JOINT CENTER

AEI-BROOKINGS JOINT CENTER FOR REGULATORY STUDIES 

A Cost-Benefit Analysis of Drug Price Controls in the U.S. 

Rexford E. Santerre and John A. Vernon *

Related Publication 04-29 

October 2004 

 *

Corresponding author. Please send correspondence to the address above or the email address: 

jvernon@business.uconn.edu. The authors thank Dennis Heffley, Carmelo Giaccotto, and John M. 

Vernon for his comments on an earlier draft of this paper. 
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 this period. Therefore, we 

approximate that the average social opportunity cost per drug developed during 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 politics. The commonly held perception 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 index for over two decades. As a 

result, the pharmaceutical industry has come under 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 markets such as Canada 

and the European Union, or more directly through 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 policy change regarding U.S. drug prices.1

 The U.S. government 

may, like all other industrialized governments around the world, soon begin regulating drug 

prices2

. In addition to several reimportation bills currently on the Senate floor, proposed 

amendments to the recently passed Medicare Modernization Act of 2003 also exist that will 

allow the U.S. government 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 generate both cost savings and improved public health, it will 

simultaneously reduce firms’ incentives to invest 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 pharmaceutical 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 paper by Kanavos et al. (2004). 
 2 

the considerable benefits of pharmaceutical innovation 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 precisely 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 costs, the former outcome often receives more attention in policy 

debates (Scherer, 2004). Interestingly, however, efforts to quantify these short-run benefits from 

a rigorous economic perspective are nonexistent. Therefore, in the current paper, we attempt to 

do just that. We also compare 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 reduced 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 forgone drug discoveries). While these studies are 

retrospective in nature (out of necessity), and consider 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 actual policy enacted in 1992 for 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 discusses 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 benefit 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 Empirical Models Of The Aggregate Consumer Demand For 

Pharmaceutical Products

We begin by assuming a one-period model in which a representative consumer, given her 

exogenous tastes and preferences, T, derives utility from consuming the units of “health 

services”, H, that flow from her health capital, and some composite good, X. Stated 

mathematically: 

U = U (H, X;T ). (1)

We also make the following standard assumptions about the individual’s utility function: 

> 0

∂

∂

H

U

; > 0

∂

∂

X

U

; 0 2

2

<

∂

∂

H

U

; 0 2

2

<

∂

∂

X

U

; 0

2

>

∂ ∂

∂

H X

U . (2) 

That is, utility is assumed to increase at a decreasing rate with respect to both health services and 

the composite good. We further assume that health services are produced with various

combinations of prescription drugs, Q (e.g., dosages), and medical services, M (such as office 

visits or inpatient days), conditioned on the representative consumer’s initial endowment of 

health capital, H0. Thus, for ease of exposition, we ignore a set of other healthcare “goods” and 

“bads” such as exercise, diet, alcohol and tobacco use, etc. and the consumer’s time involved in 

producing these healthcare activities.3

 A production function for units of health services can thus 

be written as follows: 

( , ; ) H = H Q M H0 , (3)

where H is assumed to be concave with respect to both Q and M. 

Substituting equation (3) into equation (1), allows utility to be expressed in terms of the 

production function for health services and the composite good. It is assumed that expenditures

on the two inputs that produce health services, and spending on the composite good, fully 

exhaust the consumer’s income. The consumer’s optimization problem is therefore to select the

amounts of prescription drugs, medical services, and the composite good that maximize her 

 

3

 For instance, Miller and Frech (2002) examine empirically the effect of pharmaceuticals, medical care, and various 

lifestyle factors on measures of the quality and quantity of life using a sample of OECD countries. Among other 

results, they find that pharmaceutical consumption extends and improves life. 
 4 

utility subject to the constraints of income, Y, and the out-of-pocket prices for drugs, , 

medical services, , and the composite good, . Stated more formally we have:4

 

