Willingness to pay for long-term home care services: Evidence from a stated preferences analysis

Journal Pre-proofs Willingness to pay for long-term home care services: Evidence from a stated preferences analysis Anna Amilon, Jacob Ladenburg, Anu Siren, Stine Vernstrøm Østergaard PII: DOI: Reference:

S2212-828X(20)30003-7 https://doi.org/10.1016/j.jeoa.2020.100238 JEOA 100238

To appear in:

The Journal of the Economics of Ageing

Received Date: Revised Date: Accepted Date:

18 February 2019 18 December 2019 10 January 2020

Please cite this article as: A. Amilon, J. Ladenburg, A. Siren, S. Vernstrøm Østergaard, Willingness to pay for longterm home care services: Evidence from a stated preferences analysis, The Journal of the Economics of Ageing (2020), doi: https://doi.org/10.1016/j.jeoa.2020.100238

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Article title: Willingness to pay for long-term home care services: Evidence from a stated preferences analysis Authors:

Anna Amilon (corresponding author, email: [email protected])* Jacob Ladenburg** Anu Siren* Stine Vernstrøm Østergaard*

* The Danish Center for Social Science Research, Herluf Trolles Gade 11, 1052 Copenhagen, Denmark. ** The Rockwool Foundation, Ny Kongensgade 6, 1472 Copenhagen, Denmark.

Willingness to pay for long-term home care services: Evidence from a stated preferences analysis Abstract Population aging is expected to result in an increased demand for long-term home care services world-wide. In Denmark, long-term home care is predominately provided by local municipalities and is publicly financed. This paper uses a stated preferences approach to study the willingness to pay (WTP) for various components of long-term home care services, using household taxes as the payment vehicle. In our discrete choice experiment, we ask respondents to consider a hypothetical individual – an 83-year-old woman with physical limitations who lives alone – and to choose among various service packages for her. We find that respondents, on average, have strong preferences for improving long-term home care services. However, these average results are strongly driven by positive WTP among respondents with left-wing political views. Furthermore, WTP is positively associated with age, which implies an increasing demand for improved services as longevity increases. We conclude that WTP for tax-financed, long-term home care services is closely linked to respondent characteristics. JEL Classifications: J14, J18 Keywords: long-term home care; willingness to pay; discrete choice experiment Declarations of interest: none 1. Introduction Ageing populations are considered one of the future megatrends affecting societies globally. It is projected that people over 80 years of age will account for around 10 per cent of the world’s population by 2050 (OECD, 2016). Politicians, stakeholders and researchers alike expect population ageing to have a major impact on labour markets, economic growth and social structures (Bloom et al., 2015; European Commission, 2018). The old-age dependency ratio (the ratio between the old and the working-age population), as well as public spending on pensions, health care and long-term home care are expected to increase significantly in the future (Bogetic et al., 2015), putting pressure on public budgets worldwide.

Denmark is one of the countries that is experiencing rapid population ageing, and it is estimated that by 2040 there will be twice as many Danes aged 80 and above than there are today (Ministry of Health, 2017). Denmark has a strong universal welfare model, and the care of older adults is predominately provided by local municipalities and publicly financed (Danmarks Statistik, 2011). Due to its welfare model, Denmark is the highest-taxed country in the OECD (OECD, 2017a).1 The projected increase in persons aged 80 and above is expected to place a heavy burden on Danish municipal budgets (Kirk and Wilken, 2017) and thus lead to increased pressure on already high tax rates. In particular, the demand and expenditure for municipal long-term home care services is expected to rise, as increasing numbers of older adults with care needs continue to live in their homes due to personal preferences and ageing-in-place policies (e.g. Hajek et al., 2017). Given these challenges, an efficient allocation of resources is vital for the Danish welfare model, as is striking a balance between the municipal services provided and the population’s willingness to pay (WTP) taxes to finance these services. Previous research shows that there are indeed substantial unexplained differences in the level and quality of long-term home care services across municipalities (Houlberg, 2017), which may reflect citizens’ views and willingness to pay taxes to finance such services. This paper uses a stated preferences/discrete choice experiment (DCE) approach to study the WTP for various components of long-term home care services for older people in the Danish welfare state context. Publicly financed long-term home care services are not traded on a market. Hence real choice data on the trade-off between costs and service levels are not available. Stated preference data are therefore essential in this setting.2 Knowledge about how WTP is associated with peoples’ characteristics is relevant for policy makers who wish to increase the quality of long-term home care, as it can be used to guide the distribution of additional costs among taxpayers. Moreover, associations between peoples' characteristics and

1

The employee net average tax rates for an average married worker with two children (taking into account child-related benefits and tax provisions) was 25.5 per cent in Denmark in 2016, which can be compared to the OECD average 14.3 per cent (OECD, 2017b). 2 The stated preferences method has previously been used in a number of studies, e.g. on labour supply (Delavande and Rohwedder, 2017), health economics (Ryan et al., 2006), energy economics (Ladenburg and Dubgaard, 2007), transport economics (Caussade et al., 2005), environmental economics (Adamowicz et al., 1994) and old-age care (Callan and O’Shea, 2015).

WTP provide information on how WTP may develop in the future, as the composition of the population changes. Furthermore, information on the association between political preferences and WTP may serve as guidance for policy makers as to which dimensions of long-term home care that may be improved under which political regimes. We use a large dataset representing the Danish adult population of working age (18-65 years old) to investigate the WTP for different long-term home care service attributes. We ask respondents to consider a hypothetical individual – an 83-year old woman with physical limitations who lives alone – and to choose between various municipal service-packages for her. Each service-package gives an increase in yearly taxes per household. Thus, our DCE closely resembles the actual situation in Denmark, in which the municipality provides publicly financed, long-term home care services for older people. In our setting, consistency between the attributes and levels of the choice sets and actual long-term home care packages offered by Danish municipalities was a prime concern. We therefore focus on attributes and levels corresponding to services and modes of delivery currently available in Danish municipalities. In addition, to increase the probability of all respondents being able to make informed choices we focus on services that are widely known and easy to relate to, such as meal services and cleanliness of accommodation. We find that respondents generally have significant positive preferences for improving long-term home care services. WTP varies by respondent characteristics and is positively associated with age, being politically oriented towards the left and being employed, and is negatively associated with having a high level of education, having right-wing political preferences, having children aged 6-18 years and, somewhat surprisingly, having user or work experience of the long-term home care sector. We conclude that WTP for tax-financed, long-term home care services is closely linked to respondent characteristics. Our findings are relevant for countries with tax financed long-term home care systems, which are often struggling to maintain quality within public budgets pressured by population ageing. Moreover, they are relevant for countries, e.g. in eastern and central Europe, that do not yet have a system for providing long-term care but that may wish to establish one.

Despite the challenges related to population ageing and the provision of long-term home care, few previous studies have addressed WTP for long-term home care services. Nieboer et al. (2010) conducted a DCE under the Dutch comprehensive public long-term care insurance system. In their DCE, respondents were to choose service packages and associated co-payments for four hypothetical patients. The study found that the hypothetical patient’s social and physical situation influenced respondents’ choices. For instance, respondents avoided choice sets involving nursing homes for all hypothetical patients, except for the patient described as living alone and suffering from dementia. However, the Nieboer (2010) study elicits WTP via co-payments for the hypothetical patient and not for the respondent. Thus, the respondents’ WTP for long-term care services for others (or for themselves) remains unclear. A study by Callan and O’Shea (2015) used the contingent valuation method to investigate WTP for various forms of community-based long-term care in Ireland and found strong preferences for family care – the currently predominant mode of care in the country. The study used an increase in annual taxation as the WTP payment vehicle. However, the results exhibited a low degree of convergent validity between respondents’ ranking of alternatives and their WTP values, i.e. respondents’ rankings were not consistent with their WTP. Furthermore, the study does not provide insight into the relative preferences for different long-term care service attributes, which is necessary for allowing the identification of their efficient combination. Two additional recent studies have investigated preferences for long-term care services in a stated preferences framework, but without considering WTP. Kaambwa et al. (2015) investigated preferences for consumer-directed care in Australia (in which user co-payments apply) and found that both consumers (older people) and informal carers prefer to have a high degree of flexibility concerning, for example, choice of care workers and choice of activities in their care plan. Netten et al. (2012) investigated care preferences in the UK – a country characterised by requiring users to make substantial assets- and income-related co-payments towards long-term care. They found that respondents have strong preferences for having a high degree of control over their situation and a high degree of personal cleanliness and comfort. Our study contributes to the scarce literature on WTP for long-term home care services by being the first to use a design that closely mimics the actual situation in a comprehensive welfare state setting. The study proceeds as follows: Section 2 describes the Danish setting for long-term home care. Section 3 describes the data, experimental design and variables used in the analyses. Section 4

