The weak correlation between Medicare and commercial insurance spending is due to negative correlations between each sector's price and the other sector's volume.

Laurence C. Baker, PhD; M. Kate Bundorf, PhD; and Daniel P. Kessler, JD, PhD

This retrospective, descriptive analysis investigated the source of the weak correlation across geographic areas between Medicare and private insurance spending.

- Medicare and private prices are strongly positively correlated, largely because both are keyed off of common costs. Medicare and private volumes also are strongly positively correlated.
- The weak correlation between Medicare and private spending is consistent with these 2 empirical regularities. It is mathematically due to negative correlations between each sector’s price and the other sector’s volume.
- Private prices have important spillover effects on Medicare volume. Future research on the effects of competition should consider this phenomenon.

Area variation in Medicare fee-for-service spending has been extensively documented. However, the extent to which area variation in spending by private insurers mirrors the variation in Medicare is an open empirical issue. Numerous studies have found a significant positive correlation between Medicare and the privately insured in terms of service use, such as hospital admissions, days per admission, or days in the intensive care unit at the end of life.^{1-3} In contrast, the correlation between Medicare and private spending is weakly positive or even negative, depending on the type of service and the specifics of the populations in question.^{1,4}

Understanding why Medicare and private spending are so weakly correlated is important. One hypothesis for the weak correlation in pending is that the prices paid by Medicare are weakly (or even inversely) correlated with the prices paid by private insurers. If true, this could be evidence that Medicare’s prices fail to reflect costs, perhaps due to political manipulation of reimbursement rates, or that private prices fail to reflect costs, perhaps due to provider market power. These explanations have very different implications for Medicare policy.

Yet no study has tested this hypothesis directly. More generally, no study has offered a unified framework in which to understand the relationships between the components of Medicare and private insurer spending. This study sought to fill this gap. We decomposed Medicare and private insurance spending into 2 components: a price index and price-adjusted spending, which we used as a measure of quantity. We calculated the simple correlations among the components of spending and the correlations between the components, the Medicare wage index, and price-cost markups.

These correlations rejected the hypothesis that Medicare’s prices are weakly or inversely correlated with those of private insurance. Rather, they are strongly positively correlated, largely reflecting differences in costs (as measured by the wage index) across areas. We provide evidence for an alternative explanation for the strong positive correlation between Medicare and private utilization and a weak correlation between Medicare and private spending.

**DECOMPOSING SPENDING**

We examined how the components of inpatient spending among fee-for-service Medicare beneficiaries are related to the components of spending among a nonelderly, privately insured population. We wrote inpatient spending per enrollee in either Medicare or private coverage from hospital referral region (HRR) i as the product of a price index for HRR i and quantity (price-adjusted spending) per enrollee in i:

Strictly speaking, geographic price indices for enrollees in fee-for-service Medicare and private insurance plans measure different things. Medicare reimbursement rates are determined by a complex nationwide administered price system, so geographic variation in Medicare “prices” necessarily arises out of geographic variation in whatever factors the Medicare formula considers. In contrast, commercial rates depend on numerous unrelated negotiations between insurers and providers, so geographic variation in commercial prices depends on a much less well-specified, broader range of influences.

We adopted the formula used in the Dartmouth Atlas for our Medicare price index.^{5} According to the formula, the Medicare price level in an HRR is equal to the ratio of total spending in the HRR divided by base diagnosis-related group (DRG) payments plus adjusted outlier payments. Thus, the formula captures variation across areas in the wage index and in medical education and disproportionate-share (graduate medical education, indirect medical education, and disproportionate share hospital) payments, given each HRR’s DRG mix and outlier payments. Our private insurance price index is equal to total spending in an HRR divided by what spending would have been if all hospitals in the HRR charged the national average set of DRG prices. This index captures variation across areas in the prices that insurers pay, given each HRR’s DRG mix.

