2019-06

Gender Identity and Relative Income within Households: Evidence from Canada

Maéva Doumbia Marion Goussé

Juin / June 2019

Centre de recherche sur les risques les enjeux économiques et les politiques publiques

www.crrep.ca

ABSTRACT Bertrand, Kamenica, and Pan (2015) show that among married couples in the US, the distribution of the share of the household income earned by the wife exhibits a sharp drop just to the right of 50%. They argue that this drop is consistent with a social norm related to gender identity prescribing that a man should earn more than his wife. We investigate this phenomenon over the years 1996 to 2011 in Canada at the national level but also across provinces where cultural values and gender norms appear to be different. We find a similar discontinuity at the 50% threshold and show that it comes from both matching patterns and time use behavior. We find that couples form less when the probability that a woman earns more than her husband increases. We also find that wives with greater probability of earning more than their husbands are less likely to participate in the labor force and that if they participate, they have a higher probability of earning less than their potential income. Finally, we find that when women exceeded the spouse’s income equality threshold, they also increased their time spent in housework, but this effect has disappeared in recent years. We find a stronger effect of gender roles in Quebec and in the Prairies but a less important effect in British-Columbia and in Ontario. JEL Classification: D1, J1. Keywords: Gender Wage Gap, Gender Identity, Intra-Household Allocation. Maéva Doumbia : Department of Economics, Université Laval. Marion Goussé : Department of Economics, Université Laval.

Marion Goussé gratefully acknowledges financial support from FRQ-SC. Part of the analysis presented in this paper was conducted at the Quebec Interuniversity Centre for Social Statistics which is part of the Canadian Research Data Centre Network (CRDCN). The services and activities provided by the QICSS are made possible by the financial or in-kind support of the Social Sciences and Humanities Research Council (SSHRC), the Canadian Institutes of Health Research (CIHR), the Canada Foundation for Innovation (CFI), Statistics Canada, the Fonds de recherche du Québec - Société et culture (FRQSC), the Fonds de recherche du Québec - Santée (FRQS) and the Quebec universities. The views expressed in this paper are those of the authors, and not necessarily those of the CRDCN or its partners.

1. Introduction

Whereas there has been a substantial reduction in the gender pay gap in most economically ad-

vanced nations, wage convergence, increases in female labor-force participation rates and reductions

in occupational segregation by sex have plateaued or slowed since the 1990s (Blau and Kahn, 2017).

Canada is no exception. There is a persistent pay gap in every province and in every major occu-

pational group. The gap in annual earnings between men and women has barely budged over the

past two decades, even as education levels among women have surpassed those of men (Baker and

Drolet, 2010; Moyser, 2017; Schirle, 2015; Bonikowska, Drolet, and Fortin, 2019; Fortin, 2019).

It is now well known that women’s career interruptions and prevalence of part time work are the

most significant factors in explaining the differences in earnings between men and women. A more

challenging question is the existence of a persistent gender gap in the annual number of working

hours. In spite of the rise in female participation in the labor market, men spend more time in

paid work, and women more time in housework: the gender specialization within couples remains

a quasi-universal norm. New research look into how the compliance to “gender roles” may explain

the gender division of labor (Bertrand, Kamenica, and Pan, 2015; Fortin, 2015; Bursztyn, Fujiwara,

and Pallais, 2017; Lippmann, Georgieff, and Senik, 2019). Bertrand, Kamenica, and Pan (2015),

hereafter BKP, show that the distribution of the share of income earned by women in American

households falls strikingly at the right of 0.5, the point where the wife and the husband contribute

equally to the total income. This drop highlights a phenomenon of missing couples, a lack of couples

where the wife earns more than her husband. They argue that it is consistent with a social norm

related to gender identity prescribing that a man should earn more than his wife. Compliance to

gender identity may prevent some couples to form or may impact time use behavior of women who

reduce their participation on the market and increase their housework when their income exceed the

income of their husband.1

In this paper, we investigate the effect of gender identity on marriage and time use behavior of

married women in Canada at the national level but also across provinces. We define gender identity

1Akerlof and Kranton (2000) import the concept of identity into economics. In particular, theyimport the concept of gender identity and the potentially competing identities of “homemaker” andof “career woman”.

2

as the traditional belief that the man should have a higher income than his wife in order to preserve

the traditional structure of the household in which the man is the main financial support of the

family. Our contribution is twofold. First, we show that the discontinuity at the spouse’s income

equality threshold also exists in Canada and comes from the impact of gender identity on both

matching pattern and time use behavior. Second, we document variations of gender norms across

Canadian provinces and show how they are related to different behaviors on the marriage market

and labor household division.

The existence of an aversion toward a situation where the wife has an income higher than that

of her husband is clearly identified in the wave 3 of the World Value Survey collected in 1995. Re-

spondents of the survey are asked whether they agree or not with the statement “If a woman has a

higher income than her husband, it will necessarily cause problems”. In the US, where BKP high-

light the existence of the missing couples phenomenon, 37.3% of American respondents agreed to

this statement. In Germany the agreement rate reached 43.4% and there also exists a discontinuity

in the distribution of share of the income earned by women at the 50% threshold (Wieber and Holst,

2015; Lippmann, Georgieff, and Senik, 2019). Interestingly, the discontinuity is much higher in West

Germany than in East Germany where gender norms are much less traditional.2 On the contrary,

Hederos Eriksson and Stenberg (2015) do not find such a discontinuity in Sweden although the agree-

ment rate was 30.9% among Swedish respondents. In Brazil, the agreement rate was 33.5% and a

large discontinuity is also found at the 50% threshold (Codazzi, Pero, and Albuquerque Sant’Anna,

2018).

In this paper, we first document gender norms in Canada using the European Values Study (EVS)

and the World Value Survey (WVS). Unfortunately, Canada has not been surveyed in 1995 so we do

not know the agreement rate to the statement “If a woman has a higher income than her husband,

it will necessarily cause problems” but we consider other statements related to gender norms in our

analysis. We find that the provinces of the Prairies3 have relatively more traditional gender norms

2Using the 41-year division of Germany as a natural experiment, Lippmann, Georgieff, and Senik(2019) show that East Germany gender-equal institutions created a culture that has undone themale breadwinner norm and its consequences on women labor force participation. By contrast, thenorm of higher male income is still prevalent in West Germany.3The Prairies include the provinces of Manitoba, Saskatchewan and Alberta.

3

than the other provinces whereas British-Columbia and Ontario relatively less. Second, we show

that the discontinuity at the 50% threshold also exists in Canada and is present in all provinces

except in the Atlantic provinces4 and in British-Columbia. We find a higher discontinuity in Quebec

and in the Prairies.

Two main channels may explain this missing couples phenomenon. The first channel is sorting

patterns and marriage rates as couples where the woman has a higher earning capacity than the man

may not form, or may divorce more. The second channel is time use behavior as women who would

be able to earn more than their husband may reduce their labor supply not to exceed the earnings

of their partner and comply to social norms. We investigate in turns these two mechanisms.

First, we look at how gender norms may affect marriage rate and matching patterns. Standard

economic theory says that education and earning capacity are important in determining partner’s

choice to maximize the household utility function and domestic production (Becker, 1973), however

it says nothing on which partner has to be the principal earner when both partners have the same

productivity at home and on the market. Who has to do the most of domestic production only comes

from efficiency and comparative advantages. Yet, it seems that who does what is not innocuous in

family behavior. Economic and sociological studies have shown that couples in which women work

more hours than their husband are more likely to separate (Kalmijn, Loeve, and Manting, 2007;

Heckert, Nowak, and Snyder, 1998). Experimental economics has also shown that women may hide

their career ambition in order to find a partner more easily. Bursztyn, Fujiwara, and Pallais (2017)

find that single female students report lower desired salaries and lower willingness to travel and

work long hours when they expect their classmates to see their preferences whereas married females

do not. Bertrand, Kamenica, and Pan (2015) find that an increase of one percentage point in the

probability that women earn more than men in a marriage market decreases by 0.22 percentage point

the marriage rate in this market. In this paper, using a similar methodology, we find a negative

effect of 0.26 percentage point in Canada. We find a stronger negative effect in Quebec and in the

Prairies and a non-significant effect in Ontario and in British-Columbia.

