## JOINT CENTER

AEI-BROOKIN G S JOINT CENTER FOR REG U LATORY STUDIES



## A Cost- B enefit Analysis of Drug Price Controls in the U.S.

Rexford E. Santerre and John A. Vernon *



### Related Publication 04-29 October 2004

* Corresponding author. Pl ease send correspondence to the ad dress above or the email address: jvernon@business.uconn. edu . The authors thank Dennis Heffl ey , Carm elo Giaccotto, and John M. Vernon for hi s co mments on an earlier draft of this pap e r.Executive Summary
This paper uses national data for the period 1960 to 2000 to estimate an aggregate private consumer demand for pharmaceuticals in the U.S. The estimated demand curve is then used to simulate the value of consumer surplus gains from a drug price control regime that holds drug price increases to the same rate of growth as the general consumer price level over the time period from 1981 to 2000. Based upon a 7 percent real interest rate, we find that the future value of consumer surplus gains from this hypothetical policy would have been $319 billion at the end of 2000. According to an earlier study, that same drug price control regime would have led to 198 fewer new drugs being brought to the U.S. market over th is period. Therefore, we approximate that the average social opportunity co st per drug developed dur ing this period to be approximately $1.6 billion. Recent research on the value of pharmaceuticals suggests that the social benefits of a new drug may be far greater than this estimated social opportunity cost. Based on empirical findings of the productivity of pharmaceutical R&D over a similar time period, our first approximation of the cost-benefit ratio for this hypothetical price control policy ranges between 62 and 68. This suggests price controls on pharmaceutical prices between 1980 and 2000 would have caused much more harm than good. Society may be better off discovering more efficient ways than price controls to improve access to existing drugs.1

A Cost-Benefit Analysis of Drug Price Controls in the U.S.
Rexford E. Santerre and John A. Vernon

1. Introduction

The public debate over prescription drug prices in the U.S. is one of the most contentious in the history of healthcare po litics. The commonly held percep tion that U.S. drug prices are “too high” has been fueled by the fact that real drug prices in the U.S. have been rising steadily,
and at a rate faster than that of the general consumer price inde x for over two decades. As a result, the pharmaceutical industry has come unde r intense criticism, with both politicians and special interests groups calling for new legislation that will make pharmaceuticals more affordable, either through legalized reimportation from price-regulated ma rkets such as Canada and the European Union, or more directly th rough government imposed price controls. While these calls for legislative action are not new, the U.S. does appear to be, for the first time, very close to a major polic y change regarding U.S. drug prices. 1 The U.S. government may, like all other industriali zed governments around the worl d, soon begin regulating drug prices 2 . In addition to several reimportation bills currently on the Senate floor, proposed amendments to the recently pa ssed Medicare Modernization Act of 2003 also exist that will allow the U.S. govern ment to negotiate directly with drug manufacturers for Medicare prescription drug purchases (the MMA currently has a non-interference clause), which will amount to approximately 60 percent of all U.S. drug purchases (Vernon, Santerre, and Giaccotto, 2004). While regulated drug prices in the U.S. will undoubtedly improve the public’s access to today’s medicines, and thus ge nerate both cost savings and improved public health, it will simultaneously reduce firms’ incentives to in vest in pharmaceutical R&D because of lower levels of pharmaceutical profitability. Less investment in pharmaceutical R&D will have a negative effect on the rate of future pharmaceu tical innovation. Recent research has documented

1 Approximately 10 years ago the Clinton Administration’s Health Security Act had provisions for directly controlling drug prices. In addition, the drug industry has been under the political microscope since the 1960s (Scherer, 2004) 2 There are numerous theoretical reasons why reimportation may not generate the desired prescription drug cost savings that the advocates of this and related policies expect . See, for example, the pape r by Kanavos et al. (2004).2

