1

INFORMAL CARE IN THE UK: CONSTRAINTS ON CHOICE

Fiona Carmichael*

ABSTRACT

This paper analyses the relationship between informal care provision and the

characteristics and needs of the people they care for. To do this it uses a unique

regional data set containing detailed information on 1,985 informal care-givers. The

data were provided by charitable organization providing support for carers in the

midlands region of the UK. The records provide information on carers‟

characteristics and the characteristics and needs of the people they care for. They also

contain data on caregiving provision including of hours of care, duration of the caring

episode and type of help given (personal, physical, practical or emotional). The

analysis incorporates an initial descriptive analysis of informal caregiving provision

by carers‟ age-group and gender and some contextual national evidence from the

British Household Panel Survey. The descriptive analysis is extended using

multivariate analysis to explore the interrelationships between caregiving provision

and the characteristics of both caregivers and the people they care for. The

relationship between caregiving and employment is also investigated using

instrumental-variables estimators to control for endogeneity. The results indicate that

the exogenous needs and characteristics of the cared-for play an important role in

shaping informal care provision and, indirectly, carers‟ employment. This implies that

individual decisions about caring provision are not simply a matter of choice.

Decisions are constrained, particularly after a caring episode has begun.

KEYWORDS: Caregiving, health, labor supply, unpaid work

JEL Codes: D13, J14, J17

ACKNOWLEDGMENTS

This research was supported by a grant from the British Academy. I am grateful to the

regional carers‟ support service for supplying the data and to Emma Silvester for

creating the anonymised data set. British Household Panel Survey was made available

through the Office of National Statistics and the ESRC Data Archive

*Birmingham Business School, University of Birmingham, Edgbaston,

Birmingham, B15 2TT, UK

e-mail: f.carmichael@bham.ac.uk

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INFORMAL CARE IN THE UK: CONSTRAINTS ON CHOICE

1. Introduction

Informal carers look after someone who needs support because of frailty in older age,

physical or mental health needs or illness (UK Department of Health 2006(a)).

Parents who look after children with disabilities, children caring for elderly parents

and young people providing care for siblings all fall into this category of caregiving.

Informal carers are essentially a free source of labour from the perspective of health

and social services. The costs they incur are often ignored in policy decisions, in there

is a general lack of recognition of their contribution to the health and wellbeing of

others and the difficulties they face. This is in some ways surprising since community

health and social care systems rely on family carers providing support for people who

need care but want to remain in their homes (Michael Nolan 2001). Furthermore, the

numbers involved in care are substantial. According to official estimates,

approximately one in ten of the population of England and Wales are involved in

informal care (National Statistics Online 2003). Yeandle et al. (2006) have also

additionally estimated that nearly three million workers in the UK provide informal

care. The percentage of the population who will, at some point in time, participate in

informal care is higher; among respondents involved in the first wave of the British

Household Panel Survey (BHPS) in 1991, over half (51.8 percent) of those still of

working age 10 years later had participated in informal care. These relatively high

figures for caring incidence are in line with evidence from previous research in the

UK (Heitmeuller and Inglis, 2007; Hirst, 2002) and from Australia where 3-4 percent

of employees become carers each year (Hill et al. 2008).

Nevertheless, demographic changes have prompted a growing interest in

issues related to informal care provision. This is largely because population ageing

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imposes extra demands on the health and caring services and raises the demand for

care (Hancock et al. 2003); according to some estimates, spending on personal care

alone will need to triple to £30 billion by 2026 in order to meet the needs of the aging

baby boomer generation (John Carvel 2006). At the same time, population ageing is

putting increasing pressure on pensions which has created an urgent imperative to

extend working lives. Consequently, the demand for informal care is rising at the

same time as the people‟s availability to supply care is being constrained.

In the light of these trends, it is not surprising successive UK governments

have initiated a series of policy measures that have aimed to address the needs of

carers (see Table A1 in the Appendix). These initiatives have focused mainly on

support for carers through formal services, financial support and support for working

carers. The latter reflecting government recognition that caring responsibilities can

constrain employment. For a discussion of some of the complexities underlying these

policies see for example, Twigg (2009). The impact of these policy initiatives is

difficult to judge, however, the available evidence suggests that the difficulties faced

by carers have not changed significantly (Department of Health, 2001; Challis et al,

2005; Carmichael and Hulme, 2008). Most recently, the government elected in 2010

stated an early intention to „refresh‟ the National Carers Strategy and produce a plan

of action for 2011-15. Paul Barstow, the minister for Care Services, issued a call for

views on this process (Department of Health, 2010) and a response to the views

submitted was published in November (Carers Policy Team, 2010). In the

„refreshment‟ process there is a focus on „effective early intervention‟ and

„personalisation‟. To improve the early identification of carers the Reaching out to

Carers Innovation Fund has awarded £1.35m to 79 projects run by voluntary groups

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who are „keen to support carers‟ (Department of Health, 2011). At the time of

writing, no evidence is available on the success of these projects.

Alongside these policy initiatives, research on informal caregiving provision

is also growing. This research has tended to focus on how care provision impacts on

carers‟ health, wellbeing, income employment. The latter in particular has been the

subject of considerable quantitative research (e.g. Ettner 1996; Pavalko and Artis

1997; Spiess and Schneider 2003; Heitmueller 2007). There has also been some

consideration of the related effects of employment on care (Carmichael et al., 2010;

Michaud et al. 2011). Research is also being conducted on the effect on the trade-off

between employment and caregiving of employment and workplace policies (Hill et

al., 2008; Bryan, 2011).

The underlying complexity of these relationships has also been highlighted by

in qualitative studies (Baldwin, 1985; Arksey et al. 2005; Vickerstaff et al., 2009).

For example, Arksey et al. (2005) emphasise that care-giving relationships are

characterized by uncertainty and that carers often need to respond to changes that are

beyond their control. For example, care needs can change after „critical transition

points‟ such as a medical intervention (Arksey et al. 2005:53). Relatedly, Vickerstaff

et al. (2009:27) notes that the demands of caring tend to increase over time; beginning

with relatively low level types of care such as helping with the laundry and paperwork

but ending up with more intensive care including personal care. These studies

highlight that decisions around caring and employment are constrained by myriad

factors. In particular, and once a caring episode has begun, the needs of the cared-for

and the consequent type of care provided are of particular importance. In contrast,

most quantitative research has little to say about such factors probably because of lack

of data. One exception is the study by Hassink and Van den Berg (2011) who use

5

diary data to consider allocations of time to different caring tasks. Their results

indicate that the time constraints imposed by informal care vary with the type of

caring activity undertaken. Since the latter depend on the needs of the cared-person

these are an implicated in decisions about the allocation of time.

This paper contributes to this literature by analyzing the relationship between

the amount and type of care provided and the needs and characteristics of the cared-

for. The results of this analysis are then used to investigate the relationship between

hours of care and employment. To do this the study utilizes a unique regional data set

compiled by a charitable organization in the midlands region of the UK. The

organisation is engaged in the delivery of support and advice to carers (hereafter

CSAM). The dataset contains a record for all 1,985carers who accessed the services

of CSAM between 1998 and 2008. The records provide information on caregiving

provision including indicators of hours of care supplied and duration of the caring

episode. More importantly for this study, they also provide data on the type of help

given (personal, physical, practical or emotional) and the characteristics and needs of

the people being cared for. This enables a quantitative exploration of the constraints

imposed on carers‟ time by these largely exogenous factors. This is important since

previous research, particularly in economics, has tended to focus on the „choice‟

aspect of decisions about caring, particularly in relation to employment.

The plan of the paper is as follows. First, we draw on the BHPS in order to

provide some national context on caring incidence, the characteristics of carers and

the provision of informal care. We then analyse the CSAM data base. A preliminary

descriptive analysis is reported for the whole sample by age-group and gender. We

then explore the determination of hours of care and the provision of personal care

within a multivariate framework. The analysis is extended by estimating a model of

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employment participation and hours of care using the method of instrumental

variables to control for potential endogeneity. The final section interprets the main

findings and discusses policy implications.

2. Contextual evidence from national data

In this section data from seventeen waves of the BHPS is used to provide some

contextual evidence on informal care-giving in the UK. The BHPS is an annual

survey consisting of a nationally representative sample initially composed of about

5,500 households recruited in 1991. Extension samples have since been added and the

BHPS currently covers around 10,000 households across the UK. The BHPS was

recently replaced by and incorporated into the Understanding Society Study which

follows 40,000 household in the UK. The data reported in Table 1 is derived by

pooling the first seventeen waves of the BHPS (1991-2007). The sample contains

36,536 observations for people involved in informal care and 163,825 observations

for people who were not caring in the year the data was collected.

Table 1 reports means and sample percentages by gender and caring status.

The data show that carers are significantly more likely to be female and their average

age is significantly higher than that of non-carers. Female carers are more likely to be

25 or over than female non-carers and male carers are statistically more likely to be

65 or over. Data from the Office of National Statistics (National Statistics Online,

2006(a)) indicate that the peak age for becoming an informal carer is between 45 and

64 years in the UK although just over one per cent of carers are children (aged

between 5 and 15) and five per cent of carers are aged 85 and over. Interestingly, the

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predominance of females is only a feature among carers under 75 years old, among

people 75 and over a larger proportion of men provide care. The BHPS data indicate

that 50.19 percent of informal carers 75 and over are male (see also National Statistics

Online, 2006(a)). This is because spousal care is the most frequent type of care given

among older people and more older men have living spouses than older women.

Overall, however, women provide the bulk of informal care in the UK, as they do in

other European countries, the US, and in developing countries, such as China (Eaton

2005).

The figures in Table 1 show that both male and female carers are less likely to

be employed or be in training/education than than non-carers. They are more likely to

be either unemployed, retired, on maternity leave, involved in family care or long-

term sick/disabled. Overall, these figures would be consistent with the hypotheses

that informal care responsibilities impact negatively on employment. However the

figures in Table 1 also show that compared with non-carers, carers are less likely to

have a higher educational qualification and more likely to report that they have a

health problem. Both factors could be expected to contribute to the lower employment

participation of carers as could age and gender although caring has also been

implicated in ill-health. In addition, there is a potential source of endogeneity in the

caring-employment relationship since the opportunity costs of caring are likely to be

higher for people who have a higher propensity to undertake paid work e.g. because

they have higher unobserved ability and can earn higher wages. However, previous

research in the UK and elsewhere has shown that some of the differences in

employment participation remain when carers‟ characteristics are controlled for and

endogeneity is addressed (e.g. Lilly et al. 2010; Carmichael et al, 2010; Heitmueller

and Inglis 2007; Lázaro, Moltó, and Sanchez 2004; Carmichael and Charles 1998,

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2003a, 2003b; Pickard et al. 2000; Hutton 1999; and see Lilly et al 2007 for a review

of this literature).