We also decomposed the price indices into 2 factors: the part that is due to differences in costs that are common to Medicare and the private sector, and a markup, which we allowed to vary across sectors. We used the Medicare wage index to represent common costs. We defined the markup as the residual from a regression of the log of the price index on the log of the Medicare wage index. Thus, for either the Medicare or private insurer price index in HRR i:

where f(.) is defined as exp[a + b*ln(wage index)], and a and b are coefficients from the regression described above. We provide a formal presentation of our decomposition in the**Appendix**.

**DATA**

We used data from several sources. First, we used HRR-level data from the Dartmouth Atlas website for 2007. These data included total Medicare inpatient spending per admission on fee-for-service beneficiaries, total price-adjusted inpatient spending per admission, the number of elderly beneficiaries, and the number of admissions per beneficiary. All of these variables are adjusted by Dartmouth for differences in the age, sex, and racial composition of areas. From this information, we constructed the price index described above. These data cover a total of 27,978,661 individuals in all 306 HRRs.

Second, we used Truven MarketScan Commercial Claims and Encounters data on the nonelderly privately insured from 2007. We limited our sample to individuals with preferred provider organization insurance and imposed the additional selection criteria suggested by Baker and colleagues.^{6} These data include information on the enrollees’ county of residence, which we mapped to HRRs according to the method proposed by Chernew and coauthors.^{7} We adjusted the variables for differences in age and sex across HRRs (we could not adjust for differences in race because the MarketScan data do not include race). These data cover a total of 14,902,153 beneficiaries in all 306 HRRs.

Third, we used data on the Medicare wage index by county from the Centers for Medicare & Medicaid Services website, which we mapped to HRRs using the method described above.

Fourth, we used data on hospital market structure that we constructed from individual-level 2007 data on Medicare patient flows according to the method proposed by Kessler and McClellan^{8}; we provide details in the Appendix. We counted hospitals in a common system as commonly owned.

**Table 1** presents the enrollee-weighted and unweighted means and standard deviations of the variables that we analyzed. These descriptive statistics reflect the well-known properties of Medicare and private insurance area variation. There is considerable variation across areas in Medicare spending, even after adjusting for differences in prices.^{9} Spending per enrollee in the MarketScan data is much lower, reflecting the much lower hospital admissions rate in the nonelderly population. The enrollee-weighted means of the Medicare and MarketScan price indices are both 1 by construction.

Total spending per enrollee in MarketScan is slightly less variable across areas than total spending per enrollee in Medicare; the ratio of the standard deviation to the mean is 0.159, compared with 0.181 in Medicare. But the portion of this variation due to variation in prices is much greater in MarketScan than Medicare; the standard deviation of the MarketScan price index is 0.179 compared with 0.129 in Medicare, which is consistent with previous work.^{10} This is reflected in the fact that the standard deviation across areas of the price markup over the wage index is 3 times greater in MarketScan.

**ANALYSIS**

**Table 2 **presents the correlation between Medicare and MarketScan total spending, quantities, and prices (*P* values for the null hypothesis of zero correlation are in parentheses). Total spending is weakly positively correlated (r = 0.1135, *P* = .0473), but quantity, as measured by price-adjusted spending, is very strongly positively correlated (r = 0.6467, *P* <.0001). This finding is consistent with the previous literature, which finds strong positive correlations between the rates of use of particular services in the elderly and nonelderly populations. It is also consistent with the hypothesis that physicians develop a single practice style that they use for patients of different ages and insurance statuses. Medicare and MarketScan prices are also strongly positively correlated (r = 0.4476, *P* <.0001). This rejection of the hypothesis that the prices paid by Medicare are weakly (or even inversely) correlated with the prices paid by private insurers suggests that another factor is responsible for the weak correlation between Medicare and MarketScan total spending.

