RESEARCH Open Access
Measuring gender when you don’t have a
gender measure: constructing a gender
index using survey data
Peter M. Smith1,2,3* and Mieke Koehoorn1,4*
Abstract
Background: Disentangling the impacts of sex and gender in understanding male and female differences is
increasingly recognised as an important aspect for advancing research and addressing knowledge gaps in the
field of work-health. However, achieving this goal in secondary data analyses where direct measures of gender
have not been collected is challenging. This study outlines the development of a gender index, focused on
gender roles and institutionalised gender, using secondary survey data from the Canadian Labour Force survey.
Using this index we then examined the distribution of gender index scores among men and women, and
changes in gender roles among male and female labour force participants between 1997 and 2014.
Methods: We created our Labour Force Gender Index (LFGI) using information in four areas: responsibility for
caring for children; occupation segregation; hours of work; and level of education. LFGI scores ranged from 0
to 10, with higher scores indicating more feminine gender roles. We examined correlations between each component
in our measure and our total LFGI score. Using multivariable linear regression we examined change in LFGI score for
male and female labour force participants between 1997 and 2014.
Results: Although women had higher LFGI scores, indicating greater feminine gender roles, men and women were
represented across the range of LFGI scores in both 1997 and 2014. Correlations indicated no redundancy between
measures used to calculate LFGI scores. Between 1997 and 2014 LFGI scores increased marginally for men and
decreased marginally for women. However, LFGI scores among women were still more than 1.5 points higher on
average than for men in 2014.
Conclusions: We have described and applied a method to create a measure of gender roles using survey data,
where no direct measure of gender (masculinity/femininity) was available. This measure showed good variation
among both men and women, and was responsive to change over time. The article concludes by outlining an
approach to use this measure to examine the relative contribution of gender and sex on differences in health
status (or other outcomes) between men and women.
Keywords: Gender, Sex, Labour force, Gender roles, Measurement, Survey data
* Correspondence: psmith@iwh.on.ca; mieke.koehoorn@ubc.ca
1Institute for Work & Health, 481 University Avenue, Suite 800, Toronto, ON
M5G 2E9, Canada
Full list of author information is available at the end of the article
© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Smith and Koehoorn International Journal for Equity in Health (2016) 15:82
DOI 10.1186/s12939-016-0370-4
Background
Better understanding and accounting for male and female
differences has been gaining attention in many health-
related research areas [1–3]. In the area of work and
health, men and women differ in their work exposures
and work-related health conditions. In addition, the rela-
tionships between work exposures and health outcomes
may also differ for men and women. These male/female
differences can be due to sex – referring to biological dif-
ferences between men and women – or gender – refer-
ring to social differences between men and women [4].
It is increasingly recognised that both sex and gender
matter in understanding the relationships between work-
ing conditions and health outcomes, and that research
that fails to take sex and gender into account is limited
in both quality and applicability [4, 5]. Stratifying ana-
lyses to examine the relationships between work and
health separately for men and women has been proposed
as one approach to better account for sex and gender
[6]. However, it is recognised that this approach does a
better job of understanding “sex” differences than it
does in understanding “gender” differences [7, 8]. Fur-
thermore, sex and gender often interact, suggesting that
differences between men and women might be due to a
combination of both biological (sex) and social/cultural
(gender) factors. To develop a better understanding of
the relative contribution of each of these aspects re-
quires measures of both sex and gender to be included
in analyses [5, 8].
Measuring gender and sex can take different forms
depending on the way data are being collected. When
conducting primary data collection for quantitative
studies researchers have the options to include mea-
sures of gender, such as the Bem-Sex-Role-Inventory
(BSRI) [9] which asks participants to self-identify with
personal traits, or the Masculine Gender Role Stress
scale [10]. However, are there options for researchers
to measure gender if such scales are not present in
existing data?
