Willingness to pay is key component of a consumer-based cost-benefit analysis. Presumably, a consumer assesses the benefits derived from, or harm caused by, a product or service and decides how much this is worth to him or her. For the consumer, cost and benefit are comingled and determined my multiple factors external to the service or product provided. These external factors might include their ability to pay, competing expenditures, alternative ways of acquiring a service or product, the extent to which a service invades their privacy, ease of use, and attitudes about who is responsible for payment. For example, in the health care and support service arena, consumers may feel that the government or insurance companies, and not the individual consumer, are the appropriate payers (Schulz et al., Reference Schulz2014, Reference Schulz2016). One of the limitations of this and other studies on willingness to pay is that few studies try to assess the underlying calculus used by consumers or the broad range of external factors that contribute to these assessments.
In this follow-up study (Jutkowitz et al., Reference Jutkowitz2021), the authors assessed caregivers’ willingness to pay for a nonpharmacologic dementia caregiver intervention before and after being exposed to an active treatment or control condition. The active treatment condition consisted of eight in-home sessions to provide an environmental assessment, skills training, and stress management for caregivers of persons with dementia. The attention control condition consisted of an eight-session basic education program on dementia, home safety, and home safety recommendations. What makes this study unique in the annals of research on willingness to pay is the longitudinal analysis. Although this is a major strength of the study, it is at the same time a weakness that challenges our ability to interpret the data.
Researchers studying willingness to pay focus on two key aspects of the data: what proportion are not willing to pay anything; and among those willing to pay something, how much are they willing to pay? At baseline in this study, about one-third of participants were not willing to pay anything for the sessions (treatment, 37%; control, 34%). In the follow-up analysis, participants in the active treatment condition reported a substantial increase in the proportion not willing to pay anything (9–11%), whereas control participants showed little change (2%). Clearly, participants in the active treatment condition were less enthusiastic about paying for this service once they experienced it, although no information is provided as to why this might be. Among those willing to pay something, there is a $14.00 difference between active treatment and control at baseline with control participants willing to pay substantially more (control $48.44; active treatment, $34.03). The baseline rates decreased for control participants but increased and then decreased for active treatment participants. Viewed from a randomized trial perspective, the difference in baseline values suggests that random assignment failed with respect to this variable, making it difficult to interpret the follow-up data. Making sense of the data is further compounded by the large amounts of missing data and the absence of true within-person longitudinal analyses, not fully adjusted for missing data. Finally, the measurement of willingness to pay is problematic; respondents are asked about a relatively broad intervention that may or may not have been perceived as being linked to the intervention they just experienced. We don’t know.
Studies in relatively unexplored areas often raise more questions than they answer, and this study is no exception. First and foremost, one would want to know whether willingness to pay is associated with benefits derived from the intervention. In other words, are people willing to pay more when the intervention improved their quality of life? Comparing differences between treatment and control conditions alone, as was done in this study, doesn’t address this question. For example, in discrete choice experiments, Marti (Reference Marti2012) showed that willingness to pay is linked to treatment attributes such as efficacy and side effects. In cost-utility analysis, willingness to pay for specific treatments was pegged to quality-adjusted life years (Hollingworth et al., Reference Hollingworth, McKell-Redwood, Hampson and Metcalfe2013). Basic demographic factors such as age, education, race, and income may also play a role in willingness to pay. In a national probability survey of 512 adult caregivers, Schulz et al. (Reference Schulz2016) asked how much they would be willing to pay for technology that could help monitor the care recipient and provide assistance as needed. About 20% of caregivers were not willing to pay anything, and, among those willing to pay something, the mean amount was approximately $50 per month. In multivariate models, younger caregivers, those with medium and high incomes, AD caregivers, caregivers providing fewer hours of care per week, and caregivers with positive attitudes toward technology and those with more experience with technology were willing to pay more. In a large European study (ENABLE Project), investigators assessed willingness to pay for assistive technologies to be used in the homes of people with dementia. The devices evaluated included items such as medicine reminders, locators, and gas stove monitors. In this small sample study (n < 100) recruited from five countries in Europe, the majority of family caregivers (81% on average) were willing to pay for the devices, although the maximum amount they were willing to pay was in most cases below actual cost of the technology (Duff and Dolphin, Reference Duff and Dolphin2007). In an Australian survey of members of the National Seniors Productive Ageing Research Register, three-quarters of respondents (76.8%, sample size not specified) indicated that they would be unwilling to pay the full cost of using a range of assistive technologies such as home computers and monitoring devices (National Seniors Productive Ageing Centre, 2007). Mahoney et al. (Reference Mahoney, Mutschler, Tarlow and Liss2008) also assessed the feasibility of a remote monitoring system designed to enable employed family caregivers to monitor their relative while at work. When asked about their willingness to pay a monthly system fee, 6–8 of the 16 respondents indicated that they would be willing to pay anywhere from $10 to $130 per month, depending on the features of the system.
What can we conclude from this literature? Some caregivers are willing to pay something to ease the burden of caregiving, but even when they are willing to pay, the amount typically does not cover the cost of the product or service provided. This suggests that the provision of caregiver support services will ultimately depend on government or private sector support. Obtaining this support will require compelling data on efficacy and feasibility, measured both in terms of the quality life of caregivers and care recipients but also short- and long-term health and social service costs to society. The recent comparative effectiveness review of nonpharmacological dementia caregiver intervention studies carried out by the Agency for Healthcare Research and Quality (Butler et al., Reference Butler2020) suggests that we are far from meeting this standard.