presents the econometric method. Section 5 presents the main results, while section 6 presents results from a series of robustness analyses. Section 7 discusses our findings and concludes. 2. Study context: Long-term home care in Denmark In Denmark, both health care and long-term care are tax financed and free of charge. Older people who are healthy enough to live at home but need assistance with daily activities due to functional limitations, receive long-term home care services. These services fall into two broad categories: practical help (e.g. cleaning and laundering) and personal care (e.g. bathing and dressing). The municipalities provide home care services based on an assessment of the individual’s care needs. In 2015, around 12 per cent of all persons over 65 received home care services (Ministry of Health, 2017). Since 2003, municipalities have had to provide individuals with a choice between different service providers, i.e. there must be at least two providers of long-term home care services in each municipality that individuals who have been granted long-term home care services can choose from (Ministry of Children, Gender Equality, Integration and Social Affairs, 2013). While the Social Service Act serves as the legal framework for municipalities’ services in long-term home care, the extended self-rule principle in Denmark implies that the municipalities have a high degree of freedom regarding the specific methods and the service levels that they wish to provide (ibid.). On average, Danish municipalities spent between $4,550 and $9,850 a year per person aged 65 or older on long-term home care services in 2017 (Houlberg, 2017).3 Differences in citizens’ characteristics across municipalities explain approximately 70 per cent of the variation in expenditures, but the remaining unexplained 30 per cent indicates substantial differences in the level and quality of long-term home care services across municipalities. These differences are likely due to political prioritisations and preferences that – via the democratic process – reflect citizens’ views and willingness to pay taxes to finance various municipal services.

Average exchange rates in 2009 (when the experiment was executed): $1=5.36 DKK, £1=8.36 DKK, €1= 7.45 DKK. For consistency, we use the 2009 USD exchange rate throughout the paper (i.e. all amounts are in 2009 USD). 3

In the universal welfare model, long-term home care services are financed by all citizens and not only by service recipients. While the WTP for long-term home care services through taxes remains unknown, it is likely that it is influenced by the perceived quality of care, personal experiences with the services, personal values and ideologies and individual views on the generational contract and solidarity. 3. Data and experimental design The data on WTP for long-term home care services in this study stems from a DCE (see e.g. Adamowicz and Louviere, 1998; Louviere and Woodworth, 1983). The DCE methodology builds on Lancaster’s theory, according to which it is not a good per se, but rather the bundle of attributes that a good consists of, that give utility to the consumer (Lancaster, 1966; Rosen, 1974). To identify the utility that individuals derive from the attributes, respondents in a DCE are presented with a set of alternatives (Bennett and Blamey, 2001). The alternatives define the good or service in terms of its key attributes, and different alternatives are described by varying levels of the attributes. By examining the trade-offs between attributes and attribute levels that are implicit in the choices made by respondents, it is possible to derive an estimate of the utility associated with the different attributes. 3.1 Study design The development of the questionnaire and, in particular, the design of the DCE was carried out in accordance with the guidelines in (Bateman et al., 2002) and Lancsar and Louviere (2008). Danish long-term home care services include a wide range of tasks, and it would be impossible to include them all as attributes in the choice tasks. Before deciding on which attributes to include, we made the following considerations. Several papers have established a relationship between choice complexity and model variance (DeShazo and Fermo, 2002; Swait and Adamowicz, 2001). Boxall et al. (2009) found that higher choice complexity increased the number of Status Quo (SQ) answers, thereby reducing both the information obtained on preferences/WTP and the potential welfare measure. To reduce the complexity of the choice tasks, we decided to keep the number of attributes and levels as low as possible.

Moreover, we decided only to include attributes related to practical help, as needs and preferences for personal care attributes would be strongly related to the functional capacity of the long-term home care recipient, which would bring in additional variance in the choice sets and scenario description, such as varying the physical condition of the hypothetical beneficiary. Furthermore, only including practical help in the choice task would ascertain a high degree of consistency between the choice-sets and actual long-term care packages, as the majority of Danish long-term care recipients receive practical care only. Last, we decided to prioritise attributes that were widely known and easy to relate to for respondents. In constructing our experiment, we interviewed managers in the long-term home care sector in selected municipalities to identify service attributes representing potential changes in long-term home care services that would be realistic to implement given an increase in budgets. The identified attributes were strongly inspired by recent legislative changes and the current media debate. As mentioned, long-term care recipients became eligible to choose between a public and at least one private supplier of care in 2003.4 The purpose of this reform was to make the supply of long-term home care marketable, thereby increasing the quality of services, responsiveness, equality and efficiency through competition and to give citizens the benefit of free choice (Dixon et al., 2010; Le Grand, 2009). To investigate the preferences for free choice, we included an attribute including free choice in meals. Moreover, after the reform, private companies delivering long-term home care were given the right to offer users’ additional services at their own expense. The municipalities did not have that opportunity, despite the fact that many users wished to make additional service purchases without having to choose a private supplier of long-term home care. We therefore included an attribute that involved the opportunity of buying additional services from the municipality.5 The final two service attributes “Cleaning” and “Time for socialising in connection with the cleaning” were chosen, as

4 This 5

type of market is also known as a quasi-market (Le Grand, 1991).

In 2012-2015, nine Danish Municipalities were allowed to sell additional services to care recipients as part of a

municipality trial (Hjelmar and Christiansen, 2016).

they were regularly discussed in the media at the time (and still are), see e.g. (Rostgaard and Thorgaard, 2007). The hypothetical beneficiary in our DCE was chosen to represent a “typical” user of long-term home care services – an 83-year-old woman receiving practical help only – and to provide a choice between various municipal service packages for her and other citizens in the municipality in a similar situation. Since the personal situation of the hypothetical beneficiary may influence respondents’ preferences, we provided the respondents with rather detailed information about her, as presented in Box 1.6 Box 1. The description of the hypothetical beneficiary We ask you to picture an older woman in your municipality. Below, we describe her situation. Gerda Olsen is 83 years old. She lives alone in a three-room apartment. She is fine mentally, but she suffers from various physical problems. She gets out of breath quickly and is not that strong any more. She has two children, but they live far away and therefore cannot visit and help her with her everyday chores. Besides her children, she only has regular contact with a neighbour. Gerda Olsen is currently receiving help with cleaning one hour every second week, and she gets ready-made food delivered. She cannot get any additional help from the municipality at present.

