The Impact of Canada’s Family Caregiver Amount Tax Credit in Ontario
By
Ben Segel-Brown
A research report submitted in fulfillment of the requirements for PAPM4908 as credit
towards the degree of Bachelor of Public Affairs and Policy Management [Honours]
Arthur Kroeger College of Public Affairs
Carleton University
Ottawa, Ontario
Abstract
This paper looks at the impact of the recently introduced Family Caregiver Amount Tax
Credit on care givers in Ontario. It builds a micro-economic model of the decision to
provide care at home or in an institution (nursing home) and analyses data from the General
Social Survey, Cycle 16, 2002 [Canada]: Ageing and Social Support. It finds that the tax
credit should create efficiency and equity gains, but it is unlikely to have a substantial
impact given its small size. Its findings suggest that the Family Caregiver Amount Tax
Credit should be made refundable and substantially increased in size.
Acknowledgements
Firstly, I would like to thank my supervisor, Åke Blomqvist for his time, his
guidance and his support for introducing me to research and writing in the field of health
economics. I would also like to thank thoughtful reviewers of this document who provided
valuable substantive feedback. I would also like to thank my parents for their ongoing
encouragement and support.
Table of Contents
Introduction .............................................................................................................. 1
Exposition .................................................................................................................. 4
Literature Review ................................................................................................... 13
Theoretical Framework ......................................................................................... 18
Data Analysis........................................................................................................... 36
Conclusion ............................................................................................................... 54
Works Consulted .................................................................................................... 55
1
Introduction
This report provides an economic analysis of the impacts of Canada’s Family
Caregiver Amount Tax Credit introduced by the Harper Government for the 2012 tax year,
which reduces the taxes of those who provide care for a dependant elderly person in their
home. This report focuses on three aspects of the impact of the credit: efficiency, equity,
and effectiveness. This paper shows that the Family Caregiver Amount Tax Credit could
theoretically lead to efficiency gains (i.e., it could make families better off at lower cost),
but is unlikely to be effective (i.e., its size is not substantial relative to the costs of longterm care). The data analysis suggests that the credit is regressive at lower incomes (i.e.
below $15,000) but progressive beyond the point at which positive taxes are owed. The
paper has four main sections: an exposition, a literature review, a theoretical model and a
data analysis. Each of these sections contributes to an understanding of the efficiency,
equity, and effectiveness of the tax credit.
The potential efficiency gains associated with the credit are demonstrated through a
graphical analysis of the theoretical model, which shows that the tax credit could reduce the
market distortion created by government provision of institutional care, and therefore create
efficient substitution of home care for institutional care. However, this report also shows
that these efficiency gains may be partially off-set by the inefficiencies resulting from the
federal subsidization of home care through the Medical Expenditures Tax Credit and
provincial provision of home care.
The equity impacts of the Family Caregiver Amount Tax Credit are revealed though
the exposition and data analysis. The exposition shows that the tax is regressive (i.e., it
2
provides greater tax relief to high income persons as a portion of their income) up until the
point where positive taxes are paid, but is progressive beyond that point. Likewise, the data
analysis suggests that there are substantially higher rates of care provision above the point
at which positive taxes are owed compared with below it, but that beyond this point care
provision declines with income. This indicates that the tax credit is regressive up until the
point at which positive taxes (more precisely $300 in taxes) are owed as in this range higher
income individuals receive larger tax credits and lower income individuals are less likely to
be providing care. However, beyond that point the tax credit is progressive as the credit is
an increasingly small amount of higher income families’ incomes and rates of care
provision decline with income. Because those with dependant relatives have a greater need
for income, it is debatable whether these higher income families with relatives to care for
actually have a greater ability to pay taxes, so the tax credit, while regressive, may still be
equitable.
This paper also explores how effective the Family Caregiver Amount Tax Credit is
likely to be in achieving efficiency gains (i.e., substitution of home care for institutional
care) and equity gains (i.e., addressing disparities in ability to pay taxes at a particular
income level related to care responsibilities). The exposition shows that the value of the
credit to the caregiver ($300 on top of an existing $660.30) is not substantial compared to
the costs of providing care, so little gains in efficiency or equity are likely to be achieved.
The literature review provides several measures of the benefits and costs of long-term care,
including impact on family income, impact on quality of life, and willingness to pay. The
impact of intensive personal care responsibilities (90+ minutes per day) on income and
quality of life measures is modeled as part of the data analysis. It was hoped that this would
3
provide some indication of the magnitude of the burden of care, and consequently the
degree to which the credit was compensating for that burden. However, the analysis shows
intensive care givers find life only slightly more stressful, and being a care provider was not
significantly related to life satisfaction, EI income, or health. The highest rates of care
provision were in the personal income brackets slightly below the Canadian average, but it
was not possible to determine to what degree this higher rate of care provision is a result of
a lower opportunity cost of their time as opposed to lost income resulting from providing
care. As a result no estimate of the financial burden of care in terms of lost income can be
made. Rates of care provision did not change dramatically between middle and higher
income respondents, see “Household Income Distribution and Profile of Care Providers” in
Table 1G. These results did not provide a means of creating a reliable estimate of the size
of the Tax Credit that would be needed to fully compensate intensive care givers for the
burden of care, which would in turn provide an indication of the degree to which the credit
is effective in compensating for that burden. The literature review also explores the
substitutability of outside care, care by relatives and care in institutions, finding variable
substitutability depending on medical condition. This is relevant because, regardless of
value, the tax credit will only be effective in creating substitution of home care for
institutional care if it is actually practical for families to do so.
4
Exposition
Importance of Topic
This research is timely because the Government of Ontario is facing a shortage of
places in Long-Term Care homes and at the same time pressures to reduce its deficit. With
an aging population and growing debt, both of these problems can be expected to worsen in
the coming decades, with Statistics Canada projecting that the number of dependent seniors
will triple over the next 50 years (Grignon & Bernier, 2012). A federal subsidy
compensating care givers of the elderly may offer a solution to these problems by diverting
demand away from publicly funded institutions (hospitals and nursing homes).
Defining Long-Term Care Recipients
By definition, those with long-term care needs require assistance with the
instrumental or basic activities of living (Grignon & Bernier, 2012, p. 4). To illustrate,
eating is a basic activity of living, and grocery shopping is an activity instrumental to
eating. An elderly person who needs help eating or shopping would be considered to have a
long-term care need. Long-term care is differentiated from acute or rehabilitation care in
that it is expected that care will be required for an extended period of time, often the
remainder of the patient’s life. This report distinguishes between long-term care in
institutions (nursing homes) and home care that takes place in the caregiver or recipient’s
home. Home care can be provided free of charge by friends and family, or by paid outsiders
(Grignon & Bernier, 2012, p. 5). The focus in this paper is on care recipients with severe
needs, such that they are eligible for care in an institution but could still be cared for in a
caregiver’s home with assistance. The data analysis section of this paper focuses on those
5
providing intensive (90+ minutes per day) personal care (bathing, toileting, care of
toenails/fingernails, brushing teeth, shampooing and hair care or dressing). The focus is on
personal care activities because these were most closely associated with providing care in
the home (as opposed to helping with shopping or to pay the bills) and the threshold of 90
minutes per day was chosen as an indicator of the level of need that would be needed to
qualify for a space in a nursing home. This threshold was validated by comparing the
portion of care providers above and below the intensity who believed the government
would take over if they were unable to continue providing care; those providing more than
90 minutes of care per day were almost five times as likely to believe the government
would take over if they were unable to continue providing care compared with those
providing 30 to 89 minutes per day (Table 4b).
Government Subsidies
This section outlines the current system of subsidies and the new Family Caregiver
Amount Tax Credit, the incremental impact of which will be the focus of this report. The
subsidy system described here is that of Ontario, Canada. Long-term care, both in
institutions (nursing homes) and in home care, is a peculiar part of Canada’s health care
system in that it is not covered by the Canada Health Act (Health Canada, 2004). As a
result, it is not subject to the strict requirements of universal public coverage that apply to
other aspects of health care, enabling funding mechanisms that include private financing. In
Ontario, the provincial government funds long-term care and outsider home care, while the
federal government provides tax credit for those who directly provide or pay for the care of
an elderly dependant in their home and to offset the costs of home care.
6
In Ontario, the medical and personal services provided in institutions (long-term
care/nursing homes) are fully funded by the Ontario government, which provides homes
with $152.94 per day for each resident in their care, adjusted for the needs of the home’s
resident mix. In addition there are a variety of funding envelopes for particular purposes
such as for the payment of registered practical nurses. Accommodation and food charges
for long-term care are $55.04 per day for a basic bed and more for private and semi-private
rooms, but low income seniors who cannot afford this per diem can apply for a rate
reduction that leaves them with a $132 per month comfort allowance (Ontario Ministry of
Health and Long-Term Care, 2012). In order to enter a long-term care home, seniors must
be assessed by Community Care Access Centres which will determine their eligibility and
will place them on a waiting list (Ontario Ministry of Health and Long-Term Care, 2012).
There are currently 19,000 eligible seniors in Ontario who are on the waiting list, so this
waiting list is a substantial rationing mechanism (Ontario Association of Non-Profit Homes
and Services for Seniors, 2013). This waitlist is likely inefficient as the per diem cost of
care in a hospital bed for an Alternative Level of Care (ALC) patient, i.e., a patient in a
hospital who could be cared for in a long-term care institution, is more than five times
higher, at $842/day, and many persons on the waiting list end up as ALC patients in acutecare hospitals (Ontario Home Care Association, 2012).
There are also a variety of services available to assist seniors in their home. In
Ontario, these services are delivered by family and friends, volunteers, provider
organizations, commercial retailers, and community centers. These care providers are
funded by the government, donations to voluntary organizations, private insurance plans, or
the individual (Ontario Ministry of Health and Long-Term Care, 2012). This report only
7
distinguishes between the two most common types of home care: paid outsider home care,
and unpaid care by family members. A limited amount of outsider home care is publicly
provided by the province. Medical expenditures for infirm dependants (including
expenditures on home care services) are subsidised through a federal Medical Expense Tax
Credit (Canada Revenue Agency, 2013). Family members who are providing care do not
receive any subsidy except through the non-refundable tax credit discussed below, which
does not depend on the hours of care provided. Between 66% and 84% of seniors primarily
receive care informally from friends and family free of charge (Grignon & Bernier, 2012).
For the 2012 tax year, the Harper government introduced a non-refundable Family
Caregiver Amount Tax Credit at the federal level. The impact of the introduction of this tax
credit is the focus of this paper. A tax credit is an amount deducted from your taxes owing.
Tax credits are multiplied by 15% (the lowest income bracket’s tax rate) and this is the
amount deducted from taxes owing. A non-refundable tax credit is a tax credit for which
you will not get a refund from the governement if your tax credits exceed your taxes owing
(Canada Revenue Agency, 2013). The Family Caregiver Amount Tax Credit is $2000 (a
$300 reduction in taxes), on top of an existing ‘Amount for infirm dependants age 18 or
older’ of $4,402 (a $660.30 reduction in taxes) for those who provide care for a low-income
relative in their home due to a physical or mental condition (Intuit, 2013). Because the
credit is for a fixed amount and is non-refundable, the tax is regressive up to the point
8
where the full value of the credit can be claimed and constant beyond that point as shown in
Figure 1:1
Figure 1: Absolute Value of Family Caregiver Amount Tax Credit
The term “regressive tax credit” has rarely been used in economic literature. A
regressive tax is one that takes a larger amount, as a percentage of total income, from lowincome people than from high-income people. This can occur at the individual level; for
instance, a flat tax that took the same amount of money from all people would take a larger
amount from the poor as a portion of their income. It can also occur due to aggregate
patters of consumption where lower income people spend a larger portion of their income
on a taxed good, such as cigarettes, than higher income individuals. In addition, a tax can
be more or less regressive due to the behavior response to taxation, for instance cigarette
taxes are slightly less regressive than they would otherwise be because the poor have a
1
Note that the value at which the Family Caregiver Amount Tax credit becomes useful depends on
the other deductions and credits available, the threshold of $14,000 used here is the approximate sum of the
personal amount and the existing tax credit for care givers.