PO

PM PX

MAX U = U[ ] H(Q, M;H ), X;T Subject to: 0 QPO + MPM + XPX = Y . (4) 

Using the method of Lagrange Multipliers to find the solution to this constrained 

optimization problem generates the familiar first-order conditions. Using the first-order 

conditions, we can solve for the marginal rate of substitution between drugs and the composite 

good. This yields: 

X

O

P

P

X

U

Q

H

H

U = ⎥

⎦

⎤ ⎢

⎣

⎡

∂

∂

⎥

⎦

⎤ ⎢

⎣

⎡

∂

∂

⋅ ∂

∂ −1

 . (5) 

The first partial derivative in the left-most bracket captures the marginal utility of good health 

while the second reflects the marginal productivity of drugs on good health. Equation (5) implies

that, in equilibrium, the representative consumer equates the marginal benefit of the last drug

consumed with its marginal cost, as reflected by the relative out-of-pocket price of an additional 

drug. 

For purposes later in the paper, it is important to consider here that the marginal benefit 

of an additional drug dosage is influenced by both the value that the consumer places on being in 

a state of good health and the marginal product of an additional drug on good health. As a result, 

the actual price paid for an additional drug in the marketplace captures the consumer’s 

willingness to pay for a small reduction in the probability of dying and/or a marginal 

improvement in her quality of life. This notion becomes particularly important when we use the

inverse demand curve to estimate the consumer surplus from a drug price control regime. That is, 

consumer surplus, the area under the inverse demand curve, captures the value of life and the 

marginal contribution of an additional drug to good health. 

Defining X as the numeraire, it also follows from the utility maximization process that the 

representative consumer’s quantity demanded of prescription drugs can be derived as a function 

of the relative out-of-pocket drug price, relative out-of-pocket medical price, and her real 

income. Expressed generally, the demand function takes the following form: 

 

4

 For simplicity, it is assumed that the decision to purchase pharmaceutical and medical expense insurance coverage 

has already been made. 
 5 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = 0 , , ;T,H

P

Y

P

P

P

P Q Q

X X

M

X

O . (6) 

Lastly, for purposes of empirical estimation, it is assumed that the representative consumer’s

demand for prescription drugs takes the following specific log-log form: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = +

X X

M

X

O

P

Y

P

P

P

P ln(Q) ln ln ln β0 β1 β 2 β3 . (7) 

Therefore, the coefficients in equation (7) may be interpreted as elasticities. According to the law 

of demand, the quantity demanded of prescription drugs should be inversely related to its relative 

out-of pocket market price ( 0 β1 < ). The relationship between the quantity demanded of 

prescription drugs and the relative out-of-pocket price for medical services depends on whether 

pharmaceuticals and medical services are substitutes ( 0 β2 > ) or complements ( 0 β2 < ). Finally, 

if prescription drugs can be classified a normal good, real income will have a direct impact on

the quantity demanded of prescription drugs ( 0 β3 > ). Obviously, an inverse relation holds if

prescription drugs are inferior goods. 

Time series data for the variables in equation (7) were obtained on-line either at the 

Center for Medicare and Medicaid Services, the Bureau of Labor Statistics, or the Bureau of 

Economic Analysis for the period 1960-2000. The quantity of drugs consumed by the average 

consumer is measured by real drug expenditures per capita. Dividing nominal drug expenditures 

by the pharmaceutical consumer price index and by the population produces a measure of real 

drug expenditures per capita.5

The out-of-pocket price of drugs is calculated in the following manner: 

P0 = cP . (8)

Thus, the out-of-pocket price is determined by multiplying the consumer’s out-of-pocket share, 

c, by the pharmaceutical consumer price index, P, our measure of market price. The expression 

 

5

 Note that real pharmaceutical expenditures per capita capture purchases of both existing and new drugs but a 

demand curve assumes a standardized product. Technical change over time obviously changes the mix of old and

new drugs and thus might present a problem when estimating long-run elasticities. However, variables are first 

differenced in our multiple regression model. Thus, we are estimating short-run elasticities and technical change

therefore presents less of a problem. 
 6 

in equation (8) is then divided by the general consumer price index, , to derive a real or 

relative out-of-pocket price of drugs for each year. 