We then ask respondents to choose between three hypothetical but realistic long-term home care scenarios for Gerda and other older citizens in the municipality like her. The first service package in every choice set was Gerda’s, and other similar citizens’, current level of long-term home care, i.e. her Status Quo (SQ): cleaning one hour every second week, no choice

6

See Santos-Eggimann and Meylan (2017) for a recent Vignette study investigating the impact of variation in the

condition of the hypothetical beneficiary in the long-term care context.

in the meal delivery service, no time for socialising in connection with the cleaning, no possibility of buying additional help from the municipality and no tax increase. Two of the attributes (“Choice in the meal delivery service” and “Introducing the possibility to buy additional help from the municipality”) are dichotomous by nature (possible/not possible). To keep the cognitive task at a minimum, we decided to include two levels: the SQ and one level of improvement, for the other two attributes (cleaning and social care) as well. Only including two levels imply that we cannot investigate potential attribute non-linearities in the preferences. An improvement in the level of services was connected to an increase in the respondent’s yearly household taxes, whereas keeping the SQ service package did not involve any additional tax payment. Table 1 gives an overview of the attributes and levels. Table 1 Attributes and levels in DCE Attributes

Levels

Cleaning

1 hour every second week

1 hour every week

Choice in the meal delivery

No choice

Choice between different

service Time for socialising in connection

meals No time for socialising

15 minutes every second

with the cleaning

week

Possible to buy additional services Not possible

Possible for a user fee

from the municipality Increase in household taxes

0, 93, 187, 377, 933, 1,866

($/year)7 Note that the WTP in our study refers to the respondent’s willingness to pay taxes to finance longterm home care services for the hypothetical beneficiary, and other citizens in the municipality in a

7

Amounts in DKK were 0, 500, 1,000, 2,000, 5,000 and 10,000 DKK/household/year.

similar situation, whereas in previous studies (e.g. Nieboer et al., 2010), respondents were asked to choose co-payments for the hypothetical beneficiary. We used SAS version 9 software (Kuhfeld, 2004) to design a D-optimal factorial main effect design with 36 alternatives divided to 18 choice tasks allocated across six blocks. In the design, utility parameters were assumed to be zero. The design did not take into account the attribute levels in the SQ alternative. Each respondent was randomly assigned to one of the six blocks, each including three choice tasks. Thus, for each respondent, the DCE involved three choices between three realistic hypothetical long-term home care scenarios. All the service attributes are dummy coded – including an alternative specific constant for the SQ. The tax variable is coded as a linear variable. Table 2 gives an example of a choice task. Table 2. Example of a choice task Keeping Gerda’s situation in mind, we ask you to choose between three alternative service packages. The service packages and associated increases in your yearly household taxes are made up, but we ask you to think of the alternatives as real. Also, keep in mind how an additional tax payment will affect your disposable income for other purposes. Do you prefer the Status Quo (SQ), alternative A or alternative B?

Cleaning

Status Quo

Alternative A

Alternative B

1 hour every second

1 hour every week

1 hour every second

week Choice in the meal

No choice

week No choice

delivery service

Choice between different meals

Time for socialising in No time for

No time for

15 minutes every

connection with the

socialising

socialising

second week

Cannot be offered

Possibility of buying

Cannot be offered

cleaning Additional help from the municipality Increase in yearly household taxes ($)

extra 0

93

187

3.2 Protest answers and hypothetical bias If respondents are less sensitive to hypothetical than real market tax/cost increases, hypothetical bias leads to inflated demand curves and upward biased WTPs (Bosworth and Taylor, 2012; Carlsson and Martinsson, 2003; Loomis, 2011; Murphy et al., 2005; Norwood and Lusk, 2011) Recent research shows that hypothetical bias can be mitigated by giving respondents different types of ex ante reminders in the scenario instruction, which emphasise the hypothetical nature of the stated choice, such as a “Cheap Talk” (CT) (Cummings and Taylor, 1999) and an “Opt-Out Reminder” (OOR) (Alemu and Olsen, 2019; Ladenburg and Olsen, 2014).8 A CT directly informs respondents that they may exaggerate their demand for a choice set if they do not consider its cost, by informing them of hypothetically biased WTPs found in previous studies. We employed a CT9 to reduce hypothetical bias, by including the following sentence in the instructions to respondents: “Previous studies have shown that it is easy to overestimate how much one is prepared to pay for improvements in public service. When making a choice, it is therefore very important that you are sure that you are willing to pay the level of taxes indicated at the bottom of the alternative that you choose.” Moreover, as indicated in Table 2, we included the sentence “Also, keep in mind how an additional tax payment will affect your disposable income for other purposes” in the choice tasks. Thus, we explicitly inform respondents of the negative influence an increase in the tax rate will have on their purchasing power. Besides hypothetical bias, the stated preferences in a DCE can also be biased by protest answers (Meyerhoff and Liebe, 2010; Sudman et al., 1991), resulting in both deflated and inflated demand curves. The deflated demand curves appear when respondents state zero preferences due to a lack of acceptance of the hypothetical market. In other words, despite having preferences for improving a

The OOR directs the attention of the respondent towards the choice between a status quo alternative and the alternative(s) representing a change in the good in focus. The OOR thus specifically guides the respondent to choose the status quo alternative if he/she finds the policy alternative(s) to not be worth the increase in tax/cost. When we carried out the present DCE, the method of reducing hypothetical bias with an OOR had not yet been developed, tested and published. 9 For an updated review of the effects of employing a CT and/or an OOR, see Wuepper et al., (2018). 8

good and having a positive WTP for doing so, this group of protesters state a zero demand, typically because they object to paying more taxes. Protest answers that result in inflated demand curves appear when a respondent always chooses one of the two alternatives with service improvements, while ignoring the increase in taxes/costs. This violates the assumption that the respondent considers the tax/cost changes when choosing the alternative (either the SQ-alternative or one of the two policy alternatives) that maximises utility (see Meyerhoff and Liebe (2010) and Meyerhoff et al., (2014) for a thorough review of the protest literature in SP studies). To identify the two types of protest behaviour, respondents who always opted out (always chose the SQ alternative) or always opted in (never chose the SQ alternative) were asked a series of follow-up questions. The follow-up questions were designed with the objective of detecting whether the respondents’ answers reflected their true WTP or not. We classified respondents who always opted out because they “Could not afford higher taxes”, were “Happy with the current level of services” or felt that “The increase in the level of services was not proportional to the increase in taxes” as revealing their true (zero) WTP. Respondents who always opted out because they felt that “Services should be improved, but I do not want to pay more” or because they “Could not deal with paying more” were characterised as giving protest answers and were excluded from further analysis. Respondents who always opted in because they felt that “The improvements in the level of services were great in view of the extra payment” were treated as revealing their true WTP. In contrast, respondents who answered that “Extra payments had no influence on my choices” were treated as giving protest answers and were excluded from further analysis. The first group – which always opts out – constitutes 3 per cent of the respondents, which is low in comparison to previous studies (Meyerhoff et al., 2014). The second group – which always opts in – constitutes 20 per cent of the respondents. This is significantly lower than the shares reported in other studies (e.g. Grammatikopoulou and Olsen, 2013), which may be due to inclusion of the “Cheap talk” sentence to avoid respondents’ overstating their WTP. In total, we excluded 23 per cent of the respondents due to protest answers. We elaborate on the impact of excluding protest respondents on the estimated WTP in Section 6.1.

3.3 Data collection The respondents were sampled from the Userneeds web-panel, which is a representative sample of the Danish population according to characteristics such as age, gender, education level and income. We invited respondents aged 18-65 by e-mail to participate in the study, and respondents answered the questionnaire online. A respondent’s age may have a significant impact on how long-term home care is perceived, as older respondents are closer to the age where they are likely to become users of long-term home care services themselves, or to have family members and friends who are already users. Therefore, we oversampled respondents who were 50 years old or older, so that individuals in this age group constitute 50 per cent of the sample. In the questionnaire, we ask respondents whether they have prior experiences with, or knowledge of, the long-term home care sector and include the answers as explanatory factors in our analysis. As a first step in our data collection, we conducted a pilot to investigate whether respondents understood the questionnaire and the choice sets in the DCE. The results showed that 46.9 per cent and 30.2 per cent of the respondents (N=94) had accepted the two highest tax levels of DKK 1,000 ($187) and 2,500 ($466), respectively. We therefore decided to increase the maximum tax levels to 5,000 ($933) and 10,000 DKK ($1,866) to get a more precise estimate of the WTP distribution.10 We then launched the adjusted questionnaire for a subset of respondents (soft launch). After one week of data collection, 321 answers had been collected and we analysed the data. As the results from the soft launch did not give reason to make further adjustments to the questionnaire, we decided to launch the survey to the full sample of respondents. The respondents from the soft launch were included in our analysis, whereas respondents from the pilot were excluded from further analysis.

10

In the final version of the DCE, the share of respondents that chose various alternatives varied by maximum tax-

levels as follows: DKK 1,000 ($187) - 50.3 per cent; DKK 2,000 ($377) - 41.7 per cent; DKK 5,000 ($933) - 28.9 per cent and DKK 10,000 ($1,866) - 19.3 per cent.

