1. The University of Nottingham
School of Economics
L13500 Dissertation 2014
The Rise of the Stay-At-Home Father: A Cross-Country
Comparison of the Macroeconomic Factors Contributing to a
Changing Family Structure
Thomas Lansdowne
Student ID: 4165729
Supervisor: Dr. Giammario Impullitti
Word count: 7,500
This Dissertation is presented in part fulfilment of the requirement for the completion of an undergraduate degree in
the School of Economics, University of Nottingham. The work is the sole responsibility of the candidate.
I do give permission for my dissertation proposal to be made available to students in future years if selected as an
example of good practice.

2. 1
Table of Contents
Abstract
1. Introduction
2. LiteratureReview
2.1. TechnologicalChange
2.2. MacroeconomicDrivers
2.3. ConclusionandLimitationsoftheLiterature
3. Cross-CountryComparisonandContext
4. EmpiricalAnalysis
4.1. DataLimitations
4.2. PreliminaryAnalysis
4.3. TheoreticalPredictions
4.4. EmpiricalMethod
4.4.1. U.S.RegressionAnalysis
4.4.2. CanadianRegressionAnalysis
4.5. GrangerCausalityTesting
5. Evaluation
6. Conclusion
7. References
Abstract
This paper primarily seeks to determine whether a relationship exists between
technological change and the rise in Stay-At-Home Father Households witnessed in
many Western economies. A further purpose of this study is to analyse the key
macroeconomic variables that may have given rise to this widespread change, to
offer a theoretical basis for subsequent macroeconomic analysis. Key findings of the
paper are that technology has a positive effect on the proportion of Stay-At-Home
Fathers in an economy, when Total Factor Productivity is used as a proxy for
technological change, and that the fertility rate, unemployment rate, and level of
welfare spending of an economy each offer a significant amount of explanatory
power on the proportion of Stay-At-Home Fathers.

3. 2
1. Introduction
Over recent decades there has been an undisputed rise in Stay-At-Home Father (SAHF)
Families among Western economies, reaching 2.2 million in the U.S. in 2012, according to a
nonpartisan fact tank (Pew Research Center, 2014). Whilst SAHF Households remain the least
studied and least frequent type of family structure (Kramer & McCulloch, 2010), they have
recently gathered both growing academic and media attention (The Economist; Forbes; The
Spectator, etc.).
The majority of the literature regarding SAHFs attempts to assess the factors contributing
to this growth at a micro-level, exploring a change in the decision-making of families to elect a
SAHF family structure. This largely focuses on the decreased value of time allocation to domestic
work. However, the macroeconomic determinants of the increasing prevalence seem neglected,
in part due to data limitations and contrasting definitions (Kramer & McCulloch, 2010).
This paper offers a unique approach in the analysis of this phenomenon, providing a basis
for further study of the contributing macroeconomic factors. This is achieved through the use of
a proxy variable for SAHF Households previously unexplored, which enables novel results from
time-series econometric analysis. The focus of the paper is on the effect that technological
progress has on SAHF Households, providing a comparison between the Canadian, and U.S.
economies.
The empirical investigation outlined is based on previous micro-level and macro-level
findings, as well as economic theory. Whilst technological progress has been argued to have led
to a reduction in time-allocation towards domestic work, its effect is yet to be analysed in the
framework of SAHF Households. Due to the novelty of econometric analysis on this topic, the
paper does not intend to provide a comprehensive study of all factors contributing to the rise, but
instead aims to strengthen or weaken previous claims outlined in micro-level analyses, as well as
provide a basis for further macroeconomic study.
2. Literature Review
Whilst formerly the rise in SAHF Families had been largely attributed to changing social
attitudes and gendered expectations (e.g., Ellingsæter 1998; Chelsey 2011), Kramer & Kramer
(2013) expands on previous literature to suggest that a range of social and economic factors have
been driving this development. Pew Research Center (2014) calculated that there were 2.2
million SAHF Families in the U.S. in 2012; nearly double that of 1989. Whilst in Great Britain

4. 3
research carried out for The Spectator (Brown, 2012) by The ONS noted an increase greater than
300% since 1996. Nonetheless, research into this phenomenon is limited, which Latshaw (2009)
argues to be due to the lack of correlation between rising female employment and the greater
responsibility of domestic work amongst men, coined as the ‘stalled revolution’ (Hochschild,
1989).
This review will first analyse the literature supporting the significant effect of
technological advancement on time-allocation to market work, and thus the rise in SAHFs,
through a two-pronged approach. Initially, it will examine the rising female participation rate in
the U.S. driven by the polarisation of wages, before reviewing technological progress in household
production. Secondly, it will outline the economic factors contributing to the decision of fathers
to adopt a caregiver role in nuclear family households, before referencing certain prevailing
limitations of the literature.
2.1. Technological Change
The amount of time married families allocate to market work has risen significantly since
the 1950s (Greenwood & Guner, 2004). In the U.S. in 1990 married households contributed on
average of 33.5 hours per person per week to market work, compared to 25.5 hours in 1950. This
data is deemed reliable as it was extracted from U.S. Census data. In part, the authors attribute
this change to the rise in the labour force participation rate of married women, noted as a 47%
increase over the same period, driven largely by technology.
Whilst shifts in the aggregate production function have long been attributed to technical
change (Solow, 1957), Tinbergen (1974; 1975) proposed a link between the relative demand for
skilled labour and changing technology. This is in part strengthened by Katz and Murphy (1992),
amongst others, who produced a case study of the effect of technology on wage structure in the
U.S. using data from 25 consecutive Current Population Surveys (CPS) from 1964, a significant
sample size of approximately 1.4 million people. The paper evidences a changing pattern of
employment in part from dramatic increases in both the relative wages of women as well as the
volume of women in the workplace between 1963 and 1987, driven by changing relative demand
for labour in occupations favouring women.
Extensive literature evidences a skill-biased technical change amongst OECDs. Autor et al.
(2003) aim to streamline this consensus by formalising and testing previous theories focusing on
computerisation, attributing it to the changing pattern of employment in the U.S. in favour of
service sectors. By combining information on occupational requirements from the Dictionary of
Occupational Titles (DOT) with CPS and Census data, the authors are able to comment on
changing task inputs individually across occupations and industries, as well as within differing
levels of education. Computer technology is evidenced to impose a substitution effect for

