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Sociology Working Papers

Paper Number 2012-04

Relative human capital resources and housework: a longitudinal analysis

Oriel Sullivan

Jonathan Gershuny

Department of Sociology

University of Oxford

Department of Sociology University of Oxford

Manor Road Oxford OX1 3UQ

www.sociology.ox.ac.uk/swp.html

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Relative human capital resources and housework: a longitudinal analysis

Oriel Sullivan

Jonathan Gershuny

Department of Sociology

University of Oxford

Abstract

Economic exchange, or bargaining, theory hypothesizes a strong linear dependence

between spousal relative earnings and the division of housework within couples. But

influential evidence has previously been found for a gender deviance neutralization effect,

in which women earning substantially more than their spouses compensate for their non-

gender-normative household situation through increasing their housework. We estimate, as

our indicator of spousal resources, a measure of human capital derived from economically-

salient resources based on the accumulation of educational achievement, skills,

employment and occupation over the life course, enabling the inclusion of those currently

outside employment. Using large-scale longitudinal British couples’ data allowing us to

observe whether changes in human capital have any relationship to change in the

performance or division of housework, we find strong linear effects of wives’ human

capital on both their own housework hours and those of their husbands, but no evidence for

a gender deviance neutralization effect.

Keywords: marital bargaining; economic dependency; housework; gender deviance

neutralization; domestic division of labor; time use.

Address correspondence to: Dr Oriel Sullivan, Department of Sociology, University of

Oxford, Manor Road, Oxford OX1 3UQ, U.K.

e-mail: (oriel.sullivan@sociology.ox.ac.uk)

Tel : +44 1865 281740

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Relative human capital resources and housework: a longitudinal analysis

1.Introduction

How may we best operationalize and analyze marital bargaining power? Over the past fifteen years a

series of papers based on large-scale data has investigated the relationship between economic

dependency and housework within heterosexual couples (e.g., Brines, 1994; Presser, 1994;

Greenstein, 2000; Bittman et. al., 2003; Evertsson & Nermo, 2004; 2007; Gupta, 2007; Gupta & Ash,

2008; Schneider, 2011; Killewald & Gough 2010). Their aim was to test bargaining or exchange

theory (in both of which household members trade their labor market incomes for time spent in

housework) against various other hypotheses, including gender-based explanations. Almost all of

these papers used relative (or a combination of absolute and relative) spousal earnings as their

primary independent measure, thereby equating economic bargaining power with current earned

income alone.

In this paper we make two contributions to this debate, which, in combination, shed new light

on some of the still-unresolved issues of this body of research. Firstly, we test a new measure of

economically-salient spousal resources based on the accumulation of educational achievement, skills,

employment and occupation over the life course, akin to the economists’ expected wage (as

suggested by Pollak 2005). This measure, which we call human capital, allows us to include those

who have no current earnings in analysis, and to represent the economic bargaining power of those

have taken on employment below the level that their human capital would indicate (for example,

because of the need to care for children). Secondly, we use large-scale, nationally representative,

year-on-year longitudinal British couples’ data to address the central question whether change in

couple’s relative or partner’s absolute human capital is associated with change either in partner’s

housework contributions or with couples’ share of housework. We believe we are the first in this

long-running but still active debate to combine a more inclusive single measure of economic

resources with an analysis of longitudinal data.

The assumption that economic bargaining power within couples is appropriately measured by

relative earnings levels associates resources with current employment. We suggest, though, that this

involves two conceptual difficulties. Firstly, following Blood and Wolf (1960), the literature on the

resources theory of marital power identifies a wide range of potential resources - structural, material

and emotional - which may be brought into play independently or interactively in the processes of

negotiation, conflict and bargaining within couples (see, for example, Tichenor,1999). Current

earnings alone is a rather narrow measure of such resources, and one which is likely to misrepresent

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the true bargaining power of those who are out of employment. For example, compare a couple

consisting of a well-qualified female professional with a successful career and a less well qualified

male partner, with another consisting of an unqualified woman with no consistent work history and a

male partner with the same qualifications as in the first couple. Suppose the female partner in these

couples withdraws from employment for some time in order to care for their children. Under the

relative earnings approach her earnings during this time are equal to zero and the two couples are

treated analytically as if they have identical relative bargaining power. But, we suggest, each woman

is likely to have some idea of what her time is worth in the labour market (and what her economic

well-being is likely to be should the relationship end). Under this assumption, the two couples are

likely to have quite different relative power balances, leading to different outcomes in the marital

bargaining process. The potential misrepresentation of marital bargaining power through the

association of resources with current earnings is most significant for women—those who have

chosen to be homemakers or are taking various kinds of leave or employment breaks following

childbearing or to care for elderly dependents. But it also includes men who are either seeking work

or have chosen to abstain from paid labor. This question of missing values in earnings data was

identified at an early point by Presser (1994), but has remained problematic throughout the debate,

leading some researchers to base their analyses on samples of the full time employed only (e.g.,

Gupta, 2007; Killewald & Gough, 2010). The problem is compounded in longitudinal analyses

where earnings at successive points in time are required in order to calculate a measure of change in

spousal resources (as in Evertsson & Nermo, 2007).

The second difficulty with the relative earnings approach involves an issue of explanatory

logic. The simple trade theorem that lies at the heart of Becker’s Treatise on the Family holds that

both members of a couple may stand to gain by distributing more paid work to the partner with the

higher marginal wage, and more unpaid work to the other—implying a simultaneous decision about

the distribution of hours of both unpaid and paid work. In the sociological literature on the theory of

marital power, bargaining within couples is also usually conceived of as concerning decisions about

paid and unpaid labor made simultaneously. The outcome of this bargaining process reflects both

contingent circumstances (e.g., the birth of a child), and the deployment by each spouse of a range of

embodied resources in the process of bargaining over both paid and unpaid work. Using hours of

paid work and earnings (itself the product of the wage rate and hours of paid work) as variables to

predict hours of unpaid work not only confounds the effects of paid work hours and wage rate

(Connelly & Kimmel, 2007; Killewald & Gough 2010), but also assumes that decisions about paid

work always determine those about unpaid work or childcare.

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Both problems can be dealt with by substituting, for the partners’ respective earnings, a single

continuous measure constructed from a wider range of economically-salient resources than earnings

alone, and estimated partly on the basis of employment history, rather than by current employment

status. This measure avoids the conceptual circularity involved in the use of relative earned income

as a predictive variable, and enables the inclusion of the economic resources of those currently

without paid employment. It constitutes a broader indicator of marital bargaining power than

earnings since it includes a wider range of resources, including educational attainment, occupational

status and employment history, which is why we refer to it as a measure of human capital. In its

calculation – described in more detail in section 4.3.2. and in Appendix B - it is effectively what

economists refer to as an expected wage. A few researchers (e.g., Presser, 1994; Evertsson &

Nermo, 2004; 2007) have previously included other measures of relative power in their analyses

(specifically, relative spousal educational levels and relative spousal occupational status). However,

these have been deployed as distinct variables in combination with relative earnings in order to

compare their impact, and, as such, have shared the reliance of the relative earnings measure on

current employment status.

2. Marital bargaining and economic resources

Earlier papers suggested that there was a strong linear dependence between relative earned incomes

(the usual measure of economic resources) and the division of housework within couples, supporting

the suppositions of economic bargaining theory (e.g., Brines, 1994; Presser, 1994; Greenstein, 2000).

In general, the higher the earned income of a member of the couple relative to their spouse, the less

housework they performed. But highly influential evidence was also found for a gender deviance

neutralization effect - so-called by Bittman et al. (2003) following the results of Brines (1994) and

Greenstein (2000). Brines analyzed Panel Study of Income Dynamics (PSID) data from 1985 using a

measure of relative income as the main independent variable to show that, while the housework hours

of wives largely conformed to the assumptions of conventional dependency theory (the more

economically dependent the more housework was performed), husbands who were economically

dependent (in particular those in low-income households and the long-term jobless) did less

housework than others. This finding seemed to demonstrate the importance of gender as a mediator

of economic dependency theory, and provided one possible answer to the question why it appeared

that men were failing to take up the slack in the routine performance of housework in a period when

women were increasingly entering the primary labor force.

