Women's role in the provision of long-term care, financial incentives, and the future financing of long-term care.

Abstract

Policy makers in industrialized countries fear a cost explosion in the provision of institutional long-term care (LTC) reminiscent of the one these countries have experienced in their health care sectors. However, at present the majority of persons in need of LTC live in their homes, with a daughter typically providing care to a parent or a wife to her husband [Kendig et al. (1992), Abelin/Schlettwein-Gsell (1986); for additional references, see Höpflinger (1994)]. Therefore, an important policy issue is the extent to which the future increase in the demand for LTC will continue to be met by these informal caregivers, who have to bear considerable opportunity costs in terms of leisure and wage income foregone. A measure of these opportunity costs is provided by the reservation wage for care, i.e. the quantity of money that is sufficient to compensate a respondent to provide one hour’s worth of LTC. Thus, gender-specific differences could be reflected in women’s lower reservation wage for care compared to men. Even if a gender-specific difference should not be observed, there may still be hidden differences in determinants that may become more marked in the future. In that event, a policy designed to encourage the provision of informal LTC through financial incentives directed at the caregiver, tailoring the amount of subsidy in such a way as to match her reservation wage for care, will meet with considerable problems. As an alternative, a voucher solution may be envisaged, giving the beneficiary the choice of source of care. In return, the beneficiary would have to search out caregivers whose reservation wage for care is covered by the value of the voucher or pay the difference himself.KeywordsWage RateLabor Force ParticipationInformal CaregiverReservation WageSwiss FrancThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.