Accessible Unlicensed Requires Authentication Published by De Gruyter August 28, 2013

Do Local Governments Respond to (Perverse) Financial Incentives in Long-Term Care Funding Schemes?

Henning Øien

Abstract

A highlighted issue in long-term care (LTC) financing is the presence of unfortunate incentives in financing schemes. For instance, in Norway, a high share of high-income recipients provides financial incentives to the local governments (the agencies in charge of the LTC system) to increase reliance on nursing home care relative to community housing and home-based care. This article examines the effects of the Norwegian LTC funding system on the composition of LTC services at the local government level. I use a cross-section from 2009 of 391 local governments to estimate a fractional probit model using quasi-maximum likelihood estimation. Controlling for need and geographical variations in care costs, I find that the share of “rich” elderly has a significant association with three measures of the volume of nursing home care relative to home-based care.

Funding statement: Funding: This research was financially supported by the Research Council of Norway: Project 187986/V50 “Studies in Quality and Cost of Care for the Elderly”.

FUNDING

This research was financially supported by the Research Council of Norway: Project 187986/V50 “Studies in Quality and Cost of Care for the Elderly”.

ACKNOWLEDGEMENTS

The author would like to thank two anonymous reviewers and participants at the CESifo Venice summer institute 2012 workshop on The economics of long-term care. I would like to thank Tor Iversen, Martin Karlsson, Geir Godager, Tarjei Havnes and Andreas Kotsadam for very helpful suggestions.

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  1. 1

    Formal long-term services are almost exclusively publicly provided and funded. LTC insurance is nonexistent, so taxes and out-of-pocket payments are exhaustive as sources for financing. Private provision of home-based services is limited to a few local governments. Except for the capital of Oslo, for-profit nursing homes are nonexistent (Karlsson, Iversen, and Øien 2012).

  2. 2

    For the past 30 years, all local governments have applied the maximum allowable tax rates (Borge 2010). Taxes accounted for 40% of revenue in 2011. Grants accounted for 41%, in which 36% were block grants. The allocation mechanism for block grants is designed to be dependent on structural factors outside of local governments’ direct control (Karlsson, Iversen, and Øien 2012). User fees account for 15%, and the rest is interest and other revenue.

  3. 3

    Financial incentives may distort the assignment decisions of bureaucrats and healthcare workers if they derive benefits from increasing the earnings of the LTC sector. Because I do not have individual data, I cannot analyze this issue empirically here.

  4. 4

    z captures services such as primary education and primary health care and may benefit the and types; for instance, if there are externalities in education and if there are other services that are complements of n and c.

  5. 5

    An equivalent model would be to let the utility be defined as , with U being strictly concave and increasing in both arguments, and . Using only n as an argument captures what we are interested in and makes the derivation of the model shorter and simpler.

  6. 6

    This is a strict assumption, but it greatly simplifies the problem. I relax this assumption in the empirical specification by controlling for need and preferences correlated with wealth.

  7. 7

    There are 429 local governments in Norway. I have dropped 38 local governments from the sample that did not have information on LTC recipients and assume they are missing completely at random.

  8. 8

    If the threshold is set lower, it will include more individuals, for whom it is less expensive to provide home-based care, and we should expect a weaker relationship between the share of individuals above the threshold and the nursing home share. If the threshold is increased, we should expect a stronger relationship because for increasing numbers of local governments, it will include those in which nursing home care is cheaper.

  9. 9

    The model only has continuous explanatory variables, so I will only treat this case here.

  10. 10

    The age composition is the share of the population 60 years and older in the age groups 70–74, 75–79, 80–84, 85–89, 90–94 and 95–100. The variables are denoted shXXXX in the regression model.

  11. 11

    The 15 indicators describes (1) social abilities, (2) cognitive abilities, (3) the ability to perform housework, (4) self-care and (5) the ability to take care of one’s health. Each indicator is given a discrete value between 1 and 5, where 5 is the lowest score. Individuals with an average score above 3 are defined as having a severe dependency level (Øien, Karlsson, and Iversen 2012).

  12. 12

    The index decreases linearly from 1 to 0 in the interval 0–10,000 inhabitants and equals 0 for local governments with more than 10,000 inhabitants. The index captures that the disadvantages of being small decline toward zero at 10,000 inhabitants.

  13. 13

    The survey was conducted by Professor Tor Iversen, Gunn Kristin Tjoflot and myself in February–March 2011.

  14. 14

    Given the sensitivity of the issue and the norm that local public services should be adapted to need, we emphasized that adapting LTC service capacity according to the incentive structure would free resources for other local services. In terms of the model, adapting n according to the incentive structure would free resources for z. The respondent was then asked whether this had been acted upon or considered in capacity decisions.

Published Online: 2013-8-28

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