Our study aimed at exploring preference differences across municipalities. Hence, respondents were recruited from 12 municipalities (out of 98). We selected municipalities to maximise variation in geographic conditions (degree of urbanity and east-west location), the level of service (long-term home care spending), the mayor’s political affiliation, social welfare spending (as social risks such as unemployment and depopulation are linked to respondents’ preferences for social welfare expenditure (Johansson Sevä, 2009)) and characteristics of the citizens. We invited almost 9,900 people to participate in the study, of which 3,805 completed the questionnaire. The response rate is thus 38.4 per cent, which compares favourably with surveys carried out using web-panels (Menegaki et al., 2016). 3.4 Explanatory variables We analyse the association between respondents’ characteristics and WTP for the long-term home care attributes. Knowledge on how WTP is associated with respondents’ characteristics is relevant for policy makers who wish to increase the quality of long-term home care, as it provides information on how to distribute the additional cost among taxpayers. Moreover, although the sample is not representative the associations between characteristics and WTP provide information on how WTP may develop in the future, as the composition of the population changes. Table 3 shows summary statistics of the respondents’ main background characteristics. As respondents above the age of 50 are more likely to have knowledge of the long-term home care sector, we chose to over-sample this group. Thus, 51 per cent of the respondents were 50 years old or older at the time of interview, whereas 49 per cent were between 18 and 49 (compared to 46 per cent and 54 per cent, respectively, in the general population). In addition to people aged 50 years or older, people with a high level of education, in employment and with a high level of income are over-represented in our sample. As we are mainly interested in the association between respondents’ characteristics and WTP for long-term home care services, the non-representativeness of the data is not a major problem. Nevertheless, we will keep the differences between our respondents and the general population in mind when interpreting the results. In addition to background characteristics, we include information on respondents’ political orientation and their experience with and knowledge of long-term home care services in the

Table 3. Descriptive statistics of the DCE sample Variables

Average

Standard deviation

Female

0.544

0.498

Age

46.961

11.590

Married/Cohabiting

0.763

0.425

Does not live with children 0-18 years old

0.608

0.488

Lives with children 0-5 years old

0.114

0.317

Lives with children 6-18 years old

0.283

0.450

Lower secondary education or other

0.091

0.288

VET, Upper secondary or short-cycle H.E.

0.412

0.492

Medium-cycle H.E., Bachelor or Master’s degree

0.497

0.500

In work

0.749

0.434

In education

0.049

0.217

Not in work or education

0.202

0.401

Low household income (HHI): $0-74,626

0.273

0.446

Medium HHI: $74,627 – 130,596

0.356

0.479

High HHI: $130,597 or more

0.255

0.436

HHI: Do not know or prefer not to say

0.116

0.320

Political sympathies: Left-wing

0.334

0.472

Political sympathies: Centre

0.289

0.453

Political sympathies: Right-wing

0.245

0.430

Political sympathies: Do not know/Prefer not to say

0.132

0.339

services

0.013

0.114

Second-hand knowledge of long-term home care services

0.451

0.498

Work experience of long-term home care services

0.112

0.315

Know from other sources, media or no knowledge

0.425

0.494

N. respondents

2,950

Current or previously received long-term home care

analysis. The reason for including these characteristics is the influence that they may have on respondents’ WTP for long-term home care services. Moreover, information on the association

between political preferences and WTP serves as guidance for policymakers as to which dimensions of long-term care may be improved under which political regimes. Roughly, a third of respondents defined themselves as having left-wing political sympathies, whereas a quarter of respondents defined themselves as having right-wing political sympathies. The remaining 42 per cent either had central political sympathies (29 per cent), did not know or chose not to answer the question (13 per cent in total). 1.3 per cent of the respondents were current or previous users of long-term home care services, whereas 11 per cent had work experience from the long-term home care sector. 45 per cent had second-hand knowledge of the long-term home care sector, defined as currently or previously having a family member receiving long-term home care. 4. Econometric model We apply the flexible mixed logit (MXL) model to estimate respondents’ preferences for long-term home care services, as it obviates the limitations of the conditional logit model (CL) by allowing for random taste variation and substitution over alternatives (Train, 2003). We estimate the following model: 𝑈𝑛𝑖𝑡 = 𝑉𝑛𝑖𝑡(𝑥𝑛𝑖𝑡) = 𝛽′𝑛𝑥𝑛𝑖𝑡 + 𝜀𝑛𝑖𝑡 = (𝛽 + 𝜑𝑛)′𝑥𝑛𝑖𝑡 + 𝜀𝑛𝑖𝑡 𝜀𝑛𝑖𝑡~𝐼𝐼𝐷 𝑒𝑥𝑡𝑟𝑒𝑚𝑒 𝑣𝑎𝑙𝑢𝑒 where 𝑥𝑛𝑖𝑡 is a vector of the observed long-term home care attributes of alternative i in choice task t for individual n. β is the mean attribute preference, while 𝜑𝑛 is respondent n’s deviation from the mean, β. The MXL model allows us to model correlation in the stochastic part of the utility over alternatives, attributes and choices, 𝜑𝑛′𝑥𝑛𝑖𝑡 + 𝜀𝑛𝑖𝑡. Conditional on 𝛽𝑛, the choice probability of respondent n’s choice of alternative i in choice set t is defined as

𝐿𝑛𝑖𝑡(𝛽𝑛) = 𝑃(𝑖│𝑥𝑛𝑡,𝛽𝑛) =

𝑒𝑥𝑝(𝛽′𝑛𝑥𝑛𝑖𝑡) 𝐽

∑𝑗 = 1𝑒𝑥𝑝(𝛽′𝑛𝑥𝑛𝑖𝑡)

However, allowing for examination of unobserved preference heterogeneity through 𝜑𝑛 implies that 𝜑𝑛 is unknown to the researcher. It is therefore necessary for model identification to assume a distribution of 𝑓(𝛽𝑛│𝜃). The MXL unconditional choice probability is then the integral of the conditional probability over all possible values of 𝛽𝑛 from the distribution of 𝜃.

𝑄𝑛𝑖𝑡(𝜃 ∗ ) =

∫𝐿

(𝛽𝑛│𝜃 ∗ )𝑑𝛽𝑛

𝑛𝑖𝑡(𝛽𝑛)𝑓

In this paper, we assume that all of the four long-term home care service attributes and the alternative constant coding for the SQ alternative are normally distributed. We estimate the tax parameter as a fixed parameter. Although models in WTP space can be estimated in more simple models, they do not converge in models including many interactions between the tax and non-tax variables and the socio-demographic characteristics of the respondents. As an alternative, a lognormal distribution could be applied, but this allows for very small values, which gives infinitely high levels of WTPs (Hole and Kolstad, 2012; Small, 2012). As the log-likelihood in the MXL model is too complex to solve analytically, we use maximum simulated likelihood estimation (MSLE) to simulate the log-likelihood function, using 1000 Halton draws from the mixed distribution. We estimate two types of MXL models: 1) a main effect model including only the attributes and an alternative specific constant for the SQ alternative and 2) an interaction model in which preferences across sociodemographic groups are estimated. We do this by interacting the different attributes and the SQ alternative specific constant with variables (dummy or linear) representing the sociodemographic characteristics of the respondents, such as age, education, income etc. 4.1 Deriving Estimates of WTP The focus of this study is to estimate the average WTP for specific attributes of long-term home care services (main effect model) and to analyse how WTP varies across the sample (interaction model). When a respondent chooses an alternative, he or she makes a trade-off between the longterm home care attributes and an annual tax increase, thus implicitly revealing his or her preferences. Including the tax attribute makes it possible to estimate WTP for the long-term home care service attributes. We do so by scaling the long-term home care service attribute estimate in focus by the coefficient representing the marginal utility of tax and multiplying by -1 (Louviere et al., 2000): 𝛽𝑥