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The theoretical basis for gender deviance neutralization lies in the doing gender perspective,

which focuses on the processes by which gender is continuously being constructed in interaction and

behavior. In relation to housework, women do gender by performing the bulk of feminine-defined

tasks such as routine housework, while men do gender by doing none or very little of it (West &

Zimmerman 1987). The gender deviance neutralization effect found by Brines indicated that men

who were not fulfilling their normative breadwinner role compensate by emphasizing their

masculinity through the minimal performance of housework. Brines did not find any statistically

significant support in her results for the existence of complementary gender deviance neutralization

behavior by breadwinner wives (women who earn substantially more than their spouses

compensating by emphasizing their femininity through the over-performance of housework). This

idea, however, was taken up and developed further by Greenstein (2000), whence the idea of

complementary masculine and feminine deviance neutralization effects in the performance of

housework became established in the literature. Using 1987/8 National Survey of Families and

Households (NSFH) data Greenstein modeled the gender division of housework rather than absolute

housework hours, adding a control for the gender ideology of partners. His main conclusion was that

both economically dependent men and breadwinner wives tended to neutralize their deviant identity

by undertaking less (in the case of economically dependent men) and more (in the case of

breadwinner wives) housework. That is, he reported a curvilinear relationship between housework

participation and economic dependence for both men and women, whilst Brines had only shown this

effect for men. These analyses lent strong support to gender theory by seeming to demonstrate that

in certain structural situations the power of gender can override the power of money.

Studies thereafter have not produced a unanimous verdict on the question of gender deviance

neutralization (for a review see Sullivan 2011). Using data from the Australian 1992 Time Use

Survey, Bittman et al. (2003) found curvilinear relationships between relative earnings and

housework hours in the case of women but not in the case of men. Only among couples with

breadwinner wives was the gender deviance neutralization effect evident. Bittman and his co-authors

concluded that gender does indeed “trump money” in Australia in cases where the wife earned more

than half of the total income of both partners. An additional comparative perspective was introduced

by Evertsson & Nermo (2004), who compared Swedish and U.S. couples between 1973 and 2000

using PSID and the Swedish Level of Living Survey data. They found persistent evidence of gender

deviance neutralization among women in the U.S.A. They suggested that women in the U.S.A were

dependent on their husbands to a greater extent than Swedish women, leading to a stronger gender

deviance neutralization effect. They followed up with a study of a more limited number of surviving

Swedish couples from the Level of Living Survey who had remained together over the period 1991

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to 2000 (Evertsson & Nermo 2007), finding that increases in all three of their measures of a woman’s

relative resources (education, occupation and earnings) were linearly associated with decreases in her

share of housework, and that this decrease mostly occurred through increases in the male partners’

housework hours. However, they found no evidence for a gender deviance neutralization effect in

the Swedish data. Kan’s 2008 analysis of pooled cross-sectional British Household Panel Data over

the period 1993-2003 utilized an earlier version of the human capital measure to compare the effect

of a relative income measure with a measure of gender attitudes on housework hours, and found

substantive support for a negative linear effect of relative income on housework hours. Both men’s

and women’s housework hours decreased significantly as their relative income increased (net of

gender attitudes), and she found no conclusive evidence that either highly economically dependent

men or highly economically independent women do gender by resorting to a traditional-normative

division of domestic labor. However, she did not go on to extend her cross-sectional analyses to

exploit the longitudinal nature of the BHPS.

Following the interest generated by the findings on gender deviance neutralization –

frequently misinterpreted in the media to mean that high earning professional women do an excess

of compensatory housework - more recent quantitative research has tended to focus on an

exploration of gender deviance neutralization in relation to women’s housework hours, with the

focus on full-time employed couples (in order to overcome the problem of missing values on the

earnings variable). In a series of influential articles Gupta reassessed the basis of the economic

dependency and gender deviance neutralization perspectives, arguing that it is crucial to take into

account women’s autonomous agency, and that previous findings in relation to relative earnings and

relative share of housework can be more simply explained in terms of a relationship between

women’s absolute earnings and their housework hours (Gupta, 2006; 2007; Gupta & Ash, 2008).

Using NSFH data he found that women’s relative earnings contribute little to the explanation of

housework hours when absolute hours are also included in the analytic model, and that estimates of

housework time for women’s earnings of around $20,000 per year and above were unreliable

because they were based on very sparse data. They showed again that women’s earnings were

negatively associated with their housework hours, independent of their partners’ earnings and their

share of couples’ total earnings. They concluded that an alternative model—the autonomy

perspective—fits the evidence better than either economic dependency or gender deviance

neutralization.

Exploiting the panel data of the PSID using fixed effects models, Killewald & Gough also

found no relationship between relative earnings and women’s housework (Killewald & Gough,

2010). They demonstrated a non-linear association between these women’s absolute earnings and

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housework, arguing that previous findings of a curvilinear relationship between relative earnings

and housework could be accounted for by a misspecification of this relationship as linear. The

combination of the importance of women’s absolute resources, as demonstrated by Gupta (2007),

and of their non-linear relationship with housework hours, as demonstrated by Killewald & Gough

(2010), could be sufficient to account for the contrary findings of Schneider (2011), who yet again

found evidence for a curvilinear relationship between relative earned income and wife’s housework

hours using ATUS data. However, Schneider’s models included either relative resources alone

(without absolute resources), or relative and absolute resources without the quadratic term to

express the non-linear relationship between women’s absolute resources and housework - the

omission of which, according to Killewald & Gough (2010), can result in the finding of a

curvilinear relationship.

3. Research questions

The central issue for the papers discussed above was the relative strengths of economic bargaining,

or dependency, theory against the gender deviance neutralization hypothesis. The main conclusion

has been that, over most of the range of couples’ relative incomes, economic bargaining theory is

supported. However, in some cases where couples’ employment and income situations deviated

from normative gender expectations, it appeared that a gender deviance neutralization effect came

into play. Gupta’s contribution was to show that these effects for women became much less strong

once their absolute income was added to the equation (Gupta 2007; Gupta & Ash 2008), while

Killewald & Gough (2010) demonstrated that the finding of gender deviance neutralization for

women could have been based on a model misspecification.

The advances we propose in this paper allow us to address the wider research question

regarding the relationship between housework and spousal resources using longitudinal couples’

data and deploying a measure of human capital based upon a range of economically-salient

component factors. Our analytic strategy has three strands. First, we revisit the analyses of

previous papers – this time using data from an entire population of couples, rather than just the

currently employed. We compare our measure of relative human capital both with relative wage

rate and with relative earned income - the usual operationalization of economic dependency. We

include both relative and absolute resources in our analyses, thereby addressing the woman’s

autonomy hypothesis in relation to human capital. At the same time, to test the gender deviance

neutralization hypothesis, we ask 1) whether women with much more human capital in relation to

their partners do more housework than other women, and 2) if men with very low levels of human

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capital relative to their wives do less housework than other men. We then exploit longitudinal

(panel) data in order to ask whether change in either absolute or relative human capital is associated

with change in the performance of housework. We estimate a form of Fixed Effect regression

model, which looks at the association between longitudinal changes in human capital and changes

in housework for both wives and husbands.

4. Data and measures

4.1. Data

The British Household Panel Study (BHPS)1 is a longitudinal (panel-design) national-level

representative survey based on interviews with all members of a random sample of British

households. The study, begun in 1991, re-interviews all the first wave household members, their

natural descendents, and all their current household co-residents annually. The original BHPS

sample consisted of 5050 households containing 9092 interviewed adults at Wave 1 (1991), a

response rate of 74% of eligible households, with re-interview rates rising to well above 90%.

Panel data are collected together with retrospective information on employment and other

circumstances prior to the start of the panel. For our purposes the BHPS has a number of

advantages. Firstly, the collection of information from all household members permits the direct

calculation of couples’ relative resources and contributions to domestic labor. Secondly detailed

current and retrospective information on employment, occupation and wage data was collected,

allowing us to connect historical and other accumulated personal characteristics with their current

consequences in terms of economically salient human capital. Last but not least, panel data can

give us a perspective on the causes and consequences of changes in respondents’ characteristics and

situations.

