The cost of informal care in Europe:

New estimates based on the well-being valuation method

Ulrike Schneider and Julia Kleindienst

1. Introduction

There is growing awareness that the provision of informal long- term care, an indispensible part of our health systems, carries large, though partially hidden, costs. Various studies have provided partial cost estimates of medical expenses, the opportunity cost of carers’ time and/ or foregone earnings. Colombo et al (2011), for instance, estimate the wage difference between carers and non-carers in the UK. Nepal et al (2011) compare the economic lifetime perspectives of female primary caregivers in Australia to their non-caring counterparts. For a more comprehensive cost measure, several studies (e.g. Meijer, de Brouwer et al. (2010)) have asked carers how much they would need to be paid to provide an extra hour of care (or how much they would pay to avoid it). Such contingent valuations (e.g. Carmichael and Charles (2003)) studies rely heavily on respondents being willing and able to perform a rational cost benefit calculus on the care they to provide family members. In practice, people are not used to thinking about this problem in market terms. They are likely to show sequencing or anchoring bias in their answers, or even give protest answers because it feels inappropriate to be monetising family care. In addition, an emergent literature is pointing to the existence of important benefits, not just costs, to caregivers, though estimates of their magnitude are scarce. Consequently, there is value in exploring alternative valuation methods for informal care.

Our paper will explore one methodological alternative. It follows Van den Berg and Ferrer-i-Carbonell (2007) in using the subjective well-being valuation method to estimate the cost of informal care. The underlying model assumes that life satisfaction increases with available income and is reliably reported by carers. Obviously, these are strong assumptions, but previous research on reported life satisfaction (e.g., Lepper (1998)) appears to support them. That informal care can be both a burden and a source of satisfaction, appears well-established in the literature (see, for instance, Nolan (1996)). We take an agnostic view of the balance of these effects and estimate the net effect of changes in care provision on a caregiver’s happiness. Combining this with the effect of income on subjective well-being yields a monetary measure of the life

1

satisfaction that informal care costs. Since the data includes information on the hours of care provided by each respondent, the paper can estimate the monetary value of the net burden or benefit of providing an hour of informal care.

Some background on the method used is provided below in section 2. That section also outlines expectations for some key variables. The third section will give an overview of the dataset used, key variables examined and important choices made in the specification of these variables. It shows summary statistics for both the sample as a whole, comparing carer and non-carer respondents, and for the carer sub-group examined in more detail. The fourth section presents estimation results for both groups, and their implied shadow value of informal care. These are discussed the fifth section. We highlight important findings, compare them to previous literature and attempt to explain the key results. This section also reviews limitations of the estimation method and dataset, essaying a first evaluation of the reliability of results. Finally, we conclude by summarising our answer to the question after the magnitude of shadow costs/ benefits to informal carers, note a few possible extensions of the work, and discuss some policy implications of the findings.

2. Method

The subjective well-being valuation method uses an empirical model that models life satisfaction as a function of income, the variable of interest (in this case, informal care provided) and controlling covariates. The baseline model can be described by the equation

Wij = α + β1Xij + β2Cij + β3yij + β4Dj + εij

where Wij indicates the subjective well-being of individual i in country j, Xij

describes a vector of individual determinants of life satisfaction, Cij is a vector that contains hours of care provided, yij is a measure for household income, In addition, there are dummies accounting for country-specific fixed effects. These variables are presented in greater detail below. The unobserved determinants of life satisfaction are captured by the term εij. α, β1, β2, β3, β4 represent the (vectors of) coefficients to be estimated. The full-sample estimation also includes interactions between care variables and health status, and the carer sub-sample includes indicator variables for the relationship between care provider and recipient.