The figures in Table 1 also provide some information on the hours of caring

undertaken. The figure show that the majority of people categorised as carers in the

UK care for only a few hours a week; median hours of caring are between 5 and 9 and

modal weekly hours are less than 5. The majority of carers care for fewer than 20

hours a week with only 20.60 percent of male carers and 23.62 percent of female

carers caring for more than 20 hours weekly. Nevertheless, just under 10 percent of

male carers and just under 11 percent of female carers are intensive carers who are

caring for more than 50 hours a week (Vickerstaff et al., 2009). Between 8 and 9

percent are caring for very long hours of 100 or more a week. The only other direct

information on caregiving within the BHPS relates to residency. The figures show

that 40.36 of male carers and 32.82 percent of female carers are co-resident with the

people they care for.

The figures show that carers who perform longer hours of care are more likely

to be female; 62.92 percent of those who care for twenty or more hours per week are

female. This finding is consistent with previous research in the UK and research

undertaken in other countries (e.g. Lilly et al, 2010; Arksey et al, 2005; Lázaro,

Moltó, and Sanchez 2004; Maher and Green 2002). Hours of care are also related to

age with a higher percentage of older carers providing 50 or more hours of care a

week. The BHPS data indicate that among carers 70 and over the percentage caring

for at least 50 hours a week is 15.72 percent compared with only 10.10 percent among

those between 25 and 69 (see also National Statistics Online, 2006(a)).

To summarise, the national data from the BHPS shows that compared with

non-carers, carers in the UK are older, more likely to be female, less likely to be

9

employed and more likely to suffer from some health problem, they have also

invested in less human capital. The majority of carers are caring for fewer than 20

hours a week with only under 23 percent caring for at least 20 hours a week and only

around 10 percent are intensive carers who care for 50 or more hours a week. A

minority of carers are co-resident with the people they care for.

3. The regional data set

The charity providing the regional data, CSAM, was founded over 25 years ago and is

an established provider of support to carers at the sub-county and county level. During

this time it has supported a broad range of carers including parent carers, carers of

elderly people, carers of adults with physical disabilities or learning disabilities and

young carers. CSAM‟s overall aims are to enrich carers‟ lives and promote their

wellbeing. To meet these aims CSAM employs specialist staff to address the needs of

carers and offers a wide range of services including one-to-one advice and support

and the provision of information and advice (via a newsletter and a website with links

to other agencies). It also facilitates support groups that help carers to meet each other

and by doing so feel less isolated and give mutual support. In addition, the

organisation promotes carers' rights and aims to raise the profile of carers and carer

issues by ensuring that carers have a voice that is heard by decision makers. CSAM

has recently been restructured and at the time of writing services to young carers and

adult carers are provided by separate organisations.

Since 1998 CSAM has held a database of all the carers who contacted the

organisation. The organisation kindly allowed access to this database after the entries

had been anonymised. The data covers 1998-2000 during which time 1985 carers

contacted CSAM; just under 200 carers a year. Table 2 summarises the

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characteristics of the carers who contacted CSAM over this 10 year period. Table 3

summarises the information that is available on the people they cared for and Table 4

provides some detail on the type of care provided. In each case the data is broken

down by both age-group and gender since previous research and the BHPS data

indicate that older people and women are the most intensive carers.

Characteristics of carers

In line with the national data, the majority of the carers in the sample are

female. However, the percentage of females in the regional sample (70 percent) is

higher than the national average for carers of just under 60 percent (Table 1).

Compared with the national average for carers , the CSAM carers are also older; their

average age is 59 and 46.53 percent are over 65. A somewhat larger percentage (62.8

percent) are over the statutory state pension age (SPA) since this only 60 for females

and 59.5 percent of the sample are over 60.

There is also a large group of mid-life carers between 25 and 64 years old

(41.36 percent) and a smaller group of relatively young carers; 10.7 percent are under

18 and 12.1 are under 25. The ethnicity of the sample members is predominantly

white (91.7 percent) although just fewer than 8 percent of the sample are categorised

as either Asian or Black. Although we do not have data on income, we know that 767

sample members (or the person they care for) are in receipt of some state benefit

(38.64 percent) and 173 carers (8.72 percent) are in receipt of Carer‟s Allowance. 46

others receive income support and 31 receive pension credits. 449 care recipients

(23.1 percent) are in receipt of either attendance allowance or disability living

allowance (Table 2)

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Data on employment status is available for 954 sample members. 21.28

percent of this sub-sample are in employment of some kind and just under 17 percent

are involved in training/education. Among those aged 25-64, 41.12 percent are

employed; 39.35 percent of females and 49.32 percent of males. Among females

between 25 and 59 (the SPA for this cohort) a somewhat higher percentage, 44.7

percent are in employment. Among the sub-sample aged 65 and over, 5.02 percent are

in employment (4.17 percent of women and 6.5 percent of men). None of the carers

under 25 are employed, the vast majority are involved in training/education. Overall,

females are more likely to be employed than males but this result is driven by the

higher percentage of women who are mid-life carers.

Overall, the employment participation rates among those under 65 are very

low relative to the national averages reported in Table 1. As discussed in more detail

below, part of the explanation is likely to lie in the relatively long hours of care the

CSAM sample supplies. The relatively high training/education figures among the

younger sub-sample bolster their labour market participation rates but may mask an

inability to secure employment. Some carers may also be undertaking training in

order to fulfill benefit requirements. However, there is no detail on the kind of

training being undertaken and therefore this remains conjecture.

Characteristics of the people cared-for

Table 3 provides summary data on the characteristics of 1294 of the people

cared for. However, there are fewer observations for some measures as indicated in

the Table. The data show that the gender split among the cared-for is very equal with

only fractionally more males among those cared for. Older carers and women are

more likely to be caring for men (presumably their spouses).

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The average age of those cared for is just under 61 which is only two years

older than the mean age for the carers. The small age gap is consistent with spousal

care being a large part of the care provided. Most of the 84.17 percent of those 65 and

over who are also caring for someone in the age group are also likely to be providing

spousal care. However, nearly half (49.71 percent) of midlife carers are providing

care for someone over 65 and this figure provides a very rough upper limit to the

amount of elder care being undertaken. Mid-life males in particular are more likely to

be caring for someone 65 or over than someone younger than 65. A more considered

proxy for the amount of elder care being provided is discussed below.

25.14 percent of the sample care for someone whose needs were categorised in

in terms of learning needs or mental health problems. This group includes older

person with mental health problems (n=114). 13.21 percent of carers look after

someone with physical disabilities and 15.2 percent are caring for disabled children.

In line with the age of the cared-for, the health problems of the majority (42.64

percent) were classified simply in terms of their status as an older person (65 or over).

Not surprisingly given their age, the majority of the sample (61 percent) care for

someone who is retired, however 18.9 percent care for someone who is in work and

16.2 percent look after someone who is a student.

A little less than 20 percent of the sample care for someone who receives

Attendance Allowance although this figure rises to 30 percent among older carers.

Fewer than 5 percent look after someone who receives Disability benefit. The

majority of those cared for live in their own home or a home they share jointly with

their carer. Interestingly, male carers are more likely to be looking after someone who

lives in their own (the cared-for‟s) home or a joint home and females are more likely

to care for someone who lives in their (the carer‟s) home. This is consistent with the

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male caring role predominantly being spousal care or care for relatively independent

elders. Women, on the other hand, are more likely to be caring for disabled children.

From this data we can also tentatively conjecture that they women are more likely to

be caring for less independent elders who have moved into the carer‟s home.

Type and amount of care provided

The low employment rates of caregivers reported in Table 2 are not altogether

surprising given that the carers in the sample have taken the step of contacting a

support service for help or advice. As shown in Table 4, many are caring for long

hours and some have cared for many years. Among the 869 carers for whom we have

this information the mean number of care hours per week is between 35 and 49, the

modal category is at least 100 hours and the median category is between 50 and 99.

These figures indicate that the carers in the sample are representative of the minority

of carers who provide intensive care (50 or more hours a week). Furthermore, 72

percent of the sample are co-resident (in the caregiver‟s home or a joint home) with

the person they care for and previous research suggests that these carers have more

significant caring responsibilities and care for longer hours (Vickerstaff et al.. 2009;

Heitmueller, 2007).

Within the sample, carers 65 and over care for the longest hours and have been

caring for the longest duration; among the 25-64 age group the percentage caring for

longer hours (more than 50 and more than 100 a week) is significantly lower

compared with the older age group. Nevertheless a large majority of mid-life carers

are caring for 50 or more hours a week. Among the sub-sample of carers younger

than 25 the vast majority are involved in fewer than 20 hours of care a week although

a small minority are caring for between 20 and 49 hours a week.

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The mean caring episode duration for the bulk of the sample (1926 sample

members) is between 2 and 3 years, the modal duration is 2 years and the median

duration is 3 years. However, just under 25 percent of the sample have been caring for

more than 5 years and 79 had cared for 9 or more years. The mean caring duration is

unsurprisingly higher for older carers. It is lower for women probably reflecting the

lower mean age of the people they care for. The average caring duration for younger

carers is only 2.32 years but this still indicates a considerable ongoing commitment

that could potentially impact on their future employment opportunities.

While intensive carers are usually defined as those who supply 50 or more

hours of care a week, the intensity of care in terms of how demanding it is for the

carer could be better indicated by other, more qualitative measures. For example, in

terms of the type of care provided. This would reflect the needs of the cared-for

person to some extent and would vary according to the ability of the cared-for person

to conduct core daily activities. The greater the difficulty of the person cared for to

perform these activities, the greater the need for assistance and the more demanding

or intense the associated carer responsibility (Hill et al., 2008). The provision of

personal care, for instance, implies that the care recipient faces profound limitations

on their capacity to perform basic personal care activities such as getting ready in the

morning, dressing, washing and feeding. In contrast, the provision of practical care

implies a need for help with activities such as household work, organizational

activities and transport. Other types of care could involve giving emotional support or

physical care such as physical therapy and help with mobility.