In this paper, we first use pooled BHPS data from the period 1992-20082 to analyze the

relationship between housework and human capital, comparing this relationship with that using

1 The BHPS data is collected and managed by the Economic and Social Research Council’s

Institute for Social and Economic Research at the University of Essex, England. The data is

available from the ‘major studies’ section of the UK Data Archive on http://www.data-

archive.ac.uk/findingdata/majorstudies.asp 2 Wave 1 (1991) did not include a question on housework hours.

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earned income. We then pool successive 2-year samples3 to offer a longitudinal perspective on

changes in housework in relation to changes in respondents’ circumstances.

4.2. Population

The study population consisted of men and women aged 18-59 in couples (married and living

together as married). Consistent with previous studies we excluded from our analyses same-sex

couples, those in full time education, and the long-term sick/disabled. As in previous literature the

exclusion of same sex couples is based on the recognition that the processes of economic bargaining

may operate differently within heterosexual and homosexual couples. The situation of those in full-

time education and the long-term sick/disabled in respect of economically salient human capital is

not necessarily clear (in the case of the long-term disabled or sick), or has not yet been fully

realized (in the case of those still in education). Because our primary independent variable (human

capital) is a more inclusive measure than one based on a point estimate of monthly income we do

not exclude from our analyses, as some other studies have done, those who have already retired by

age 60. Following these exclusions we arrive at a total sample size of 32,359 couple observations

for the pooled cross-sectional analyses, and 16 successive 2-year pooled samples provided by 6,541

couples with multiple panel observations for the longitudinal analyses4.

4.3. Measures

Ranges, means and standard deviations for the variables described in this section are shown in

Appendix A.

4.3.1. The dependent variable

In previous literature the dependent measure of time spent in housework has usually been derived

from a question asking respondents to estimate their weekly hours spent in housework (or in various

components of housework). The two main data sources for research into the relationship between

economic dependency and housework have been the PSID (which has a single household

3 We weight the analyses conservatively using the standard panel Wave 1 weights for the

cross-sectional analysis and the standard weights for the first wave of each 2-year pooled

sample for the longitudinal analysis.

4 Following standard practice we use a clustering procedure (the ‘cluster’ option in Stata) in

order to counteract for the effect of repeated observations of the same individuals in this data.

While the values of the regression coefficient themselves are not affected, standard errors are

more robust (i.e. larger) in this procedure.

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informant) and the NSFH because of their sample size and coverage of a large range of socio-

economic and demographic variables - advantages which are shared by the BHPS. However, a few

studies in this area (e.g. Bittman et al. 2003; Connelly and Kimmel 2007; Schneider 2011) have

based their analyses on time use diary information, which is generally acknowledged to yield a

more accurate measure of time spent in specific activities (Robinson 1985). Nevertheless, we

considered that for the purposes of this study the advantage of having panel couple data from the

BHPS – permitting longitudinal analysis – outweighed the disadvantages associated with the use of

stylized questions.

The BHPS question about housework time is similar to that of the PSID (although it is

collected directly from both partners):

“About how many hours do you spend on housework in an average week, such as time spent

cooking, cleaning and doing the laundry?”

The BHPS, as in the NSFH, collects matched information from both partners of couple

households since all individuals in the household are interviewed. In the PSID one partner (usually

the man) reports on the hours of housework contributed by their spouse, a methodology generally

regarded as problematic (see Bryant et al. 2003 for an assessment of spousal reporting errors). The

ATUS, similarly, only collects information on time use from one member of each household, so that

no measure of relative share of housework is calculable.

For the purposes of our longitudinal analyses, we use both measures of year-on-year change

in the weekly hours of housework for wives and husbands, and change in the wife’s proportion of

weekly housework hours as the dependent variables.

4.3.2. Independent variables

All the papers referred to above have used the calculation of income transfer introduced by

Sorensen & Maclanahan (1987) as a measure of relative economic resources. However, this

measure has the two previously mentioned conceptual difficulties when used for modeling the

effects of marital bargaining processes on spouses’ housework. In the theoretical literature on

marital power the distribution of paid work within couples forms part of the same bargaining

process which determines the distribution of unpaid labor. It follows that paid work hours - a

central determinant of earned income - are at least partly endogenous to the behavior which we are

trying to explain, and so should not be treated as exogenous factors; that is, they should not be used

as explanatory variables in multivariate analyses. In addition, the absence of a current wage for

people who are not in employment does not necessarily mean that they have zero economic

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bargaining power within the relationship. Again, the theoretical literature informs us that a wide

range of resources potentially determine marital power, and it is reasonable to suppose that human

capital as we define it provides bargaining power irrespective of current earnings (as in our

hypothetical comparison of couples described on page3). The gap between a measure of relative

earned income and actual bargaining potential is likely to be most acute for women because of the

burden of caring responsibilities. At any one point in time a woman’s income may represent a

substantial underestimation of her resources (and therefore her bargaining power) as measured

through her education, occupation and employment history. The use of a measure of usual income

to some extent alleviates this problem, but a woman working part time in order to care for children

(or dependent adults) may well have a usual income below the level that her human capital imparts

in the marital bargaining process.

The measure of human capital we estimate takes into account a range of economically-

salient factors likely to affect the distribution of marital bargaining power within couples. This

measure represents only one element of the combination of social, economic and cultural resources

which comprise the total embodied capital of any individual (Bourdieu, 1984). But unlike previous

measures it takes account of factors such as educational achievement and employment history

traced over the life course5. It is calculated from retrospective (recall) and prospective (panel study)

evidence on the educational levels, employment histories, occupations, and work hours (for those

currently in employment) of married and cohabiting couples from the British Household Panel

Study6. We use a continuously scaled indicator, designed originally as a tool to investigate patterns

of differentiation in life chances (Kan & Gershuny, 2006). It is constructed from individuals’

educational qualifications, recent experience in employment and non-employment, and present or

previous occupational membership using data from all the currently available waves of the BHPS.

The calculation of the human capital score is a based on a wage equation estimating the value of

these economically-salient embodied characteristics from their effect on wage-earning capacity. In

the wage equation we include: 1) age and age squared; 2) educational attainment dummy variables;

3) a count of months in employment and family care over the past four years to indicate labor

market attachment; 4) standardized mean occupational wages (MOW scores) as indicators of job

quality; 5) the product of dummy variables for high-end MOW-scores and age (and age squared) to

account for occupational differences in age/earnings trajectories. We deploy a standard Heckman

5 Support for this approach comes from Evertsson and Nermo (2007), who compare spousal

relative education, social status of job and earned income as measures of relative resources

and find very similar patterns of relationship to spousal housework hours over time. 6 The calculation of this measure is described in more detail in section 4.3.2., and in Appendix

B.

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estimation approach, using a dummy variable for sex only in the selection equation, so that

individual characteristics have the same consequence in the calculation for women and men. The

resulting measure covers the entire adult population with non-missing data on these items. For the

employed population (i.e. those who have a current earned income) correlations between this

measure and earned income and wage rate (the more usual measures of resources) are in the order

of 0.4-0.5 for both men and women (exact correlations are noted in Appendix A).

The relative human capital measure used in these analyses is calculated as the wife’s human

capital score minus that of her husband, divided by the overall combined score of husband and wife.

We include squared terms in order to allow for a non-linear relationship between the effects of both

relative and absolute human capital and the time spent in housework. The inclusion of a squared

term for absolute resources is prompted by Killewald and Gough’s finding that the negative

relationship between absolute earned income and housework is non-linear over the range of values

of earned income, declining in strength as income rises (Killewald & Gough, 2010). They show

this non-linearity through the use of a spline set at different centiles of the distribution of earned

income. The quadratic term that we include should capture a distribution shaped similarly to the

one they demonstrate.