The ratio of the estimated coefficients β2 /β3 represents the marginal rate of substitution between care hours and income. It is the shadow price of an hour on

2

informal care. Using income as a base good requires only the assumption that money enters positively into the utility function as it can be transformed into valued consumption goods. It is not necessary that a reallocation of time away from care actually enable the respondent to increase their disposable income. Thus, estimates are possible even for those respondents that are no longer working (the majority of the sample). It should only be inappropriate for people who can no longer derive utility from income, perhaps because they are bed- bound and in very poor health, and incapable of enjoying anything beyond the care they are already receiving. Such respondents are not in this sample, however. The selection bias leaving out the physically worst- off subjects is an unfortunate, but unavoidable feature of the data. In order to avoid assuming cardinality of subjective life satisfaction, the model estimated is an ordered probit, with robust standard errors (clustered at the country level).

The subjective well-being valuation method is well-tested in environmental economics and other fields that require cost estimations outside of market contexts. That work, along with supporting literature on life satisfaction or happiness, provides guidance for the variables needed in the model. Specifically, life satisfaction is regressed on age, gender, health, employment and marital status, as well as household income, as these factors have been found significantly associated with life satisfaction in previous studies.

Life satisfaction has been shown to be systematically related to age. Blanchflower and Oswald (2008), (2009) present evidence for ‘u-shaped’ subjective life satisfaction over the life cycle, regardless of cohort effects. Employment status is another crucial control. Although, unsurprisingly, over half the SHARE sample is retired, this may include respondents that were forced into retirement rather than choosing to go or early retirees. They, along with the unemployed, would be expected to have lower life-satisfaction than their in-work counterparts. Lucas et al. (2004) use German panel data to show that losing one’s job has persistent negative effects on life satisfaction.

Similarly, those authors (2005) show persistent effects from divorce. Therefore, dummy variables for being single, married or in a registered partnership, divorced or widowed are also included as control variables. Clark et al. (2008) also find weak evidence for a gender effect in adaptation of life satisfaction to changing life circumstances, requiring another control.

Health status as a control is a potentially difficult case, owing to adaptation effects. Theoretical approaches like Sen’s capability concept argue at least for an

3

indirect effect of health on life satisfaction. In addition, previous applications of the method (see van den Berg, 2007) argue for the importance of some health proxy as a control variable. In our sample, life satisfaction is robustly and strongly associated with good health, regardless of the measure of health status explored.

The inclusion of absolute income, crucial for calculating the shadow cost of care, may also be contentious, given the fact that adaptation and reference group choice are important in valuing money for life satisfaction (see, for instance Easterlin (2001) or Ferrer-i-Carbonell (2005)). The large body of work that uses this approach (see Clark et al. (2008) for a survey), however, suggests that this is not an insurmountable problem. Life satisfaction in our sample is indeed increasing in income. To capture the diminishing marginal utility of income, and reduce the effect of outliers, we include income in its natural logarithm form.

That care provision systematically affects well-being is an implicit hypothesis of this paper, and the basis for using the SWBV method to find its shadow price. Van den Berg et al. (2007) find a net negative effect on well-being. This is unsurprising, given the amply documented physical and emotional burdens care-giving imposes (see, for instance, Wilson et al, (2007)). It is not inevitable, however. Brouwer at al. (2005) show that in a large sample of Dutch caregivers, carers gained process utility form the provision of informal care to their relatives. Their survey results suggest that happiness would decline if care were provided by someone else, pointing to carer motivation beyond pure altruism (perhaps a warm glow effect). If the benefits of care provision outweigh the costs imposed, the net effect of care provision on well-being may well be positive.

3. Data and Measures

This paper uses the second wave of data, gathered 2006-2007, for the Survey of Health, Ageing and Retirement in Europe (SHARE) (c.f. (Börsch-Supan and Jürges 2005)). Some summary statistics, contrasting carers and non-carers are shown in table 1 below. Our sample (restricted to the 33498 respondents with sufficient data available) contains more women than men. Unsurprisingly, given the mean age of 67, over forty percent of them are retired. The vast majority (over 70%) are married, or living with a partner. Having

4

discussed expectations for some of the major variables in the previous section, we now provide more detail on the measures used and key features of the data.