PX

As noted by some researchers, consumer price indices are not measured without error 

because substitution effects and quality changes over time are not fully incorporated (e.g., 

Hausman, 2003). Several authors have also pointed out the biases that previously existed in 

pharmaceutical price indices because of (1); the undersampling of new drugs, (2); the failure to

treat generic drugs as lower-priced substitutes for branded drugs rather than new drugs, and (3);

the use of list instead of transaction prices (Berndt, Griliches, and Rosett, 1993; Scherer, 1993). 

Nevertheless, the pharmaceutical price index represents the best available time series indicator of 

drug price swings in the U.S. (Beginning in 1995, the Bureau of Labor Statistics has taken steps

to correct some of these biases in the pharmaceutical price index.) Moreover, since we are

examining changes in the ratio of the pharmaceutical and general CPI measures over time, some

of the substitution and quality bias in the numerator and denominator may tend to cancel out. In 

addition, average year-to-year parameter estimates are obtained in the multiple regression 

analysis. These short-run estimates may avoid some of this bias because sufficient time does not

pass for substitution effects and quality changes to fully work themselves out. It should be kept

in mind, however, that any remaining measurement error biases the parameter estimates towards 

zero if the rest of the model is properly specified. 

Similarly, the out-of-pocket real price of medical care is obtained by multiplying the ratio

of the medical care consumer price index and the general price index by the percent of medical 

care expenditures that were out-of-pocket expenses. Real income is measured by real GDP per

capita. Before presenting our multiple regression results, we first discuss how the dependent

variable and the two out-of-pocket prices in equation (7) changed over time. This information is 

provided in Exhibit 1. Notice that real drug expenditures per capita increased rapidly from 1960 

to 1975, continued to increase but at a diminishing rate from 1976 to 1994, and then began 

increasing at an increasing rate once again from 1995 to 2000. It may not be coincidental, but 

instead reflective of the law of demand, that out-of-pocket real prescription drug prices inversely 

mirrored these movements in per capita real drug expenditures during this time period. For

instance, the out-of-pocket real price of drugs declined rapidly from 1960 to 1975 and from 1993 

to 2000, the two time periods when real drug expenditures 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 correlation 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 also 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 included to control to for unobserved demand factors 

like tastes and preferences, T, and the initial endowment of health, H0, as specified in equation 

(6). Estimation of the first-differenced model by ordinary least squares produced the coefficient 

estimates and corresponding t-statistics reported in the second column of Exhibit 26,7 We note 

that the adjusted R2

 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 elasticity of demand is negative and statistically 

significant. Our relatively price-inelastic estimate 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) estimate of –0.34 and also falls within previous estimates ranging 

 

6

 Mathematically, the intercept term in equation (7) falls out after first differencing of the variables. 

Statistically, an intercept term proved to be no different from zero. 7

 As noted in the text, a one-period lagged value of Q (in logs) was also included in the specification to control for 

changing tastes and preferences. Because a lagged value of the dependent variable is specified 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 run. For example, Schwartzman 

(1976) finds no evidence to support economies in manufacturing pharmaceuticals. While the market price of 

pharmaceuticals may be independent of consumption at a point in time, market price may change over time given 

varying supply conditions, allowing 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 depreciated health capital, elderly individuals are 

likely to face fewer health-related choices than the general population, and thus may possess less 

price-elastic demands for pharmaceuticals. 

The positive and statistically significant cross-price elasticity estimate suggests that 

medical care and prescription drugs are substitute 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 demanded. The direct relation 

between the price of medical care and the quantity 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 finding is consistent with Lichtenberg’s (1996) 

research, which found that increased expenditures on pharmaceuticals leads to reduced 

expenditures on hospitalizations, ambulatory care, and physician services. 

