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Journal of Happiness Studies
An Interdisciplinary Forum on
Subjective Well-Being
ISSN 1389-4978
Volume 16
Number 6
J Happiness Stud (2015) 16:1539-1555
DOI 10.1007/s10902-014-9577-5
Gender Differences in Subjective Well-
Being and Their Relationships with Gender
Equality
Gerhard Meisenberg & Michael
A.Woodley
1 23
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RESEARCH PAPER
Gender Differences in Subjective Well-Being
and Their Relationships with Gender Equality
Gerhard Meisenberg •Michael A. Woodley
Published online: 20 September 2014
Springer Science+Business Media Dordrecht 2014
Abstract Although most surveys of happiness and general life satisfaction find only
small differences between men and women, women report slightly higher subjective well-
being than men in some countries, and slightly lower subjective well-being in others. The
present study investigates the social and cultural conditions that favor higher female rel-
ative to male happiness and life satisfaction. Results from more than 90 countries repre-
sented in the World Values Survey show that conditions associated with a high level of
female relative to male happiness and life satisfaction include a high proportion of Mus-
lims in the country, a low proportion of Catholics, and absence of communist history.
Among indicators of gender equality, a low rate of female non-agricultural employment is
associated with higher female-versus-male happiness and satisfaction. Differences in the
rate of female non-agricultural employment explain part of the effects of communist
history and prevailing religion. They may also explain the recent observation of declining
female life satisfaction in the United States.
Keywords Happiness Life satisfaction Women Gender equality World Values
Survey
1 Introduction
…women have made substantial progress toward gender equality over the past
25 years across a number of dimensions. Gender differences in labor force
G. Meisenberg (&)
Department of Biochemistry, Ross University Medical School, Picard Estate, Portsmouth, Dominica
(Eastern Caribbean)
e-mail: gmeisenberg@rossu.edu
M. A. Woodley
Department of Psychology, University of Arizona, Tucson AZ, USA
123
J Happiness Stud (2015) 16:1539–1555
DOI 10.1007/s10902-014-9577-5
Author's personal copy
participation have narrowed sharply….Differences between men and women in
occupations, types of education, and rates of self-employment have been greatly
diminished; and women have narrowed the gender wage gap substantially. (Blau
1998, p. 160).
The above quotation celebrates progress toward gender equality as a success for women.
Although not saying so explicitly, it implies that women are now better off than before as a
result of greater gender equality. If women are indeed the main beneficiaries of these
developments, we can predict that women’s subjective well-being has improved not only in
absolute terms, but also in relation to men’s. However, there is no empirical support for
this prediction. In the United States, female happiness and life satisfaction may have
declined marginally since the 1970s, despite a secular trend towards greater gender
equality. Women, who had higher subjective well-being than men until the early to mid-
1980s, have reported lower life satisfaction than men since at least the late 1990s
(Blanchflower and Oswald 2002; Ross 2011; Stevenson and Wolfers 2009; but see also
Herbst 2011). In Britain, the gender difference in life satisfaction has remained essentially
unchanged between 1972 and 1998 (Blanchflower and Oswald 2002). If gender equality
does promote the subjective well-being of women, either it raises male well-being to at
least the same extent, or the positive effects on female well-being are cancelled by other
trends that are unfavorable for women.
Possible reasons for the lack of progress in female well-being are being debated. Ste-
venson and Wolfers (2009, p. 27) speculate about the possibility that certain general social
trends, such as rising neuroticism, decreased social cohesion and greater household risk
had greater impact on women than men. Gender-specific effects of the contraceptive pill
have been proposed as a more specific reason (Pakaluk and Burke 2010,cf. Pezzini 2005).
The introduction of mutual consent divorce laws has been proposed as a possible coun-
tervailing trend that may have cancelled beneficial effects of gender equality on female
subjective well-being (Pezzini 2005).
Most of the studies cited above are analyses of subjective well-being trends in advanced
industrial societies. Alternatively, gender gaps can be studied in cross-sectional studies at
the country level. At least three studies of this kind have been performed to date. The first,
by Sabrina Vieira-Lima, studied a sample of 80 countries from the World Values Survey
(WVS), concluding that’’ women are happier than men in most African and many
developing countries, and less happy in around 15 European and other industrialized
countries’’ (Vieira Lima 2011, p. 1), and ‘‘objective matters of female rights and
achievements display a negative impact on women’s happiness, whereas national beliefs
that would favour men at the expense of women in economic and political terms would
grant them happiness’’ (ibid., page 15). The second study, by Arrosa and Gandelman
(2013), found women happier than men in most countries. Relating this difference to
objective conditions, they concluded that ‘‘the happiness gap cannot be explained by
observables, quite the contrary, the differences in the objective individual determinants of
happiness suggest women should be less happy than men’’ (Arrosa and Gandelman 2013,
p. 19). In a third study (Tesch-Ro
¨mer et al. 2008), the authors reported a correlation of
-.10 between relative female life satisfaction and relative female economic activity rate
for a sample of 57 countries. These authors propose that cultural attitudes to economic
gender equality determine the direction in which objective gender equality is related to
subjective life satisfaction.