the considerable benefits of pharmaceutical in novation in terms of improved U.S. longevity (Lichtenberg 2002; Miller and Frech, 2002) as well as the sensitivity of R&D investment to real pharmaceutical prices (Giaccotto, Santerre , and Vernon, 2003, 2005) and profits (Vernon, 2004a). Thus, in addition to the short-run benefits associated with lower, regulated drug prices, there will also be long-run costs. This is precis ely the tradeoff the U.S. patent system tries to balance by awarding limited-term patents to new drug products. Even though a policy of regulated drug prices in the U.S. involves a tradeoff between short-run benefits and long run co sts, the former outcome often re ceives more atten tion in policy debates (Scherer, 2004). Interestingly, however, effo rts to quantify these s hort-run benefits from a rigorous economic perspective ar e nonexistent. Therefor e, in the current pa per, we attempt to do just that. We also compar e our findings with the results from an earlier study—one that employed the same data and modeling techniques, but which measured the economic costs of the same U.S. price control policy—in terms of re duced levels of pharmaceutical innovation. Thus, we are able to weigh the benefits of pharmaceutical price controls (in terms of consumer surplus gains) against the costs (measured in terms of fo rgone drug discoveries). While these studies are retrospective in nature (out of necessity), and co nsider only one type of U.S. price control policy (one that requires pharmaceutical prices grow no faster than the CPI), the price control policy simulated is, nevertheless, similar to an actua l policy enacted in 1992 fo r drugs purchased by the government for the Veterans Administration (VA) health system. Moreover, and for the first time, a formal cost-benefit analysis of a particular type of drug price control is possible, and this may offer new insights. Our paper proceeds as follows. In Section II we develop an empirical model of the aggregate consumer demand for pharmaceuticals in the United States. We also outline our empirical strategy and describe the data. Section III reports and discusse s our empirical estimates. In Section IV we simulate the consumer surplus gains from a hypothetical price control policy in the U.S.: one that limits the growth rate of pharmaceutical prices to that of the CPI from 1981-
2000. We then assess the net bene fit of this policy by comparing the gains of consumer surplus to some fairly plausible estimates of the value of the R&D (and drugs) that would be lost had the policy been enacted. Section V provides a summary and offers some conclusions.

If this occurs, direct price regulation will have to follow to procure the cost savings that such reimportation policies were intended to achieve.3

2. Conceptual And Empirica l Models Of The Aggregate Consumer Demand For
Pharmaceutica l Products

We begin by assum i ng a one-period m odel in which a repr e s enta tive consum er, given her exogenous tastes and preferences, T , derives utility from consum ing the units of “health services”, H , that flow from he r health capital, and som e composite good, X . Stated

m a them atically: ). ; , ( T X H U U = ( 1 )
We also m a ke the following standard assum p ti ons about the individual’s utility function:

0 > ∂ ∂

H U ; 0 > ∂ ∂ X

U ; 0 2 2 < ∂ ∂

H

U ; 0 2 2 < ∂ ∂

X

U ; 0 2 > ∂ ∂

∂ X H U

. (2)

That is, utility is assum e d to in crea se at a decr eas ing ra te with respe c t to b o th hea lth s e rvices and
the com posite good. We further assum e that health services are produced with various

com b inations of prescription drugs, Q (e.g., dosages), and m e dical services, M (such as office visits or inpatient days), c onditioned on the representative c onsum er’s initial endowm e nt of health capital, H 0 . Thus, for ease of exposition, we ignor e a set of other h ealthcare “goods” and “bads” such as exercise, diet, alcohol and tobacc o use, etc. and the consum er’s tim e involved in producing these heal thcare activities. 3 A production function for units of health services can thus
be written as f o llows: ) ; , ( 0 H M Q H H = , ( 3 )

where H is assum e d to be concave w ith respect to both Q and M

. Substitu ting equation (3 ) into equa tion (1), allo ws utility to be expresse d in term s of the production function for health serv ices and the com posite good. It is assum e d that expenditures on the two inputs that produce health servic es, and spending on the com posite good, fully
exhaust the consum er’s incom e . The consum er’s op tim izatio n problem is the r ef ore to sele ct the

am ounts of prescription drugs, m e dical servic es, and the com posite good that m a xim i ze her

3 For i n stance , Miller and Fre c h (2002) e x a m ine em pirical ly the effect of phar m aceuticals, m e dical care, and va rious lifestyle factors on m easures of the quality and qua ntity of life usi n g a sa m p le of OE CD countries. Am ong othe r results, they fi nd that pha rm a ceutical co nsu m pti on e x t e n d s an d i m pro v es l i f e.4

utility subje c t to the c onstra i nts o f incom e , Y, and the out-of-pocket prices for drugs,
,

m e dical serv ices,
, and the com posite good, . Stated m o re form ally we have: 4 O P

M 

### P X 

### P

MAX U = Subject to: 

## [ ] T X H M Q H U ; ), ; , ( 0 Y XP MP QP X M O = + +

. (4) Using the m e thod of Lagrange Multip liers to find the solution to this cons trained optim ization problem generates the fa m iliar fi rst-ord e r conditions . Using the first-o r der conditions, we can solve for the m a rginal rate o f substitutio n between drugs and the com posite good. This yields:

X O P P X U Q H H U

= ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ∂

∂ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ∂ ∂

⋅ ∂ ∂ − 1

. (5)