9
more elastic demand for cigarettes – their consumption and therefore tax burden decrease
more in response to an equal increase in tax (Remler, 2004).
It follows that a regressive tax credit is one that reduces the taxes, as a percentage of
total income, of higher income individuals more than that of lower income individuals. An
example of this is the income tax deduction for federal taxes paid that exists in several US
states, which greatly benefits the rich because they both have a larger deduction (because
they pay federal taxes on a larger income at a higher rate) and get a larger reduction in taxes
owing, as a result of a given deduction (because their remaining income is taxed at a higher
rate) (Citizens for Tax Justice, 2009). Figure 2 shows that, as a portion of the income of a
care provider, the tax credit is regressive up to the threshold at which positive taxes of $300
would otherwise be owed (around $16,000 with only the Basic Personal Amount and the
existing Caregiver Amount). Whether the tax credit is considered progressive or regressive
beyond this point depends on the point of comparison. The tax credit will always be worth
more (as a percentage of income) to those with higher incomes than it is for those who do
not owe positive taxes (for whom it was worth nothing), but its value as a percentage of
income does substantially decline with increasing income meaning it is technically
progressive in this range. Figure 2 does not capture the differential tax impacts arising from
the higher rate of care provision of lower income individuals still owing positive taxes.
Note also that, the value technically depends on the income of the higher wage earner in a
family with pooled resource, but this does not greatly affect the conclusions here given the
very strong relationship between personal and family income.
10
Figure 2: The value of the Family Caregiver Amount Tax Credit as a Percentage of Personal Income.
The non-refundable Family Caregiver Amount Tax Credit introduced for the 2012
tax year is likely to have a negligible impact in terms of substitution between institutional
and home care as a $2000 tax credit ($300 per year) is not a meaningful amount relative to
the costs and burdens of providing long-term care. For example, the public subsidy for each
nursing home patient is about $54,000 per year over and above the private cost of around
$20,000 per year (Ontario Ministry of Health and Long-Term Care, 2012). Another
problem is that the tax credit is non-refundable, meaning that it is only deducted from taxes
owing and not reimbursed to low income individuals whose credits exceed their taxes
owing. As a result it will only influence the decisions of individuals in families with an
individual who currently pays positive taxes. Further, if the family does not pool resources,
it is possible the care decision maker will not receive much benefit from the tax credit (for
11
instance if their spouse counts it against their taxes owing and keeps the savings in a private
account spent for their own benefit). If the tax credit was made refundable, it would impose
no additional burden on families to require the credit be claimed by the caregiver, ensuring
it influences their decision.
Research Question
This report asks: Is the Family Caregiver Amount Tax Credit efficient (in the sense
of reducing costs to government while improving quality of care) equitable (in the sense of
redistributing the tax burden away from those with less ability to pay) and effective (in the
sense of creating substantial substitution from institutional to home care or compensating
care givers for the burden of care)? This reflects my interpretation of the two potential
microeconomic2 purposes of the tax credit, to relieve the burden on institutional care
settings, and to help relieve the burden of care. This paper suggests that there are potential
efficiency improvements associated with the tax credit and that it is progressive, although
not necessarily inequitable. However, the small size of the credit means it is essentially
meaningless in the scale of decisions involving care for the elderly. This section more
explicitly defines efficiency, equity, and effectiveness. It also outlines the results I expected
my analysis to produce, i.e., the initial hypotheses.
Efficiency Proposition
Efficiency improvements are a micro-economic concept that refers to a net utility
gain, i.e., a situation where the benefits to all actors outweigh the costs of a policy option.
2
The credit was introduced as part of Canada’s Economic Action Plan suggesting it was intended to
serve the macro-economic purpose of promoting growth, but this doesn’t make much sense given we are no
longer in a recession. I also exclude rationales rooted in the potential political advantage to be gained. The
assigning of the name ‘Family Caregiver Amount Tax Credit’ to what is effectively an increase in the
‘Amount for infirm dependents age 18 or older’ was almost certainly done for the political reasons of being
able to attract publicity and gain political advantage.
12
In this case, I expected to find that the elderly are happier and less expensive to care for
when cared for in a relative’s home (as opposed to an institution), but the caregiver bears
greater private costs when providing care in the home. Given the existing market
distortions, including free rationed institutional care and limited public funding of outsider
home care, this paper’s theoretical analysis (presented later) supports the claim that the tax
credit could result in an efficiency improvement, although this benefit may be partially
offset by the existence of inefficiency in the home care market.
Equity Proposition
I had also expected that the credit would be equitable in the sense of having
desirable distributional consequences. By equity, I refer to vertical equity, a principle that
requires that individuals with a less ability to bear the tax burden should pay less. I
expected the Tax Credit to have desirable consequences in terms of vertical equity, as I
expected the policy to reduce the tax burden on the poor, women, and families with
dependent elderly persons.
A tax credit that provides greater benefits to the poor promotes vertical equity in
that it provides an additional means of reducing the tax burden on those with the least
ability to pay. In addition, I expected the poor to be more likely to substitute (and therefore
benefit) as a result of the credit, as fixed sums (like the tax credit) are more significant
incentives for them due to the diminishing marginal utility of consumption. I also expected
that the policy would benefit women, who are generally thought to be disproportionally
care givers for the elderly and care recipients in the home. Finally, I expected that the credit
would promote vertical equity in that it would redistribute the tax burden away from those
families with an elderly relative to care for, and thus less ability to pay, to families with a
13
greater ability to pay. The data analysis presented later shows that the tax credit is
progressive above $15,000 in personal income and would disproportionately benefit
women who are 4.5 times as likely to provide care (Table 4b). The general proof of utility
gains resulting from insurance against risk (here the risk of an elderly relative needing care)
could be applied to show the value of equity gains.
Effectiveness Proposition
By effectiveness, I refer to the degree to which the Family Caregiver Amount Tax
Credit results in substitution leading to efficiency improvements or provides substantial
compensation relative to the burden of care. With a value of $300, the Family Caregiver
Amount Tax Credit is insubstantial relative to the cost to government and burden on care
givers associated with long-term care. This is discussed in the graphical analysis and data
analysis presented later in this report.
Literature Review
There have been cost-benefit or cost-effectiveness analyses of various programs
related to long-term care including: leave to provide care for dying relatives (Negera,
2009), treatment of traumatic brain injury (Faul, 2007) and neonatal intensive care for
premature infants (Walker, 1984). However, as one meta-analysis of economic studies on
long-term care put it, “Few studies attempt any evaluation and those that do use varied,
inconsistent and controversial methodologies” (Smith and Wright, 1994). This may be a
result of the improbability of any government deciding to eliminate long-term care, moral
objections to the likely conclusion under existing methodologies that long-term care is not
worthwhile, or the plethora of theoretical and practical challenges involved in performing
such an analysis, which are outlined in detail in the literature. Because this paper must draw
14
on a variety of research areas to address the aspects of its cost-benefit analysis, this review
offers a broad overview of existing literature.
There have been some limited applications of contingent valuation to estimate the
marginal costs and benefits of home and institutional care for care givers and patients.
Contingent valuation assigns a value to a service by directly asking people, in a survey,
how much they would be willing to pay (WTP) to receive or not to have to provide it.
Several studies have found that contingent valuation can be effectively applied, but there is
substantial variation between individuals and studies result in estimates of marginal cost
(WTP) from £9.52 euros, about $12 US, to $10.54 US per hour (denBerg et al., 2005).
Interestingly, this is slightly larger than the value of care to recipients. Another study put
the marginal value at a less likely value of £0.38 or about $0.60 US per hour (Mentzakis,
Ryan, & McNamee, 2011). . However, all these studies looked at the marginal impact of an
increase or decrease in the number of hours of care provided, not the overall value of care.
Further these studies do not capture the significant psychological cost to care givers or care
recipients arising from their placement in a nursing home as opposed to receiving care at
home (Abel, 1990).
Other relevant empirical studies have taken approaches other than contingent
valuation. For instance, Fevang et al. (2012) looked at labour force participation to show
that, in the years prior to their parent’s death, children are less likely to be employed (all
other things being equal), with employment at 0.5 to 1% lower among sons and 4% lower
among daughters. Dunham and Dietz (2003) additionally showed that in many cases
women change jobs in order to receive the flexibility they need to take care of aging
15
relatives, suggesting that providing care impacts both quantity and productivity of
employment.
In terms of the substitutability of care by family for paid home care or institutional
care, the results are mixed, suggesting that the substitutability of care depends on the care
recipient’s medical condition. Brach and Jette (1962) used multiple regression to identify
the factors that lead individuals to enter long-term care and how differences between
populations in home care and institutional care in terms of age, need for ambulatory aids,
mental disorientation, and needs for assistance with the activities of daily living, may limit
the substitutability of home care. Dunham and Dietz (2003) showed that informal care is an
effective substitute for long-term care as long as the needs of the elderly are low and
require unskilled care. Lee and Young-Sook (2012) confirmed this result finding high
substitutability for diabetics and low substitutability for patients with high blood pressure
and mental illness. This severity-dependant substitutability has been picked up in the
theoretical models discussed below.
Another approach to determining substitutability is to look at the skills required to
perform the tasks currently being performed by paid outsiders. Unregistered workers, i.e.,
home care providers other than registered nurses, licensed practical nurses, or social
workers, represented just over 75% of formal home care providers in 2001 (Grignon &
Bernier, 2012). In a study in British Columbia, only 10% of outside home care providers
were registered professionals (Grignon & Bernier, 2012). Consistent with this finding,
71.2% of care (by hour) funded by the government of Ontario was for personal
support/homemaking, 24.8% for nursing and 3.9% for therapy providers (Ontario Home
Care Association, 2012). This suggests that there is substantial substitutability of paid home
16
care for care by relatives because unregistered care providers do not have any specialized
training and their work could be done by a physically able relative.
Another approach to economic valuation is the discrete choice experiment
methodology which imputes preferences by asking care providers and recipients to choose
between two bundles of different types of care and money. Mentzakis et al. (2011) assessed
care recipient and care provider preferences for combinations of different types of care and
money. While formal care and informal care generally acted as substitutes, they found that
formal care complements informal supervision, and argue this finding occurred because the
close emotional relationship between care givers and receivers made it difficult for them to
agree to substitute away from assisting with the activities of daily living. While
complementary in terms of value, this suggests there is significant inefficiency associated
with outsider home care in that it necessitates more supervisory work for family care
givers, despite reducing other burdens. This finding of a complementary relationship might
cause one to worry that removing the subsidy for home care (and thereby increasing its
price) might decrease the amount of home care provided by family. However, this
relationship only existed for supervision by family which does not directly contribute to
care and not for other care activities like personal care by family. Thus, while supervision
would be expected to decrease with an increase in the price of home care, there is no reason
to believe this decreased supervision will negatively impact care recipients. Monetary
incentives were also found to be significant for younger care givers, but not for older care
givers. However, the external validity of these results is questionable as Mentzakis et al.’s
estimates of the value of care, about $0.60 US per hour, seems implausibly low. It also
relied on a small sample with a low response rate.
