender proportions differ across the joint work-family trajectories of college educated
computer science and engineering majors in ages 30–40, and how do these compare to
natural science and non-STEM majors?* (2) *How do the trajectories relate to gender earnings
gaps, measured by annual earnings in ages 41–43?*

<figure><img src="image_3.png" /><figcaption>**Fig.1** Share of women across STEM fields in Finland 1987–2022.</figcaption></figure>

*Notes:* Data from authors’ calculations based on Finnish register data 1987–2022.

This study focuses on computer science and engineering majors with a college degree
(or tertiary education) to illuminate gender disparities in persistently male dominated fields.
Natural science and non-STEM² majors are included as points of comparison, allowing us to

biology, biochemistry, environmental sciences, natural environments and wildlife, chemistry, earth sciences, physics, mathematics, and statistics (four-digit level).

² Non-STEM includes all fields outside of computer science and engineering, and natural science.
contrast gendered patterns in the most male dominated fields (computer science and
engineering) with those observed in more gender mixed fields (natural science and non-
STEM).<fnref n="3" /> This is important, because men do not necessarily serve as an appropriate
benchmark for understanding women's commitment to computer science and engineering
careers. According to the *leaky pipeline* hypothesis, women are expected to disproportionately
exit male dominated fields and occupations (Calhoun, Jayaram, and Madorsky 2022).

However, women exhibit lower persistence than otherwise comparable men in other
professional domains as well, at least in part due to family constraints (Glass et al. 2013).
Including natural science and non-STEM majors thus allows us to compare women's
retention in computer science and engineering careers with that of similarly skilled women in
other careers.

Beyond gender imbalances in employment, differential returns to work trajectories
figure centrally in understanding gender disparities. The commonly observed fatherhood
premium has been interpreted as the result of selection into parenthood (Mari 2019) and
increased household specialization following parenthood (Becker 1985; 1991; Glauber 2018;
Ludwig and Brüderl 2018). However, when it comes to the most male dominated fields, we
continue to know less about the relationship between trajectories in later stages of adulthood
and the gender earnings gap. How do the returns to distinct work-family trajectories compare
between men and women? And, how do these returns differ for computer science and
engineering, natural science, and non-STEM majors?

Exploiting longitudinal Finnish register data, this study assesses the longer-term
trajectories of men and women with computer science and engineering majors, and the returns
to these trajectories in terms of annual earnings. We contribute to the literature in three ways.

<footnote n="3">³ Figure A1 provides the share of women across STEM fields in the analytical population, showing that natural science was gender mixed in all cohorts analyzed. Figure A1 is to be compared to Figure 1, which provides shares for the total Finnish population. All tables and figures designated with an “A” appear in the online appendix.</footnote>
First, our study adopts a life course perspective to illuminate gender dynamics in the STEM
fields in later stages of adulthood. Prior studies have focused on gendered educational choices
in STEM fields (Cimpian, Kim, and McDermott 2020; Delaney and Devereux 2019; Griffith
2010; Koch et al. 2022; Speer 2023) and gender differences in labor market entry (Sassler,
Michelmore, and Smith 2017; Schwerter and Ilg 2023; White and Smith 2022). By contrast,
less attention has been paid to gender disparities in the STEM fields through to the forties.
Second, we examine how men and women majoring in STEM fields fare in the labor market,
in terms of annual earnings. This is central in improving our understanding of the equality of
earnings in the most male dominated fields. Third, we go beyond describing gaps and assess
within-gender differences to gain insight into the mechanisms underlying observed gender
disparities. We distinguish between gendered rewards to family and career paths and assess to
what extent they differ across majors.

We situate our research in Finland, as it presents a particularly interesting case for
understanding gender disparities in STEM. During the late 1980s, the Nokia company was a
large employer in Finland. Since then, a sizable share of the Finnish population has continued
to work in the technology industry and, on an international scale, the proportion of students
majoring in computer science and engineering is higher than in most other European countries
(Rouvinen and Ylä-Anttila 2003). Yet, gender disparities in these education fields and
occupations remain high (Tekniikan akateemiset TEK 2024). Studying Finland further
provides opportunities to work with longitudinal individual-level register data that cover the
full population, and to assess gender disparities in a setting where differences likely provide
lower bound estimates, because Finland follows a dual-earner or earner-carer family policy
model (Korpi, Ferrarini, and Englund 2013; Tekniikan akateemiset TEK 2024).

In the following, we discuss the prior literature on gender disparities in STEM and
computer science and engineering as well as the theoretical background, leading us to make
four distinct hypotheses. Then, we discuss the context, data, and methods used. Finally, we present the results and conclude.

## Literature review and theoretical framework

The STEM fields play a crucial role in offering high-earning job opportunities and have been argued to drive economic growth and boost productivity (European Institute for Gender Equality 2017). Thus, the fields have undergone intense scrutiny, consistently revealing a significant underrepresentation of women within them. Women are less likely to enroll in STEM education and more prone to drop out than men (Delaney and Devereux 2019; Griffith 2010; Koch et al. 2022; Speer 2023). However, even among the select few who major in STEM, women face lower chances of being hired in the STEM sector than men with equal qualifications (Sassler, Michelmore, and Smith 2017) and they experience disproportionate attrition from STEM employment (Cech and Blair-Loy 2019). This gender imbalance is often attributed to a leaky pipeline, reflecting that despite women's interest and ability to succeed in STEM industries, their representation tends to decrease over the course of their career (Calhoun, Jayaram, and Madorsky 2022).

Although research in STEM has extensively examined its gendered characteristics, the vast majority of the literature has concentrated on educational transitions and thus primarily focused on young individuals. More limited attention has been paid to pathways beyond schooling. The growing body of literature on the labor market destinations of STEM majors is based on data from the United States and focuses on the first jobs after graduation (for some notable exceptions with evidence for the UK and Germany, see White and Smith 2022; Schwerter and Ilg 2023). Existing studies on the gendered trajectories after graduation have thus predominantly concentrated on labor market entry, leaving a notable gap in our understanding of gender dynamics in later stages of adulthood.
Gender differences in computer science and engineering

Gender disparities are particularly pronounced in computer science and engineering education
fields of study and employment (Cimpian, Kim, and McDermott 2020; Sassler, Michelmore,
and Smith 2017; White and Smith 2022). In the United States, gender imbalances are
consistently larger in physics, engineering, and computer science than in other STEM fields,
with about four men to every woman (Cimpian, Kim, and McDermott 2020). Student
attributes, such as parental socioeconomic status, explain most of the gender difference
among high achievers but only one third among low achievers. Other explanations include
gendered educational experiences, societal gender pressures, a lack of female role models,
biological differences, an inherent masculine worldview, and a chilly climate for girls and
women in STEM (Blickenstaff 2005).

When it comes to employment in the computer science and engineering sector,
Sassler, Michelmore, and Smith (2017) find that women who major in these fields are about
ten percentage points less likely to work in STEM than their male counterparts. Women with
a computer science major are particularly less prone than their male peers to work in
computer science occupations (Sassler, Smith, and Michelmore 2023). Computer science and
engineering majors are also the main driver of gender differences in early-career outcomes in
Germany, where the transition rate into STEM occupations is lower for women than for men
(Schwerter and Ilg 2023).

Examining different career stages, White and Smith (2022) find that women with
STEM majors in the UK are less likely than their male peers to gain graduate-level
employment, to work in highly-skilled STEM jobs, and in managerial positions in the year
after graduation. These differences are largely attributable to the fact that male dominated
subject areas have higher graduate employment rates. Still, within-subject differences in
graduate level-work are also evident, and largest in computer science and engineering.
Among older graduates, women are underrepresented in managerial positions, and when found in managerial positions, they are less likely to work in science and industry.

Family formation has been highlighted as a potential explanation for the underrepresentation of women in STEM employment (Buffington et al. 2016; Correll, Benard, and Paik 2007; Williams, Phillips, and Hall 2016). Different domains of individuals' social lives are inherently intertwined and the accumulation of resources in one domain, such as work, tends to facilitate or hinder other life domains, such as family (Bolano and Berchtold 2021). However, the ways in which life domains interact are clearly gendered (Sirniö, Kauppinen, and Martikainen 2017). For example, family formation may hinder women's careers in STEM, while promoting men's. Yet, prior evidence remains inconclusive. Whereas Sassler, Michelmore, and Smith (2017) show that demographic characteristics and family formation account for half of the gender gap, Schwerter and Ilg (2023) find no such contribution in Germany. Assessing the propensity of computer science and engineering majors to work in the field, Sassler, Smith, and Michelmore (2023) find that family factors contribute to gender difference in employment in the field, but that half of the difference remains unexplained. This suggests that family formation may contribute at least partly to gender differences in employment in the field. This evidence leads us to our first hypothesis:

*Hypothesis 1 (H1: Leaky pipeline hypothesis): Women, and particularly mothers, are underrepresented in computer science and engineering occupations relative to men.*

## Gender earnings gaps in STEM

The STEM sector not only lacks gender balance, but it also features earnings disparities that place women at a disadvantage (Michelmore and Sassler 2016; Cech 2013). Women have lower entry-level wages than men (Sterling et al. 2020) and their wage growth falls behind that of men (Xu 2015; Bairoh 2019). In computer science jobs, the hourly wages earned by
women are on average 13 percentage points lower than those for their male peers and compositional differences in age and family formation only explain part of these gender earnings gaps (Sassler and Meyerhofer 2023). However, in comparison to other occupations, technology and science occupations feature smaller gender earnings gaps. This has been in part attributed to higher work flexibility in the technology industry (Goldin 2014).

Across occupations, men tend to earn more than women, yet gender earnings gaps are accentuated by parenthood (Blau and Kahn 2017; Boye, Halldén, and Magnusson 2017; Kleven et al. 2024). An established literature has addressed the effects of parenthood on earnings, highlighting the role of selection into fatherhood (Mari 2019). Unobserved characteristics, such as ambition, health, and motivation, can be important for becoming a father and are simultaneously remunerated in the labor market. Likewise, fatherhood may provide a signal to employers that the person is trustworthy and dependable. In general, we do not expect that selection into fatherhood markedly differs across occupations. The impact of parenthood is thus expected to differ systematically by gender but not by occupation.

*Hypothesis 2* (H2: Consistent parental gender gap hypothesis): Parents will exhibit larger gender earnings gaps compared to non-parents, and these gaps will be consistent in magnitude across occupations.

Conversely, specialization theory argues that commonly observed fatherhood premiums and motherhood penalties, or smaller motherhood premiums, are the result of increased household specialization following parenthood (Becker 1985; 1991; Bygren, Gähler, and Magnusson 2021; Glauber 2018; Jalovaara and Fasang 2020; Ludwig and Brüderl 2018). Whereas men spend more time and energy on paid work after becoming parents, women tend to shoulder greater responsibility for childcare (Glauber 2008; Killewald and García-Manglano 2016).
Fatherhood is often associated with a modest increase in housework, no change in paid work hours, and an increase in earnings (Glauber 2008; Killewald and García-Manglano 2016; Ludwig and Brüderl 2018). By contrast, motherhood tends to be associated with an increase in housework, a decrease in paid work hours, and a decrease in earnings (Glauber 2008).