The first partial derivative in the left-m ost bracket captures the m a rginal utility of good health
while the second reflects the m a rginal productivity of drugs on good health. Equation (5) im plies that, in equilibrium , the represen tative consum e r equates the m a rg inal benef it of the last d r ug

consum ed with its m a rginal cos t , as reflected by the relative out-of-pocke t price of an additional drug.
For purpose s later in th e paper, it is im portant to consider he re that the m a rginal benef it of an additional drug dosage is influenced by both the value that the cons um er places on being in a state of good health and the m a rg inal product of an additional drug on good healt h. As a result,
the actu a l price paid for an additional drug in the m a rketp l ace captures the consum er’s willingn ess to pay f o r a sm all reduction in the prob ability of dying and/o r a m a rginal
im prove m e nt in her quality of life. This notion b ecom e s particula r ly im portan t when we use th e inverse dem a nd curve to estim a te the consum er surp lus from a drug price c ontrol regim e . That is,

consum er surplus, the area unde r the inverse dem a nd curve, captu res the value of life and the

m a rginal contribution of an a dditional drug to good health. Defining X a s the num eraire, it a l so f o llows f r om the utility m a xim i zation p r ocess tha t the representative consum e r’s quantity dem a nded of prescription drugs can be derived as a function of the relative out-of-pocket dr ug price, relative out-of-p o cket m e dical price, and her real
incom e . Expressed generally, the dem a nd function takes the following form :

4 For sim p licity, it is assum e d that th e decisi on to purc hase pha rm aceutical an d m e dical e xpe nse i n sura nce covera ge has al rea d y be en m a de.5

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

= 0 , ; , , H T P Y P P P P Q Q

X X M

X O

. (6)

Lastly, for purposes of em pirical estim ation, it is assum e d that the representative consum er’s
dem a nd for prescription drugs takes th e f o llowing specif i c log

- log f o rm :

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + =
X X M

X O P Y P P P P Q ln ln ln ) ln( 3 2 1 0 β β β β

. (7)

Therefore, the coefficients in equation (7) m a y be inte rpre te d as elas ticities. Accord ing to the la w of de m a nd, t h e quantity dem a nded of prescriptio n dr ugs should be inversely related to its relativ e out-of pocket m a rket price ( 

### 0 1 

### < β ). The relationship between the quantity dem a nded of prescription drugs and the relativ e out-of-pocket price for m e dical services depends on whether pharm aceuticals and m e dical services are subs titutes ( 

### 0 2 

### > β ) or complem e nts ( 

### 0 2 

### < β ). Fina lly, if prescription drugs can be cla ssified a norm a l good, real incom e will have a direct im pact on the quantity dem a nded of prescription drugs ( 0 3 > β ). Obviously, an inverse relation holds if prescription drugs are inferior goods.
Tim e series data for the variables in equa tion (7) were obtained on-line either at the
Center for Medicare an d Medicaid Services, th e Bureau of Labor S t atistics, or the Bureau of
Econom ic Analysis for the period 1960-2000. The quantity of drugs c onsum ed by the average

consum er is m easured by real drug expenditure s per capita. Dividing nom i nal drug expenditures by the pharm aceutical consum er price ind e x an d by the po pulation pro duces a m easure of real drug expenditures per capita. 5 The out-of-pocket price of drugs is c a lculated in the f o llowin g m a nner:

cP P =

0 . ( 8 )
Thus, the o u t-of -pocke t price is d e term ined by multiply ing the consum er’s ou t-of -p ocket sha r e,

c , by the ph arm aceutical consum er price index, P , our m eas ure of m a rket price. The expression

5 Note that rea l pha rm aceutic al expe ndit u re s per ca pita c a pture purc has e s of both e x isting a nd ne w drugs but a d e m a n d cur v e assu m e s a stan d a rd ized pr oduct. Techn i cal ch ang e ov er ti me o b v i ou sly ch ang e s t h e mix of o l d and

n e w drug s and thu s m i g h t p r esen t a p r o b le m wh en esti ma tin g lo ng -run elasticities. Howev e r, v a ri ab les are first d i fferen ced i n o u r m u ltip le reg r ession m o d e l. Thu s , we are estim a tin g sh ort-run elasticities an d techn i cal ch an g e therefore prese n ts less of a problem

.6

in equation (8) is then divided by the general consum er price index, , to derive a real or
relative ou t-of-pocket price of drugs for each year. X 