For comparison with the human capital measure a standard relative earned income measure

is also calculated in the usual way: the wife’s weekly earned income minus the husband’s weekly

earned income divided by the total weekly earned income for husband and wife combined. A

further comparison is provided by testing our measure of human capital against a measure of wage

rate (i.e. weekly earned income divided by weekly hours in employment). Wage rate overcomes the

problem of conceptual circularity in the calculation of economic dependency from relative earned

incomes since it removes hours of employment from the right-hand-side of the predictive equation,

but shares with earnings the problem of excluding significant population groups not in current

employment. For the purposes of our longitudinal analyses we employ measures of year-on-year

change in both the absolute and relative human capital of the respondents (Arellano, 2003), and a

measure of housework share based on the percentage of housework done by the female partner.

4.3.3. Control variables

For our multivariate regressions we follow the analytic strategy of previous authors and control for

age of the respondent (and its squared term)7 and the number of own children aged under 18 in the

7 The inclusion of age (and age squared) as controls in these analyses is not contraindicated

by the use of these variables in the estimation of human capital since the estimation is based

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household. Since we are using a data set pooled over many years we also include a marker for

survey wave and its squared term8.

5. Results

We illustrate first the extent and likely impact of the omission of cases when the non-employed are

excluded from analyses of the relationship between relative resources and housework. Table 1

shows the distribution of employment status at different levels of the distribution of relative spousal

human capital – effectively a comparison of the distribution of cases using the human capital

measure and one based on current earnings. A progressive decline in employment rates was evident

for both men and women as their partner’s human capital increased relative to their own. Indeed, at

the extreme end of the distribution where women’s human capital was at its highest relative to their

male partners, the male employment rate was only 54%. The equivalent percentage for women at

the opposite end of the distribution of relative human capital was even smaller, at 39%. Forty-six

percent of husbands and 61% of wives at these points of the distribution of relative spousal human

capital had no current earnings and have therefore been treated in previous earnings-based analyses

as either (1) excluded, or (2) having zero resources. The impact of this exclusion and its effect on

interpretation is discussed below in section 5.3, with reference to Figure 1.

not just on age itself but on interaction terms between age and occupation. That age-related

variation still remains to be explained in the human capital model is indicated by the fact that

the regression coefficients for age and age squared are very similar across models based on all

three measures of resources (see Table 1). 8 The squared term for survey wave is added because recent years have shown a leveling off of the

rate of decline in the proportion of domestic labor done by women, leading some commentators to

refer to a slowing or stalling of the trend towards convergence.

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Table 2. OLS Regression of housework hours on various measures of resources: pooled sample

(*** P<.001 ** P<.01 * P<.0.5)

Women’s housework hours Men’s housework hours

Earned

income

Wage

Rate

Human

Capital

Earned

Income

Wage

Rate

Human

Capital

Relative resources -1.248** -1.190** -1.899* 1.545*** 1.159*** 5.494***

Rel res squared .197 .807 -0.789 -1.293*** -.614 3.389*

Own resources -.012*** -.303*** -3.738*** - .000 -.014 -.414*

..res squared/100 .000*** .126*** 17.733*** .000 .009 2.726**

Employment hrs -.282*** -.134***

...hrs squared/100 .238*** .101***

Wave -.466*** -.535*** -.584*** -.120*** -.115** -.117***

Wave squared/100 1.090** 1.265*** 1.988*** .650*** .691*** .585***

N of children<18 2.024*** 3.189*** 3.609*** .625*** .416*** .347***

Age .534*** .361*** .768*** .001 .000 -.043

Age squared/100 -.415*** -.130 -.633*** -.018 -.025 .046

Constant 10.666*** 6.159*** 11.719*** 9.830*** 5.781*** 8.095***

Model R-squared .245 .175 .226 .055 .021 .037

N of observations 22077 21736 32057 22059 21765 30733

N of clusters 3275 3259 4095 3276 3258 3939

Table 1. Employment rates by spousal relative human capital; pooled sample

Relative human capital (woman-man)/(woman+man)

-0.60 –

-0.40 to

-0.59

-0.20 to

- 0.39

-0.19 to

0.19

0.20 to

0.39

0.40 to

0.59

0.60+

N of

couples

% Women in

employment

em e

employment

emp

employment

39.1 63.4 72.3 82.0 86.0 92.2 100 24560

% Men in

employment

employment

96.6 96.9 94.6 89.2 82.2 77.5 54.8 29557

N of couples 414 4719 11666 11380 3534 604 42 32359

16

5.1. Model comparisons; measures of resources

In order to select the best combination of explanatory variables for our pooled models we first

tested nested cross-sectional OLS regression models for each of our resource variables separately

(human capital, earnings and wage rate), entering different combinations of respondents’ and

spouses’ absolute resources and their relative resources9. (We did not, as some other authors have

done, include the model containing the respondent’s own, spouse’s own and relative resources since

in preliminary data testing this was found to be over-determined, leading to a high degree of

multicollinearity between these variables). The results of these nested comparisons (not shown)

suggested that the most parsimonious model across the board in predicting housework hours for

both men and women was the one which combined the respondent’s absolute and relative resources

(including both squared terms). This finding supports both Gupta’s argument that the model using

relative income without absolute income in the prediction of women’s housework hours does not

best capture the real relationship between women’s resources and their housework, and the

argument of Killewald & Gough concerning the curvilinear nature of the relationship between

absolute resources and women’s housework hours.

Having decided on the best generic model, Table 2 shows regression statistics and

coefficients from multivariate cross-sectional OLS regression models predicting women’s and

men’s housework hours using the three different measures of resources: earned income; wage

rate and human capital. Note first the differences in the number of observations in the pooled

samples. The model based on human capital included around ten thousand more observations

than models based on measures of earned income. This difference represents the currently

non-employed, who constituted a substantial one-third of the overall number of observations.

Model fit (R squared) ranged between 25% of variance explained (women’s housework hours

by earned income model) to 2% (men’s housework hours by wage rate model); as is usual in

multivariate analyses of domestic labor time, it proves easier to predict women’s time than

9 All models include Wave, Wave Squared, Age, Age Squared and Number of Children aged

<18 in the household. Models for earned income also include Hours of Employment per

week and Hours of Employment Squared. To these variables: Model (1) adds Own Resources

and Own Resources Squared; Model (2) adds Own Resources, Own Resources Squared,

Spouse’s Resources and Spouse’s Resources Squared; Model (3) adds Relative Resources and

Relative Resources Squared; Model (4) adds Own Resources, Own Resources Squared,

Relative Resources and Relative Resources Squared. The final option (the model including

own, spouse’s and relative resources) is over-determined, leading to a high degree of

multicollinearity between these variables.

17

men’s. The best fitting model (highest R2) for both men and women was that using relative

earned income (including a control for hours of employment). However, this model excludes

a substantial proportion of the population. We would in any event expect it to show an

inflated model fit due to the presence of endogenous variables (hours in employment and

income – the product of wage rate and hours in employment) in the right-hand-side of the

equation. The wage rate measure of relative resources takes hours of employment out of the

right-hand-side of the equation. The effect is to reduce the variance explained; R2s for this

model were substantially lower for both women and men than in the model for earned

income. The R2 for the human capital measure, however, was substantially larger for both

men and women than that for wage rate. The human capital model is conceptually more akin

to that for wage rate, treating employment hours as an endogenous variable, yet its

explanatory power in relation to time spent in housework was considerably larger. For

women, the R2 for the human capital model was much closer to that of the model based on

earned income. And, most important, this model contained a substantially larger number of

cases.

5.2. Model predictions; absolute and relative resources

In relation to the effect of absolute (own) resources shown in Table 2 there was an interesting

difference between women and men. The effect of own resources was much more important for

women than for men, for whom it was non-significant in two of three of the models when relative

resources were also included. For women, the higher her resources, the less housework she did, and

indeed in all the models the statistical significance of own resources was greater than the effect for

relative resources. As was also found by Killewald and Gough (2010), the positive squared term of

absolute resources for women (and for men, though only in the case of the human capital model)

implies a non-linear relationship, such that the decline in the line predicting the association between

resources and housework slows and eventually turns upwards again at higher levels of own absolute

resources. The gender deviance neutralization hypothesis has frequently been (mis)interpreted as

meaning that well-off professional women do excessive compensatory amounts of housework, and

there would appear to be some support for that idea in this finding. However, since the coefficients

of any regression line predict values of the dependent variable according to a mathematical

relationship continuing beyond the existing observed values, it is not clear from the coefficients

alone whether there is in fact any turn-up within the existing range of the independent variable, or

merely a slowing in the rate of decline. In order to establish this, it is necessary to graph the

18

regression line by instantiating the regression prediction over the actual range of values of absolute

resources (see section 5.3 below).

