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Dynamic Price Transmissions among Prices for U.S. Health Care Services: a preliminary analysis

Gregory D. Wozniak* & Ronald A. Babula**

"The slow growth in health spending appears to result more from very low inflation in the rest of the economy rather than from any permanent changes in the health sector. We still need to address the underlying causes that are driving up health care spending faster than the other costs and distorting our health care delivery system." Donna E. Shalala Secretary of Health and Human Services November, 1994

*Director, Outcomes Analytics, American Medical Association (AMA), Chicago, IL, greg.wozniak@ama-assn.org **Associate Professor of Economics and Finance at Keimyung University, 1095 Dalgubeol-daero, Daegu 704-701, Daegu, Republic of Korea, rbabula@kmu.ac.kr. The views expressed here do not necessarily represent those of the AMA or Keimyung University. (filename medprice 06192014)  

 

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Introduction

Since the early 1980s measured medical care prices have risen faster than the Consumer

Price Index (CPI) and the implicit price deflator for Gross Domestic Product (GDP).

Expenditures in most categories of medical care services have also grown faster than GDP

(Jencks and Schieber, 1991). Previous research has also shown that for the period 1970-1990,

nearly one-half of the growth in personal health expenditures was due to economy wide inflation

(Levit, et al., 1992). More recently, hospital spending increased 4.9 percent in 2012 compared to

the 3.5-percent growth in 2011. Physician and clinical services spending increased 4.6 percent in

2012 and 4.1 percent in 2011. The accelerated growth in 2012 was influenced by growth in both

prices and use or intensity of services. As in previous periods, we would expect that growth in

health spending over this period is driven by how prices of medical care behave relative to prices

of other goods and services.

The actuarial methods typically utilized to decompose expenditure changes, however, tell

us little about the long-run relationships among the consumer prices for health care, health

insurance and the other goods and services, and tell us nothing about the dynamic process

through which those sectors interact. Knowledge of the relationships among these sectors, and

how the rates of adjustment in prices differ through time or across medical care commodities

should prove especially useful in evaluating public and private sector "cost containment"

strategies, including policies to review insurance premiums, taxes on very generous (‘Cadillac’)

health insurance plans, and other provisions contained in the 2010 Affordable Care Act (ACA).

The effectiveness of these provisions are dependent on how prices respond to economic

incentives2 and how and how quickly prices respond.

 

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In this study, we use an empirical model to examine how consumer prices for health care

(ie, physician services, hospital services, nursing services, and prescription drugs), health

insurance and other consumer goods have historically adjusted within long run relations.3 The

empirical model is a cointegrated vector autoregression (cointegrated VAR) model. The results

indicate that the price of consumer goods is weakly exogenous, hence influencing the medical

care prices without a feedback response from those prices; and that health insurance premiums

are endogenous to the system of prices, so that premiums both influence prices for medical care

and are influenced by those prices in the system. Policy implications of legislative provisions

related to health insurance coverage and premiums are presented.

The Model

Granger and Newbold (1986) note that economic time series such as those introduced

below for medically-related prices often fail to achieve conditions of weak stationarity (also

known as stationarity and ergodicity) required of valid inference and in some cases unbiased

estimates. Before Engle and Granger (1987), the approach frequently was to first-difference non-

stationary data that were integrated of order-one to achieve stationarity and to avoid

compromised inference. However, such individually non-stationary series sometimes form

stationary linear combinations whereby the group of series moves in a stationary manner through

time. This results when individually non-stationary series are cointegrated and comprise an

error-correcting system (Engle and Granger 1987; Johansen and Juselius 1990). Differencing

such cointegrated series would achieve stationarity, but at the expense of introducing

misspecification bias of the regression estimates from the omission of important long run

components, ie, the cointegrating relationships or stationary linear combinations critical to

explaining the behavior of the system. It is well known that the cointegrated VAR model

 

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permits the retention of levels-based information deleted by differencing, and in the reduced-

rank or stationary form of the correction space described below, thus avoiding, misspecification.

We utilize a monthly cointegrated VAR model of the following six U.S. prices (United

States Department of Labor, Bureau of Labor Statistics 2013):

PALLITEMS - general U.S. retail price level denoted by the U.S. consumer price index or CPI, all urban consumers, all items: Labor, BLS series no. CUUR0000SA0.

PPHYSICIAN - price of physician services denoted by the U.S. CPI for physician services: Labor, BLS series no. CUUR0000SEM01.