Cross-temporal and cross-country approaches are complementary. Both are confounded
by unmeasured variables that correlate with trends or cross-country differences,
1540 G. Meisenberg, M. A. Woodley
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respectively, in measures of subjective well-being and gender equality. These unmeasured
confounding variables can be different in cross-temporal and cross-country studies.
In this study we use a cross-sectional approach to test the hypothesis that greater gender
equality raises female relative to male well-being. We predict that in ‘‘patriarchal’’ soci-
eties with traditional gender roles, men will report higher subjective well-being than
women; and in societies with greater gender equality, women’s subjective well-being will
be at least as high as that of men. More generally, the question is: what social, economic
and cultural conditions favor female over male subjective well-being and vice versa? In
theory, knowledge about the conditions that favor either male or female well-being can be
used to predict differential effects of social changes on male versus female subjective well-
being.
2 Methods
2.1 Subjective Well-Being Measures
Measures of Happiness and Satisfaction are from the WVS Official Aggregate v.20090901,
2009, available at www.worldvaluessurvey.org. Interviews were conducted between 1981
and 2008 with 355,298 respondents in 96 countries and territories. Answers to two
questions were used: (1) Taking all things together, would you say you are very happy—
quite happy—not very happy—not at all happy; and (2) All things considered, how satisfied
are you with your life as a whole these days? 10-step scale. The country-level correlation
between these two subjective well-being measures is .744. For ease of presentation, raw
scores were converted to a scale with zero as the lowest and ten as the highest possible
score. The measure of gender differences was the unstandardized B coefficient in country-
level regressions predicting happiness or satisfaction with gender, age and survey year.
Positive values indicate higher scores for females. Sample sizes ranged from 986 (Zim-
babwe) to 11,203 (Spain).
2.2 Development Indicators
Intelligence is the average of IQ (Lynn and Vanhanen 2012) and school achievement in
international testing programs for those countries that have both measures, with weighting
for data quality as described in Meisenberg and Lynn (2011). IQ or school achievement
alone was used for countries having only one of these measures. The correlation between
school achievement and IQ is .885 (N =100 countries).
Education measures length of schooling for adults 15?years old (1995–2010 average),
based on the Barro-Lee data set (http://www.barrolee.com/data/dataexp.htm). Missing data
points were extrapolated from World Bank and United Nations sources.
lgGDP is log-transformed per capita GDP (1985–2005 average) adjusted for purchasing
power from the Penn World Tables (Heston et al. 2011), with missing data extrapolated
from the World Development Indicators of the World Bank.
No corruption is a composite of Transparency International’s Corruption Perception
Index for the years 1998–2003 (http://www.transparency.org) and the no corruption
measure of the World Bank’s Governance Indicators, 1996–2005. Scores from these two
sources correlate with a Pearson’s rof .971 for the 135 countries having both measures.
High values indicate low corruption.
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Political freedom is averaged from two source variables: (1) averaged scores of political
rights and civil liberties from Freedom House at http://www.freedomhouse.org/research/
freeworld, average 1985–2008; and (2) the Voice and Accountability measure of the World
Bank’s Governance Indicators, 1996–2008 average, from www.govindicators.org. The
correlation between these two measures is r=.963, N =177 countries.
2.3 Measures for the Status of Women
Income ratio is the female/male income ratio. The measure is averaged from data reported
in the 2004 and 2005 Human Development Reports covering the time between 1991 and
2002, published at http://hdr.undp.org/en/reports/global/.
m–f years in school is the male–female difference in length of schooling for the pop-
ulation aged 15?, 1985–2005 average according to the Barro-Lee data set, high values
indicating more female schooling.
f/m enrolment is the average of the gender parity indices (ratio of girls/boys enrolled)
for secondary education and tertiary education published by the United Nations. (http://
mdgs.un.org/unsd/mdg/Data.aspx), average of the years 2000, 2005 and 2010.
f in parliament is the proportion of seats in parliament occupied by women, 1990–2011
average, from the Millennium Development Goals indicators of the United Nations at
http://mdgs.un.org/unsd/mdg/SeriesDetail.aspx?srid=557.
f managers is the proportion of females among legislators, officials and managers,
1995–2005 average, published by the World Bank at data.worldbank.org.
f/m labor force is the female/male ratio in the labor force participation rate, 1990–2010
average from the United Nations at http://data.un.org/Data.aspx?q=labour&d=
GenderStat&f=inID%3a106.
f non-agricultural employment is female non-agricultural employment as percent of the
total, 1990–2010 average from the United Nations at http://data.un.org/Data.aspx?d=
MDG&f=seriesRowID%3A722.