5. 4
unskilled workers performing routine tasks, whilst complementing industries in which problem-
solving and creativity is necessitated (high-skilled labour). While the latter is pronounced
amongst both genders, it is however larger for women, suggesting that the changing relative
demand for labour is indeed in favour of female-rich occupations, contributing to the increase in
the average wage rate of women.
Autor and Dorn (2009) expand on this concept by outlining a displacement of less
educated workers towards low-skill service occupations, which are difficult to automate, creating
an increase in service sector wages. Adding weight to this conclusion, the authors also test for
various alternative hypotheses such as demographic and economic shifts, and off-shoring, none
of which provide statistically robust evidence to counter the null hypothesis. In tandem with an
increase in high-skilled labour, this phenomenon contributes to the polarisation of the U.S. labour
force. Thus, when taking into account the overrepresentation of males in middle-skill
occupations, this may act as further evidence for the strong performance of females in the labour
market relative to males (Acemoglu & Autor, 2011), reducing the value of their labour in
household production. Atesagaoglu et al. (2014) substantiates this claim with a life-cycle model
attributing 93% of the reduction in the gender unemployment gap to falling demand and wages
in male-heavy occupations, due to technological change. This increase in the wage ratio of female
to male earnings may partially explain the consistent rise in SAHFs, since this makes production
of domestic work by women more costly to the family (Mincer, 1962).
An additional component of technological progress that alters time-allocation of domestic
work is the gains in efficiency that reduce the hours needed to complete equivalent tasks over
time (Greenwood and Guner, 2004). Greenwood (2012) develops this hypothesis in referencing
the contributions that domestic inputs such as dishwashers, washing machines, and the internet,
have had on reducing the need for domestic labour. The paper highlights various channels of
technological progress that provide economies of scale in household maintenance. However,
since this paper focuses on divorce, it offers little by way of explanation for the rise in SAHFs.
It is clear that many academics have developed over time the economic theory explaining
the effects of technological advancement on time-allocation of market work and the gender wage
gap. However, very little quantitative analysis has attempted to determine the subsequent impact
on SAHFs, particularly across OECD countries, thus providing an interesting topic for exploratory
study.
2.2. Macroeconomic Drivers
The significant rise in SAHFs has been documented across a large amount of OECD
countries in recent decades, with the largest proportion of this change arising from fathers that
are choosing to be primary caregivers (Pew Research Center, 2014; Kramer & McCulloch, 2010).

6. 5
The latter study finds that this characteristic is prevalent in the U.S. amongst SAHF Families in
which the wife earns 100%, 90% and 75% of household income within each decade between
1968 and 2009. However, whilst a reliable and well-suited source of data is referenced (U.S. CPS),
a significant limitation of findings is that it is impossible to infer whether a greater percentage of
domestic work is in fact completed by the working mother and not the SAHF.
The division of labour amongst households is motivated by the substitutability of market
and domestic work between individuals (Becker, 1981). Whilst biological differences remain
prevalent in driving women to act as primary caregivers for the family, changes in experience and
investment into human capital may contribute to growing substitutability between male and
female labour.
Kramer & Kramer (2013) attempt to quantify the effect on stay-at-home fatherhood of
greater human capital of mothers relative to their male counterparts. Using logistic regression
analysis, the author finds strong evidence to support the claim that greater educational
attainment of the wife in a household over that of the husband, largely increases the likelihood of
a SAHF Family. However, one limitation of this conclusion is that although educational attainment
is viewed as a strong correlate of human capital, recognising the specific discipline of higher
education and amount of market work experience may enable a closer estimate of human capital
and thus develop the author’s findings.
Greenwood (2012) also references a dramatic increase in the rate of women in higher
education, stimulated in part by a rising college premium, raising the contribution made by
married women to household income. The increase in female education alone augments their
earning potential which may motivate some families to adopt a SAHF household income structure
(Kramer & McCulloch, 2010).
Additionally, how macroeconomic fluctuations effect the number of SAHF Households is
unclear. Whilst high male unemployment as a result of an economic downturn may stimulate
female participation through the ‘added worker’ effect, as households compensate for falling
household income, increasing female unemployment can discourage married women from
joining the labour force (Jaumotte, 2003). Kramer & Kramer (2013) provide deeper analysis of
the effect of macroeconomic fluctuations by separating caregiving SAHFs with those unable to
work. The authors provides evidence that the unemployment rate does not affect the amount of
caregiving SAHFs, which increases over time linearly, but does increase the likelihood of unable-
to-work SAHF Families by 8.1% for every 1% increase in unemployment. However, when using a
dummy variable to reflect periods of recession, as a means of isolating these economic
fluctuations, to avoid attributing the changing volume of SAHFs to changes in unemployment
alone, the results obtained were contrary to the previous. They indicate that recessions may in
fact reduce the amount of caregiving SAHF Families, and provide no significant correlation with

7. 6
those that are unable to work. It appears as though this area of study requires further
consideration.
The individual design and focus of economic policy across countries, with particular
reference to taxation and benefits, may also have a consequential impact on time-allocation to
market and domestic work of individual family members (Anxo, 2007). Time-use surveys are
analysed across four OECD countries which differ in terms of welfare policy. In Sweden, where a
low gender gap in time-allocation is present, public policy is characterised by individualised
taxation and extensive welfare support for childcare and parental leave. Contrastingly, Italy offers
restricted public support to families, with strong protection for those in permanent employment,
contributing to female unemployment as women are often seen as labour-market entrants.
Furthermore, the paper describes a contrast in the extent to which welfare support affects time
allocation across countries through empirical analysis. A limitation of the paper’s preference for
time-use surveys, however, is that they were undertaken by separate statistical authorities
during different time-periods, arguably skewing the results obtained when comparing
internationally. Jaumotte (2003) also found evidence to suggest that the specific tax treatment of
second earners, and the use of taxation to incentivise couples to divide market work, have an
effect on the likelihood of mothers to engage in market work.
2.3. Conclusion and Limitations of the Literature
To summarise, the literature states that technological advancement has led to an increase
in the amount of time families contribute to market work over domestic work, whilst reducing
the comparative advantage that women have previously assumed for domestic work. This is
driven by dramatic increases in the relative wages of women and the volume of women in the
workplace, thus increasing the relative cost to families of women allocating time to domestic
duties. Developments in both skill-biased change in favour of women, as well as economies of
scale in household maintenance, act as partial drivers of this change, potentially leading to an
overall increase in SAHFs. Empirical evidence suggests that the largest percentage of the rise in
SAHF Families is accounted for by those choosing to be primary caregivers. The literature
proposes that factors driving this change include greater substitutability of market and domestic
work between genders, rising human capital of mothers relative to fathers and the extent to
which economies offer welfare support. The effect of macroeconomic fluctuations on SAHFs
remains ambiguous.
Finally, whilst empirical evidence seems in support of these determinants causing an
increase in SAHF Households, certain limitations to these explanations must be acknowledged.
Definitions and characteristics of SAHFs differ both over time and between countries, causing
difficulty in assuring accurate time-series and cross-country analyses. Additionally, when