The concept of gender diagnosticity was first introduced
by Lippa and Connelly [11] to estimate the probability of
being male or female, based on some gender-related
diagnostic indicator. In their original study, Lippa and
Connelly used occupational preference ratings as a meas-
ure of gender-diagnosticity, finding that these preferences
were distinct from responses to the Personal Attributes
Questionnaire (PAQ) [12] and the BSRI [9]. They also
reported that occupational preference was more predictive
of being a man or a woman than either the BRSI or PAQ
indices [11]. This suggests that a gender diagnostic ap-
proach offers an alternative method to measure social
differences between men and women based on their roles
and preferences, compared to indices such as the PAQ
and BRSI that are based on gender stereotypes [11].
A decade later Lippa and colleagues used this same
approach (occupational preferences) to examine the role
of sex and gender on mortality [13]. In this study they
found masculinity (as measured by occupational prefer-
ences) was predictive of mortality among both men and
women, resulting in the highest mortality rate being
observed among the most masculine men, and the lowest
mortality rate observed among the most feminine women
[13]. The objective in a gender-diagnostic approach is to
identify indicators that best differentiate between different
gender-based groups. As Lippa and Connelly noted in
their original paper, multiple indicators would ideally be
used to form a gender index, resulting in a more reliable
scale [11].
Most recently a variation on this approach has been
used to examine the impact of sex and gender on cardio-
vascular risk factors among individuals with premature
acute coronary syndrome [14]. In this study the gender
index was comprised of information on whether the re-
spondent was the primary earner in their household;
their personal income; the number of hours and respon-
sibility for housework; and level of stress at home –
along with measures of masculinity and femininity from
the BSRI [14]. Similar to the previous gender diagnostic
studies, this paper found that both sex and gender were
important in predicting many cardiovascular risk factors,
but that the gender score was generally more important
than sex (male/female) in predicting risk in multivariable
models [14].
The preceding studies relied on primary data collec-
tion where the concept of a gender index or gender-
diagnostic approach was part of the study design. This is
not always feasible in population-health or health ser-
vices research studies that rely on existing surveys and
administrative health records, despite the growing body
of literature that indicated that gender differences matter
to primary prevention and health care practices [15–17].
In this paper the aim is to develop a gender-index
using the Canadian Labour Force Survey (LFS). The LFS
was selected because it has a number of questions that
are commonly available in other data sources, and a
gender-index using this data may be readily applied (and
modified or expanded upon) to other secondary data
sources. We use data from the 1997 and 2014 Labour
Force Surveys to accomplish three objectives: to develop
a gender-index using existing population health survey
data; to examine the distribution of our gender index
across males and females (i.e. to ensure that it measured
a separate concept to sex); and to examine if there have
been changes in gender roles (as measured by the index)
among male and female labour market participants be-
tween 1997 and 2014. We then discuss how this index
(or a similarly constructed index) could be used in re-
search that exploits secondary data to better understand
Smith and Koehoorn International Journal for Equity in Health (2016) 15:82 Page 2 of 9
the relative contribution of aspects of gender and sex in
male/female differences in health outcomes.
Methods
Data source
This paper uses secondary data from Statistics Canada’s
LFS. The LFS is a monthly survey carried out by Statis-
tics Canada with the objective of providing information
on trends in labour market participation and hours of
work across major occupational and industrial sectors in
Canada [18]. The LFS surveys approximately 56,000
Canadian households per month. Households remain in
the sample for six consecutive months, with one sixth of
the sample rotated out, and replaced by a new group of
households representing one sixth of the sample each
month. The target population for the LFS is the civilian,
non-institutionalised population 15 years of age and over
residing in all of Canada’s provinces and territories. Per-
sons living on Aboriginal reserves, full-time members of
the Canadian Armed Forces, and the institutionalised
population are excluded from coverage, as are households
in extremely remote areas. Statistics Canada estimates
these groups represent less than 2 % of the Canadian
population aged 15 and over, and that the LFS is represen-
tative of its target population [18]. For the purpose of this
analysis, the Public Use files from the 1997 and 2014
Labour Force Surveys were used through Statistics
Canada’s Data Liberation Initiative [19]. The year 1997
was chosen as the start point for the analysis, as the
questions asked in the LFS changed in this survey year.
For each survey cycle, the analysis was restricted to
respondents who were currently working for pay or
profit in the past month, excluding unpaid family
workers, regardless of the number of hours worked.
