Why Are Medicare and Commercial Insurance Spending Weakly Correlated? | Page 2
Published Online: January 20, 2014
Laurence C. Baker, PhD; M. Kate Bundorf, PhD; and Daniel P. Kessler, JD, PhD
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.
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.
Instead, according to Table 2, the weak correlation between sectors’ total spending is due to the negative correlation between each sector’s price and the other’s quantity. In other words, Medicare volume is low where private prices are high (r = –0.4324), and MarketScan volume is low where Medicare prices are high (r = –0.3748). Mathematically, this is what is causing the weak correlation between Medicare and MarketScan total spending. (If Medicare spending is the product of prices a and quantities b, and MarketScan spending is the product of prices c and quantities d, then Cov(ab, cd) ≈ w1Cov(a,c) + w2Cov(a,d) + w3Cov(b,c) + w4Cov(b,d), where w1...w4 are weights.11) This is consistent with a wide volume of literature that finds that price reductions in Medicare induce increases in private volume.12
Table 3 investigates this result further. It presents the correlations of the sectors’ prices and quantities with the Medicare wage index and each sector’s markup. According to the first column, the wage index is strongly positively correlated with both sectors’ prices, although more strongly with Medicare prices (r = 0.9001) than with private prices (r = 0.5262). It is also negatively correlated with both Medicare(r = –0.2548) and MarketScan (r = –0.4123) quantities. For example, the 3 HRRs with the lowest price-adjusted Medicare spending (Bend, Oregon, at $2644; San Luis Obispo, California, at $2677; and Santa Barbara, California, at $2763) all have relatively high wage indices (1.04, 1.12, and 1.12, respectively); by comparison, the 3 HRRs with the highest adjusted Medicare spending (Alexandria, Louisiana, at $5666; McAllen, Texas, at $5836; and Monroe, Louisiana, at $5925) all have relatively low wage indices (0.78, 0.87, and 0.79, respectively). If we assume the wage index measures costs common to both sectors, when the wage index falls, both Medicare and private patients become absolutely more profitable, but Medicare patients become relatively less profitable than private patients, because the correlation between the wage index and the Medicare price is higher. This explains why a unit decrease in the wage index leads to increases in both Medicare and private quantities, with a greater increase in the private quantity.
According to the second column, the MarketScan markup is strongly positively correlated with the MarketScan price (r = 0.8739), and negatively correlated with both the Medicare (r = –0.3576) and the MarketScan (r = –0.2958) quantities. When the MarketScan markup rises, private quantity falls, and Medicare quantity falls even more. The Figure translates the negative correlation between MarketScan markup and Medicare quantity into dollar terms. The HRRs with the lowest MarketScan markups have average price-adjusted Medicare spending of $4426, whereas those with the highest markups have Medicare spending of $3808—a difference of almost 1 standard deviation. This is consistent with a model in which private demand slopes downward and providers respond to the declining profitability of private patients by increasing the services they deliver to Medicare patients.
According to the third column of Table 3, the Medicare markup is strongly positively correlated with the Medicare price (r = 0.4431, P <.0001), but roughly uncorrelated with the MarketScan price and the Medicare or MarketScan quantity. This is likely due to the minimal amount of variation in the Medicare markup; we discuss alternative explanations for this finding in the Discussion section.
Area variation in Medicare spending has long been viewed as evidence of inefficiency in the program.13 At the same time, health services researchers have observed that area variation in private insurance spending is only weakly positively (or even negatively) correlated with Medicare variations. Given that physicians likely have a single practice style that they use for both elderly and nonelderly patients, this presents a puzzle: if utilization is correlated across sectors, why isn’t spending?
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