8. 7
completing surveys, participants may be implicitly incentivised to be dishonest when referencing
their motivations for staying at home, by claiming to be unable to work, due to the pressure of
gender expectations and stigma of SAHFs (Zimmerman, 2000). This may deflate the figure of how
many fathers choose to act as primary caregivers.
3. Cross-Country Comparison and Context
In order to provide an informative comparative study of SAHF Households in Canada and
the U.S., the historical context of their prevalence in each country will be analysed, aiming to offer
potential explanations for key deviations in trends.
The below graph depicts the proxy used for total SAHF Households, as a percentage of
Husband-Wife families, for both the U.S. and Canada between 1978 and 2007.
Figure 2. SAHF Households: Canada and the U.S.
A steady upward trend is presented for both countries throughout the 29-year period,
with a 2.5 percentage point increase in the U.S., and a 3.8 percentage point increase in Canada.
Thus, whilst the direction of this change is common between countries, the magnitude varies
significantly.
One possible explanation for this difference is the contrasting income tax procedures.
Whilst Canadian couples must file their income tax returns separately, the U.S. Internal Revenue
Service (IRS) allows for joint filings. Those couples that decide to file jointly benefit from
0
1
2
3
4
5
6
7
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
SAHF,%ofHusband-WifeFamilies
Stay-At-Home Father Families
SAHF USA SAHF CAN

9. 8
significant tax exemptions, as well as qualifying for multiple tax credits such as Child and
Dependent Care Tax Credit, and Earned Income Tax Credit (IRS.gov). This incentivises dual-
earner families, posing an additional opportunity cost for the husbands in SAHF Households in
the U.S. to stay out of the labour force, and act as the sole caregiver of children. This may partially
explain why the rise in SAHF Households in Canada is greater than that of the U.S.
A further potential explanation may be the contrasting fertility rates of the two countries,
and the subsequent effect on female labour force participation. Both countries faced a very similar
fertility rate in the late 1970s, however from 1980 onwards, Canada experienced a rate that
fluctuated between 1.5 and 1.7 (World Bank), whilst that of the U.S. was above 2 for
approximately 50% of the 29-year period, reaching 2.12 in 2007 (Statistics Canada), as shown in
the figure below.
Figure 3. Fertility Rate: Canada and the U.S.
In their cross-country empirical investigation into the effects of fertility on female labour
force participation, Bloom et al. (2007) discovered a strong negative effect resulting from a
combination of factors. They argue that a decline in the fertility rate leads to a reduction in
population growth and increase in the capital-labour ratio. Simultaneously, an increase in the
ratio of the working-age population is noted, which combined with the previous effects,
contributes to a rise in female labour force participation. Thus, it can be argued that a lower
fertility rate in Canada makes it less likely for married women to adopt the role of primary
caregiver in Husband-Wife families, leading to a greater increase in SAHF Households in Canada
relative to the U.S.
0
0.5
1
1.5
2
2.5
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
AverageFertilityRate
Fertility Rate: USA/CAN
Fertility USA Fertility CAN

10. 9
4. Empirical Analysis
Based on both economic theory and past literature, various data have been collected to
analyse the direction and magnitude of several key variables through time-series econometric
modelling. This section will outline the advantages and limitations of using a proxy dependent
variable, as well as introduce the reader to the other variables used in the empirical analysis,
before outlining the econometric method adopted.
4.1. Data Limitations
The scarcity of academic study on SAHF Households, and its focus on micro-level analysis,
is partially explained by two reasons. Firstly, very few national and international databases
collect data relating to SAHF Households. Secondly, several definitions of varying restrictiveness
have been used when analysing SAHFs, leading to contrasting results (Kramer & McCulloch,
2010). By using a proxy variable for SAHFs for which there is attainable data, a common definition
can be used that enables a comparative study between the two countries, thus providing the
potential for original results.
Data for ‘Husband-Wife Families in which the Wife is the sole earner, as a percentage of total
Husband-Wife Families’ is used to represent the proportion of SAHF Households. The sources used
are the Bureau of Labor Statistics, and Statistics Canada, both of which ensure reliable results.
However, one main drawback of using a proxy variable with such a broad definition is that
couples without children may be included, albeit a very similar trend is expected as shown below.
This restricts the practical interpretation of any results specific to SAHFs.
The below graph depicts the data of the proxy variable as a line graph mapped against
SAHF data collected from the Integrated Public Use Microdata Series of the Current Population
Survey, of three different definitions of varying restrictiveness. The data used is taken from
Kramer & McCulloch’s (2010) paper. The three contrasting definitions are SAHF Households in
which the wife earns 100%, >90%, and >75% of total income, allowing for part of total household
income to be earned by the father. A further restriction is that each family also has one child of 18
years of age or below.

11. 10
Figure 1. SAHF Households: Alternative Definitions
The graph shows that the proxy selected fits rather closely with the three definitions,
suggesting that the potential inclusion of couples without children does not hugely inflate the results.
All variables used in the empirical analysis are presented and described in the table below,
referencing their original sources. Due to data constraints, the empirical analysis will use all data
between 1981 and 2007, totalling 27 years. Whilst performing an econometric regression with 27
entries is not optimal, it does allow for significant results, yet this will be considered in the evaluation
of the paper’s findings.
Table 1. Summary Table of Variables
Variable Measure Definition Units of
Measure
Years
Available
Source
SAHFt Stay-At-Home
Fathers
Proportion of
Husband-Wife
families in which
the Wife is the sole
earner
Percentage of
total Husband-
Wife familes
USA:
1967-2007
CAN:
1976-2011
USA:
Bureau of Labor
Statistics (CPS)
CAN:
Statistics
Canada
GERDt Gross
Expenditure on
Research and
Development
Gross Government
Expenditure on
Research and
Development as a
proportion of GDP
Total
Expenditure as
a % of GDP
USA:
1981-2012
CAN:
1981-2013
OECD
TFPt Total Factor
Productivity
Business sector
Multifactor
Productivity
Measured as a
% change year-
on-year
USA:
1948-2014
CAN:
1961-2011
USA:
FRBSF Working
Paper 2012-19
(March 2014)
CAN:
Statistics
Canada
Patentst Total Patents Number of Utility
Patent Grants in
all industries
Total number
per calendar
year
1963-2012 US Patent Office
0
1
2
3
4
5
6
1968-1979 1980-1989 1990-1999 2000-2009
%ofSAHF SAHF USA: Alternative Definitions
SAHF USA (100%) SAHF USA (>90%)
SAHF USA (>75%) SAHF USA (proxy)