With respect to relative resources, the effects were also as expected from previous literature.

The greater the relative resources of the woman in a couple, the less housework she did (negative

coefficient) and the more her partner did (positive coefficient). There was no evidence among

women for a non-linear relationship with relative resources. This finding supports the predictions

of economic bargaining theory and not the idea of a gender deviance neutralization effect among

women who have considerably more resources than their male partners. For men, the effects of

relative resources were much more important than those of absolute resources – the reverse of the

result for women. However, there were significant squared terms evident in the models based on

earned income and human capital. For the model based on earned income there was evidence for a

slowing or reversal of the increase in men’s housework time as their female partner’s resources

increased relative to their own (a positive coefficient for earned income and a negative one for

earned income squared). This result has been interpreted before as supporting the gender deviance

neutralization hypothesis for men whose partner’s income significantly exceeds their own. For the

human capital model, however, the direction of the squared term was reversed, implying an

increased rate of rise in men’s housework time as their partner’s resources increased relative to their

own. It is, though, not clear how much should be made of this effect, since sample sizes are very

low and standard errors correspondingly very high at the extreme deciles of the distribution, where

men’s resources are very substantially less than their partner’s. This problem has been noted before

in Bittman et al. (2003), Gupta & Ash (2008) and Killewald (2011).

Findings were consistent in the expected directions for the control variables. There was an

overall decrease in the time spent on housework over the period 1992 -2008, which was greater for

women than for men (although the statistically significant squared term does suggest a slowing of

this decline in more recent years). The number of children aged under 18 in the household had a

significant positive effect on housework time for both genders, while age was positively associated

with housework for women, but had no effect for men, suggesting that older women do more

housework (while holding other variables constant). For the model based on earned income,

employment hours had the expected negative effect on housework time for both women and men.

19

5.3. Model predictions; instantiations of relative resources

At this point in our analyses we continue with the models for wage rate and human capital. We

have compared them with the earned income model in order to enable comparison with previous

research using this measure, but, as we have argued, we believe that earned income is not the best

indicator of marital power because of its link to employment status and employment hours. If the

concern is to capture economic power as indicated through earnings capacity, then wage rate

provides a better measure.

Figure 1, then, shows a prediction of the regression coefficients from the wage rate and

human capital models, instantiated for respondents aged 42 in 2004, with one dependent child aged

under 18 in the household. These instantiations of the regression predictions for specific values of

the variables included in the model allow us to see graphically the combined effect of the regression

coefficients across the full observed range of couple’s relative resources. The horizontal axis shows

the range (in deciles) of couples with different combinations of resources – from women in the

bottom decile/men in the top decile on the left, to women in the top decile/ men in the bottom decile

on the right (the mean values of the decile distribution of resources for couples in these different

combinations are used in the instantiation). The vertical axis shows the predicted housework hours.

Note first the contrasting curves for men and women. As women’s relative resources increase they

did less housework, while their male partners did more. However, the effect was more dramatic for

women than for men, particularly for the human capital model. The outcome of these trends was

that the total combined time spent in housework by spouses is considerably greater in those

households where men’s resources substantially outstrip those of their wives, than in those where

the women’s resources substantially outstrip those of their husbands. This difference (previously

noted by, for example, Bianchi et al., 2006) reflects the fact that men have not filled the domestic

labor gap left as women’s resources rise and their housework hours decline10

.

10

Note that this difference is unlikely to be due to the effect of outsourcing, since only 7% of

full-time employed British couples with children in the household employ any cleaning

assistance, and the employment of such assistance does not have an impact in multivariate

analysis on women’s overall hours of housework (Sullivan & Gershuny, under review; see

also Killewald, 2011).

20

Figure 1. Modeled housework hours for women and men by couples’ joint human capital

and wage rate decilesi : pooled sample

i. Models are instantiated for 2004, for a respondent aged 42 with 1 dependent child in the household

and include relative resources; relative resources squared; own absolute resources; and own absolute

resources squared.

0.0

5.0

10.0

15.0

20.0

25.0

womenbottommen top

women4 men 7

women7 men 4

womentop menbottom

ho

urs

of h

ou

se

wo

rk p

er

we

ek women's hours from wage rate

women's hours from human capital

men's hours from wage rate

men's hours from human capital

<--woman relatively less JOINT HUMAN CAPITAL DECILES woman relatively more-->

21

Note that there is no evidence in Figure 1 for a gender deviance neutralization effect either

for men or for women. This is especially clear for the human capital model where there was a

substantial upturn in men’s domestic work contributions at the extreme end of the relative resources

distribution, compared to the much flatter (although still rising) line for the wage rate model.

Indeed, at the extreme end of the curve where women’s human capital is at its highest and their

male partner’s at its lowest, the gender division of housework hours approached equity. In the

difference between the modeled lines for relative human capital and relative wage rate we see the

effects of the inclusion of the non-employed (i.e., non-earning) population. As expected, at the

extreme ends of the relative human capital distribution, employment rates are at their lowest (see

Table 1) and housework hours correspondingly the longest for those partners with the lower level of

human capital. In the substantial upturn of the modeled line for men’s relative human capital

observable at the right-hand side of the graph, we see the effects of the contribution to housework

made by men married to women who have significantly more human capital than themselves, 45%

of whom are non-earning (the majority unemployed). For women, the line for relative human

capital is steeper than that for wage rate, starting at a higher level of housework hours because it

includes the housework contributions of non-employed women. However, it continues to decline

steeply towards the right hand side of the graph where women’s human capital far outstrips that of

their male partners, and dips below the line predicted from the wage rate model. On this evidence

there is no suggestion whatsoever of a gender deviance neutralization effect.

5.4. Model predictions; instantiations of absolute versus relative human capital

By instantiating model predictions for different combinations of the distribution of couples’ relative

human capital deciles we can shed more light on the gender difference between the coefficients for

absolute and relative human capital shown in Table 2. Figure 2a shows the effect on housework

hours where couples shared the same resource decile (so that relative human capital is held roughly

constant). This graph demonstrates why the effect of absolute human capital is so much weaker for

men, when holding relative human capital constant. As men’s human capital increased in line with

that of their female partners, their housework hours hardly changed. In contrast, the coefficient for

absolute human capital was highly statistically significant for women (Table 2), and this is reflected

in a steep decline in their housework hours as their human capital increased in line with that of their

male partners. Figure 2b, in contrast, shows the effect of varying women’s human capital while

holding that of their male partner’s constant at two contrasting human capital decile levels (in other

words, varying both the woman’s absolute and the couple’s relative human capital). The lines for

22

women’s housework hours demonstrate the comparative unimportance of changes in relative human

capital for women, since those showing the decline in women’s housework hours for both levels of

their partner’s human capital were very similar and almost identical in shape to that shown in Figure

2a. However, for men we see an increase in their housework hours as their female partner’s human

capital rises; in other words, as was shown in Table 2, for men the main effect was a relative one. In

addition, being in the lowest human capital decile as opposed to the middle decile had an obvious

positive effect on men’s housework hours throughout the range of their female partner’s human

capital. The same is not true for women where there was relatively little difference in their

housework hours according to whether their male partner was in the bottom or the middle human

capital decile.

Figure 2a. Modeled housework hours by Figure 2b. Modeled housework hours by

couples’ joint human capitali : pooled sample woman’s human capital

i : pooled sample

0.0

5.0

10.0

15.0

20.0

25.0

1 2 3 4 5 6 7 8 9 10

Ho

use

wo

rk

ho

urs p

er w

ee

k

JOINT HUMAN CAPITAL DECILES<--Both spouses less Both spouses more-->

women's hours

men's hours

0.0

5.0

10.0

15.0

20.0

25.0

1 2 3 4 5 6 7 8 9 10

Ho

use

wo

rk h

ou

rs p

er

we

ek

WOMEN'S HUMAN CAPITAL DECILES

women's hours; man in decile 1

women's hours; man in decile 5

men's hours; man in decile 1

men's hours; man in decile 5

i. Models are instantiated for 2004, for a woman aged 42 with 1 dependent child in the household

and include relative resources; relative resources squared; the woman’s absolute resources; and the

woman’s absolute resources squared.

