Per-period co-payments and the demand for health care: evidence from survey and claims data

Health Econ. 2013 Sep;22(9):1111-23. doi: 10.1002/hec.2955. Epub 2013 Jun 17.

Abstract

When health insurance reforms involve non-linear price schedules tied to payment periods (for example, fees levied by quarter or year), the empirical analysis of its effects has to take the within-period time structure of incentives into account. The analysis is further complicated when demand data are obtained from a survey in which the reporting period does not coincide with the payment period. We illustrate these issues using as an example a health care reform in Germany that imposed a per-quarter fee of €10 for doctor visits and additionally set an out-of-pocket maximum. This co-payment structure results in an effective 'spot' price for a doctor visit that decreases over time within each payment period. Taking this variation into account, we find a substantial reform effect-especially so for young adults. Overall, the number of doctor visits decreased by around 9% in the young population. The probability of visiting a physician in any given quarter decreased by around 4 to 8 percentage points.

Keywords: co-payments; health economics; natural experiment; non-linear pricing.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Age Factors
  • Deductibles and Coinsurance / economics
  • Deductibles and Coinsurance / statistics & numerical data*
  • Female
  • Germany
  • Health Care Reform / economics
  • Health Care Reform / organization & administration
  • Health Care Reform / statistics & numerical data*
  • Health Services Needs and Demand / economics
  • Health Services Needs and Demand / organization & administration
  • Health Services Needs and Demand / statistics & numerical data*
  • Humans
  • Insurance Claim Review / economics
  • Insurance Claim Review / statistics & numerical data
  • Insurance, Health / economics
  • Insurance, Health / organization & administration
  • Insurance, Health / statistics & numerical data
  • Male
  • Middle Aged
  • Models, Statistical
  • Sex Factors
  • Time Factors
  • Young Adult