PPREDRUG - price of prescription drugs denoted by the U.S. CPI for prescription drugs: Labor, BLS series no. CUUR0000SEMF01.

PNURSE - price of nursing and related services denoted by the U.S. CPI for nursing home and related adult day services: Labor, BLS series no. CUUR0000SEMD02.

PHOSPITAL - price of hospital and related services denoted by the U.S. CPI for hospital and related services: Labor, BLS series no. CUUR0000SEMD.

PINSURANCE – total health insurance premium plus return on the invested part of the premium) denoted by the U.S. producer price index for direct health and medical insurance carriers: Labor, BLS series no. PCU241145241142.1

The monthly data are not seasonally adjusted. Because monthly data on health plan

prices (PINSURANCE) are only available from Labor BLS starting in January, 2003, the data

used are for the 2003:01 – 2013:08 period. All analysis was carried out using RATS, Version

8.2 (Estima 2012) or CATS Version 2 (Dennis, 2006).

Following Juselius (2006), we begin with a traditional levels VAR model that posits each

endogenous variable as a function of k=3 lags2 of itself and of each of the remaining variables

(Sims 1980). Deterministic and trend components are added as the analysis unfolds. The levels

VAR of the above six variables may be compactly re-written as the following unrestricted vector

error correction or VEC model (Johansen and Juselius 1990; Juselius 2006):

Δx(t) = Γ(1)*Δx(t-1) + . . . + Γ(k-1)*Δx(t-k+1) + Π*x(t-1) + ΦD(t) + ε(t) (1)

                                                            1Labor, BLS does not provide or publish price indices for health insurance in its consumer price index data base, such that we chose this series within the producer price index data base. 2 We applied Tiao and Box’s lag selection procedure on the data for the 2003:01 – 2013:08 sample and results suggested a lag structure of k=3. Due to space considerations, the Tiao-Box results are not reported and are available from the authors on request. 

 

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where p = 6, the number of endogenous variables; ε(t) are white noise residuals; delta is the

difference operator; x(t) and x(t-1) are p x 1 vectors of the endogenous variables in current and

lagged levels, respectively; Γ(1), . . ., Γ(k-1) are p x p matrices of short run regression

coefficients; Π is a p x p long run error correction (EC) term to account for endogenous levels;

and ΦD(t) is a set of deterministic variables, including an array of binary (dummy) variables that

will be added to address stationarity and specification issues as the analysis unfolds below. The

EC term is decomposed as follows:

Π = α*β’ (2)

where α is a p x r matrix of adjustment coefficients (r is the number of cointegrating relationships

or the reduced rank of Π discussed below), and β is a p x r vector of cointegrating parameters.

The EC term retains the levels-based and other long run information: linear

combinations of non-differenced and individually I(1) levels variables (under cointegration);

permanent shift binaries to capture more enduring effects of policy/market events (presented

below); and a linear trend. The term [Γ(1)*Δx(t-1) . . .Γ(k-1)Δx(t-k+1), ΦD(t)] collectively

comprises the model’s short run/deterministic component (hereafter denoted short run

component) that includes the permanent shift binaries in differenced form, observation-specific

outlier binaries (introduced below), and seasonal binaries.

Granger and Newbold (1986) note that the potential adverse econometric consequences

of failing to utilize information inherent in the modeled endogenous data’s non-stationarity

elements included compromised inference, spurious regressions, and in some cases biased

estimates. We follow Juselius’ (2006) procedure to capture such non-stationary elements into a

well-fitting unrestricted levels VAR and unrestricted VEC equivalent before exploiting the

 

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system’s cointegration properties. Such analysis on our six series suggested inclusion of a linear

trend (TREND) and various permanent shift dummies or binaries presented below.

We initially included the following permanent shift binaries in levels form in the long run

component and in differenced form in the short run/deterministic component of the model:

ACA_PASSAGE: 1 from 2010:03 to 2013:08 , and 0 otherwise, this binary captures the effects on the modeled price system of the President Obama, March 23, 2010 signing into law of the Affordable Care Act (ACA).

EFFECTS_SEPT23.2010: 1 from 2010:09 to 2013:08, and 0 otherwise, this binary captures impacts of the September 23, 2010 establishment of the ACA’s principal private sector measures that include permission for young people less than age-26 to remain on parental health insurance plans and mandated coverage of certain preventative services (e.g., mammograms and colonoscopies) without charging deductibles and co-payments (among other measures).  