Vickerstaff et al. (2009) note that most of the carers in their sample who cared

for 50 or more hours a week also provided personal care while those caring for fewer

hours did not. This suggests that the provision of long hours of care and intimate

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personal care are complements. This may reflect the long-term nature of many caring

relationships which, as observed by Vickerstaff et al. (2009:27) gradually “become

more intensive, time consuming and intimate.” Recent research additionally suggests

that personal care may also be the most difficult type of care to fit around other

activities such as paid work since related tasks often need to be completed at

particular times of the day (Hassink and Van den Berg, 2011).

The figures in Table 4 show that over 40 percent of the sub-sample for whom

we have the relevant information (n=988) provide personal care (the vast majority

with physical care and only 1.72 percent on its own). Women are significantly more

likely to provide personal care but the gender difference is only significant among the

older cohort who are anyway more likely to providing this type of care. Nearly twenty

percent provide only practical care (including drug administration, n= 2) and just over

17 percent provide emotional or „other‟ care. 17.11 percent of this sub-sample of 988

are categorised as young carers providing either excess chores (the majority) or

sibling care.

It would be useful to have a measure of intergenerational caring provision, in

particular, the degree to which the sample members are providing elder, spousal, or

child care. Unfortunately, there is no explicit data on the familial relationships

between carers and cared-for. However, we do know that 15.2 percent of the cared-for

are disabled children of a sample member (Table 3). 12.10 percent of the sample are

also under 25 and these relatively young carers are likely to providing care for a

sibling or a parental. We also know that a majority of the cared-for people are 65 and

over. As noted above, this doesn't necessarily mean that the majority of care

undertaken by the sample is elder care since many of the carers are older people

themselves and are probably giving spousal care. Some of the mid-life carers who are

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caring for people 65 and over are also providing spousal care. However, 26 percent

of the sample for whom we have data on both the carer‟s and the cared-for person‟s

age (n= 1103) are caring for someone who is both at least 60 years old and at least 17

years old than the carer (Table 4). This figure gives an approximate indication of the

extent of elder care within the sample. The majority of carers who are 65 or over are

caring for older people who are less than 17 years older than themselves (n= 411).

The group is likely to be undertaking spousal care. Only two sample members who

are 65 or over are caring for people who are 20 or younger, these carers are probably

caring for their grandchildren. Taken together these estimates suggest that

approximately 47% of the care provided is spousal or „other‟ care.

4. Multivariate analysis

4.1 Empirical specification

Caring provision

In this part of the paper we explore the determinants of informal caring provision. We

consider how and to what extent caring provision is influenced by the characteristics

and status of the caregiver (including their employment status and whether they are in

receipt of Carer‟s Allowance) the characteristics of the cared-for person and the type

of care given (e.g. personal, physical or practical). Initially the focus is on caring

intensity measured by time involved in care, T and the estimated relationship is given

by:

T = β0 + β1X1 + β2 X2 + β2 X3 + ε (1)

where T is hours of care provided, X1 is a vector of covariates reflecting the

characteristics and status of the caregiver, X2 is a vector of covariates reflecting the

characteristics of the cared-for person, X3 is a vector of covariates indicating the type

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of care given and the duration of the caring episode, ε is the error term. This

specification tests whether and how the largely exogenous characteristics of the

cared-for person impact on care provision. In addition, the relationship between type

of care provided and hours of caring is explored.

As discussed, provision of personal care is an alternative indicator of the

intensity of caring. We therefore estimate a version of (1) with a dependent variable

indicating whether or not the caregiver provides personal care (P). In this estimation

the vector X3 includes a more restricted set of covariates indicating type and

duration of care.

Four dichotomous indicators of the time involved in caregiving are used in the

estimations, three are dichotomous and one is an ordered variable. HOURS>50pw

(No=0, Yes=1) records whether an individual cares for at least 50 hours a week;

HOURS>100pw (=0,1) records whether an individual cares for at least 100 hours a

week; CAREHOURScat (=0, 1,2,3,4,5,6) is an ordered variable and records weekly

hours of caring by category (less than 5, 5-9, 10-19, 20-34, 35-49, 50-99, 100 or

more); HOURS<20pw (=0,1) is an alternative, negative measure of time committed

to care provision. It records whether an individual cares for fewer than 20 hours a

week. The indicator of personal care provision (PERSONAL CARE) is also a

dichotomous variable.

For the dichotomous variables, we model the probability of undertaking at

least T hours of care (T =50, 100) less than T hours (T = 20) or providing personal

care (P) using logit and for ease of interpretation report odds ratios. In the case of the

measures of hours of care provided, logit models the mean probability of undertaking

at least/less than T hours of care ( 0,1iT ) by maximising:

18

, ,

1

ln ln ( ) (1 ) ln(1 ( ))N

i j j i i j j i

i

L T F x T F x (1)

where 1

, ,1( ) (1 exp( ))

k

j j i j j ijF x x is the logistic function. The xjs are the

individual carer and cared-for characteristics and indicators of type of care that are

available in the dataset (Tables 2-4). The βjs are the corresponding coefficients (log-

odds ratios). An equivalent specification corresponds to the logit estimation for the

supply of personal care. For the regressions in which the dependent variable is the

ordered variable CAREHOURScat we use ordered logit. The ordered logit model

generalises the approach of the binary-choice logit model to the notion of multiple

thresholds where larger values are taken to correspond to higher outcomes.

For each of these estimations we adopt a stepped approach that involves

estimating 4 models. The stepped approach enables identification of significant

influences that would otherwise be obscured because of collinearity with other

variables. Model 1 regresses the measure of caring on only those variables reflecting

the characteristics or circumstances of individual carers: gender (MALE); age (AGE);

whether the carer is employed (EMPLOYED); whether the carer receives Carer‟s

Allowance (CARERS ALLOWANCE); ethnicity (ETHNIC BLACK/ASIAN,

ETHNIC OTHER, the reference group is white ethnic).

Model 2 regresses the measure of caring on only those variables that reflect

the characteristics and needs of the cared-for (CF) person. These include gender

(CF_MALE) and age (CF_AGE). Two variables indicating whether the cared-for

person receives Attendance Allowance or Disability Living Allowance are also

included. Model 2 includes the indicators of the needs of the cared for person

classified in terms of their health condition as either a disabled child, an adult with a

physical disability, and adult with mental health or learning needs (includes older

19

adults) or as an elderly person. Other included variables indicate the living

arrangements of the cared-for and whether they are employed (CF_EMPLOYED) or

retired (CF_RETIRED). For cared-for people of working age, whether they are

working or not provides some indication of their degree of independence. As such,

CF_EMPLOYED is expected to be negatively related to hours of care provided

although it may also be correlated negatively with CF_AGE.

Model 3 regresses the dependent variable only on indicators of the duration of

the caring episode and the type of care given and. The measure of the duration of the

caring episode is in years (DURATION). The indicators of type of care cover co-

residential care (CO-RESIDENTIAL) and a proxy measure for elder care

(ELDERCARE). The latter indicates whether care is given to an elderly person over

60 and at least 17 years older than the care-giver. Also included are an indicator of

personal care provision (PERSONAL CARE) and a variable indicating that the only

care given is emotional or „other‟ care (EMOTIONAL/OTHER CARE). The

reference category for these variables is the provision of only physical or only

practical care. These two measures are excluded when the dependent variable is

PERSONAL CARE.

Model 4 is the last step in the procedure and estimates a best-fit equation that

regresses the dependent variable on only those independent variables that are

significant in either Model 1, 2 or 3. Table A2 in the Appendix provides definitions of

all the variables used in the analysis.

Caring provision and employment

We additionally explore the relationship between caregiving and employment

participation for a restricted sample of carers aged 19-69 (among those older than 65

20

two sample members younger than 70 are working but only five carers aged 70-80 are

employed). The oldest employed sample member is 80). In the analysis we take steps

to control for possible endogeneity in the relationship between caring and

employment. As already referred to, the issue of endogeneity arises because time

spent caring incurs an opportunity cost in terms of the monetary value of forgone time

(see for example, Heitmueller, 2007; Carmichael et al., 2010). Decisions about caring

and employment are likely to be jointly determined because the shadow price of time

involved in care will be higher for some people (e.g. those employed in full-time jobs,

those working inflexible hours and those earning a relatively high income). Even

after a caring episode has begun, a variety of “uncertainties and unknown factors

and/or external constraints” including the carer‟s own health and job insecurity are

likely to influence decisions about both paid work and care (Arksey et al., 2005: 150).

More specifically, endogeneity arises because of omitted variables in the

estimated regression of employment participation, E (or other measure employment

status) against informal care commitment, C, given by:

E = β0 + β1C + β2X + ε (2)

where X is a vector of covariates reflecting individual characteristics and ε is the error

term. In this estimation there are unobserved covariates such as unobserved ability

that potentially impact on both employment participation and caring commitment.

The omission of these covariates violates the zero conditional mean assumption

E(ε|X) = 0 since changes in such variables will alter both C and E. This implies that

OLS estimates of (2) will be inconsistent. For example, inasmuch as people with

higher unobserved ability have a higher propensity for employment

(βUnobservedAbility > 0) and a lower propensity for caregiving then (Cov(ε, C) < 0)

OLS estimates of β1 will be biased downward.

21

Researchers have adopted a variety of approaches in an attempt to control for

endogeneity and selection into caring. For instance, they have used panel data in

conjunction with fixed or random effects models to allow for the effect of individual

characteristics on labor supply (Bolin et al, 2008; Heitmueller, 2007). Another

approach has been to control for individual characteristics by using longitudinal data

to estimate a relationship between changes in informal care provision and

simultaneous changes in employment status (Carmichael et al. 2010; Spiess and A.

Schneider 2003; Pavalko and Artis 1997).

The most common method used to address endogeneity in the caring-

employment relationship is to specify an instrument for caring commitment, C

(Ettner, 1996). This method is adopted here. In order to address the identification

problem, the selected instrument for caring commitment, z, needs to satisfy two

criteria. First, it needs to be strongly correlated with the measure of care. Second it

needs to be exogenous so that is uncorrelated with the error term in the estimated

employment relationship (2). To satisfy the first criteria, we construct instruments

from those variables that are significant in the estimated regressions in which the

relevant measure of caring commitment is the dependent variable. We then use 2SLS

to combine these multiple instruments into one optimal instrument and generate

consistent estimation of β1 in (2).1

We cannot observe ε, and therefore we cannot directly test for the second

assumption of zero correlation between z and ε (Wooldridge, 2002). However, to

address this point the proposed instrument is constructed only from variables

reflecting the characteristics of the cared-for and their care-needs. It is unlikely that

these measures are correlated with unobserved determinants such as the carer‟s

1 Conceptually, the procedure involves running an auxiliary regression with the dependent variable C

and the multiple instruments as independent variables. The optimal instrument is generated by the

predicted values of this estimation although the „two-stage‟ estimator is calculated in one computation.