4The Atlantic provinces include Newfoundland and Labrador, Nova Scotia, Prince Edward Islandand New Brunswick.

4

Second, we look at how gender norms may affect women attachment to the labor market. In her

study on women’s participation in the labor market in OECD countries, Fortin (2005) finds that the

impact of gender identity is significant. Her analysis of the World Value Survey data suggests that

countries where people mostly agree to the statement “When jobs are scarce, men must have priority

over women in the granting of work” have low employment rates among women and have a higher

wage gap between men and women. Discriminatory attitudes towards women continue to play an

important role in slowing down access to equality between men and women in the labor market. In

a more recent paper, Fortin (2015) documents the evolution of gender role attitudes and women’s

labor market participation in the US. She first shows that traditional gender roles after a steady

decline in the 1970s and 1980s began to stabilize in the mid-1990s, even increasing in the late 1990s

for some cohorts. She shows that the progression of gender role attitudes explains at least a third of

the recent leveling-off in female labor force participation. Bertrand, Kamenica, and Pan (2015) find

that an increase of one percentage point in the probability that a wife earns more than her husband

decreases by 0.14 percentage point her labor force participation. Wieber and Holst (2015) also find

a negative effect in Germany but only in West Germany for women working full time. In this paper,

we find decrease of 0.09 percentage point in Canada using a similar methodology. We find similar

effects across provinces.

Finally, we look at how gender norms may affect time spent in housework. Despite their grow-

ing presence in the labor market and the relative rise in their incomes, women remain primarily

responsible for domestic work (Lachance-Grzela and Bouchard, 2010). This behavior contradicts

standard theory where women should reduce the amount of time they spend doing housework as

their income and the time they spend on the job market increase. Greenstein (2000) finds that

women housework decreases with their labor income until their labor income reaches the level of

their husband’s labor income, then their time spent in housework ceases to decrease and even in-

creases significantly. This behavior has been shown in the US (Bertrand, Kamenica, and Pan, 2015)

and in Germany but only in West Germany and not in East Germany (Lippmann, Georgieff, and

Senik, 2019). Using confidential micro-data files of the General Social Survey of 2010 and 2015 on

time use, we find a similar behavior in Canada in the 2010 survey but not in the 2015 survey anymore.

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In the next section, we show how gender norms vary across Canadian provinces. In section 3, we

present the distribution of within couples relative income and investigate the role of gender identity

on marriage rates in section 4. In section 5 we look at the impact of gender identity on women labor

supply then we focus on housework in section 6 before concluding.

2. Gender norms in Canada

To document gender norms in Canada, we use the European Values Study (EVS) and the World

Value Survey (WVS) which are two large-scale, cross-national and longitudinal survey research

programs. They include a large number of questions, which have been replicated in 364 surveys.

Both programs have been conducted from 1981 to 20145. Canadian data are only available on

the 1990 wave of the EVS and on the wave 2000 and 2005-2008 of the WVS. The questions on

gender role attitudes are included in a section that asks respondents whether they agree strongly,

agree, disagree or disagree strongly with a number of statements. Fortin (2005) has identified four

statements which are correlated with women labor force participation in the OECD countries. She

shows that agreement with the statement that “When jobs are scarce, priority must be given to

men in the granting of work” appears to be the most powerful explanatory factor of cross-country

differences in female employment and in the gender pay gap. This statement capture perceptions

of the man as the main breadwinner, as well as anti-egalitarian views or discriminatory attitudes

against working women. Perception of the woman as homemaker, measured as agreement with the

statement “Being a housewife is just as fulfilling as working for a salary” also has a significant

explanatory power. For individual choices, she also finds that agreement with the statement that

“A working mother can establish just as warm and secure a relationship with her children as a

mother who does not work” is closely associated with the employment status and is a measure of

some literature calls “mother’s guilt”. We also look at the statement “Husband and wife should both

contribute to income”.

Using those statements, we build four dummies to compute the share of individuals who have

traditional view of gender norms. We build the dummy “Men first” which is equal to 1 if the

5EVS (2015): European Values Study Longitudinal Data File 1981-2008 (EVS 1981-2008). GESISData Archive, Cologne. ZA4804 Data file Version 3.0.0, doi:10.4232/1.12253WVS (2015). World Value Survey 1981-2014 official aggregate v.20150418, 2015. World ValuesSurvey Association (www.worldvaluessurvey.org). Aggregate File Producer: JDSystems, Madrid.

6

individual agrees to the statement “When jobs are scarce, priority must be given to men in the

granting of work” and which is equal to 0 if the individual answers that he disagrees or that he

neither disagrees nor agrees. We build the dummy “Housewife fulfilling” which is equal to 1 if

the individual answers that he agrees or strongly agrees to the statement “Being a housewife is

just as fulfilling as working for a salary” and which is equal to 0 if the individual answers that he

disagrees or strongly disagrees. We build the dummy “Warm if not working” which is equal to 1 if

the individual answers that he disagrees or strongly disagrees to the statement “A working mother

can establish just as warm and secure a relationship with her children as a mother who does not

work” and which is equal to 0 if the individual answers that he agrees or strongly agrees. Finally

we build the dummy “Both should not contribute” which is equal to 1 if the individual answers

that he disagrees or strongly disagrees to the statement “Husband and wife should both contribute

to income” and which is equal to 0 if the individual answers that he agrees or strongly agrees.

Table 1 then shows the share of individuals who have traditional view of gender norms in Canada

(at the national level and at the provincial level) and in other countries overtime. We find that

agreement rates to give priority to men in a context of scarce jobs are decreasing over time for

all countries. However, they are much lower in North America and Sweden than in France and

Germany (except in the Quebec province where the rate is particularly high). Similarly the share

of individuals who agree with the statement that both members should not contribute is decreasing

with time in all countries and provinces. This share is higher in North America than in Europe

(except in the Quebec province where the share is lower). This rate is also particularly high in

the Prairies. The share of individuals agreeing with the statement that women who do not work

may establish a warmer and more secure relationship with their children than working mother is

also decreasing with time in all countries and provinces. In 2000, it is close to 20% in all countries

(except in Sweden where it is slightly lower at around 15%). Surprisingly, in North America, the

agreement rate to the statement that being a housewife is as fulfilling than working is much higher

than in Europe and has increased overtime whereas it has decreased in Europe. In 2006, around

80% of individuals agreed to this statement in North America whereas they were only 40% to 50%

in Europe (in 1990, they were 70% in North America and between 40% and 60% in Europe). This

fact has also been noted by Fortin (2005). All in all, we remark that gender norms appear to be less

7

traditional overtime. They also appear to be less traditional in Sweden where gender identity has

no effect on within household income according to Hederos Eriksson and Stenberg (2015) and more

traditional in the US where BKP find a strong effect of gender identity on within-household relative

income. As gender norms in Canada seem to be close to the gender norms in the US, we expect to

find similar gender behavior.