Labour Force Gender Index (LFGI)
Gender is a multidimensional construct that includes
four dimensions: gender roles (behavioural norms ap-
plied to men and women); gender identity (how an
individual sees themselves on the male/female con-
tinuum); gender relationships (how individuals are treated
by others based on their ascribed gender); and institution-
alized gender (how power and influence are distributed
differently among men and women) [4]. The LFGI con-
structed from the LFS focused primarily on the dimen-
sions of gender roles and institutionalised gender among
labour force participants. Given the data available, the
LFGI was comprised of four main measures: responsibility
for caring for children; occupational segregation; hours of
work relative to partner/spouse; and education relative to
partner/spouse. Differences in male and female participa-
tion rates in education in Canada and other developed
countries have changed considerably since the early
1970’s,with women outnumbering men in university and
post-secondary education completions [20, 21]. However,
education was used in the construction of the LFGI as it is
a measure of educational attainment relative to one’s
partner/spouse, not a measure of absolute educational
attainment. Each measure in the index is described in
detail below.
Responsibility for caring for children
In each cycle of the LFS respondents are asked if they
were away from work (either completely or partially) in
the last week, and the reason for this absence, with one
option being personal or family responsibilities. Re-
spondents working less than 30 h per week are also
asked the main reason they are not working more
hours per week, with one option being caring for chil-
dren and another being other personal or family re-
sponsibilities. Using responses to these questions, the
following three category variable was created: 0 = no re-
duction in labour market participation due to personal
or family responsibilities; 1 = part or full week absence
due to personal or family responsibilities; 2 = working
part-time due to personal or family responsibilities.
Occupational segregation
Self-reported occupation is coded into 47 major groups
based on the National Occupational Classification sys-
tem [22]. For the LFGI responses to the 1997 LFS were
used to classify each of these 47 occupations into one of
four groups: 0 = occupations where less than 26 % of
workers were women; 1 = occupations where 26 to 50 %
of workers were women; 2 = occupations where 51 to
74 % of workers were women; and 3 = occupations where
75 % or more of workers were women. Occupations with
the lowest participation of women are conceived as the
most masculine occupations, while occupations with the
highest participation of women are conceived as the most
feminine occupations.
Hours of work relative to partner/spouse
Respondents are asked the usual number of hours they
usually work each week. For respondents who are liv-
ing with a spouse they are also asked the number of
hours their spouse usually works per week. Using both
these sources of hours worked each respondent was
grouped into one of the following four categories: 0 =
respondent working, but spouse not in the labour
force; 1 = respondent working more hours than their
spouse; 2 = respondent working the same number of
hours as their spouse; and 3 = respondent working less
hours than their spouse. If respondents did not have a
spouse they were grouped with respondents working
more hours than their spouse.
Smith and Koehoorn International Journal for Equity in Health (2016) 15:82 Page 3 of 9
Education level relative to partner/spouse
Respondent’s and spouse’s highest level of education are
reported in the following six categories: 0 to 8 years of
education; some secondary education; graduated from
high school; some post-secondary education; post-
secondary certificate or diploma; and university degree.
Using this information respondents were grouped into
one of the following three categories: 0 = respondents
with a higher level of education than their spouse; 1 = re-
spondents with the same level of education as their
spouse; 2 = respondents with a lower level of education
than their spouse. Similar to work hours, respondents
without a spouse were grouped with respondents with a
higher level of education than their spouse.
To create the LFGI the values for the above four mea-
sures (caring for children, occupational segregation,
hours or work and education level) were summed for
each respondent providing a score ranging from 0 to 10,
with higher scores indicating more traditionally feminine
gender labour market roles of respondents and lower
scores indicating more traditionally masculine gender
labour market roles.
Analysis
Correlations between the four measures of the LFGI
were examined, and between each component and the
final LFGI score. LFGI scores were then compared
for men and women, and for the 1997 and 2014 LFS.