12. 11
The majority of the data used have been collected from reliable national or international
statistics databases, such as the OECD, the World Bank, the U.S. Bureau of Labor Statistics,
Statistics Canada, and the U.S. Patent Office. Where possible, data for both countries have been
selected from a common source. This is the case for four of the eight variables, to ensure a reliable
comparative study that is not skewed by differences in data collection and definitions. There are
three proxy variables for technological progress; Gross Expenditure on Research and
Development (GERD) as a percentage of GDP; Total Factor Productivity (TFP); and Total Patents.
These will be used individually in seperate models to strengthen the reliability of any
interpretation of the effect of technological progress on SAHF Households.
There are several prevailing limitations of the reliability of certain variables which must
be addressed. Patent data has been collected from the U.S. Patent Office for both Canadian and
U.S. patents, which represents all Utility Patent Grants approved in the U.S. Therefore, Canadian
patents granted solely in Canada would not be included in these figures. However, this does not
greatly hinder the reliability of the results, as it can be assumed that the vast majority of Canadian
patents are also granted in the U.S.
A further limitation is that the data for Relative Secondary Education is only obtainable at
five-yearly intervals, thus, for them to be used in the statistical model, any missing data points
Fertilityt Fertility Rate Average expected
number of
children born to a
woman assuming
they reach the end
of childbearing
years
Average
number of
births per
woman
USA:
1960-2012
CAN:
1960-2011
USA:
World Bank
CAN:
Recent Social
Trends in
Canada, L.
Roberts (1960-
2002)
Statistics
Canada (2003-
2011)
Unempt Unemployment
Rate
Population aged
16+ actively
seeking
employment as a
proportion of total
labour force
Percentage of
total labour
force
USA:
1968-2009
CAN:
1976-2011
USA:
Bureau of Labor
Statistics (CPS)
CAN:
Statistics
Canada
RelEd_St Relative
Secondary
Education
Relative female
secondary
education as
Population of 15-
64 year olds
Ratio of women
to men
1970-2015 World Bank
Welfaret Welfare
Spending
Total government
welfare spending
as a proportion of
GDP
Total
Expenditure %
of GDP
1980-2011 OECD

13. 12
have been interpolated. This should not hinder the reliability of the model since the variable does
not appear to fluctuate significantly.
4.2. Preliminary Analysis
The below table provides an analysis of the key features of all variables, before the formal
econometric model is introduced.
Table 2. Key Statistical Features of the Variables
Firstly, across all three technology measures, Canada has a significantly lower mean and
median value, suggesting that each proxy consistently estimates a greater level of technological
advancement of the U.S. economy. Secondly, the mean and median value of welfare spending as a
proportion of GDP are greater for Canada, and the maximum is over four percentage points
higher. Thus, the two economies appear to prioritise welfare spending to varying degrees.
Furthermore, by examining the maximum and minimum values of each variable, it appears as
though there are no outliers in the data. Finally, the table shows that there is no unique number
of observations, which means that the model will have to use the range of data points of the
variable with the lowest amount of observations. As previously mentioned, this implies 27 data
points.
SAHFt GERDt TFPt Patentst Fertilityt Unempt RelEd_St Welfaret
Mean USA 4.19 2.52 0.88 54496 1.96 5.49 1.06 14.23
CAN 4.30 1.64 0.08 2060 1.63 7.92 1.03 16.76
Median USA 4.25 2.54 1.16 52742 2.00 5.83 1.06 14.30
CAN 4.25 1.62 0.15 1986 1.66 8.25 1.02 16.30
Maximum USA 5.60 2.65 3.37 89823 2.12 9.71 1.12 15.80
CAN 6.00 2.04 3.42 3606 1.76 12.00 1.07 20.50
Minimum USA 3.10 2.27 -2.77 30074 1.76 3.97 1.01 12.80
CAN 2.20 1.20 -2.70 867 1.49 6.00 0.99 13.20
Std. Dev. USA 0.71 0.10 1.44 20299 0.11 1.38 0.03 1.09
CAN 1.19 0.26 1.47 926 0.08 1.70 0.03 1.67
Skewness USA 0.42 -0.88 -0.64 0.36 -0.44 0.95 0.02 -0.04
CAN -0.43 0.13 0.21 0.28 -0.15 0.43 0.33 0.61
Observations USA 30 27 30 30 30 30 30 28
CAN 30 27 30 30 30 30 30 28

14. 13
4.3. Theoretical Predictions
Before outlining the empirical method, it is useful to discuss the theoretical predictions of
the regressors:
Technology;
As discussed in depth in the literature review, a positive correlation is expected between
technology and SAHF Households. As theorised by Katz and Murphy (1992), technological
progress has led to the polarisation of wages in the U.S., thus leading to a rise in female labour
force participation due to the overrepresentation of males in middle-skilled jobs. Also,
Greenwood (2012) argues that technology has contributed to changes in household production,
reducing the necessity of time allocation to domestic work.
Fertility Rate;
Consistent with the findings of Bloom et al. (2007), a fall in fertility rate is expected to
have a positive effect on SAHF Households, due to it increasing the volume of women in work,
with fewer of the constraints of having children.
Unemployment;
The effect of unemployment is ambiguous. As expressed in the literature review,
unemployment as a consequence of economic downturn may lead to the ‘added worker’ effect,
stimulating female participation, though it may equally discourage married women from joining
the labour force as they perceive there to be a high level of unemployment (Jaumotte, 2003).
Relative Education;
Kramer & Kramer (2013) evidence greater educational attainment increasing the
likelihood of SAHF Households, thus a similar effect is anticipated from an increase in the ratio of
relative secondary education of women to men.
Welfare Spending;
Since this variable does not provide specific information as regards to the specific target
of welfare spending, its effect on SAHF Households in this model is ambiguous. Anxo (2007),
argues that governments can increase female labour force participation by targeting welfare
spending at specific family policies and employment regimes.
4.4. Empirical Method
The formal econometric model attempts to provide empirical evidence supporting the
predictions stated. The analysis will focus on the U.S. example, before outlining any differences or
similarities for the Canadian data. As is typical of regression analysis, a general regression for