17
There have also been a variety of attempts to address theoretical challenges in
valuing long-term care and family decision making. Kuhn and Nuscheler (2011) developed
a model of health care with two levels of care (high need and low need) and two settings
(home and institution). Institutional care provides better health care, which creates greater
utility gain for high needs than for low needs patients, but those in institutional care
suffered a fixed disutility. This model showed that those with more severe needs will selfselect to receive long-term care in institutions. Their model further incorporates information
asymmetries to conclude that care in institutions will be overprovided due to its
informational superiority, i.e., because the government can easily tell care recipients
actually need care. Alternately, Pestieau and Sato (2008) assumed complete information
and looked at efficiency in the context of mixing provision of formal and informal care
with different levels of child income. Rodrigues and Schmidt (2010) complement these
mathematical models with a broader explanation of the rationale for state intervention in
long-term care. This result arises from their consideration of the value of insurance and the
necessity of government intervention to make insurance work due to moral hazard and
adverse selection. Similarly, Wan outlines considerations for the evaluation of long-term
care programs.
Another interesting body of economic research looks at the political economy of
long-term care. This literature is useful because it takes a more comprehensive view of the
costs and benefits of long-term care, including both financial and psychological burdens, as
well as how they might be measured. Roeder and Nuscheler’s model shows that public
spending on long-term care reduces need related income inequality. From an empirical
public-economy angle, public polling shows that self-interest is a significant factor in
18
support for medical benefits for the elderly (Day, 1993), and that support is weakest in
young adults with less contact with their grandparents (Silverstein and Parrott, 1997).
Theoretical Framework
Introduction
This research is broadly built on the theoretical framework of micro-economics
because of its primary concern with efficiency, equity and effectiveness -- key concepts
which are used to evaluate policy options. This paper also draws on the framework of
health economics by focusing on measures such as Quality Adjusted Life Years. More
specifically, this paper builds on the theoretical model of Kuhn and Nuscheler (2011),
whose two-setting model of home and institutional care showed that that those with more
severe needs will self-select to receive institutional care, and institutional care will be
overprovided relative to home care due to its informational superiority, i.e., the government
can tell the person needs care and measure that it was delivered.
Incorporating Interdependent Utilities
This paper looks at a caregiver maximizing a weighted sum of the utilities of family
members. This approach differs somewhat from the classical microeconomics assumption
of an independent self-interested decision-maker. The main reason for this adaptation is
that, in the context of long-term care decision-making, it is a central and common feature of
the existing models discussed above that they include altruism whereby the utility of the
caregiver is partially dependent on that of the care recipient. Recipients of long-term care,
particularly those in nursing homes, may also lack the mental capacity to understand what
is in their best interests due to dementias, so the legal power over their assets and in health
19
care decisions (Power of Attorney) is often granted to their caregiver (Ontario Ministry of
the Attorney General, 2008).
Economics provides a variety of concepts that could be used to describe this
relationship. For example, the model could state that care for the elderly provides a positive
caring externality to their care givers (i.e., there are benefits not captured in the market
transaction between care supplier and care recipient), or one could define a utility function
maximized by the care giver that includes the happiness of the care recipient. This second
approach is adopted by Khun and Nusheler who assume a fully altruistic3 only-child acting
as a decision maker for the care of a single parent. However, they give the function for the
child’s decision in a more general form that allows for multiple levels of altruism: Child’s
Utility = Comfort (Own Consumption) + Altruism * Parent’s Comfort (Care)
This altruistic decision maker approach is functionally equivalent to treating the
family as a rational decision making unit with pooled resources4, and consistent with this,
the child and family are used interchangeably as the actor in narrative descriptions of
decision making by Khun and Nusheler. Another equivalent concept that could be applied
is that of agency, where care givers partially act as agents for their elderly relatives. This
understanding would be consistent with the expectation that care givers will speak on
behalf of their parent’s interests in relations with health providers (Power of Attorney for
Personal Care) and over their assets (Continuing Power of Attorney For Property).
However, the only ‘enforcement’ mechanisms present are internal and social pressures (or
3
In full altruism, equal weight is given to the happiness of the parent and child.
Not all families will function in this way and this could impact the efficiency impacts of the tax
credit. For instance if a wife provides care, but the tax credit is counted against her employed husband’s
income (perhaps because she did not reach the income threshold at which positive taxes are owed and the
credit would be valuable) and they do not share resources, her decision may be less influenced by the tax
credit.
4
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inheritances) and the idea of a care giver acting as an agent for a care recipient whose
interests are in competition with their own self-interested desires is inconsistent with the
focus of microeconomics on financial incentives.
Model Focus
This report’s model assumes a family with one employed potential care provider
and one care recipient where resources are pooled and the recipient’s condition is severe
enough that they are eligible for care in a nursing home, but manageable enough that they
still could be cared for in the provider’s home. This is the basic unit required for the tax
credit to be relevant. A care recipient with no potential providers cannot claim the tax
credit, regardless of whether they are purchasing care in their home or receiving care in a
nursing home. The care provider would likely have to be employed in order to reach the
threshold at which positive taxes are owed and non-refundable tax credits become
relevant.5 It does not particularly matter whether the care provider is a child or spouse,
except that a spouse is less likely to be working and therefore less likely to reach the
threshold at which non-refundable tax credits become relevant. The pooling of resources
(i.e., pensions and benefits of the care recipient are controlled by the care provider) is
significant because it simplifies the problem to a tradeoff of time and monetary resources
between the direct benefits to the provider and care for the care recipient, which is critical
in the graphical analysis below. It should be noted that the Family Caregiver Amount Tax
Credit does not benefit care recipients with no potential care providers who are willing to
look after the care recipient in their home. Where the care recipient could get by on care in
5
Alternately, the care provider could have non-employment (interest and dividend income) or could
have a spouse who was employed, but in the data analysis below most care givers have employment income.
21
their own home, but instead inefficiently substitutes care in a caregiver’s home to receive
the tax credit, the credit likely creates an efficiency loss.
Mathematical Theoretical Model
The mathematical theoretical model presented here is intended to describe the major
factors in a caregiver deciding how to care for a dependent care recipient who is eligible for
institutional care. The care provider maximizes a family utility function (the maximum
possible W value between equations 1a and 2a), a weighted (x) sum of the care recipient’s
utility (UR) and the care providers’ self-interested utility (UP), subject to the time (TP) and
budget (CP) constraints. In this basic form, the model describes the choice of care options
for any period up to the death of the elderly relative. The care provider’s utility is a function
of their leisure (LP) and consumption (CP).6 The care recipient’s utility is a constant (URI) in
institutional care, otherwise it is a function of the number of hours of care provided at home
by relatives (AP) and by paid home care providers (D).7 The time function (Equations 1b
and 2b) represents that the care provider’s time (TP) must be allocated between hours of
work (KP), hours of leisure (LP) and hours of care for the care recipient (AP). The budget
function (Equations 1c and 2c) represents that the amount left for the care giver’s
consumption is the care giver’s employment income plus the government benefits and
pension income of the elderly relative (BR) and the care provider (BP), less the costs of care.
Employment income is the product of the hours worked (KP) and the care giver’s wage net
6
Consumption and Leisure includes any altruistic value the care provider receives from spending
time or money on activities with other family members such as children or grandchildren. Their utility is also
dependent on the utility of their parent, but this altruism is separately captured in the weighing of the decision
making criteria, the focus of this term is on their self-interested utility.
7
Although of some relevance, consumption of the parent is excluded here for the sake of simplicity,
because the primary concern of the care provider is with their health, rather than happiness, and many
receiving care may be physically or mentally incapable of making consumption decisions.
22
of tax (n). The financial costs of institutional care (I)8 may depend on the care recipient’s
income (BR),9 while the cost of outside home care is the product of the hours worked by
paid home care providers (D) and their hourly rate net of subsidy (r).
Home Care
Institutional Care
WHome = x * UR(AP,D) + UP(LP, CP)
(1a)
WInstitution = x * URI + UP(LP, CP)
(2a)
TP = KP+LP+AP
(1b)
TP = KP+LP
(2b)
CP = KP*n + BR + BP – D*r
(2b)
CP= KP*n + BR + BP – I(BR)
(2c)
In practice, many elderly persons receive home care for some time before moving
into institutional care. The model here is thus not static, but rather dynamic as UR(AP,P) and
URI change over time as the care recipient’s health declines until they reach the point
modeled by Kuhn and Nuscheler where the greater medical care available in institutions
tilts the balance, such that the care provider decides to rely on institutional care. However,
the same factors that make home care more or less desirable as a static choice are the same
factors that will delay this decision as to when to turn to institutional care, so this static
model is still useful for modeling this dynamic decision.
First Order Conditions
Focusing on the home care system of equations, the equations can be seen as having
three choice variables, hours of care provided (AP), hours of leisure (LP), and the amount of
paid care purchased (D). The objective function can be written as
WHome = x * UR(AP,D) + UP[LP, (TP-AP-LP)*n + BR + BP – D*r]
8
9
For the details of the cost arrangements see page 6.
This may include income other than government benefits such as dividends and interest of savings.
23
The first order conditions for this equation are:
Equation F1 shows that the opportunity cost of leisure is the utility gain associated
with consumption that could have been achieved by working at the wage rate for the same
amount of time. The application of the equi-marginal principle suggests that at the margin
(in the absence of market distortion), care givers value an hour of leisure at their wage rate.
Equation F2 suggests that the opportunity of cost for an hour of care by relatives is
equal at the margin to the utility gain associated with consumption that could have been
achieved by an hour of work at the care givers’ wage rate. The application of the equimarginal principle suggests that (in the absence of market distortion) the weighted marginal
value of care to the care provider (i.e., the utility they derive from it being provided to the
care recipient) is equal to the care givers’ wage rate.
Equation F3 indicates that the after-subsidy opportunity cost to the care provider of
an hour of care by paid care providers is the utility gain associated with consumption that
could have been achieved with the hourly cost of care. The application of the equi-marginal
principle suggests that (in the absence of market distortion) the weighted value of care to
24
the elderly relative is equal at the margin to the value of the money spent for consumption
of the care provider.
The most significant equation for this report is equation F2 as it indicates the factors
that will lead a care provider to choose to provide more care. Aside from emphasizing the
importance of caring for elderly relatives, there is little the government can do to affect x,
the weight put on the utility of the care recipient. Sociological solutions to effect x, for
instance policies that promote workforce (and therefore caregiver) migration, promote
physical separation which may weaken emotional bonds (Smith, 1998). Coercive solutions
could also be used, such as legally compelling children to care for their parents as is the
case in some US states, British Columbia, Nova Scotia, New Brunswick and Singapore
(Martin, 2010). For example, in British Columbia under the 1922 Family Relations Act “A
child is liable to maintain and support a parent having regard to the other responsibilities
and liabilities and the reasonable needs of the child” (Spencer, 2011). When tempered by
the needs of the child in this way, these liability laws essentially mandate a minimum value
of x. Government might be able to affect δUR/δA, the productivity of care in providing
utility for the care recipient, for instance, by training care providers. An increase in taxes
would decrease the wage net of tax (n), but increase δUR/δC, the degree to which the care
provider values their income. The impact on informal care provision of these competing
substitution effects (work earns less) and income effects (money is valued more) is
theoretically ambiguous. A per hour subsidy for care by relatives, or removal of the subsidy
for care by outsiders, would most directly create substitution towards care provision by
family members.
25
These first order conditions assume that there is an internal solution, i.e., that the
optimal solution to the families’ maximization problem relies on home care, rather than
institutional care. Assuming substitutability between home and institutional care, even if a
tax credit does not replace the subsidy for care by outsiders, it would still increase the
provision of care by families because it makes informal care more appealing relative to
nursing home care. Because it would only have an income, rather than substitution effect
for current care providers, a fixed-rate subsidy like the tax credit would likely not
substantially affect the hours of care provided by family.