MEDICARE_JAN1.2011: 1 from January, 2011:01 to 2013:08, and 0 otherwise. This binary captures the impacts of major Medicare provisions that became effective on January 1, 2011.

FINALREGS_ MAY19.2011: 1 from 2011:05 to 2013:08, and 0 otherwise; this binary captures the effects of the Obama Administration May 19, 2011 announcement of the final ACA regulations for a program to review rates charged by insurance companies.

RECESS: 1 from 2007:12 to 2009:06, and 0 otherwise, this binary captures the impacts of the U.S. economic recession.

A variable was added and retained based on diagnostic test values presented in Table 1

(Juselius, 2006). All five of the above permanent shift binaries, a trend variable, and selected

short run outlier binaries were included. Further estimation was carried out using this

specification. When a potential outlier was deemed of potentially extraordinary influence, an

appropriately specified binary variable was included in differenced deterministic component in

equation 2 and retained if the battery of diagnostic values suggested improved specification.3

                                                            3 We followed Juselius’ (2006) procedure for the analysis of potentially extraordinary impacts based on the Bonferoni criterion: INVNORMAL[(1.0 – 0.025) (1/T) where INVNORMAL is RATS’ instruction for the inverse of the normal distribution function that returns the variable for the density function of the standard normal distribution (Estima, 2007). The effective sample was T = 125. The absolute standardized residual value was 3.5 or more. Having realized that there were some month-specific events with potentially extraordinary effects with absolute standardized residual values of about 3.0, we opted for a more conservative Bonferoni absolute value criterion of 3.0 rather than 3.5. Observations with absolute standardized residual values of 3.0 or more were thereby considered as potential outliers, and we specified an appropriately defined variable for relevant observations for the sequential estimation procedure noted above. Twelve binaries were ultimately included. Due to space limitation

 

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An adequately specified underlying unrestricted model should generate statistically

normal residuals and achieve reasonable literature-established diagnostic standards. Table 1

provides a battery of diagnostic test values recommended by Juselius and Toro (2005), Juselius

(2006), and Juselius and Franchi (2007) and were generated by our model estimated after the

above-noted efforts at specification enhancement. The trace correlation, a goodness of fit

indicator, increased by about 66% to 0.52. Serial correlation and heteroscedasticity were not

issues at the 1% or 5% significance levels. Doornik-Hansen (D-H) values test the null

hypothesis that the estimated residual behavior approximates multivariate normality. Univariate

D-H values suggest, that for the system and for each endogenous equation, we do not find

sufficient evidence to reject the null of normally behaving estimated residuals. And finally, the

statistically adequate model generated skewness and kurtosis values that fell within established

levels.

Cointegration: Testing for and Imposing an Appropriate Reduced Rank

The endogenous variables are shown below to be non-stationary. Juselius (2006, p. 80)

notes that cointegrated variables are driven by common trends and stationary linear

combinations called cointegrating vectors (CVs). The π-matrix in equation 2 is a p by p (here 6

by 6) matrix equal to the product of two p by r matrices: β of error correction estimates that

under cointegration combine into r < p stationary CVs of the six individually non-stationary price

series and α of adjustment coefficients (beta, alpha estimates, respectively). Under cointegration,

the rank of β’x(t) is reduced despite the non-stationarity of x(t)’s six series.

                                                                                                                                                                                                considerations, we do not report these binaries as they are part of the estimated model’s short run component that is not a focus of this study on long run cointegration relationships. The binaries are available from the authors on request.  

 

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The EC space’s reduced rank has been traditionally based on the widely applied trace

tests developed in part by Johansen and Juselius (1990). However, Juselius and Toro (2005),

Juselius and Franchi (2007), and Juselius (2006) strongly recommend against sole reliance on

such trace test results in determining reduced rank r < p, and in turn the number of cointegrating

vectors that error-correct the system. More specifically, they suggest that the determination of

reduced rank should consider other sources of evidence. We thereby chose a reduced rank, r,

based on three sources of relevant evidence: the traditionally consulted nested trace tests;

patterns of characteristic roots in companion matrices generated under relevant r-values; and an

examination of the plotted behavior of CVs potentially included in the error correction space

(Juselius, 2006). These three sources of evidence suggest that an appropriate r should be 1 or 2,

with most evidence collectively suggesting that r is more likely 1 than 2.

A strict reading of the nested trace test results (Table 2) suggests that r = 2. Evidence at

the 5% percent significance level is sufficient to reject the first two null hypotheses, and

insufficient to reject the third null (r ≤ p), which in turn, given the tests’ nested nature, suggests

that r =2.