Given greater work flexibility in the technology industry, specialization theory leads us to expect smaller gender earnings gaps for parents in computer science and engineering occupations relative to parents in other occupations. Substantiating this idea, studies actually indicate a positive association between motherhood and earnings in the STEM sector (Buchmann and McDaniel 2016; Michelmore and Sassler 2016), and a motherhood premium in earnings for married women (Beutel and Schleifer 2022). This leads us to expect that gender earnings gaps differ across occupations.

*Hypothesis 3 (H3: CSE gender gap hypothesis):* Parents in computer science and engineering occupations will experience smaller gender earnings gaps relative to parents in natural science and non-STEM occupations.

Beyond describing gaps, we illuminate potential mechanisms underlying observed disparities by assessing within-gender variation. As aforementioned, gaps may be more pronounced for parents than non-parents because fathers receive fatherhood premiums (Ludwig and Brüderl 2018; Mari 2019). Conversely, this difference may be driven by women facing motherhood penalties or smaller motherhood premiums (Bygren, Gähler, and Magnusson 2021). Prior studies indicate stronger variation by family trajectory for men's than women's earnings in Finland (Jalovaara and Fasang 2020). This leads us to expect that fatherhood premiums play a central role. With regards to differences in gender earnings gaps across work trajectories, prior evidence shows that technology and science occupations feature smaller gaps (Goldin
2014). This leads us to expect that women especially gain from pursuing computer science and engineering careers.

*Hypothesis 4 (H4: Within-gender hypothesis): Fatherhood premiums and rewards accrued by women in computer science and engineering careers will help explain trajectory-specific differences in the gender earnings gap.*

There is a dearth of studies addressing gender disparities in earnings among STEM majors from a life course perspective. To gain deeper insight into how women with majors in the most male dominated STEM fields fare, it is therefore crucial to take a life course perspective and examine both family and work life concurrently.

## Context

Finland experienced a significant technology boom in the late 1980s, marked by the rapid expansion of Nokia and the broader technology industry. This period is commonly referred to as the Nokia boom (Rouvinen and Ylä-Anttila 2003). Although the economy faced a severe downturn in the early 1990s, recovery was facilitated by the established technology sector. Since then, the proportion of students pursuing STEM majors has remained high in Finland, and markedly higher than in Sweden, Denmark, and notably Norway, where, only 15% of students majored in STEM fields compared to Finland's 26% (Rouvinen and Ylä-Anttila 2003).

Finland further has a high degree of gender equality that is particularly apparent in the domains of education and work (European Institute for Gender Equality 2023). Education is free at all levels and a well-established system of study grants and loans provides education opportunities to everyone. Similarly, affordable high-quality day care and policies promoting
a more equitable distribution of parental leave have contributed to high labor force
participation among women. Part-time employment is somewhat more common among
women, although on an international comparison, it is relatively low and gender differences
are small (Öun 2012; Kuitto and Kuivalainen 2021).

Yet, gender segregation both within the educational system and labor market persists.
Most students in the fields of health, teaching, and humanities are women, whereas men
dominate in computer science and engineering (Statistics Finland 2021). This gender disparity
is significant even on an international scale, revealing differences that are similar in
magnitude to the OECD average (OECD 2022). The same pattern is reflected in the labor
market (Teräsaho and Närvi 2019). Across occupations, there is a 16% gender earnings gap
that disadvantages women (Statistics Finland 2022). The gender earnings gap in STEM fields
is at least as pronounced as in other sectors, and it tends to widen with age (Ojala 2020).

Data

Our analysis is based on Finnish individual-level register data that cover the total population
between 1987 and 2022. Each person can be observed longitudinally on a rich set of
demographic and socioeconomic indicators. The data provide us with information on
individuals' education level and field of study. The data further provide yearly information on
individuals' employment status, occupation, family situation, and earnings. All data access,
data preparation, and analyses were performed within Statistics Finland's remote access
system FIONA. The contract number is TK-52-694-18.

Population restrictions

We make several successive population restrictions (see Table 1). Our analysis follows six
entire birth cohorts (individuals born in 1974–1979) from ages 30 to 43 (between the calendar
years 2004 and 2022). We restrict our analysis to individuals who are living in Finland at the
end of every calendar year between ages 30 and 43, to avoid problems resulting from missing information (excluding 13%). We start observing individuals at age 30, as most have completed their education by this age. The average age of obtaining a master's degree is 29 in Finland (OECD, 2024). By ages 40-43, most men and women have stable work and family situations which allows us to assess their labor market position (employment status, occupation, and earnings) and family situation (cohabitation, marriage, and parenthood).

<table><caption>**Table 1** Population restrictions</caption><thead><tr><th>Finnish register data</th><th>Person-years</th><th>Individuals</th><th>Percentage excluded</th></tr></thead><tbody><tr><td>Full population file</td><td>195,319,818</td><td>7,670,456</td><td></td></tr><tr><td>1. Restrict to six birth cohorts</td><td>5,700,154</td><td>434,832</td><td>94%</td></tr><tr><td>2. Restrict to individuals observed yearly ages 30-43</td><td>5,278,994</td><td>377,071</td><td>13%</td></tr><tr><td>3. Restrict to individuals with college educ or higher</td><td>2,199,848</td><td>157,132</td><td>58%</td></tr><tr><td>4. Restrict to the Finnish born population</td><td>2,112,558</td><td>150,897</td><td>4%</td></tr><tr><td>5. Restrict to individuals with info on graduation year</td><td>2,111,144</td><td>150,796</td><td>0.1%</td></tr><tr><td>Final population</td><td></td><td>150,796</td><td></td></tr><tr><th>Education field of study</th><th>All</th><th>Male</th><th>Female</th></tr><tr><td>Computer science and engineering majors (CSE)</td><td>38,860</td><td>30,927</td><td>7,933</td></tr><tr><td>Natural science majors</td><td>5,364</td><td>1,997</td><td>3,367</td></tr><tr><td>Non-STEM majors</td><td>106,572</td><td>27,290</td><td>79,282</td></tr></tbody></table>

*Notes*: Data are from authors' calculations based on Finnish register data 1987-2022.

Subsequently, we restrict our analytical population to individuals with college degrees (excluding 58%). Disentangling the field at levels below the college degree is difficult because all students in academic tracks in high school pursue generic programs. We further restrict our analysis to the Finnish-born population and to individuals with information on all variables (excluding 4% and 0.1%, respectively). Our analytical population comprises 150,796 individuals: 38,860 computer science and engineering majors, 5,364 natural science majors, and 106,572 non-STEM majors. Our analysis of non-STEM majors is restricted to a random
sample of 40,000 individuals, as we were unable to compute the multi-channel sequence
analysis on 106,572 individuals in Statistics Finland's remote access system.

## Education field of study

Throughout the analysis, we differentiate between individuals who majored in: computer science and engineering, hereinafter abbreviated to CSE; natural science; and non-STEM. As aforementioned, CSE includes information and communication technology, engineering, manufacturing, and construction (based on the ISCED 2013 classification at the two-digit level). Natural science includes natural science, mathematics, and statistics. Non-STEM includes teaching, arts and humanities, social science, business, administration, and law, agriculture, health, and services. Non-STEM is included as a point of comparison to assess whether gendered patterns are specific to STEM, though it should not be over-interpreted.

## Employment status and occupation

Finnish register data provide yearly information on each individual's employment status and occupation. This allows us to identify whether the person is employed or self-employed, unemployed, or outside of the labor force (e.g., a homemaker, on disability pension, studying, or in the army) at the end of each calendar year. Individuals on parental leave are recorded as employed. For individuals who work, we assess the recorded occupation.

Occupations are categorized as CSE, natural science, and non-STEM occupations. Occupational classifications available in Finnish register data have changed over time, in line with ISCO classifications. In years 2004–2009, Statistics Finland's classification of occupations 2001 is available (similar to ISCO-88 at the three-digit level). Since 2010, Statistics Finland's classification of occupations 2010 is available (following ISCO-08 at the three-digit level). In order to identify STEM occupations, we follow, to the greatest extent possible, a classification provided by the European Institute for Gender Equality (2017, 82).
Given that we have access to more detailed occupation codes (at the three- rather than two-digit level), we are further able to refine the existing categorization. We provide the full list of occupations classified as CSE and natural science occupations in Tables A1 and A2.

Given that occupation structures differ across contexts and change over time, there is no one established way of classifying STEM and CSE occupations. Whereas some studies include actuaries among CSE and technology occupations (Goldin 2014), we categorize them as natural science occupations because we cannot differentiate between mathematicians, actuaries, and statisticians (ISCO-08). Others include health scientists among STEM occupations (Goldin 2014). We are, however, unable to differentiate between health professionals and health scientists, and therefore categorize health professionals as non-STEM. With this in mind, we recognize that differences across classifications can be viewed as a source of bias in estimating gender differences. We have explored different descriptive statistics to assess our categorization in Tables A1 and A2 and Figures A2 and A3, and elaborate on these in the Appendix.

## Family situation

With regard to individuals' family situation, the data provide yearly information on cohabitation and marriage, as well as whether children are in the household. This allows us to distinguish between individuals who are in couples with children (including married and cohabiting couples), in couples without children or childless couples, single parents, and singles at the end of every calendar year.

## Earnings

The information on earnings is derived from tax registers and refers to total annual taxable income from employment and self-employment, alongside taxable social benefits, adjusted to 2022's prices using the consumer price index. To circumvent problems in which information
from the same ages would be used in the multi-channel sequence analysis identifying work-family trajectories and in the earnings analysis, we analyze average earnings between ages 41 and 43. This ensures that the trajectories are distinct from patterns observed in the earnings analysis.

Given that women's earnings tend to be more volatile during prime working ages than men's (Brenner 2010) and women's earnings often peak later in life than men's (Mincer and Polachek 1974), we would ideally capture earnings over a longer time frame. However, by the forties the ratio of male to female earnings has been found to stabilize and current earnings provide a reasonable proxy of lifetime earnings (Goldin 2014; Haider and Solon 2006). We therefore rely on average earnings in the early 40s to proxy differences in life time earnings (Finnish Centre for Pensions 2020).