Understanding why Medicare and private spending are so weakly correlated is important. One hypothesis for the weak correlation in pending is that the prices paid by Medicare are weakly (or even inversely) correlated with the prices paid by private insurers. If true, this could be evidence that Medicare’s prices fail to reflect costs, perhaps due to political manipulation of reimbursement rates, or that private prices fail to reflect costs, perhaps due to provider market power. These explanations have very different implications for Medicare policy.

Yet no study has tested this hypothesis directly. More generally, no study has offered a unified framework in which to understand the relationships between the components of Medicare and private insurer spending. This study sought to fill this gap. We decomposed Medicare and private insurance spending into 2 components: a price index and price-adjusted spending, which we used as a measure of quantity. We calculated the simple correlations among the components of spending and the correlations between the components, the Medicare wage index, and price-cost markups.

These correlations rejected the hypothesis that Medicare’s prices are weakly or inversely correlated with those of private insurance. Rather, they are strongly positively correlated, largely reflecting differences in costs (as measured by the wage index) across areas. We provide evidence for an alternative explanation for the strong positive correlation between Medicare and private utilization and a weak correlation between Medicare and private spending.

We examined how the components of inpatient spending among fee-for-service Medicare beneficiaries are related to the components of spending among a nonelderly, privately insured population. We wrote inpatient spending per enrollee in either Medicare or private coverage from hospital referral region (HRR) i as the product of a price index for HRR i and quantity (price-adjusted spending) per enrollee in i:

Spending per enrollee in HRR = price index

in HRR × quantity per enrollee in HRR

in HRR × quantity per enrollee in HRR

Strictly speaking, geographic price indices for enrollees in fee-for-service Medicare and private insurance plans measure different things. Medicare reimbursement rates are determined by a complex nationwide administered price system, so geographic variation in Medicare “prices” necessarily arises out of geographic variation in whatever factors the Medicare formula considers. In contrast, commercial rates depend on numerous unrelated negotiations between insurers and providers, so geographic variation in commercial prices depends on a much less well-specified, broader range of influences.

We adopted the formula used in the Dartmouth Atlas for our Medicare price index.

We also decomposed the price indices into 2 factors: the part that is due to differences in costs that are common to Medicare and the private sector, and a markup, which we allowed to vary across sectors. We used the Medicare wage index to represent common costs. We defined the markup as the residual from a regression of the log of the price index on the log of the Medicare wage index. Thus, for either the Medicare or private insurer price index in HRR i:

Price index in HRR = f(Medicare wage index in HRR)

× markup in HRR,

× markup in HRR,

where f(.) is defined as exp[a + b*ln(wage index)], and a and b are coefficients from the regression described above. We provide a formal presentation of our decomposition in the

We used data from several sources. First, we used HRR-level data from the Dartmouth Atlas website for 2007. These data included total Medicare inpatient spending per admission on fee-for-service beneficiaries, total price-adjusted inpatient spending per admission, the number of elderly beneficiaries, and the number of admissions per beneficiary. All of these variables are adjusted by Dartmouth for differences in the age, sex, and racial composition of areas. From this information, we constructed the price index described above. These data cover a total of 27,978,661 individuals in all 306 HRRs.

Second, we used Truven MarketScan Commercial Claims and Encounters data on the nonelderly privately insured from 2007. We limited our sample to individuals with preferred provider organization insurance and imposed the additional selection criteria suggested by Baker and colleagues.

Third, we used data on the Medicare wage index by county from the Centers for Medicare & Medicaid Services website, which we mapped to HRRs using the method described above.

Fourth, we used data on hospital market structure that we constructed from individual-level 2007 data on Medicare patient flows according to the method proposed by Kessler and McClellan

Total spending per enrollee in MarketScan is slightly less variable across areas than total spending per enrollee in Medicare; the ratio of the standard deviation to the mean is 0.159, compared with 0.181 in Medicare. But the portion of this variation due to variation in prices is much greater in MarketScan than Medicare; the standard deviation of the MarketScan price index is 0.179 compared with 0.129 in Medicare, which is consistent with previous work.

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