The plots of cointegrating relations 1 and 2, the second source of evidence, are

respectively provided in Figures 1 and 2. We follow Juselius (2006) and present two versions of

each CV: the BETA*x(t) plots are for the model that includes short run effects, and BETA*R(t)

are CV plots for the model corrected for short run effects, with the latter generally considered the

most reliable. Behavior of a CV that should be included in the EC space exhibits stationary

behavior. CV1 behavior in Figure 1 appears stationary for the most part: the plot frequently and

repeatedly reverts to the zero mean; exhibits relatively constant durations of variation; and, aside

from 2007, does not exhibit prolonged episodes of cycling. Figure 2, on the other hand, suggests

 

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that behavior of CV2 is relatively more non-stationary than the behavior CV1: while CV2 does

mean-revert to a degree, variation levels are more volatile and there are a number of enduring

episodes of prolonged cycling – particularly during 2007-2010 followed by supra-zero episode

during 2011-2012. These two plots suggest that CV1 is more stationary than CV2 and that CV1

should be included in, and CV2 excluded from, the EC space.

The third source of rank-relevant evidence lies in the patterns of characteristic roots of

the companion matrix generated under appropriate r-assumptions. If the chosen r is appropriate,

then the companion matrix under r should generate (p-r) unit roots, with the next “(p-r+1)st

being substantially below unity. Under r=2, the companion matrix had (p-r) = 4 unit roots, with

the (p-r+1)st or 5th root of 0.93 approaching unity. Following Juselius (2006), we deemed this as

evidence that r=2 should be reduced to r=1.

Based on the three sources of evidence recommended by the cited literature, we conclude

that the reduced rank of the error correction space is r=1 with a single error-correcting CV.

Hypothesis Tests and Inference on the Cointegrating Relationship

We begin with the unrestricted CV that emerged after imposition of reduced rank r=1 on

the cointegration space. Due to space considerations, we do not report this initially emergent

unrestricted CV, but rather report the finally restricted CV below. We tested a series of

hypothesis tests on the CV based on economic theory, econometric theory, and market

knowledge. Such hypothesis tests take the form:

β = H*φ (3)

 

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Above, β is a p1 by r vector of coefficients included in the cointegration space,4 and H is a p1 by

s design matrix, with s being the number of unrestricted or free beta coefficients. The φ is an s by

r matrix of unrestricted beta coefficients. The hypothesis test value or statistic is:

2ln(Q) = T*∑ [(1-λi*) / (1-λi)] for i= 1 (=r) (4)

Asterisked (non-asterisked) eigenvalues (λi , i = 1) are generated with (without) the tested

restrictions imposed.

We followed the recommendations of Juselius and Toro (2005), Juselius(2006), and

Juselius and Franchi (2007): instead of a univariate test such as the Dickey-Fuller or Phillips-

Perron unit root test, we conducted as the first set of hypothesis tests six system-based and rank-

dependent unit root tests on the endogenous prices. The six unit root tests suggested that all

endogenous variables are nonstationary in logged levels.5

The second group of five hypotheses arose from consultation of economic and

econometric theory as well as market knowledge and expertise. They were tested and accepted

by the data using equations 3 and 4 and were imposed on the EC space that was then re-

estimated using reduced rank estimator (Johansen and Juselius, 1990).6

                                                            4The p1 equals 12: it is the sum of p=6 endogenous variables, five permanent shift binary variables, and a trend that were restricted to lie in the cointegration space. 5 More specifically, Equation 9 is re-written as βc = [b,φ]. Let p1 be the new dimension of 12 to reflect the six endogenous variables and the six deterministic variables restricted to lie in the EC space. The βc is a p1 by r or 11 by 1 matrix with one of the variable’s levels restricted to a unit vector and b is a p1 by 1 or 12 by 1 vector with a unity value corresponding to the variable the stationarity of which is being tested. The φ is a p1 by r-1 matrix that vanishes under r=1 since (r-1) is zero. Given the rank of 1, the test values and parenthetical p-values for the four stationarity tests are as follows with the null of stationarity rejected for p values below 0.05: 29.65 (0.0000058) for CPIALL; 19.82 (0.00054) for PPHYSCNS; 23.03 (0.00012) for PHOSPIT; 23.63 (0.000095) for PPREDRUG; 18.72 (0.00089) for PNURSE; and 26.28 (0.000028) for PINSURANCE.  6The Chi-square test value (5 degrees of freedom) using the Bartlett small sample correction programmed in CATS2 by Dennis (2006) was 2.66 and had a p-value of 0.753. Thus evidence at the 5% significance level was insufficient to reject, and hence strongly accepted, the five restrictions. The five restrictions that were tested, accepted, and imposed on the finally estimated model included zero restrictions on the beta coefficients of PPREDRUG, MEDICARE_JAN1.2011, EFFECTS_SEPT23_2010, ACA_PASSAGE, and RECESS.