## Descriptive statistics

A larger number of women graduate from college (tertiary education) than men in Finland, with about 91,000 women in the study population graduating from college versus 60,000 men (last row of Table 2). Among those with a college degree, two thirds of men and women graduate from lower tertiary education or obtain a Bachelor degree. However, the education fields of study and occupations held by men and women point to stark gender differences. More than 50% of men major in CSE, and 40% work in such occupations at age 30. The corresponding numbers for women are 9% and 7%, respectively. The share of individuals in natural science (both in the educational system and the labor force) is considerably lower and points to smaller gender differences, with about 4% of men and women pursuing natural science in college and 2% working in commensurate occupations. We also find that a higher share of women than men is non-employed, or outside of the labor force, at age 30 (13% of women versus 5% of men). Nearly half of women who have a college degree are in couples with children at age 30, while one in three men is single at age 30.
<table><caption>**Table 2** Education, work and family outcomes of women and men at age 30</caption><thead><tr><th></th><th>Men</th><th>Women</th><th>Total</th></tr></thead><tbody><tr><td><strong>Education level</strong></td><td></td><td></td><td></td></tr><tr><td>Lower tertiary</td><td>0.66</td><td>0.68</td><td>*** 0.67</td></tr><tr><td>Upper tertiary</td><td>0.34</td><td>0.32</td><td>*** 0.33</td></tr><tr><td><strong>Field of study<sup>1</sup></strong></td><td></td><td></td><td></td></tr><tr><td>Computer science and engineering (CSE)</td><td>0.51</td><td>0.09</td><td>*** 0.26</td></tr><tr><td>Natural science</td><td>0.03</td><td>0.04</td><td>*** 0.04</td></tr><tr><td>Non-STEM</td><td>0.45</td><td>0.88</td><td>*** 0.71</td></tr><tr><td>Average age finish education</td><td>27</td><td>27</td><td>27</td></tr><tr><td><strong>Occupation and employment status</strong></td><td></td><td></td><td></td></tr><tr><td>Computer science and engineering (CSE)</td><td>0.39</td><td>0.07</td><td>*** 0.20</td></tr><tr><td>Natural science</td><td>0.02</td><td>0.01</td><td>*** 0.02</td></tr><tr><td>Non-STEM</td><td>0.50</td><td>0.75</td><td>*** 0.65</td></tr><tr><td>Unemployed</td><td>0.04</td><td>0.04</td><td>*** 0.04</td></tr><tr><td>Non-employed</td><td>0.05</td><td>0.13</td><td>*** 0.10</td></tr><tr><td><strong>Family status</strong></td><td></td><td></td><td></td></tr><tr><td>Couple with children</td><td>0.36</td><td>0.46</td><td>*** 0.42</td></tr><tr><td>Couple without children</td><td>0.35</td><td>0.29</td><td>*** 0.31</td></tr><tr><td>Single parent</td><td>0.01</td><td>0.03</td><td>*** 0.02</td></tr><tr><td>Single</td><td>0.29</td><td>0.22</td><td>*** 0.25</td></tr><tr><td>N</td><td>60,214</td><td>90,582</td><td>150,796</td></tr></tbody></table>

*Notes:* The population is restricted to individuals with college education. Data are from authors' calculations based on Finnish register data 1987–2022.

¹This share does not correspond to the one reported in Tables 3–5, as proportions within gender are provided here. That is, Table 2 indicates that, among college educated women, 9% major in computer science and engineering. By contrast, Tables 3–5 refer to proportions within education field of study. That is, the share of women among college educated computer science and engineering majors.

# Methods

## Multi-channel sequence analysis

We use multi-channel sequence analysis to study individuals' trajectories, which is a widely used approach (Jalovaara and Fasang 2020; Sirniö, Kauppinen, and Martikainen 2017; Fasang and Aisenbrey 2022; Aisenbrey and Fasang 2017). Using this method, we capture individuals' entire work and family trajectories defined by multiple discrete states between ages 30 and 40 (Abbott and Forrest 1986). For individual work trajectories, the annually measured states are defined as (1) non-employed, (2) unemployed, (3) employed in non-STEM, (4) employed in
natural science, and (5) employed in CSE. Family trajectories relate to information recorded
on the couple (including non-marital cohabitation) and children. The annually measured states
are (1) single, (2) single parent, (3) childless couple, and (4) couple with children.

The multi-channel sequence analysis proceeds in three steps. First, we construct a
work trajectory (sequence) and a family trajectory for each individual during the observation
window of eleven years (ages 30–40, years 2004–2019). In each year, an individual takes one
of the five work and four family statuses. Secondly, we use optimal matching to identify
similarity between sequences (MacIndoe and Abbott 2004). Similarity between any two
sequences is systematically determined by the total “costs” of transforming one sequence into
another, also called substitution costs (Pollock 2007; Gauthier et al. 2010). In contrast to other
methods for dealing with multi-dimensional sequences, substitution costs in multi-channel
sequence analysis are first specified separately for each dimension (i.e., work and family).
Then, the two dimensions are aligned jointly, and the total substitution costs at each time
point are summed up from the two separate cost matrices. This approach generates one large
combined substitution cost matrix. Therefore, interactions between the two dimensions are
taken into account by combining the separate cost matrices into one large cost matrix that
spans work and family trajectories. This joint substitution cost matrix sums all possible
combinations of states from the two dimensions, allowing us to use the transition probability
between any two different work and family states. We have assessed other cost specifications
and obtained similar results.

Thirdly, the joint substitution cost matrix is used in cluster analyses that group similar
sequences. We determine the best number of clusters using Ward weighted clustering (Studer
2013). Guided by several cluster cut-off criteria (see Figure A4), we retain six clusters as the
most suitable groupings. This number also proved substantively most meaningful, satisfying
the criterion of construct validity (Studer 2013). All analyses are conducted separately for CSE, natural science, and non-STEM majors.

We visualize the work-family trajectories of the six clusters using sequence distribution plots (Tables 3–5). These plots visualize the distribution of the different states by rendering a series of stacked bar charts at each position of the sequence (i.e., over age). The plots do not visualize individual trajectories, instead they display aggregated distributional information in repeated cross-sections. Gender proportions in each cluster inform about gender differences in trajectories without separating men and women a priori. Clusters are ordered with the aim of enhancing intuitive understanding and to highlight points of interest.

## Earnings regressions

Having identified six clusters (i.e., the most common work-family trajectories), we assess how different clusters relate to men's and women's average earnings between ages 41 and 43. We analyze men and women jointly and include an interaction term between gender and cluster. This approach informs both on the earnings gaps between men and women who belong to the same cluster (i.e., follow similar work-family trajectories) and within-gender differences. We specify the model:

$$Y_i = \alpha + \beta \text{female}_i + \delta \text{cluster}_i + \mu(\text{female}_i \times \text{cluster}_i) + \gamma X_i + \epsilon_i, \quad (1)$$

where $Y_i$ refers to average log earnings in ages 41–43 for individual $i$. The outcome variable is thus continuous and we estimate linear regressions with ordinary least squares. Due to the skewed distribution of earnings, we use the log transformation of the variable. The intercept is referred to as $\alpha$. The term $\text{female}_i$ is a dummy indicator equal to one when individual $i$ is female, and zero when individual $i$ is male. $\beta$ represents the corresponding coefficient vector, which gives the average difference in earnings between women and the reference category
(men). The term *cluster*ᵢ is a set of dummy variables that indicates the cluster individual i pertains to between ages 30 and 40, ranging from two to six. The (omitted) reference category is the first cluster. The corresponding coefficient vector, δ, provides the difference in earnings, in each cluster, compared to the reference group (the first cluster). We also include interaction terms between female and cluster. This allows the gender earnings gap to vary across clusters. μ refers to the coefficient vector for all possible combinations between the two sets of dummies (in total five coefficients, i.e., five for women). Xᵢ is a set of time-constant control variables and γ is the corresponding coefficient vector. The control variables include educational attainment, age when finished education, being a Swedish speaker, region of residence at age 41, and occupation fixed effects (at age 41). The error term is represented by εᵢ. We present adjusted predictions, which provide the estimated mean earnings in the six clusters for men and women (Figure 3).<fnref n="4" />

# Results

## Work-family clusters

We begin by assessing whether women are underrepresented in CSE occupations relative to men, and the extent to which this relates to having children. Table 3 presents the work and family trajectories of CSE majors obtained from multi-channel sequence analysis. Columns 2 and 3 provide sequence distribution plots for the six clusters. Thereafter, gender proportions in each cluster inform about gender differences in trajectories. We also provide the percentage of individuals (with a CSE majors) in each cluster and the total number of individuals in each cluster. The last row shows that a mere 20% of all CSE majors are women.

<footnote n="4">⁴ Adjusted predictions are estimated using the Stata command: margins, at.cluster=(1(1)6) female=(0 1)) post (Williams, 2012).</footnote>
The first two clusters (from the top) comprise *parents in CSE* and *parents in non-STEM*. Based on their family trajectories, we find that individuals in both clusters tend to be in couples with children throughout their 30s. However, the clusters are distinct with regards to their work trajectories. Whereas individuals in the first cluster predominantly work in CSE occupations, individuals in the second cluster generally work in non-STEM occupations. The share of women also differs markedly across the two clusters. Whereas only 22% of parents in CSE are women, about one in three parents in non-STEM is a woman (32%). Women are thus proportionately (under)represented among parents in CSE, relative to the overall share of women among CSE majors (20%). By contrast, they are overrepresented among parents in non-STEM. Parents in CSE comprise 37% of CSE majors or the largest cluster, whereas parents in non-STEM are a smaller group (15%). In short, a considerable share of CSE majors work in CSE occupations and become parents.

The two clusters of *later parents in CSE* and *singles in CSE* comprise particularly few women. Only 14% and 12% are women in these clusters, respectively. Later parents have children in their mid-30s, whereas singles remain unpartnered and childless between ages 30 and 40. Singles in CSE are a small group (12% of all CSE majors), whereas later parents in CSE comprise 20% of CSE majors or the second largest cluster.

The remaining two clusters comprise *childless partners in CSE (mixed)* and *singles with mixed work trajectories*. About 20% of each cluster are women, which is close to the average. Among childless couples in CSE (mixed), individuals are married or live in cohabitation but do not have children throughout their 30s. A majority or more than half of childless couples in CSE (mixed) work in CSE occupations, whereas the remainder is in non-STEM occupations, unemployed, or non-employed. The terminology *mixed* aims to reflect this heterogeneity, while showing that there is one dominant occupation. Conversely, singles in mixed work trajectories are relatively evenly split across employment in CSE, employment
in non-STEM, unemployment, and non-employment. Both of these clusters are small in size, comprising 9% and 7% of CSE majors, respectively.

<table><caption>**Table 3** Work-family trajectories of computer science and engineering majors for ages 30-40</caption><thead><tr><th>Clusters</th><th>Work<br>trajectories</th><th>Family<br>trajectories</th><th>Percent<br>women</th><th>Percent<br>of total</th><th>N</th></tr></thead><tbody><tr><td>Parents in<br>CSE</td><td></td><td></td><td>22%</td><td>37%</td><td>14,262</td></tr><tr><td>Parents in<br>Non-STEM</td><td></td><td></td><td>32%</td><td>15%</td><td>5,874</td></tr><tr><td>Later<br>parents in<br>CSE</td><td></td><td></td><td>14%</td><td>20%</td><td>7,806</td></tr><tr><td>Childless<br>couples in<br>CSE<br>(mixed)</td><td></td><td></td><td>23%</td><td>9%</td><td>3,566</td></tr><tr><td>Singles in<br>CSE</td><td></td><td></td><td>12%</td><td>12%</td><td>4,717</td></tr><tr><td>Singles<br>with mixed<br>work<br>trajectories</td><td></td><td></td><td>22%</td><td>7%</td><td>2,635</td></tr><tr><td>Total</td><td></td><td></td><td>20%</td><td>100%</td><td>38,860</td></tr></tbody></table>

*Notes:* Results from sequence distribution plots presented. CSE stands for computer science and engineering. Non-STEM stands for all occupations outside of computer science and engineering and natural science. Percent women stands for the percentage of women in each cluster. Percent of total stands for the percentage of individuals (with a CSE major) in each cluster. N provides the total number of individuals in each cluster. Data are from authors' calculations based on Finnish register data 1987-2022.
Among natural science majors (Table 4), women are underrepresented among *parents in CSE*. That is, 63% of natural science majors are women, whereas 45% of parents in CSE are women. This cluster is relatively small, comprising 8% of natural science majors. Women are instead overrepresented in the three clusters that make up a bulk of natural science majors (67%): *parents in non-STEM*, *parents in natural science (mixed)*, and *later parents in non-STEM*.