The income elasticity estimate is also positive and statistically significant, indicating that 

pharmaceutical products can, on average, be treated as normal goods. The income elasticity is 

fairly sizable suggesting that a 10 percent increase in real income per capita produces a 5.1 

percentage increase in the quantity of drugs demanded. 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 positive and statistically different from zero. This 

coefficient estimate can be used in conjunction with the short-run estimates to calculate long-run 

elasticity estimates.9

 According to the calculations, the long run own-price, cross-price, and 

income elasticities are –0.89, 1.03, and 0.94 respectively. 

Although predominately influenced by private demand (because public health insurance 

for pharmaceuticals was largely absent on an outpatient basis during the sample period), public 

financing may also influence real drug expenditures per capita, the dependent variable in 

equation (7). During the time period under investigation, 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 covered by one of the various public health insurance 

programs. The government also paid for prescription drugs for outpatient care under the Veterans 

Administration and Medicaid programs. Reimbursement 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 control variable should be included in the multiple 

regression equation for the percentage of pharmaceutical 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 variable (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 public financing variable is not statistically 

different from zero. Public financing may have had no independent impact on pharmaceutical 

consumption because of its relatively low percentage over time, at least until very recently, or 

because our public financing variable captures 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 column 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 price 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 would 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 depicted in Exhibit 1. Moreover, 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 the same drug price control regime. These authors 

assumed Congress enacted a law in 1980 requiring pharmaceutical prices in the U.S. to grow no 

faster than the general price level. This approach mirrors one of the actual mandates in the 
 10 

Veteran’s Health Care Act of 1992: namely, that drug prices paid by federal agencies cannot 

grow at a rate faster than the urban consumer price index. Thus, while our experiment is

couched within a hypothetical and historical context, it will nevertheless reflect an actual 

approach employed by the Federal Government to control pharmaceutical prices (albeit on a 

relatively small scale). By combining the results from these two studies, some insight may be

gained into the net consumer benefits arising from a drug price control system that holds the rate

of growth of drug prices to the same rate of growth as the overall CPI. 

Retrieving the elasticities from the multiple regression results in Exhibit 2, we can

express a representative consumer’s constant elasticity of demand curve for drugs as the 

following: 

0.456

1

0.483 0.560 0.512

−

−

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = t

X X

M

X

O Q P

Y

P

P

P

P Q A (9)

Or more simply, we can write: 

−0.483

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

X

O

P

P Q K (10)

and define, 

0.456

1

0.560 0.512

⎟ − ⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = t

X X

M Q P

Y

P

P K A (11)

It follows that K, the amount of drug consumption that results from all factors other than the real 

out-of-pocket price, can be determined by dividing Q, real pharmaceutical expenditures per 

capita, by for each year between 1981 and 2000. K changes over time in response to 

adjustments in the relative out-of-pocket price for medical care, real income and the lagged 

measure of the dependent variable. 

−0.483 PO

To estimate the consumer surplus associated with the representative consumer’s 

additional drug purchases under our price ceiling program, we have to invert equation (10) and 

solve for the market price of pharmaceutical products by employing the expression for the outof-pocket price depicted by equation (8), which gives: 

0.483

1

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = Q

K

c

P P x (12)
 11 

Using standard integration techniques, the area under the inverse demand curve can be measured 

over the range between the actual real drug expenditures observed and the real drug expenditures 

that would have resulted from a price control 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 demand 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 status. Because our demand equation is specified 

on a per capita basis, it reflects the aggregated demands of the insured (both partially and fully) 

and the uninsured as well as the sick and the healthy (zero consumption). Thus, following 

Nyman, the resulting estimate of consumer surplus may be biased downward because it 

implicitly incorporates the demands of the uninsured and healthy. On the other hand, the estimate 

of consumer surplus may be biased upward because it incorporates the inefficiencies of moral 

hazard through c, the out-of-pocket fraction. To remove the potential inefficiencies caused by 

moral hazard, estimates for consumer surplus are also reported assuming that c = 1. However, 

we are unable to correct for the possible lower bound nature of the consumer surplus estimate 

because of the inclusion of the healthy and uninsured into the demand specification. 