𝑊𝑇𝑃𝑥 = ― 𝛽𝑡𝑎𝑥/5.36,

where βx is the coefficient of the attribute of interest and βtax is the tax coefficient. We divide the WTP by the average exchange rate (5.355) between DKK and USD ($) in 2009 (Danmarks Nationalbank, 2019). In the interaction model, preferences can vary across the respondents’ sociodemographic characteristics. However, the estimated preference differences can originate from actual preference differences or differences in model error variance, due to the confounding of the estimated parameters by the error variance of the model through the scaling factor (Davis et al., 2016; Louviere and Eagle, 2006). In order to circumvent this problem, preference differences are only compared in terms of WTP. The scaling factor is thus cancelled out, when estimating WTP for the jth sociodemographic groups of respondents relative to the relevant reference groups. In the WTP estimation, each observation is weighted with the respective sample share of each sociodemographic group (presented in Table 3). Using the marginal difference in WTP between male and female respondents as an example of a sociodemographic group, the WTP function for a selected service attribute is

𝑚𝑎𝑟𝑔𝑖𝑛𝑎𝑙 𝑊𝑇𝑃𝐹𝑒𝑚𝑎𝑙𝑒𝑥 =

(

(𝛽𝑥 + 𝛽𝐹𝑒𝑚𝑎𝑙𝑒𝑥 + ∑𝑤𝑗𝛽𝑗𝑥) ― (𝛽𝑡𝑎𝑥 + 𝛽𝐹𝑒𝑚𝑎𝑙𝑒𝑡𝑎𝑥 + ∑𝑤𝑗𝛽𝑗𝑡𝑎𝑥)

―

(𝛽𝑥 + ∑𝑤𝑗𝛽𝑗𝑥) ―(𝛽𝑡𝑎𝑥 + ∑𝑤𝑗𝛽𝑗𝑡𝑎𝑥)

)

/5.36

Where wj is the sample share (weight) of the jth sociodemographic group. 5. Results: Preferences for long-term home care and WTP This section presents the estimated average preferences and WTP for the sample as a whole (main effect model – Table 4) and for groups of respondents with different sociodemographic characteristics (interaction model – Table 5). 5.1 Average preferences and WTP The estimated average preferences in Table 4 point towards the respondents having significant positive preferences for improving long-term home care services (βCleaning, βMeals, βSocialise and βAdditional services > 0). The estimated constant for the SQ is negative (βSQ < 0), implying that respondents have a negative utility associated with preserving the SQ. In other words, the respondents have a positive utility associated with making changes to long-term home care services that goes beyond the utility associated with the different service attributes estimated in the model. For instance, respondents may have preferences for general improvements in long-term home care, but not for the specific attributes investigated here, and hence may not differentiate between

attributes but choose the alternative that implies a service improvement that they consider to be worth the additional tax. Alternatively, the respondents may associate the combination of service attributes with additional utility that is not captured by the service attributes that are included in the model. As expected, the tax parameter is significant and negative (βTax < 0). Furthermore, all random diagonal parameters are significant, which indicates substantial preferences heterogeneity. This heterogeneity is illustrated by the estimated shares of the respondents in the sample that have a negative utility associated with each of the four long-term home care attributes (between 27.0-36.8 per cent). Most of the off-diagonal elements in the variance/covariance matrix are significant and positive. These results suggest that respondents who have positive (negative) preferences for one service attribute also have positive (negative) preferences for the three remaining service attributes. Table 4. Sample average preferences and WTP, main effect MXL model Sociodemographic Tax Cleaning (DKK) variable Parameter -0.000448*** 0.528*** estimates [0.0000174] [0.0655] WTP 219.9*** ($/household/year) [24.88] Random parameters, variance/covariance matrix Cleaning Meals

Meals

Socialise

0.817*** [0.0650] 340.5*** [22.93]

1.073*** [0.0724] 446.9*** [25.87]

Socialise

2.469*** [0.343] Meals 0.607** 2.023*** [0.221] [0.298] Socialise 0.787** 1.244*** 3.052*** [0.229] [0.291] [0.407] ** *** Additional services 0.531 1.023 1.530*** [0.208] [0.229] [0.237] SQ 0.108 0.478 0.389 [0.345] [0.344] [0.318] N respondents 2,950 N choices 8,850 LL(0) -9,722.7 LL(β) -8,073.2 2 McFadden R 0.170 Notes: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001 Cleaning

Additional services 0.482*** [0.0617] 200.9*** [24.19]

SQ

Additional services

SQ

-

-

Negative preference s (per cent) 36.8

-

-

28.3

-

-

27.0

1.208*** [0.323] 0.100 [0.332]

-

33.0

1.898** [0.572]

73.0

-0.843*** [0.0974] -351.1*** [43.56]

The results show that, on average, respondents are willing to pay $220/year for weekly cleaning, $341/year for a choice in meals, $447/year for 15 minutes of social interaction every second week and $201/year to provide the opportunity to buy additional services from the municipality. To make results comparable, the WTP for social interaction is $894 per hour11, or more than eight times the WTP for additional cleaning. This result strongly implies that, on the margin, if a care worker can spend an additional 15 minutes with a care service user every second week, these 15 minutes should be spent interacting rather than cleaning. Furthermore, on average respondents are willing to pay $351/year for a general (not service-attribute-specific) improvement in long-term home care (the WTP for the SQ-alternative is –$351/year). 5.2 WTP for subgroups of respondents The results in Table 4 give insights into the sample average preferences and the unobserved preferences heterogeneity around the estimated means. We now explore the sources of heterogeneity in preferences and WTP as a function of the respondents’ sociodemographic characteristics. Appendix A presents MXL results from a model, in which interactions between the sociodemographic characteristics and the four service attributes, increases in the level of taxes and the SQ variables are included. The marginal WTPs for each of the sociodemographic groups are shown in Table 5. When interpreting the WTP results, it is important to keep in mind that the WTPs are ratios between the long-term home care service attributes and the tax attribute. Differences in WTP can thus be a function of differences in the preferences for the service attribute, for the tax attribute or for both. This is elaborated on in the presentation of the WTP results below.

11

Assuming linearity in the WTP in the interval 15 minutes to 1 hour and that there are four weeks in a month.

Additional cleaning refers to going from 1 hour every second week to 1 hour every week, i.e. an additional 2 hours a month. Hence, the WTP is $110/ year per additional hour of cleaning. Additional social interaction refers to going from 0 hours a month to 15 minutes every second week, i.e. 0.5 hours per month. Hence, the WTP is $894/year per hour of additional social interaction.

Table 5: Marginal WTP ($/year/household) for subgroups, interaction MXL model

Femalea Age Married/ Cohabitingb Children 0-5 yearsc Children 6-18 yearsc VET, Upper secondary or short-cycle H.Ed Medium-cycle H.E., Bachelor’s or Master’s degreed HHI:

$74,627-130,596e

HHI: $130,597 or moree HHI: Do not know or prefer not to saye In workf In educationf Receive/received helpg Second-hand knowledge of helpg Work experienceg

Cleaning

Meals

Socialise

Status Quo

37.09 [52.08] -1.699 [1.597] 108.2+ [59.59] -49.17 [72.28] 14.88 [53.69] -12.57 [86.40] -108.0 [86.93]

Additional services 56.39 [49.02] 0.438 [1.446] 70.40 [56.64] -49.60 [69.45] 62.46 [50.49] 18.70 [80.07] -65.07 [80.60]

5.43 [50.17] 1.249 [1.530] 85.75 [58.04] -52.87 [71.36] 11.78 [52.07] -124.1 [82.80] -180.6* [82.92]

41.61 [44.69] 3.808* [1.576] 51.08 [51.17] -60.94 [63.69] -41.35 [46.11] -47.21 [73.31] -135.7+ [73.69]

-134.8* [63.80] -18.14 [71.98] -17.67 [79.18] -18.59 [58.65] -13.31 [140.4] -83.60 [209.8] 19.08 [47.73] -174.0+ [95.29] 186.2** [59.92] 114.4 [70.68] 2.8 [84.21]

-94.79+ [56.91] -3.563 [63.98] -8.039 [70.34] 18.78 [51.95] 33.58 [124.0] -399.5* [191.9] -43.71 [42.26] 68.95 [81.21] 136.4** [52.30] 91.72+ [54.63] 32.6 [74.76]