15. 14
preliminary analysis is first selected, before specifying a final model based on econometric
testing.
One crucial assumption of time-series regression analysis is that all variables are
stationary. It is common, however, for time-series data to have time-dependant movements.
Failing to correct for this may lead to a spurious relationship (Granger & Newbold, 1974).
As is clear from the graphs below, eyeballing the trends of the variables can indicate
whether non-stationarity is expected from the formal tests conducted.
Figure 2. Line Graphs of Variables

16. 15
At first glance, the majority of the variables seem to be non-stationary, with the exceptions
of TFP, and GERD. The Augmented Dickey-Fuller (ADF) test is a useful way of determining non-
stationarity, as it does not rely on the assumption that each variable has a random walk, instead
it allows for trends, and considers this when selecting the critical values to be tested against. The
below table displays the results from the individual ADF tests.
Table 3. ADF Tests: Results
Variables Country
Test
Statistic
1% Critical
Value
5% Critical
Value
10% Critical
Value
SAHF USA -2.139 -4.343 -3.484 -3.23
CAN -1.965 -4.343 -3.484 -3.23
GERD USA -3.018 -2.492* -1.711 -1.318
CAN -1.904 -4.371 -3.596 -3.238
TFP USA -4.992 -3.723* -2.989 -2.625
CAN -3.385 -2.473* -1.703 -1.314
Patents USA -3.064 -4.343 -3.584 -3.23
CAN -2.982 -4.343 -3.584 -3.23
Fertility USA -1.73 -4.343 -3.584 -3.23
CAN -0.677 -4.343 -3.584 -3.23
Unemp USA -2.344 -4.343 -3.584 -3.23
CAN -1.831 -4.343 -3.584 -3.23
RelEd_S USA -2.97 -4.343 -3.584 -3.23
CAN -1.557 -4.343 -3.584 -3.23
Welfare USA -3.303 -4.343 -3.584 -3.23
CAN -1.969 -4.362 -3.592 -3.235
For the majority of cases, the ADF test-statistics are greater than the 10% critical values.
Thus, for these variables we are unable to reject the null hypothesis that a unit root is present,
suggesting that they suffer from non-stationarity. Conversely, TFP, for the U.S. and Canada is
statistically significant at the 1% level, so too is GERD for the U.S. alone.
In order to correct for this, the first difference of the logarithm for non-stationary
variables will be used in the econometric model. After having conducted further ADF tests on each
of the variables generated by first differencing, it can be confirmed that they no longer suffer from
non-stationarity (see appendix).

17. 16
To begin the formal econometric analysis, a model including two lags of both the
dependent and explanatory variables is selected, with the view to remove any insignificant lags if
signified by the initial results. The first technology proxy to be modelled is TFP, with U.S. data.
4.4.1. U.S. Regression Analysis
Equation 1.
𝑫_𝑺𝑨𝑯𝑭 = 𝑪(𝟏) + 𝑪(𝟐)𝑫_𝑺𝑨𝑯𝑭(−𝟏) + 𝑪(𝟑)𝑫_𝑺𝑨𝑯𝑭(−𝟐) + 𝑪(𝟒)𝑻𝑭𝑷 + 𝑪(𝟓)𝑻𝑭𝑷(−𝟏)
+ 𝑪(𝟔)𝑻𝑭𝑷(−𝟐) + 𝑪(𝟕)𝑫_𝑭𝒆𝒓𝒕𝒊𝒍𝒊𝒕𝒚 + 𝑪(𝟖)𝑫_𝑭𝒆𝒓𝒕𝒊𝒍𝒊𝒕𝒚(−𝟏) + 𝑪(𝟗)𝑫_𝑭𝒆𝒓𝒕𝒊𝒍𝒊𝒕𝒚(−𝟐)
+ 𝑪(𝟏𝟎)𝑫_𝑼𝒏𝒆𝒎𝒑 + 𝑪(𝟏𝟏)𝑫_𝑼𝒏𝒆𝒎𝒑(−𝟏) + 𝑪(𝟏𝟐)𝑫_𝑼𝒏𝒆𝒎𝒑(−𝟐)
+ 𝑪(𝟏𝟑)𝑫_𝑹𝒆𝒍𝑬𝒅_𝑺 + 𝑪(𝟏𝟒)𝑫_𝑹𝒆𝒍𝑬𝒅_𝑺(−𝟏) + 𝑪(𝟏𝟓)𝑫_𝑹𝒆𝒍𝑬𝒅_𝑺(−𝟐)
+ 𝑪(𝟏𝟔)𝑫_𝑾𝒆𝒍𝒇𝒂𝒓𝒆𝑮𝑫𝑷 + 𝑪(𝟏𝟕)𝑫_𝑾𝒆𝒍𝒇𝒂𝒓𝒆𝑮𝑫𝑷(−𝟏) + 𝑪(𝟏𝟖)𝑫_𝑾𝒆𝒍𝒇𝒂𝒓𝒆𝑮𝑫𝑷(−𝟐)
Figure 3. Baseline Model: TFP (U.S.)

18. 17
When modelling time-series data, serial correlation is often found. This is when the residuals
of the variables used in the model are correlated with the residuals of the variables’ lagged
counterparts. Failing to account for serial correlation can exaggerate the goodness-of-fit, often shown
by an inflated R2, and can also lead to estimates with biased coefficients. The Breusch-Godfrey LM test
is used to determine the presence of first and second order serial correlation.
Figure 4. Test for First Order Serial Correlation (TFP, U.S.):
Figure 5. Test for Second Order Serial Correlation (TFP, U.S.):

