Outside Instruments for Contribution Rate and Non-Rate Attributes

Attributes

Likely candidates for the instruments for the contribution rate or non-rate attributes are variables that are either directly or indirectly related to the cost or revenue side of the individual funds and the health status or economic well being of the population. The fact that the risk structure adjustment negates most of the revenue per insured variation, makes it harder to find revenue related variables, but not necessarily impossible.

A major problem is point 3) above, because fund specific cost data exist only for a very limited number of funds and this cost data are hard to interpret, because of the risk structure adjustment.121 Therefore all cost related instruments have to be indirectly related to a fund’s cost, thus come from other (non-fund) sources and are therefore aggregated at a higher geographic level122 than the individual fund.

6.8.1.1

The Cost Structure of the Funds

121

For a limited number of funds annual reports, which often include cost data broken down into several categories, are available, but an attempt in 2003/2004 to compile a sufficiently extensive database of the costs data failed due to the reluctance of too many funds to provide these reports.

122 _{The geographic aggregation levels are city, county, district (usually five to 20 counties, some states are}

comprised of one district only), region (applies only in North Rhine-Westphalia, which is the only state in which a fund can operate only in one half of the state) and state.

• Administrative costs (about 5% of total) that incur predominantly at the site of the fund’s headquarters, but for funds with an extensive branch level also wherever the branches are located.

• Ambulatory care costs (about 26% of total) are negotiated between each fund and the physician’s organization at the district level of the headquarters. It is basically a capitation fee that each fund negotiates for its members.

• Stationary care costs (about 35% of total): Hospitals charge the funds a mix of per diem and case-based lump sums. Major investments (construction, etc.) are usually financed with public funds at the state level while the sickness funds generally pay for the operating costs of the hospitals. Each hospital negotiates the different reimbursement rates with the head organizations of the sickness funds. Therefore geographic variation exists on the cost side.

• Other costs include pharmaceuticals, sick pay and other. Sick pay is a percentage of the income and should thus be correlated with the wage level of the location of the member. Prices for pharmaceuticals are uniform across Germany.

6.8.1.2

The Selected Instruments

The data for the instruments come from a number of sources. The population and area of the cities where a fund is headquartered were derived from the German Wikipedia site.123 All socio-economic district and state data as well as hospital statistics were obtained from the

Statistische Bundesamt or Eurostat.124 The smoking habits and body-mass-index numbers are part of the 2003 Mikrozensus.125

Per capita GDP is available at the county level for all years. It can be interpreted as a rough measure of per capita income, which should be correlated with pay roll cost for the funds’ employees and possibly with the capitation fee for ambulatory care. Therefore per capita GDP should be positively correlated with cost and also the contribution rate. The problem with per capita GDP is that it greatly overstates the per capita income in the bigger cities that employ a lot of commuters and understates the per capita income of the surrounding counties. Unfortunately GNP is not available at the disaggregated levels. Fortunately very few sickness funds seem to be headquartered in suburbs – the vast majority are headquartered either in the big cities or in smaller centers so any bias is at least in the same direction for most funds. Per capita disposable income should be a superior measure, but is only available at the district level for all years. Also these indicators of the population’s economic well being should be positively correlated with the fund’s non-price attributes.

One problem with all of these variables is that they are also positively correlated with the fund’s revenue side and thus negatively correlated with the contribution rate, because the RSA negates only 92% of the inter fund variation of the financial base from which funds receive their revenue. Whether the two effects are canceling each other or one dominates the other is a priori not clear.

Population and population density for the headquarters are available at the city and county level for 2005 only. There is likely a correlation between wage level and city population, especially because in Germany the suburbs are more likely incorporated than in

124 _{See Appendix A} 125

the U.S. Furthermore the population density of the city is likely correlated with the real estate prices which need to be paid for the headquarter unless it is owned by the fund. The problem with population numbers is that more populated cities tend to also have a higher population density and at the same time represent more potential members close to the fund headquarter, which in most cases also serves as a branch. Thus there could be a correlation between the instrument and the membership. Data for whether a county consisted of a single city are also available, which would give the county a higher population density by not including the surrounding less populated areas.

Hospital beds per capita and hospital admission rates are available at the district level for all years. The funds within a region finance the variable cost of the hospitals and thus either one of these variables should be positively correlated with hospital care cost and thus the contribution rate.

Smoking rates and BMI (body mass index) data exist at the state level for 2003. These variables are positively correlated with morbidity rates, and thus also with health care cost and thus the contribution rate. Also these instruments should be correlated with the non- price attributes, because members with a lower health status are more likely to demand broader services.

The cost and revenue side of a fund is obviously determined not only by the cost and revenue structure in the city/county/region/state where the fund is headquartered but in the entire geographic market of its operation. Therefore markets of operation-based instruments are constructed from variables that are available at the state level. The state’s population serves as weights when computing the averages.126

An additional benefit of these markets of operation-based instruments is that using them adds variation to the predicted funds’ contribution rates. If the location of the headquarters were the only determinant of the instruments, all funds that are headquartered in the same city would have the same predicted contribution rate. Table 14 shows all instruments, broken down by geographic aggregation level and whether they are computed for the region of the fund’s headquarters or the entire market of operation.