Graphical Analysis
One could simplify the model above to a trade-off in the allocation of time and
money between activities that contribute directly to the utility of the care provider (aka.
Full income, a function of consumption and leisure) and activities that contribute to the
utility of the care recipient (a function of the number of hours of care by family and paid
providers in home care or a low constant and a minimal cost for institutional care). When
framed in this way, there is a downward sloping budget constraint showing the
combinations of utilities that can be achieved with care in the home (paid or provided by
family) and a point that represents the option of institutional care (which as a set price and a
low constant for quality of care) as shown in (Figure 3):
26
Figure 3: Illustration showing a decision to place the care recipient in institutions in a trade-off between care
giver utility and care recipient utility
27
This model does not produce any unambiguous results as to the impact of the tax credit
except that it makes home care more appealing relative to nursing home care, potentially
creating substitution as shown in Figure 4:
28
Figure 4: Illustration showing a decision to substitute home for institutional care as a result of the tax credit in a
trade-off between care giver utility and care recipient utility
In this possible outcome, the credit results in a decision to care for the elderly relative in the
home (saving government money) and resulting in higher quality care, but less caregiver
consumption/leisure. It can be shown that this represents an efficiency improvement over
the same decision without a tax credit as shown in Figure 5:
29
Full Income
or UP
Potential Efficiency Gains with Tax Credit
Institutional Care Public Cost
Avoided
Cost
Institutional Care Private Cost
Cost of
Credit
Indifference Curves
Old, New
Home Care Budget Constraint
with Tax Credit
UR (a function of care setting,
paid hours, and family hours)
Figure 5: Potential Efficiency Gains with Tax Credit
With the indifference curves assumed above, the tax credit makes this family better
off (allows them to achieve a higher indifference curve), while having a net negative cost
(because the cost of the credit is less than the avoided cost of care). Because the families
can be made better off at lower cost, there is a potential efficiency gain associated with the
tax credit. However, it is worth noting that the current tax credit represents less than 2% of
the person’s income, even at its peak (see data analysis below), so very few families are
likely to be close enough to the margin for this result to occur. In addition, the cost of the
credit to government (as they cannot perfectly distinguish between those who will and will
not substitute) will exist for many families that are not close enough to the margin for it to
make a difference. From an economic perspective, ineffective credits that do not shift the
balance of a decision are a neutral transfer, their cost is exactly offset by the benefits to
30
families, exempting the economic cost (inefficiency) associated with raising tax revenue.
The benefits received from families at the margin substituting would have to be weighed
against the costs of raising tax revenue.
An additional caution is that the gain from substitution may be partially offset by
the inefficiency in the home care market due to distortion between care provided by family
and paid help. Both subsidization and the public provision of a limited amount can be
shown to be inefficient in some circumstances (As shown in Figures 6 through 8 below).
Likewise, the incentive might primarily serve to reduce the waiting list, rather than reduce
the number of people receiving institutional care, which is a worthwhile activity, but would
not produce cost savings in the way shown above (except where care recipients moving to
home care are waiting in hospitals).
Figure 6: Illustration of how the home care mix might be affected the Medical Expenditure Tax Credit (a subsidy)
31
Figure 7: Illustration of the inefficiency resulting from the subsidy in Figure 6
Figure 8: Illustration of how the home care mix might be affected the limited provincial provision of care
32
If the budget constraint is shifted to the right (from original to new) because the
government provides a fixed amount of care in the home (the publically provided amount),
as a result (in this example) much more care is provided by family (from F1 to F2) and a
little more paid care is provided (from P1 to P2), and the family is much better off (can
reach a higher indifference curve). This situation might occur if the person only required a
fixed amount of paid care (ie. Has a near vertical indifference curve).
Figure 9: Illustration of the inefficiency resulting from the public provision in Figure 8
This diagram compares the result that can be achieved with some public care
provision to that which could be achieved with a cash grant, the theoretically optimal
option. It shows that where there is a corner solution (ie. the family chooses not to purchase
any paid care beyond the care that the government provides), there is inefficiency (because
33
same indifference curve could have been achieved at lower cost to government). Therefore
if paid care is partially publically provided there is an inefficient distortion of behavior.
Extensions
There are several extensions to this model that would significantly contribute to its
realism but which are not applied here. If families are able to borrow or have savings to
deplete then the budget function does not have to be balanced for a particular period, but
rather over a lifecycle10 within the family (l).
represents the discount rate for healthy life
(near-term years being more valuable than further away years) and r is the interest rate.
The discounted lifecycle utility and budget functions can be expressed as:
Discounted lifecycle utility
∑( (
Lifecycle budget function
))
∑(
)
(
)
Where for healthy years x = 0 as
both parent and child act in their own
interests as consumers.
Another important extension that could be made is to consider the possibility of
competing interests within the family. For instance, if a tax credit is claimed by the working
spouse of the caregiver and the caregiver does not fully share in the benefits of that income,
but they retain control over deciding whether to provide care or not, their care decision may
be less impacted by the introduction of the tax credit. Alternately, if the elderly person does
not delegate Power of Attorney to a caregiver and does not want to impose a burden on
10
I use life cycle instead of lifetime because in general one can assume that the amount borrowed to
take care of one generation must be paid off by the time the next generation needs to be taken care of.
34
their caregiver, they may place insufficient weight on their own well-being and enter
institutional care when it is inefficient to do so.
Optimal Solutions
The system that would be created by making this tax credit refundable given these
assumptions would be equivalent to a cash grant to a rational and full altruistic caregiver. A
cash subsidy that could be adjusted for health status and was the only form of subsidization
for home care could theoretically lead to the optimal level of care provision. The optimal
(efficiency maximizing) cash grant would create substitution in all situations where it is
efficient to do so. If institutional care provided the same quality care at the same cost as
home care for the same severity of care needs, this would suggest that the combined private
cost of institutional care ($55.04/day or about $20,000/year) and private opportunity cost
from not receiving the tax credit should equal the total cost of institutional care ($152.94
per resident public cost, plus the marginal envelope costs that could be avoided, plus
$55.04/day private cost, for a total cost of about $75,000/year). This suggests that, if home
and institutional care were equally cost effective, the tax credit should offer a $55,000
benefit to caregivers (the public share of the costs of institutional care).
In fact, home care has been demonstrated to provide a higher quality of care at
lower cost, so the efficient subsidy would be even higher. This finding exists across all
levels of care including the highest (chronic) level of care and when including caregiver
out-of-pocket expenses and caregiver time valued at market wage (Hollander, 2003).
Although the tax credit would have to be adjusted for the severity of the care recipient’s
condition, this suggests that the efficient level of tax credit for the average person currently
in institutional care who is also facing the current out-of-pocket private cost would be in in
35
the order of $55,000. Theoretically, this would result in all care recipients with a potential
care provider receiving care in the home at a substantially the same cost to the government
(this cost reduction would likely occur in most individual cases as well as in aggregate as
the current per bed subsidy is already adjusted for the severity of care needs).
However, a $55,000 tax credit would be very difficult to implement, as among other
issues it would create a very strong incentive for fraud through exaggerating the severity of
the care recipient’s condition or failing to report their death as has occurred in Japan (BBC,
2010). This would not be the cost-minimizing solution to government if government in
unable to distinguish those individuals who would substitute from those who will not.
Furthermore, the incentive would be attractive to potential care providers at much lower
subsidy levels; for most Canadians, a subsidy level of $55,000 would make it more
lucrative to provide care in their home than to be in paid employment. It would also be
difficult to prevent the inefficient substitution of care in the care giver’s home or live in
care in the care recipient’s home and it would force government to face the costs of care for
all care recipients.
The market distortion between home care by outsiders and family could be
eliminated by replacing the current home care subsidies with a larger refundable tax credit,
as shown in Appendices 5b and 5d. The limited public provision is efficient in many cases,
and could be made theoretically optimal by allowing the funds set aside for home care to be
used to employ family members to provide home care. Without eliminating the home care
subsidy resulting from the federal Medical Expenditures Tax Credit, a per hour subsidy for
family care would be needed to correct the market distortion Appendix (5d), but such a
36
system would likely face administrative challenges as it would be difficult to audit reported
hours of care provided by family members.
Data Analysis
This section assesses the equity implications of the distribution of benefits of the
Family Caregiver Amount Tax Credit with reference to empirical data. It also explores the
impact of care on the care provider in an attempt to suggest the order of magnitude of the
burden of care on the care provider. Data was not available to explore the benefits of care to
recipients, for reasons discussed below. This section addresses three questions:
1. Who, statistically, will benefit from the tax credit? How are they different from the
rest of the population? This question breaks down care providers by age, gender,
marital status, children in the home, level of education, province of residence,
whether they are employed, their employment schedule, and their family income.
To put this profile in perspective, this section also compares rates of care provision
across characteristics as show in the introduction to Appendix A.
2. What is the impact of care responsibilities on the provider’s quality of life (health,
happiness, stress level) and income (employment, unemployment, job schedule and
personal income)?
3. What factors predict whether somebody will be a care provider? This section builds
a binary logistic regression model that shows the relationship between a set of
independent predictors (age, sex, marital status, and personal income) and care
provision.
37
Description of Data Set
The General Social Survey, Cycle 16, 2002 [Canada]: Ageing and Social Support
datasets is part of a series of surveys collected to “gather information on changes in the
living conditions and well-being of Canadians over time and to provide data on specific
social policy issues of current or emerging interest”. They are produced by Statistics
Canada and collected using Computer Assisted Telephone Interviewing (Statistics Canada,
2005).
This particular data set focused on aging and social support and sampled from all
persons over 45 residing in Canada’s ten provinces excluding full-time residents of
institutions. The sample was selected from people who responded to the 2001 Canadian
Community Health Survey, which had drawn a national geographically-stratified sample.
An approximately equal number of people from each 10 year age bracket were included in
the sample. The total sample was 24,855 cases with a response rate of almost 84%
(Statistics Canada, 2005).
The dataset included many variables which I expected to influence or be influenced
by whether or not someone is a caregiver, including demographics, family structure,
participation in leisure activities, health-related quality of life, whether care providers had
employment income and their income bracket. Because of the overall large sample and the
stratification by age, there were a large number of elderly persons receiving care within the
sample. However, because the survey excluded full-time residents of institutions of nursing
homes, this data did not enable comparisons of outcomes between institutional and home
care. Further, because of the particular questions asked, it was difficult to identify
individuals who were not providing care but had a close relative who needed care.
38
A major limitation of the main dataset file was that it did not provide an indication
of the intensity of care. To distinguish between the intensity and incidence of care, the main
file was integrated with the General Social Survey Care Giving by Respondent (45-64) file,
which asked for the number of minutes of personal care provided per day to each care
recipient. For the purposes of this analysis, I distinguish between care givers with a
moderate burden (30 to 89 minutes per day) and an intensive burden (90+ minutes per day)
of personal care (bathing, toileting, care of toenails/fingernails, brushing teeth, shampooing
and hair care or dressing). Where a respondent provided care to multiple people, the data
for the respondents’ relationship with the care recipient for whom the most personal care
was provided the data in the merged data set (while the credit can be claimed for multiple
elderly dependents, there were too few such providers in my sample to draw significant
results). Even the moderate criterion may be too strict as the total number of people in the
population represented by the intensive (221,683) and moderate (58,886) care provision
groups (obtained using the person weights provided by Statistics Canada) is still less than
one would expect given the government’s estimate of the number of potential claimants
(over 500,000 when including all infirm dependents) (Government of Canada, 2012).