Among non-STEM majors (Table 5), women are underrepresented in four of the six clusters: *later parents in non-STEM*, *parents in mixed work trajectories*, *childless couples in non-STEM*, and *singles in non-STEM*. That is, 75% of non-STEM majors are women, whereas women make up less than 70% of each of these clusters. Conversely, women are overrepresented among *parents in non-STEM* and *single parents in non-STEM*. We also find that nearly 50% of non-STEM majors are represented by the cluster *parents in non-STEM*.

These findings provide mixed support for H1, which posits that women, and particularly mothers, are underrepresented in CSE occupations. On the one hand, women are underrepresented in CSE careers, but more strongly when these careers are coupled with later parenthood and staying single than with parenthood. This indicates that family constraints are not the main deterring factor, which keeps women from pursuing a career in CSE. On the other hand, among CSE majors women are the most strongly overrepresented among parents in non-STEM. This suggests that factors, such as a chilly climate for women in STEM and a lack of female role models, likely contribute to women leaving CSE. We likewise find that about 4% of men and 5% of women, who worked in CSE occupations at age 30, have left these occupations by age 40. In natural science and non-STEM, few majors pursue CSE occupations, and yet the small group of natural science majors who go on to work in CSE are the cluster in which women are the most strongly underrepresented.
<table><caption>**Table 4** Work-family trajectories of natural science majors for ages 30-40</caption><thead><tr><th>Clusters</th><th>Work trajectories</th><th>Family trajectories</th><th>Percent women</th><th>Percent of total</th><th>N</th></tr></thead><tbody><tr><td>Parents in CSE</td><td></td><td></td><td>45%</td><td>8%</td><td>443</td></tr><tr><td>Parents in Non-STEM</td><td></td><td></td><td>69%</td><td>22%</td><td>1,180</td></tr><tr><td>Parents in Natural Science (mixed)</td><td></td><td></td><td>69%</td><td>29%</td><td>1,566</td></tr><tr><td>Later parents in Non-STEM</td><td></td><td></td><td>67%</td><td>16%</td><td>866</td></tr><tr><td>Childless couples in mixed work trajectories</td><td></td><td></td><td>60%</td><td>12%</td><td>620</td></tr><tr><td>Singles in mixed work trajectories</td><td></td><td></td><td>49%</td><td>13%</td><td>689</td></tr><tr><td>Total</td><td></td><td></td><td>63%</td><td>100%</td><td>5,364</td></tr></tbody></table>

*Notes:* Results from sequence distribution plots presented. CSE stands for computer science and engineering. Non-STEM stands for all occupations outside of computer science and engineering and natural science. Percent women stands for the percentage of women in each cluster. Percent of total stands for the percentage of individuals (with a natural science major) in each cluster. N provides the total number of individuals in each cluster. Data are from authors' calculations based on Finnish register data 1987-2022.
<table><caption>**Table 5** Work-family trajectories of non-STEM majors for ages 30-40</caption><thead><tr><th>Clusters</th><th>Work trajectories</th><th>Family trajectories</th><th>Percent women</th><th>Percent of total</th><th>N</th></tr></thead><tbody><tr><td>Parents in Non-STEM</td><td></td><td></td><td>78%</td><td>48%</td><td>19,024</td></tr><tr><td>Single parents in Non-STEM</td><td></td><td></td><td>83%</td><td>10%</td><td>4,066</td></tr><tr><td>Later parents in Non-STEM</td><td></td><td></td><td>71%</td><td>12%</td><td>4,674</td></tr><tr><td>Parents in mixed work trajectories</td><td></td><td></td><td>70%</td><td>10%</td><td>3,910</td></tr><tr><td>Childless couples in Non-STEM</td><td></td><td></td><td>71%</td><td>7%</td><td>2,861</td></tr><tr><td>Singles in Non-STEM</td><td></td><td></td><td>65%</td><td>14%</td><td>5,465</td></tr><tr><td>Total</td><td></td><td></td><td>75%</td><td>100%</td><td>40,000</td></tr></tbody></table>

*Notes:* Results from sequence distribution plots presented. CSE stands for computer science and engineering. Non-STEM stands for all occupations outside of computer science and engineering and natural science. Percent women stands for the percentage of women in each cluster. Percent of total stands for the percentage of individuals (with a non-STEM major) in each cluster. N provides the total number of individuals in each cluster. Data are from authors' calculations based on Finnish register data 1987-2022.
## Gender earnings gap

Next, we assess gender differences in the returns to distinct work-family trajectories using log earnings in ages 41–43. We begin by plotting the distribution of men’s and women’s earnings across education fields of study (Figure 2). Women tend to earn less than men in all fields. Yet, gender differences are more pronounced for CSE and non-STEM majors than for natural science majors. Log earnings are close to normally distributed, motivating our methodological choice to estimate linear regressions with ordinary least squares in the subsequent analysis.

Figure 3 presents estimates based on Equation 1. Models are estimated separately for CSE, natural science, and non-STEM majors and reported as adjusted predictions. We provide the coefficients and standard errors of the control variables in Table A3. This table shows that few individuals, or less than 150, were excluded due to zero income.

Figure 3 shows that women earn less than men, but gender earnings gaps differ substantively across clusters. Among CSE majors (top panel), gender earnings gaps are most pronounced among parents in CSE, parents in non-STEM, and later parents in CSE. By contrast, gender differences are smaller for childless couples in CSE (mixed) and singles in CSE, when we compare individuals with similar individual level characteristics and who work in the same occupations. Differences in the gender earnings gaps are largely attributable to across-cluster-variation in men’s earnings. That is, women have similar earnings across clusters, while men appear to receive a strong fatherhood premium. The sizeable gender earnings gaps observed in the clusters parents in CSE, parents in non-STEM, and later parents in CSE are therefore attributable to men receiving higher earnings. We observe an outlier group of singles in mixed work trajectories, in which men and women have much lower earnings and experience no gender differences.
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Earnings distribution across education fields of study and gender.</figcaption></figure>

*Note:* Figures provide results from kernel density estimations of the average log earnings in ages 41–43. Note that outliers are excluded. Data from authors’ calculations based on Finnish register data 1987–2022.
Gender disparities are less pronounced among natural science majors (middle panel). Still, we also find significant gender earnings gaps, and particularly so among parents in natural science (mixed) and later parents in non-STEM. We observe the same outlier group of singles in mixed work trajectories, who are characterized by lower earnings and no gender earnings gap. In contrast to the patterns observed for CSE majors, we find that men's *and* women's earnings differ across clusters.

Among non-STEM majors (bottom panel), the gender earnings gap is largest for parents in mixed work trajectories. It is also sizeable for parents in non-STEM, single parents in non-STEM, and later parents in non-STEM. Similar to patterns observed among natural science majors, earnings vary across clusters for men and women. We also note an outlier group of singles in non-STEM, for whom we observe no gender earnings gap.

These findings corroborate H2, which posits the existence of consistent parental gender gaps across occupations, rather than H3, which posits reduced gaps in CSE. They are also consistent with H4, which predicts fatherhood premiums to play a considerable role. Still, patterns are more heterogeneous when it comes to the rewards accrued by women in CSE careers. Among CSE majors, the larger gender earnings gaps observed for parents appear fully attributable to men's fatherhood premium, as women's earnings do not vary across clusters. Even though some women are therefore able to combine CSE employment and parenthood, they do not seem to benefit in terms of earnings. We similarly observe a fatherhood premium among natural science and non-STEM majors, but women who major in these fields also benefit from different career choices.
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Gender earnings gaps across majors and clusters.</figcaption></figure>

*Note:* Figures provide the estimated earnings from Equation 1. Adjusted predictions are reported. Regressions are estimated separately for computer science and engineering (CSE), natural science, and non-STEM majors. Coefficients and standard errors are provided in Table A3. Data from authors’ calculations based on Finnish register data 1987–2022.
## Sensitivity analysis

We ran sensitivity analyses excluding individuals on parental leave anytime in ages 41–43 (Figure A5). They revealed similar patterns to those presented in the main analysis, although gender earnings gaps were not statistically significant for singles in CSE and childless couples in mixed work trajectories.

We also ran the main analysis focusing on the first three birth cohorts, in order to assess whether our results capture earnings shocks related to the COVID-19 pandemic (Figure A6). The results reveal similar patterns, though some gender earnings gaps that were small in the main analysis, are not statistically significant.

Finally, we assessed individuals' earned income from employment in ages 41–43, rather than relying on individuals' total taxable income (Figure A7). The results reveal similar gender differences, although the overall values are lower because earned income from employment excludes social benefits.

## Discussion and conclusion

This study has assessed gender differences in the longer-term work trajectories of college educated computer science and engineering majors, and how they are linked to family formation. Exploiting longitudinal Finnish register data, we used multi-channel sequence analysis to identify individuals' joint work (employment status and occupation) and family trajectories (couple and children) in ages 30–40. We further assessed gender differences in lifetime earnings by comparing men and women with similar work-family trajectories, and within-gender differences.

The analysis revealed three main findings. First, we found that women are able to combine a career in computer science and engineering and having children. This suggests that family constraints related to having children are not the main deterring factor that keeps
women from pursuing a career in computer science and engineering. This could be facilitated
by the fact that the technology industry provides work flexibility (Goldin 2014). Notably, we
found that women are more strongly underrepresented in computer science and engineering
careers when following non-normative (later parenthood and staying single between 30 and
40) rather than normative family trajectories (parenthood). Strong cultural norms that promote
stable marriage with children may pose significant challenges for individuals who do not
follow these traditional pathways in Finland, and may be particularly pronounced for those in
gender-mismatched occupations (Jalovaara and Fasang 2020; Thomson, Winkler-Dworak,
and Kennedy 2013).

Yet, women are strongly overrepresented among parents in non-STEM, whereas they
are proportionally represented among parents in computer science and engineering. This
suggests that work flexibility promotes mothers in computer science and engineering
occupations, but only to a certain extent. Societal gender pressures, a lack of female role
models, a chilly climate for women in STEM, and gender discrimination, among others, likely
present barriers that keep women out of these male dominated sectors (Blickenstaff 2005).
Our findings thus provide mixed support for the leaky pipeline hypothesis (H1), according to
which women, and particularly mothers, are underrepresented in computer science and
engineering occupations (Calhoun, Jayaram, and Madorsky 2022; Glass et al. 2013). On the
one hand, we did find that women tend to be underrepresented in computer science and
engineering occupations, which goes in line with prior evidence from the United States
(Sassler, Michelmore, and Smith 2017; Sassler, Smith, and Michelmore 2023). On the other
hand, our findings point to important variation across family trajectories. Whereas mothers
proportionally pursue computer science and engineering careers, non-normative family
trajectories are more clearly linked with an underrepresentation in computer science and
engineering careers.
Second, the returns to combining a career in computer science and engineering with parenthood are much lower for women than for men. In broader terms, we found that the gender earnings gap is largest for parents and consistently so across occupations, corroborating H2, which posits that parents exhibit larger gender earnings gaps than non-parents and that these gaps are consistent in magnitude across occupations. This result qualifies the CSE gender gap scenario (H3) following the specialization theory (Becker 1985; 1991) and suggests that mothers face similar earnings gaps in male dominated and gender mixed occupations. It further suggests that work flexibility enables women to combine work with family, but does not reduce the earnings gap between fathers and mothers in computer science and engineering as these industries are simultaneously characterized by a high prevalence of “greedy jobs” (Goldin 2021).