 

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Among the accepted restrictions on the finally estimated model was a zero restriction on

β(PPREDRUG). This suggests that movements in prescription drug prices do not appear to

influence the other prices in the long run, although PPREDRUG influences are still present in the

short run component of the model. The combination of Medicaid price controls on prescriptions,

private insurers’ drug formularies requiring the use of generics, and the $5.00 30-day and $15.00

90-day generics may account for the lack of a long run relationship among PPREDRUG and the

other modeled medical care prices. One example of the dominance of generics is from 2009 to

2013 when nearly 95% of antihypertension medications were generics. The zero restriction of

the shift binaries related to the ACA may be due to the health care systems failure to fully

integrate their influences since the provisions came into effect.

The Cointegrating Relationship: Medical Price Transmission Mechanism

The restricted cointegrating relationship which is a reduced form price transmission

among the six modeled prices is presented in Equation 5 with the pseudo-t values placed below

the beta estimates. The absolute critical value to test the hypothesis that a β-estimate in equation

1 (or α-estimate discussed below) is statistically zero is 2.60 at the 5% significance level

(Juselius 2006).7

PPHYSICIAN = -0.57*PALLITEMS + 0.54*PHOSPITAL - 1.39*PNURSE (-5.21) (5.58) (-7.39) +1.83*PINSURANCE – 0.0099*FINALREGS_MAY19.2011 (6.77) (-2.83) +0.0039*TREND (6.85) (5)

                                                            7 Juselius (2006) discusses that these pseudo-t values are not distributed in the same way as are Student t-values, and as a result, the pseudo-t critical values differ from those of the Student t-values.

 

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Endogeneity versus Weak Endogeneity

We adopt the following interpretation concerning endogenous variables of a cointegrated

system (Juselius, 2006):

A fully endogenous player in the system whereby it influences and is influenced by (or adjusts to) the error correction mechanism, as reflected by statistically significant beta and adjustment or alpha coefficient estimates, or,

Displays the property of “no levels feedback,” or alternatively, weak exogeneity, where

the variable injects a “one-way” influence into the EC mechanism without itself adjusting to or being affected by the EC mechanism. Weak exogeneity is reflected with statistically non-zero beta coefficients while its α-estimates are not statistically significant.

The adjustment or α-coefficient estimates with their pseudo-t variables in parentheses are

as follows: -0.053 (-1.14) for PALLITEMS; -0.105 (-2.78) for PPHYSICIAN; +0.0034 (0.063)

for PHOSPITAL; -0.051 (-1.27) for PDRUG; -0.078 (-2.33) for PNURSE; and 0.0119 (7.40) for

PINSURANCE. The results presented in equation 5, the above α-estimates and pseudo-t values

provide evidence regarding the endogeneity of the variables in the cointegrated system.

The price for all consumer goods is weakly exogenous as reflected by the PALLITEMS

β-estimate’s significant pseudo-t value of -5.21 and its insignificant α-estimate that the pseudo-t

of -1.14 suggests is far from significant at the 5% level. So the economy-wide prices appear to

influence the medical related prices, physician services prices in particular, without responding

to the prices in the health care sectors. This would support Donna Shalala's claim regarding the

relationship between health care expenditures and inflation, at least in terms of health care prices

and inflation, some 20 years later.

Hospital services price appears weakly exogenous insofar as its beta t-value of 5.58 is

strongly significant, and its α-estimate is insignificant (pseudo-t = 0.063). The price of hospital

services influences PPHYSICIAN and the other medical prices in a one-way manner without

 

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feedback from the other five variables in the CV or EC mechanism. The Medicare and Medicaid

policies and rate-setting for hospitals currently in place in a number of states (Ginsburg and

Pawlson, 2014) may be a factor impacting this long-run price transition relationship.