23

5.5. Longitudinal analyses; the effect of change in absolute and relative human

capital

To this point we have focused on cross-sectional models to provide a comparison with

previous research. There are however several problems with the cross-sectional approach,, which is

dogged by problems of selection bias, or unobserved heterogeneity. In particular, in this case,

couples where one partner has significantly more resources than the other may be different in

unmeasured ways from those where resources are distributed more equally, and this may account

for some of the differences in housework hours. One way of dealing statistically with such

unobserved heterogeneity is to use fixed effect models (as in Kan & Gershuny, 2009). A related

way of getting around the same problem is to use longitudinal analyses which predict change in the

dependent variable on the basis of changes in the independent variables (Arellano, 2003). Since the

temporal sequence is clear in longitudinal analysis, causality is easier to infer and, as in other sorts

of fixed effect models, unobserved heterogeneity is effectively controlled for. The remainder of our

analyses, therefore, focuses on the effects of longitudinal measures of change in both relative and

absolute human capital on change in the performance of housework among couples.

Table 3 shows the regression statistics and coefficients from multivariate OLS regressions

of: change in women’s hours of housework; change in men’s hours of housework and change in the

gender division of housework on changes in the respondent’s human capital and relative human

capital. Change is measured on a year to year basis across successive waves of the BHPS panel,

yielding 16 pairs of successive years (t, t+1). The t and t+1 year pairs are then pooled for analysis.

From the values of R2

shown in Table 3, the longitudinal model works best for the prediction of

changes in women’s housework hours, at 24% of variance explained. Note also that for both

women and men the model predicting change in housework hours from changes in absolute and

relative resources provides a less good fit than the same cross-sectional models (compare R2s from

Table 3 with those from Table 2). The lower R2 for the longitudinal model is to be expected since

the variance of change in housework hours is considerably less than that in housework hours itself,

and it also excludes the effects of unobserved heterogeneity. Taking into account that we are

predicting a single year’s change in housework, 24% is a remarkably high proportion of variance

explained by a longitudinal model11

.

11

Indeed, if we predict the level of housework in year t+1 (rather than change between years t

and t+1) using the same set of variables, the variance explained rises to over 50%

24

Table 3. OLS Regression of change in housework hours on human capital: pooled

sample (*** P<.001 ** P<.01 * P<.05)

Change in

women’s hours

Change in

men’s hours

Change in

gender division

of houseworka

Dependent variable previous year -.451*** -.386*** -.319***

Relative human capital previous year -1.405** 2.016*** -6.617***

Rel hum cap previous year squared .410 1.188 -2.550

Change in relative human capital -1.312 1.464** -6.532***

Change in relative human capital sq -6.135* -3.020* 6.474

Own human capital previous year -1.291*** -.329** -.672*

Own human capital previous year sq .058*** .019** .004

Change in own human capital -.369*** -.043 -.300*

Change in own human capital sq

.013 -.009 -.041

Wave -.265*** -.084** .063***

Wave squared/100 1.080*** .449** -.490***

N of children<18 previous year 1.454*** .131** .776***

Change in number of children<18 2.735*** .279** 2.182***

Age .270*** .034 .292**

Age squared -.002** -.000 -.003*

Constant 5.539*** 2.927*** 16.906***

Model R2

.243 .187 .155

Number of observations 28982 28982 28976

Number of clusters 3561 3561 3561

a. Model including women’s human capital and age variables

The important coefficients for our purposes from among those shown in Table 3 are those

indicating change. As expected from existing literature, the birth of a child (represented as an

increase in the number of dependent children aged under 18) led to a significant increase in the

number of hours spent on housework for both women and men. Also as expected, it led to a

significant change in the gender division of housework (shown in the third column of coefficients),

25

such that women took on relatively more housework. With respect to those variables representing

change in human capital, we see a significant relationship between change in own (absolute) human

capital and change in housework hours for women: a year-on-year increase in own human capital

was associated with a significant decline in hours spent on housework. This finding provides a

powerful, longitudinal, example of the importance of changes in women’s absolute economic

resources in the determination of housework.

In the model for the gender division of housework an increase in the woman’s human capital

was also associated, albeit less strongly, with a decrease in her proportion of housework, while there

was little (direct) effect of changes in own human capital evident for men. Overall, the gender

difference in respect of change in absolute and relative resources is of great significance: for men, a

change in relative human capital was the only change in resources which had a statistically

significant effect on their housework time, whereas for women, change in own absolute resources

proved to be the only statistically significant resource change. The implication of this is that

changes in women’s absolute resources are the primary driving force behind changes in the time

that both partners spend doing housework.

5.6. Longitudinal analyses; instantiations of the change model

In order to understand better the effect of these various changes we instantiated the regression

coefficients shown in Table 3 in Figures 3a and 3b. These figures show predicted housework hours

for women and men in couples with different combinations of human capital, under different

conditions of change in absolute and relative resources. The models are instantiated for 2003 for

men and women aged 4112

with one dependent child in the household. As in Figure 1, the

horizontal axis shows the range (in deciles) of couples with different combinations of resources –

from women in the bottom decile/men in the top decile on the left, to women in the top decile/ men

in the bottom decile on the right. The trend lines shown in the body of the graph are instantiations

(model predictions) of changes in housework hours. They show before and after levels of

housework associated with changes in human capital, for wives and husbands respectively. The

middle line of each graph shows no change (i.e., before) and is identical to the line shown in Figure

1. The other lines show the effect on housework hours of a positive and negative change of one

decile of human capital for wife and husband respectively.

12

This date and age is chosen for consistency with the cross-sectional instantiations shown in

Figure 1 based on those aged 42 in 2004. Since we are modeling change over a year, year t-1

is 2003 and the sample was one year younger (age 41 in 2003).

26

A striking difference is evident from Figures 3a and 3b in the effects of change for husbands

and wives, reflecting the coefficients shown in Table 3. For a woman it is clear that an increase in

her male partner’s human capital of one decile led to very little increase in her housework hours

across the range of joint human capital deciles (in fact in Figure 3a one can hardly distinguish this

line from that for no change). This effect represents change in relative resources (since her own

resources are held constant) which is - not surprisingly – a statistically non-significant effect for

women in Table 3. In contrast, the effect of an increase in her own human capital led to a

pronounced decrease in her housework hours. This asymmetry reflects the far greater importance in

the determination of a woman’s housework hours of a change in her own resources as opposed to a

change in her partner’s (i. e. the couple’s relative) resources.

Figure 3a. Women’s housework hours by changes in human capitali

11.0

13.0

15.0

17.0

19.0

21.0

23.0

25.0

womenbottom

mentop

women4 men

7

women7 men

4

womentopmen

bottom

Axi

s H

ou

rs o

f h

ou

sew

ork

per

wee

k

JOINT HUMAN CAPITAL DECILES <--woman relatively less woman relatively more-->

Woman's hours: man up 1 decileWoman's hours: no changeWoman's hours: woman up 1 decile

27

Figure 3b. Men’s housework hours by changes in human capitali

i. Models are instantiated for 2003, for women and men aged 41 with 1 dependent child in

the household and include: relative human capital and annual change in relative human

capital (and their squared terms); woman‘s human capital and annual change in woman’s

human capital (and their squared terms)

For men the picture appeared more symmetrical (note the difference in the vertical scale

between the two graphs): an increase of one decile in their own human capital did lead to a

corresponding decrease in housework hours – however, from Table 3 the effect of a change in own

absolute resources is seen to be weak and not statistically significant. This effect is mirrored on the

opposite side of the no change line by an increase in men’s housework hours following an upwards

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

womenbottom

mentop

women4 men 7

women7 men 4

womentopmen

bottom

Ho

urs

of

ho

use

wo

rk p

er w

eek

JOINT HUMAN CAPITAL DECILES <--woman relatively less woman relatively more-->

Man's hours: man up 1 decile

Man's hours: no change

Man's hours: woman up 1 decile

28

shift in their female partner’s human capital (reflecting a change in relative human capital whilst

holding own human capital constant).