Third, we found that, even though larger gender gaps observed for parents are consistent across occupations, they stem from different underlying within-gender differences. Distinct mechanisms may consequently be at play. The larger gender gaps observed for parents were expected to result from men earning more when they become fathers (H4). Consistent with this expectation and past research (Jalovaara and Fasang 2020; Bygren, Gähler, and Magnusson 2021; Ludwig and Brüderl 2018), our findings confirm this expectation: men's earnings differ considerably by family status and indicate a clear fatherhood premium. Conversely, work trajectories appear less important for men's earnings.

For women, patterns are more heterogeneous and indicate distinct rewards for pursuing a career in computer science and engineering. Among computer science and engineering majors, women's earnings remain constant across work and family trajectories. Mothers in computer science and engineering experience neither motherhood premiums nor child related career costs. This finding paints a more pessimistic picture than prior evidence from the United States, which indicates a positive association between motherhood and
earnings in the STEM sector (Buchmann and McDaniel 2016; Michelmore and Sassler 2016; Beutel and Schleifer 2022). These differences might reflect that we distinguish computer science and engineering from natural science (in the latter, we observe indications of a motherhood premium).

By contrast, among natural science and engineering majors women's earnings vary across work trajectories. Women with a STEM major earn considerably less when they follow mixed work trajectories as compared to other career paths. This is likely due to the fact that women are more often homemakers in this cluster. Even though men also experience a reduction in earnings in this cluster, it is less severe than for women.

Among natural science majors, women earn more when they move into computer science and engineering and non-STEM careers, while we do not observe the same rewards among computer science and engineering majors. This distinction may be due to differences in how women select into computer science and engineering occupations across these two majors. They may also be related to differences in the subfield and work activity pursued (Goldin 2014). Women tend to be segregated into the less core technical subfields and social work activities, and this seems to be particularly the case for women who major in the field (White and Smith 2022). Conversely, women with a major in natural science may be more likely to pursue “greedy jobs” within these occupations, entering more prestigious and higher-paid positions (Goldin 2021). This will be important to explore further in future research.

Our study has some limitations that future research could help address. First, we are unable to address selection. Individuals select into their education fields of study and subsequent work-family trajectories. We expect that selection into fatherhood plays a non-negligible role in understanding observed parent gaps but were unable to examine this directly. Still, here we aimed at comparing individuals across different trajectories. Second, we assess individuals' earnings in ages 41–43. We would ideally follow individuals over a
longer time period, but are restricted by the statistical years available. Third, we analyze men and women, but do not capture individuals' partners. This will be pertinent for gaining a better understanding of gender inequalities at the individual versus the family level in future research.

In conclusion, we make three key contributions. First, we extend STEM research by adopting a longitudinal perspective to assess joint work and family trajectories in later stages of adulthood, revealing that women are able to combine employment in computer science and engineering and parenthood. Second, we assess individuals' earnings and show that, despite combining work and family, women earn considerably less than their male counterparts. Third, in addition to uncovering gender gaps, we show that a major mechanism underlying parental gaps is that men receive notable fatherhood premiums across education fields of study and work trajectories. For women, findings reveal more heterogeneous patterns. Among computer science and engineering majors, women have similar earnings across trajectories. In natural science and non-STEM, women gain from pursuing computer science and engineering and non-STEM careers.

## Acknowledgements

We are grateful to Joseph Cimpian, Roujman Shahbazian, David Card, and Andrea Weber for their comments on earlier versions of this manuscript. We would also like to thank participants at the RC28 Paris 2023 meeting and the Sciences Po seminar at the Center for Research on Social Inequalities for their helpful input. This work was supported by the Swedish Research Council for Health, Working life and Welfare (Förskiningsrådet för Hälsa, Arbetsliv och Välfärd, Grant numbers 2021-00026 and 2023-00609), Stiftelsen för Åbo Akademi for the DemSwed internal centre of excellence (2019-2024), and the Society of Swedish Literature in Finland (application number 7965).
References

Abbott, Andrew, and John Forrest. 1986. "Optimal Matching Methods for Historical Sequences." *The Journal of Interdisciplinary History* 16 (3): 471–94.
https://doi.org/10.2307/204500.

Aisenbrey, Silke, and Anette Fasang. 2017. "The Interplay of Work and Family Trajectories over the Life Course: Germany and the United States in Comparison." *American Journal of Sociology* 122 (5): 1448–84.

Atalay, Enghin, Phai Phongthiengtham, Sebastian Sotelo, and Daniel Tannenbaum. 2020. "The Evolution of Work in the United States." *American Economic Journal: Applied Economics* 12 (2): 1–34. https://doi.org/10.1257/app.20190070.

Bairoh, Susanna. 2019. "Valitaanko Pätevä Vai Nainen?" *Loppuraportti: TEK akateemiset*. https://www.tek.fi/sites/default/files/attachments/tek_pateva-vai-nainen_raportti_2019.pdf.

Becker, Gary. 1985. "Human Capital, Effort, and the Sexual Division of Labor." *Journal of Labor Economics* 3 (1): S33–58. https://doi.org/10.1086/298075.

———. 1991. *A Treatise on the Family*. Enlarged edition. Cambridge, MA: Harvard University Press.

Beutel, Ann M., and Cyrus Schleifer. 2022. "Family Structure, Gender, and Wages in STEM Work." *Sociological Perspectives* 65 (4): 790–819.
https://doi.org/10.1177/07311214211060032.

Blau, Francine D., and Lawrence M. Kahn. 2017. "The Gender Wage Gap: Extent, Trends, and Explanations." *Journal of Economic Literature* 55 (3): 789–865.
https://doi.org/10.1257/jel.20160995.

Blickenstaff, Jacob Clark. 2005. "Women and Science Careers: Leaky Pipeline or Gender Filter?" *Gender and Education* 17 (4): 369–86.
https://doi.org/10.1080/09540250500145072.

Bolano, Danilo, and André Berchtold. 2021. "The Analysis of Inequality in Life Trajectories: An Integration of Two Approaches." In *The Routledge Handbook of Contemporary Inequalities and the Life Course*, 63–80. Routledge.
https://doi.org/10.4324/9780429470059-8.

Boye, Katarina, Karin Halldén, and Charlotta Magnusson. 2017. "Stagnation Only on the Surface? The Implications of Skill and Family Responsibilities for the Gender Wage Gap in Sweden, 1974–2010." *The British Journal of Sociology* 68 (4): 595–619.
https://doi.org/10.1111/1468-4446.12252.

Brenner, Jan. 2010. "Life-Cycle Variations in the Association between Current and Lifetime Earnings: Evidence for German Natives and Guest Workers." *Labour Economics* 17 (2): 392–406. https://doi.org/10.1016/j.labeco.2009.03.006.

Buchmann, Claudia, and Anne McDaniel. 2016. "Motherhood and the Wages of Women in Professional Occupations." *RSF: The Russell Sage Foundation Journal of the Social Sciences* 2 (4): 128–50. https://doi.org/10.7758/rsf.2016.2.4.05.

Buffington, Catherine, Benjamin Cerf, Christina Jones, and Bruce A. Weinberg. 2016. "STEM Training and Early Career Outcomes of Female and Male Graduate Students: Evidence from UMETRICS Data Linked to the 2010 Census." *American Economic Review* 106 (5): 333–38. https://doi.org/10.1257/aer.p20161124.

Bygren, Magnus, Michael Gähler, and Charlotta Magnusson. 2021. "The Constant Gap: Parenthood Premiums in Sweden 1968–2010." *Social Forces* 100 (1): 137–68.
https://doi.org/10.1093/sf/soaa097.
Calhoun, Linda, Shruthi Jayaram, and Natasha Madorsky. 2022. “Leaky Pipelines or Broken Scaffolding? Supporting Women’s Leadership in STEM.” *Stanford Social Innovation Review*.

Cech, Erin A. 2013. “Ideological Wage Inequalities? The Technical/Social Dualism and the Gender Wage Gap in Engineering.” *Social Forces* 91 (4): 1147–82.
https://doi.org/10.1093/sf/sot024.

Cech, Erin A., and Mary Blair-Loy. 2019. “The Changing Career Trajectories of New Parents in STEM.” *Proceedings of the National Academy of Sciences* 116 (10): 4182–87.
https://doi.org/10.1073/pnas.1810862116.

Cimpian, Joseph R., Taek H. Kim, and Zachary T. McDermott. 2020. “Understanding Persistent Gender Gaps in STEM.” *Science* 368 (6497): 1317–19.
https://doi.org/10.1126/science.aba7377.

Correll, Shelley J., Stephen Benard, and In Paik. 2007. “Getting a Job: Is There a Motherhood Penalty?” *American Journal of Sociology* 112 (5): 1297–1339.
https://doi.org/10.1086/511799.

Delaney, Judith M., and Paul J. Devereux. 2019. “Understanding Gender Differences in STEM: Evidence from College Applications.” *Economics of Education Review* 72:219–38.
https://doi.org/10.1016/j.econedurev.2019.06.002.

Deming, David J., and Kadeem Noray. 2020. “Earnings Dynamics, Changing Job Skills, and STEM Careers.” *The Quarterly Journal of Economics* 135 (4): 1965–2005.
https://doi.org/10.1093/qje/qjaa021.

European Institute for Gender Equality. 2017. “Gender Segregation in Education, Training and the Labour Market.” https://data.consilium.europa.eu/doc/document/ST-14624-2017-ADD-2/en/pdf.

European Institute for Gender Equality. 2017. “Economic Benefits of Gender Equality in the EU.” 10.2839/652355.

———. 2023. “Towards a Green Transition in Transport and Energy.” Publications Office of the European Union. https://eige.europa.eu/gender-equality-index/2023.

Fasang, Anette Eva, and Silke Aisenbrey. 2022. “Uncovering Social Stratification: Intersectional Inequalities in Work and Family Life Courses by Gender and Race.” *Social Forces* 101 (2): 575–605. https://doi.org/10.1093/sf/soab151.

Finnish Centre for Pensions. 2020. “Women’s Income Development Slows Markedly at Family Formation Age.” 2020. https://www.etk.fi/en/topical-issues/womens-income-development-slows-markedly-at-family-formation-age/.

Gauthier, Jacques-Antoine, Eric D Widmer, Philipp Bucher, and Cédric Notredame. 2010. “Multichannel Sequence Analysis Applied to Social Science Data.” *Social Methodology* 40 (1): 1–38. https://doi.org/10.1111/j.1467-9531.2010.01227.x.

Glass, Jennifer, Sharon Sassler, Yael Levitte, and Katherine Michelmore. 2013. “What’s So Special about STEM? A Comparison of Women’s Retention in STEM and Professional Occupations.” *Social Forces* 92 (2): 723–56.
https://doi.org/10.1093/sf/sot092.

Glauber, Rebecca. 2008. “Race and Gender in Families and at Work: The Fatherhood Wage Premium.” *Gender and Society* 22 (1): 8–30.
https://doi.org/10.1177/0891243207311593.

———. 2018. “Trends in the Motherhood Wage Penalty and Fatherhood Wage Premium for Low, Middle, and High Earners.” *Demography* 55 (5): 1663–80.
https://doi.org/10.1007/s13524-018-0712-5.

Goldin, Claudia. 2014. “A Grand Gender Convergence: Its Last Chapter.” *American Economic Review* 104 (4): 1091–1119. https://doi.org/10.1257/aer.104.4.1091.
———. 2021. *Career & Family: Women's Century-Long Journey toward Equity*. Princeton NJ: Princeton University Press.