Health premiums and nursing services prices appear to be endogenous to the system (and

not weakly exogenous) insofar as both prices have significant beta and alpha estimates.8 Health

insurance premiums appear to influence and in turn respond to PALLITEMS and the five other

medical prices in the modeled system. Variation in prices of health care services charged by

health care providers, particularly physicians and hospitals, are well documented. In the private

insurance market providers are constrained by negotiated payment rates, determined as part of

annual contracting. On the public payer side, Medicare and state Medicaid policies set a fixed

price per admission, per patient day, or by DRG. The recent Medicare move to Value-Based

Payment for hospital payment, bundled payment, and the soon to be introduced Value-Based

Modifier for Medicare physician payments could strengthen this exogeneity of health insurance

premiums.

The increased use of tiered networks by private insurers is intended to provide incentives

for patients to select lower price (cost, more efficient) providers, and to steer them away from

higher cost providers. The lower tier providers generally also have lower out of pocket costs (ie,

co-pays, deductibles) to patients. Little evidence exists, however, that these lower cost providers

are also the high quality providers.

The insurance premium response to medical care services prices might also be capturing

the feedback from increased demand and expenditures for physician and hospital services.

Long-run increases in insurance premiums are consistent with the use of experience rating;

                                                            8PINSURANCE’s beta and alpha estimates are both strongly significant with pseudo-t values of 6.77 and 7.40, respectively. PNURSE has a significant pseudo-t value of -7.69 for its beta and a marginally significant pseudo t value of 2.32 on its alpha estimate, which led us to treat PNURSE as a fully endogenous variable.

 

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insurers may charge higher or lower premiums at renewal based on claims history or changes in

health status. Greater than expected health care costs of insurance claims filled in previous years

could bring about premium increases under experience rating.

The Dynamic Price Transmission Mechanism

The CV appears to be a reduced form long run price transmission relationship among the

system of six prices that includes four medical-related prices plus the CPI. The first observation

is the inverse response in prices of physician services and the less than proportional change in the

prices of goods in the all-items consumer price index. The estimate of β(PPHYSICAN) of -0.57

suggests that on average, each percentage price rise in CPI elicits a -0.57 percent decline in

prices of physicians’ services (Equation 5). The reduced form transmission mechanism may be

picking up demand influences, suggesting that as the general U.S. price level reflected by the

CPI rises and real income levels ease, demand for physician office visits, and other physician

services and procedures may fall, suggesting that prices of physician services serve as a brake on

inflation. Inclusion of a quantity variable for physician services (unavailable at this writing)

would likely provide more of a structurally interpretable demand-side or supply-side

cointegrating relationship and would help clarify this murky relationship between PPHYSICIAN

and PALLITEMS, and would be a well-placed focus for future research.

The estimate of β(PHOSPITAL) of +0.54 suggests that generally, relative increases in

physician services has been far more moderate than increases in prices of hospital services

generally, with each percentage rise in hospital services prices having elicited, on average, a far

less proportional 0.54% rise in prices of physicians services. The interpretation of this reduced

form result is that hospital services and physician services are demand substitutes, such that

 

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escalating prices for hospital services will generate increases in utilization of services provided

by physicians. This reflects the historical and ongoing substitution of services provided in the

inpatient setting with services provided in physician ambulatory practices and other outpatient

facilities.

The β(PNURSE) of -1.39 suggests that physician and nursing services are treated by

demanders as complements. With the aging of the population there has been an increase in

demand for post-acute care (ie, post-operative care and rehab, with cardiovascular care being a

particularly high growth sector). As a result the demand for nursing care services and physician

services have increased together.

The β(PINSURANCE) of 1.83 suggests that on average, each percentage rise in health

insurance elicits a 1.8% rise in the price of physician services. This may capture effects of a

preference structure that treats health insurance and physicians’ services as substitutes. Such

could occur if increased health insurance premiums lead to a drop in coverage, and in turn,

reduced utilization of physician services. In addition, self-pay (uninsured) patients pay higher

prices for physician treatment and procedures than patients with coverage because of the

discounts health plans and self-insured employers negotiate with providers. In addition, in many

plan designs higher premiums are accompanied by lower out-of-pocket deductibles or copays,

which increase the demand for physician services. Evidence exists that there is self-selection

into high deductible plans for those with lower utilization of physician services (Schellhorn,

2001).

