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
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?
In this study, we explain the source of this apparent anomaly. We decomposed Medicare and private insurance spending into 2 components: a price index and price-adjusted spending, which we use as a measure of quantity or volume. We showed that the weak correlation in overall spending is not due to weak or inverse correlation in Medicare and private prices. Not surprisingly, Medicare and private prices are strongly positively correlated across areas, largely because both are keyed off common costs (as measured by the Medicare wage index). Instead, the weak correlation is due to the negative correlation between each sector’s price index and the other’s volume.
We documented 2 channels through which this negative cross-correlation might occur. First, increases in common costs are associated with increases in both Medicare and private insurance prices, and increases in private prices are associated with decreases in private volumes. Second, increases in private insurance markups are associated with decreases in Medicare volumes. Previous research on the sources of Medicare variation has focused on beneficiary health, socioeconomic status, and preferences. Our results point to another possibility: prices in the private, under-age-65 insurance market. In analyses not reported in the tables, we showed that hospital market concentration is a likely source of this second effect: the correlation between private markups and the hospital HHI is 0.3797 (P <.0001).
To our knowledge, ours is the first study to identify this effect. The literature on the spillover effects of prices on utilization focuses on the impact of changes in public prices on private quantities rather than the impact of private prices on public quantities.11 We provide empirical evidence for the hypothesis proposed by Chernew and colleagues: that providers respond to the declining profitability of private patients by reducing the share of time and resources they devote to them compared with public patients.12
Our analysis has significant limitations. It does not identify the causal relationships that are at the root of the negative association between one sector’s prices and the other’s volume. For example, we cannot say whether high private prices themselves cause low Medicare volumes, or whether some underlying third factor (like common costs or hospital market concentration) causes both. For purposes of policy, distinguishing between these alternatives is important. To do this would require specification of a formal model of the process through which prices and volumes are determined, which was beyond the scope of this study. In addition, our analysis was limited to inpatient acute care hospital spending. Results for outpatient services, prescription drugs, and postacute care may differ, thus complicating the explanations that we offer for the weak correlation in overall spending. Finally, our study design had only minimal independent variation in public prices. Therefore, the fact that we did not find significant spillovers from public prices to private volumes cannot be interpreted as evidence of the absence of such an effect; understanding the relative importance of private-to-public and public-to-private spillovers is an important topic for future work.
Nonetheless, our work shows that the weak correlation between spending in the 2 sectors does not, by itself, imply anything about the processes by which prices are determined, or about the relative efficiency in one sector versus the other. However, our finding that hospital market concentration is strongly positively correlated with private payer markups supports the concern voiced by other investigators that private sector purchasers are more vulnerable to provider market power.1 Future research on the policy implications of area variations should take more careful account of this issue.
Author Affiliations: Frome Stanford University (LCB, KB, DPK), Stanford, CA.
Funding Source: None
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (LCB, KB, DPK); acquisition of data (LCB, KB, DPK); analysis and interpretation of data (LCB, KB, DPK); drafting of the manuscript (LCB, KB, DPK); critical revision of the manuscript for important intellectual content (LCB, KB, DPK); statistical analysis (LCB, KB, DPK); obtaining funding (LCB, KB), administrative, technical, or logistic support (LCB, KB, DPK); and supervision (LCB, KB, DPK).
Address correspondence to: Dr Daniel P. Kessler, Stanford University Law, GSB, and Hoover Institution, 434 Galvez Mall, Stanford, CA 94305. E-mail: email@example.com.
1. Chernew ME, Sabik LM, Chandra A, Gibson TB, Newhouse JP. Geographic correlation between large-firm commercial spending and Medicare spending. Am J Manag Care. 2010;16(2):131-138.
2. Baker LC, Fisher ES, Wennberg JE. Variations in hospital resource use for Medicare and privately insured populations in California. Health Aff (Millwood). 2008;27(2):w123-w134.
3. Stiefel M, Feigenbaum P, Fisher ES. The Dartmouth Atlas applied to Kaiser Permanente: analysis of variation in care at the end of life. Perm J. 2008;12(1):4-9.
4. Franzini L, Mikhail OI, Skinner JS. McAllen and El Paso revisited: Medicare variations not always reflected in the under-sixty-five population. Health Aff (Millwood). 2010;29(12):2302-2309.
5. Gottlieb DJ, Zhou W, Song Y, Andrews KG, Skinner J, Sutherland J. Technical Report: A Standardized Method for Adjusting Medicare Expenditures for Regional Differences in Prices. The Dartmouth Institute for Health Policy and Clinical Practice, Center for Health Policy Research, Dartmouth College. http://www.dartmouthatlas.org/downloads/papers/std_prc_tech_report.pdf. Published January 2010. Accessed
6. Baker L, Bundorf M, Royalty A. Private insurers payments for routine physician office visits vary substantially across the United States. Health Aff (Millwood). 2013;32(9):1583-1590.
7. Chernew ME, Sabik L, Chandra A, Newhouse JP. Would having more primary care doctors cut health spending growth? Health Aff (Millwood). 2009;28(3):1327-1335.
8. Kessler DP, McClellan MB. Is hospital competition socially wasteful? Q J Economics. 2000;115(2):577-615.
9. Gottlieb DJ, Zhou W, Song Y, Andrews KG, Skinner JS, Sutherland JM. Prices don’t drive regional Medicare spending variations. Health Aff (Millwood). 2010;29(3):537-543.
10. Philipson T, Seabury S, Lockwood LM, Goldman D, Lakdawalla D. Geographic variation in health care: the role of private markets. Brookings Papers on Economic Activity. Spring 2010:325-355.
11. Bohrnstedt GW, Goldberger AS. On the exact covariance of products of random variables. J Am Stat Assoc. 1969;64(328):1439-1442.
12. Chernew M, Baicker K, Martin C. Spillovers in Health Care Markets: Implications for Current Law Projections. http://www.cms.gov/ Research-Statistics_Data-and-Systems/Statistics-Trends-and-Reports/ReportsTrustFunds/downloads/spillovereffects.pdf. Published April 2010. Accessed June 2, 2013.
13. Skinner J, Fisher E, Wennberg J. The efficiency of Medicare. In: Wise D, ed. Analyses in the Economics of Aging. Chicago, IL: University of Chicago Press; 2005:129-160.