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Willingness to pay is key component of a consumer-based cost-benefit analysis. Presumably, a consumer assesses the benefits derived from, or harm caused by, a product or service and decides how much this is worth to him or her. For the consumer, cost and benefit are comingled and determined my multiple factors external to the service or product provided. These external factors might include their ability to pay, competing expenditures, alternative ways of acquiring a service or product, the extent to which a service invades their privacy, ease of use, and attitudes about who is responsible for payment. For example, in the health care and support service arena, consumers may feel that the government or insurance companies, and not the individual consumer, are the appropriate payers (Schulz et al., Reference Schulz2014, Reference Schulz2016). One of the limitations of this and other studies on willingness to pay is that few studies try to assess the underlying calculus used by consumers or the broad range of external factors that contribute to these assessments.
In this follow-up study (Jutkowitz et al., Reference Jutkowitz2021), the authors assessed caregivers’ willingness to pay for a nonpharmacologic dementia caregiver intervention before and after being exposed to an active treatment or control condition. The active treatment condition consisted of eight in-home sessions to provide an environmental assessment, skills training, and stress management for caregivers of persons with dementia. The attention control condition consisted of an eight-session basic education program on dementia, home safety, and home safety recommendations. What makes this study unique in the annals of research on willingness to pay is the longitudinal analysis. Although this is a major strength of the study, it is at the same time a weakness that challenges our ability to interpret the data.
Researchers studying willingness to pay focus on two key aspects of the data: what proportion are not willing to pay anything; and among those willing to pay something, how much are they willing to pay? At baseline in this study, about one-third of participants were not willing to pay anything for the sessions (treatment, 37%; control, 34%). In the follow-up analysis, participants in the active treatment condition reported a substantial increase in the proportion not willing to pay anything (9–11%), whereas control participants showed little change (2%). Clearly, participants in the active treatment condition were less enthusiastic about paying for this service once they experienced it, although no information is provided as to why this might be. Among those willing to pay something, there is a $14.00 difference between active treatment and control at baseline with control participants willing to pay substantially more (control $48.44; active treatment, $34.03). The baseline rates decreased for control participants but increased and then decreased for active treatment participants. Viewed from a randomized trial perspective, the difference in baseline values suggests that random assignment failed with respect to this variable, making it difficult to interpret the follow-up data. Making sense of the data is further compounded by the large amounts of missing data and the absence of true within-person longitudinal analyses, not fully adjusted for missing data. Finally, the measurement of willingness to pay is problematic; respondents are asked about a relatively broad intervention that may or may not have been perceived as being linked to the intervention they just experienced. We don’t know.
Studies in relatively unexplored areas often raise more questions than they answer, and this study is no exception. First and foremost, one would want to know whether willingness to pay is associated with benefits derived from the intervention. In other words, are people willing to pay more when the intervention improved their quality of life? Comparing differences between treatment and control conditions alone, as was done in this study, doesn’t address this question. For example, in discrete choice experiments, Marti (Reference Marti2012) showed that willingness to pay is linked to treatment attributes such as efficacy and side effects. In cost-utility analysis, willingness to pay for specific treatments was pegged to quality-adjusted life years (Hollingworth et al., Reference Hollingworth, McKell-Redwood, Hampson and Metcalfe2013). Basic demographic factors such as age, education, race, and income may also play a role in willingness to pay. In a national probability survey of 512 adult caregivers, Schulz et al. (Reference Schulz2016) asked how much they would be willing to pay for technology that could help monitor the care recipient and provide assistance as needed. About 20% of caregivers were not willing to pay anything, and, among those willing to pay something, the mean amount was approximately $50 per month. In multivariate models, younger caregivers, those with medium and high incomes, AD caregivers, caregivers providing fewer hours of care per week, and caregivers with positive attitudes toward technology and those with more experience with technology were willing to pay more. In a large European study (ENABLE Project), investigators assessed willingness to pay for assistive technologies to be used in the homes of people with dementia. The devices evaluated included items such as medicine reminders, locators, and gas stove monitors. In this small sample study (n < 100) recruited from five countries in Europe, the majority of family caregivers (81% on average) were willing to pay for the devices, although the maximum amount they were willing to pay was in most cases below actual cost of the technology (Duff and Dolphin, Reference Duff and Dolphin2007). In an Australian survey of members of the National Seniors Productive Ageing Research Register, three-quarters of respondents (76.8%, sample size not specified) indicated that they would be unwilling to pay the full cost of using a range of assistive technologies such as home computers and monitoring devices (National Seniors Productive Ageing Centre, 2007). Mahoney et al. (Reference Mahoney, Mutschler, Tarlow and Liss2008) also assessed the feasibility of a remote monitoring system designed to enable employed family caregivers to monitor their relative while at work. When asked about their willingness to pay a monthly system fee, 6–8 of the 16 respondents indicated that they would be willing to pay anywhere from $10 to $130 per month, depending on the features of the system.
What can we conclude from this literature? Some caregivers are willing to pay something to ease the burden of caregiving, but even when they are willing to pay, the amount typically does not cover the cost of the product or service provided. This suggests that the provision of caregiver support services will ultimately depend on government or private sector support. Obtaining this support will require compelling data on efficacy and feasibility, measured both in terms of the quality life of caregivers and care recipients but also short- and long-term health and social service costs to society. The recent comparative effectiveness review of nonpharmacological dementia caregiver intervention studies carried out by the Agency for Healthcare Research and Quality (Butler et al., Reference Butler2020) suggests that we are far from meeting this standard.