The subjective well-being assessment is fairly standard. Respondents are asked “On a scale from 0 to ten, how satisfied are you with your life?” The dependent variable displays reasonable variation, although the distribution shows the typical left skew, with over 90% of responses given as five or above. Some respondents (about 1% of the sample) could not answer the question because the questionnaire was being completed by a proxy. This suggests that the sample may exclude those with the worst health status, as these are no longer able to complete the survey. In the section on health, over 94% of respondents answered themselves (rather than by proxy). Life satisfaction is significantly higher for the carer group.

Health status is an important control variable in the regressions run. The SHARE dataset provides a wealth of information on respondents’ health. The crucial choice for this project was between using self- assessed (subjective) health, or a more objective measure, such as the number of chronic conditions diagnosed. While the former provides a more comprehensive and nuanced measure than the number of diagnosed illnesses, it has two limitations. Firstly, not all respondents chose to answer that question, limiting the usable dataset if it were to be included. More importantly, however, it is plausible that idiosyncratic personality factors affect the framing of both subjective satisfaction and health assessments. Then including the latter as a regressor would introduce bias. The number of chronic conditions, on the other hand, fails to capture the severity of mobility limitations implied. It has been suggested, that grip strength (measured for both hands in the survey) is a useful proxy for physical health (see Andersen-Ranberg, K., I. Petersen, et al. (2009)), although measures do show a North- South gradient even after controlling for age and chronic conditions. Given our inclusion off country fixed effects, this is not a concern here.

Each respondent’s grip strength was measured four times by interviewers. Following the authors above, the grip strength variable used is the maximum grip strength attained in these four measurements. These measurements range between 0 and 86, with a left- skewed distribution. The missing values are overwhelmingly (over 60%) from respondents unable to take the measurement because they were ill, injured or not personally answering questions. As such, they are underrepresented in the carer subsample. In other words, missing values

5

for grip strength prevail in the part of the sample not identified as providing care.

Carers have statistically significantly higher grip strength than do the non- carers. The same pattern holds for other measures of health status, which are all highly correlated amongst each other and cannot be explained by the age profile of the groups, which is very similar. As may be expected, then, we find a health selection effect in care-giving.

The survey asks about many possible sources of income. However, merely summing across the income sources from responses in the dataset would further limit the usable dataset, due to the prevalence of missing values. To retain as much data as possible, the imputations of Christelis (2011) for SHARE data are used. The author provides five sets of generated variables. In order to make efficient use of the information, all five are used in Stata’s “Multiple Imputations” estimation. The income variable generated at the individual level includes transfers, pension payments, income from savings or financial investments. Household level income also includes net income from property, and is the measure used. Unfortunately, intra-household distributions, reflecting the disposable income actually under any respondents’ control, are impossible to make out through this dataset. The dataset shows a wide range of household incomes, higher for carers than non- carers on average.

The final important measure pertains to the definition of caregiver. Carers (about a third of the sample) were defined as those who answered that they had spent time helping (this excludes child care) someone outside their household. Two unavoidable limitations are the lack of a similar question about the amount of help provided to a household member, excluding the many carers that help their co-habiting spouses, and the lack of information about the kind of care provided. In particular, the health and functional status of the care recipient are not recorded, so that the physical and emotional burden, that both vary wildly between care-giving arrangements, may be only indirectly investigated.

Education is controlled for by including the ISCED category of the respondent’s highest educational attainment. Given the difference in educational attainment between carers and non-carers, it was felt to be an important control. It has a positive, but not statistically significant association with life satisfaction. Since the more standard specification of three categories of educational attainment was not statistically significant either, only the ISCED variable was included for greater parsimony.