Since the cointegrated VAR model presented here was estimated in natural logarithms

(logs), interpreting coefficient estimates generated by binary (or dummy) variables follow the

Halvorsen and Palmquist (1980) method. The Halvorsen and Palmquist (HP) values calculated

 

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for the β-estimates generated by binary variables indicate, on average, the percentage by which

PPHYSICIAN is above (for positive β-estimates), or below (for negative β-estimates), for the

period the binary variable indicates the presence of the event versus the period of the sample

when the event was not present.9 The HP values provide an important avenue of the cointegrated

VAR model by which policy-analytic results may be obtained. The HP value for the beta

estimate on FINALREGS_MAY19.2011 is -0.0099 that generates an HP value of -0.985. This

suggests that the May 19, 2011 announcement of the final ACA regulations for a program to

review rates charged by insurance companies have led to about a 1 percent drop in U.S.

physicians’ services prices over the last 6 or 7 months that have elapsed since that time at this

writing. Along with the positive β-estimate on insurance premiums, the tax on Cadillac health

plans and the rate review provisions of ACA would put downward pressure on health insurance

premium increases which in turn reduce physician services prices. This result suggests that the

ACA will reduce health care expenditures via physicians’ services price, and consequently place

a relative small burden of cost containment on physicians in the form of lower prices.

Limitations

Following a substantial body of past and recent literature, we invoked the vector

autoregression reduced form properties and modeled the markets for medical services

represented in the above list of medical prices as reduced form price equations. We

acknowledge that modeling each of the downstream medical service markets for physician

services, hospital services, nursing related services, etc. with both a price (which we procured)

and a quantity (which were unable to procure) would have been preferable. But such medical

                                                            9 As noted in Halvorsen and Palmquist (1980), for log/log estimations such as ours, one takes “e,” the base of the natural logarithm; raises it to the power of the binary’s β-estimate; subtracts 1.0; and then multiplies the result by 100 to render he noted HP value for that estimated coefficient.

 

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services quantities are not publically available. Consequently each of the represented medical

service markets were modelled with a single reduced form price equation that captures as much

of each medical service sector’s salient elements as was possible. The estimated set of

equations, however, met established standards of diagnostic quality nonetheless without the

desired quantity variables.

Many of the provisions of the ACA did not take effect until 2013 (eg, increasing

payments for primary care services provided by primary care doctors to 100 percent of the

Medicare rates in 2013 for those newly eligible for Medicaid) or 2014 (eg, the individual

mandate to purchase insurance.

Conclusions

The small negative effect on the price of physicians’ services may be mitigated by future

impacts of the ACA provisions. For example, the CBO (2010) projected that the ACA would

increase health insurance coverage by 32 million people. However, expansion of coverage is not

an expansion of access to care or actual care. The newly insured will bring significant demand

for physician and hospital services, on a delivery system with significant workforce shortages,

requiring an additional 8,000 physicians by 2025. This is in addition to the 33,000 additional

physicians needed to accommodate population growth, and 10,000 additional physicians needed

to accommodate population aging (Petterson et al., 2012). In addition, CBO (2009) estimates

that the ACA reforms would result in non-group premiums falling by 10–13 percent, on average

by 2015, with no significant change in group premiums. The net effect of future increases in

coverage and increases in demand for physician services, along with reduced premiums is

ambiguous.

 

18  

REFERENCES

Congressional Budget Office. 2010. “H.R. 4872, Reconciliation Act of 2010.” Letter to the Honorable Nancy Pelosi (March 18). Congressional Budget Office, Washington, DC, http://www.cbo.gov/ftpdocs/113xx/doc11355/hr4872.pdf. Accessed June 17, 2014.

Congressional Budget Office. 2009. “An Analysis of Health Insurance Premiums Under the

Patient Protection and Affordable Care Act.” Letter to the Honorable Evan Bayh (November 30). Congressional Budget Office, Washington, DC, http://www.cbo.gov/doc.cfm?index=10781

Dennis, J. 2006. CATS 2: Cointegration Analysis of Time Series, Version 2. Evanston , IL:

Estima. Engle, R. and Granger, C.W.J. 1987. Cointegration and error correction: Representation,

estimation, and testing. Econometrica 55, 251-276.

Estima. 2012. RATS: Regression analysis of time series, Version 8.2. Evanston, IL: Estima. Ginsburg P., and G. Pawlson. Seeking Lower Prices Where Providers Are Consolidated: An

Examination Of Market And Policy Strategies. 2014. Health Affairs, 33, no. 6, http://content.healthaffairs.org/content/early/2014/05/13/hlthaff.2013.0810)

Granger, C., and P. Newbold. 1986. Forecasting economic time series. NY: Academic Press. Halvorsen, R., and R. Palmquist. 1980. The Interpretation of dummy variables in

semilogarithmic equations. American Economic Review, 9, 474-475.