### P

As noted by som e researchers, consum er pr ice indices are not m e asured without error
because sub s titu tion effects and qu ality chang e s over tim e are not fu lly incorpo r ated (e.g.,
Haus m a n, 2003). Several authors have also pointe d out the biases that previously existed in pharm aceutical price ind i ces becau se of (1); the undersam pling of new drugs, (2); th e failure to

trea t gene ric drugs as lo wer-priced s ubstitu tes f o r branded dr ugs rathe r th an new drugs, and (3) ; the use of list instead of transaction pri ces (Berndt, Griliches, and Rosett, 1993; Scherer, 1993).
Nevertheles s , the pharm aceutical price index rep r esen ts th e b e st av ailable tim e series indicato r of drug price swings in the U.S. (Beginning in 1995, the Bureau of Labor Statistics has taken steps

to correct s o m e of these biases in the pharm aceutical price index.) Moreover, s i nce we are

exam ining changes in th e ratio of th e pharm aceutical and general CPI m easures over tim e, som e of the substitution and quality bias in the num erat or and denom i nator m a y tend to cancel out. In addition, av erage year-to-year param e ter estimat es are ob tained in the m u ltiple regres sion analys is. Th ese short-ru n estim ates m a y avoid som e of this bias because sufficient tim e does not pass f o r sub s titu tion ef f

ects and qua lity changes to f u lly wor k them selves out. It sho u ld be kep t in m i nd, however, that any rem a ining m easurem ent error biases the param e ter estim ates towards
zero if the rest of the m o del is properly specified.
Sim ilarly, th e out-of

- poc ket re al p r ic e of m e dical care is ob tained by m u ltiplying th e ra tio
of the m e dical care consum er price index and th e general price index by the percen t of m e dical care expend itures that w e re out-of-p o cket expen s es. Real in com e is m e asured by real GDP per capita. Bef o re pr esenting our m u ltiple regr essi on results, we f i rst d i s c uss how th e dependen t variable and the two out-of-pocke t p r ices in equ a tion (7) ch anged ove r tim e . This inf o rm ation is provided in Exhibit 1. Notice that real drug expe nditures per capita increased rapidly from 1960 to 1975, continued to increase but at a dim i ni shing rate from 1976 to 1994, and then began increasing at an increasing rate once again from 1995 to 2000. It m a y not be coincidental, but
instead reflective of the law of de m a nd, that out-o f-pocket real prescription drug prices inversely

m i rrored these m ove ments in per capita real drug expenditures during this time period. F o r instance, the out-of-pocket real price of drugs declined rapidly from 1960 to 1975 and from 1993 to 2000, the two tim e periods w h en real drug e xpenditures per capita were rising quickly.7

Attention should also be drawn to the fact that the out-of-pocket real price for medical services generally declined over the entire time period, but at a relatively slow rate.

3. Empirical Results

A high degree of serial correlati on necessitated first differencing of the data (after taking logarithms). First differencing of the data seems particularly appropriate because diagnostic tests revealed the presence of unit roots in several of the time series data (i.e ., real drug spending per capita and real GDP per capita) before they were first differenced. Unit roots can result in spurious correlations among variables. We al so included in our empirical model a lagged measure of real drug expenditures per capita as an additional independent variable (after first differencing). The lagged measure was include d to control to for unobserved demand factors like tastes and preferences, T, and the initial endowment of health, H 0 , as specified in equation (6). Estimation of the first-differenced model by ordinary least squares produced the coefficient estimates and corresponding t-statistics re ported in the second column of Exhibit 2 6,7 We note that the adjusted R 2 of 0.58 is quite high for this type of time-series model (i.e., models using first-differenced data). 8 As anticipated, the estimated own-price elasti city of demand is negative and statistically significant. Our relatively price-inelastic estim ate of –0.48 suggests that a 10 percent decrease (increase) in the real out-of-pocket price of prescription drugs increases (decreases) the quantity demanded of prescription drugs by about 4.8 percent, ceteris paribus . This estimate is very close to Coulson and Stuart’s (1995) es timate of –0.34 and also falls w ithin previous estimates ranging

6 Mathematically, the intercept term in equation (7) fa lls out after first differencing of the variables. Statistically, an intercept term pr oved to be no different from zero. 7 As noted in the text, a one-period lagged value of Q (in l ogs) was also included in th e specification to control for changing tastes and preferences. Because a lagged value of the dependent variable is specifi ed on the right hand side of the equation, the typical Durbin Watson statistic cannot be used to detect serial correlation. We remind the reader, however, that our specification contains first differences of the variables and not levels so we report the Durbin Watson statistic. We also experimented with other diagnostic tests such as the Q-statistic and Durbin’s alternative test (Maddala, 1992). These tests failed to detect the presence of serial correlation. 8 The specification of equation (7) assumes that the market price of drugs, which helps to make up the out-of-pocket real price of drugs, is independent of the amount of existing drugs produced. An assumption of independence or constant returns to scale in production is not unreasonable , particularly in the short ru n. For example, Schwartzman (1976) finds no evidence to support economies in manufacturing pharmaceuti cals. While the market price of pharmaceuticals may be independent of consumption at a poi nt in time, market price may change over time given varying supply conditions, allowi ng us to identify a demand curve for pharmaceuticals.8