6

Summary Statistics carers non-carers Description mean (sd) min max mean (sd) min maxDependent Variable

life satisfactionSubjective life satisfaction on a scale from 1-10 7.81 0 10 7,44 0 10

(1.59) (1.85)Independent Variables

grip strengthHighest grip strength measurement; health status 36.69 0 100 33,19 0 92

(11.89) (12.11)age 66.80 29 106 66,83 26 107

(10.60) (10.53)gender 1 for females 0.56 0 1 0.55 0 1

(0.497) (0.497)employed 0.38 0 1 0.258 0 1

(0.485) (0.437)retired 0.424 0 1 0.527 0 1

(0.494) (0.499)unemployed 0.033 0 1 0.025 0 1

(0.179) (0.155)disabled 0.035 0 1 0.036 0 1

(0.183) (0.186)homemaker 0.112 0 1 0.135 0 1

(0.317) (0.342)single 0.041 0 1 0.042 0 1

(0.143) (0.144)

marriedmarried or living with a partner 0.726 0 1 0.706 0 1

(0.481) (0.479)divorced 0.075 0 1 0.051 0 1

(0.19) (0.17)widowed 0.087 0 1 0.142 0 1

(0.402) (0.258)education ISCED categories 2.91 1 6 2.51 1 6

(1.40) (1.39)

net incomeYearly household income in Euros 133354 0

4656233 85840.19 0

4656233

(208888.1)(152730.2

)N 10551 22947

7

Table two shows details for caregivers only. Care hours reported have been converted into the average hours provided per week in the last year, for better comparability. The average number of care hours provided is low at about twelve a week, especially given the fact that most respondents are not employed. The variance is very large, however. The table includes not just average weekly care hours but also the hours provided by two groups of intensive carers, providing between 10 and 30 hour a week, and over 30 hours a week, respectively. Given the non-linearities in the effect of care provision found on life satisfaction, the full specification includes these three groups of care hours.

A set of category variables captures the relationship between care-giver and recipient. The two largest groups are those caring for a non- relative and for a parent, accounting for over half of all carers together. For an alternative specification, care relationships are grouped by generation, although there is some ambiguity in assigning non-relatives to these groups. The largest group comprises those caring for a member of their generation (mostly a partner or ex- partner, followed by siblings).

Summary Statistics carers Description Q1 median Q3total carehours average weekly carehours in the last year 0.47 2 7high carehours weekly carehours more than 30 36 49 70medium carehours weekly carehours 10-30 14 15.5 21

mean (sd) min maxparent Main care recipient is a parent 0.249 0 1

(0.431)in-law Main care recipient is a parent/child in-law 0.093 0 1

(0.289)partner Main care recipient is a spouse/ partner 0.039 0 1

(0.197)sibling Main care recipient is a sibling 0.061 0 1

(0.24)child Main care recipient is a child 0.224 0 1

(0.419)other Main care recipient is another relative 0.067 0 1

(0.25)friend Main care recipient is not a relative 0.262 0 1

(0.44)older care recipient is of an older generation 0.243 0 1

(0.429)younger care recipient is of a younger generation 0.276 0 1

(0.444)same care recipient is of the same generation as carer 0.389 0 1

(0.487)n= 9794

8

4. Results

Below are regression results for the ordered probit estimation for the entire sample. Reassuringly, the effect on well-being associated with a rise in income is (highly robustly) positive and statistically significant. This is a minimum requirement for the shadow cost calculation to be plausible. In all specifications, we find a positive effect of providing care on subjective well-being. Reported life satisfaction seems to increase robustly, but non-linearly with the hours of care provided, as the effect for additional hours above 10 is negative. Thus, the benefits are practically wiped out for very intensive care-giving arrangements (those over thirty hours a week), presumably overwhelmed by the more familiar burdens associated with care, that increase as hours committed to it rise.

Several other results are as the literature would lead us to expect; subjective well-being is positively and robustly associated with better health, for instance. Married respondents are happier relative to the reference group of singles, divorcees and widowed people less happy. Respondents who are unemployed (or not working due to a disability) are also less satisfied relative to those still employed. No such significant difference can be found for homemakers or retirees.

