Crowding out of long-term care insurance: evidence from European expectations data

Health Econ. 2015 Mar:24 Suppl 1:74-88. doi: 10.1002/hec.3148.

Abstract

Long-term care (LTC) is the largest insurable risk that old-age individuals face in most western societies. However, the demand for LTC insurance is still ostensibly small in comparison with the financial risk. One explanation that has received limited support is that expectations of either 'public sector funding' and 'family support' crowd out individual incentives to seek insurance. This paper aims to investigate further the aforementioned motivational crowding-out hypothesis by developing a theoretical model and by drawing on an innovative empirical analysis of representative European survey data containing records on individual expectations of LTC funding sources (including private insurance, social insurance, and the family). The theoretical model predicts that, when informal care is treated as exogenously determined, expectations of both state support and informal care can potentially crowd out LTC insurance expectations, while this is not necessarily the case when informal care is endogenous to insurance, as happens when intra-family moral hazard is integrated in the insurance decision. We find evidence consistent with the presence of family crowding out but no robust evidence of public sector crowding out.

Keywords: family crowding out; long-term care; long-term care insurance; old-age dependency; public sector crowding out.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Europe / epidemiology
  • Family
  • Female
  • Health Services Needs and Demand / economics
  • Health Services Needs and Demand / statistics & numerical data
  • Home Nursing
  • Humans
  • Insurance, Long-Term Care / statistics & numerical data*
  • Long-Term Care / economics
  • Long-Term Care / methods
  • Long-Term Care / statistics & numerical data
  • Male
  • Middle Aged
  • Models, Theoretical
  • Social Security / statistics & numerical data
  • Young Adult