Linear regression analyses then examined if the rela-
tionship between sex (male versus female) and LFGI
scores changed between 1997 and 2014, after adjust-
ment for differences in age, province, and month of
survey participation between the 1997 and 2014 sur-
veys. To examine if gender scores had changed for
men and women between 1997 and 2014 a multi-
plicative interaction term between sex (male/female)
and survey year was included in the model. The re-
gression analysis was based on a 10 % random sample
to avoid the possibility of a Type I error given the
size of the LFS samples. All analyses were weighted
to account for the initial probability of selection for
each household, non-response and coverage errors, as
specified by Statistics Canada [18]. Analyses were
conducted using SAS Version 9.3 [23].
Results
Table 1 presents the distribution of each of the LFGI
measures for men and women in the 1997 and 2014
Labour Force Surveys. Women were more likely to have
taken time off and be working part time due to house-
hold responsibilities in both 1997 and 2014, and they
were also more likely to be working fewer hours than
Table 1 Distribution of gender index components for Canadian men and women in 1997 and 2014
1997 LFS (N = 696,350) 2014 LFS (N = 729,132)
Men Women p-value
for diff
Men Women p-value
for diff
Responsibility for caring for children
No absence from work due to family or household responsibilities 98.8 % 91.1 % < 0.001 98.0 % 91.3 % < 0.001
Part or full-week absence due to family or household responsibilities 1.0 % 2.7 % 1.7 % 4.5 %
Works part-time due to family or household responsibilities 0.2 % 6.3 % 0.3 % 4.2 %
Occupation (based on 1997 LFS only)
Less than 26 % women 45.3 % 7.5 % < 0.001 46.0 % 7.7 % < 0.001
26 to 50 % women 29.8 % 22.7 % 27.7 % 21.8 %
51 % to 74 % women 22.0 % 43.4 % 22.4 % 45.5 %
75 % women 2.9 % 26.5 % 3.9 % 25.0 %
Hours of work
Respondent works spouse does not 18.9 % 7.9 % < 0.001 15.1 % 8.6 % < 0.001
Respondent works more than spouse/respondent does not have a spouse 62.6 % 41.8 % 63.4 % 46.3 %
Respondent works same amount as spouse 13.5 % 16.2 % 15.0 % 15.5 %
Respondent works less than spouse 5.0 % 34.2 % 6.6 % 28.6 %
Education
Respondent higher level of education than spouse/respondent does
not have a spouse
20.2 % 18.9 % < 0.001 14.5 % 18.6 % < 0.001
Respondent same education as spouse 62.2 % 63.3 % 68.1 % 69.0 %
Respondent lower education than spouse 17.6 % 17.9 % 17.5 % 12.4 %
Respondents to Statistics Canada’s Labour Force Survey
Smith and Koehoorn International Journal for Equity in Health (2016) 15:82 Page 4 of 9
their spouse in both time periods. As expected, given
that occupation categories were based on 1997 labour
market participation, we observed women were more
likely to be working in occupations with a greater pro-
portion of women, and men in occupations with a
greater proportion of men. Distribution across occupa-
tional segregation groups for men and women only
changed to a small extent between 1997 and 2014. Dif-
ferences in education were also noted for men and
women, although these were smaller in magnitude than
observed for other measures. In 1997 an almost identical
proportion of men and women had lower levels of edu-
cation than their spouse (conceptualised as being the
most feminine category), but by 2014 men were more
likely than women to have lower education than their
partner/spouse.
Figures 1a and b present the distribution of LFGI
scores for men and women in 1997 (Fig. 1a) and 2014
(Fig. 1b). The distribution of LFGI scores was relatively
similar for men and women in 1997 and 2014, with
women scoring higher (more feminine) on the LFGI
than men. It is important to note, in each year males
and females were represented across the range of LFGI
scores from 0 to 10, highlighting the distinction between
gender as measured by the LFGI and biological sex.