2.4 Other Indicators
World regions are defined based on the system developed in Inglehart et al. (2004). They
include Protestant Europe, Catholic Europe and Mediterranean (including Greece, Cyprus,
Israel), the English-speaking countries (Britain, Ireland, USA, Canada, Australia, New
Zealand), the ex-communist countries of Eastern Europe and the former Soviet Union,
Latin America, the Muslim Middle East (defined here as the predominantly Muslim
countries from Morocco to Pakistan), South and Southeast Asia (from India to Indonesia
and the Philippines excluding Singapore, which has an ethnic Chinese majority), East Asia
(China, Taiwan, South Korea, Japan, Hong Kong, Singapore), and Sub-Saharan Africa.
2.5 Weighting of Country Samples
In correlations and regressions (reported in Tables 1,2,3,4,5,6), the country average was
the unit of analysis independent of the country’s size or number of respondents in the
WVS. When averages were formed for different world regions (reported in Fig. 1), each
country was weighted by the number of respondents interviewed in that country. In the
WVS, larger countries (e.g., Spain) usually have larger sample sizes than smaller countries
in the same world region (e.g., Andorra).
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Table 1 Correlations of country averages in subjective well-being measures (Happy, Satisfied) and their gender differences (f–m Happy, f–m Satisfied, positive values
indicating higher female happiness or satisfaction) with development indicators and with measures of gender equality or female status
Happy Satisfied f–m Hap f–m Sat Intellig. Educ. lgGDP No Corr. Freedom N
Satisfied .739*** 1 93
f–m Happy .197 .108 1 93
f–m Satisfied .136 -.095 .768*** 1 93
Intelligence -.022 .351** .076 -.065 1 93
Education -.093 .266* .052 -.120 .752*** 1 93
lgGDP .264* .642*** .116 -.104 .797*** .750*** 1 93
No corruption .455*** .679*** .204 .015 .631*** .554*** .821*** 1 93
Political freedom .368*** .650*** .011 -.148 .548*** .584*** .750*** .813*** 1 93
f/m Income ratio -.123 -.127 -.079 -.031 .213* .201 -.025 .201 .172 89
f–m years in school .131 .340** -.200 -.374*** .345** .474*** .374*** .359** .512*** 84
f/m enrolment .022 .394*** -.085 -.233* .553*** .704*** .625*** .385*** .473*** 87
f in parliament .175 .255* -.049 -.030 .313** .248* .299** .491*** .397*** 90
f managers -.010 .075 -.213 -.281* .102 .330** .113 .151 .335*** 80
f/m labor force -.207* -.171 -.334** -.305** .206* .288** -.019 .145 .197 92
f non-agric. empl. -.073 .220* -.318** -.420*** .586*** .606*** .502*** .427*** .514*** 90
N=number of countries
Statistical significance (2-tailed): * p\.05; ** p\.01; *** p\.001
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2.6 Strategy of the Study
The relationship of gender differences in subjective well-being with culturally defined
world regions was investigated initially (Fig. 1), based on the expectation that major
cultural differences in gender roles might plausibly be related to gender differences in
subjective well-being. The remainder of the study consists of an effort to define which
aspects of cultural variation and specifically, which aspects of gender equality are related
to these gender differences.
Table 2 Correlations among indicators of female status (Pearson’s r)
f/m inc.
ratio
f–m Y in
Sch
f/m
enrolment
fin
parliament
f
managers
f/m labor
f.
f–m years in school .325** 1
f/m enrolment .032 .653*** 1
f in parliament .480*** .269** .150 1
f managers .295** .732*** .676*** .233* 1
f/m labor force .821*** .479*** .218* .492*** .483*** 1
f non-agric.
employm.
.604*** .707*** .562*** .439*** .701*** .798***
N=92 countries
Statistical significance (2-tailed): * p\.05; ** p\.01; *** p\.001
Table 3 Regression models predicting average happiness in countries (males and females combined)
12345678
Intelligence -.126 -.082 -.070 -.151 -.065 -.091 -.131 -.097
Education -.159 -.183 -.282 -.133 -.152 -.151 -.167 -.177
lgGDP .083 .211 .086 -.141 .050 .277 .118 .117
No corruption .323 .202 .401* .416 .336 .302 .301 .232
Political
freedom
.089 -.004 -.064 .005 .049 -.235 .078 -.071
Communism -.456*** -.560*** -.497*** -.562*** -.486** -.585*** -.471** -.633***
f/m income
ratio
.089
f–m years in
School
.235*
f/m school
enrolment
.246*
fin
parliament
.010
f managers .184
f/m labor
force
.033
f non-agric.