22

unobserved ability. The latter are likely to be determined in the carer‟s formative

years not by the onset or nature of the cared-for person‟s illness or disability.

4.2 Results

Hours of care supplied: Tables 5-8

In table 5 the dependent variable is HOURS>50. The figures reported are odds-ratios.

MALE is insignificant in Model 1 suggesting that among this sample of carers,

females are no more or less likely to supply longer hours of care. While employment

status (EMPLOYED) is weakly and negatively significant in Model 1 it is not

significant in Model 4, the best fit estimation, which includes variables reflecting

cared-for characteristics and indicators of type/duration of care. However, carer‟s age

retains significance in Model 4 confirming that longer hours of care are provided by

older carers even after allowing for other factors. The reported odds ratio in Model 4

suggests that the odds of providing at least 50 hours of care a week increase by 12

percent with each year of a carer‟s life. Receipt of Carer‟s Allowance also retains

positive significance in Model 4 which is not surprising given the eligibility

conditions for receipt of this allowance (see Appendix); the odds of supplying at least

50 hours of care a week are higher by a factor of 7.89 for those in receipt of Carer‟s

Allowance.

The cared-for characteristics that retain significance in Model 4 are age

(CF_AGE) and receipt of either Attendance Allowance or Disability Living

Allowance and living arrangments. Age of the cared-for person is negatively related

to hours of care supplied in contrast to carer‟s age. The positive significance of either

state benefit is in line with the stringent needs-based eligibility conditions for receipt

of these benefits. As such receipt of these benefits is likely to identify those cared-for

23

people with the greatest care needs. It is therefore not altogether surprising that after

allowing for receipt of benefits, none of the variables indicating the specific health

needs of the cared-for are significant. Living arrangements appear to be important,

particularly whether the cared-for person lives in institutionalized accommodation

which retains negative significance in Model 4 (relative to the reference category of

living in the cared-for‟s own/joint home).

In Model 3, the provision of co-residential care, elder care, personal care and

emotional or other care are all positively related to hours of caring, but only co-

residential care and personal care retain significance in Model 4, both positively. The

odds ratio associated with personal care in Model 4 indicates that supplying this type

of care raises the odds of caring for at least 50 hours a week by a factor of 4.47 (or

347 percent). Providing co-residential care raises the odds by a somewhat smaller

factor of 2.43 or 143 percent.

The results in Tables 6-8 are largely in line with those in Table 5. However,

there are some exceptions suggesting that there are threshold effects related to hours

of care. For instance, in Table 6 where the weekly hours threshold is 100 hours

(HOURS>100pw= 1) the employment status variable (EMPLOYED) is negatively

significant in both Models 1 and 4. The odds ratio in Model 4 indicates that the odds

of caring for 100 or more hours a week are lower by a factor of 0.39 (61 percent) if

the carer is employed. In contrast, employment status is insignificant in Table 5 where

the weekly hours threshold is only 50 hours. This suggests that only the most

intensive carers trade-off care hours and hours of work. Another difference is that the

age of the cared-for person is not significantly related to the 100 hours threshold while

the proxy for eldercare is negatively significant. Since carer age is positively

significant in both sets of estimations, this difference can be interpreted as suggesting

24

that the most intensive carers are older and caring for someone who also elderly.

Presumably this is mostly spousal as opposed to eldercare. Personal care is positively

and significantly related to both the 50 and 100 hours thresholds. However the

positive significance of providing emotional/other care in Table 6 is difficult to

interpret.

The 50 and 100 hour thresholds also appear to be related to living

arrangements in different ways; when the cared-for person lives in the carer‟s home

the odds of caring for at least 100 hours a week are higher by a factor of 2.72 (172

percent). Interestingly, the Model 2 estimates in Table 6 indicate that carers looking

after an adult with a physical disability are significantly more likely to provide 100 or

more hours of care a week. However, this indicator of specific care need does not

retain significance in Model 4 when carer characteristics and type/duration of care

variables are included. As indicated in Table 9, this could be because the provision of

personal care is strongly related to care need.

Another notable difference is that when the dependent variable marks the 100

hours threshold, duration of the caring episode (DURATION) is negatively

significant; the odds of caring for 100 or more hours a week are lower by a factor of

0.58 (42 percent) for each extra year involved in caregiving. This result may be

interpreted as suggesting attrition or possibly a tradeoff between time spent caring

and the duration of the caring episode.

In Table 7 the dependent variable is the ordered variable CAREHOURScat

and the estimation procedure is ordered logit. The results are largely consistent with

those in Tables 5-6 but there are some differences. For instance, the variables

indicating that the cared-for person lives either in institutionalised accommodation

and the carer‟s home retain significance in Model 4, the former negatively, the latter

25

positively. The significance of these variables in Table 7 (but not Tables 5-6) suggests

that the living arrangements of the cared-for person impact on hours of caring in a

graduated way that is captured by the ordered measure of care hours but not the

dichotomous measures; the results imply that the odds of providing longer hours of

care are 3.95 times higher if the cared-for person is living in the carer‟s home and

0.30 times smaller if they live in insitutionalised accommodation. As in Table 5 (but

not Table 6) employment participation is insignificant. As in Table 6 but not Table 5,

the cared-for person‟s age and co-residential care are insignificant while

DURATION and the proxy for eldercare are both negatively significant in Model 4,

although the latter only weakly so.

In Table 8 where the dependent variable, HOURS<20pw, is a negative

measure of caring commitment, the estimates provide a consistency test of the

alternative specifications in Tables 5-7. In these estimations the variables indicating

receipt of either Attendance Allowance of Disability Living Allowance cannot be

included. This is because no carers with a dependent in receipt of these benefits cared

for fewer than 20 hours per week. As would be expected the results are otherwise

almost a mirror image of those in Tables 5 and 6. For example, younger carers and

those looking after someone living in institutionalized accommodation are more likely

to be caring for fewer than 20 hours a week. An exception is the negative significance

of EMPLOYED however this result is probably being driven by the very low

employment participation rates of younger carers who also care for fewer hours. In

Model 3, the indicators of type or amount of care provided are all negatively

significant with the exception of CO-RESIDENTIAL. However, only the provision of

emotional or other care retains significance in Model 4 and positively so: the odds of

26

supplying fewer than 20 hours a week are higher by a factor of 10.58 if the carer

provides only emotional or other care.

Provision of personal care: Table 9

In Table 9 the dependent variable indicates whether the carers provides personal care

(PERSONAL_CARE). As discussed the provision of personal care is an alternative

measure of the intensity of caregiving since it implies that the cared-for person has

profound limitations. The results indicate that gender (MALE) is an important

determinant of the provision of personal care; the odds of supplying personal care are

lower for males by a factor of 0.56 (44 percent). This is an interesting result as gender

is insignificantly related to hours of care (Tables 5-8) while PERSONAL_CARE is

positively significant. Thus gender is implicated indirectly in the time devoted to

caregiving. Equally interesting is the lack of significance of the receipt of either

Attendance Allowance or Disability Living Allowance in Table 9. In contrast, carers

of an adult with a physical disability are more likely to supply personal care. As in

Tables 5-8, living arrangements are important and co-residential carers (particularly

those sharing their home with the cared-for person) are more likely to provide

personal care. One possible interpretation is that the least independent people live in

their carer‟s home, and interesting their carers are more likely to be women.

Employment participation and hours caring: Table 10

The results of estimating the employment participation equation (2) are shown in

Table 10. In these estimations the dependent variable, EMPLOYED (N0 =0,Yes=1)

indicates whether the sample member is in paid work . Since the dependent variable is

dichotomous, logit would be an appropriate estimator. However, we additionally wish

27

to address endogeneity using the instrumental variables specification and in Stata

this is only available with probit or tobit. We therefore estimate equation (2) using

probit.

In these estimations EMPLOYED is regressed on either HOURS>100pw

(Model 6) or CAREHOURScat (Model 7). The other independent variables reflect

carer characteristics. The selection of the latter is restricted by availability to age,

gender and ethnicity. To capture diminishing returns to experience, proxied by age,

the square of age (AGE_squared) is also included in these estimations. The only other

available carer characteristic variable records receipt of Carer‟s Allowance. This

variable is excluded from this analysis because the earnings eligibility condition for

receipt of this benefit imply that it is not exogenously determined. Because of the

very low employment participation rates of younger and very elderly sample

members, the sample for these estimations is restricted to those aged 19 to 69.

The first estimation in Table 10 (Model 5) is included only for comparative

purposes and does not include the hours of caring measures. The inclusion of the

latter in estimations in Models 6 and 7 has very little effect on the carer

characteristics. The estimates show that employment participation is significantly and

negatively related to both HOURS>100 and CAREHOURScat. The marginal effect of

HOURS>100pw indicate that caring for 100 hours or more reduces the probability of

being employed by 0.26 (holding all variables at their mean value). The marginal

effect associated with the ordered variable CAREHOURScat is smaller and indicates

that longer hours of care reduce the probability of being employed by 0.1.

However, as already discussed there are unobserved covariates in these

estimations, such as unobserved ability, that potentially impact on both employment

participation and caring commitment. The results in Models 6 and 7 are therefore

28

likely to be biased due to the endogeneity of the caring measures. To address the

potential endogeneity, Models 6 and 7 were re-estimated using the probit conditional

maximum likelihood estimator with the instrumental variables specification. An

instrument for caregiving was constructed from indicators reflecting the

characteristics of the cared-for and their care-needs. The instruments were selected on

the basis of their significance in the estimations in Models 2-3 in Tables 6 and 8. As

argued above, it is unlikely that these measures are correlated with unobserved

determinants such as the carer‟s unobserved ability since the latter is likely to be

determined prior to the onset of illness of the cared-for person. However, this may not

be true for carers who have continued to care from a young age. Young carers are

excluded from this analysis but it is possible that the sub-sample contains adult carers

we were also young carers.