Jencks, S., and G. Schieber. 1991. Containing U.S. health care costs: What bullet to bite? Health Care Financing Review, Annual Supplement, 1-12.

Johansen, S., and K. Juselius. 1990. Maximum likelihood and inference on cointegration: With applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52, 169-210.

Juselius, K. 2006. The cointegrated VAR approach: methodology and applications. Oxford, UK:

Oxford University Press. Juselius, K., and M. Franchi. 2007. Taking a DSGE model to the data meaningfully.

Economics,1, 1-38. Juselius, K., and J. Toro. 2005. Monetary transmission mechanisms in Spain: The effect of

monetization, financial deregulation, and the EMS. Journal of International Money and Finance 24: 509-531.

Levit, K, et al., 2002. Inflation Spurs Health Spending in 2000. Health Affairs. 172–181.

 

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Schellhorn, M. 2001. The effect of variable health insurance deductibles on the demand for physician visits. Health Economics. Volume 10: 441–456.

Sims, C. 1980. Macroeconomics and reality. Econometrica 48: 1-48. Tiao, G., and G. Box. 1978. Modelling multiple time series: With applications. Journal of the

American Statistical Association 76: 802-816. Petterson, S, et al. 2012. Projecting US Primary Care Physician Workforce Needs: 2010–2025.

Annals of Family Medicine, Vol. 10, No. 6 (November/December) 503–509]. http://www.annfammed.org/content/10/6/503.full.pdf. Accessed June 16, 2014.

U.S. Department of Labor, Bureau of Labor Statistics. 2013. Producer and consumer price index

data bases. www.bls.gov. Accessed on August 25, 2013.

 

20  

Table 1. Mis-specification Tests for the Unrestricted VEC After Specification Enhancement Efforts

Test &/or equation

Null hypothesis &/or test explanation.

Value after specification efforts

Trace correlation

System-wide goodness of fit indicator. Large proportion desirable.

0.52

LM (2)test, serial correlation

H0: No serial correlation. Reject for p≤ 0.5

35.00 (p = 0.52)

LM test, ARCH(2) H0: No heteroscedasticity. Reject for p ≤ 0.05 37.93 (p =.0.093)

Doornik_Hansen, system-wide test.

H0: Model system of estimated residuals behaves normally. Reject for p ≤ 0.05.

18.62 (p = 0.10)

Doonik-Hansen univariate tests

H): An equation’s estimated residuals behave normally. Reject for values ≥ 9.2.

∆PALLITEMS 1.05

∆PPHYSICNS 4.47

∆PHOSPITAL 2.24

∆PPREDRUG 0.65

∆PNURSE 6.71

∆PINSURANCE 3.73

Skewness (kurtosis), univariate values.

Skewness: Ideal is zero; “small” values desirable.

Kurtosis: acceptable values below 5.0.

∆PALLITEMS -0.034 (2.47)

∆PPHYSICNS 0.43 (3.55)

∆PHOSPITAL -0.034 (3.42)

∆PPREDRUG 0.46 (3.12)

∆PNURSE 0.54 (3.19)

∆PINSURANCE 0.41 (3.39)

Table 2. Nested Trace Test Statistics and Reduced Rank Determination. Null Hypothesis Trace Value 95% Fractile Result Rank or r ≤ 0 173.26 128.25 Reject null that r ≤ 0.

Rank or r ≤ 1 108.66 99.35 Reject null that r ≤ 1.

Rank or r ≤ 2 65.36 74.46 Reject null that r ≤ 2.

Rank or r ≤ 3 40.18 53.57 Fail to reject that r ≤ 3.

Rank or r ≤ 4 22.71 36.53 Fail to reject null that r ≤ 4.

Rank or r ≤ 5 7.49 23.28 Fail to reject null that r ≤ 5.

Notes. -- As recommended by Juselius (2006), CATS2–generated fractiles are increased by 6*1.8 or 7.2 to account for the six permanent shift binary variables restricted to lie within the cointegration space.

 

21  

 

Figure 1. Cointegrating relation 1.

 

 

Figure 2. Cointegrating relation 2.

Beta1'*Z1(t)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013-4

-2

0

2

4

6

Beta1'*R1(t)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013-3

-2

-1

0

1

2

3

4

Beta2'*Z1(t)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013-5

-3

-1

1

3

Beta2'*R1(t)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013-4

-3

-2

-1

0

1

2

3