Griffith, Amanda L. 2010. "Persistence of Women and Minorities in STEM Field Majors: Is It the School That Matters?" *Economics of Education Review* 29 (6): 911–22.
https://doi.org/10.1016/j.econedurev.2010.06.010.

Haider, Steven, and Gary Solon. 2006. "Life-Cycle Variation in the Association between Current and Lifetime Earnings." *American Economic Review* 96 (4): 1308–20.
https://doi.org/10.1257/aer.96.4.1308.

Jalovaara, Marika, and Anette Eva Fasang. 2020. "Family Life Courses, Gender, and Mid-Life Earnings." *European Sociological Review* 36 (2): 159–78.
https://doi.org/10.1093/esr/jcz057.

Killewald, Alexandra, and Javier García-Manglano. 2016. "Tethered Lives: A Couple-Based Perspective on the Consequences of Parenthood for Time Use, Occupation, and Wages." *Social Science Research* 60:266–82.
https://doi.org/10.1016/j.ssresearch.2016.03.007.

Kleven, Henrik, Camille Landais, Gabriel Leite Mariante, and Mary Reader. 2024. "Child Penalty Atlas." 2024. https://childpenaltyatlas.org/?place=FI&metric=gap.

Koch, Amanda J., Paul R. Sackett, Nathan R. Kuncel, Jeffrey A. Dahlke, and Adam S. Beatty. 2022. "Why Women STEM Majors Are Less Likely than Men to Persist in Completing a STEM Degree: More than the Individual." *Personality and Individual Differences* 190:111532. https://doi.org/10.1016/j.paid.2022.111532.

Korpi, Walter, T. Ferrarini, and S. Englund. 2013. "Women's Opportunities under Different Family Policy Constellations: Gender, Class, and Inequality Tradeoffs in Western Countries Re-Examined." *Social Politics: International Studies in Gender, State & Society* 20 (1): 1–40. https://doi.org/10.1093/sp/jxs028.

Kuitto, Kati, and Susan Kuivalainen. 2021. "Gender Inequalities in Family Leaves, Employment and Pensions in Finland." In *Challenges to the Welfare State*. Cheltenham, UK: Edward Elgar Publishing.

Ludwig, Volker, and Josef Brüderl. 2018. "Is There a Male Marital Wage Premium? New Evidence from the United States." *American Sociological Review* 83 (4): 744–70.
https://doi.org/10.1177/0003122418784909.

MacIndoe, Heather, and Andrew Abbott. 2004. "Sequence Analysis and Optimal Matching Techniques for Social Science Data." In *Handbook of Data Analysis*, 387–406. Sage.

Mari, Gabriele. 2019. "Is There a Fatherhood Wage Premium? A Reassessment in Societies With Strong Male-Breadwinner Legacies." *Journal of Marriage and Family* 81 (5): 1033–52. https://doi.org/10.1111/jomf.12600.

Michelmore, Katherine, and Sharon Sassler. 2016. "Explaining the Gender Wage Gap in STEM: Does Field Sex Composition Matter?" *RSF: The Russell Sage Foundation Journal of the Social Sciences* 2 (4): 194–215. https://doi.org/10.7758/rsf.2016.2.4.07.

Mincer, Jacob, and Solomon Polachek. 1974. "Family Investments in Human Capital: Earnings of Women." *Journal of Political Economy* 82 (2, Part 2): S76–108.
https://doi.org/10.1086/260293.

OECD. 2022. "Did Someone Say Women in Science?" 2022.
https://www.oecd.org/coronavirus/en/data-insights/did-someone-say-women-in-science.

Ojala, Satu. 2020. "Teollisuusalojen naisten ja miesten työtulot koulutusaloittain ja toimialoittain neljällä kohortilla." In *Pirstoutuvatko työurat? Teollisuusalat talouden ja teknologian muroksissa*, edited by Satu Ojala and Pasi Pyöriä. Tampere University Press.
Öun, Ida. 2012. "Work-Family Conflict in the Nordic Countries: A Comparative Analysis." *Journal of Comparative Family Studies* 43 (2): 165-84.
https://doi.org/10.3138/jcfs.43.2.165.

Pollock, Gary. 2007. "Holistic Trajectories: A Study of Combined Employment, Housing and Family Careers by Using Multiple-Sequence Analysis." *Journal of the Royal Statistical Society Series A: Statistics in Society* 170 (1): 167-83.
https://doi.org/10.1111/j.1467-985X.2006.00450.x.

Prieto-Rodriguez, Elena, Kristina Sincock, and Karen Blackmore. 2020. "STEM Initiatives Matter: Results from a Systematic Review of Secondary School Interventions for Girls." *International Journal of Science Education* 42 (7): 1144-61.
https://doi.org/10.1080/09500693.2020.1749909.

Rouvinen, Petri, and Pekka Ylä-Anttila. 2003. "Little Finland's Transformation to a Wireless Giant." In *The Global Information Technology Report: Toward an Equitable Information Society*, edited by Soumitra Dutta, Bruno Lanvin, and Fiona Paua. New York and Oxford: Oxford University Press with World Economic Forum.

Sassler, Sharon, and Pamela Meyerhofer. 2023. "Factors Shaping the Gender Wage Gap among College-Educated Computer Science Workers." *PLOS ONE* 18 (10): e0293300. https://doi.org/10.1371/journal.pone.0293300.

Sassler, Sharon, Katherine Michelmore, and Kristin Smith. 2017. "A Tale of Two Majors: Explaining the Gender Gap in STEM Employment among Computer Science and Engineering Degree Holders." *Social Sciences* 6 (3): 69.
https://doi.org/10.3390/socsci6030069.

Sassler, Sharon, Kristin E. Smith, and Katherine Michelmore. 2023. "Cohort Differences in Occupational Retention among Computer Science Degree Holders: Reassessing the Role of Family." *Sociological Perspectives* 66 (6): 1060-83.
https://doi.org/10.1177/07311214231195024.

Schwerter, Jakob, and Lena Ilg. 2023. "Gender Differences in the Labour Market Entry of STEM Graduates." *European Journal of Higher Education* 13 (3): 308-26.
https://doi.org/10.1080/21568235.2021.2010226.

Sirniö, Outi, Timo M. Kauppinen, and Pekka Martikainen. 2017. "Intergenerational Determinants of Joint Labor Market and Family Formation Pathways in Early Adulthood." *Advances in Life Course Research* 34:10-21.
https://doi.org/10.1016/j.alcr.2017.09.001.

Speer, Jamin D. 2023. "Bye Bye Ms. American Sci: Women and the Leaky STEM Pipeline." *Economics of Education Review* 93.
https://doi.org/10.1016/j.econedurev.2023.102371.

Statistics Finland. 2021. *Gender Equality in Finland 2021*.
—. 2022. "Basic Information on Gender Equality between Women and Men." 2022.
https://stat.fi/tup/tasaarvo/perustietoja-sukupuolten-tasa-arvosta_en.html.

Sterling, Adina D., Marissa E. Thompson, Shiya Wang, Abisola Kusimo, Shannon Gilmartin, and Sheri Sheppard. 2020. "The Confidence Gap Predicts the Gender Pay Gap among STEM Graduates." *Proceedings of the National Academy of Sciences* 117 (48): 30303-8. https://doi.org/10.1073/pnas.2010269117.

Studer, Matthias. 2013. "WeightedCluster Library Manual: A Practical Guide to Creating Typologies of Trajectories in the Social Sciences with R." *LIVES Working Papers* 24.
http://dx.doi.org/10.12682/lives.2296-1658. 2013.24.

Tekniikan akateemiset TEK. 2024. "More than Half of the Gender Pay Gap in Tech Is Unexplained." 2024. https://www.tek.fi/en/news-blogs/more-than-half-of-the-gender-pay-gap-in-tech-is-unexplained.
Teräsaho, Mia, and Johanna Närvi. 2019. “Näkökulmia Sukupuolten Tasa-Arvoon – Analyyseja Tasa-Arvobarometrista 2017 [Perspectives on Gender Equality – Analyses of the Gender Equality Barometer 2017].” National Institute for Health and Welfare (THL). https://www.julkari.fi/handle/10024/137765.

Thomson, Elizabeth, Maria Winkler-Dworak, and Sheela Kennedy. 2013. “The Standard Family Life Course: An Assessment of Variability in Life Course Pathways.” In *Negotiating the Life Course*, edited by Ann Evans and Janeen Baxter, 35–52. Dordrecht: Springer.

White, Patrick, and Emma Smith. 2022. “From Subject Choice to Career Path: Female STEM Graduates in the UK Labour Market.” *Oxford Review of Education* 48 (6): 693–709. https://doi.org/10.1080/03054985.2021.2011713.

Williams, Joan, Katherine Phillips, and Erika Hall. 2016. “Tools for Change: Boosting the Retention of Women in the STEM Pipeline.” *Journal of Research in Gender Studies* 6 (1): 11. https://doi.org/10.22381/JRGS6120161.

Williams, Richard. 2012. “Using the Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects.” *The Stata Journal: Promoting Communications on Statistics and Stata* 12 (2): 308–31. https://doi.org/10.1177/1536867X1201200209.

Xu, Yonghong. 2015. “Focusing on Women in STEM: A Longitudinal Examination of Gender-Based Earning Gap of College Graduates.” *The Journal of Higher Education* 86 (4): 489–523. https://doi.org/10.1353/jhe.2015.0020.
# Appendix

We have further explored two sets of descriptive statistics to assess our categorization of CSE, natural science, and non-STEM occupations: the top ten occupations of computer science and engineering majors, and the top ten occupations by gender.

Tables A1 and A2 show that about half of computer science and engineering majors work in three occupations: (1) architects and engineering science professionals, (2) physical, chemical, and engineering science technicians, and (3) computing professionals (2001 classification), and (1) engineering professionals, (2) software and applications developers and analysts, and (3) physical and engineering science technicians (2010 classification). By contrast, natural science majors are often teachers and administrative professionals, besides life science, physical, and earth science professionals.