Table 2 presents the polychoric correlations between
the LFGI and its four component measures. Correla-
tions for respondents in 1997 are presented below the
0%
5%
10%
15%
20%
25%
30%
35%
0 1 2 3 4 5 6 7 8 9 10
Men Women
Feminity gender role index score
0%
5%
10%
15%
20%
25%
30%
35%
0 1 2 3 4 5 6 7 8 9 10
Men Women
Feminity gender role index score
a
b
Fig. 1 a Distribution of gender index score (higher scores = greater feminine gender roles) for Canadian men and women. 1997 Labour Force
Survey. b Distribution of gender index score (higher scores = greater feminine gender roles) for Canadian men and women. 2014 Labour
Force Survey
Smith and Koehoorn International Journal for Equity in Health (2016) 15:82 Page 5 of 9
diagonal and correlations for respondents in 2014 are
presented above the diagonal. The relationship between
the LFGI and its component measures were similar at
both time points. The LFGI was most strongly corre-
lated with occupation segregation and hours of work,
and weakly correlated with education. Focusing on the
measures included in the LFGI the highest correlation
was observed between caring for children and hours of
work in each survey year. Correlations indicated no re-
dundancy between measures.
Table 3 presents the results of the linear regression
model examining the interaction between sex and survey
year on LFGI index scores after adjustment for age,
province of residence and survey month. A statistically
significant interaction was observed between sex and
survey year. Although women had higher gender scores
than men and gender scores increased between 1997
and 2014, this increase was not the same for men and
women. To examine this interaction further, separate
models were constructed for men and women. These
models demonstrated that LFGI scores increased (indi-
cating higher feminine gender roles) for men between
1997 and 2014, but decreased for women during the
same time period (results not shown but available on re-
quest). The adjusted mean scores for the gender index
for men increased from 2.81 in 1997 to 3.01 in 2014. For
women the adjusted mean scores for the gender index
decreased from 4.78 in 1997 to 4.64 in 2014.
Discussion
Disentangling the impacts of sex and gender in under-
standing male and female differences is increasingly recog-
nised as an important aspect for advancing research and
addressing knowledge gaps in the field of work-health [5].
However, achieving this goal in secondary data analyses
where direct measures of gender, such as the BRSI or
PAQ, have not been collected is challenging. The objective
of this paper was to demonstrate how a gender index –
based primarily on gender roles – could be developed
using routinely collected information from the Canadian
Labour Force Survey. A second objective was to examine
how gender scores were distributed among men and
women (i.e. sex) and if there had been changes in gender
roles among working Canadian men and women over the
17 year period between 1997 and 2014. Differences were
observed between men and women in each component of
the LFGI, with women generally having higher LFGI
scores (indicating greater feminine gender roles) com-
pared to men. While we found that women had higher
LFGI scores in both 1997 and 2014 small increases in
LFGI scores were observed for men between 1997 and
2014, and small decreases in LFGI scores for women over
the same time period.
These study results should be interpreted taking the fol-
lowing strengths and limitations into account. The
household-based sampling strategy employed by Statistics
Canada in conducting the LFS resulted in a truly represen-
tative sample of the Canadian labour market, and findings
can be generalised to Canadian labour market participants
over the study time period. However, the large sample also
increases the possibility of a Type I error and the inference
of a meaningful difference that has no practical or mean-
ingful importance. This may be the case for the observed
differences in the LFGI score over time and the interpret-
ation that men are taking on greater feminine gender roles
while women are taking on greater masculine gender
roles. In each of these cases the differences over time
periods were less than 0.5 on an index that ranges from 0
to 10. To put this into context, if LFGI scores continue to
increase among men and decrease among women at the
same rate as observed over the 19-year study period (1997
to 2014), it will take until 2097 for men and women to
have similar LFGI scores, indicating gender-equity in
relation to labour market roles.