Table 1. Agreement rates to gender norms statements

Men first Housewife Both should Warm iffulfilling not contribute not working

1990 2000 2006 1990 2000 2006 1990 2000 1990 2000

Canada 16.1 12.2 10.2 69.9 81.6 80.3 32.0 23.7 27.5 19.6Atlantic provinces 12.7 13.1 6.9 66.7 82.5 87.0 28.3 25.4 31.9 19.2Quebec 20.5 15.2 16.8 80.7 86.1 79.6 25.9 13.6 23.7 18.1Ontario 17.1 8.7 9.2 63.3 75.1 76.4 33.0 25.1 30.0 19.7Prairies 13.8 10.7 6.9 70.6 85.7 80.1 37.1 35.2 27.7 22.4British-Columbia 8.7 13.3 9.4 67.3 77.6 74.6 37.3 27.0 24.4 20.6United-States 20.9 8.1 5.6 72.2 78.9 79.1 33.5 30.8 25.2 19.8France 31.4 19.7 15.6 56.3 61.3 50.3 20.6 20.3 25.6 21.0Germany 27.0 22.6 16.5 41.0 34.1 39.0 25.3 17.1 50.6 23.0Sweden 6.6 1.6 1.7 60.6 47.8 47.3 13.5 11.4 27.2 15.2

Data are from the integrate survey of EVS and WVS 1981 to 2014. The sample includes individuals between 18 and

65 years of age. Atlantic provinces include Newfoundland and Labrador, Nova Scotia, Prince Edward Island andNew Brunswick. The Prairies include the provinces of Manitoba, Saskatchewan and Alberta.

Provinces of Canada differ in several characteristics which may be correlated with values. To

obtain a measure of the strength of gender norms in each province, we perform a linear regression of

each of our gender norm dummies on age, education6, sex, year and provinces fixed effects. Ontario

is the province of reference. We also perform a regression on an index built as the sum of the four

dummies previously defined. The index may then takes five values from 0 to 4. The higher the

index, the more traditional the individual. As all statements are not available each year, it is only

computed for individuals present in wave 1990 and 2000. We present the province fixed effects for

the five regressions in figure 1 (all regression results are presented in appendix in table 11). Results

do not show a clear pattern of gender norms across Canada. The province fixed effect for the index

shows that the provinces of the Prairies may be more traditional than the others. We could expect

6Education level is only available in the WVS. We use the information on the leaving school age,which is available in both the EVS and the WVS age to control for education.

8

that gender identity will play an important role in women behavior in the Prairies. The province

of Quebec is particular as it appears to be significantly more traditional than Ontario with respect

to give priority to men for jobs and with respect to the statement that housewifery is as fulfilling

as working. However, it is significantly less traditional than Ontario with respect to the statement

that working women may secure a warm relationship with children and with the statement that

both members should contribute. However, Fortin (2005) show that the former two statements are

those which matter the most in explaining women employment and full-time participation. We could

expect that gender identity will also play an important role in women behavior in the province of

Quebec. The Atlantic provinces are less traditional than Ontario with respect to give priority to

men for jobs but significantly more traditional with respect to the statement that being as housewife

is as fulfilling as working for pay. It is non significantly different from Ontario otherwise. British-

Columbia is not significantly different from Ontario. We expect gender identity to have a less

important role in British-Columbia and Ontario than in other provinces.

Figure 1. Gender norms fixed effects across Canadian provinces

-0.03*

0.09***

-0.02

0.000.01

0.04***

0.10***

-0.05**

-0.09***

0.01

-0.01

0.07***

0.01

0.08***

0.13**

-0.02

0.01

-0.02

0.04

0.01

Men F irst Housewife fu l f i l l ing

Warm i f not working

Both should not contr ibute

Index

Atlantic Quebec Prairies British-Columbia

Note : Data are from the integrate survey of EVS and WVS 1981 to 2014. The sample includes individuals between18 and 65 years of age. Atlantic provinces include Newfoundland and Labrador, Nova Scotia, Prince Edward Islandand New Brunswick. The Prairies include the provinces of Manitoba, Saskatchewan and Alberta. The bars show the

province fixed effects obtained after regressing gender norms dummies on age, sex and education of respondents,year and province fixed effects. All regression coefficients are presented in appendix in table 11. ***significant at 1%

level, **at 5%, *at 10%.

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3. Relative earnings within household

In this section, we are interested in computing the distribution of the relative income of women

in Canadian households that is the share of the household income which is earned by the wife. We

use the 1996 to 2011 cross-sections of the Survey on Labor and Income Dynamics (SLID) which

provides data on employment and earnings of Canadian individuals and families. We restrict the

data to couples7 aged between 18 and 65 year old where both members earn positive labor market

earnings. We then compute the relative income as

relativeIncomei =wifeIncomei

wifeIncomei + husbIncomei

where wifeIncomei and husbIncomei respectively represent the individual labor market earnings

of the wife and the husband in couple i and includes income from self-employment.

The distribution of these relative incomes are presented in figure 2. The relative income is divided

by twenty bins of 0.05. The blue dots on the densities represent the fraction of couples in each bin.

We use a lowess function, and estimate the distribution of the relative income to the left and right

of the 0.5 point. We observe a large discontinuity at the 0.5 threshold as in BKP.

The estimation of the discontinuity is sensitive to the presence of a mass of couples at the 0.5

threshold in the distribution (Binder and Lam, 2018; Hederos Eriksson and Stenberg, 2015). Ex-

cluding couples in which both members earn the same income may eliminate the discontinuity. In

our data, we observe an important mass point at the 0.5 threshold as 1.3% of couples earn the

same income. We will consequently test for the presence of the discontinuity both in including and

in excluding same-income couples. We perform McCrary tests (McCrary, 2008) to ensure that the

observed fall corresponds to a break in the distribution density. The principle of this test is to esti-

mate non-parametrically the density to the left (f−) and to the right (f+) of the threshold where a

discontinuity is suspected. The size of the discontinuity is then equal to θ = ln f+ − ln f−. We test

for a break at the 0.5 threshold when we exclude same-income couples whereas we test for the break

at the right of the threshold (at 0.501) when we include same-income couples. We present in figure

3 the resulting estimated densities with solid lines curves. Figure 3.A presents the results when we

include same-income couples from the sample and figure 3.B the results when we exclude them. We

7We include married and unmarried couples.10

observe on figure 3.A the presence of a mass point at 0.5 where the wife and the husband earn the

same income. In both graphics, we observe a discontinuity. Binder and Lam (2018) show that the

densities estimated with McCrary algorithm and consequently the test results are sensitive to the

choice of the bandwidth. We present test results for two different bandwidths: the optimal band-

width h∗ chosen by the McCrary algorithm and a smaller bandwidth equal to 0.75h∗. We present in

table 2 the size of the discontinuities estimated both with and without same-income couples and with

different bandwidths. In all cases, we find a significant discontinuity at the 0.5 threshold. However,

the discontinuity is smaller when we exclude same-income couples. Using the optimal bandwidth as

in BKP, we find a discontinuity of -0.102 (pvalue < 0.01) in Canada which is very close to the -0.12

found by BKP. The discontinuity varies across provinces. It is much higher in the Prairies (-0.176,

pvalue < 0.01) and it is non significant in the Atlantic provinces and British-Columbia8

We have identified a missing couples phenomenon at the 50% threshold. We now want to inves-

tigate how it is related to gender identity that is to a social norm prescribing that a man should

earn more than his wife. It may come from the impact of gender identity on matching patterns or

on time use behavior. We investigate these two channels in the following sections.

Figure 2. Distribution of Relative Income

Data are from the 1996 to 2011 SLID cross-sections. The sample include married and cohabiting couples where bothpartners earn positive income and are between 18 and 65 years of age. Each dot is the fraction of couples in a 0.05

relative income bin. The vertical line indicates the relative income share = 0.5. The solid line is the lowess smoother

applied to the distribution allowing for a break at 0.5.

8In appendix, we present on figure 4 and 5 the estimated densities across provinces.11

Figure 3. Discontinuity at 0.5 in Relative Income

05

1015

-.5 0 .5 1 1.5

(a) With same income couples

02

46

8-.5 0 .5 1 1.5

(b) Without same income couples

Data are from the 1996 to 2011 SLID cross-sections. The sample include married and cohabiting couples where bothpartners earn positive income and are between 18 and 65 years of age. Estimated densities to the left and right of

the threshold are estimated using McCrary algorithm with a bin size of 0.0011. On the left panel, same income

couples are included, and the break is set at 0.501. The optimal bandwidth h∗ = 0.159 is used. On the right panel,same income couples are excluded, and the break is set at 0.5. The optimal bandwidth h∗ = 0.155 is used.