-10.70 [66.22] -83.13 [74.42] -150.9+ [82.40] 100.1+ [60.52] 39.74 [148.6] 102.3 [202.9] 30.43 [49.46] 117.3 [97.36] 353.2*** [63.18] 64.82 [64.98] 77.0 [88.77]

51.65 [61.86] 65.52 [69.48] -14.16 [77.17] 27.57 [57.31] -59.85 [138.7] -40.28 [201.8] 34.87 [46.52] 156.7+ [90.72] 3.819 [58.03] 134.5* [60.06] 106.2 [82.60]

21.15 [98.08] 14.31 [96.10] 79.30 [117.4] -149.1+ [87.43]] -322.7 [242.8] -295.1 [309.1] 22.00 [71.76] 54.49 [138.1] -65.61 [92.76] 262.4** [89.73] -63.4 [129.5]

-39.43 [75.22] -8.184*** [2.237] 188.9* [88.26] 79.26 [105.0] 130.3+ [77.09] -34.90 [127.4] -30.58 [127.8]

Political orientation: lefth Political orientation: righth Political orientation: don’t know/prefer not to sayh Notes: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Reference groups: a) Male, b) Single c) No children, d) Lower secondary education or other, e)Household income <$74,627, f) Not in work or education, g) Knowledge from other sources, media or no knowledge, h)Political orientation centred.

The results show that marginal WTP for each of the four attributes is not significantly different for male and female respondents. However, there is a positive association between age and marginal WTP for giving older citizens the possibility of choosing between different meals and for general improvements in long-term care services (improving services over and above the SQ). More specifically, for each additional year of life, respondents are willing to pay an additional $4/year for giving older citizens the opportunity to choose between different meals and $8/year for a change away from the SQ (i.e. a general improvement in long-term home care services). Figure 1 illustrates WTP as a function of age and shows that respondents that are between 60 and 70 years of age are willing to pay between $248-336/year more for giving older citizens the possibility of choosing between meals and $533-$722/year more for a general improvement in long-term home care service, relative to 20-year-olds (i.e. they have a negative WTP for preserving the SQ). 12 336**

400 248** 200

$/year/household

50**

173**

107**

0 -200

-108*** -231***

-400

-371***

-600

-533***

-800 30

40

50

60

-722*** 70

Age of the respondent Choice between different meals

Preserve SQ

Fig. 1. Willingness to pay as a function of age (reference age: 20 years).

12

Although the variable “age” is linear, the interaction between tax and age, which appears in the numerator of the

WTP estimation function, is non-linear, and so a non-linear relationship between WTP and age appears in Figure 1.

Consequently, WTP for improvements in long-term home care services are strongly positively correlated with age. Thus, respondents who are closer to an age at which they may become users of long-term home care services are also more willing to increase tax payments to improve such services. However, the age gradient could also reflect differences in the present discounted value of provided services at age 83 for different age groups rather than different preferences. Interestingly, potential soon-to-be users of long-term care are not willing to pay for socialising with the care worker – which was the attribute that received the highest average WTP (Table 4). Respondents who are married or cohabiting are willing to pay $108 for social interactions and $189 to preserve the SQ situation in relation to respondents who live alone. Respondents with 7-18-yearold children have a significant WTP for not improving long-term home care services over and above the SQ, compared to respondents without children. The WTP is $130/year. Having younger children (0-5 years) does not influence WTP for long-term home care. Accordingly, being a consumer of public schools (but not of public day-care) influences general WTP negatively.13 The income measure that we include in the model reflects non-equivalised household income. Thus, the result may partly reflect a more constrained financial situation among parents of school-age children. The result implies that parents with school-age children prioritise other types of consumption than improving the quality of long-term home care services, for instance private consumption or improving the quality of other types of public service (i.e. state schools). This interpretation is in accordance with a previous study showing that Danish parents have a positive WTP for improving public school services (Krassel et al., 2016). Turning to the associations between socio-economic characteristics and WTP for long-term home care services, the results show that respondents with a high level of education (defined as mediumcycle higher education, a bachelor’s degree or master’s degree) have a significantly lower WTP for additional cleaning services and for offering older citizens the possibility of choosing between different meals, as compared to respondents with a low level of education (defined as lower secondary education) (the WTP levels are -$181 and -$136, respectively). Tests reveal no significant differences in WTP between respondents with a high level and respondents with an intermediate level of education (defined as VET, Upper secondary or short-cycle H.E). Although

13

The vast majority of Danish children are in publicly financed day-care or schools.

difficult to explain from a theoretical perspective, the results indicate that when making the tradeoff between, on the one hand, offering older citizens additional cleaning and the possibility to choose between different meals and, on the other, increased taxes, high-education respondents apparently prefer avoiding the latter to a greater extent. Respondents who do not wish to reveal their income level have a significantly lower WTP for social interaction. Furthermore, respondents in the mid-income category ($74,627-$130,596) have a lower WTP for more cleaning (-$135) and the possibility of choosing between different meals (-$95) compared to respondents in low income households. Tests reveal that the WTPs between respondents in mid-income category ($74,627-$130,596) and the high-income category (-$130,597 or more) are not significantly different. Keeping in mind that the WTP estimates are ratios between the long-term home care service attributes and the tax attribute, it is interesting that the parameter estimates for the tax attribute are significant and positive for both the mid- and the high-income groups (Appendix A), which points towards a generally lower WTP among mid- and high-income respondents, as compared to low income respondents (though WTP differences are only statistically significant for the mid-income group in Table 5). These results are surprising, as theory predicts a positive association between WTP and income. However, the empirical support for this theoretical prediction is mixed, as reviews by Schläpfer (2006) and Jacobsen and Hanley (2009) find a significant and positive WTP-income relation in 63 and 39 percent of the included studies, respectively. To investigate whether occupation influences WTP for long-term home care services, we have divided respondents into three groups: In work, in education and a reference group including respondents who are retired, on sick-leave or unemployed. The results suggest that students’ preferences are not significantly different from those of the reference group. However, employed respondents are willing to pay an additional $100/year for social interaction and $149/year for a general improvement in long-term home care services. The more stable financial situation of employed respondents can probably explain this result.14

14

This interpretation is supported by the main driver of the WTPs ratios having a significantly lower sensitivity towards

increased taxes in the MXL model in Appendix A, although it is only significant on a 90 per cent level of confidence.

Somewhat surprisingly, having knowledge of, or experience with, long-term home care negatively influences WTP of long-term home care services. Respondents who currently receive, or have previously received, long-term home care services have a significantly lower WTP for offering older citizens the possibility of choosing between different meals (-$400 per year) compared to respondents without experience of the long-term home care sector (the reference group). Satisfaction with the current level of meal-services may explain this result. Respondents with secondary knowledge (i.e. who have a family member who has received care) do not have a significantly different WTP for any of the long-term home care services compared to the reference group. However, respondents who work in the long-term home care sector have a significantly lower WTP for improving the cleaning service (-$174/year). Workers in the long-term home care sector may not want to increase the amount of cleaning, due to disutility associated with this task (as they are the ones who will do the additional cleaning). Alternatively, respondents with work-experience of long-term home care may find that the current level of cleaning provided for the hypothetical beneficiary (and other citizens like her) is appropriate. However, respondents with work experience have a higher WTP ($157/year) connected to giving users of long-term care the possibility to purchase additional services from the municipality. Political orientation is strongly associated with WTP for long-term home care services. In fact, the positive average levels of WTP displayed in Table 4 are strongly driven by respondents with leftwing political sympathies. Respondents with left-wing political sympathies are willing to pay $186/year for offering older citizens additional cleaning services, $136/year for offering older citizens the possibility of choosing between different meals and $353/year for offering older citizens 15 minutes of social interaction with the service staff twice a month. On the other hand, respondents with right-wing political sympathies constitute the only group among those investigated here that has a significant WTP ($135/year) for offering older citizens the possibility to purchase additional services from the municipality. They also have a positive WTP for giving the older adults the opportunity to choose between difference meals ($92/year). Finally, they have a positive WTP of $262/year for preserving the SQ. Figure 2 compares the WTP of respondents with left-wing political sympathies to that of respondents with right-wing political sympathies.