To test whether the chosen instruments were appropriately uncorrelated with

the error term we re-ran the probit estimations using Newey's two-step estimator for

probit with instrumental variables. This enabled us to obtain Amemiya-Lee-Newey's

Chi-squared test statistic to test for the validity of the instruments (using the Stata

command overid). This test is the equivalent of the Sargan test for TSLS. Use of the

Amemiya-Lee-Newey's Chi-squared test statistic suggested that the it was not

appropriate to use indicators for receipt of Attendance Allowance or Disability Living

Allowance as instruments in the analysis. This is perhaps unsurprising since receipt

of these benefits is an eligibility condition for receipt of Carer‟s Allowance which as

argued above is likely to be jointly determined with employment participation. Use of

the dichotomous indicators of co-residential status and whether the cared-for person

lived in institutional accommodation were also ruled out by this test. This suggests

that decisions about residential status, caring commitment and employment are jointly

29

determined. The indicators of personal care and emotional/other care provision were

not used as instruments since the evidence of Hassink and Van den Berg (2011)

suggests that this would be inappropriate.2 With the included instruments as listed in

the notes to Table 10, the Amemiya-Lee-Newey's Chi-squared test statistic is only

weakly significant (at the 10 percent level) in Model 6a in Table 10 and it is

insignificant in Model 7a (see notes to Table 10). Therefore it is possible to accept the

null hypothesis that the included instruments are uncorrelated with the error term.

A further complication is that in Stata, the probit estimator with instrumental

variables (ivprobit) assumes that the endogenous regressors are continuous and

neither HOURS >100 nor CAREHOURScat are continuous variables. As an

alternative to probit we estimated linear probability models using GMM to obtain

robust standard errors. The results from these estimations are reported in Table A3 in

the Appendix. For these estimations we report the Hansen J statistic which is the

GMM equivalent of the Sargan test for TSLS. Neither statistic is significant indicating

the null hypothesis of independence of the instruments and the disturbance process

need not be rejected.

After instrumenting, the marginal effects associated with HOURS>100 and

CAREHOURScat in Models 6a and 7a are absolutely larger than in Models 6 and 7.

This suggests that the endogeneity causes the influence of caring on employment to

be underestimated. The Wald test for endogeneity is weakly significant at the 10

percent level in estimation 6a and significant at the 5 percent level in estimation 7a.

This indicates that there is sufficient information to reject the null of no endogeneity

and that therefore the estimates in Models 6 and 7 are biased (downwards).

2 However, inclusion of these indicators as instruments was not ruled out by the Amemiy-Lee-Newey

test although their inclusion reduced the number of available observations and reduced the size of the

marginal effects associated with the instrumented care hours measures. Interestingly, inclusion of these

measures of instruments also resulted in the insignificance of the Wald test statistic for endogeneity

suggesting that indicators of type of care are potentially exogenous omitted variables.

30

Summary of results

The results indicate that time devoted to caregiving is influenced directly by the

characteristics of the cared-for person in terms of their age and their living

arrangements. Carers also care for longer hours when the needs of the people they

care for are severe enough for them to qualify for either Attendance Allowance or

Disability Allowance. Hours of care are also longer when the carer is providing

personal care. Not surprisingly, given the qualifying conditions, carers in receipt of a

Carer‟s Allowance provide longer hours of care.

The provision of personal care is more likely if the carer is an older female.

Co-residential carers are also more likely to supply personal care, particularly if the

shared home is the carer‟s home and the cared-for person is an adult with a physical

disability. The results relating to the employment-caregiving relationship are

consistent with those of previous research in that after addressing endogeneity, the

negative effect of longer hours of informal care on employment participation remains

statistically significant. Indeed, the size of the effect is larger after instrumenting.

5. Summary and implications

The research reported here is based on regional data relating to a sample of carers

living in the midlands region of the UK. As well as information on the carers

themselves and the number of hours of care they provided, the data include

information on the characteristics and needs of the people cared for as well as the type

of help given. The availability of this data allowed us to explore the relationships

between caregiving provision and these other factors in some depth. While the data

set is relatively small, the richness of the data in respect of care needs and the

31

characteristics of the cared-for leads to some new insights on the roles of these

factors in caring provision.

A limitation of the analysis is that the regional sample is composed of those

carers most heavily involved in informal care in terms of their time commitment, their

co-residency status and to a lesser extent the duration of their caring episodes. To the

extent that the results of this research are generalizable, this will only be true in

respect of carers in a similar position who represent only a minority of carers

nationally (Table 1). Nevertheless, it is important to examine the needs of the carers

who fall into this category as arguably it is this group that is in most need of support.

The results show that intensive carers providing longer hours of care are

older, more likely to be co-resident with the person they are caring for (particularly if

this is the carer‟s home) and giving personal care. Not surprisingly, those involved in

the longest hours are more likely to be in receipt of Carer‟s Allowance and the person

they care for is likely to be in receipt of either Attendance Allowance or Disability

Allowance.

The provision of personal care provides an alternative measure of the demands

or intensity of the caring role and the analysis indicates that this type of care is more

likely to be provided by older carers, those in receipt of Carer‟s Allowance, and co-

resident carers (particularly when the shared home is the carer‟s home). In addition

personal care is more likely to be given by women and more likely to be received by

older people. However, personal care is less likely to be given by those providing

elder care indicating that the bulk of personal care is likely to be spousal. We also find

a positive relationship between hours of caring and the provision of personal care.

This confirms that the provision of personal care and longer hours of care are

complements and possibly jointly determined. Nevertheless, whether personal care is

32

provided or not depends in part on the exogenously determined needs of the person

cared-for.

We find a negative relationship between the duration of the caring episode and

the provision of 100 or more hours of care. This suggests a potential tradeoff between

hours spent caring and the duration of a caring episode. Perhaps, those who manage to

supply care over the very long term find ways to reduce or contain the time they are

involved in care e.g. by organizing more help.

The analysis of the relationship between care-giving and employment

highlighted the negative relationship found in previous research between hours of

caring and employment participation. After instrumenting for hours of care the

negative coefficient on the measures of caring are absolutely larger suggesting that the

caring effect on employment is underestimated when endogeneity is not addressed. To

the extent that we are able to generalize from these results they confirm that carers

who care for substantial hours are less likely to be either willing or able to remain in

employment directly as a consequence of their caring responsibilities.

The evidence of this paper is that the care undertaken by informal carers is in

part determined by the characteristics and needs of the people they care for. Since all

the sample members were existing carers not much can be said about the effect on

employment of the initial decision to undertake care. However, the results can be

taken to imply that once the decision to provide care has been made, the amount and

type of care undertaken is at least to some extent exogenously determined by need.

Since this can change, some carers will have little discretion over their caring

provision. Notably, the results suggest that among carers who already care for quite

long hours, females are not significantly likely to be caring more hours. However,

female carers are more likely to be providing personal care, arguably the most

33

demanding or intensive type of care. This suggests that even among this group of

already time committed carers gendered norms of responsibility still apply.

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Table 1: National data: Carers and caregiving (British Household Panel Survey,

1991–2007)

Carers Non-carers

All respondents (no. of observations) 36,536 163,825

Percentage of all respondents 18.24 81.76

Percentage male 40.31*** 48.55

MEN (no. of observations) 14,726 79,534

Percentage of males 15.6 84.4

Average age 50.01*** 40.86

Percentage under 25 10.46*** 21.12

Percentage 25-64 67.14 66.68

Percentage 65 and over 22.40*** 12.31

Percentage of 25-64 year olds employed 68.36*** 81.54

Percentage of 25-64 year olds not employed

and not in training or educationb

27.67*** 14.48

Percentage with some health problem 49.85*** 34.31

Percentage with higher educational

qualification (higher or first degree, teaching

qualification, other)

37.19*** 38.17

Modal weekly hours of caring 0-4 -

Median weekly hours of caring 5-9 -

Percentage caring more than 20 hours a week 20.60 -

Percentage caring more than 50 hours a week 9.64 -

Percentage caring more than 100 hours a week 8.15 -

Percentage co-resident with cared-for person 40.36 -

WOMEN (no. of observations) 21.810 84.291

Percentage of females 20.6 79.4

Average age 49.06*** 39.81

Percentage under 25 8.00*** 21.20

Percentage 25-64 73.99*** 68.85

Percentage 65 and over 18.03*** 10.00

Percentage of 18-64 year olds employed 55.48*** 66.16

Percentage of 18-64 year olds not

employed/training/educationb 40.66*** 33.78

Percentage with some health problem 30.00*** 29.80

Percentage with higher educational

qualification (higher or first degree, teaching

qualification, other)

28.65*** 31.94

Modal weekly hours of caring 0-4 -

Median weekly hours of caring 5-9 -

Percentage caring more than 20 hours a week 23.62 -

Percentage caring more than 50 hours a week 10.98 -

Percentage caring more than 100 hours a week 9.19 -

Percentage co-resident with cared-for person 32.82 -

Notes: aEither unemployed, retired, maternity leave, family care, long-term sick/disabled other economic status

***; Independent samples test indicates that the null hypothesis value of a zero difference does not fall within

the 99%, 95% or 90% confidence interval implying that the reported mean is statistically significantly different

from the comparable figure for non-carers (at 1% level of significance) on basis of t statistic in two sample test.

Table 2: Characteristics of caregivers by age and gender

Carer characteristics

All carers

Younger carers: under 25

Mid-life carers: 25-64

Older carers: 65 and over

All Women Men All Women Men All Women Men All Women Men

Number of observations (sample %)

1985 (100%)

1383 602

206 (12.10%)

108

98 704 (41.36%)

561

143 792 (46.53%)

521

271

% female 69.67% 52.43%## 79.69%## 65.78%

Mean age n=1702

59.01 59.16 58.65 13.95%## 14.32 13.55 52.99## 52.69* 54.20 76.07 75.43** 77.30

% Employed n=954

21.28% 22.99%* 17.25% 0%## 0% 0% 41.12%## 39.35% 49.32% 5.02% 4.17% 6.5%

% In training or education n=954

16.88% 13.28%** 25.35% 99.37%## 99% 100% 0.24% 0.3% 0% 0% 0 0

% Not employed/ training/ Education n=954

60.48% 62.09% 56.69% 0.63%## 1% 0% 55.47%## 57.10% 47.95% 94.99% 95.83% 93.50%

% White ethnicity (British, Irish, European, other)

91.74% 92.77%*** 89.37% 91.75% 93.52% 89.80% 90.34% 92.16%** 83.22% 91.04% 91.36% 90.41%

% Asian or Black ethnicity

7.96% 6.94%** 10.30% 7.77% 5.56% 10.20% 9.09% 7.31%** 16.08% 8.97% 8.64% 9.59%

% other ethnic 0.30% 0.29% 0.33% 0.49%# 0.94% 0% 0.57%## 0.54% 0.7% 0 0% 0%

% in receipt of Carers Allowance

8.72% 9.76%** 6.31% 0%## 0% 0% 18.75%## 18.72% 18.89% 4.92% 5.57% 3.69%

% either carer and/or cared-for receives some state benefita

38.64% 40.13%* 35.22% 0%## 0% 0% 44.46% 45.10% 41.96% 47.98% 47.79% 48.34%

Notes: sample = 1985 unless n is given as less, percentages are by column **, *: Within age group and for row characteristic, mean of female sub-sample significantly different from mean of male sub-sample at 1% or 5% level ##