However, very few care givers (5.5%) providing care provided less than 30 minutes, so the
results would not be substantially different with a different grouping and 30 minutes seem
like a reasonable threshold that indicates the caregiver and recipient live in the same home,
a requirement for receiving the tax credit.
39
Data Analysis Procedure
Because the original data was stratified so as to produce approximately equal
numbers of respondents in each age group and region, and in order to minimize nonresponse bias, in this analysis the data is weighted to be representative of the general
population. For example, fewer men aged 45-54 (11% of those over 45) were sampled than
exist in the general population (20% of those over 45, based on the census). To avoid the
underrepresentation of these younger men, their results must be assigned greater weight in
order for the sample to be representative of the population. The process used to establish
weights (the number of people in the population represented by a person in the sample) are
outlined in the User Guide. Statistics Canada states in the User Manual for the dataset that
“The survey weights must be used when producing estimates or performing analyses in
order to account as much as possible for the geographic over- or under-representation and
for the over- or underrepresentation of age-sex groups in the unweighted file” (Statistics
Canada, 2005). However, these weighted values inflate the significance of statistical tests.
Following Statistics Canada’s directions, the weights used here are adjusted (specifically,
divided by the mean weight), such that the sample size does not change with weighting and
statistical tests are not inflated. This paper uses these adjusted weights to compensate for
under representation of certain groups without inflating the significance of statistical tests.
The statistical profile of care givers (Question 1) is presented in a series of crosstabulations (Annex 1A-J). The table can be read horizontally to state the rate of care
provision within a particular group or vertically to state the proportion of care providers
who belong to a particular group. Beneath each cross tabulation table, the Chi-Square Test
is presented. The significance of the Chi-Squared value is the probability that the observed
40
result would have occurred if there was in fact no difference between cases in care
provision. Where the Chi-squared value is less than .001, there is a less than 0.1% chance
the result would have occurred based on chance alone and we can conclude that there is a
relationship between the categories and care provision.
To identify the impacts of being a caregiver (Question 2), I compare the frequency
of care provision across the various categories of the variables that the literature suggests
would provide measures of the impact of the care burden, namely life stress, life
satisfaction, health utility, whether they are receiving EI, their job schedule, and their
personal income. There is no clear directionality of the causal relationship of any of these
variables, for instance, a caregiver might provide care because they are unemployed or not
provide care because their health (measured in the health utility) prevents them from doing
so. Likewise, the literature and survey results suggest care providers might quit their job
and suffer a decline in health as a result of the care burden. As before, Chi-Squared tests are
used to compare provision between groups, with the addition of an independent means ttest for the health utility index, which is continuous.
Instead of comparing those who provided care to those who did not, it is useful for
the purposes of moving towards isolating the impact of the burden of care to compare those
who provided care compared to those who had an elderly relative who would have
benefited from care, but did not provide care to them. Running the analysis only for these
cases would greatly improve internal validity, our ability to attribute the change in the
dependent variable (ex. Income) to a change in an independent variable (in this case taking
on the burden of care). Unfortunately, a major limitation of this data is that it does not
distinguish between those who do and do not have an elderly relative who needs care,
41
making it difficult to attribute differences to care responsibilities, as opposed to merely the
impact of having a relative who needs care. Future studies should include these questions to
allow comparisons of those who do and do not opt to provide care.
To build a comprehensive model predicting whether somebody will provide care
based on their demographics (Question 3), I use binary logistic regression. Binary logistic
regression builds a model that predicts which of two categories a person is likely to belong
to given certain other information (Field, 2009). In this case, a variety of categorical
variables representing the demographic features, such as employment income, are used to
predict whether or not someone is a caregiver. Fewer variables are included here because
logistic regression requires that predictors not be too highly correlated. The model was
highly significant, with a less than 0.1% chance the observed results would have occurred
in the absence of any relationship between the predictors and care provision. All predictors
were significant at the .05 level using the significance of the Wald values based on the
standard errors. This means that there is a less than 0.5% chance that the observed results
would occur due to chance alone if there was no relationship between the predictor and care
provision.
Statistical Profile and Distinguishing Characteristics of Care Providers
This section outlines some of the major characteristics of informal care givers to
provide a picture of who, statistically, will benefit from the program.
Care provision peaks in the 50 to 54 age range with 1.1% of people in this group
providing moderate care and 3.5% providing intense care (Table 1A). Age is a statistically
significant (p=0.001) predictor of care provision. This relationship likely occurs because
42
those over 55 are more likely to be physically incapable of providing care or their parents
may have already passed away.
Women were substantially more likely to provide care at both the moderate (1.3%
provided compared with 0.3% of men) and high levels (4.8% provided compared with 1.1%
of men) and just over 80% of both types of care providers were women (Table 1B). This
difference in rates of care provision among caregivers aged 45 to 64 is highly significant
(p<0.001). This confirms the equity hypothesis that the tax credit would help compensate
women for their disproportionate burden of care and thus promote horizontal equity by
moving towards an equalization of the social burden for care across genders.
While most care providers aged 45 to 64 are married (66.3%), the highest rates of
care provision are actually in the widowed, divorced, and separated group (Table 1C). The
differences in rates of care provision by marital status are highly significant (p<.001). The
higher rate of care provision among the widowed, divorced, and separated group may be
explained by its association with being female (73.2% of this group is female).
While the focus of this paper on the impact of the federal tax credit in Ontario, I
also looked at the profile and distribution of care responsibilities by province as this
provides some indication of how other policy options may affect care provision. Most care
providers aged 45 to 64 lived in Ontario in Quebec, but the highest rates of intense care
provision were found in New Brunswick (4.7%), Nova Scotia (4.9%), and Newfoundland
and Labrador (4.5%), while the lowest rates were in Quebec (2.2%) and Alberta
(2.8%)(Table 1D). These differences by province are highly significant (p<0.001). The
lower rate of care provision in Quebec may reflect the greater availability of paid and
public care in Quebec. The contrast between the Maritime Provinces and Alberta may
43
reflect their demographic structure or the greater tendency to provide care where the family
lives in close geographic proximity, as discussed in the section above.
Most care givers 45 to 64 (75-80%) are employed (Table 1E). There is no difference
in provision of moderate care between those with and without employment income, but the
employed were more likely to provide intensive care (3.3% provided care compared with
2.2% of those without employment income). This result is statistically significant (p<.01).
This result likely reflects a mix of the association of unemployment with older age and
therefore decreased physical ability to provide care and the need for employment income to
offset the costs of providing care.
There was a highly significant (p<.001) relationship between the respondent’s
highest level of education and the rates of care provision among those aged 45 to 64 (Table
1F). Most care providers had either a college diploma/certificate (39.1%) or a university
degree (20.9%). Those with only a college diploma had the highest rate of care provision
(4.5%) followed by those with some university or college (3.0%) and those with a
university degree (2.9%). The peak at a college level of education likely reflects competing
effects of the higher opportunity costs of time associated with higher levels of education
and the need for a sufficient income to be able to bear the burden of care – a similar peaked
relationship is found between care and personal income (below). The very low rate of care
provision among those without their high school diploma (1.8%) can likely be explained by
its association with older age.
Canadian born respondents aged 45 to 64 were substantially more likely to be
providing both moderate (0.9% provided compared with 0.3% among immigrants) and
intensive care (3.1% provided compared with 2.7% among immigrants) (Table 1G). This
44
result is statistically significant (p<.001), and may be a result of the geographical separation
of first-generation immigrants and their parents. Greater frequency of religious attendance
and having three generations living in the same home11 were slightly associated with
greater care provision, but these effects were not significant (P>.1).
The largest chunk of care providers aged 45 to 64 were providing care to their
mother (35.2%), a much larger group than those providing care to their father (11.5%) or
spouse (1.8%) (Table 5). A similar relationship existed with in laws where 4.5% were
providing care to their mother in law while only 2.2% were providing care to their fatherin-law. These results support the equity claim that the benefits of care go disproportionately
to women. The lower rate of care provision for spouse is likely a function of the
requirement the care recipient be over 65 or over and care provider under 65 and few
couples would fall in this range. This is much less of an issue for the other relationships
shown.
Impact of Care Responsibilities
Measurement Challenges
The literature gives examples of several strategies for valuing the benefits and costs
of a health policy option (for example, see Mentzakis et al, 2011). This section explains and
outlines the issues and limitations associated with the four dominant approaches: Increased
Productivity, Quality Adjusted Life Years, market value, and contingent valuation.
11
The challenge with this indicator is that many families span three generation regardless of needs as
a result of cultural norms and many care providers’ children will have already left home. The difference was
between 2.7% of three generation families providing care compared with 2.0% of non-providers.
45
Increased Productivity/Opportunity Cost Method
Cost-benefit analysis attempts to value health outcomes in monetary terms. An early
approach was to value health improvements based on the increase in the patient’s
productivity, as measured by their wage rate, as a result of the medical intervention.
However, this measurement is not feasible for those receiving long-term care, as they do
not have a wage rate. However, the logic of valuing costs and benefits by the gain or loss of
productive time valued at the affected persons’ wage rate has been extended to care givers.
This logic would suggest that the cost of care givers’ time should be valued at their market
wage. For instance, if the burden of care means a care provider earning $50,000 per year is
unable to work, the cost of care could be valued at its opportunity cost (the cost of the next
best use of the time), which would be the $50,000 in lost income. Some shortcoming of this
strategy are that it does not include satisfaction the caregiver receives from providing care
(the value of which should be subtracted from the cost of time), nor the physical and mental
health consequences of the burden of care (the value of which should be added to the cost
of time).
To include this measure, I compare the family incomes of those providing intensive
care to those not providing care (Table 2D). The results were non-significant but suggest
those incidence of providing care increased with income up to about $50,000/year (just
below the average Canadian household income at the time of $68,500/year). In line with
this finding, care providers also were more likely to be employed and less likely to be
receiving employment insurance.
46
Quality Adjusted Life Years
Cost-utility analysis is commonly applied to the evaluation of health programs.
Using this method, the benefits of a program are measured in Quality-Adjusted Life-Years,
a measure that considers both the increased length of life and increased quality of life for
the patient associated with a medical intervention. However, because there is no
compassionate/ethical alternative treatment option to long-term care, one could not
ethically construct an experimental design to evaluate the benefits associated with various
care options. While estimates have been made of the Quality of Life Adjustment for those
in home care and in institutions, little can be inferred from the much greater quality of life
of those at home (HUI = 0.797) compared with those in institutions (HUI = 0.120) because
those with more severe conditions will be admitted and self-select to receive institutional
care (Jones & Feeny, 2007). The Health Utility Index (HUI) captures changes in health
status, such as depression, resulting from care responsibilities. The difficulty in attributing
changes in HUI to care setting is compounded by the measures of the index which relies
primarily on physical conditions in estimating quality of life.
While it cannot ethically be applied to care recipients, the literature indicates that
care responsibilities have an impact on the health of care providers (Mentzakis et al., 2011).
This suggests there may be a role for Quality Adjusted Life Years as a measure of the costs
of home care to the care provider. To include this measure, I compare the HUI, general life
satisfaction, and level of stress between care providers and non-care providers.
47
Willingness to Pay/Willingness to Accept
Two approaches are possible to estimate the value of the benefits of a program in
terms of the patient’s willingness to pay for those benefits. First, a patient’s willingness to
pay for a treatment can be inferred based on people’s actual choices, where the value of that
good is reflected in a market price. One reference point for judging people’s willingness to
pay based on actual behavior is the market for unsubsidized nursing care. If people
purchase this care, it can be inferred that the market price is less than the value they place at
the margin on receiving care.