400

353*** 288***

300

200

186** 136**

$/year/household

114 100

262**

72

135* 92+

65

45

4 0

-66

-100 -131* -200

-300 -328** -400 1 hour every week

Choice between different meals

Left-wing respondents

No changes in the 15 minutes socializing Posibility to buy additional help from long-term home care every 2nd week the municipality service (preserve SQ)

Right-wing respondents

Difference left|right

Fig. 2 WTP and political preferences. We find no significant differences as regards WTP for additional cleaning services or for the possibility of choosing between different meals. However, respondents with left-wing political sympathies are willing to pay $288/year more for offering older citizens 15 minutes of social interaction with the service staff twice a month than respondents with right-wing political sympathies. Respondents with right-wing political sympathies are willing to pay $131/year more than respondents with left-wing political sympathies for offering older citizens the possibility to purchase additional services. Moreover, respondents with left-wing political sympathies are willing to pay an additional $328/year for a general increase in long-term home care services compared to the right-wing respondents. These results indicate that political preferences are strong drivers of WTP for long-term home care services.

5.3 WTP and costs of service Our analyses reveal that the respondents are generally willing to pay additional taxes to improve long-term home care services. In this section, we estimate how the reported WTP measure up to the costs of producing the additional services (additional cleaning, time for social interaction and choice of meals). We estimate the costs of providing additional cleaning and social interaction to $65/hour based on average hourly rates for long-term home care services in 2009.15 Furthermore, we estimate the cost associated with giving long-term care recipients a choice in meals to $173/month.16 In Table 6, we calculate the yearly costs of delivering additional cleaning, social interaction and choice in meals to one long-term care recipient and compare the costs to the number of households needed to finance these services. Table 6: Comparing production costs and number of households needed to finance the costs

Service improvement

Two hours

Estimated cost

Average WTPa

Number of households needed

($/year)

($/year/household)

to cover the costs

2*65*12=1,560

219

≈7

0.5*65*12=390

449

<1

173*12=2,064

340

≈6

cleaning/month

30 minutes of social interaction/month

Free menu a)

Based on WTP estimates in Table 4.

15

This average is based on all 98 Danish municipalities (Socialstyrelsen, 2019).

16

This estimate is the difference between the most and the least expensive meal plan provided by municipalities in 2007

(Kommunernes Landsorganisation and Velfærdsministeriet, 2008). Unfortunately, there is no data on which meal plans allowed for a choice.

Even though estimated costs do not account for consumption taxes, the simple analysis in Table 6 points towards welfare benefits being highest if long-term care recipients are offered 30 minutes of social interaction a month. 6. Robustness analysis In this section, we report results from a series of robustness analyses by comparing our main estimation results in Tables 4 and 5 to a series of alternative specifications (Appendices B-D present a more detailed description of the methodology and full estimation results). Overall, the analyses show that the WTP results from the interacted model (Table 5) are robust to alternative specifications. 6.1 Exclusion of protester In our main models in Tables 4 and 5, we exclude 23 per cent of respondents due to protest behaviour (see Section 3.3). In particular, excluding the 20 per cent of respondents who always opt in may push WTPs downwards, as these protesters probably have particularly strong preferences for long-term home care service improvements. Table 7 presents the sample mean WTPs of a main effect model when including protesters and compares the results to the corresponding results when excluding protesters (from Table 4). Table 7. Comparing mean WTPs ($/household/year) when excluding and including protesters, main effect MXL models

Cleaning Meals Socialise Additional services SQ N respondents

Without protesters

With protesters

WTP 219.9*** [24.88] 340.5*** [22.93] 446.9*** [25.87] 200.9*** [24.19] -351.1*** [43.56] 2,950

WTP 296.3*** [25.66] 453.9*** [24.22] 586.2*** [28.06] 273.6*** [25.27] -540.4*** [56.41] 3,805

ΔWTP 76.4* [35.74] 113.3*** [33.35] 139.3*** [38.17] 72.7* [34.99] -189.3** [71.27]

Notes: Standard errors in brackets, * p < 0.05, ** p < 0.01, *** p < 0.001. The full main effect MXL model including protesters appears in Appendix (Table B2).

As expected, WTPs are significantly higher for all long-term home care attributes and significantly lower for the SQ alternative, when including protesters. However, although WTP increases in absolute terms, it remains the same in relative terms (i.e. the WTP is still highest for social interaction, followed by choice in meals etc.). We estimate logit models to determine which respondents have a higher propensity to exhibit protest behaviour in Appendix, Table B1. This analysis shows that men, respondents with a high household income and respondents with right-wing political sympathies have higher probabilities of always opting out, whereas in particular older respondents have an increased probability of always opting in. As the propensity to protest varies by sociodemographic characteristics, the exclusion of protesters may also influence marginal WTPs of different socio-demographic groups. We estimate an MXL interaction model in Appendix, Table B3, which includes protesters (but is otherwise equivalent to the model presented in Table 5), and compare the marginal WTPs of the two models in Appendix, Table B4. This analysis shows that there are no significant differences in marginal subgroup WTP between the models including and excluding protesters, respectively. Accordingly, excluding protesters does not influence the marginal WTPs for different subgroups significantly. 6.2 Municipality fixed effects To investigate whether Municipality Fixed Effects (MFE) influence the results, we estimate a main effect model and an interaction model with MFE (Appendix tables C1 and C2) and compare the results to the corresponding models without MFE (Tables 4 and 5). The average WTPs based on the main effect MXL MFE model and the differences in WTP compared to the corresponding model without MFE (from Table 4) are shown in Table 8. None of the estimated differences in WTPs are significant. The average WTPs thus appear robust to including MFE.17

Although the vast majority of the specific municipality fixed effects estimates in Appendix C2 are insignificant, there are exceptions. Significant estimates may stem from variation in the level of long-term home care services between municipalities. Although we have fixed the SQ service level in the description of the hypothetical beneficiary, the present service level in the respondent’s municipality may influence preferences. For instance, a respondent who believes that the current service level is better than the one described in the SQ may increase the number of SQ-choices and vice versa. Such preference relationships have been reported in Kataria et al., (2012). We do not have information 17

Table 8: Comparing average WTP ($/household/year) with and without fixed effects, main effect MXL models

Cleaning Meals Socialise Additional services SQ N respondents

No MFE

With MFE

WTP 219.9*** [24.88] 340.5*** [22.93] 446.9*** [25.87] 200.9*** [24.19] -351.1*** [43.56] 2,950

WTP 193.6** [66.09] 377.0*** [58.39] 382.5*** [68.14] 133.5* [65.21] -445.6*** [101.4] 2,950

ΔWTP 26.23 [72.62] 36.52 [62.73] -64.37 [72.89] -67.49 [69.56] -94.53 [110.38]

Notes: Standard errors in brackets, * p < 0.05, ** p < 0.01, *** p < 0.001.