employm
.216
N 9389848790809290
Adj. R
2
.494 .504 .534 .543 .493 .541 .488 .529
Standardized bcoefficients are shown
Statistical significance (2-tailed): * p\.05; ** p\.01; *** p\.001
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Measures of general social and economic development were included although these are,
in most cases, expected to affect male and female well-being in similar ways. To the extent
that these development indicators are associated with gender differences in subjective well-
being measures, we hypothesize that these relationships are mediated by one or another aspect
of gender equality that impacts men and women in different ways. For example, gender roles
may be related systematically to prosperity, democracy, or communist history.
Table 1reports the zero-order correlations of subjective well-being measures and their
gender differences with plausible correlates. Because correlations between measures that
operationalize different aspects of gender equality are not always high (Table 2), and there
is no theoretical reason to expect that all aspect of gender equality have the same well-
being effects, composite measures of gender equality were avoided. Because gender
equality can be related to overall subjective well-being for men and women combined, as
well as to gender differences, regression models were used in which the outcome variable
was either the average level of happiness or life satisfaction (Tables 3,4), or their gender
difference (Tables 5,6). All statistical analyses were done using SPSS 16.0.
3 Results
3.1 Magnitude and Geography of Gender Differences
Among the 95 countries in the sample, happiness was greater for women in 50 countries, and
greater for men in 45 countries. Life satisfaction was higher for women in 49 countries and
higher for men in 46 countries. Most gender differences were small, with average absolute
size of .178 for happiness and .159 for satisfaction, both on the zero-to-10 scale. Gender
Table 4 Regression models predicting average life satisfaction (males and females combined)
123456 78
Intelligence -.121 -.045 -.102 -.067 -.047 -.081 -.100 -.086
Education -.289* -.285* -.389** -.373** -.280* -.247 -.265 -.298*
lgGDP .626** .584* .662** .417* .579** .709*** .525* .649**
No corruption .117 .118 .178 .252 .129 .132 .177 .049
Political freedom .277* .242 .135 .141 .247 .039 .309* .183
Communism -.171 -.201 -.165 -.272* -.207 -.260* -.126 -.276*
f/m income ratio -.042
f–m years in
school
.212*
f/m school
enrolment
.328**
f in parliament -.004
f managers .111
f/m labor force -.092
f non-agric.
employm.
.117
N 938984879080 9290
Adj. R
2
.599 .588 .601 .642 .596 .615 .599 .601
Standardized bcoefficients are shown
Statistical significance (2-tailed): * p\.05; ** p\.01; *** p\.001
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differences reached a statistical significance level of p\.05 (two-tailed ttest) in 43 countries
for happiness and in 33 countries for life satisfaction: Women were significantly happier than
men in 27 countries and more satisfied in 21 countries; and men were significantly happier
than women in 16 countries and more satisfied in 12 countries. This confirms the finding of
Arrosa and Gandelman (2013) that women are happier than men in most countries. Gender
differences for happiness correlated with those for life satisfaction at r =.772. The corre-
lations of absolute sex differences with sample size are -.142 for happiness and -.164 for life
satisfaction (both non-significant with N =95 countries). Because true effect sizes of gender
differences scatter closely around zero, and because smaller sample sizes cause larger
deviations from the true values, the small magnitude of these negative correlations indicates
that insufficient sample size plays a minor role in the measured sex differences.
The geographic distribution of gender differences is shown in Fig. 1. We see that
women tend to be happier and more satisfied than men in the Muslim countries (‘‘Middle
East’’), followed by East Asia. The three world regions in which men report higher hap-
piness and life satisfaction than women are the ex-communist countries, Catholic Europe,
and Latin America. Some of these differences are statistically significant. For example,
comparison of the 9 countries of the Muslim Middle East with the remaining 86 countries
produces significant differences for happiness (p=.018) and life satisfaction (p=.003).
Comparing the 23 ex-communist countries with the rest of the world produces significance
Table 5 Regression models predicting gender differences in average happiness
12345678
Intelligence .291 .171 .143 .176 .181 .212 .294 .271
Education .496** .515** .510** .517* .473* .549** .572** .527**
lgGDP -.453 -.450 -.249 -.277 -.409 -.549* -.819** -.424
No corruption .317 .389 .357 .249 .430 .470* .605** .433*
Political
freedom
-.512** -.455* -.586** -.448* -.487** -.506* -.391* -.238
Communism -.710*** -.604** -.608** -.620** -.629*** -.602** -.473** -.259*
Happiness
avg.