An alternate method for valuing benefits in cost-benefit analysis is contingent
valuation, whereby patients and their families would be directly asked how they would
behave in hypothetical situations. However, those in long-term care may lack the mental
competency to be able to complete a contingent valuation questionnaire. This method also
does not resolve the inconsistency between the impact of income on people’s willingness to
pay and the principle that the allocation of heath resources should not depend on people’s
incomes. The effect of income can be mitigated by asking how much they would be willing
to accept to stay out of institutional care, rather than what they would be willing to pay to
receive it. The data set did not provide any means of estimating willingness to pay but
estimates were already discussed in the literature review section above.
48
Analysis
This section explores the possible impacts of care responsibilities on the quality of
life of the care provider.
The majority (51.3%) of intensive care providers aged 45 to 64 find their life
somewhat stressful (Table 2A). There are significantly (p<.01) higher rates of intensive
care provision among those who found life somewhat stressful (3.4% in group provided) or
very stressful (3.2% in group provided). This likely reflects a combination of the stress
associated with having someone you care for unwell and the added stress resulting from
care responsibilities. These differences are not enormously different from the popular
average rate of provision of 3.1%. There was no significant relationship between life
satisfaction and whether or not a respondent provided care (p>.1) (Table 2B). Together,
these indicators do not suggest a clear utility burden on care providers resulting from their
responsibilities, possibly because those who are socially integrated are more likely both to
provide care and have lower baseline levels of stress and higher life satisfaction that could
obfuscate the impact of care responsibilities.
There was a weak relationship between personal income and care provision (p<.05)
(Table 2C) with the highest rate of care provision (1.1% providing moderate and 3.7%
providing intensive care) in the $15,000 to $29,999 wage range (below the Canadian
average of 34,200 at the time of the survey). This suggests that the tax may be slightly
progressive as a function of aggregate patterns of behavior, i.e., those with below average
incomes are more likely to provide care, so the tax provides slightly greater benefits to the
those with lower personal incomes. This relationship may exist because those with lower
incomes have lower opportunity costs when providing care (as outlined in the model
49
above). However, there was no significant relationship between family (household) income
and care provision (p>.1) so with the assumption of pooled resources this contradicts the
proposition that families with lower abilities to pay would be more likely to claim the credit
(Table 2D). Oddly, there was no significant difference in care provision between those
receiving and not receiving employment insurance, despite the existence of Employment
Insurance Compassionate Care Benefits, which should theoretically provide short-term
relief to care givers unable to work due to their care burden at the end of the recipient’s life
(Government of Canada, 2013) (Table 2E). Perhaps respondents did not regard these
benefits as employment insurance income or the period of time for which it can be claimed
had expired.
The difference in health utilities between care providers and non-providers in the
subsample is statistically insignificant (p>.05) although clinically meaningful (.008>.005)
(Table 2F) (Jones & Feeny, 2007). Surprisingly, the health utility of intensive care
providers is actually higher than that of non-providers. While this result can partially be
explained by the greater ability of the healthy to provide care and as a statistical fluke, the
absence of any significant or even negative relationship here has real world significance in
that it implies that care responsibilities may not substantially impact the health of providers,
and certainly does not provide a means of quantifying the burden of care.
It is difficult to assign monetary values to the changes in life stress and satisfaction.
This would be valuable because it would make it possible to estimate the change in income
(created through the tax credit) that would leave the respondent just as well off, and
determine the degree to which the credit is effective in compensating for that burden.
Unfortunately, while greater care was associated with higher stress, so was higher (or very
50
low) income, so it we were to determine the change in income that would create a
equivalent variation in stress (as a measure of well-being) the answer would be nonsensical negative, i.e., it would suggest care providers be hugely taxed to reduce their
stress. Life satisfaction was negligibly lower for intensive care providers and slightly higher
for moderate providers. As a rough estimate of the change in income that would create an
equivalent variation in overall well-being, the .02 difference is about 26% of the difference
between the$15,000 to $30,000 brackets overall well-being and that of the $30,000 to
$50,000 well-being bracket. 26% of the difference in the middle values of these brackets is
about $4,500. However, it would be problematic to look only at this indicator when the
difference between means was not statistically significant and life stress as a measure of
well-being would have provided such a different answer.
Total household income (grouped)
How stressful is life:12
Overall life satisfaction13
No income or loss
Less than $15,000
$15,000 to $29,999
$30,000 to $49,999
$50,000 to $79,999
$80,000 or more
Total
2.14
2.51
2.70
2.61
2.49
2.35
2.52
2.27
1.99
1.82
1.75
1.71
1.60
1.74
Intensity of Care
Not a care provider
30 to 89 minutes per day
90 minutes or more per day
Total
12
13
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
How stressful is life
Overall life satisfaction
2.55
1.75
2.27
1.67
2.30
1.77
2.54
1.75
Means of rating on a four point scale where lower values indicate higher stress
Means of rating on a four point scale where lower values indicate higher satisfaction
51
An important guide to interpreting these results on the impact of care is the
perception of care providers as to the impact of care responsibilities on their lives (Table
4a). For intensive personal care providers, the most common impact was that they had extra
expenses (58.1%), changed their social life (54.9%), changed holiday plans (40.5%),
suffered declines in health (30.7%), changed sleep patterns (29.9%), changed work patterns
(28.9%), or reduced their hours of work (22.9%). Only 17.9% reported a reduction in
income, which while significant suggests that the primary impact of care responsibilities is
through additional expenditures and impacts on health and well-being rather than lost
income. Future studies should inquire to the size of the additional expenditures necessary to
provide care.
Logistic Regression Model
The purpose of the logistic regression model (Appendix 3) is to draw together
demographic and income factors and show how they relate to intensive (90+ minutes /day)
personal care provision among those aged 45 to 64, how strongly they are related, and the
significance of this relationship. Specifically, the model includes age, gender, marital
status, and personal income. Related measures, such as whether the person was employed,
had to be excluded because the model’s statistical techniques assumes independence of
predictors. The full model in included as Appendix 3.
The overall model was highly significant based on the Chi-squared test (Chisquared = 196.788) which had a probability of less than 0.1% (p<0.001) of occurring if the
null hypothesis was true, i.e., there was an effect of all the individual variables taken
together on the dependent variable (Institute for Digital Research and Education, 2013). All
52
predictors were significant at the .05 level, i.e., there is a less than 5% chance that the
observed difference would have occurred in the absence of a relationship between the
predictor and care provision.
A logistic regression model expresses the change in probability relative to a
reference population, arbitrarily chosen here to be aged 45 to 54, male, living common law,
and having a net income of zero or a loss. The odds ratio expresses the change in
probability associated with a criterion being true, rather than belonging to the reference
population. For example, the odds ratio associated with being female is 4.502 meaning that
(all other factors being equal) women are 4.5 times as likely to be providing intensive care
compared to men. Odds ratios greater than 1 indicate a higher probability of providing care
than the reference population, while ratios less than 1 indicate a lower probability than the
reference population.
Criteria
Age 45 to 5414
Age 55 to 64
Male15
Female
Living common-law
Married
Widowed/Divorced/Separated
Single (Never married)
No personal income
Personal Income Less than $15,000
Personal Income $15,000 to $29,999
Personal Income $30,000 to $49,999
Personal Income $50,000 to $79,999
Personal Income $80,000 or more
14
Significance of
Predictor
.027
.027
.000
.000
.024
.018
.012
.003
.046
.029
.004
.004
.002
.041
Odds Ratio
Reference
.782
Reference
4.502
Reference
1.922
2.074
2.568
Reference
1.697
2.000
1.968
2.186
1.862
This does not appear in the model output, it’s statistical significance is that of its binary opposite,
belonging to the 55 to 64 age group.
15
This does not appear in the model output, it is only implied as the opposite of female.
53
The effects found here are not all consistent with those discussed in the statistical
profile above because in the regression all variables are forced into the model at the same
time so, for instance, the higher rates of care provision in the 55 to 64 age group seen in the
cross tabulations might be explained by the greater number of women in the age group, and
when the model controls for gender the effect could be reversed, as was the case here.
Some changes in effect may also be due to random error, for instance it would be
problematic to read too much into the double peaked rates of care provision by personal
income; the difference between the $15,000 to $30,000 bracket and $30,000 to $50,000
bracket is negligible.
Overall, care provision was now significantly (p<.05) higher in the younger 45 to 54
age group, meaning that when controlling for the other variables, belonging to the younger
age group actually increased ones odds of providing care. Gender remained a highly
significant predictor (p<.001) and the strongest one, with women being 4.5 times as likely
to be providing intensive care compared to men. Whereas previously the highest rates of
care provision were among the widowed, divorced and separated, the highest rate when
controlling for other variables is among the single, suggesting that the greater number of
widowed women may have been responsible for the higher rate of care provision in that
group. All marital statuses were significant predictors (p<.05). Personal income was also a
significant predictor (p<.05) of whether or not a respondent provided intensive care, with
those in the middle income brackets ($15,000 to $80,000) being around twice as likely to
provide care as those with no income or a loss, and having substantially higher rates of care
provision than the $0 to $15,000 and over $80,000 brackets. This relationship between
income and care provision suggests that those with incomes under $15,000 have difficulty
54
bearing the burden of care. The lower rate of care provision in the highest $80,000 and over
bracket might be explained by the ability of these higher income respondents to afford
private institutional care and home care workers and thus alleviate the personal burden of
care.
Conclusion
This report provided an economic analysis of the impacts of the Family Caregiver
Amount Tax Credit on the care decision of care givers for elderly persons with severe longterm care needs. It focuses on three aspects of the impacts of the credit: efficiency, equity
and effectiveness. This paper shows that the Family Caregiver Amount Tax Credit could
theoretically lead to efficiency gains (i.e., it could make families better off at lower cost),
but is unlikely to be effective (i.e., its size is not substantial relative to the costs of longterm care). The data analysis here suggests that the credit is progressive (i.e., it provides
greater benefits to those with lower incomes) above the personal income point at which
$300 in taxes are owed but fairly neutral with respect to family income.
The findings of this paper suggest that the Family Caregiver Amount Tax Credit has
a design that should lead to efficiency and equity improvements. To maximize its
equitability and efficiency, the credit should be made refundable and claimable only by the
care provider. To be effective in actually creating improvements in efficiency and equity,
the credit would need to be radically increased in size.
55
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Appendix A: Profile of Care givers and Comparison of Rate of Care
Provision Across Groups
This analysis was run on a merged dataset of the General Social Survey, Cycle 16, 2002
[Canada]: Ageing and Social Support, Main File and General Social Survey, Cycle 16,
2002 [Canada]: Ageing and Social Support, Care Giving by Respondent (45-64). The later
data set is used to identify individuals proving moderate (30 to 89 mins/day personal care)
and high (90+ mins/day personal care) intensity care.
The data was weighted using the adjusted weight so as to compensate for the stratification
of the sample geographically and by age without inflating statistical tests. The analysis
below is run only for individuals ages 45 to 64 (N = 12167), as the care giving by
respondent data set only covered this age range.
The cross-tabulation below show the rate of care provision between groups (italicized and
bold in the sample below) as well as the portion of care providers who belong to a
particular group (underlined in the sample below). For example, in the sample below we
can see that women were substantially more likely to provide care at both the moderate
(1.3% compared with 0.3%) and intensive levels (4.8% compared with 1.1%) and that just
over 80% of both types of care providers are women.