from –0.06 to –0.64 as noted by these authors. Earlier research focused on the demand for pharmaceuticals by elderly individuals. Our own-price elasticity is most likely higher because it captures the demand of the representative consumer, rather than the representative elderly individual. Because of their relatively more depr eciated health capital, elderly individuals are likely to face fewer health-rela ted choices than the general po pulation, and thus may possess less price-elastic demands for pharmaceuticals. The positive and statistic ally significant cros s-price elasticity es timate suggests that medical care and prescription drugs are substitu te products. For example, based upon our crossprice elasticity estimate of 0.56, a 10 percent increase in the price of medical care is associated with a 5.6 percent increase in the quantity of prescription drugs dema nded. The direct relation between the price of medical care and the quant ity of prescription drugs demanded suggests that decision-makers have some ability to substitute one good for the other in the production of good health when relative price changes. This fi nding is consistent with Lichtenberg’s (1996) research, which found that increased expenditures on pharmaceuticals leads to reduced expenditures on hospitalizations, ambula tory care, and physician services. The income elasticity estimate is also positive and statistically signifi cant, indicating that pharmaceutical products can, on average, be treate d as normal goods. The income elasticity is fairly sizable suggesting that a 10 percent increase in real in come per capita produces a 5.1 percentage increase in the quantity of drugs demande d. Studies tend to suggest that health care is a normal good (Santerre and Neun, 2004). The estimated coefficient on the lagged measure of real pharmaceutical spending per capita is positiv e and statistically different from zero. This coefficient estimate can be used in conjunction w ith the short-run estimates to calculate long-run elasticity estimates. 9 According to the calculations, the lo ng run own-price, cross-price, and income elasticities are –0.89, 1.03, and 0.94 respectively. Although predominately influenced by privat e demand (because public health insurance for pharmaceuticals was largely absent on an outp atient basis during the sample period), public financing may also influence re al drug expenditures per capita, the dependent variable in equation (7). During the time period under inve stigation, the government paid for drugs

9 For example, the long run price elasticity estimate is calculated by dividing the short run elasticity by one minus the coefficient estimate on the lagged dependent variable. The other long-run estimates are determined in a similar fashion.9

administered to inpatients if they were cove red by one of the various public health insurance programs. The government also paid for prescrip tion drugs for outpatient care under the Veterans Administration and Medicaid programs. Reimbursem ent for pharmaceutical is fairly restrictive under the latter two programs, but the programs do provide some of the population with financial access to pharmaceuticals. In any case, a contro l variable should be in cluded in the multiple regression equation for the percentage of pha rmaceutical expenditures paid by government. The ratio of government expenditures on pharmaceutical to total pharmaceutical expenditures ranged from a little under 3 percent in 1960 to slightly under 22 percent in 2000. Inclusion of this additional independent va riable (after taking first differences in logarithms), results in the multiple regression findings reported in the third column of Exhibit 2. Notice that the multiple regression findings remain virtually the same as those reported in column 2, and the coefficient estimate on the pub lic financing variable is not statistically different from zero. Public financing may have had no independent impa ct on pharmaceutical consumption because of its relatively low percenta ge over time, at least until very recently, or because our public financing variable captu res a host of widely diverse government
reimbursement schemes.

4. Simulating Consumer Gains From A Drug Price Control Regime

We use the multiple regression results in colu mn 2 of Exhibit 2 to estimate the future value of the consumer surplus from a drug price control policy that holds drug price increases to the same rate of growth as the general pri ce level over the period from the beginning of 1981 through the end of 2000. To measure the consumer surplus for each year, we estimate the area
under the inverse demand curve over the range between actual real drug expenditures for that year and the real drug expenditures that woul d result from a lower controlled price. We pick the years between 1981 and 2000 to conduct the experiment because real drug prices increased throughout that period, as depi cted in Exhibit 1. More over, in a recent study, Giaccotto, Santerre, and Vernon (2003, 2005) used that same period to estimate the number of new drugs that would have been lost from th e same drug price control regime. These authors assumed Congress enacted a law in 1980 requiring pha rmaceutical prices in the U.S. to grow no faster than the general price level. This a pproach mirrors one of the actual mandates in the10