-.067 .023 -.070 -.002 -.027 .000 -.056 .109
f/m income
ratio
-.019
f–m years in
School
-.095
f/m school
enrolment
-.124
fin
parliament
-.143
f managers -.118
f/m labor
force
-.375**
f non-agric.
employm.
-.537***
N 93 89848790 809290
Adj. R
2
.249 .221 .257 .200 .223 .252 .300 .339
Standardized bcoefficients are shown
Positive values indicate an effect favoring female over male happiness
Statistical significance (2-tailed): * p\.05; ** p\.01; *** p\.001
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levels of p\.001 for both happiness and satisfaction. These observations show that the
global patterning of gender differences in happiness and life satisfaction is not entirely
random.
3.2 Gender Differences and Development Indicators
Earlier studies have shown that in factor analysis of the WVS (at both the individual and
country levels), the subjective well-being measures load on the dimension that Inglehart
and Baker (2000) described as ‘‘survival versus self-expression’’ and that Meisenberg
(2004) called ‘‘postmodern.’’ This dimension is related most strongly to freedom from
corruption and to material wealth (Meisenberg 2004). Therefore the relationships between
the well-being measures, their gender differences, and five different development indica-
tors were investigated. Their correlations are included in Table 1. As expected, correlations
among the development indicators are high. Higher absolute levels of happiness and life
satisfaction (men and women combined) are robustly related to the absence of corruption,
greater political freedom, and higher per-capita GDP. Intelligence and education appear to
be less important. The correlations are higher for life satisfaction than for happiness.
Table 6 Regression models predicting gender differences in average life satisfaction
1 2 34 5 678
Intelligence .374 .312 .272 .363 .299 .227 .341 .332
Education .337 .350 .443* .327 .339 .310 .396* .336
lgGDP -.543* -.569 -.492 -.529 -.544 -.669* -.890** -.481
No
corruption
.274 .322 .318 .316 .312 .481* .604** .419*
Political
freedom
-.388* -.379* -.395* -.423* -.384* -.319 -.255 -.178
Communism -.674*** -.643*** -.535** -.605*** -.633*** -.529** -.420* -.303
Satisfaction
avg.
-.228 -.194 -.184 -.144 -.204 -.226 -.268 -.150
f/m income
ratio
.016
f–m years in
School
-.236
f/m school
enrolment
-.069
fin
parliament
-.024
f managers -.159
f/m labor
force
-.393**
f non-agric.
employm.
-.514**
N 93 89 8487 90 809290
Adj. R
2
.220 .176 .258 .168 .174 .220 .273 .277
Positive values indicate an effect favoring female over male satisfaction
Standardized bcoefficients are shown
Statistical significance (2-tailed): * p\.05; ** p\.01; *** p\.001
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However, gender differences in happiness and life satisfaction are virtually unrelated to the
development indicators.
3.3 ‘‘Status’’ of Women and Gender Differences in Subjective Well-Being
The seven indicators of female status defined in the Methods section include one measure
of earnings equality, and two each of female education (years in school and school
enrolment ratios), representation of women in high-status positions (parliamentarians,
officials and managers), and gainful employment (labor force participation, non-agricul-
tural employment). Table 2shows that correlations between these measures are generally
positive, but not always large.
Table 1shows that in general, the female status indicators are positively correlated with
the development indicators, showing greater gender equality or higher female status in the
more prosperous and complex societies. We further see in Table 1that although some
indicators of female status are related to higher life satisfaction, the relationships are
modest in size. More surprising is that the correlations between the gender equality
indicators and relative female (vs. male) subjective well-being have negative signs: If
anything, higher female status and/or greater gender equality are associated with lower
female relative to male happiness and life satisfaction. Specifically, female well-being
appears to be compromised (or male well-being enhanced) by high female involvement in
gainful employment, and perhaps to some extent by prolonged female schooling.
Fig. 1 Gender differences for
average self-reported happiness
and life satisfaction in different
world regions. Positive values
indicate greater female happiness
or satisfaction. Numbers in
parentheses behind world regions
are the numbers of countries.
Country averages are weighted
for sample size.
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3.4 Gender Equality as a Correlate of General Happiness or Satisfaction
Before examining the determinants of differences between male and female subjective
well-being, we need to examine whether gender equality has any specific relationship with
the general level of subjective well-being (males and females combined), independent of
other social and economic conditions with which gender equality is correlated. A standard
linear model was specified as follows:
SWB ¼b0þb1Intelligence þb2Education þb3lgGDP þb4noCorr þb5
Freedom þb6Communism þb7Equality:
SWB is a measure of subjective well-being, either happiness or life satisfaction; noCorr
is freedom from corruption, Freedom is political freedom, Communism a history of
communist rule, and Equality is one of the 7 indicators of gender equality described under
Methods. The gender equality measures were used one at a time to avoid unnecessary
collinearity and to reduce the risk of false positives by limiting the number of analyses
performed for each dependent variable. Tables 3and 4show the results.