Table 2. Discontinuity estimates

Canada Atlantic Quebec Ontario Prairies British-Columbia

Same-income couples included, break at 0.501h∗ -0.353*** -0.237*** -0.345*** -0.398*** -0.408*** -0.328***

(0.015) (0.028) (0.035) (0.027) (0.032) (0.054)0.75× h∗ -0.399*** -0.219*** -0.393*** -0.426*** -0.453*** -0.400***

(0.017) (0.032) (0.040) (0.031) (0.036) (0.061)Bin size 0.0011 0.0025 0.0025 0.0020 0.0022 0.0037Automatic bandwidth h∗ 0.159 0.259 0.162 0.143 0.143 0.136

Same-income couples excluded, break at 0.5h∗ -0.102*** 0.015 -0.116*** -0.122*** -0.176*** -0.058

(0.016) (0.037) (0.038) (0.031) (0.031) (0.055)0.75× h∗ -0.064*** 0.060 -0.096** -0.068* -0.143*** -0.018

(0.019) (0.044) (0.044) (0.036) (0.036) (0.063)Bin size 0.0011 0.0025 0.0025 0.0020 0.0022 0.0038Automatic bandwidth h∗ 0.155 0.166 0.156 0.145 0.175 0.154

Data are from the 1996 to 2011 SLID cross-sections. The sample include married and cohabiting couples where both

partners earn positive income and are between 18 and 65 years of age. Atlantic provinces include Newfoundland and

Labrador, Nova Scotia, Prince Edward Island and New Brunswick. The Prairies include the provinces of Manitoba,Saskatchewan and Alberta. Standard errors are reported in parenthesis. ***significant at 1% level, **at 5%, *at

10%.

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4. Marriage market

We analyze in this section how the marriage rate varies with the distribution of potential incomes

of men and women. We group individuals into marriage markets under the criterion of homophily.

How the changes in the woman’s income relative to that of a man in a marriage market affects the

marriage rate in this market ? We first define M marriage markets where individuals have the same

age group, level of education and province. We define three levels of education (primary education,

secondary education and some superior education) and three age groups which differ for men and

women as usually men marry younger women. We define the age group by 18-35, 36-45 and 46-65

year old for men and 18-33, 34-43 and 44-65 year old for women. We then obtain 3 × 3 × 10 = 90

marriage markets each year, that is 90×16 = 1440 observations. In table 12 in appendix, we present

how couples sort within different groups. More than 70% of men marry within their age group and

more than 50% of men marry within their education group.

For each marriage market m ∈ [1;M ] and year t, we calculate the probability that when a woman

encounters a man, she has an higher income than his. We proceed as follows. We randomly select

couples among each market. That is, in each market for each year, we replicate 20 times each

individual observation and we match men and women randomly. Then we compute the rate of women

earning a higher income than their assigned husband to obtain the variable PrWomanEarnsMoremt

which is then the probability that women earn more than men in the marriage market m at time

t. For each marriage market and year t, we also compute the observed marriage rate of men. Table

13 in appendix presents descriptive statistics of these variables. The average marriage rate is 66.8%

and the average probability that a woman earns more than a man is 35.5%. Then we perform a

linear regression of the marriage rate on the variable PrWomanEarnsMoremt. The causal effect

is identified if there are no omitted variables that are simultaneously correlated with the marriage

rate and the probability that women earn more than men. We therefore add several sets of variables

of controls and fixed effects. All regressions include the log of the average men income, the log

of the average women income and the deciles of the men and women income distributions in the

market each year. We add marriage market fixed effects, and year fixed effects interacted with age

groups, education groups and provinces. The unit of observation is a marriage market in a year.

Standard errors are clustered by province, and each observation is weighted by the number of women

13

in the marriage market. Results for this baseline specification are presented in column (1) of table

3. Column (2) adds a control for average women’s income divided by the sum of average men’s and

women’s income and includes several additional controls: the sex ratio (the share of men among the

market population), the average number of years of schooling for men and women, and the number

of men and women in the market. The impact of PrWomanEarnsMoremt on the marriage rate is

negative and significant equal to -0.28 (pvalue < 0.01).

One may still worry that the variable PrWomanEarnsMoremt is endogenous as women earn-

ings depend on their marital situations. Following BKP, we use two alternatives measures of women

income to compute the variable PrWomanEarnsMoremt and the variables of controls for income.

First, we use a measure of predicted income based on demographic characteristics. For each demo-

graphic group (gender, age, education, province and year)9, we compute the distribution of income

for working men and women. Then, we assign potential income to each men and women by drawing

from the earning distribution of those in their demographic group. Results are presented in columns

(3) and (4) of table 3. The coefficients are very stable. The impact of PrWomanEarnsMoremt

on the marriage rate is still negative and significant and is equal to -0.25 (pvalue < 0.01). Fi-

nally, we build a distribution of incomes derived from a Bartik instrumental methodology that

isolates the variation in relative income which is plausibly unrelated to the factors that directly

affect the marriage market. We use the variation of wages among different industries as shares of

men and women are different by industry. In particular, we compute predicted Bartik wages at the

p = {5th, 10th, ...95th} percentiles the following way

wB,pgeaprt =

∑j

γjgepr,1996× wj,p

geat,−pr

where g indexes gender, e education group, a age group, j industry10, t the year and pr the province.

Variable wj,pgeat,−pr

is the pth percentile of the national income distribution in year t in industry j

for workers of a given gender, education, and age group, excluding province pr. γjgepr,1996is the

9Demographic groups are defined more precisely than marriage markets. We group age into sixyear intervals and we define 5 groups of education: less than secondary, secondary education, non-university post secondary education, partial university education, university degree.10We consider 11 industry groups: agriculture and forestry; construction; manufacturing; trans-portation; trade; finance, insurance, and real estate; business, personal, and repair services; teachingservices, health care and social assistance, other services; and public administration.

14

fraction of individuals with gender g and education e in province pr who are working in industry

j, as of the base year 1996. Those percentiles are strongly correlated with predicted percentiles by

demographic group but should not be correlated with marriage market rates. Results are presented

in columns (5) and (6) of table 3. The impact of PrWomanEarnsMoremt on the marriage rate is

still negative and significant. One point of percentage increase in the probability that women earn

more than men in a marriage market decrease by 0.16 percentage point the marriage rate in this

marriage market.

We now investigate the effect of gender identity on marriage rates across provinces and we add

in the regressions the interaction variable area × PrWomanEarnsMore. We present the effect

of gender identity on marriage rate in each province in table 411. The first row gives the es-

timate for this effect that is the sum of the coefficients of variables PrWomanEarnsMore and

area×PrWomanEarnsMore when actual earnings are used and all control variables are included.

The second row gives the F-stat of a significativity test on that sum. Rows (3) and (4) show the

results when predicted income by demographic group is used in the estimation whereas rows (5)

and (6) show the results when the Bartik specification is used. We find a strong and significant

negative effect in each area when actual income is used. When using predicted income and Bartik

instrument, we find a strong and significant negative effect in the Atlantic provinces, in Quebec and

in the Prairies but a non-significant effect in British-Columbia and even a positive effect in Ontario.

Our results then highlight a negative effect of gender identity on marriage rates as in BKP. As

women have strongly increased their labor income over the last fifty years, a part of the decline in

marriage rates overtime may be explained by compliance to gender identity.