, #

: For row characteristic, mean within age group significantly different from mean of 65 and over age group (the modal category) a Either carer receives Carers Allowance, Income Support or Pension Credit and/or cared-for receives either Attendance or Disability Living Allowance

Table 3: Characteristics of cared-for person by caregivers’ gender and age

Cared-for characteristics All carers Younger carers: under 25 Mid-life carers: 25-64 Older carers: 65 and over

All Women Men All Women Men All Women Men All Women Men

% Male n= 1294 50.39% 61.61%** 23.9% 52.46% 50% 55.29% 45.25%## 51.50%** 19.23% 55.58% 77%** 11.83%

Mean age n= 1124 60.97 59.83** 63.98 22.03## 20.57 23.75 57.83## 54.96** 70.19 76.05 75.81 76.54

% older person (65 and over) n= 1124

58.27% 57.13% 61.29% 1.06## 0% 3.28% 49.71%## 47.70%** 58.33% 84.17% 82.96% 86.67%

% disabled child n=1257 15.20% 16.41%* 12.26% 5.15%## 49.46% 53.85% 16.57%## 19.85%** 19.61% 0.41% 0.62% 0%

% adult with physical disability n=1257

13.21% 11.12%** 18.26% 22.49%## 19.78% 25.64% 12.71% 10.66%** 21.57% 10.18% 9.57% 11.38%

% adult mental health or learning needs (includes older people) n= 1257

25.14% 25.71% 23.71% 17.75%# 21.98% 12.82% 28.18% 29.48% 22.55% 24.24% 21.61%** 29.34%

% employed n=1103 18.86% 18.91% 18.73% 32.89%## 32.53% 33.33% 22.04%## 22.14% 21.59% 11.12% 11.26% 10.96%

% retired n=1103 60.47% 59.14% 63.81% 2.01%## 0%** 4.55% 53.81%## 50.78%** 67.05% 86.56% 86.35% 87.00%

% adult not working/retired/student n= 1103

17.14% 16.50% 18.73% 32.89%## 32.53% 33.33% 19.28%## 18.75% 21.59% 9.80% 9.22% 10.96%

% student/pupil n= 1103 16.23% 17.40%* 13.33% 46.98%## 40.96% 54.55% 21.40%## 25.26%** 4.55% 1.14% 1.37% 0.68%

% receives Disability Benefit n=1985

4.23% 4.19% 4.32% 0##% 0% 0% 6.82%## 6.60% 7.69% 4.04% 3.26% 5.54%

% receives Attendance Allowance n= 1985

18.89% 18.94% 18.77% 0%## 0% 0% 11.79%## 12.30% 9.79% 30.05% 30.13% 29.89%

% live in own home or joint home (with carer) n= 1267

73.64% 68.64%** 85.56% 93.82%## 92.71% 95.12% 58.9%## 53.85%** 80.81% 81.76% 81.33% 82.63%

% live in caregivers home n= 1267

22.34% 27.10%** 10.96% 3.93%## 3.13% 4.88% 35.99%## 41.03%** 14.14% 15.23% 16.27% 13.17%

% live in medical/care/ residential/ sheltered/ supported home n= 1267

3.31% 3.46% 2.94% 0.56% 1.04% 0% 4.17% 4.2% 4.04% 3% 2.41% 4.19%

Notes: n = sub-sample size. Percentages are by column. **, *: Within age group and for row characteristic, mean of female sub-sample significantly different from mean

of male sub-sample at 1% or 5% level: ##

, #. For row characteristic, mean within age group significantly different from mean of 65 and over age group (the modal category)

Table 4: Type and amount of care-giving provided

Caring provision All carers Younger carers: under 25 Mid-life carers: 25-64 Older carers: 65 and over

All Women Men All Women Men All Women Men All Women Men

% fewer than 20 hrs per week n= 869

21.29% 17.00%** 30.86% 95.48%## 92.78%* 98.75% 2.69%## 2.61% 3.08% 1.04% 1.14% 0.88%

% between 20 and 49 hrs per week n= 869

12.66% 14.00%* 9.67% 45.20% 7.22%* 1.25% 20.16%## 20.85% 16.92% 6.57% 6.29% 7.02%

% at least 20 hrs per week 78.71% 83.00%** 69.15% 4.52%## 7.22%* 1.25% 97.31% 97.39% 96.92% 98.96% 98.86% 99.12%

% at least 50 hrs per week 66.05% 69.00%** 59.48% 0%## 0% 0% 77.15%## 76.55% 80% 92.39% 92.57% 92.11%

% at least 100 hrs per week n= 869

43.73% 46.33%* 37.92% 0%## 0% 0% 47.04%## 48.21% 41.54% 64.36% 65.14% 63.16%

Mean years caring n= 1926

2.77 2.66** 3.03 2.32## 2.27 2.39 2.33## 2.25* 2.65 3.30 3.23 3.44

% co-residential care n=1267

46.00% 46.06% 45.85% 43.69%## 42.59% 44.90% 50.28% 50.98% 47.55% 53.16% 53.17% 53.14%

% elder care (cared-for > 60 and min. 17 years older than carer) n=

26.02% 26.88% 23.78% 1.50%## 0% 3.28% 46.17%## 42.86%** 60.42% 10.85% 11.90% 8.67%

% gives personal care (with/without physical care) n= 988

41.70% 45.79%** 32.33% 2.82%## 4.17% 1.24% 47.42%# 48.71% 41.56% 53.45% 57.53%* 46.51%

% gives personal and physical care n= 988

39.98% 44.33%** 30.00% 2.26%## 3.13% 1.24% 46.01% 47.57% 38.96% 51.44% 55.71% 44.19%

% give only practical care (includes drug administration) n= 988

19.23% 19.33% 19.00% 0%## 0% 0% 24.65% 25.50% 20.78% 22.41% 19.64% 27.13%

% young carer excess chores/sibling care n= 988

17.11% 13.37%** 25.67% 94.35%## 93.75% 95.06% 0% 0% 0% 0% 0% 0%

% gives only emotional/’other ‘ care n= 988

18.42% 19.04% 17.00% 2.83%## 2.08% 3.70% 24.41%# 23.50% 28.57% 19.25% 19.18% 19.38%

Notes: n = sun-sample size, percentages are by column. **, *: Within age group and for row characteristic, mean of female sub-sample significantly different from mean of male sub-sample at 1% or 5% level.

##, #

: For row characteristic, mean within age group significantly different from mean of 65 and over age group (the modal category)

Table 5: Logit Regressions: Dependent variables is HOURS>50pw (1 =provides care for at least 50 hours a week; 0 = fewer than 50 hours) Independent variable Model 1

Carer characteristics

Model 2 Cared-for

characteristics

Model 3 Type of care

Model 4 Pooled

(significant variables only)

Odds ratio

95% CI

Odds ratio

95% CI

Odds ratio

95% CI

Odds ratio

95% CI

Carer characteristics

MALE 0.75 0.5-1.2

AGE 1.09*** 1.1-1.1 1.12*** 1.1-1.5

EMPLOYED 0.64* 0.4-1.0 0.69 0.4-1.3

CARERS ALLOWANCE 5.23*** 2.7-10 7.89*** 3.5-17.9

ETHNIC BLACK/ASIAN 2.76 0.8-9.3

ETHNIC OTHER 4.00 0.1-114

Cared for characteristics

CF_MALE 1.29 0.9-1.9

CF_AGE 1.02* 1-1.1 0.96*** 0.9-1

ATTENDANCE ALLOWANCE

4.06*** 1.8-9.4 3.12** 1.2-8.4

DISABILITY LIVING ALLOWANCE

4.13** 1.2-14.2

4.38* 0.8-24.5

DISABLED CHILD 0.67 0.2-1.9

ADULT WITH PHYSICAL DISABILITY

1.40 0.7-2.8

ADULT WITH MENTAL HEALTH or LEARNING NEEDS

1.24 0.7-2.2

CF_EMPLOYED 1.54 0.6-3.7

CF_RETIRED 2.81* 0.9-8.9 1.03 0.3-3.2

INSTITUTIONALISED ACCOMMODATION

0.24*** 0.09-0.6

0.28** 0.8-1.0

LIVES IN CARER’S HOME

120.11*** 9.8-41.2 1.88 0.8-4.7

Type/amount of care provided

CO-RESIDENTIAL 6.88*** 4.3-11.0 2.43*** 1.2-5.0

DURATION (years) 1.01 0.8-1.2

ELDERCARE 1.62* 1.0-2.7 1.722 0.5-5.7

PERSONAL CARE 13.85*** 8.2-23.4 4.47*** 2.3-8.9

EMOTIONAL/OTHER CARE

3.79*** 2.3-6.3 0.58 0.3-1.2

No. of observations 763 687 683 638

Log likelihood -279.58 -314.54 --305.50 -180.91

Log likelihood Χ2

413.09*** 199.65*** 238.29*** 406.50***

Pseudo R2

0.425 0.241 0.281 0.529

Notes: Reference categories: Carer ethnic group; white ethnic. Carer economic/employment status; adult not working, adult/young person training/education. Cared-for need category; older person (65 and over) with mental or physical health needs. Cared-for economic status; not employed, student/pupil. Cared-for living arrangements; lives in own home. Type of help given; young carer chores /sibling care, only practical care (includes drug administration); only physical care ***, **, *: estimate significantly different from zero at 1%, 5% and 10% levels respectively

Table 6: Logit Regressions: dependent variable is HOURS>100pw (1 = provides care for at least 100 hours a week; 0 = fewer than 100 hours) Independent variable Model 1