The large sample allows for a rich specification, including not only the key variables outlined above, but also interaction terms between care hours and grip strength, our proxy variable for health status. The interaction terms are statistically significant and large and have some interesting implications. For the care-hours under 10/week, satisfaction gained from an hour of care is lower for care-givers in better health, than comparable ones in worse health. The opposite holds for higher weekly care hours, however. Translating these results into monetary benefits, the estimated coefficients imply that a carer with the sample’s median health status and income, providing low intensity care (below 10 hour a week) derives a benefit valued at about €137 for providing an extra hour each week on average, that is, an extra 52 hours per year. Thus, the value of a single hour would be about €2.60. A similar carer with slightly worse health (grip strength three units lower) is expected to derive a benefit equivalent to an additional €1 / hour. At higher care- intensities, say, 25 hours a week, the median carer derives a benefit of about €1.50 / hour. With the same deterioration in health as considered above, this hypothetical carer would see this benefit turn negative.

9

The table reports the proportion of life satisfaction observations that the fitted model predicts correctly, or within one unit of the given answer. This provides a goodness-of-fit measure in place of the more usual pseudo R-squared, which was not obtainable for the ordered probit model using imputed data. Clearly, these are not comparable directly to other goodness-of-fit measures, but were useful in guiding our choice of specification, and selecting variables to include.

Dependent Variable: subjective life satisfaction (1-10)

Variable Estimated Coefficient standard error p-valuecarehours 0.0441838 0.0180841 0.015carehours (10-30) -0.0343972 0.0164265 0.036carehours (>30) -0.0442146 0.0181272 0.015health 0.01443 0.0017979 0.000 carehours*health -0.0010469 0.0004119 0.011carehours (10-30)*health 0.0008649 0.0003302 0.009carehours (>30)*health 0.0010481 0.0004127 0.011log income 0.1117652 0.0145769 0.000 female 0.1640422 0.0341618 0.000age 0.0172306 0.0135523 0.204age squared -0.0001073 0.0000931 0.249 education -0.0000727 0.0013655 0.958retired -0.0427904 0.0198388 0.031unemployed -0.4784577 0.0748724 0.000disabled -0.4641743 0.0584549 0.000homemaker -0.0077087 0.0305857 0.801reference category: employed married 0.1450297 0.0110363 0.000divorced -0.231302 0.0487387 0.000widowed -0.133672 0.0353124 0.000reference category: single n= 29471Not reported: Country fixed effects and cut points

Correctly predicted life satisfaction observations: 46.52%

Predicted life satisfaction observations one category off: 53.47%

10

The second table (below) shows results for the carer sub-sample only. This proved too small to include interaction effects, but the specification for care hours is the same as for the full sample. The estimated coefficients on care hours have the same sign as in the full sample, though they are smaller. For comparison, they would imply a shadow benefit to a carer providing less than ten hours a week of about € 1.20/hour. They fall short of statistical significance at the ten percent level. More parsimonious specifications result in more significant coefficients, but the pattern of estimated coefficients is robust to these variations.

Restricting the sample to carers only allows us to include variables capturing the relationship between caregiver and recipient. Exploration found no systematic difference between the effects of caring for family members as opposed to less closely related carers, but this may simply be due to working with too small a sample size. When care relationships are grouped by generation (see below), the emotional care burden seems to be greater for those caring for members their own or a younger generation, than caring for someone older than themselves. The discussion below offers some speculation on why this is so. In order to translate the results into something more readily interpretable, imagine two caregivers of the same health and marital status, both with the median income, both providing the same number of hours of care. Then the difference in expected life satisfaction between the one caring for an older relative (like a parent), and the one caring for someone in their own generation for a year would be valued at €1.600.

11

Dependent Variable: subjective life satisfaction (1-10)

12

Variable Estimated Coefficient standard error p-valuecarehours 0.0030911 0.0016818 0.011carehours (10-30) -0.0020342 0.0014523 0.014carehours (>30) -0.0030733 0.0017271 0.012 log income 0.1356025 0.0192366 0.000health 0.0105347 0.0014451 0.000care recipient younger -0.0532406 0.0314303 0.090 care recipient from same generation -0.0693635 0.0254501 0.006

reference category: care recipient is older than carer female 0.1449282 0.0357037 0.000 age 0.0233085 0.0140416 0.097age squared -0.0001066 0.0001063 0.316education -0.0034733 0.002326 0.135retired -0.0138191 0.0229377 0.547unemployed -0.4866278 0.0971019 0.000disabled -0.3256991 0.053251 0.000homemaker 0.0169202 0.0391252 0.665reference category: employed married 0.1534883 0.0145204 0.000divorced -0.2230966 0.052626 0.000widowed -0.2718058 0.0725157 0.000reference category: single Carer sub-sample: n= 9768Not reported: Country fixed effects and cut points