11All parameters are presented in table 14 in Appendix.15

Table 3. Potential relative income and marriage rate in Canada

Dependent variable : marriage rateActual earnings Predicted earnings Bartik instrument(1) (2) (3) (4) (5) (6)

PrWomanEarnsMore -0.324*** -0.285*** -0.276*** -0.250*** -0.126** -0.161***(0.0435) (0.0411) (0.0421) (0.0354) (0.0403) (0.0416)

Log average in men’s income 0.0218 0.0627** 0.0129 0.0555* -0.00652 -0.0564(0.0182) (0.0260) (0.0174) (0.0266) (0.0553) (0.0587)

Log average in women’s income 0.0388* 0.00347 0.0454* 0.00947 -0.0279 -0.0152(0.0200) (0.0319) (0.0222) (0.0375) (0.0795) (0.0726)

Share of Women Income 0.152 0.154 -0.134(0.119) (0.136) (0.0939)

Men average schooling 0.0268 0.0325 0.0400(0.0255) (0.0250) (0.0247)

Women average schooling 0.0384** 0.0453** 0.0392**(0.0164) (0.0149) (0.0152)

Number of men 2.38e-05 9.96e-06 9.05e-05(7.87e-05) (7.01e-05) (8.01e-05)

Number of women 4.83e-06 1.75e-05 -5.27e-05(7.80e-05) (6.97e-05) (7.64e-05)

Sex ratio -0.153 -0.158 -0.245**(0.102) (0.0925) (0.0929)

Constant 0.543*** 0.195 0.501*** 0.0999 0.245*** 0.0805(0.0405) (0.152) (0.0370) (0.144) (0.0680) (0.173)

Observations 1,440 1,440 1,440 1,440 1,440 1,440R-squared 0.984 0.985 0.984 0.985 0.983 0.984

Data are from the 1996 to 2011 SLID cross-sections. The unit of observation is a marriage market by year. All

specifications include marriage market fixed effects, year fixed effects, and the year interacted with the age group,the education group, the province and deciles of men’s and women’s income. Regressions are weighted by the

number of women in the marriage market. Standard errors clustered at the province level are in parenthesis.***significant at 1%, **at 5%, *at 10%.

Table 4. Potential relative income and marriage rate in Canada. Ef-fects by province.

Atlantic Quebec Ontario Prairies British-ColumbiaActual earnings -0.361*** -0.189*** -0.244*** -0.287*** -0.097***

[36.60] [15.39] (0.051) [23.78] [16.11]Predicted earnings -0.322*** -0.217*** -0.064 -0.281** -0.061

[28.88] [19.66] (0.070) [6.18] [3.21]Bartik instrument -0.172** -0.348** 0.122* -0.223** -0.031

[9.62] [6.27] (0.061) [8.17] [0.09]F-stats are reported in brackets. Standard errors in parenthesis. ***significant at 1% level, **at 5%, *at 10%.

16

5. Women labor supply

5.1. Women labor force participation. In this section, we want to estimate the effect of gender

identity on participation of married women in the labor market. We use the SLID cross-sections

from 1996 to 2011 and we restrict the data to couples where the husband earns a positive income.

As in the previous section, we also construct a predictive measure of income according to demo-

graphic characteristics. For each couple i, we estimate the distribution of the potential income of

a married woman according to her demographic group. For this purpose, we define the percentiles

of income that we denote wpi , p = {5th, 10th, ...95th} among the population of working women in

the demographic group of married women (age group, level of education, province of residence). We

define the probability that a married woman will have a higher income than her husband, on the

basis of the demographic group to which she belongs computing the following variable:

PrWifeEarnsMorei =1

19

∑p

1(wpi >husbIncomei)

where husbIncomei is the income of her current spouse. Whatever her current professional status,

this calculation captures the probability of her earning more income than her spouse if her income

was randomly drawn from the labor force of women in her demographic group.

We establish wifeLFPi, wife’s labor market participation for a couple i, as a binary variable

that takes the value 1 when the married woman is in the labor force all year. We present summary

statistics of our computed variable in table 15 in appendix. The average probability of wives earning

more than their husband is equal to 27% and the participation rate of women is of 67%. We estimate

the effect of gender identity on women labor force participation using a linear probability model.

Formally, we simply run the following regression:

wifeLFPi = β0 + β1PrWifeEarnsMorei + β2logHusbIncomei + β3Xi + βdwdi + εi

where logHusbIncomei is the logarithm of the husband’s income, wdi controls for the potential

income of the wife at each decile. And Xi is a matrix of non-income controls: age group, education

level, province of residence. Results are presented in column (1) of table 5. One point of percentage

increase in the probability that a wife earns more than her husband decreases by 0.02 percentage

17

point (pvalue < 0.05) the probability that she participates to the labor market. To control for the

husband income, we add a cubic polynomial of the logarithm of the income of the husband (column

(2)), and in our last specification (columns (3)), we add the presence of children, the interaction

variable logMedianWifeIncome× logHusbIncome and the wife demographic groups fixed effects.

We find that one point of percentage increase in the probability that a wife earns more than her

husband decreases by 0.09 percentage point (pvalue < 0.01) the probability that she participates to

the labor market which is close to the -0.14 point found in BKP.

In columns (4) to (6), we investigate the effect of gender identity on labor force participa-

tion of women across provinces and we add in the regressions the interaction variable area ×

PrWifeEarnsMore. We present the effect in each province in table 6. The first row gives the

estimate of the effect that is the sum of the coefficients of the variables PrWifeEarnsMore and

area×PrWifeEarnsMore when all control variables are included. The second row gives the F-stat

of a significativity test of that sum. We find strong and significant similar negative effects across

provinces.

18

Table 5. Potential relative income and wife’s labor force participation

Dependent variable : wife in the labor force all year(1) (2) (3) (4) (5) (6)

PrWifeEarnsMore -0.0230** -0.0562*** -0.0923*** -0.0222 -0.0665*** -0.0868***(0.0112) (0.0137) (0.0140) (0.0156) (0.0173) (0.0172)

Atlantic× PrWifeEarnsMore 0.00534 0.0298* -0.00575(0.0157) (0.0157) (0.0150)

Quebec× PrWifeEarnsMore -0.0246 -0.00540 -0.0216(0.0194) (0.0192) (0.0176)

Prairie× PrWifeEarnsMore 0.0202 0.0260* 0.0116(0.0149) (0.0149) (0.0136)

British Columbia× PrWifeEarnsMore -0.0197 -0.0150 -0.0265(0.0168) (0.0173) (0.0167)

logHusbIncome -0.00814** -0.495*** -0.553*** -0.00793** -0.500*** -0.551***(0.00380) (0.0663) (0.0669) (0.00382) (0.0666) (0.0670)

SquareLogHusbIncome 0.0690*** 0.0586*** 0.0696*** 0.0582***(0.00778) (0.00761) (0.00782) (0.00763)

CubicLogHusbIncome -0.00300*** -0.00265*** -0.00302*** -0.00263***(0.000290) (0.000282) (0.000291) (0.000283)

logMedianWifeIncome × logHusbIncome 0.0147*** 0.0148***(0.00141) (0.00145)

Has children -0.0465*** -0.0466***(0.00591) (0.00591)

Constant 0.223*** 1.201*** 1.405*** 0.219*** 1.205*** 1.401***(0.0423) (0.179) (0.176) (0.0422) (0.179) (0.176)

Observations 181,631 181,631 181,631 181,631 181,631 181,631R-squared 0.075 0.078 0.090 0.075 0.078 0.090Wife demog. group No No Yes No No Yes

Data are from the 1996 to 2011 SLID cross-sections. The sample consists of couples where both the wife and the

husband are between 18 and 65 years old and the husband is working. All regressions include controls for log

husband’s income, deciles of the wife’s potential income, wife’s and husband’s education (five categories), wife’s andhusband’s six-year age group, year, and province fixed effects. Standard errors are reported in parenthesis.