6. Discussion and conclusions

This paper extends the marital bargaining approach to housework through the use of a new measure

of economic resources akin to an expected wage rate based on theaccumulation of educational

achievement, skills, employment and occupation over the life course. This measure, which we have

termed human capital, allows us to include those who are not currently employed, and to represent

the bargaining power of those have taken on employment below the level that their human capital

would indicate (for example, because of the need to care for children). In particular, employment-

based measures are likely to be gender-biased by the differential exclusion or underestimation of

women’s economic resources. The human capital measure also overcomes to a large extent the

conceptual circularity involved in measuring the time partners spend on housework as an outcome

of the time they spend in paid employment and the income which they receive for it. We showed,

firstly, that the measure of human capital yields results which are not only consistent with those

from previous research based on earnings, but represents a significant improvement (in terms of

model fit) over a model based on actual wage rate. This gives confidence that our measure, more

inclusive than earned income, does indeed capture key variation in spousal resources found to be

important in the previous literature on marital bargaining.

The second contribution of this paper lies in the findings concerning the debate on absolute

versus relative resources. We found that the best fitting generic model included the absolute human

capital of respondents, together with their spousal relative resources (including the squared terms).

We excluded the overdetermined model including both partners’ absolute resources and their

relative resources, which was found to result in high levels of multicollinearity between the

variables. These findings support those of Gupta (2007; Gupta & Ash, 2008), who argued that

earlier models which tried to predict women’s housework hours based on a measure of relative

income alone suffered from the exclusion of women’s absolute resources, and those of Killewald

and Gough (2010) who demonstrated a curvilinear relationship between women’s absolute

resources and their housework time. In our models we found an interesting difference between men

and women. For women their own absolute resources proved highly significant, while the effect of

relative resources was also significant, but much less convincingly so. For men, relative resources

appeared to be more important than absolute resources in predicting housework time. Our

conclusion is that the most important factor in determining both women’s and men’s housework

29

time is women’s own resources - the effect of which is reflected in the model for men in the relative

resources term.

We see no evidence for the gender deviance neutralization effect in the graphed regression

predictions, instantiated for particular combinations of independent variables across spousal

combinations of human capital. According to this hypothesis, men and women in domestic

economic circumstances that contradict normative gender expectations (i.e. women who

significantly out-earn their husbands, and men in marginal economic situations) compensate

through adjusting their housework to conform to gendered expectations. Several high-profile papers

based on large-scale data have investigated and re-investigated this hypothesis, and much has been

made of this apparent contradiction of economic bargaining theory which seems to fly in the face of

research documenting the empowering effect of high status employment among women (Benjamin

& Sullivan, 1999; Crompton & Lyonette, forthcoming). In the assessment of a gender deviance

neutralization effect, much importance has previously been accorded to the squared term of relative

resources. Where this term has been statistically significant and its sign has been opposite to the

effect of the non-squared term this has been regarded as the statistical proof of a gender deviance

neutralization effect. However, as we have argued, this interpretation is not necessarily correct. An

opposite sign on the squared term of relative resources does not necessarily mean that there is an

actual upturn in the curve within the range of the observed values of relative resources – it could

simply mean that there is a diminution in the rate of decline. In our analysis of women’s housework

hours (Table 2), the squared effect of relative resources, though not statistically significant, was

positive in sign, in contrast to the significant negative effect of relative resources. However, when

we instantiated the regression prediction over the actual range of couples’ relative resources (Figure

1), it became clear that, far from there being an upturn in women’s performance of housework at the

extreme end of the relative human capital distribution (in favor of women), women’s housework

time decreased further. Where women’s resources were highest relative to their male partners, the

gender division of housework within couples was at its most equal (even though women continued

to do more in overall terms).

For men, again contrary to the predictions of the gender deviance neutralization hypothesis,

in the graphed instantiations of our regression predication we see that those with the lowest

resources married to women with the highest resources approached gender equality in the

performance of housework. The steep rise in men’s contributions to housework among the group of

men with the lowest human capital relative to their wives in combination with the fact that 46% of

men in this group are not in employment suggests that unemployed men are spending significant

amounts of their time in the performance of housework (see Figure 1). Models based on relative

30

earnings have missed the significance of this finding either through 1) excluding non-earners from

analysis or 2) according to every non-earning man a bargaining power of zero.

Last but not least, we analyzed change models based on large-scale, nationally-

representative year-on-year longitudinal data. By focusing on changes in housework time

associated with changes in the independent variables we can overcome the problem of unobserved

fixed differences between couples that dogs cross-sectional analysis. The statistical issue is

unobserved heterogeneity, which we have dealt with using change models which focus on the

relationship between changes in partners’ resources and changes in housework time. Echoing our

cross-sectional results, in these year-on-year change analyses (Table 3) only changes in a woman’s

own human capital resources were found to have a strong (and strongly statistically significant)

effect in predicting change in her housework time, while the effect of changes in relative resources

was significant for predicting change both in men’s housework hours and in the gender division of

housework13

. In support of Gupta’s women’s autonomy hypothesis, it appears that it is change in a

woman’s own human capital that produces change in her housework time, as opposed to a change

in relative resources. For men, in contrast, our analyses showed that it is predominantly change in

relative human capital resources which predict change in housework time, and not change in their

own human capital. The implication is that for a man what proves most important in determining

housework time is change in his female partner’s human capital. This asymmetry is consistent with

the findings of Evertsson and Nermo (2007) who found that, over a period of nine years, increases

in Swedish women’s relative resources were associated with increases in their male partner’s

housework hours, but not the reverse. It also lends support to the conclusions of the lagged

adaptation thesis, according to which men make adjustments in their unpaid labor over an extended

period of time following changes in their female partner’s employment status (Gershuny et al.,

2005).

In sum, these longitudinal analyses both confirm and extend statistical relationships that

have been found in cross-sectional analyses, and, more recently, in a more limited number of

13

Because of the way in which the measure of human capital was calculated, such increases may

reflect the effect of changes in employment status, position in job hierarchy, educational

qualifications or occupation on the calculation of the life-course accumulation of human capital.

The intention was to identify a single measure of marital bargaining power to substitute for that of

current earnings since the central theoretical question which all previous studies have sought to

address is whether (overall) economic bargaining power within the household is related to

housework hours. Note that a similar consideration would apply to change analyses based on

earnings - a change in earnings can arise from several quite distinct causes: a change of job; a change

in number of hours worked; a promotion or pay cut; a seniority increment etc.

31

longitudinal studies. From these results we can state: 1) that as women’s absolute resources increase

year-on-year, the time they spend in housework decreases; and 2) that men’s housework increases

year on year as their partner’s resources increase relative to their own.

In the context of the wider theoretical debate over economic bargaining theory versus doing

gender, although we have found no support for gender deviance neutralization, in the asymmetry of

the findings for men and women an important effect of gender is nonetheless evident. Our findings

both underline the fact that women continue to do more of the housework even when their

resources significantly outstrip those of their husbands, and point to the key significance of

women’s absolute resources in determining both their own and their partner’s housework time.

This conclusion emphasizes the negative consequences of the gender wage gap for equality both in

the public and the domestic sphere, lending strong support to feminist efforts to improve women’s

employment opportunities and status as a means of achieving greater gender equality both in the

public and in the domestic sphere.

Finally, while our analyses are based on British data, we have two reasons to expect that our

findings are also applicable to the U.S. context. U.S. data over the past half century displays very

similar trends to other Anglophone countries (the U.K., Canada and Australia) in the gender

division of domestic labor (Gershuny & Robinson, 1987; Kan, Sullivan & Gershuny, 2011). And

many of the conclusions from our analyses are fully consistent with those of recent papers on this

topic based on U.S. data (e.g. Gupta, 2007; Gupta & Ash, 2008; Killewald & Gough, 2010).