Given that men and women pursue distinct occupations, we also provide the top ten occupations by gender for CSE, natural science, and non-STEM majors in Figures A2 (2001 classification) and A3 (2010 classification). Gender differences are relatively small among CSE majors, although men are more often physical, chemical, and engineering science technicians (Figure A2). Women are instead more often architects, planners, surveyors, and designers (Figure A3). Among non-STEM majors, gender differences are more notable. About 15% of women are in nursing compared with about 4% of men (Figures A2 and A3), whereas men are most often finance and sales associate professionals and business professionals (2001 classification), or sales and purchasing agents and brokers (2010 classification).
<table><caption>**Table A1** List of computer science and engineering and natural science occupations and the fraction of computer science and engineering and natural science majors working in each occupation, based on Statistics Finland's classification of occupations 2001 (relied on for years 2004-2009)</caption><thead><tr><th>Occupation code</th><th>Occupation name</th><th>Fraction of CSE majors<fnref n="1" /></th><th>Fraction of natural science majors<fnref n="2" /></th></tr></thead><tbody><tr><td colspan="4"><strong>Computer Science and Engineering</strong></td></tr><tr><td>213</td><td>Computing professionals</td><td>0.14</td><td>0.02</td></tr><tr><td>214</td><td>Architects and engineering science professionals</td><td>0.24</td><td>0.05</td></tr><tr><td>311</td><td>Physical, chemical, and engineering science technicians</td><td>0.24</td><td>0.06</td></tr><tr><td>312</td><td>Computer associate professionals</td><td>0.04</td><td>0.01</td></tr><tr><td>711</td><td>Miners, shotfirers, stone cutters and carvers</td><td>0.01</td><td>0.00</td></tr><tr><td>712</td><td>Building frame and related trades workers</td><td>0.01</td><td>0.00</td></tr><tr><td>713</td><td>Building finishers and related trades workers</td><td>0.01</td><td>0.00</td></tr><tr><td>714</td><td>Painters, building strucutre cleaners and related trades workers</td><td>0.01</td><td>0.00</td></tr><tr><td>721</td><td>Metal moulders, sheet-metal workers, structural metal preparers, and related trades workers</td><td>0.01</td><td>0.00</td></tr><tr><td>722</td><td>Blacksmiths, tool-makers and related trades workers</td><td>0.01</td><td>0.00</td></tr><tr><td>723</td><td>Machinery mechanics and fitters</td><td>0.01</td><td>0.00</td></tr><tr><td>724</td><td>Electrical, electronic and telecommunications equipment mechanics and fitters</td><td>0.01</td><td>0.01</td></tr><tr><td>811</td><td>Mining and mineral-processing-plant operators</td><td>0.01</td><td>0.00</td></tr><tr><td>812</td><td>Metal-processing plant operators</td><td>0.01</td><td>0.00</td></tr><tr><td>814</td><td>Wood-processing- and papermaking-plant operators</td><td>0.01</td><td>0.01</td></tr><tr><td>816</td><td>Power-production and related plant operators</td><td>0.01</td><td>0.01</td></tr><tr><td>817</td><td>Industrial robot operators</td><td>0.01</td><td>0.00</td></tr><tr><td>821</td><td>Metal- and mineral-products machine operators</td><td>0.01</td><td>0.00</td></tr><tr><td>823</td><td>Rubber- and plastic-products machine operators</td><td>0.01</td><td>0.00</td></tr><tr><td>824</td><td>Wood-products machine operators</td><td>0.01</td><td>0.00</td></tr><tr><td colspan="4"><strong>Natural Science, Mathematics and Statistics</strong></td></tr><tr><td>211</td><td>Physicists, chemists, and related professionals</td><td>0.01</td><td>0.11</td></tr><tr><td>212</td><td>Mathematicians, statisticians and related professionals</td><td>0.01</td><td>0.02</td></tr><tr><td>221</td><td>Life science professionals</td><td>0.01</td><td>0.08</td></tr><tr><td>321</td><td>Life science technicians and related associate professionals</td><td>0.01</td><td>0.01</td></tr><tr><td>813</td><td>Glass, ceramics and related plant operators</td><td>0.01</td><td>0.00</td></tr><tr><td>815</td><td>Chemical-processing-plant operators</td><td>0.01</td><td>0.01</td></tr><tr><td>822</td><td>Chemical-products machine operators</td><td>0.01</td><td>0.00</td></tr><tr><td colspan="4"><strong>Top Ten Occupations for Computer Science and Engineering Majors</strong></td></tr><tr><td>214</td><td>Architects and engineering science professionals</td><td>0.24</td><td></td></tr><tr><td>311</td><td>Physical, chemical, and engineering science technicians</td><td>0.24</td><td></td></tr><tr><td>213</td><td>Computing professionals</td><td>0.14</td><td></td></tr><tr><td>341</td><td>Finance and sales associate professionals</td><td>0.05</td><td></td></tr><tr><td>312</td><td>Computer associate professionals</td><td>0.04</td><td></td></tr><tr><td>247</td><td>Public service administrative professionals</td><td>0.03</td><td></td></tr><tr><td>123</td><td>Other specialist managers</td><td>0.02</td><td></td></tr><tr><td>241</td><td>Business professionals not elsewhere classified</td><td>0.02</td><td></td></tr><tr><td>522</td><td>Shop, stall and market salespersons and demonstrators</td><td>0.01</td><td></td></tr><tr><td>122</td><td>Production and operations managers</td><td>0.01</td><td></td></tr><tr><td colspan="4"><strong>Top Ten Occupations for Natural Science Majors</strong></td></tr><tr><td>232</td><td>Secondary education teaching professionals</td><td>0.23</td><td></td></tr><tr><td>247</td><td>Public service administrative professionals</td><td>0.16</td><td></td></tr><tr><td>211</td><td>Physicists, chemists and related professionals</td><td>0.11</td><td></td></tr><tr><td>221</td><td>Life science professionals</td><td>0.08</td><td></td></tr><tr><td>311</td><td>Physical, chemical and engineering science technicians</td><td>0.06</td><td></td></tr><tr><td>214</td><td>Architects and engineering science professionals</td><td>0.05</td><td></td></tr><tr><td>231</td><td>College, university and higher education teaching professionals</td><td>0.04</td><td></td></tr><tr><td>222</td><td>Health professionals (except nursing)</td><td>0.02</td><td></td></tr><tr><td>322</td><td>Health associate professionals (except nursing)</td><td>0.02</td><td></td></tr><tr><td>213</td><td>Computing professionals</td><td>0.02</td><td></td></tr></tbody></table>

Notes: The prevalence of the occupation at age 30 is reported. Data are from authors' calculations. Data are from authors' calculations based on Finnish register data 1987-2022.

<footnote n="1">¹ Individuals who studied and majored in computer science and engineering (CSE).</footnote>

<footnote n="2">² Individuals who studied and majored in natural science.</footnote>
<table><caption>**Table A2** List of computer science and engineering and natural science occupations and the fraction of computer science and engineering and natural science majors working in each occupation, based on Statistics Finland's classification of occupations 2010 (following the structure of ISCO-08, relied on for years 2010 and onwards)</caption><thead><tr><th>Occupation code</th><th>Occupation name</th><th>Fraction of CSE majors<fnref n="1" /></th><th>Fraction of natural science majors<fnref n="2" /></th></tr></thead><tbody><tr><td colspan="4" style="text-align: center; font-weight: bold;">Computer Science and Engineering</td></tr><tr><td>132</td><td>Manufacturing, mining, construction, and distribution managers</td><td>0.04</td><td>0.01</td></tr><tr><td>133</td><td>ICT service managers</td><td>0.02</td><td>0.01</td></tr><tr><td>214</td><td>Engineering professionals (excl. electrotechnology)</td><td>0.16</td><td>0.04</td></tr><tr><td>215</td><td>Electrotechnology engineers</td><td>0.08</td><td>0.01</td></tr><tr><td>216</td><td>Architects, planners, surveyors and designers</td><td>0.02</td><td>0.01</td></tr><tr><td>251</td><td>Software and applications developers and analysts</td><td>0.15</td><td>0.03</td></tr><tr><td>252</td><td>Database and network professionals</td><td>0.02</td><td>0.01</td></tr><tr><td>311</td><td>Physical and engineering science technicians</td><td>0.12</td><td>0.04</td></tr><tr><td>312</td><td>Mining, manufacturing, and construction supervisors</td><td>0.01</td><td>0.00</td></tr><tr><td>313</td><td>Process control technicians</td><td>0.01</td><td>0.01</td></tr><tr><td>351</td><td>ICT operations and user support technicians</td><td>0.03</td><td>0.01</td></tr><tr><td>352</td><td>Telecommunications and broadcasting technicians</td><td>0.01</td><td>0.00</td></tr><tr><td>711</td><td>Building frame and related trades workers</td><td>0.01</td><td>0.00</td></tr><tr><td>712</td><td>Building finishers and related trades workers</td><td>0.01</td><td>0.00</td></tr><tr><td>713</td><td>Painters, building structure cleaners and related trades workers</td><td>0.01</td><td>0.00</td></tr><tr><td>721</td><td>Sheet and structural metal workers and moulders and welders, and related workers</td><td>0.01</td><td>0.00</td></tr><tr><td>722</td><td>Blacksmiths, toolmakers and related trades workers</td><td>0.01</td><td>0.00</td></tr><tr><td>723</td><td>Machinery mechanics and repairers</td><td>0.01</td><td>0.00</td></tr><tr><td>741</td><td>Electrical equipment installers and repairers</td><td>0.01</td><td>0.00</td></tr><tr><td>811</td><td>Mining and mineral processing plant operators</td><td>0.01</td><td>0.00</td></tr><tr><td>812</td><td>Metal processing and finishing plant operators</td><td>0.01</td><td>0.00</td></tr><tr><td>814</td><td>Rubber, plastic and paper products machine operators</td><td>0.01</td><td>0.00</td></tr><tr><td>817</td><td>Wood processing and papermaking plant operators</td><td>0.01</td><td>0.00</td></tr><tr><td>818</td><td>Other stationary plant and machine operators</td><td>0.01</td><td>0.00</td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">Natural Science, Mathematics and Statistics</td></tr><tr><td>211</td><td>Physical and earth science professionals</td><td>0.01</td><td>0.11</td></tr><tr><td>212</td><td>Mathematicians, actuaries, and statisticians</td><td>0.01</td><td>0.02</td></tr><tr><td>213</td><td>Life science professionals</td><td>0.01</td><td>0.11</td></tr><tr><td>314</td><td>Life science technicians and related associate professionals</td><td>0.01</td><td>0.01</td></tr><tr><td>813</td><td>Chemical and photographic products plant and machine operators</td><td>0.01</td><td>0.01</td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">Top Ten Occupations for Computer Science and Engineering Majors</td></tr><tr><td>214</td><td>Engineering professionals (excl. electrotechnology)</td><td>0.16</td><td></td></tr><tr><td>251</td><td>Software and applications developers and analysts</td><td>0.15</td><td></td></tr><tr><td>311</td><td>Physical and engineering science technicians</td><td>0.12</td><td></td></tr><tr><td>215</td><td>Electrotechnology engineers</td><td>0.08</td><td></td></tr><tr><td>332</td><td>Sales and purchasing agents and brokers</td><td>0.04</td><td></td></tr><tr><td>243</td><td>Sales, marketing, and public relations professionals</td><td>0.04</td><td></td></tr><tr><td>132</td><td>Manufacturing, mining, construction, and distribution managers</td><td>0.04</td><td></td></tr><tr><td>351</td><td>ICT operations and user support technicians</td><td>0.03</td><td></td></tr><tr><td>242</td><td>Administration professionals</td><td>0.03</td><td></td></tr><tr><td>216</td><td>Architects, planners, surveyors, and designers</td><td>0.02</td><td></td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">Top Ten Occupations for Natural Science Majors</td></tr><tr><td>233</td><td>Secondary education teachers</td><td>0.19</td><td></td></tr><tr><td>213</td><td>Life science professionals</td><td>0.11</td><td></td></tr><tr><td>211</td><td>Physical and earth science professionals</td><td>0.11</td><td></td></tr><tr><td>242</td><td>Administration professionals</td><td>0.05</td><td></td></tr><tr><td>311</td><td>Physical and engineering science technicians</td><td>0.04</td><td></td></tr><tr><td>231</td><td>University and higher education teachers</td><td>0.04</td><td></td></tr><tr><td>214</td><td>Engineering professionals (excl. electrotechnology)</td><td>0.04</td><td></td></tr><tr><td>234</td><td>Primary school and early childhood teachers</td><td>0.03</td><td></td></tr><tr><td>251</td><td>Software and applications developers and analysts</td><td>0.03</td><td></td></tr><tr><td>235</td><td>Other teaching professionals</td><td>0.03</td><td></td></tr></tbody></table>

Notes: The prevalence of the occupation at age 40 is reported. Data are from authors' calculations based on Finnish register data 1987-2022.