***significant at 1% level, **at 5%, *at 10%.

Table 6. Potential relative income and wife’s labor force participa-tion. Effects by province

Atlantic Quebec Ontario Prairies British-ColumbiaCoefficient -0.0926*** -0.1084*** -0.0868*** -0.075*** -0.133***

[31.38] [32.81] (0.017) [22.41] [34.64]F-stats are reported in brackets. Standard errors are reported in parenthesis.

***significant at 1% level, **at 5%, *at 10%.

5.2. Difference between income earned by the wife and her potential income. The cost

of a definitive departure from the labor market is very high. To comply with gender identity, a

woman may instead only reduce her participation at the intensive margin which would be less costly

financially. In this section, we intend to estimate the difference between the income a married

woman achieves and her potential income, that is the income she could realize according to her19

demographic characteristics if she did not modify her behavior on the labor market in order to

preserve the traditional structure of her household. We compute for each women i the variable

incomeGapi =wifeIncomei − wifepotentiali

wifepotentiali

where wifeIncomei is the actual income earned by the wife and wifepotentiali is the prediction

of the potential income of the woman according to her demographic group. In this section, we

restrict our estimation to married women who work and earn a positive income. We use the same

linear probability model and the same explanatory variables as in section 5.1 and run the following

regression:

incomeGapi = β0 + β1PrWifeEarnsMorei + β2lnHusbIncomei + β3Xi + βdwdi + εi

Results are presented in table 7. One point of percentage increase in the probability that a wife

earns more than her husband decreases by 0.19 percentage point the income gap between her current

income and her potential income. To control for the husband income, we add a cubic polynomial of

the logarithm of the income of the husband (column (2)), and in our last specification (columns (3))

we add the presence of children, the interaction variable logMedianWifeIncome× logHusbIncome

and the wife demographic groups fixed effects. We find smaller estimates when we add these controls.

Our estimate becomes non-significant in our last specification.

In columns (4) to (6), we investigate the effect of gender identity on the income gap across

provinces and we add in the regressions the interaction variable area × PrWifeEarnsMore. We

present the effect for each province in table 10. The first row gives the estimate of the effect that

is the sum of the coefficients of the variables PrWifeEarnsMore and area× PrWifeEarnsMore

when all control variables are included. The second row gives the F-stat of a significativity test of

that sum. We find a strong and significant negative effect Quebec and in the Prairies only. In those

regions, we find that one point of percentage increase in the probability that a wife earns more than

her husband decreases by 0.07 her income gap which is close to the -0.11 point found in BKP.

Our results then highlight the existence of a negative effect of gender identity on women labor

force participation.

20

Table 7. Potential relative income and income gap

Dependent variable : Income gap(1) (2) (3) (4) (5) (6)

PrWifeEarnsMore -0.185*** -0.0502* -0.0333 -0.178*** -0.0274 -0.0116(0.0220) (0.0263) (0.0272) (0.0274) (0.0334) (0.0340)

Atlantic× PrWifeEarnsMore 0.0312 0.000623 0.0307(0.0266) (0.0277) (0.0271)

Quebec× PrWifeEarnsMore -0.0191 -0.0452 -0.0560**(0.0283) (0.0317) (0.0280)

Prairie× PrWifeEarnsMore -0.0417 -0.0508* -0.0600**(0.0260) (0.0269) (0.0268)

British Columbia× PrWifeEarnsMore -0.00844 -0.0129 -0.0174(0.0449) (0.0458) (0.0455)

logHusbIncome 0.0297*** 0.0717 0.390*** 0.0291*** 0.0661 0.379***(0.00814) (0.136) (0.138) (0.00812) (0.136) (0.138)

SquareLogHusbIncome -0.0288* -0.0342** -0.0282* -0.0326**(0.0161) (0.0161) (0.0161) (0.0160)

CubicLogHusbIncome 0.00194*** 0.00222*** 0.00192*** 0.00216***(0.000625) (0.000622) (0.000624) (0.000620)

logMedianIncWife × logHusbIncome -0.0281*** -0.0284***(0.00272) (0.00269)

Has children -0.143*** -0.142***(0.0135) (0.0135)

Constant 0.310*** 0.823** -0.342 0.303*** 0.834** -0.311(0.0879) (0.383) (0.380) (0.0875) (0.382) (0.379)

Observations 120,072 120,072 120,072 120,072 120,072 120,072R-squared 0.032 0.035 0.050 0.032 0.036 0.051Wife demog. group No No Yes No No Yes

Data are from the 1996 to 2011 SLID cross-sections. The sample consists of couples where both the wife and the

husband are between 18 and 65 years old and the husband is working. All regressions include controls for loghusband’s income, deciles of the wife’s potential income, wife’s and husband’s education (five categories), wife’s and

husband’s five-year age group, year, and province fixed effects. Standard errors are reported in parenthesis.

***significant at 1% level, **at 5%, *at 10%.

Table 8. Potential relative income and income gap. Effects by province

Atlantic Quebec Ontario Prairies British-ColumbiaCoefficient 0.0191 -0.0677** -0.0116 -0.0716** -0.0290

[0.40] [4.45] (0.034) [5.21] [0.56]F-stats are reported in brackets. Standard errors in parenthesis.

***significant at 1% level, **at 5%, *at 10%.

6. Relative income and housework

In this section, we analyze the effect of gender identity on women’s time spent on housework.

For this purpose, we use the confidential micro-data files of the General Social Survey of 2010 and

2015 on time use. These data provide information on time spent on unpaid work, on paid work

and provides for the exact amount of income earned by the respondent and the exact amount of the21

total income earned by the household. However, the income of the spouse is not directly observed,

so we infer it from the difference between the total household income and the respondent’s personal

income. To reduce the risk of errors in calculating spouses’ income, we keep only the households in

which both spouses work, and we remove households in which more than two members contribute

to the total household income. We measure domestic work, as the sum of the amount of time spent

in household chores or maintenance.12 It is measured in hours per week for each respondent i by

our variable HouseworkT imei. We restrict the data to individuals aged between 18 and 65, who

are married or living in a common-law relationship and who have a non-zero income. Finally, we

build the variable wifeEarnsMorei which is equal to 1 if the woman has a higher income than

her spouse. Summary statistics are presented in table 15. In 2010, wives spend an average of 18.8

hours a week on chores, compared with 12.7 hours for husbands and the wife earns more than the

husband in 25 percent of the couples. In 2015, wives spend an average of 19.7 hours a week on

chores, compared with 13.9 hours for husbands and the wife earns more than the husband in 30

percent of the couples. From 2010 to 2015, the ratio of husbands’ housework over wives’ housework

has then slightly increased from 68% to 71%. In the GSS, we observe information on time use for

only one respondent per household. Thus, to analyze how relative income impacts the division of

home production, we focus, as in BKP, on the interaction between the impact of gender and the

impact of relative income on time use. Specifically, we consider the baseline OLS model:

HouseworkT imei =β0 + β1wifeEarnsMorei + β2femalei × wifeEarnsMorei + β3femalei

+ β4lnWifeIncomei + β5femalei × lnWifeIncomei

+ β6lnHusbIncomei + β7femalei × lnHusbIncomei

+ β8lnTotalIncomei + β9femalei × lnTotalIncomei

+ β10Xi + β11femalei ×Xi + εi

where femalei is a dummy indicating if the individual is a woman, and Xi includes levels of

education of both spouses (three categories), a quadratic polynomial of the age of both spouses, a

12It includes meal preparation, house cleaning, dish washing, tidying, laundry, ironing, repair, orga-nizing, doing the groceries, gardening, ...

22

cubic polynomial of the logarithm of income of the husband, the wife and the total income. We

also add the income share of the woman, dummies indicating whether the household has zero child,

one child, two children, three children or more than three children, dummies indicating whether the

household has zero child under four year old, one child, two children, three children or more than

three children under four year old, province fixed effects and day of the week fixed effects. We only

keep weekdays observations.