7. Acknowledgements

The writing of this paper was funded by ESRC large grant RES-060-25-0037,

“Establishing the Centre for Time Use Research”.

32

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34

Appendix A. Variable means, standard deviations (in brackets) and ranges by survey waves

Year of survey

All years

N

1992-1996 1997-2002 2003-2008

Housework hours Women 19.9

(13.1)

0, 99

17.2

(10.9)

0, 99

16.1

(9.8)

0, 99

17.8

(11.5)

0, 99

32057

Men 5.2

(5.5)

0, 65

5.0

(5.1)

0, 90

5.4

(5.0)

0, 56

5.2

(5.2)

0, 90

30733

% done by women 77

(19.9)

0, 100

75

(20.2)

0, 100

73

(20.4)

0, 100

75

(20.2)

0, 100

Human Capital Women 4.8

(2.0)

1.7, 16.2

5.4

(2.1)

2.1, 15.4

6.3

(2.5)

2.4, 17.3

5.5

(2.3)

1.7, 17.3

32359

Men 6.3

(2.5)

1.6, 17.5

6.7

(2.4)

1.9, 18.4

7.5

(2.8)

2.5, 22.5

6.8

(2.6)

1.6, 22.5

32359

Year-on-year change

in human capital

Women .13

(.81)

-8.77, 8.09

32359

Men .12

(.94)

-7.99, 8.68

32359

Relative Human

Capital (woman-

man/woman+man)

-0.13

(.20)

-0.68, 0.69

-0.11

(.18)

-0.60, 0.57

-0.09

(.18)

-0.60, 0.54

-0.11

(.19)

-0.68, 0.69

32359

Year-on-year change

in relative human

capital

+.00

(.09)

-.67, .57

32359

Earnings (£/week) Women 166

(128)

0, 1937

185

(151)

0, 4000

208

(190)

0, 7879

186

(159)

0, 7879

25110

Men 327

(233)

0, 5468

345

(290)

0, 13386

369

(281)

0, 7759

347

(270)

0, 13386

27844

Relative earnings

(woman-

man/woman+man)

-0.31

(.36)

-1.0, 1.0

-0.28

(.38)

-1.0, 1.0

-0.25

(.40)

-1.0, 1.0

-0.28

(.38)

-1.0, 1.0

22578

Wage rate

(earnings/hour)

Women 5.7

(5.6)

0, 242

6.2

(5.4)

0, 390

7.1

(7.4)

0, 297

6.3

(6.2)

0, 390

24704

Men 7.7

(6.9)

0, 277

8.3

(7.1)

0, 225

9.1

(7.2)

0, 173

8.4

(7.1)

0, 277

27351

Relative wage rate

(woman-

man/woman+man)

-0.13

(.31)

-1.0, 1.0

-0.12

(.33)

-1.0, 1.0

-0.10

(.35)

-1.0, 1.0

-0.12

(.33)

-1.0, 1.0

21896

Woman’s Age 40

(10.2)

20, 59

41

(10.2)

20, 59

42

(10.0)

20, 59

41

(10.2)

20, 59

32359

35

Year of survey

All years

N

1992-1996 1997-2002 2003-2008

Number of

dependent

children/household

0.90

(1.1)

0, 9

0.88

(1.1)

0, 7

0.88

(1.1)

0, 7

0.89

(1.1)

0, 9

32359

Year-on-year change

in number of

dependent children

-.00

(.34)

-4, 4

32359

Correlations (Pearson’s R) between measures of resources:

Human capital x earned income (employed population only) = 0.531 (women); 0.431 (men)

Human capital x wage rate (employed population only) = 0.401 (women); 0.434 (men)

36

Appendix B. Estimation of the human capital measure

We follow the conventional economists’ procedure (Heckman 1976) of combining an

estimation of the probability of an individual’s selection into employment, with an

appropriately adjusted regression estimate of the economic value of the various

characteristics for those actually in employment. Firstly we estimate the quality of jobs by

their market valuation (i.e. the expected income levels of those doing them). We construct

a Mean Occupational Wage (MOW) scale of job quality by pooling all the eighteen waves

of the BHPS responses (yielding 239,043 observations), adjusting hourly wage rates by the

Retail Price Index, and calculating the mean for each 2-digit group in the standard

occupational classification. We take the natural log of mean income for each occupational

category, and then normalise the result so that the lowest-income job is scored 0, and the

highest is scored 100. We estimate the equation using a pooled file of the full set of 18 waves of

BHPS data.

The regression stage of the Heckman procedure estimates the equation:

Eq. (A.1.) lwage = (age agesq mow mowsq higra agegr agrsq medgra agemd agmsq

educ1 to educ6, jobtot1 to jobtot4, unmtot1 to unmtot4)

where:

lwage is the log of the hourly wage rate;

higra is a dummy variable indicating membership of the top 10% of the MOW scale

(83-100) and medgra indicates membership of the next 30% (60-82);

agegr agrsq agemd agmsq are the products and squared products of age and the high

and medium grade dummies, introduced to allow for differing age/earnings curves

across high, medium and low level occupations;

educ1 to educ6 provide dummy variables for, respectively, Higher Degree, 1st Degree,

other tertiary qualification, A-Level, O-Level/higher grade GCSE and other

GCSE/CSE;

jobtot_ and unmtot_ represent respectively months in employment, and unemployment

in each of the four years immediately preceding the date of interview.

Table A1 shows the regression coefficients and standard errors for this estimation. Note

that the selection stage of the Heckman procedure includes the same variables, plus sex to

identify the equation. Despite its inclusion in the selection equation, which means in turn

that its effects are used indirectly to adjust the size of the coefficients in the regression

stage of the equations, there is no sex coefficient used in the imputation of our measure of

human capital. Therefore any statistical association between sex and human capital is a

result of associations with the incidence of values of its component variables.

In the final step we use the coefficients from the Heckman regression stage to

estimate a predicted value for the log (predicted) wage rate for each respondent for each

wave of the BHPS. In our analysis the human capital score is the exponential of that

predicted log wage rate. Occupational level (MOW scores), the age-occupational level

interactions, educational attainment and its interaction with occupational level, and job

history (48 months employment and unemployment counts) emerge as the most important

determinants of the human capital score.

37

Appendix B Table 1: Human Capital estimation equation, BHPS Respondents aged 16-64, 1991-2008 (dependent variable log hourly wage)

Coef. SE

age age 0.038688 4.81E-06

agesq age squared -0.00045 0.063717

mow mean occ. Wage (MOW) 0.001584 0.003095

mowsq MOW squared 0.000116 3.74E-05

higra MOW=83 to 100 (dummy) -0.99859 0.032069

agegr higra*age 0.043437 0.001652

agrsq higra*age squared -0.00046 2.07E-05

medgra MOW=61 to 82 (dummy) -0.46233 0.010008

agemd medgra*age 0.02282 0.007459

agmsq medgra*age squared -0.00025 0.006095

educ1 higher degree (dummy) 0.561255 0.004631

educ2 first degree (dummy) 0.454359 0.004345

educ3 other tertiary (dummy) 0.285776 0.006358

educ4 university entrance (dummy) 0.17904 0.010687

educ5 medium school (dummy) 0.104599 4.81E-06

educ6 low school (dummy) 0.034821 0.063717

No school qualifications (omitted)

higrahied higra*(educ1 or educ2) -0.05507 0.008696

medgrahied medgra*(educ1 or educ2) -0.02673 0.000692

jobtots months in employment year-3 0.003743 0.000551

jobtotr months in employment year-3 0.004684 0.000552

jobtotq months in employment year-1 0.003859 0.000443

jobtotp months in employment this year 0.005298 0.001604

unmtots months unemployment year-3 -0.01099 0.001394

unmtotr months unemployment year-3 -0.00404 0.001362

unmtotq months unemployment year-1 -0.00421 0.00121

unmtotp months unemployment this year -0.00815 0.001848

wave year count 1991=1 0.030046 7.67E-05

wavesq wave squared -1.9E-05 2.79E-05

wavemow wave*MOW -0.00062 1.49E-08

wavemowsq wave*M OW squared 2.19E-07 0.023634

cons intercept 0.087318 0.008696

R 0.633