19. 18
The null hypothesis of the Breusch-Godfrey LM test is that serial correlation is not present
in the estimation. Thus, at the 5% and 10% critical values of the tests for first and second order
serial correlation, there is insufficient evidence to reject the null hypothesis, since 0.8754 and
0.4417 are larger than 0.1, implying that the estimation does not suffer from serial correlation.
Therefore, the inclusion of additional lags is not necessary.
Heteroskedasticity is also tested for, which occurs when the variance of the error term is
not constant, and varies depending on the value of the explanatory variables. This can lead to
incorrect standard error terms which alters the confidence intervals, potentially allowing
variables to be accepted or refused incorrectly at a given significance level. The Breusch-Pagan-
Godfrey test is used to test for linear heteroskedasticity, as a lack of observations in the model
prevents the White test, a popular test of heteroskedasticity, from being estimated.
Figure 6. Test for Heteroskedasticity (TFP, U.S.):
The null hypothesis of this test is that the variances of the error terms are equal, i.e. there
is no linear heteroskedasticity. Again, at the 5% and 10% levels, there is failure to reject the null
hypothesis, allowing the assumption that the model does not suffer from heteroskedasticity.
Furthermore, it is important to account for the possibility of over-specification. Therefore,
any insignificant lags of the variables that do not explain the variation in the dependent variable
are removed. This is based on the size of the probability value and t-statistics. As they can vary
once certain variables are removed, it is essential to eliminate any insignificant lags in stages.
Figure 7. Variables D_SAHF(-1), and D_RelEd_S(-2) are removed:

20. 19
Equation 2.
𝑫_𝑺𝑨𝑯𝑭 = 𝑪(𝟏) + 𝑪(𝟐)𝑫_𝑺𝑨𝑯𝑭(−𝟐) + 𝑪(𝟑)𝑻𝑭𝑷 + 𝑪(𝟒)𝑻𝑭𝑷(−𝟏) + 𝑪(𝟓)𝑻𝑭𝑷(−𝟐)
+ 𝑪(𝟔)𝑫_𝑭𝒆𝒓𝒕𝒊𝒍𝒊𝒕𝒚 + 𝑪(𝟕)𝑫_𝑭𝒆𝒓𝒕𝒊𝒍𝒊𝒕𝒚(−𝟏) + 𝑪(𝟖)𝑫_𝑭𝒆𝒓𝒕𝒊𝒍𝒊𝒕𝒚(−𝟐) + 𝑪(𝟗)𝑫_𝑼𝒏𝒆𝒎𝒑
+ 𝑪(𝟏𝟎)𝑫_𝑼𝒏𝒆𝒎𝒑(−𝟏) + 𝑪(𝟏𝟏)𝑫_𝑼𝒏𝒆𝒎𝒑(−𝟐) + 𝑪(𝟏𝟐)𝑫_𝑹𝒆𝒍𝑬𝒅_𝑺
+ 𝑪(𝟏𝟑)𝑫_𝑹𝒆𝒍𝑬𝒅_𝑺(−𝟏) + 𝑪(𝟏𝟒)𝑫_𝑾𝒆𝒍𝒇𝒂𝒓𝒆𝑮𝑫𝑷 + 𝑪(𝟏𝟓)𝑫_𝑾𝒆𝒍𝒇𝒂𝒓𝒆𝑮𝑫𝑷(−𝟏)
+ 𝑪(𝟏𝟔)𝑫_𝑾𝒆𝒍𝒇𝒂𝒓𝒆𝑮𝑫𝑷(−𝟐)
Re-testing for first and second order serial correlation and the presence of
heteroskedasticity finds that the model does not suffer from any of those properties (see
appendix). Thus, having tested the robustness of the model, it can be concluded that it is a suitable
model with which to proceed to analysis. Due to there being lagged versions of the dependent and
explanatory variable, the model is defined as an autoregressive distributed lags model,
ARDL(1,2,2,2,1,2).

21. 20
The adjusted R2 is used to assess the strength of the model as a useful predictor of
variation in the dependant variable. This measures the explanatory power of the independent
variables in predicting variation in the dependant variable, taking into consideration the number
of variables in the model. This shows that 59.92% (2.s.f) of the variation in SAHF Households is
explained by the model, which is relatively high. When comparing this to the Baseline Model in
Figure 3, it is clear that removing unnecessary lags has improved the explanatory power of the
model by approximately 10 percentage points, suggesting that the previous model was over
specified.
The coefficients of the three TFP lags individually represent the average marginal
percentage change in the dependant variable arising from a unitary percentage point change at
each lagged period, this is because TFP is measured as a growth rate. Thus, since the first
difference of the logarithm of all other variables has been taken, these coefficients have the same
interpretation. In this case, at the 5% significance level we can conclude that when all other
factors are held constant, on average a 1% increase in TFP leads to a 0.0872% (4.s.f) decrease in
the amount of SAHF Households in the economy, as a proportion of all Husband-Wife families.
An interesting insight arises when the long-run effect of a change in TFP is considered.
This is calculated by summing the coefficients of the TFP variable and its respective lags. In the
long-run it has a positive effect on SAHF Households suggesting that a 1% increase in TFP leads
to on average a (= -0.087235 + 0.137781 =) 0.0505% (4.s.f) increase in the proportion of SAHF
Households. Since the second lag of TFP is not significant at the 10% level it is not included in this
calculation. This result is consistent with the theoretical prediction that technological progress
has reduced the value of female time allocation towards domestic work, and has subsequently
increased the likelihood of the Wife being the sole-earner of a single-earner family. The initial
negative relationship between technology and SAHF Households, however, provides an
interesting insight. This suggests that after an increase in technological advancement, there is a
delay in this affecting the propensity of families to opt for a SAHF family structure. This is
unsurprising as decisions to enter or exit the labour market are often constrained by the time it
takes to find employment, as well as the legal obligation to forewarn employers in advance of
quitting a job.
Furthermore, at the 5% significance level, a 1% increase in the growth of unemployment
would lead to an average 0.6185% (4.s.f) increase in the proportion of SAHF Households in the
long-run, ceteris paribus. This appears to be an economically significant result. It may suggest that
the ‘added worker’ effect, which stimulates female labour force participation, outweighs the
discouragement experienced by married women from searching for employment arising from the
perception of high unemployment (Jaumotte, 2003).