Three of the four measures that comprised the LFGI
distinguished between social and occupational roles of
men and women in the expected direction. However, a
similar number of men and women had lower education
Table 2 Polychoric correlations between gender index and its
components
1 2 3 4 5
1. Gender Index 1.00 0.64 0.79 0.75 0.35
2. Responsibility for caring for children 0.72 1.00 0.21 0.35 -0.05
3. Occupation
(based on 1997 LFS only)
0.79 0.27 1.00 0.20 -0.08
4. Hours of work 0.78 0.46 0.26 1.00 0.00
5. Education 0.43 0.03 -0.01 0.06 1.00
Correlations below diagonal are for 1997 LFS. Correlations above diagonal are
for 2014 LFS
Table 3 Adjusted ordinary least squared (OLS) estimates for sex,
survey year and their interaction in gender index score
Est se p-value
Sex
Male ref
Female 1.57 0.01 < 0.001
Survey Year
2014 ref
1997 -0.21 0.01 < 0.001
Interaction
Survey year/sex multiplicative interaction term 0.37 0.02 < 0.001
Respondents to the 1997 and 2014 LFS (N = 142,558; 10 % random sample)
Est OLS regression estimate, se standard error
Estimates additionally adjusted for age, age2, province/territory of residence
and survey month
Smith and Koehoorn International Journal for Equity in Health (2016) 15:82 Page 6 of 9
than their partner/spouse in 1997, while men were more
likely than women to have lower education than their
partner/spouse in 2014. To some extent this result re-
flects the changing nature of characteristics previously
thought of as masculine or feminine [24]. For example,
the BRSI has “ambitious” and “analytical” as masculine
traits [9] while the PAQ has “likes math and science”
and “intellectual” as masculine traits [12]. Given changes
in educational participation between men and women,
along with the information presented in this paper, fu-
ture work that creates indexes/measures that reflect
gender roles and institutionalised gender may choose to
exclude education (as both an absolute measure and in
relation to the respondent’s partner/spouse) as a compo-
nent of such measures.
Finally, the construction of the LFGI represents the
sum of scores for the relative components. While this
approach has the advantage of simplicity, making the
approach easy to replicate, it does make assumptions
about the relative contribution of each of the compo-
nents of the index in relation to overall labour market
gender roles, which may not be valid. Alternative ap-
proaches to constructing the index (e.g. factor analyses
or cluster analyses) may be warranted, and researchers
should weigh the advantages and disadvantages to
each analytic approach if they choose to replicate the
work in this paper.
How could the LFGI be used to better understand
processes that create male/female differences in
health?
While the LFS provided us with the most representative
annual estimates for the Canadian labour market, it does
not contain information on health indicators. This infor-
mation, if available could have been used to further
demonstrate how the LFGI might be applied to research
to examine male/female differences in health status. To
address this gap, a conceptual overview of how the LFGI
might be included and interpreted in analyses using sec-
ondary data is provided below (Fig. 2a-c). We do this
using directed acyclic graphs (DAGs), which provide a
a
b
c
Fig. 2 a A simple DAG linking male/female to a health outcome of interest. b An extended DAG to include gendered labour market factors
(as measured by the LFGI). c A complete DAG to examine the factors that contribute to male/female differences in a given health outcome
Smith and Koehoorn International Journal for Equity in Health (2016) 15:82 Page 7 of 9
useful approach to understanding the causal relation-
ships between variables and interpretation of effects in
epidemiological analyses [25–27].
Figure 2a presents a simple DAG where there is a
difference in a health outcome for men compared to
women (note this difference could be in either direc-
tion – i.e. more prevalent among men compared to
women, or more prevalent among women compared
to men). If a difference in the health outcome is
present among men and women, path “A” in this DAG
will be not equal to zero and will be statistically sig-
nificant. For theoretical purposes, the relationship be-
tween male/female and the health outcome (path A) is
assumed to be adjusted for all confounders, and that
male/female and the health outcome (along with the
additional variables included in Fig. 2b and c below)
have been measured without error.
In order to understand why the risk of the health outcome
is greater for men (or women), the DAG is extended to in-
clude intermediate or mediating variables, such as the LFGI,
to understand the impact of “sex” and “gender” in male/fe-
male differences in the health outcome. This extended
model is presented in Fig. 2b. Again, for theoretical pur-
poses, we make no confounding for all paths and no meas-
urement error assumptions for all variables. We now have a
direct path (path A′) and an indirect path (paths B and C)
linking male/female to the health outcome. The magnitude
of the indirect path will be determined by the strength of
the relationship between male/female at the LFGI (path B)
and the relationship between the LFGI and the health out-
come (path C). The estimate for path B is equivalent to the
regression estimate for female (relative to male) presented in
Table 3 previously. It is important to note that the estimate
from Table 3 indicates that the LFGI score was strongly
influenced, but not completely explained by whether the
respondent was male or female. The estimate for the direct
effect (path A′) can be interpreted as the difference in the
health outcome for men compared to women that would
remain if men and women had similar roles in relation to
labour market status (i.e. if men and women had similar
scores on the LFGI). The difference between path A and
path A′ (which in an ordinary least-squared model will be
equivalent for the product term of path B and C) [28], can
be interpreted as the amount of the originally observed
difference in the health outcome for men and women that
can be explained by differences in labour market roles (as
assessed by the LFGI) between men and women [27].