Correctly predicted life satisfaction observations: 34.65%

Predicted life satisfaction observations one category off: 40.19%

13

5. Discussion

Carers in this sample clearly derive some benefit from this activity, perhaps via an increased sense of self worth, altruistic enjoyment of the care-recipient’s improved life and/ or the greater social interaction necessitated by the care provision. Previous papers (See, for instance, Brouwer et al, (2005)) have suggested that informal carers may experience process utility in providing this help. Hunt’s (2003) review article cites increased self-esteem or uplifting experiences as possible positive results from providing care. In our results, the marginal positive effect of an hour of care decreases as hours provided increase. This appears to support the idea that providing informal care does yield some satisfaction for the carer as well. If the counterbalancing physical and emotional strain do not become too large (as, here, when the time commitment mounts, and the carer’s health status is insufficiently good) the net effect can be strongly positive.

Another interesting result, which we believe to be new in this context, is the importance of an interaction between health status and care hours. Coefficients of those interaction terms are robustly statistically significant. They are also non-trivial in size, as the calculations above illustrate. The estimation suggests that for low care burdens, an hour of care provides more enjoyment to a person with worse grip strength, than a comparable one with better health status. Perhaps the opportunity cost of these visits is higher when care-givers are in better health, and have more options open to them. It is also possible that carers simply evaluate their health issues as less troublesome, or give them less salience in their general appraisal of well-being because they use the care recipient- almost surely in worse health- as a reference point. Such a health perception bonus would be more important for respondents in objectively worse health, and could also help explain this relationship. The relationship reverses for higher care burdens: over ten hours, and even more strongly over 30, the life satisfaction gained by an hour of care increases with improving health status. Plausibly, the higher care burden seems to be easier to handle for people in better health.

The one previous application of the subjective well-being valuation method to informal care was published by van den Berg and Ferrer-i-Carbonell in 2007. In their investigation of a sample of Dutch caregivers, they find a negative net effect of care provision on well-being, their calculations implying a cost of informal care of € 9-12 /hour. Many of our findings agree with their results. The authors find, for instance, that care costs are higher for family members than non-relative caregivers. Although we use slightly different categories to depict the relationship between caregiver and recipient, our results seem to confirm that it is an important factor

14

mediating the impact of care benefits and burdens on the carer. The positive value of informal care to many in our sample, however, is a significant difference. For the purposes of comparison, it should be noted that there are small differences in methodology, and the variables used and large differences in the sample considered. As explained above, all our estimations are ordered probit models, to avoid the questionable assumption that the subjective well-being responses are cardinal. Using a simple OLS model does not yield different signs on coefficients, however. The differences between our findings and van den Berg et al.’s are unlikely to be driven by model specification. In particular, the signs of coefficients are robust to the inclusion of other variables, use of a different health measure and also the inclusion of income in non-logarithm form.

The main difference between our work and theirs seems to be the sample considered. The carers we identified in the SHARE data are a very specific group; those who provide care to someone in a different household. Given that our carers visit another household to provide care, unlike the Dutch sample, they are likely insulated from the worst emotional strain. Even the few that essentially spend all day providing help must have respite when they go home. It is likely that the social contact provided by these visits is a salient feature. As detailed in the data section, most of them also provide relatively few care hours. The sample mean is far below the care hours provided by Van den Berg’s sample. Even if the Dutch family caregivers over-report their hours (49 hours a week, significantly above the country average), this seems an especially committed group of caregivers. Finally, we do estimate a far smaller benefit when we restrict our sample to include only carers (as the Van den Berg study does). Apparently, the value of that first hour of care provided (which can be estimated only when comparing respondents to non-carers) is large.