22. 21
Additionally, at the 5% significant level, a 1% increase in the percentage of welfare
spending as a proportion of GDP is shown to cause a 0.6412% (4.s.f) decrease on average in SAHF
Households at the second lag. This implies that U.S. welfare spending is not particularly targeted
at the specific family policies or employment regimes said to promote female labour force
participation outlined in Anxo’s 2007 paper.
Now, the same baseline regression is performed on the remaining two alternative proxy
variables of technology in turn. Consistent with the previous regression, two lags of the
dependant and explanatory variables are used in the initial estimation.
Tests for first and second order serial correlation, using the Breusch-Godfrey LM test,
were conducted on the resulting estimations.
Figure 8. Test for First Order Serial Correlation (GERD%GDP, U.S.):
Figure 9. Test for Second Order Serial Correlation (GERD%GDP, U.S.):
Both tests fail to reject the null hypothesis of no serial correlation at the 5% and 10%
levels, implying that the GERD model does not suffer from serial correlation.
Figure 10. Test for First Order Serial Correlation (PATENTS, U.S.):

23. 22
Figure 11. Test for Second Order Serial Correlation (PATENTS, U.S.):
Again, at the 5% and 10% critical values we fail to reject the null hypothesis for the model
using Patents as a proxy for technology, suggesting serial correlation is not present in the model.
Next, both models are tested for linear heteroskedasticity using the Breusch-Pagan-
Godfrey test.
Figure 12. Test for Heteroskedasticity (GERD%GDP, U.S.):
Figure 13. Test for Heteroskedasticity (PATENTS, U.S.):
The results of both tests show failure to reject the null hypothesis of no heteroskedasticity
at the 5% and 10% levels, thus allowing the assumption that both models have homoscedastic
error terms.
After removing any insignificant lags of the independent variables, based on their
probability values, the resulting model with GERD as the technology proxy is as follows:

24. 23
Figure 14. Variables GERD_GDP, D_FERTILITY, D_FERTILITY(-1), D_RELED_S, D_RELED_S(-2), and
D_WELFAREGDP are removed:
The first thing to notice is that the adjusted R2 is negative, which severely weakens the
interpretation of any findings as it has very insignificant explanatory power. This can be
interpreted as having an adjusted R2 of zero. This may be caused by the lack of observations used
due to the insufficient data available. Alternatively, it may be as a result of too many variables
being included.
Figure 15 below shows the results of removing the insignificant lags of the independent
variables in the Patents model.

25. 24
Figure 15. Variables D_SAHF(-1), D_SAHF(-2), D_PATENTS(-2), D_RELED_S, D_RELED_S(-2), and
D_WELFAREGDP are removed:
Similarly to the previous example, the adjusted R2 is very low at 0.0328 (4.s.f) thus
rendering the findings of the model of little use in terms of their interpretation as predictors of
the proportion of SAHF Households in the economy.
It can be concluded from these three models that the technology proxy with the greatest
explanatory power on the proportion of SAHF Households is TFP, and so it will be used to provide
the analysis of the macroeconomic factors contributing to the rise in SAHF Households.
4.4.2. Canadian Regression Analysis
In the same manner as with the U.S. example, separate models for each technology proxy
with two lags of the dependent and independent variables are estimated. They are then tested for
serial correlation and heteroskedasticity, resulting in three final models once insignificant lags
are removed, which are re-tested for these characteristics. The complete stages of these processes
are presented in the appendix. The three resulting models are given below.

26. 25
Figure 16. Final Model (TFP, Canada), Variables D_SAHF, D_FERTILITY, D_UNEMP(-1), D_RELED_S are
removed:
Figure 17. Final Model (GERD%GDP, Canada), Variable D_WELFAREGDP(-2) are removed:

27. 26
Figure 18. Final Model (GERD%GDP, Canada), Variables D_SAHF(-1), D_PATENTS, and D_PATENTS(-2)
are removed:
After conducting the same tests as outlined previously, not one of the three models
showed an indication of first or second order serial correlation, nor of linear heteroskedasticity.
Interestingly, contrarily to the results of the three U.S. models, all three adjusted R2 statistics show
that the models are well specified and have strong explanatory power. The adjusted R2 shows
that 66.15%, 55.50%, and 56.06% (2.s.f) of the variation in the dependant variable SAHF
Households can be explained by the independent variables in the TFP model, the GERD model,
and the Patents model respectively. Since the model with the greatest explanatory power is the
one that uses TFP as a proxy for technology, the findings of this model will be explored in more
depth.
The first noteworthy piece of information arises in the analysis of TFP. At the 1%
significance level, a 1% increase in the growth of TFP would lead to, on average, a 0.1741% (4.s.f)
increase in SAHF Households as a proportion of all Husband-Wife families. However, the effect of
the lags of TFP are not statistically significant even at the 10% level. Whilst this is slightly
dissimilar from the results obtained in the U.S. example, TFP is still shown to have a positive effect
on SAHF households, thus remains consistent with the theoretical prediction.
Furthermore, a novel insight from this model is that the lagged explanatory variable
D_SAHF(-2) is statistically significant at the 5% level, suggesting that a 1% increase in SAHF
Households would lead to a 0.4507% (4.s.f) decrease in SAHF Households after two lagged

28. 27
periods on average, other factors held constant. Interestingly, this is contrary to economic theory
regarding changing gendered expectations, which argues that as couples increasingly perceive
SAHF Households around them, their reluctance to adopt a SAHF family structure reduces, due to
a deterioration in gender role stereotypes (Kramer & Kramer, 2013; Chesley, 2011). A possible
explanation for the negative relationship shown by the model is that in the relatively short term
couples may be deterred from choosing a SAHF Household the more they come into contact with
an existing one. This may be as a result of an increased awareness of the negative stereotype of
stay-at-home fatherhood before the positive effect on changing gendered expectations is realised.
This model also has some predictive power in the long-run. At the 10% significance level,
a 1% increase in the fertility rate causes a 9.6275% (4.s.f) increase in the proportion of SAHF
households on average, ceteris paribus. This result has particular economic significance due to its
magnitude, however, the implications of this are more restricted due it not being significant at
the 5% level. Furthermore, this is inconsistent with the theoretical prediction evidenced by
Bloom et al. (2007), that a higher fertility rate increases the constraints imposed on women of
having children, thus decreasing the proportion of SAHF Households. A possible explanation of
this result is that an increase in the average number of children per family may in fact increase
the likelihood of a couple opting for a sole-earner family structure. This would most likely
increase the volume of SAHF Households as well as Stay-At-Home Mother Households, the
increased proportion of SAHFs may therefore be a result of there being a greater propensity of
the father choosing to stay at home than the mother.
Finally, welfare spending as a % of GDP is also significant at the 10% level, suggesting that
on average a 1% increase in welfare spending leads to a 0.3566% (4.s.f) increase in SAHF
Households in the long-run. This is consistent with the theoretical predictions, yet inconsistent
with the U.S. example. It suggests that in comparison with the U.S., Canada targets a greater
proportion of welfare spending at family and employment policies that aim to incentivise female
labour force participation.
4.5. Granger Causality Testing
To further test the significance of the models’ findings, Granger Causality tests, which
have been used extensively in econometric analysis since its conception in 1969, were conducted
to determine whether any causal relationships exist between variables. Granger Causality is
deemed to be in effect if the historic values of a given variable provide statistically significant
information on future values of another variable. This predictive power is based solely upon
lagged values of a variable (Granger, 1969). The below figures display the results of conducting
Granger causality tests for both countries. Each includes 2 lags of the variables keeping consistent
with the previous models.

