title: Redistributional consequences of community rating.
abstract: OBJECTIVE
  To predict the geographical effects of community rating of health insurance premiums on the amount individuals pay for insurance.
  
  
  METHODS
  We estimate premiums and health expenditures for a 5 percent sample of Californians from the 1990 U.S. Census (the Public Use Microdata Sample) and use data from Blue Cross of California to adjust for regional price differences in services.
  
  
  METHODS
  We use an episodic health simulation model to estimate health expenditures for 975,074 Californians. Because the simulations do not reflect expenditure differences due to price variation in cost of services, we adjust these data for relative price differences by county. This leaves us with a sample of Californians for whom we have estimated health expenditures. We then compute average expenditures within areas of different sizes (all California, two regions, within counties) to estimate community-rated premiums. We then compare these premiums with actual expenditures on a county-by-county basis.
  
  
  RESULTS
  With a single California-wide premium, rural residents pay premiums that exceed their use of care, while urban residents pay premiums that are less than their use of care. These transfers are substantial. Dividing California into regional risk pools at the county level still results in poorer communities providing substantial subsidies to their more wealthy counterparts.
  
  
  CONCLUSIONS
  Mandated community rating of premiums in a heterogeneous state such as California results in large unintended transfers of wealth from poorer, rural communities to urban, wealthier communities. Allowing premiums to vary with the regional cost of medical care would eliminate some of the transfers without sacrificing the benefits of community rating. Subsidies to low-income families could also effectively mitigate this redistribution. UTILITY: This article points out some potentially regressive consequences of geographic community rating and suggests ways to mitigate them.
authors.affiliation: RAND, Santa Monica, CA 90407-2138, USA.
authors.forename: D P
authors.initials: DP
authors.lastname: Goldman
authors.forename: A
authors.initials: A
authors.lastname: Leibowitz
authors.forename: J L
authors.initials: JL
authors.lastname: Buchanan
authors.forename: J
authors.initials: J
authors.lastname: Keesey
mesh_terms: D002140:California; D002391:Catchment Area, Health / Q000191:economics; D003153:Community Health Services / Q000145:classification / Q000191:economics / Q000706:statistics & numerical data; D005249:Fees and Charges / Q000592:standards; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006801:Humans; D015271:Insurance Pools; D007348:Insurance, Health / Q000191:economics*; D008962:Models, Theoretical; D011907:Rate Setting and Review / Q000379:methods*; D012424:Rural Population; D014505:Urban Population