Given the net benefit to carers estimated, it is worth taking time to review the limitations of this work and evaluate how reliable it is likely to be, as well as mapping out some future research avenues on this topic. The subjective well-being valuation method itself obviously relies on a respondents’ ability to accurately and consistently report their current well- being. As the discussion in section one lays out, this seems plausible enough to make the calculations interesting, but should certainly be kept in mind when interpreting them. They should be seen as a first indication, rather than the last word on the subject. Both consistency checks with differently administered surveys, and qualitative exploration should be valuable here. When the next wave of SHARE data becomes available, repeating the calculations on panel data will improve our confidence in the results. By exploiting variation in each respondent’s care situation and self-assessed life satisfaction, this will deal with unobserved heterogeneity that may be biasing results.

15

To better model the balance of positive and negative effects of care-giving, it would be crucial to include information on the care recipient’s functional status. Trukeschitz, Schneider et al (2010),for instance, show that the functional status of their care recipient is a key determinant of the strain experienced by employed caregivers. After controlling for the number of hours provided, the care recipient’s health status is crucial in determining how much physical and, more importantly, emotional strain their care involves. It is possible that this is what the generational variables are actually picking up, in the carer-subsample estimation. Perhaps the carers that visit their siblings or ex-partners (care recipients of their own generation) to help them do so because the situation is particularly dire. Unlike an elderly relative, someone of the carers’ generation is less likely to need help with household tasks, financial matters or personal care unless they are in very bad health. It is also possible that this is confounding the effect found associated with care hours; those carers with difficult care situations (caring for people in extremely poor physical health, or perhaps with dementia) might also be the ones required to provide the most hours. This alone does not seem to adequately account for the positive association between hours of care and life satisfaction for the less intensive care arrangements, however.

Finally, the income data used does include imputations and results should be interpreted with extra caution. In particular, the calculated shadow costs should be treated as no more than indications of relationships between different variables, not necessarily reliable in their magnitude.

6. Conclusion

We have seen, then, that providing care out of home may be associated with substantial benefits to the caregiver. These will be eroded by very large time commitments and bad health of intensive caregivers. The relationship between care-giver and -recipient seems to be an important factor in mediating the balance of positive and negative effects of care-giving, though more information on the care recipient would certainly be useful to shed light on this question. Although this study suffers from several limitations (like the lack of ability to control for personal idiosyncrasies in assessing life satisfaction, or the imputed income data), it does indicate that quantitative approaches can go some way towards investigating potential benefits of care-giving. The methodological approach thus offers a useful complement to the qualitative studies still emerging on the issue.

16

As these results are tentative first indications, so, necessarily, are any policy recommendations we might make. The findings do suggest that carer support policies that focus on alleviating the negative consequences of informal care-giving on caregivers might allow them to enjoy providing care. Investigating a slightly different approach, an ongoing intervention study for a carer support programme also focuses on benefit-finding as a coping strategy (Cheng, Lau et al. 2012). The informal care system, well-understood and supported, could be more than a way of off-loading costs of care onto relatives. The difference in experiences of in-home and out-of-home caregivers provides indirect evidence for the importance of respite care. More importantly, the results clearly indicate that good health is a crucial coping resource for intensive care-givers. Interventions to improve it thus offer dividends beyond lower future healthcare costs.

References

Andersen-Ranberg, K., I. Petersen, et al. (2009). "Cross-national differences in grip strength among 50+ year-old Europeans: results from the SHARE study." European Journal of Ageing 6(3): 227-236.

Blanchflower, D. G. and A. J. Oswald (2008). "Is well-being U-shaped over the life cycle?" Social Science & Medicine 66(8): 1733-1749.

Blanchflower, D. G. and A. J. Oswald (2009). "The U-shape without controls: A response to Glenn." Social Science & Medicine 69(4): 486-488.