It is important to note that if path A′ is still associated
with the health outcome then this indicates that male/
female differences in the health outcome are not com-
pletely explained by differences in labour market roles
only. The remaining differences between men and
women (path A′) will likely be a combination of other
biological (sex) differences between men and women that
are relevant to the outcome, and other social (gender)
differences between men and women that are both rele-
vant to the outcome, and not captured in the LFGI. This
has been explicitly described in Fig. 2c. If data was avail-
able to construct – either individually or as part of an
index – all other sex and gender related factors that are
relevant to the outcome of interest, then path A″ in Fig. 2c
would approach zero, and one could examine the relative
contribution of biological factors (paths F and G) and gen-
der factors related to labour market roles (paths B and C)
and non-labour market roles (paths D and E). The caveat
for Fig. 2c is that each of the three pathways can be
measured and estimated as distinct from each other. This
highlights the need for the integration of “sex” and
“gender” into the study design and data collection phase
as part of a comprehensive research process [4], so that a
more thorough examination of sex and gender into male/
female differences in health status can be routinely under-
taken using the approach outlined above.
Conclusions
In this paper we developed a measure of feminine and
masculine gender roles, using self-reported survey data,
where no direct measure of gender (masculinity/femin-
inity) was available. This measure had face validity in
terms of being related to, but distinct from sex (male/
female), and was also responsive to change over time.
Future research should examine the relative importance
of including additional measures to an index such as the
LFGI. For example, in the study by Pelletier and col-
leagues [14] primary earner status was the measure most
strongly related to masculine BRSI scores, while the
number of hours spent doing housework and responsi-
bility for doing housework were the measures most
strongly associated with high feminine BRSI scores. The
LFGI in this paper included some indication of primary
earner status and some indication of household respon-
sibilities for respondents. However, a more detailed or
direct measure of primary earner status and household
responsibilities (in particular housework) may have
allowed further refinement or distinction between mascu-
line and feminine roles in the LFGI. In addition, it would
be interesting to examine how an index with a reduced
number of measures would perform in differentiating
gender roles for men and women. As mentioned in the
introduction to this paper, the early work by Lippa and
colleagues focused only on differences in occupational
preferences between men and women [11, 13]. Interest-
ingly, occupational segregation, as well as hours worked,
was the measure most strongly correlated with the LFGI
in our study. We also suggest that research examining
work-related health outcomes should (and in many cases
can) integrate measures of sex and gender, using an ap-
proach similar to the one outlined in this paper.
Smith and Koehoorn International Journal for Equity in Health (2016) 15:82 Page 8 of 9
Abbreviations
BSRI, Bem-Sex-Role-Inventory; LFGI, Labour Force Gender Index; LFS, Canadian
Labour Force Survey; PAQ, personal attributes questionnaire.
Acknowledgements
Peter Smith and Mieke Koehoorn are both supported by Research Chairs in
Gender, Work & Health from the Canadian Institutes of Health Research.
Authors’ contributions
PS and MK were both involved in the conceptual development of this paper.
PS performed all data analyses and wrote the first draft of the manuscript.
MK provided feedback on the manuscript. Both authors read and approved
the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Author details
1Institute for Work & Health, 481 University Avenue, Suite 800, Toronto, ON
M5G 2E9, Canada. 2School of Public Health and Preventive Medicine, Monash
University, Melbourne, Australia. 3Dalla Lana School of Public Health,
University of Toronto, Toronto, ON, Canada. 4School of Population and Public
Health, Faculty of Medicine, University of British Columbia, 2206 East Mall,
Vancouver, BC V6T 1Z3, Canada.
Received: 7 October 2015 Accepted: 19 May 2016
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