Veteran’s Health Care Act of 1992: nam e ly, that drug prices paid by federal agencies cannot grow at a rate faster than the urban consum er price index. Thus, while our experim e nt is couched within a hypo thetical and historical c ontext, it will neverth e le ss reflect an actual approach employed by the Federal Governm e nt to contro l pharm aceutical prices (albe it on a
rela tive l y s m all scale). By com b ining the resu lts from these two studies, som e insight m a y be gained into the net consum er benefits arising from a drug pr ice contro l s y stem that holds the rat e of growth of drug prices to the sam e rate of grow th as the overall CPI.
Retriev i ng the elasticities f r om the m u ltip le r e gress i on re sults in Exhibit 2, we can express a representative consum er ’s constant elasticity of dem a nd curve for drugs as th e following:

456

. 0 1 512

. 0 560

. 0 483

. 0

− − ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

= t X X M

X O Q P Y P P P P A Q ( 9 )

Or m o re simply, we can write:

483

. 0

− ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

=
X O P P K Q ( 1 0 )

and define,

456

. 0 1 512

. 0 560

. 0

− ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

= t X X M Q P Y P P A K ( 1 1 )

It f o llows th at K , the am ount of drug consum p tion that results from all factors other than the real out-of-pocket price, can be determ ined by dividing Q , real pharm aceutic al expenditures per capita, by for each year between 1 981 and 20 00. K chan ges over tim e in respo n se to adjustm e nts in th e r e la tive out-of

- p o cket p r ic e for m e dical care, real incom e and the lagged

m easure of the dependent variable. 483

. 0

− O P

To estim ate the consum er surplus associated with the represen tative consu m er’s additional drug purchases under our price ceiling pr ogram , we have to invert equation (10) and
solve for th e m a rket price of phar m aceutical p r oducts by employing th e expression for the out

of-pocket price depicted by e quation (8), which gives:

483

. 0 1 ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

= Q K

c P P x ( 1 2 )11

Using standard integration techni ques, the area under th e inverse demand curve can be measured over the range between the actual real drug expe nditures observed and the real drug expenditures that would have resulted from a price contro l regime that held the rate of growth in pharmaceutical prices to that of the general consumer price index. For the purposes of our simulation, the price control policy holds the ratio of the market price of pharmaceutical goods to all other goods at the same level observed in 1980 (but not necessarily the relative out-of-pocket price because c, the out-of-pocket share, changes over time). Some disagreement exists in the literature regarding the appropriate measure of consumer surplus when health care is involved because of the presence of third-party payers. Health economists typically argue that the uninsured de mand curve when people are healthy should be used to estimate consumer surplus to remove the distortion caused by moral hazard. Nyman (2003) insists on the insured demand curve when people are ill because insurance provides access to health care that many could not otherwise afford and because the amount of utility received from medical care depends on health st atus. Because our demand equation is specified on a per capita basis, it reflects the aggregated demands of the in sured (both partially and fully)
and the uninsured as well as th e sick and the healthy (zero consumption). Thus, following Nyman, the resulting estimate of consumer su rplus may be biased downward because it implicitly incorporates the demands of the unins ured and healthy. On the other hand, the estimate of consumer surplus may be biased upward becaus e it incorporates the inefficiencies of moral hazard through c, the out-of-pocket fraction. To re move the potential ine fficiencies caused by moral hazard, estimates for consumer surplus are also reported assuming that c = 1. However,
we are unable to correct for th e possible lower bound nature of th e consumer surplus estimate because of the inclusion of the healthy a nd uninsured into the demand specification. Theory suggests that wil lingness to pay should be meas ured by the compensated demand curve because a price change also typically triggers an income effect. However in this particular case the income effect is very tiny because of both a small drug expenditure share (about 1 percent on average for the sample) and an income elasticity of demand le ss than one. Applying the Slutsky equation, the uncompens ated and compensated price elas ticities of demands differ by only .005 (i.e., -0.483 versus –0.478). Given this minor discrepancy, we measure consumer surplus with the uncompensated demand curve.12