Börsch-Supan, A. and H. e. Jürges (2005). The Survey of Health, Aging, and Retirement in Europe - Methodology. Mannheim, Mannheim Research Institute for the Economics of Aging (MEA).

Brouwer, W. B. F., N. J. A. van Exel, et al. (2005). "Process utility from providing informal care: the benefit of caring." Health Policy 74: 85-99.

Carmichael, F. and S. Charles (2003). "The opportunity costs of informal care: does gender matter?" Journal of Health Economics 22: 781-803.

Cheng, S., R. Lau, et al. (2012). "A benefit-finding intervention for family caregivers of persons with Alzheimer disease: study protocol of a randomized controlled trial " Trials 13(1): 98.

Christelis, D. (2011). "Imputation of Missing Data in Waves 1 and 2 of SHARE." CSEF Working Papers, 2011: 51.

Clark, A. E., E. Diener, et al. (2008). "Lags And Leads in Life Satisfaction: a Test of the Baseline Hypothesis." The Economic Journal 118(529): F222–F243.

17

Colombo, F., A. Llena-Nozal, et al. (2011). Help Wanted? Providing and Paying for Long-Term Care. OECD Health Policy Studies, OECD Publishing. Paris, OECD.

Easterlin, R. A. (2001). "Income and Happiness: Towards a unified theory." The Economic Journal 111: 465-484.

Ferrer-i-Carbonell, A. (2005). "Income and well-being: an empirical analysis of the comparison effect." Journal of Public Economics 89: 997-1019.

Hunt, C. K. (2003). "Concepts in Caregiver Research." Journal of nursing scholarship 35(1): 27-32.Lepper, H. S. (1998). "Use of other-reports to validate subjective well-being measures." Social Indicators

Research 44: 367-379.Lucas, R. E. (2005). "Time Does Not Heal All Wounds : A Longitudinal Study of Reaction and Adaptation to

Divorce." Psychological Science 16 (12): 945-995.Lucas, R. E. C., Andrew E. ; Georgellis,Yannis and Diener, Ed (2004). "Unemployment Alters the Set Point for

Life Satisfaction." Psychological Science 15(1): 8-13.Meijer, C., W. de Brouwer, et al. (2010). "The value of informal care: A further investigation of the feasibility

of contingent valuation in informal caregivers. ." Health Economics 19: 755-771.Nepal, B., L. Brown, et al. (2011). "A Comparison of the Lifetime Economic Prospects of Women Informal

Carers and Non-carers, Australia, 2007." The Australian Journal of Social Issues 46(1): 91-108.Nolan, M. G., Gordon; Keady, John (1996). Understanding family care: A multidimensional model of caring

and coping Buckingham and Bristol, PA, USA, Open University Press Trukeschitz, B., U. Schneider, et al. (2010). Does informal eldercare affect stress at work for working

cargeivers? Working Paper. W. F. f. Altersökonomie. Wien, WU Wien.van den Berg, B. and A. Ferrer-i-Carbonell (2007). "Monetary valuation of informal care: the well-being

valuation method." Health Economics 16(11): 1227-1244.Wilson, M., C. Van Houtven, et al. (2007). "Depression and Missed Work among Informal Caregivers of

Older Individuals with Dementia." Journal of Family and Economic Issues 28(4): 684-698.

Acknowledgement: This paper uses data from SHARELIFE release 1, as of November 24th 2010 or SHARE release 2.5.0, as of May 24 th

2011. The SHARE data collection has been primarily funded by the European Commission through the 5th framework programme (project QLK6-CT-2001- 00360 in the thematic programme Quality of Life), through the 6th framework programme (projects SHARE-I3, RII-CT- 2006-062193, COMPARE, CIT5-CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and through the 7th framework programme (SHARE-PREP, 211909 and SHARE-LEAP, 227822). Additional funding from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, Y1-AG-4553-01 and OGHA 04-064, IAG BSR06-11, R21 AG025169) as well as from various national sources is gratefully acknowledged (see http://www.share-project.org for a full list of funding institutions).

18