The MFE are statistically significant on a 99 per cent level and a 90 per cent level of confidence in the main effect and the interacted model, respectively. This fall in level of significance suggests that the sociodemographic variables capture some of the municipality level variation. Nevertheless, the MFE might influence results across subgroups. We compare the marginal WTP with MFE to the corresponding results from the model without MFE (from Table 5) in Appendix table C3. As there are no significant differences in WTP between the two models, we conclude that our main results are robust to including MFE and that the MFE-specific preferences seem to capture preference heterogeneity that is orthogonal to the variation explained by the respondents’ socio-demographic characteristics. Our results are interesting in light of the substantial differences in the level and quality of long-term home care services across municipalities, as indicated by the findings of Houlberg (2017). Recall that the McFadden R2 is 0.170 in the MXL average preference model in Table 4. When we add

on the respondents’ perception of the municipality specific service-level and hence cannot test whether it influences the probability of choosing the SQ alternative. An alternative method would be to test whether the actual initial service level among the municipalities is associated with the municipality-specific SQ-estimate. Such data are not available for 2009, however.

socio-demographic characteristics to the model, the McFadden R2 increases to 0.188, which is an increase of 10.5 percent. Including MFE, the McFadden R2 increases to 0.195, i.e. a further increase of 3.7 percent. Accordingly, the additional explanatory power of the MFE seems to be of a modest magnitude compared to the findings in Houlberg (2017). 6.3 Non-linear age relations A final robustness analysis investigates non-linearities related to age. We estimate WTP among respondents below and above 50 years of age, respectively. As the preferences of recipients and non-recipients of care may differ, we also condition on respondents not currently receiving care. The WTPs appear in Table 9 and the full MXL main effect models appear in Appendix tables D1 and D2. As previously found (Fig. 1), age only significantly influences Choice in meal and the SQ alternative specific constant. Table 9: Comparing WTPs of respondents below and above 50 years who do not receive long-term home care. Younger than 50 years WTP estimate Cleaning 178.8*** [32.32] Meals 269.8*** [29.51] Socialise 440.9*** [33.02] Additional 180.6*** services [30.24] SQ -230.9*** [50.33]

Older than 49 years WTP estimate 255.5*** [38.06] 429.4*** [35.43] 428.9*** [40.70] 213.1*** [38.62] -584.9*** [79.63]

Difference WTP estimate 76.71 [49.93] 159.54*** [46.11] -11.99 [52.41] 32.50 [49.05] -353.99*** [94.20]

Notes: Standard errors in brackets, * p < 0.05, ** p < 0.01, *** p < 0.001.

We further explore potential differences in WTPs by estimating the marginal differences in WTP in an MXL interaction model equivalent to the one presented in Table 5. We estimate differences in WTP by first calculating the mean age among non-care recipients below (37.9 years) and above (56.8) 50 years. Then, using these average ages levels, we estimate differences in WTP for the meal attribute to $127.98**/year and for general improvements in long-term home care relative to the SQ to $301.49***/year. The results showing that older respondents have significantly higher WTP for

free choice in meals and a general improvement in the long-term home care service relative to the SQ situation are thus robust to alternative specifications. 7. Discussion and conclusion Considering its pivotal importance in relation to population aging, WTP taxes to finance long-term home care services is an under-researched area. We use data from a DCE to investigate the WTP to improve the quality of long-term home care services of older citizens in Denmark using household taxes as a payment vehicle. Our DCE setting thus closely resembles the actual situation in a welfare state context. Our results point towards respondents’ on average having positive WTP for improvements in long-term home care services for older citizens for all of the investigated service attributes. However, the levels of WTP vary substantially by service attribute. For instance, on average, respondents are prepared to pay eight times more for a service worker to spend time on socialising with an older care recipient than for a service worker to spend the same amount of time on cleaning. This result may be due to frequent reports in Danish media about the staff in long-term home care being stressed (Rysgaard, 2009) and about a large proportion of older citizens feeling lonely and depressed (Henriksen, 2016). Allowing time for staff and care recipients to socialise may potentially alleviate both of these problems. This result strongly suggests that respondents’ solidarity not only includes WTP for the more instrumental tasks of long-term home care, but also for more general improvements of the well-being of older citizens. However, a more thorough analysis reveals that the high levels of average WTP are strongly associated with respondents’ characteristics – in particular their political preferences. More specifically, the positive WTPs for the specific attributes that we find in this study are primarily driven by respondents with left-wing political preferences. This group of respondents is the only one with a positive WTP for improvements in cleaning, meal services and social interaction. However, right-wing respondents have a higher WTP when it comes to offering older citizens the possibility to purchase additional services than left-wing respondents. These results imply that political preferences are strong drivers of WTP for long-term home care services and that increasing taxes to improve such services will be more feasible politically under a left-wing government. Another important finding is the strong positive association between age and WTP for long-term home care services – a result that may reflect either differences in preferences or differences in the

present discounted value of provided services at age 83 for different age groups. Regardless, the result has important implications for the future allocation of public resources. Indeed, as populations age not only might the demand for long-term home care services increase, but the political pressure for improved quality of services might also increase. In a welfare state setting such as Denmark, characterised by high taxes and an increasing dependency ratio, this result implies that reductions in the quality of service in other sectors may become necessary in the future. Such potential quality reductions may explain why parents of 7-18-year-old children have a positive WTP for preserving the SQ (i.e. for not improving long-term home care). This result implies that parents prioritise, for instance, improving the quality of other types of public service (i.e. state schools or childcare) (Krassel et al., 2016; Ladenburg et al., 2019) or increasing their private consumption over improving the situation of recipients of long-term care. Interestingly, the specific attributes that are associated with a negative WTP (amongst certain subgroups) are cleaning services and giving care recipients a choice between different meals. Both of these types of services can be bought on the ordinary market in Denmark, although the quality and content may differ from the corresponding services provided by the municipality. Therefore, one interpretation of the result is that some respondents object to increasing the public (tax-financed) level of services over and above the level that the municipality is already providing, because service users who want improved services have the option of buying these on the market. This interpretation is supported by the fact that people who currently receive, or have previously received, long-term home care services have a negative WTP for meal services. There are some important limitations to our study that should be kept in mind when interpreting the results. Firstly, we exclude about 20 per cent of the respondents due to their answers not reflecting their true WTP (due to protest behaviour). Although this share is not high in comparison to previous studies (Meyerhoff et al., 2014), this exclusion of respondents might lead to sample selection bias (Grammatikopoulou and Olsen, 2013). However, our robustness analysis shows that including these respondents does not influence subgroup marginal WTP, nor relative levels of average attributespecific WTP. Secondly, we limit our sample to data from 12 municipalities, and hence the results may not reflect the WTP pattern of the general Danish population. Nevertheless, robustness analysis indicates that the main results and WTP by subgroup are not influenced by Municipality Fixed Effects.

Thirdly, as we include only two levels for each long-term home care attribute (SQ and an improvement) we cannot identify trade-offs and/or non-linear WTP patterns for varying levels of improvement within and among long-term home care attributes. Fourthly, we provided respondents with a rather detailed description of the hypothetical beneficiary. Although this approach has the advantage of making choice sets more realistic and engaging, it also has the disadvantage of potentially reducing the external validity of the results (see Nieboer et al., (2010) for results showing that the situation of the hypothetical beneficiary does influence WTP). Nevertheless, our study sheds important light on WTP for long-term home care services in a welfare state setting by demonstrating the importance of respondents’ characteristics and political preferences in this context. Moreover, it shows that WTP varies by service attribute. Lastly, the results point towards increased future political pressure to improve long-term home care services due to ageing populations. Potentially, policy makers can use these results when planning, organising and prioritising long-term home care services. Acknowledgements We acknowledge the assistance of Stine Bendsen, Jørgen Jordahl Jørgensen and Morten Hørman in constructing the questionnaire that was used to collect the data in this study. Amilon, Siren and Østergaard acknowledge the financial contribution of the Innovation Fund Denmark (grant 615800002B) to this research. References Adamowicz, W., Louviere, J., 1998. Introduction to Attribute-Based Stated Choice Methods Introduction to Attribute-Based Stated Choice Methods. Alternatives 105, 1339–1342. Adamowicz, W., Louviere, J., Williams, M., 1994. Combining revealed and stated preference methods for valuing environmental amenities. J. Environ. Econ. Manage. https://doi.org/10.1006/jeem.1994.1017 Alemu, M.H., Olsen, S.B., 2019. Linking Consumers’ Food Choice Motives to their Preferences for Insect-based Food Products: An Application of Integrated Choice and Latent Variable Model in an African Context. J. Agric. Econ. 70, 241–258. https://doi.org/10.1111/1477-9552.12285 Bateman, I., Carson, R., Day, B., Hanemann, M., Hanley, N., Hett, T., Jones-Lee, M., Loomes, G.,

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Conflict of Interest

The authors declare that they have no conflicts of interest.

Anna Amilon: Conceptualization, Writing – original draft preparation, reviewing and editing Jacob Ladenburg: Conceptualization, Methodology, Software, Formal analysis, Visualization Anu Siren: Project administration, Writing – reviewing and editing, Funding acquisition Stine Vernstrøm Østergaard: Data curation, Formal Analysis