Low happiness is, above all, predicted by a history of communist rule. Intelligence,
education and, more surprisingly, political freedom do not raise average happiness in
countries when the other variables are controlled. Of the indicators of female status, those
describing high female versus male schooling are related to higher happiness. Significant
relationships are not observed with other gender-related indicators, although all have
positive signs in the regressions.
The major predictor of high life satisfaction is high per-capita GDP (lgGDP). Education
and communist history tend to reduce life satisfaction, while freedom from corruption and
political freedom have positive signs. As in the happiness regressions of Table 3, more
female than male schooling is related to higher life satisfaction.
3.5 Regression Models for Gender Differences in Happiness and Life Satisfaction
The correlations in Table 1suggest that gender equality or high female status do not
necessarily lead to higher female than male happiness and life satisfaction. This impression
is further explored in the regression models of Tables 5and 6. One observation is that
communist history, political freedom and possibly high per-capita GDP seem to define
conditions under which happiness and life satisfaction are greater for men than for women.
Female subjective well-being appears to be favored by prolonged schooling and possibly
freedom from corruption. Most of the ‘‘feminist’’ indicators, with the unsurprising
exception of high female/male earnings, have negative signs. Of the seven indicators, high
female non-agricultural employment and high female-versus-male labor force participation
are associated significantly with gender differences in both happiness and life satisfaction.
Because each outcome measure was explored with seven alternative measures of gender
equality, false positive findings can arise through multiple testing, which capitalizes on
chance. Testing of multiple relationships is considered a main reason for irreproducible
results in the scientific literature (Ioannidis 2005). Therefore a Bonferroni correction was
applied, which is considered the most stringent control for multiple testing (Perneger
1998). This reduced the statistical significance of female/male labor force participation and
female non-agricultural employment to p\.05 and p\.01, respectively, with the gender
difference in either happiness (Table 5) or life satisfaction (Table 6) as the dependent
variable.
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The interpretation of bcoefficients in Tables 5and 6is not straightforward, especially
for the development indicators. These predictors are highly collinear while having only
small zero-order correlations with the outcome variables, as shown in Table 1. Therefore
their bcoefficients in Tables 5and 6are predictably inflated and should not be used as
indicators of the true effect sizes. However, the indicators of gender equality do not show
this high collinearity. For example, the highest variance inflation factors (VIFs) in model 8
of Table 6are 8.3 for lgGDP and 5.2 for freedom from corruption, and the lowest are 2.7
for life satisfaction and 2.6 for female non-agricultural employment. Their bcoefficients
are not much higher than the zero-order correlations shown in Table 1, and can be con-
sidered realistic indicators for the strength of the relationship.
3.6 Spatial Analysis of the Female Employment Effect
Variations in any country characteristic can manifest at different scales of aggregation. For
example, they may be primarily present between larger world regions, such as Europe, East
Asia and Latin America, or between neighboring countries within any of the world regions.
In general, neighboring countries tend to be similar on many dimensions, and therefore
data points are not strictly independent. In consequence, this spatial autocorrelation can
inflate correlations among country-level indicators and produce false positives in statistical
significance testing (Eff 2004). For this reason, the statistical significance levels in the two-
tailed ttests reported in Tables 1,2,3,4,5,6need to be interpreted conservatively.
Some of the relationship between female employment and gender differences in sub-
jective well-being appears to exist at the level of differences among the major world
regions, as is the case with the patterning of gender differences in subjective well-being
shown in Fig. 1. When we correlate female non-agricultural employment with gender
differences in happiness and life satisfaction at the level of the world regions, we obtain
Pearson’s correlations of -.352 for happiness and -.645 for satisfaction. These correla-
tions are in the expected direction, but fail to reach conventional statistical significance.
However, statistical significance cannot be expected because of the small sample size of
only nine world regions.
As a conventional control for spatial autocorrelation we determined, for each of the
countries, female non-agricultural employment and gender differences in happiness and
life satisfaction of immediately neighboring countries. For example, for Switzerland the
average of France, Germany, Austria and Italy was used. Countries separated by expanses
of water were used when few or no countries with available data had land boundaries with
the focal country. For example, Algeria and Spain were used as neighboring countries of
Morocco although Spain and Morocco are separated by the Strait of Gibraltar. Next, the
difference between the value of the focal country and the average of the neighboring
countries was derived. This procedure was used to test whether there is a tendency for
countries that have higher female employment than neighboring countries to also have
systematically higher or lower female-versus-male happiness and life satisfaction when
compared with these same countries. The correlations of the difference score in female
employment with the difference score of female-versus-male happiness (Pearson’s r) was
-.170 (p=.102), and for satisfaction it was -.278 (p=.007). Using non-parametric
correlation, we obtained a Spearman’s qof -.198 (p=.055) for happiness and -.268
(p=.009) for satisfaction. These correlations and significance levels are smaller than
those reported in Table 1. However, this is expected because there is severe range
restriction when neighboring countries are compared, and the calculation of difference
scores between each country and the average of its neighboring countries amplifies
1550 G. Meisenberg, M. A. Woodley
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measurement error. The results support the robustness of the female employment effect,
and suggest that it exists both on the global scale and between neighboring countries.