Intensity of Care
Count
Male
Not a care
30 to 89
90 minutes
provider
minutes per
or more per
day
day
8118
22
89
% of men providing care of this Intensity
98.7%
% of care providers of this gender
50.7%
0.3%
16.8%
1.1%
18.0%
7881
109
405
93.9%
1.3%
4.8%
% of care providers of this gender
49.3%
83.2%
82.0%
Count
15999
131
494
% Overall Providing Intensity of Care
96.2%
0.8%
3.0%
Sex of
Count
respondent
% of women providing care of this
Female
Total
Intensity
The Chi-Squared tables beneath each cross-tabulation show the statistical significance of
the result, ie. The probability that a difference of this magnitude between groups would
62
occur by chance alone. In the case of this sample table, p<0.001 meaning that there is a less
than 0.1% chance that men and women in fact provide equal amounts of moderate and
intensive care and we can reject this hypothesis.
63
Table 1A: Age Distribution and Profile of Care Providers
Intensity of Care
Not a care
30 to 89
90
provider
minutes
minutes or
per day
more per
day
Count
Age group of
the
5138
42
160
45 to 49 % within group proving care of this intensity
96.2%
0.8%
3.0%
% of care providers in this group
32.1%
32.1%
32.3%
4475
53
164
50 to 54 % within group proving care of this intensity
95.4%
1.1%
3.5%
% of care providers in this group
28.0%
40.5%
33.1%
3591
18
105
55 to 59 % within group proving care of this intensity
96.7%
0.5%
2.8%
% of care providers in this group
22.4%
13.7%
21.2%
2795
18
66
60 to 64 % within group proving care of this intensity
97.1%
0.6%
2.3%
% of care providers in this group
17.5%
13.7%
13.3%
Count
15999
131
495
% Overall Providing Intensity of Care
96.2%
0.8%
3.0%
Count
respondent:
5-year
groups
Count
Count
Total
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
21.959a
6
.001
Likelihood Ratio
22.074
6
.001
Linear-by-Linear Association
5.300
1
.021
N of Valid Cases
16625
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 22.69.
64
Table 1B: Gender Distribution and Profile of Care Providers
Intensity of Care
Not a care
30 to 89
90 minutes
provider
minutes per
or more per
day
day
Count
Male
Sex of
8118
22
89
% of men providing care of this Intensity
98.7%
0.3%
1.1%
% of care providers of this gender
50.7%
16.8%
18.0%
7881
109
405
93.9%
1.3%
4.8%
% of care providers of this gender
49.3%
83.2%
82.0%
Count
15999
131
494
% Overall Providing Intensity of Care
96.2%
0.8%
3.0%
Count
respondent
Female
Total
% of women providing care of this
Intensity
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
261.796
a
2
.000
Likelihood Ratio
283.726
2
.000
Linear-by-Linear Association
246.613
1
.000
N of Valid Cases
16624
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 64.85.
65
Table 1C: Marital status Distribution and Profile of Care Providers
Intensity of Care
Not a
30 to 89
90
care
minutes
minutes
provide
per day
or more
r
Marital
Living
Count
common-
% within group proving care of this intensity
law
% of care providers in this group
Married
status of
per day
1036
7
16
97.8%
0.7%
1.5%
6.5%
5.3%
3.2%
Count
11253
96
328
% within group proving care of this intensity
96.4%
0.8%
2.8%
% of care providers in this group
70.5%
72.7%
66.3%
2532
26
106
the
Widowed/
Count
respondent
Divorced/
% within group proving care of this intensity
95.0%
1.0%
4.0%
Separated
% of care providers in this group
15.9%
19.7%
21.4%
Single
Count
1147
3
45
(Never
% within group proving care of this intensity
96.0%
0.3%
3.8%
married)
% of care providers in this group
7.2%
2.3%
9.1%
Count
15968
132
495
% within Marital status group proving care of
96.2%
0.8%
3.0%
Total
this intensity
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
26.784a
6
.000
Likelihood Ratio
29.064
6
.000
Linear-by-Linear Association
14.266
1
.000
N of Valid Cases
16595
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 8.42.
66
Table 1D: Provincial Distribution of Care Providers
Intensity of Care
Non-
30 to 89
90
provider mins/day mins/day
Newfoundland Count
and Labrador
% within province proving care of this intensity
294
6
14
93.6%
1.9%
4.5%
71
1
3
94.7%
1.3%
4.0%
491
10
26
93.2%
1.9%
4.9%
394
10
20
92.9%
2.4%
4.7%
4123
32
92
97.1%
0.8%
2.2%
6005
39
187
96.4%
0.6%
3.0%
538
2
25
95.2%
0.4%
4.4%
461
4
16
95.8%
0.8%
3.3%
1451
17
42
96.1%
1.1%
2.8%
2171
11
69
Prince Edward Count
Island
Nova Scotia
Province
of
Count
% within province proving care of this intensity
New
Count
Brunswick
% within province proving care of this intensity
Quebec
residence
of the
% within province proving care of this intensity
Ontario
respondent
Manitoba
Saskatchewan
Alberta
Count
% within province proving care of this intensity
Count
% within province proving care of this intensity
Count
% within province proving care of this intensity
Count
% within province proving care of this intensity
Count
% within province proving care of this intensity
British
Count
Columbia
% within province proving care of this intensity
96.4%
0.5%
3.1%
Count
15999
132
494
% overall proving care of this intensity
96.2%
0.8%
3.0%
100.0%
100.0%
100.0%
Total
% of care providers in this group
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
63.973
a
18
.000
Likelihood Ratio
54.687
18
.000
.859
1
.354
Linear-by-Linear Association
N of Valid Cases
16625
a. 7 cells (23.3%) have expected count less than 5. The minimum expected count is .60.
67
Table 1E: Employment Distribution and Profile of Care Providers
Intensity of Care
Not a
30 to 89
90
care
minutes
minutes
provider
per day
or more
per day
Count
11511
96
395
% of employed providing care of this intensity
95.9%
0.8%
3.3%
wages/ salaries/
% of care providers in this group
73.8%
74.4%
80.8%
commissions/
Count
4081
33
94
97.0%
0.8%
2.2%
% of care providers in this group
26.2%
25.6%
19.2%
Count
15592
129
489
% overall providing care of this intensity
96.2%
0.8%
3.0%
Income from
tips/ selfemployment
Yes
No
Total
% of unemployed providing care of this
intensity
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
11.928
a
2
.003
Likelihood Ratio
12.693
2
.002
Linear-by-Linear Association
11.405
1
.001
N of Valid Cases
16210
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 33.49.
68
Table 1F: Highest Level of Education Distribution and Profile of Care
Providers
Intensity of Care
<30 or 30 to 89
none
Doctorate/ masters
/bachelor's degree
Highest
level of
education
obtained
by the
respondent
Count
min/day min/day
3434
32
103
% of group who provided care of this intensity
96.2%
0.9%
2.9%
% of care providers in this group
21.7%
24.8%
20.9%
4065
38
193
94.6%
0.9%
4.5%
25.7%
29.5%
39.1%
2169
32
67
Diploma/
Count
certificate from
% of group who provided care of this intensity
community
college or trade/
90
% of care providers in this group
technical school
Some university/
Count
community
% of group who provided care of this intensity
95.6%
1.4%
3.0%
college
% of care providers in this group
13.7%
24.8%
13.6%
2615
12
64
% of group who provided care of this intensity
97.2%
0.4%
2.4%
% of care providers in this group
16.5%
9.3%
13.0%
3550
15
67
High school
diploma
Count
Some secondary/
Count
elementary/ no
% of group who provided care of this intensity
97.7%
0.4%
1.8%
schooling
% of care providers in this group
22.4%
11.6%
13.6%
Count
15833
129
494
% within Highest level of education obtained
96.2%
0.8%
3.0%
Total
by the respondent
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
76.817
a
8
.000
Likelihood Ratio
75.271
8
.000
Linear-by-Linear Association
29.737
1
.000
N of Valid Cases
16456
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 17.78.
69
Table 1G: Country of Birth Distribution and Profile of Care Providers
Intensity of Care
Not a
30 to
90
care
89
minutes
provider minutes or more
per day per day
Country of
Canada
11964
118
387
% of native Canadians providing care of this
95.9%
0.9%
3.1%
75.2%
90.1%
78.2%
3936
13
108
intensity
% of care providers in this group
birth of the
respondent
Count
Country
Count
outside
% of immigrants proving care of this intensity
97.0%
0.3%
2.7%
Canada
% of care providers in this group
24.8%
9.9%
21.8%
Count
15900
131
495
% within Country of birth of the respondent
96.2%
0.8%
3.0%
Total
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
17.484
a
2
.000
Likelihood Ratio
20.672
2
.000
Linear-by-Linear Association
5.662
1
.017
N of Valid Cases
16526
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 32.16.
70
Appendix 2: Income and Utility Comparisons among (weighted, for those aged
45-54 with one living parent)
Table 2A: Differences between Moderate and Intensive Personal Care
Providers and the general population (45-64) in terms of Life Satisfaction
Intensity of Care
very
stressful
somewhat
stressful
How stressful is
not very
life
stressful
not at all
stressful
Not a
30 to 89
90 +
care
minutes
mins/day
provider
per day
Count
1999
28
66
% of group providing intensity of care
95.5%
1.3%
3.2%
% of care intensity provided by group
13.1%
21.5%
13.4%
Count
7082
50
253
% of group providing intensity of care
95.9%
0.7%
3.4%
% of care intensity provided by group
46.5%
38.5%
51.3%
Count
4291
41
132
% of group providing intensity of care
96.1%
0.9%
3.0%
% of care intensity provided by group
28.2%
31.5%
26.8%
Count
1784
11
42
% of group providing intensity of care
97.1%
0.6%
2.3%
% of care intensity provided by group
11.7%
8.5%
8.5%
75
0
0
100.0%
0.0%
0.0%
% of care intensity provided by group
0.5%
0.0%
0.0%
Count
15231
130
493
% within How stressful is life
96.1%
0.8%
3.1%
Count
No opinion % of group providing intensity of care
Total
Chi-Square Tests
Value
Pearson Chi-Square
df
Asymp. Sig. (2-sided)
20.404a
8
.009
22.791
8
.004
Linear-by-Linear Association
7.325
1
.007
N of Valid Cases
15854
Likelihood Ratio
a. 2 cells (13.3%) have expected count less than 5. The minimum expected count is .61.
71
Table 2B: Differences between Non-providers, Moderate and Intensive
Personal Care Providers and the general population (45-64) in terms of
Life Satisfaction
Intensity of Care
Not a
30 to
90
care
89
minutes
provider minutes or more
per day per day
Count
Very satisfied
4655
52
145
% of group providing intensity of care
95.9%
1.1%
3.0%
% of care intensity provided by group
30.7%
40.3%
29.5%
9548
69
319
% of group providing intensity of care
96.1%
0.7%
3.2%
% of care intensity provided by group
62.9%
53.5%
64.8%
703
7
21
% of group providing intensity of care
96.2%
1.0%
2.9%
% of care intensity provided by group
4.6%
5.4%
4.3%
154
1
5
% of group providing intensity of care
96.3%
0.6%
3.1%
% of care intensity provided by group
1.0%
0.8%
1.0%
122
0
2
% of group providing intensity of care
98.4%
0.0%
1.6%
% of care intensity provided by group
0.8%
0.0%
0.4%
Count
15182
129
492
% within Overall life satisfaction
96.1%
0.8%
3.1%
Count
Satisfied
Count
Overall life
satisfaction
Dissatisfied
Count
Very
dissatisfied
Count
No Opinion
Total
Chi-Square Tests
Value
Pearson Chi-Square
Likelihood Ratio
Linear-by-Linear Association
N of Valid Cases
df
Asymp. Sig. (2-sided)
8.605a
8
.377
9.557
8
.297
.363
1
.547
15803
a. 4 cells (26.7%) have expected count less than 5. The minimum expected count is 1.01.