Our coefficient of interest is given by β2. A positive coefficient indicates that, ceteris paribus, in

couples where the wife earns more than the husband, she also spends more time on housework. Table

9 presents our results. Column (1) presents our estimates for observations the year 2010 whereas

column (3) presents our estimates for 2015. We find a significant and positive effect of 4.10 hours

per week in 2010 (pvalue < 0.05). This effect is higher than the effect of 2.7 hours found in BKP.

The effect is not significant anymore in 2015.

In columns (2) and (4), we investigate the effect of gender identity on housework across provinces

and we add in the regressions the two interaction variables female× area× wifeEarnsMore and

area × wifeEarnsMore. Our coefficient of interest is then given by the sum of the coefficients

of the variables female × area × wifeEarnsMore and female × wifeEarnsMore. We present

this estimate and its F-stat for each province and each year in table 10. We only find a significant

and positive effect in the Atlantic provinces (4.91, pvalue < 0.1) and in British-Columbia (6.63,

pvalue < 0.1) for the year 2010. We do not find any significant effect in 2015.

Our results show that in 2010, when women exceeded the spouse’s income equality threshold,

they also increased their time spent in housework. This behavior is in contradiction with standard

theory. Compliance to gender identity is the most likely explanation. This behavior does not exist

anymore in 2015, which may be related to a decline in traditional gender norms.

23

Table 9. Relative income and wife’s housework

Dependent variable : Housework (Hours per week)2010 2015

(1) (2) (3) (4)

wifeEarnsMore -2.42* -0.51 1.40 2.55(1.48) (2.05) (1.30) (1.89)

female× PrWifeEarnsMore 4.10** 2.21 0.28 0.19(1.93) (2.73) (1.64) (2.47)

Atlantic× wifeEarnsMore -1.70 -3.40(2.70) (2.37)

Atlantic× female× wifeEarnsMore 2.71 2.47(3.55) (3.16)

Quebec× wifeEarnsMore -4.25 0.59(3.27) (2.42)

Quebec× female× wifeEarnsMore 1.79 -3.26(4.31) (3.26)

Prairie× wifeEarnsMore -2.44 -2.60(2.71) (2.39)

Prairie× female× wifeEarnsMore 2.87 2.19(3.70) (3.28)

British Columbia× wifeEarnsMore -3.83 0.10(3.26) (3.10)

British Columbia× female× wifeEarnsMore 4.42 -1.87(4.37) (4.14)

Observations 2585 4075R-squared 0.108 0.110 0.114 0.115

Data are from confidential micro-data files of the GSS 2010 and 2015 on time use. The sample consists of coupleswhere both the wife and the husband are between 18 and 65 years old and earn a positive income. All regressions

include controls for a cubic polynomial of the logarithm of the incomes of the husband, the wife and the totalincome, for wife’s and husband’s education (three categories), quadratic polynomial of wife’s and husband’s age,

controls for children, day of the week and province fixed effects. We only keep weekdays. Standard errors are

reported in parenthesis. ***significant at 1% level, **at 5%, *at 10%.

Table 10. Relative income and wife’s housework. Effects by province

Canada Atlantic Quebec Ontario Prairies British-Columbia2010 4.10** 4.91* 3.99 2.21 5.07 6.63*

(1.93) [2.63] [1.11] (2.73) [2.37] [2.63]2015 0.28 2.65 -3.07 0.19 2.38 -1.68

(1.64) [1.02] [1.30] (2.47) [0.72] [0.19]F-stats are reported in brackets. Standard errors are reported in parenthesis.

***significant at 1% level, **at 5%, *at 10%.

7. Conclusion

In this paper, we show that the distribution of the share of income held by the woman among the

Canadian households drops to the right of 0.5. As in BKP, we believe that this drop may be linked

to gender identity, that is the traditional belief that the man should have a higher income than his

wife in order to preserve the traditional structure of the household. We find that couples form less24

when the probability that a woman earns more than her husband increases and that the wives with

greater probability of earning more than their husbands are less likely to participate in the labor

force. If they participate, they have a higher probability of earning less than their potential income.

We find a stronger effect of gender norms in Quebec and in the Prairies but a less important effect

in British-Columbia and in Ontario.

Other explanations of the missing couple pattern have been raised in the recent literature and

complement the gender norm hypothesis. One is related to the existence of gender difference in

reporting income in surveys. Murray-Close and Heggeness (2018) analyse this phenomenon with US

data and Roth and Slotwinski (2018) with Swiss data. These papers find that the gap between a

husband’s survey and administrative earnings is higher if his wife earns more than he does, and the

gap between a wife’s survey and administrative earnings is lower if she earns more than her husband

does. These findings suggest that gendered social norms can influence survey reports of seemingly

objective outcomes and that their impact may be heterogeneous not just between genders but also

within gender. Another explanation is related to what women think is a fair distribution of relative

working hours within the household. Fleche, Lepinteur, and Powdthavee (2018) present evidence

that in addition to the fact that women may have preferences for not wanting to out-earn their

husband, there is also an aversion to a situation where women work significantly longer hours than

their husbands. They show that women’s propensity to opt-out of the labor force is significantly

higher in couples where the wife’s working hours exceed the husband’s. They also show that life

satisfaction is significantly lower among women whose work hours exceed their partners, holding the

share of wife’s income constant. Men, by contrast, are not affected by working longer or fewer hours

than their wives. They argue that these patterns are best explained by perceived fairness of the

division of labor within the household.

All these results highlight the impact of gender-unequal norms on men and women behavior. This

is important as traditional gender norms such as the male breadwinner norms are prevalent around

the world and are still present in developed countries where they have stabilized (Fortin, 2015). As

norms are cultural and can be done and undone by institutions (Lippmann, Georgieff, and Senik,

2019), policy-makers have a role to play in the construction of these norms and their consequence

on individual behavior.

25

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8. Appendix

Table 11. Gender norms

(1) (2) (3) (4) (5)Men First Warm If Not Working Housewife fulfilling Both should not contribute Index

Female -0.00884 -0.114* 0.0481 0.0920 -0.0393(0.0383) (0.0589) (0.0496) (0.0621) (0.130)

25-39 year old -0.0168 0.0839** 0.0370 0.140*** 0.223**(0.0303) (0.0413) (0.0399) (0.0437) (0.0926)

40-54 year old -0.00316 0.137*** 0.0864** 0.208*** 0.379***(0.0317) (0.0432) (0.0417) (0.0458) (0.0971)

55-65 year old 0.0513 0.242*** 0.190*** 0.284*** 0.750***(0.0364) (0.0504) (0.0474) (0.0533) (0.113)

Female × 25-39 year old 0.0561* 0.00246 0.0135 0.0452 0.136(0.0309) (0.0481) (0.0400) (0.0509) (0.106)

Female × 40-54 year old 0.0500 0.0149 0.0375 -0.00204 0.168(0.0310) (0.0493) (0.0400) (0.0522) (0.109)

Female × 55-65 year old 0.0158 -0.0574 0.0105 -0.0905 -0.126(0.0351) (0.0577) (0.0453) (0.0611) (0.128)

Leave school [17-21] -0.0685*** -0.0241 -0.00762 0.0834** -0.0547(0.0219) (0.0342) (0.0286) (0.0360) (0.0760)

Leave school after 21 -0.128*** -0.0375 -0.0140 0.111*** -0.111(0.0218) (0.0346) (0.0286) (0.0364) (0.0772)

Female × Leave school [17-21] -0.0279 0.0189 -0.0304 -0.121** -0.134(0.0291) (0.0454) (0.0377) (0.0480) (0.100)

Female × Leave school after 21 -0.0484* -0.0205 -0.0503 -0.128*** -0.227**(0.0292) (0.0462) (0.0378) (0.0489) (0.102)

2000 -0.0314 -0.0135 0.213*** 0.0163 0.179*(0.0341) (0.0441) (0.0443) (0.0466) (0.0978)