The results of the consumer surplus simu lation are shown in Exhibit 3 assuming an insured demand based upon the actual figures for the out-of-pocket fraction in each period. The second column shows the consumer surplus resulting from the drug price ceiling for the representative consumer during each of the years. Notice how individual consumer surplus increases throughout the period as actual drug pr ices continue to increase relative to the controlled drug prices.
By multiplying the total population in the U.S. by the representative consumer’s surplus figures, aggregate consumer surplus can be derived. Aggregate consumer surplus for each of the years is shown in column 3. Assuming no inte rest and compounding of earnings, these yearly figures are summed, and result in a total of $206 billion by the end of 2000. Finally, the fourth column lists the future value of the aggregate consumer surplus that would have been earned over 20 years at a 7 percent real rate of return. Over the last few decades, the average rate of return in the stock market has exceeded general price inflation by about 7 percent. According to the figure shown at the bottom of column 4, the total future value of the aggregate consumer surplus would equal roughly $319 billion if the entir e savings from the drug price control regime were invested at a 7 percent annual real rate of return. The future value of the consumer surplus for the uninsured demand (found by setting c equa l to 1) amounts to $149 billion. For purposes of comparison, we also report in Exhibit 4 the future value of the consumer surplus for the
insured and uninsured demands at different rates of return. 10 Over this same time period, Giaccotto, Sant erre, and Vernon (2003, 2005) estimated that this same price control regimen would ha ve caused firms to reduce pharmaceutical R&D expenditures (in $2000) by between $264.5 and $293.1 billion, because of lower profit expectations and possibly reduced levels of internal funds (which are the primary source of R&D finance) 11 . This reduced investment in R&D would have led to approximately 38 percent fewer new drugs being brought to market in the global economy. If this 38 percent figure is applied to the total number of new chemical entities approve d for marketing during this period in the U.S.,
we can use our simulation results to calculate the aver age social opportunity cost per new drug.

10 Over the 1980 to 2000 period, the rate of return in the stock market averaged roughly 17 percent and the general price inflation rate averaged 4 percent. 11 The latter would be the case if capital market imperfections imparted a cost advantage to internal funds over external debt and equity (see Vernon, 2004a).13

During the period from 1980 to 2000, 520 new chemical entities were approved for marketing in the U.S. 12 This figure suggests that 198 drugs would have been “lost” if the assumed price control regime was imposed. Divi ding the $319 billion c onsumer surplus gains from price controls over the period from 1980- 2000 by the number of new drugs “lost” due to price controls, we estimate that, on average, cons umers (in the aggregate) gave up $1.6 billion in consumer surplus per new drug developed. This raises the question of whether or not the benefit of a new drug brought to market during this period was greater than or less than $1.6 billion. If the former is the case, then the fact that price controls were not imposed (in the manner we describe and model) was, on net, good for the U.S. If, however, th e latter is the case, then not imposing price controls had a net
social cost for Americans. While recent rese arch has documented the significant benefits associated with medical and pharmaceutical rese arch, and even suggested that U.S. may be currently underinvesting in R&D (Murphy and T opel, 2003), very few studies have documented the value (in dollar terms) of new drugs or pha rmaceutical R&D. A notable exception is the econometric study by Lichtenberg (2002). In his study, which covered a similar time period in the U.S. (1960-1997), he approximated that, on average, every $1,345 spent on pharmaceutical R&D “produced” an additional U.S. life year. While speculative, we can use this average productivity measure of R&D to compare the benefits of price controls (as modeled in this study) and the costs in terms of “lost” R&D and drugs 13 . To do this we divide Giaccotto and collea gue’s estimated range of forgone capitalized pharmaceutical R&D, by $1,345 and multiply this by $100,000, which is one measure of the value of a U.S. life year (C utler and McClellan, 2001). Of course, both higher and lower estimates exist. When this first approximation of the cost of price controls is compared with the $319 billion gain in consumer surplus, the resulting cost-benefit ratio ranges from about 62 to 68. This suggests that, from a social welfare perspec tive, price controls would have done much more harm than good.

12 Federal Drug Administration at http://www.FDA.gov.

13 See the papers by Vernon, Santerre , and Giaccotto (2004), a nd Vernon (2004b) for a discussion of the caveats involved.14

5. Conclusion

Rising drug prices have captured the attention of the media, va rious public interest groups, and politicians. Some have pointed to price controls as a way of re ining in what are perceived by many to be “runaway” drug prices. But as econo mists have known for centuries, price controls simply represent “bad economics”. Economic theory suggests that price controls often create shortages, reduce quality, lead to price disc rimination, and can harm incentives for innovation. The only benefit to price controls is that some individuals gain, at the expense of others, through an increase in their consumer surplus as a result of the lower controlled prices. In this paper, we estimate the consumer su rplus, or benefit, resulting from a hypothetical price control regime in the U.S. To accomplish th is objective, we use national data from 1960 to 2000 to estimate the aggregate private demand for pharmaceuticals. Based upon our empirical results, the demand for pharmaceuticals is shown to be inversely related to its own-price and directly related to the both medical prices and real income. Moreover, the empirical estimation generates elasticity estimates with plausible magnitudes that are in general agreement with previous research findings. We then use the estimated demand curve to simulate the consumer surplus gains associated with an assumed price control policy that holds the increase in pharmaceutical prices to the rate of growth of the general consumer price index. Giaccotto, Santerre, and Vernon (2003, 2005) have conducted this same experiment, and found that this same price control regime would have reduced the number of ne w drug innovations by about 38 percent. For the nation as a whole, we estimate that the future value of the consumer surplus from the assumed price control regime would equa l approximately $319 b illion in 2000. On a per drug basis, we estimate that the social opportunity cost of not imposing th is price control policy was approximately $1.6 billion. However, when compared to the estimated benefits of the additional pharmaceutical R&D that was undertaken because these hypothetical price controls were not implemented, these costs appear to be very small. Given our results, and t hose reported in prior research, society may be better off discovering more efficient ways than price controls to improve access to existing drugs.15