72
Table 2C: Differences between Non-providers, Moderate and Intensive
Personal Care Providers and the general population (45-64) in terms of
Personal Income of Care Providers
Intensity of Care
Not a
30 to
90
care
89
minutes
provider minutes or more
per day per day
Count
No income
Less than
$15,000
Annual
personal
income of
the
respondent
(grouped)
$15,000 to
$29,999
$30,000 to
$49,999
$50,000 to
$79,999
$80,000 or
more
Total
902
5
23
97.0%
0.5%
2.5%
% of care providers in this group
7.0%
4.8%
5.7%
Count
2282
18
83
% within group proving care of this intensity
95.8%
0.8%
3.5%
% of care providers in this group
17.8%
17.3%
20.4%
2509
30
97
% within group proving care of this intensity
95.2%
1.1%
3.7%
% of care providers in this group
19.5%
28.8%
23.8%
3363
22
107
% within group proving care of this intensity
96.3%
0.6%
3.1%
% of care providers in this group
26.2%
21.2%
26.3%
2478
17
73
% within group proving care of this intensity
96.5%
0.7%
2.8%
% of care providers in this group
19.3%
16.3%
17.9%
1300
12
24
% within group proving care of this intensity
97.3%
0.9%
1.8%
% of care providers in this group
10.1%
11.5%
5.9%
Count
12834
104
407
% overall proving care of this intensity
96.2%
0.8%
3.0%
% within group proving care of this intensity
Count
Count
Count
Count
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
20.507a
10
.025
Likelihood Ratio
21.133
10
.020
Linear-by-Linear Association
4.425
1
.035
N of Valid Cases
13345
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 7.25.
73
Table 2D: Household Income Distribution and Profile of Care Providers
Intensity of Care
Non-
30 to 89
90
provide minutes minutes
r
per day or more
per day
No income
or loss
Less than
$15,000
Total
household
income
(grouped)
$15,000 to
$29,999
$30,000 to
$49,999
$50,000 to
$79,999
$80,000 or
more
Total
Count
34
0
0
100.0%
0.0%
0.0%
0.3%
0.0%
0.0%
783
7
24
96.2%
0.9%
2.9%
% of care providers in this group
6.1%
6.5%
5.8%
Count
1725
15
53
% within group proving care of this intensity
96.2%
0.8%
3.0%
% of care providers in this group
13.4%
13.9%
12.8%
2879
21
106
% within group proving care of this intensity
95.8%
0.7%
3.5%
% of care providers in this group
22.4%
19.4%
25.5%
3504
21
118
% within group proving care of this intensity
96.2%
0.6%
3.2%
% of care providers in this group
27.3%
19.4%
28.4%
3925
44
114
% within group proving care of this intensity
96.1%
1.1%
2.8%
% of care providers in this group
30.5%
40.7%
27.5%
Count
12850
108
415
% overall proving care of this intensity
96.1%
0.8%
3.1%
% within group proving care of this intensity
% of care providers in this group
Count
% within group proving care of this intensity
Count
Count
Count
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
11.420
a
10
.326
Likelihood Ratio
12.657
10
.243
.049
1
.825
Linear-by-Linear Association
N of Valid Cases
13373
a. 2 cells (11.1%) have expected count less than 5. The minimum expected count is .27.
74
Table 2E: Differences between Non-providers, Moderate and Intensive
Personal Care Providers and the general population (45-64) in terms of
Employment insurance Income
Intensity of Care
Not a
30 to
90
care
89
minutes
provider minutes or more
per day per day
Count
1367
10
36
Yes % of group providing intensity of care
96.7%
0.7%
2.5%
% of care intensity provided by group
8.8%
7.9%
7.4%
14168
116
452
No % of group providing intensity of care
96.1%
0.8%
3.1%
% of care intensity provided by group
91.2%
92.1%
92.6%
Count
15535
126
488
% within Income from Employment insurance
96.2%
0.8%
3.0%
Income from
Employment
Count
insurance
Total
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
1.304a
2
.521
Likelihood Ratio
1.366
2
.505
Linear-by-Linear Association
1.301
1
.254
N of Valid Cases
16149
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 11.02.
75
Table 2F: Differences between Non-Providers and Intensive Personal Care
Providers and the general population (45-64) in terms of Health Utility
Index
Group Statistics
Intensity of Care
Health utility
index
N
90 minutes or more per 493
day
Not a care provider
15244
Mean
.86326
Std. Deviation Std. Error
Mean
.195632
.008811
.85541
.209942
Independent Samples t-Test
Levene's
t-test for Equality of Means
Test for
Equality of
Variances
F
Sig. t
df
Sig. Mean
(2- Difference
taile
d)
Equal
variances
Health assumed
utility Equal
index variances
not
assumed
3.401 .065 .819 15735 .413 .007850
.875 529.31 .382 .007850
6
.001700
Std.
Error
Differen
ce
95% Confidence
Interval of the
Difference
Lower Upper
.009587 -.010942 .02664
2
.008973 -.009778 .02547
7
76
Appendix 3: Logistic Regression Model
Logistic Regression
Data
Filter
Weight
General Social Survey Merged Data Set
Individuals under 65 years of age
Adjusted weight (Provided by StatsCan)
Case Processing Summary
Unweighted Cases
Included in Analysis
Selected Cases
Missing Cases
Total
Total
Dependent Variable Encoding
Original Value
Not intensive personal care
Providing intensive personal care (90+ mins/day)
N
9702
2465
12167
12167
Internal
Value
0
1
Percent
79.7
20.3
100.0
100.0
77
Omnibus Test of Model Coefficients
Chi-square
df
Sig.
196.788
10
Model Summary
Step
-2 Log
likelihood
Cox & Snell R Nagelkerke R
Square
Square
1
.015
3449.755a
.000
.061
Variables in the Equation
Aged 55 to 64
B S.E. Wald
-.245 .111 4.903
Female
1.505 .132 130.173 1
Living common-law
df
1
Sig.
.027
Exp(B)
.782
.000
4.502
.653
9.445
.276 5.613
3
1
.024
.018
1.922
Widowed/Divorced/Separated .730
.290 6.318
1
.012
2.074
Single (Never married)
.943
.313 9.093
1
.003
2.568
Less than $15,000
.529
11.260
.242 4.784
5
1
.046
.029
1.697
$15,000 to $29,999
.693
.239 8.440
1
.004
2.000
$30,000 to $49,999
.677
.238 8.080
1
.004
1.968
$50,000 to $79,999
.782
.249 9.869
1
.002
2.186
$80,000 or more
.622
.305 4.161
1
.041
1.862
Constant
.367 235.990 1
5.631
.000
.004
Married
No income
78
Appendix 4: Caregiver Perceptions of care burden
Table 4A: Caregiver Perceptions of Impacts of Care
Impact of Assisting Seniors
% of those affected providing care of this intensity
Changes in Yes
% care providers of intensity affected
your social
% of those affected providing care of this intensity
activities
No
% care providers of intensity affected
% of those affected providing care of this intensity
Changes in Yes
% care providers of intensity affected
your holiday
% of those affected providing care of this intensity
plans
No
% care providers of intensity affected
Postponed Yes % of those affected providing care of this intensity
% care providers of intensity affected
educational/
training
% of those affected providing care of this intensity
No
plans
% care providers of intensity affected
% of those affected providing care of this intensity
Moved in Yes
% care providers of intensity affected
with care
% of those affected providing care of this intensity
recipient
No
% care providers of intensity affected
% of those affected providing care of this intensity
Yes
Senior
% care providers of intensity affected
moved
% of those affected providing care of this intensity
closer to you No
% care providers of intensity affected
% of those affected providing care of this intensity
Turned
Yes
% care providers of intensity affected
down a job
offer or a
% of those affected providing care of this intensity
No
promotion
% care providers of intensity affected
% of those affected providing care of this intensity
Changed
Yes
sleep
% care providers of intensity affected
patterns
No % of those affected providing care of this intensity
Duration of
personal care
(mins/day)
Under 30 to 90+
30
89
83.0% 4.1% 12.9%
30.7% 60.4% 54.9%
93.4% 1.4% 5.3%
69.3% 39.6% 45.1%
82.9% 4.2% 12.9%
22.7% 45.5% 40.5%
92.2% 1.6% 6.2%
77.3% 54.5% 59.5%
78.0% 6.4% 15.6%
3.4% 11.0% 7.8%
90.4% 2.1% 7.5%
96.6% 89.0% 92.2%
71.7% 10.1% 18.1%
2.5% 14.0% 7.2%
90.5% 2.0% 7.5%
97.5% 86.0% 92.8%
83.6% 4.5% 11.9%
8.0% 16.8% 13.0%
90.5% 2.1% 7.4%
92.0% 83.2% 87.0%
80.7% 3.5% 15.8%
2.3% 4.0% 5.2%
90.2% 2.2% 7.6%
97.7% 96.0% 94.8%
76.8% 7.2% 16.1%
12.4% 45.5% 29.9%
92.1% 1.5% 6.4%
79
Extra
expenses
Health
affected
Yes
No
Yes
No
Reduction in Yes
hours of
work
No
Yes
Quit a job
No
Change your Yes
work
patterns
No
Reduction in
income
Yes
No
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
% of those affected providing care of this intensity
% care providers of intensity affected
87.6%
84.4%
36.3%
93.4%
63.7%
77.8%
12.0%
91.9%
88.0%
80.0%
11.0%
91.3%
89.0%
76.2%
1.2%
90.1%
98.8%
80.4%
14.5%
91.7%
85.5%
78.8%
7.4%
90.9%
92.6%
54.5% 70.1%
3.8% 11.8%
65.0% 58.1%
1.3% 5.3%
35.0% 41.9%
4.9% 17.3%
30.3% 30.7%
1.8% 6.3%
69.7% 69.3%
5.5% 14.5%
29.7% 22.9%
1.8% 6.9%
70.3% 77.1%
3.2% 20.6%
2.0% 3.8%
2.2% 7.6%
98.0% 96.2%
5.6% 14.0%
40.0% 28.9%
1.6% 6.6%
60.0% 71.1%
4.6% 16.6%
16.8% 17.9%
2.1% 7.0%
83.2% 82.1%
Table 4b: Caregiver Perceptions of Government Role
Intensity of Care
(mins/day)
30 to 89
90+
% of those providing care of this 2.1%
10.2%
Would the government take
Yes
intensity
over assisting the long term
receiver if you were unable
% of those providing care of this 97.9%
89.8%
No
to do so?
intensity
80
Table 5: Relationship between care provider and recipient (selected
categories, not all possible responses included)
Intensity of Total
Care (minutes
of personal
care per day)
30 to 90 or
89
more
% of caregivers with this relationship
18.2% 81.8% 100.0%
Spouse/
partner
% of those providing care of this intensity 1.5% 1.8% 1.8%
% of caregivers with this relationship
28.8% 71.3% 100.0%
Father
% of those providing care of this intensity 17.6% 11.5% 12.8%
The care
% of caregivers with this relationship
23.0% 77.0% 100.0%
recipient Mother
% of those providing care of this intensity 39.7% 35.2% 36.2%
is the care
% of caregivers with this relationship
26.7% 73.3% 100.0%
provider’s Father-inlaw
% of those providing care of this intensity 3.1% 2.2% 2.4%
[…]
29.0% 71.0% 100.0%
Mother-in- % of caregivers with this relationship
law
% of those providing care of this intensity 6.9% 4.5% 5.0%
% of caregivers with this relationship
12.9% 87.1% 100.0%
Close friend
% of those providing care of this intensity 3.1% 5.5% 5.0%
% of caregivers with this relationship
21.0% 79.0% 100.0%
Total
100.0 100.0 100.0%
% of those providing care of this intensity
%
%