4 Discussion
The cross-sectional approach used in the present study permits us to investigate those
country-level conditions that have differential effects on male and female well-being. First,
we can see that some aspects of the socio-cultural environment appear to affect males and
females to different degrees. Communist history not only predicts lower happiness for
everyone (Table 3), but this effect is stronger for women than men (Table 5). In Model 1
of Table 5, the differential effect of communist history amounts to .396 points on the zero-
to-10 scale of happiness (95 % confidence interval .252–.539), which is 16.4 %
(10.5–22.3 %) of the standard deviation for happiness in Russia (males and females
combined, N =8148). Similar results are obtained for education and political freedom.
Prolonged schooling for everyone appears to be more detrimental for men than for women,
and political freedom appears to raise subjective well-being more for men than for women.
More interesting are the effects of gender equality. First, Table 2shows that although
alternative measures of female equality or status are positively correlated, the correlations
are not always high and the measures are not interchangeable. Therefore we advise against
the use of composite indices of women’s rights, gender equality and related constructs
(e.g., Cingranelli et al. 2013; OECD 2009) in basic research. Tables 3and 4reveal that
greater gender equality has few significant effects on overall subjective well-being (males
and females combined), except for a slight association of more female (relative to male)
education with higher well-being.
Most of the gender equality measures do not predict differences between male and
female subjective well-being, neither when considering zero-order correlations (Table 1)
nor in regression models that control for plausible covariates (Tables 5,6). Therefore we
can confirm the conclusion of Vieira Lima (2011) that greater gender equality or higher
female status does not usually benefit women more than men. For example, a higher
proportion of women in high-status occupations does not raise the average subjective well-
being of all women, although it is likely to do so for the minority of highly ambitious
women competing for these positions. High female labor force participation and non-
agricultural employment emerge as conditions that appear to reduce female relative to
male (or raise male relative to female) well-being (Tables 5,6). This result confirms and
extends the observation of Tesch-Ro
¨mer et al. (2008) of a predominantly negative rela-
tionship between relative female life satisfaction and relative female economic activity
rate. One possible explanation is that in many (though not necessarily all) countries, the
disutility of work is greater for women than men. In other words, women dislike gainful
work in a modern economy more than men do.
Another possibility is that high female non-agricultural employment does not cause lower
female happiness and life satisfaction, but that it is a proxy measure for cultural conditions
that are detrimental for women. One of these cultural factors, history of communist rule, is
included in Tables 5and 6. In the absence of any gender equality measure, communist history
is a robust predictor of lower female than male well-being. Comparison of the bcoefficients
for models 1 and 8 in Tables 5and 6shows that the effect of communism is reduced by 63.5
and 55 %, respectively, when female non-agricultural employment is included in the model.
Therefore at least half of the negative effect of communism on female relative to male
subjective well-being is statistically ‘‘explained’’ by higher rates of female employment in the
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communist and ex-communist countries. Tables 5and 6further show that the negative effect
of democracy on relative female well-being is attenuated when female non-agricultural
employment is included in the model. Like communism, modern democracy is associated
with a normative expectation of gender equality, which has resulted in efforts at socializing
women into traditionally male economic roles. Like communism, political freedom is
associated with high female non-agricultural employment (Table 1). Revealingly, neither
communism nor liberal democracy have attempted in earnest to educate men into tradi-
tionally female economic and social roles.
Results are similar when cultural factors are indexed by the prevailing religion. A high
percentage of Catholics in the country favors male over female subjective well-being,
while % Muslims has the opposite effect (see Fig. 1). When the percentage of Muslims in
the country is added to model 1 in Tables 5and 6, % Muslims predicts higher female than
male happiness and life satisfaction (p=.002 in both cases). When % Muslims is included
together with female non-agricultural employment, employment remains a negative pre-
dictor for relative female happiness (p=.015) and satisfaction (p=.038), and % Muslim
still has marginal positive effects with p=.211 for happiness and p=.039 for satisfac-
tion. The negative effect of % Catholics is significant for happiness (p=.007) but not
satisfaction (p=.295). In regression models containing both female non-agricultural
employment and % Catholics, employment remains a significant predictor for both hap-
piness (p\.001) and satisfaction (p=.001), and Catholicism remains a significant
negative predictor of happiness (p=.007).