## Exhibit 1:

Real Drug Expenditures per capita, O ut-of-Pocket Real Price of Drugs and Out-of-Pocket Real Price of Medica l Care Over Time in the U.S.

R e a l P e r Ca p i t a D r u g E x p e n d it u r e s a n d R e la t i v e P r ic e s ( i n Lo gs )

-2

. 2 0 -1

. 7 0 -1

. 2 0 -0

. 7 0 -0

. 2 0

1960 1965 1970 1975 1980 1985 1990 1995 2000
LN ( Q ) LN ( P q/ P x ) LN ( P m

/ P

x )16



## Exhibit 2:
Multiple Regression Results (d ependent variable is the first difference of the log of real pharm aceutical expenditures per capita)

Variable Basic Results Extended Results Log of out-of-pocket real price of drugs (P Q 

### /P X 

### ) -0.483 (3.73) -0.469 (3.51) Log of out-of-pocket real price of medical care (P M 

### /P X 

### ) 0.560 (3.04) 0.506 (2.38) Log of real GDP per capita (Y/P X 

### ) 0.512 (2.99) 0.528 (3.00) One-year lagged measure of real pharmaceutical expenditures per capita 0.456 (4.06) 0.492 (3.70) Log of percent of total pharmaceutical expenditures paid by government -0.039 (0.52)
Adjusted R 2 

### 0.584 0.575

Durbin Watson Statistic 1.81 1.84

Observations 39 3917



## Exhibit 3:

Determination of the Aggr egate Consumer Surplus Fr om a Drug Price Contr o l Regime

Y e a r M o n e y value of the

consumer

surplus from one individual Money Value of Aggregate Consumer Surplus (i.e., the second

column times

population) Future va lue of consumer surplus (invested at 7 percent until the end of the year 2000)

1 9 8 1 $ 0

. 2 6 $60,472,360 $234,008, 952 1 9 8 2 $ 1

. 8 4 $426,554, 876 $1,542,647,455 1 9 8 3 $ 4

. 5 7 $1,069,482,312 $3,614,777,785 1 9 8 4 $ 6

. 9 6 $1,642,217,474 $5,187,461,537 1 9 8 5 $ 9

. 8 8 $2,350,009,621 $6,937,613,212 1 9 8 6 $ 1 3

. 6 3 $3,271,366,127 $9,025,802,326 1 9 8 7 $ 1 6

. 8 0 $4,071,236, 379 $10,497,822,037 1 9 8 8 $ 2 0

. 7 9 $5,082,849, 457 $12,248,879,350 1 9 8 9 $ 2 5

. 3 7 $6,260,541, 229 $14,099,938,298 1 9 9 0 $ 3 1

. 7 4 $7,918,736, 937 $16,667,768,902 1 9 9 1 $ 3 8

. 5 5 $9,722,865, 584 $19,126,348,231 1 9 9 2 $ 4 3

. 9 3 $11,201,883, 372 $20,594,205,681 1 9 9 3 $ 4 6

. 7 9 $12,061,384, 335 $20,723,703,874 1 9 9 4 $ 5 0

. 0 3 $13,021,977, 945 $20,910,450,971 1 9 9 5 $ 5 4

. 3 6 $14,286,584, 751 $21,440,311,361 1 9 9 6 $ 5 9

. 8 9 $15,881,839, 547 $22,275,101,543 1 9 9 7 $ 6 7

. 2 3 $18,003,459, 448 $23,598,862,810 1 9 9 8 $ 7 9

. 1 1 $21,375,526, 811 $26,185,939,492 1 9 9 9 $ 9 7

. 9 7 $26,716,982, 518 $30,588,273,285 2 0 0 0 $ 1 1 5

. 3 2 $31,747,3 55,082 $33,969,669,938

TOTAL $ 2 0 6 , 1 7 3 , 326,166 $319,469, 587,04018



## Exhibit 4
Future Value of the Consumer Surplu s for Various Rates of Return and Insured versus Uninsured Demands

Real Rate of Return Uninsured Demand Insured Demand 5% $128 Billion $280 Billion 9% $174 Billion $366 Billion 13% $241 Billion $488 Billion19

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