<footnote n="1">¹ Individuals who studied and majored in computer science and engineering (CSE).</footnote>

<footnote n="2">² Individuals who studied and majored in natural science.</footnote>
<table><caption>Table A3 Coefficients from ordinary least squares regressions on the average earnings of computer science and engineering, natural science, and non-STEM majors in ages 41-43 across clusters and gender</caption><thead><tr><th colspan="2">Computer Science and Engineering</th><th colspan="2">Natural Science</th><th colspan="2">Non-STEM</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(1)</th><th>(2)</th><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td colspan="7"><strong>Cluster (ref. Parents in CSE)</strong></td></tr><tr><td>Parents in Non-STEM</td><td>-0.01<br>(0.01)</td><td>-0.01<br>(0.01)</td><td>Parents in non-STEM</td><td>-0.09*<br>(0.03)</td><td>-0.02<br>(0.04)</td><td>Single parents in Non-STEM</td><td>-0.08***<br>(0.02)</td><td>-0.07***<br>(0.02)</td></tr><tr><td>Later parents in CSE</td><td>-0.04***<br>(0.01)</td><td>-0.03***<br>(0.01)</td><td>Parents in Natural Science (mixed)</td><td>-0.12***<br>(0.04)</td><td>-0.12**<br>(0.04)</td><td>Later parents in Non-STEM</td><td>-0.10***<br>(0.02)</td><td>-0.08***<br>(0.02)</td></tr><tr><td>Childless couples in CSE (mixed)</td><td>-0.14***<br>(0.01)</td><td>-0.11***<br>(0.01)</td><td>Later parents in Non-STEM</td><td>-0.24***<br>(0.04)</td><td>-0.13**<br>(0.05)</td><td>Parents in mixed work trajectories</td><td>-0.12***<br>(0.02)</td><td>-0.15***<br>(0.02)</td></tr><tr><td>Singles in CSE</td><td>-0.19***<br>(0.01)</td><td>-0.16***<br>(0.01)</td><td>Childless couples in mixed work trajectories</td><td>-0.25***<br>(0.05)</td><td>-0.19***<br>(0.05)</td><td>Childless couples in Non-STEM</td><td>-0.30***<br>(0.03)</td><td>-0.24***<br>(0.03)</td></tr><tr><td>Singles in mixed work traj.</td><td>-0.70***<br>(0.02)</td><td>-0.59***<br>(0.01)</td><td>Singles in mixed work traj.</td><td>-0.57***<br>(0.05)</td><td>-0.45***<br>(0.05)</td><td>Singles in Non-STEM</td><td>-0.54***<br>(0.02)</td><td>-0.44***<br>(0.02)</td></tr><tr><td colspan="7"><strong>Gender (ref. Male)</strong></td></tr><tr><td>Female</td><td>-0.37***<br>(0.01)</td><td>-0.30***<br>(0.01)</td><td>Gender (ref. Male)</td><td>-0.22***<br>(0.04)</td><td>-0.17***<br>(0.04)</td><td>Gender (ref. Male)</td><td>-0.32***<br>(0.01)</td><td>-0.23***<br>(0.01)</td></tr><tr><td colspan="7"><strong>Cluster × Female<br>(ref. Parents in CSE × Female)</strong></td></tr><tr><td>Parents in Non-STEM × Female</td><td>-0.01<br>(0.02)</td><td>0.03<br>(0.02)</td><td>Parents in Non-STEM × Female</td><td>-0.01<br>(0.04)</td><td>0.03<br>(0.04)</td><td>Cluster × Female<br>(ref. Parents in Non-STEM × Female)</td><td>Single parents in Non-STEM × Female</td><td>0.01<br>(0.03)</td><td>0.01<br>(0.02)</td></tr><tr><td>Later parents in CSE × Female</td><td>0.11***<br>(0.02)</td><td>0.08***<br>(0.02)</td><td>Parents in Natural Science (mixed) × Female</td><td>-0.12*<br>(0.05)</td><td>-0.05<br>(0.05)</td><td>Later parents in Non-STEM × Female</td><td>-0.04<br>(0.02)</td><td>-0.05**<br>(0.02)</td></tr><tr><td>Childless couples in CSE (mixed) × Female</td><td>0.11***<br>(0.02)</td><td>0.10***<br>(0.02)</td><td>Later parents in Non-STEM × Female</td><td>-0.02<br>(0.06)</td><td>-0.04<br>(0.05)</td><td>Parents in mixed work traj. × Female</td><td>-0.23***<br>(0.03)</td><td>-0.16***<br>(0.02)</td></tr><tr><td>Singles in CSE × Female</td><td>0.25***<br>(0.02)</td><td>0.20***<br>(0.02)</td><td>Childless couples in mixed work traj. × Female</td><td>-0.03<br>(0.06)</td><td>0.02<br>(0.06)</td><td>Childless couples in Non-STEM × Female</td><td>-0.18***<br>(0.03)</td><td>0.13***<br>(0.03)</td></tr><tr><td>Singles in mixed work traj. × Female</td><td>0.38***<br>(0.04)</td><td>0.31***<br>(0.04)</td><td>Singles in mixed work traj. × Female</td><td>0.21**<br>(0.07)</td><td>0.20**<br>(0.07)</td><td>Singles in Non-STEM × Female</td><td>0.31***<br>(0.03)</td><td>0.25***<br>(0.02)</td></tr><tr><td colspan="7"><strong>Education (ref. Lower tertiary education)</strong></td></tr><tr><td>Upper tertiary education</td><td>0.22***<br>(0.01)</td><td>0.17***<br>(0.01)</td><td>Upper tertiary education</td><td>0.36***<br>(0.05)</td><td>0.23***<br>(0.05)</td><td>Education (ref. Lower tertiary education)</td><td>Upper tertiary education</td><td>0.32***<br>(0.01)</td><td>0.22***<br>(0.01)</td></tr><tr><td colspan="7"><strong>Age finish education (ref. below age 25)</strong></td></tr><tr><td>25-29</td><td>-0.03***<br>(0.01)</td><td>-0.03***<br>(0.01)</td><td>25-29</td><td>-0.10**<br>(0.04)</td><td>-0.07<br>(0.04)</td><td>Age finish education (ref. below age 25)</td><td>25-29</td><td>-0.02***<br>(0.01)</td><td>-0.01<br>(0.01)</td></tr><tr><td>30+</td><td>-0.03**<br>(0.01)</td><td>-0.03**<br>(0.01)</td><td>30+</td><td>-0.07<br>(0.04)</td><td>-0.07<br>(0.04)</td><td>30+</td><td>0.07***<br>(0.01)</td><td>0.03<br>(0.01)</td></tr><tr><td>Swedish speakers (ref. Finnish speakers)</td><td>0.01<br>(0.02)</td><td>0.01<br>(0.02)</td><td>Swedish speakers (ref. Finnish speakers)</td><td>0.09*<br>(0.04)</td><td>0.07<br>(0.04)</td><td>Swedish speakers (ref. Finnish speakers)</td><td>0.02<br>(0.02)</td><td>0.02<br>(0.02)</td></tr><tr><td>Region of residence FE</td><td>Yes</td><td>Yes</td><td>Region of residence FE</td><td>Yes</td><td>Yes</td><td>Region of residence FE</td><td>Yes</td><td>Yes</td></tr><tr><td>Occupation FE</td><td>Yes</td><td>Yes</td><td>Occupation FE</td><td>Yes</td><td>Yes</td><td>Occupation FE</td><td>Yes</td><td>Yes</td></tr><tr><td>Constant</td><td>10.93***<br>(0.06)</td><td>10.76***<br>(0.06)</td><td>Constant</td><td>10.96***<br>(0.09)</td><td>10.60***<br>(0.15)</td><td>Constant</td><td>10.85***<br>(0.06)</td><td>10.65***<br>(0.05)</td></tr><tr><td>R<sup>2</sup></td><td>0.20</td><td>0.29</td><td>R<sup>2</sup></td><td>0.14</td><td>0.27</td><td>R<sup>2</sup></td><td>0.19</td><td>0.32</td></tr><tr><td>N<sup>1</sup></td><td>38,746</td><td>38,746</td><td>N<sup>2</sup></td><td>5,328</td><td>5,328</td><td>N<sup>3</sup></td><td>39,854</td><td>39,854</td></tr></tbody></table>

Notes: Robust standard errors in parentheses. Time varying characteristics (region of residence and occupation) are measured at age 41. Data are from authors' calculations based on Finnish register data 1987-2022.

¹Observations with zero income are excluded from the total of 38,860 (N=114).

²Observations with zero income are excluded from the total of 5,364 (N=36).

³Observations with zero income are excluded from the total of 40,000 (N=146).

* p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed).
<figure><img src="image_6.png" /><figcaption>**Fig.A1** Share of women across STEM fields in Finland in the analytical population.
*Notes:* Data from authors' calculations based on Finnish register data 1987–2022.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig.A2** Top ten occupations for men and women with computer science and engineering, natural science, and non-STEM majors at age 30.
*Notes:* Based on Statistics Finland's classification of occupations 2001 (relied on for years 2004-2009). Data from authors' calculations based on Finnish register data 1987–2022.</figcaption></figure>
## Computer Science and Engineering (%)

<figure><img src="image_8.png" /></figure>

## Natural Science (%)

<figure><img src="image_9.png" /></figure>

## Non-STEM (%)

<figure><img src="image_10.png" /></figure>

**Fig.A3** Top ten occupations for men and women with computer science and engineering, natural science, and non-STEM majors at age 40.

*Notes:* Based on Statistics Finland's classification of occupations 2010 (following the structure of ISCO-08, relied on for years 2010 and onwards). Data from authors' calculations based on Finnish register data 1987–2022.
<figure><img src="image_11.png" /><figcaption>**Fig.A4** Cluster cut-off criteria for Ward's cluster analysis derived from multi-channel sequence analysis.</figcaption></figure>

*Notes*: ASW = Average Silhouette Width; PBC = Point Biserial Correlation; HGSD = Hubert's gamma (Sommer's D). Data from authors' calculations based on Finnish register data 1987–2022.
<figure><img src="image_12.png" /><figcaption>**Fig.A5** Gender earnings gaps across majors and clusters, excluding individuals who are on parental leave anytime in ages 41–43.</figcaption></figure>

*Notes:* Figures provide the estimated earnings from Equation 1. Adjusted predictions are reported. Regressions are estimated separately for computer science and engineering (CSE), natural science, and non-STEM majors. Data from authors’ calculations based on Finnish register data 1987–2022.
<figure><img src="image_13.png" /><figcaption>**Fig.A6** Gender earnings gaps across majors and clusters, focusing on the first three cohorts (1974–1976).</figcaption></figure>

*Notes*: Figures provide the estimated earnings from Equation 1. Adjusted predictions are reported. Regressions are estimated separately for computer science and engineering (CSE), natural science, and non-STEM majors. Data from authors’ calculations based on Finnish register data 1987–2022.
<figure><img src="image_14.png" /><figcaption>**Fig.A7** Gender earnings gaps across majors and clusters, based on earned income rather than total taxable income.</figcaption></figure>

*Notes:* Figures provide the estimated earnings from Equation 1. Adjusted predictions are reported. Regressions are estimated separately for computer science and engineering (CSE), natural science, and non-STEM majors. Data from authors’ calculations based on Finnish register data 1987–2022.