2006 -0.0403 0.142***(0.0344) (0.0449)

Female × 2000 0.00867 0.0781** -0.0854*** 0.0207 0.0315(0.0241) (0.0312) (0.0312) (0.0332) (0.0693)

Female × 2006 -0.0177 -0.0350(0.0241) (0.0312)

25-39 year old × 2000 -0.0145 -0.0710 -0.00916 -0.105** -0.190*(0.0371) (0.0479) (0.0480) (0.0507) (0.106)

25-39 year old × 2006 0.0126 0.0189(0.0379) (0.0492)

40-54 year old × 2000 0.00191 -0.136*** -0.113** -0.141*** -0.396***(0.0381) (0.0491) (0.0493) (0.0521) (0.109)

40-54 year old × 2006 0.0166 -0.0706(0.0384) (0.0498)

55-65 year old × 2000 0.0138 -0.157*** -0.124** -0.187*** -0.443***(0.0434) (0.0561) (0.0558) (0.0595) (0.124)

55-65 year old × 2006 -0.0158 -0.123**(0.0427) (0.0552)

Atlantic -0.0256* -0.0189 0.0896*** 0.00150 0.0118(0.0144) (0.0241) (0.0186) (0.0256) (0.0536)

Quebec 0.0405*** -0.0464** 0.0989*** -0.0880*** 0.0148(0.0130) (0.0203) (0.0167) (0.0215) (0.0446)

Prairies -0.0127 0.00541 0.0708*** 0.0758*** 0.130**(0.0150) (0.0233) (0.0194) (0.0251) (0.0523)

British-Columbia -0.0217 -0.0192 0.0131 0.0413 0.0109(0.0171) (0.0273) (0.0219) (0.0290) (0.0603)

Constant 0.233*** 0.260*** 0.575*** 0.0889* 1.231***(0.0335) (0.0481) (0.0440) (0.0507) (0.107)

Observations 4,706 3,018 4,507 2,967 2,798R-squared 0.049 0.036 0.040 0.042 0.050

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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Table 12. Percentage of wives marrying husbands of particular ageand education

Husband Age groupWife Age group 18-35 36-45 46-6518-33 0.8092 0.1744 0.016434-43 0.1263 0.6983 0.175444-65 0.0090 0.1106 0.8804

Husband Education groupWife Education group Secondary Non-university University

and less post secondarySecondary and less 0.5246 0.4052 0.0701Non-university, post secondary 0.3070 0.5418 0.1512University 0.1240 0.3750 0.5009

Data are from the 1996 to 2011 SLID cross-sections. Each cell reports the percentage of wives and husbands in eachcategory.

Table 13. Summary statistics - Marriage market sample

Mean Standard DeviationPrWomanEarnsMore - Actual 0.355 0.479PrWomanEarnsMore - Predicted 0.316 0.465PrWomanEarnsMore - Bartik 0.335 0.472Male marriage rate 0.668 0.202

Data are from the 1996 to 2011 SLID cross-sections. Each observation is a marriage market defined by the

interaction of province, year, age-group and education-group. N = 1440.

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Table 14. Potential relative income and marriage rate in Canada, byprovince

Dependent variable : marriage rateActual earnings Predicted earnings Bartik instrument(1) (2) (3) (4) (5) (6)

PrWomanEarnsMore -0.334*** -0.244*** -0.174** -0.0642 0.199* 0.122*(0.0498) (0.0511) (0.0630) (0.0700) (0.0898) (0.0610)

Atlantic× PrWomanEarnsMore -0.0598 -0.117 -0.156 -0.258** -0.338*** -0.294***(0.108) (0.104) (0.0996) (0.105) (0.0993) (0.0621)

Quebec× PrWomanEarnsMore 0.106 0.0557 -0.0690 -0.153* -0.460*** -0.469***(0.0682) (0.0593) (0.0620) (0.0795) (0.105) (0.114)

Prairie× PrWomanEarnsMore 0.0144 -0.0430 -0.139 -0.216 -0.410*** -0.344***(0.0837) (0.0803) (0.145) (0.133) (0.0714) (0.0467)

British Columbia× PrWomanEarnsMore 0.215** 0.147* 0.117** 0.00343 -0.165* -0.153*(0.0724) (0.0676) (0.0518) (0.0616) (0.0736) (0.0797)

Log average in men’s income 0.0197 0.0581* 0.0137 0.0529 0.000876 -0.0528(0.0188) (0.0267) (0.0165) (0.0315) (0.0536) (0.0580)

Log average in women’s income 0.0400* 0.00737 0.0434* 0.0126 -0.0244 -0.00950(0.0201) (0.0340) (0.0214) (0.0412) (0.0793) (0.0719)

Share of Women Income 0.138 0.132 -0.142(0.124) (0.148) (0.0948)

Men average schooling 0.0267 0.0329 0.0362(0.0258) (0.0247) (0.0261)

Women average schooling 0.0404** 0.0481** 0.0401**(0.0177) (0.0148) (0.0148)

Number of men 2.47e-05 2.28e-05 8.74e-05(7.90e-05) (7.55e-05) (7.39e-05)

Number of women 2.00e-06 4.63e-06 -5.98e-05(7.84e-05) (7.56e-05) (7.41e-05)

Sex ratio -0.144 -0.157 -0.249**(0.102) (0.0914) (0.0900)

Constant 0.572*** 0.221 0.531*** 0.134 0.241*** 0.0993(0.0513) (0.152) (0.0335) (0.140) (0.0725) (0.183)

Observations 1,440 1,440 1,440 1,440 1,440 1,440R-squared 0.985 0.985 0.984 0.985 0.983 0.984

Data are from the 1996 to 2011 SLID cross-sections. The unit of observation is a marriage market by year. All

specifications include marriage market fixed effects, year fixed effects, and the year interacted with the age group,

the education group, the province and deciles of men’s and women’s income. Regressions are weighted by thenumber of women in the marriage market. Standard errors clustered at the province level are in parenthesis.

***significant at 1%, **at 5%, *at 10%.

Table 15. Summary statistics - Time uses

N Mean St. Dev.Data source : SLID. 1996 - 2011PrWifeEarnsMore 181631 0.270 0.305Wife Labor Force Participation 181631 0.672 0.470Income Gap 120072 0.101 0.691Data source : GSS. 2010WifeEarnsMore 3988 0.25 0.43Wife Housework time (hours per week) 3292 18.82 16.69Husband Housework time (hours per week) 2691 12.74 15.45Data source : GSS. 2015WifeEarnsMore 5948 0.30 0.46Wife Housework time (hours per week) 3218 19.66 17.86Husband Housework time (hours per week) 2746 13.93 17.30

Data are from the 1996 to 2011 SLID cross-sections and from the GSS cross-sections available in CDR.

30

Figure 4. Discontinuity at 0.5 in relative income by provinces

02

46

-.5 0 .5 1 1.5

(a) Atlantic. With same inc. couples

01

23

4-.5 0 .5 1 1.5

(b) Atlantic. Without same inc. couples

02

46

8

-.5 0 .5 1 1.5

(c) Quebec. With same inc. couples

01

23

4

-.5 0 .5 1 1.5

(d) Quebec. Without same inc. couples

02

46

810

-.5 0 .5 1 1.5

(e) Ontario. With same inc. couples

01

23

4

-.5 0 .5 1 1.5

(f) Ontario. Without same inc. couples

31

Figure 5. Discontinuity at 0.5 in relative income by provinces (continued)

02

46

8

-.5 0 .5 1 1.5

(a) Prairie. With same inc. couples

01

23

4-.5 0 .5 1 1.5

(b) Prairie. Without same inc. couples

01

23

45

-.5 0 .5 1 1.5

(c) British-Columbia. With sameinc. couples

01

23

-.5 0 .5 1 1.5

(d) British-Columbia. Withoutsame inc. couples

32