Carer characteristics

Model 2 Cared-for

characteristics

Model 3 Type of care

Model 4 All significant variables in Models 1-3

Odds ratio

95% CI

Odds ratio

95% CI

Odds ratio

95% CI

Odds ratio

95% CI

Carer characteristics

MALE 0.64*** 0.4-0.9 1.03 0.6-1.7

AGE 1.05*** 1.0-1.1 1.02*** 1.0-1.0

EMPLOYED 0.42*** 0.3-0.6 0.39*** 0.2-0.7

CARERS ALLOWANCE 2.78*** 1.8-4.3 4.22*** 2.3-7.7

ETHNIC_BLACK_ASIAN 1.87 0.7-5.3

ETHNIC_OTHER 7.04 0.5-92

Cared for characteristics

CF_MALE 1.32 0.9-1.8

CF_AGE 0.99 0.97-1

ATTENDANCE ALLOWANCE

3.14*** 1.9-5.3 3.16*** 1.6-6.1

DISABILITY LIVING ALLOWANCE

2.37** 1.1-5.1 3.38*** 1.3-8.7

DISABLED CHILD 1.18 0.5-3.0

ADULT WITH PHYSICAL DISABILITY

2.03** 1.1-3.7 0.85 0.4-1.8

ADULT WITH MENTAL HEALTH/LEARNING NEEDS

0.97 0.6-1.6

CF_EMPLOYED 1.66 0.8-3.7

CF_RETIRED 6.27*** 2.17-18 1.5 0.7-3.1

INSTITUTIONALISED ACCOMMODATION

0.87 0.3-2.3

LIVES IN CARER’S HOME

5.03*** 3.2-8 2.72*** 1.4-5.2

Type/amount of care provided

CO-RESIDENTIAL 3.05*** 1.9-4.8 1.26 0.7-2.3

DURATION (years) 0.68*** 1.9-4.8 0.58*** 0.5-0.7

ELDERCARE 0.49*** 0.3-0.8 0.29*** 0.1-0.6

PERSONAL CARE 8.78*** 5.8-13.3 6.13*** 3.7-10.2

EMOTIONAL/OTHER CARE

3.75*** 2.3-6.1 2.33*** 1.3-4.3

No. of observations 763 687 683 605

Log likelihood -423.01 -424.27 --362.81 -275.68

Log likelihood Χ2

198.85*** 99.76*** 211.11*** 283.05***

Pseudo R2

0.190 0.105 0.225 0.339

Notes: Reference categories as Table 4. ***, **, *: estimate significantly different from zero at 1%, 5% and 10% levels respectively

Table 7: Ordered Logit Regressions: dependent variable is CAREHOURScat (weekly caring hours ordered by category; less than 5; 5-9; 10-19; 20-34; 35-49; 50-99; 100+) Independent variable Model 1

Carer characteristics

Model 2 Cared-for

characteristics

Model 3 Type of care

Model 4 All significant variables in Models 1-3

Odds ratio

95% CI

Odds ratio

95% CI

Odds ratio

95% CI

Odds ratio

95% CI

Carer characteristics

MALE 0.56*** 0.4-0.8 0.77 0.5-1.1

AGE 1.08*** 1.1-1.1 1.07*** 1.1-1.1

EMPLOYED 0.84 0.59-1.2

CARERS ALLOWANCE 4.14*** 2.7-6.3 5.01*** 3.0-8.3

ETHNIC_BLACK_ASIAN 2.84*** 1.3-6.1 1.69 0.7-4.3

ETHNIC_OTHER 7.92* 0.8-76.9 4.53 0.4-50.5

Cared for characteristics

CF_MALE 1.29* 1.0-1.7 0.84 0.6-1.2

CF_AGE 1.01 1.0-1.0

ATTENDANCE ALLOWANCE

3.15*** 1.9-5.2 2.94*** 1.6-5,2

DISABILITY LIVING ALLOWANCE

-3.20*** 1.5-6.6 3.10*** 1.4-7.0

DISABLED CHILD 0.69 0.3-1.4

ADULT WITH PHYSICAL DISABILITY

1.99*** 1.14-3.5

1.20 0.7-2.1

ADULT WITH MENTAL HEALTH/LEARNING NEEDS

0.99 0.65-1.5

CF_EMPLOYED 1.56 0.8-2.9

CF_RETIRED 4.79*** 2.1-11.2

1.09 0.6-2.1

INSTITUTIONALISED ACCOMMODATION

0.43* 0.2-1.0 0.30** 0.1-0.8

LIVES IN CARER’S HOME

13.75*** 8.4-22.6

3.95*** 2.3-6.8

Type/amount of care provided

CO-RESIDENTIAL 4.07*** 2.9-5.8 1.18 0.8-1.8

DURATION (years) 0.73*** 0.7-0.8 0.74*** 0.7-0.8

ELDERCARE 1.36* 1.0-2.0 0.57* 0.3-1.0

PERSONAL CARE 10.07*** 7.0-14.5 5.06*** 3.3-7.7

EMOTIONAL/OTHER CARE

4.05*** 2.7-6.1 1.60* 1.0-2.6

No. of observations 763 687 683 631

Log likelihood -921.77 -908.82 -904.87 -677.98

Log likelihood Χ2

533.18*** 235.20*** 290.40*** 553.15***

Pseudo R2

0.224 0.115 0.138 0.290

Notes: Reference categories as Table 4. ***, **, *: significantly different from zero at 1%, 5% and 10% levels respectively

Table 8: Logit Regressions: dependent variable is HOURS<20pw (1= provides care for less than 20 hours a week; 0 = at least 20 hours of care) Independent variable Model 1

Carer characteristics

Model 2 Cared-for

characteristics

Model 3 Type of care

Model 4 Pooled All significant

variables in Models 1-3

Odds ratio

95% CI

Odds ratio

95% CI

Odds ratio

95% CI

Odds ratio

95% CI

Carer characteristics

MALE 2.92** 1.2-7.3 1.17 0.3-4.5

AGE 0.88*** 0.9-1.0 0.83*** 0.8-0.9

EMPLOYED 0.23*** 0.7-0.8 0.13** 0.02-0.8

CARERS ALLOWANCE 0.10*** 0.2-0.5 0.05*** 0.01-0.5

ETHNIC_BLACK_ASIAN 0.92 0.1-7.1

ETHNIC_OTHER 0.67 0.003-143

Cared for characteristics

CF_MALE 1.04 0.5-2.0

CF_AGE 0.94*** 0.9-0.97

1.03 1.0-1.1

ATTENDANCE ALLOWANCE

-- --

DISABILITY LIVING ALLOWANCE

-- --

DISABLED CHILD 2.51 0.6-10.4

ADULT WITH PHYSICAL DISABILITY

1.37 0.5-4.1

ADULT WITH MENTAL HEALTH/LEARNING NEEDS

1.07 0.4-3.1

CF_EMPLOYED 0.72 0.3-2.1

CF_RETIRED 0.31 0.1-1.7

INSTITUTIONALISED ACCOMMODATION

12.92***

3.3-51.4

70.08*** 8-614

LIVES IN CARER’S HOME 0.01*** 0.001-0.02

0.11** 0.01-0.9

Type/amount of care provided

CO-RESIDENTIAL 1.31*** 1.2-1.63

1.4 0.3-6.7

DURATION (years) 0.03*** 0.05-0.2

1.29 0.8-2.1

ELDERCARE 0.03*** 0.1-0.08

0.32 0.03-3.3

PERSONAL CARE 0.03*** 0.01-0.07

0.30 0.03-3.3

EMOTIONAL/OTHERCARE 0.07*** 0.03-0.2

10.58*** 1.8-61

No. of observations 763 687 683 626

Log likelihood -95.20 -135-56 -198.86 -46.40

Log likelihood Χ2

598.56*** 346.35*** 278.57*** 524.72***

Pseudo R2

0.759 0.561 0.412 0.850

Notes: Reference categories as Table 4. ***, **, *: significantly different from zero at 1%, 5% and 10% levels respectively

__ variable dropped as insufficient variation with respect to dependent variable; predicts failure perfectly

Table 9: Logit Regressions: dependent variable is PERSONAL CARE (1= provides personal care; 0 = physical, practical, emotional or other care) Independent variable Model 1

Carer characteristics

Model 2 Cared-for

characteristics

Model 3 Type of care

Model 4 All significant variables in Models 1-3

Odds ratio

95% CI Odds ratio

95% CI

Odds ratio

95% CI

Odds ratio

95% CI

Carer characteristics

MALE 0.58*** 0.4-0.8 0.56*** 0.4-0.8

AGE 1.04*** 1-0-1.1 1.03*** 1.0-1.0

EMPLOYED 1.17 0.8-1.7

CARERS ALLOWANCE 2.33*** 1.6-3.5 1.87*** 1.2-2.8

ETHNIC_BLACK_ASIAN 3.29*** 1.6-3.5 2.05 0.8-5.2

ETHNIC_OTHER 1.33 0.1-16.9

Cared for characteristics

CF_MALE 1.45** 1.1-2 1.07 0.8-1.5

CF_AGE 1.03*** 1.01-1.04

1.01* 1.0-1.0

ATTENDANCE ALLOWANCE

1.30 0.8-2.1

DISABILITY LIVING ALLOWANCE

1.49 0.7-3

DISABLED CHILD 1.89 0.8-4.6

ADULT WITH PHYSICAL DISABILITY

3.84*** 2.1-7 3.25*** 2.0-5.3

ADULT WITH MENTAL HEALTH/LEARNING NEEDS

0.79 0.5-1.3

CF_EMPLOYED 0.59 0.3-1.2

CF_RETIRED 1.18 0.5-3.1

INSTITUTIONALISED ACCOMMODATION

1.52 0.7-3.5

LIVES IN CARER’S HOME

6.18*** 3.9-9.8 3.38*** 2.1-5.6

Type/amount of care provided

CO-RESIDENTIAL 2.75*** 1.9-4 1.48* 1.0-2.3

DURATION (years) 1.00 0.9-1.1

ELDERCARE 1.58** 1.1-2.3 0.92 0.5-1.7

EMOTIONAL/OTHER CARE

__ __

No. of observations 809 768 802 800

Log likelihood -482.15 -463.77 -526.59 -466.33

Log likelihood Χ2

121.64*** 118.30*** 32.63*** 159.50***

Pseudo R2

0.112 0.113 0.03 0.146

Notes: Reference categories as Table 4. ***, **, *: significantly different from zero at 1%, 5% and 10% levels respectively

__ variable dropped as insufficient variation with respect to dependent variable; predicts failure perfectly

Table 10: Probit Regressions of employment on caregiving: dependent variable is EMPLOYED (1= employed; 0 = not working; either not employed, in training or education, looking for work or training)

Model 5 Carer characteristics only

Model 6 Carer characteristics and

HOURS>100

Model 7 Carer characteristics and

CAREHOURScat

Model 6a Probit IV (CMLE)

HOURS>100 instrumented

Model 7a Probit IV (CMLE) CAREHOURScat

instrumented

Independent variable

Marginal Effect

95% CI S.E. Marginal Effect

95% CI S.E. Marginal Effect

95% CI S.E. Marginal Effect

95% CI S.E

. Marginal

Effect 95% CI S.E.