29. 28
Figure 19. Granger Causality Test (U.S.):
Figure 20. Granger Causality Test (Canada):

30. 29
Looking at the Canadian results, it is interesting to note is that by analysing the probability
values, we can see that the fertility rate, unemployment rate, and proportion of welfare spending
as a percentage of GDP all appear to have a causal influence on SAHF Households, since the null
hypothesis of the variables not G-Causing SAHF Households can be rejected. Whilst
unemployment and welfare spending are both significant at the 10% level, the fertility rate is
significant at the 5% level, strengthening the previous evidence of a correlation with SAHF
Households. Importantly, a conclusion of causality can not necessarily be drawn, since the
relationship between the two variables may occur as a result of an unspecified process having
predictive power over both variables in question.
The results of the U.S. example, however, suggest no significant causal relationships,
which arguably limit the implications of the Canadian findings. One possible reason for an
underestimation of causality is the small number of lags used, as increasing the lags augments the
likelihood of any dynamic relationships being accounted for.
Finally, an additional dynamic relationship seemingly captured in the Canadian example
is that of the proportion of SAHF Households having a causal effect on the growth of TFP at the
5% significance level. Whilst this relationship requires further testing and analysis for it to be
considered as significant, it does offer an interesting insight into the potential implications of an
increasing proportion of SAHF Households. As previously stated, the ARDL model noted a
statistically significant positive relationship between TFP and SAHF Households. A positive causal
effect of the proportion of SAHF Households on TFP may be explained by the existence of gender
stereotypes and expectations dis-incentivising females from joining the labour force in place of
males in Husband-Wife families. Due to this, females would need to be able to significantly
outperform their male counterparts in work for the couple to elect a SAHF family structure.
Therefore, when SAHF Families do arise, this may contribute to a higher level of economic growth
and efficiency, evidenced by a rise in TFP. This finding appears to support ‘exchange theory’
(Kramer & McCulloch, 2010), suggesting that there is a greater likelihood of families to have a
SAHF Household when the wife’s earnings potential is significantly greater than that of wives in
other family structures. However, whilst the implications of a causal effect may offer an
interesting interpretation, it must be realised that the Granger Causality test implies that the
values of SAHF Households reflect useful predictive information with regards to the direction of
TFP growth, above true causality (Hamilton, 1994).

31. 30
5. Evaluation
The results of econometric modelling are ambiguous in the conclusion of the paper’s
primary research question: the effect of technological change on SAHF Households. Of the six
estimations specified, two provide statistically and economically significant evidence of a positive
correlation. At the 1% significance level TFP is shown to have a positive impact on SAHF
Households, a result that is consistent across both countries, albeit at contrasting lags. In neither
country is there a significant relationship evidenced between SAHF Households and GERD or
Total Patents, as proxies for technology. Noteworthy is the success of TFP as a predictor of
technical change (Easterly & Levine, 2001), which has been well documented due to its
interpretation as a measure of total output not caused by factor inputs, often referred to as the
‘Solow residual’.
The paper’s secondary objective is to assess the explanatory power of certain
macroeconomic variables on SAHF trends. The paper has evidenced a strong positive relationship
between the fertility rate and SAHF Households, prevalent across all Canadian estimations,
restricting the negative relationship documented by Bloom et al. (2007) to female labour force
participation. The paper also evidences a net positive effect of unemployment on SAHF
Households in the U.S., suggesting the ‘added worker effect’ outweighs any discouragement to
join the labour force resulting from the perception of high unemployment. However, contrasting
results across Canadian estimations significantly limit the strength of this conjecture.
Furthermore, the explanatory power of welfare spending on SAHF Households is inconclusive
due to the lack of in-depth analysis into the specific target of welfare spending regimes in each
country. The presence of contrasting results across the two countries provides a motivation for
further academic study due to the significance that this difference may have for public policy,
potentially highlighting certain benefits of targeting welfare spending. Equally, the possibility of
an increased volume of SAHF Households having a negative effect on the likelihood of other
couples adopting a SAHF family structure in the short-term, provides a novel in the literature,
offering a topic for further exploratory study.
The paper’s success in achieving these objectives, however, is hindered by certain data
limitations which must be evaluated, especially considering the novelty of the use of econometric
analysis in the literature. The broad definition of the proxy variable for SAHF Households
allowing for the inclusion of couples without children, limits the interpretation and implication
of any findings. The data that is needed for a successful cross-country analysis that accounts for
this limitation is currently unavailable, thus would necessitate a change in the collection of data
regarding family structure by international databanks. Equally, a lack of sufficient data for several
key variables has led to estimations with very few observations, limiting the strength of the

32. 31
conclusions that can be drawn. Having insufficient observations increases the likelihood of the
model not accounting for true relationships, or overestimating others.
6. Conclusion
The effect of technology on SAHF Households has previously been unexplored. This paper
uses extensive time-series econometric modelling to analyse the potential relationship that exists
as a result of a combination of the role technology has played in increasing female labour force
participation, and reducing the necessity of time allocation to domestic work. In considering the
issues faced by economists when empirically analysing the effects of technological change, three
separate technology proxies are selected in a bit to strengthen the paper’s conclusion. The
strength of the argument that technology has had a positive effect on SAHF Households relies on
the suitability of TFP as a proxy. Whilst further exploratory study is needed to substantiate this
conjecture, the paper nonetheless contributes to the literature by being the first to evidence a
positive relationship between technology and SAHF Households, as well as by providing
empirical support for several other macro-level determinants which must be accounted for in
future studies.
The paper offers two novel insights which, if supported by further academic study, may
have significant implications for public policy. Firstly, the paper evidences contrasting effects on
SAHF Households of the two countries’ welfare spending regimes. Future studies should focus on
the consequences for SAHF Families of varying welfare states, as this may offer key insights into
the significance that certain benefit structures may have for micro-level decision-making
regarding family structure. Secondly, the paper argues that an increase in SAHF Households may
have a positive effect on TFP itself, which could enable governments to restructure welfare
spending in a way that might benefit long-term economic growth.

33. 32
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