The results suggest that much or even most of the apparent detrimental effect of female
non-agricultural employment on relative female well-being is related to specific effects of
female employment, rather than to associated ‘‘cultural’’ factors. Conversely, some of the
cultural effects of Islam (as well as communism and democracy) on female-versus-male well-
being appear to be related to female non-agricultural employment. Only the effect of
Catholicism appears unrelated to female employment. One possibility is that it is not only
female employment but the normative expectation of female employment that is detrimental
for women (or favorable for men). In the communist and ex-communist countries, low female
well-being coincides with an ideology of gender equality that expected, demanded, and
largely achieved, full participation of women in the labor force. The Muslim countries
represent the other extreme. Here gender roles are still differentiated, with men earning the
money and women responsible for domestic chores, and low scores on the index of female
non-agricultural employment. The comparison of male versus female happiness and life
satisfaction suggests that this cultural framework, and this division of labor, is more favorable
for women than for men—or less detrimental for women than for men.
One possibility is that higher female life satisfaction in countries with traditional gender
roles is caused by lower female expectations. However, in this case we would expect that
traditional gender roles favor higher self-reported female life satisfaction but not neces-
sarily happiness. Inspection of Fig. 1shows this not to be the case. Also the inclusion of a
measure of acquiescent response style (Meisenberg and Williams 2008) in the regression
models did not affect gender differences in happiness and life satisfaction (data not shown).
Some Western scholars may find these conclusions counterintuitive. However, there is
evidence from Western societies indicating that unlike men, women do better with part-
time than full-time employment (Booth and van Ours 2010; Gash et al. 2010; Willson and
Dickerson 2010). In Europe, women who are housewives or are employed part-time are
slightly happier than those in full-time employment (Treas et al. 2011), and low male
participation in housework further reduces female happiness (Mencarini and Sironi 2012).
The latter observation is a possible explanation for low relative female well-being in
1552 G. Meisenberg, M. A. Woodley
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Southern Europe (see Fig. 1), where male participation in housework is generally low. We
can add that the trend for declining subjective well-being for women relative to men in the
United States (Ross 2011; Stevenson and Wolfers 2009), if real, not only coincided with
rising female employment, but can possibly be explained by it.
Studies have shown repeatedly that Western women average lower than men in
ambition, competitiveness, risk taking and materialism (Croson and Gneezy 2009; Lynn
1992). Vocational interests differ between men and women in ways that generally conform
to gender stereotypes, and within genders they are related to sexual orientation and to
androgen-influenced physical traits such as voice pitch and amount of body hair (Ellis and
Ratnasingam 2012). While some social scientists attribute both the gender differences and
individual differences to biology (Ellis and Ratnasingam 2012; Sapienza et al. 2009; Van
Vugt 2009), and in consequence are inclined to tolerate them, others are experimenting
with methods for making women more competitive, enabling them to succeed in tradi-
tionally male-dominated careers (Balafoutas and Sutter 2012; Beaman et al. 2012; Villeval
2012). The under-representation of women in traditionally male-dominated careers such as
mathematics, engineering and computer science seems to be a major concern (Ceci and
Williams 2011).
Although surprising for some social scientists, our observations concur with experiences
made in Israeli kibbutzim, which were founded based on an ideology of strict gender
equality. Women were expected to work like the men, and to participate in political
governance of the kibbutz. However, despite this strong ideological commitment, women
were soon found to avoid traditionally male occupations, and to drift from agricultural
work groups and machine repair shops to the communal kitchen, laundry and the children’s
house; and many were disinterested in politics and decision making in the kibbutz. Within
one generation, gender roles became more strongly differentiated in the kibbutz than in
Israeli society at large. Unlike the initial ideology of strict gender equality, which had been
originated mainly by men, the subsequent development of gender role differentiation was
driven by female preferences (Spiro 1979; Tiger and Shepher 1975). These experiences
show that even in a modern society, female preferences can diverge substantially from
male preferences.
The present study is strictly cross-sectional. However, it shows that greater gender
equality is not associated with higher subjective well-being of women relative to men. It
even suggests that high rates of female employment, or possibly a value system that insists
on female employment, have the potential to reduce female well-being. Therefore we need
to be aware of the possibility that continued efforts at educating women out of traditional
female roles and into traditional male roles can reduce female subjective well-being, as has
happened in the communist and ex-communist countries. But is this really surprising? Men
would not be happy and satisfied either if they were forced out of traditional male roles and
into traditional female roles. Perhaps the implicit belief among many social scientists that
male-typical preferences, values and social roles are in some way superior to traditional
female ones needs to be re-evaluated.
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