Carer characteristics

MALE 0.11**

0.005-0.210

0.05 0.122** 0.007-0.239

0.6 0.13** 0.009-0.241

0.06 0.13** 0.001-0.26

0.07 0.35** 0.009-0.693

0.18

AGE 0.07*** 0.047-0.100

0.14 0.071*** 0.042-0.1

0.015 0.08*** 0.055-0.113

0.02 0.05*** 0.016-0.081

0.02 0.20*** 0.101-0.289

0.05

AGE_squared -0.001*** -0.001- -0.0005

0.0001 -0.001*** -0.001- -0.0005

0.0001 -0.001*** -0.001 – -0.0006

0.0001

-0.001*** -0.0009- -0.0003

0.0002 -0.002*** -0.003- -0.001

0.0005

ETHNIC BLACK or ASIAN

0.07 -0.147- -0.291

0.11 0.048 -0.234-0.33

0.144 0.07 -0.221- 0.351

0.15 0.22 -0.127-0.57

0.18 0.61 -0.241-1.47

0.44

Indicators of hours of caring provision

HOURS>100pw -0.26*** -0.345- -0.18

0.04 -0.43*** -0.617- -0.26

0.09

CAREHOURScat -0.10*** -0.139 - -0.062

0.02 -0.20*** -0.86 -0.316

0.05

No. of observations

580 466 466 398 385

Log likelihood -321.96 -241.78 -247.10 -456.58 -736.2

Log likelihood Χ2

82.34*** 102.01*** 91.37*** 79.66*** 78.43***

Pseudo R2

0.113 0.174 0.156 - -

Wald exogeneity Χ

2

3.08* 4.25**

Notes: CMLE is the conditional maximum likelihood estimator. ***, **, *: significantly different from zero at 1%, 5% and 10% levels respectively Sample restricted to carers over 18 and younger than 70 (the oldest employed sample member is 80) Marginal effect is for a discrete change of a dummy variable from 0 to 1 Additional instruments for HOURS>100 and CAREHOURScat are ADULT WITH PHYSICAL DISABILITY, CF_RETIRED, LIVES IN CARER’S HOME, DURATION and ELDERCARE. CF_MALE is a an additional instrument for CAREHOURScat (CF_MALE is not significant in Model 2 in Table 6). Amemiya-Lee-Newey Χ

2 statistics for the Newey two-step estimation of Models 6a and 7a are 3.67 and 6.191. Neither is statistically significant signalling that the null hypothesis that the

instruments are uncorrelated with the error term can should not be rejected.

APPENDIX Definitions of allowance available to carers and cared-for Attendance Allowance is a benefit for disabled people aged 65 or over, who find it difficult to care for themselves because of a disability or long term health problem. You can get Attendance Allowance if you find things like dressing and washing very difficult, if you need someone to make sure that you are safe, or you have a terminal illness.

Disability Living Allowance (DLA) is a benefit paid to people aged under 65 who need to help to look after themselves and/or to get around because of a long-term health problem or disability. This might be because they: Need help to get washed, dressed, take medication etc; Need someone to keep an eye on them to make sure that they are safe; Have a terminal illness; Have problems walking when out of doors

Carer's Allowance is the main state benefit for carers. Carer's Allowance is currently worth £53.90 a week (2010-2011 rate). To qualify carers need to be 16 years old or over ; look after someone for at least 35 hours a week; the person they look after must receive a qualifying disability benefit (AA or DLA); must not earn more than £100 a week (2010-2011 rate); must not receive one of a list of other benefits; must be living in the UK; must not be a full-time student

Table A1: UK policy initiatives supporting and responding to the needs of informal

carers

Year Title Issues addressed (indicative)

1995 Carers

(Recognition and

Service) Act (1995)

Recognition of the role of informal carers in society, raising

the profile of carers and creating awareness of carers‟ issues.

1998 Modernising

Social Services

Highlighted a need to support carers

1998 Fairness at Work Proposals to give all employees the right to time-off to

deal with family emergencies

1999 National Strategy

for carers

Recognition of carers‟ role and carers‟ needs.

1999 Working Families

Tax Credit

Parents with disabled children entitled to claim

childcare costs for children up to the age of 16

(previously 12)

2000 Carers and

Disabled Children

Act

Entitled carers to an assessment in their own right and

required local authorities to provide direct services to

carers to meet their assessed needs.

2001 Health and Social

Care Act

Direct Payment Scheme entitled carers to direct cash

payments from their local council to pay for short

breaks, nursery placement providing specialist support

for children, assistance to attend an activity, and

personal care

2002 The State Second

Pension

Provided a more generous additional State Pension for

low and moderate earners, some carers, and people

with long-term illness or disability.

2002 Amendment to

The Employment

Relations Act

Parents of disabled children under 18 entitled to request

flexible working arrangements and unpaid time off in

an emergency

2003 Fair Access to

Care

Provided councils with a framework for setting their

eligibility criteria for adult social care to create fairer

and more consistent eligibility decisions across the

country.

2004 Carers (Equal

Opportunities)

Act

The Act came into force in April 2005. It focuses on

health, employment, and life-long learning issues for

carers.

2006 Work and

Families Act

Carers of some adults given the right to request flexible

working arrangements

2006 White Paper More support for carers promised in Our Health, Our

Care, Our Say: A New Direction for Community

Service

2007 New Deal for

Carers

Extra funding of £33 million in the Carers grant

dedicated to respite care, a carer training programme

and national helpline

2008 New Carers‟

Strategy

Strategy incorporating the changes in the 2006 White

Paper

2010 Next steps for the

carers strategy:

Response to the

call for views

Identifies the actions that the Government will take

over the next four years to support carers. Priority areas

identified as: early identification; education and

employment; personalised support; carers‟ health

2010/11 Reaching out to

Carers Innovation

Fund

£1.35m funding for 79 projects „to improve early

identification of carers so that they can be supported in

considering their various options and make informed

choices about their lives‟.

Updated from Carmichael et al. (2008)

Table A2: Definitions of variables used in the analysis

Variable Definition

Type/amount of care provided

HOURS>50pw Caregiver provides at least 50 hours of care a week (=0,1).

HOURS>100pw Caregiver provides at least 100 hours of care a week (=0,1).

HOUR<20pw Caregiver provides fewer than 20 hours of care a week (=0,1).

CAREHOURScat Ordered variable indicating whether caregiver cares for less than 5, 5-9, 10-19, 20-34, 35-49, 50-99 or 100 or more hours a week (=0, 1,2,3,4,5,6).

CO-RESIDENTIAL Caregiver provides co-residential care, either in own home or home of cared-for (=0,1).

DURATION (years) Number of years has been registered with CSAM (proxy for number of years has provided care).

ELDERCARE Caregiver provides care for a person who is 60 or over and at least 17 years older than the caregiver (=0,1, proxy for elder care).

PERSONAL CARE Caregiver provides personal care (=0,1). Reference category is caregiver is either a young carer (chores or sibling care), or provides only practical care (include drug administration) or only physical care.

EMOTIONAL/OTHER CARE

Caregiver provides emotional or ‘other’ care (=0,1). Reference category is caregiver is either a young carer (chores or sibling care), or provides only practical care (include drug administration) or only physical care.

Carer

characteristics/status

MALE Caregiver is male (=0,1)

AGE Caregiver’s age

EMPLOYED Caregiver is employed (=0,1). Reference category is adult not working or young person in training or education.

CARERS ALLOWANCE Caregiver receives Carers Allowance (=0,1)

ETHNIC_BLACK_ASIAN Caregiver’s ethnic group is ‘black or Asian’ (=0,1). Reference category is white.

ETHNIC_OTHER Caregiver’s ethnic group is ‘other’ (=0,1). Reference category is white.

Cared for characteristics

CF_MALE Cared-for is male (=0,1).

CF_AGE Age of cared-for person.

ATTENDANCE ALLOWANCE

Cared for receives Attendance Allowance (=0,1).

DISABILITY LIVING ALLOWANCE

Cared for receives Disability Living Allowance (=0,1).

DISABLED CHILD Cared-for is a disabled child of the caregiver (=0,1). Reference category is older person (65 and over) not categorised as having mental health or learning needs.

ADULT WITH PHYSICAL DISABILITY

Cared-for is an adult with a physical disability (=0,1). Reference category is older person (65 and over) not categorised as having mental health or learning needs.

ADULT WITH MENTAL HEALTH/LEARNING NEEDS

Cared-for is an adult with mental health or learning needs, includes older people with mental health or learning needs (=0,1). Reference category is older person (65 and over) not categorised as having mental health or learning needs.

CF_EMPLOYED Cared-for is employed (=0,1). Reference category is adult not working or young person in training or education.

CF_RETIRED Cared-for is retired (=0,1). Reference category is adult not working or young person in training or education.

INSTITUTIONALISED ACCOMMODATION

Cared-for lives in institutionalised accommodation e.g. nursing home (=0,1). Reference category is cared-for lives in own home

LIVES IN CARER’S HOME

Cared-for lives in caregiver’s home (=0,1). Reference category is cared-for lives in own home

Table A3: GMM Linear Probability Model Regressions of employment on caregiving: dependent variable is EMPLOYED (1= employed; 0 = not working; either not employed, in training or education, looking for work or training)

Model 6a IV (GMM)

HOURS>100 instrumented

Model 7a IV (GMM)

CAREHOURScat instrumented

Independent variable Coefficient Robust S.E. Coefficient Robust S.E.

HOURS>100pw -0.438*** 0.116

CAREHOURScat -0.215*** 0.056

MALE 0.9085 0.06 0.094 0.061 AGE 0.035*** 0.013 0.05*** 0.016

AGE_squared -0.0004*** 0.0001 -0.0006*** 0.0001

ETHNIC BLACK or ASIAN -0.203 0.138 0.699 0.538

Constant -0.00003 0.362 0.699 0.538

No. of observations 398 385

F 18.19*** 18.98***

Centered R2

0.161 0.107

Uncentered R2 0.431 0.394

Root MSE 0.428 0.441

Hansen j statistic ( Χ2 for test

of overidentification of instruments)

4.776 4.663

Notes: ***, **, *: significantly different from zero at 1%, 5% and 10% levels respectively Sample restricted to carers over 18 and younger than 70 (the oldest employed sample member is 80) Additional instruments for HOURS>100 and CAREHOURScat are as for Table 10