 Theory suggests that willingness to pay should be measured 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 less than one. Applying 

the Slutsky equation, the uncompensated and compensated price elasticities 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 simulation 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 prices 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 interest 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 entire 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 equal 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, Santerre, and Vernon (2003, 2005) estimated that 

this same price control regimen would have 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 approved for marketing during this period in the U.S., 

we can use our simulation results to calculate the average 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. Dividing the $319 billion consumer 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, consumers (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, the latter is the case, then not imposing price controls had a net 

social cost for Americans. While recent research has documented the significant benefits 

associated with medical and pharmaceutical research, and even suggested that U.S. may be 

currently underinvesting in R&D (Murphy and Topel, 2003), very few studies have documented 

the value (in dollar terms) of new drugs or pharmaceutical 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 

drugs13. To do this we divide Giaccotto and colleague’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 (Cutler 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 perspective, 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), and Vernon (2004b) for a discussion of the caveats 

involved. 
 14 

5. Conclusion

Rising drug prices have captured the attention of the media, various public interest groups, and 

politicians. Some have pointed to price controls as a way of reining in what are perceived by 

many to be “runaway” drug prices. But as economists have known for centuries, price controls 

simply represent “bad economics”. Economic theory suggests that price controls often create 

shortages, reduce quality, lead to price discrimination, 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 surplus, or benefit, resulting from a hypothetical 

price control regime in the U.S. To accomplish this 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 new 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 equal approximately $319 billion in 2000. On a per drug basis, we 

estimate that the social opportunity cost of not imposing this 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 those 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, Out-of-Pocket Real Price of Drugs and 

Out-of-Pocket Real Price of Medical Care Over Time in the U.S. 

Real Per Capita Drug Expenditures and Relative Prices

(in Logs)

-2.20

-1.70

-1.20

-0.70

-0.20

1960 1965 1970 1975 1980 1985 1990 1995 2000

LN(Q) LN(Pq/Px) LN(Pm/Px)
 16 

Exhibit 2: 

Multiple Regression Results (dependent variable is the first 

difference of the log of real pharmaceutical expenditures per capita) 

Variable Basic 

Results 

Extended 

Results 

Log of out-of-pocket real price of drugs (PQ/PX) -0.483 

(3.73) 

-0.469 

(3.51) 

Log of out-of-pocket real price of medical care 

(PM/PX) 

0.560 

(3.04) 

0.506 

(2.38) 

Log of real GDP per capita (Y/PX) 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 R2 0.584 0.575 

Durbin Watson Statistic 1.81 1.84 

Observations 39 39 
 17 

Exhibit 3: 

Determination of the Aggregate Consumer Surplus From a Drug 

Price Control Regime 

Year Money

value of the 

consumer 

surplus 

from one 

individual 

Money Value of 

Aggregate 

Consumer Surplus 

(i.e., the second 

column times 

population) 

Future value of consumer 

surplus (invested at 7 

percent until the end of the 

year 2000) 

1981 $0.26 $60,472,360 $234,008,952

1982 $1.84 $426,554,876 $1,542,647,455

1983 $4.57 $1,069,482,312 $3,614,777,785

1984 $6.96 $1,642,217,474 $5,187,461,537

1985 $9.88 $2,350,009,621 $6,937,613,212

1986 $13.63 $3,271,366,127 $9,025,802,326

1987 $16.80 $4,071,236,379 $10,497,822,037

1988 $20.79 $5,082,849,457 $12,248,879,350

1989 $25.37 $6,260,541,229 $14,099,938,298

1990 $31.74 $7,918,736,937 $16,667,768,902

1991 $38.55 $9,722,865,584 $19,126,348,231

1992 $43.93 $11,201,883,372 $20,594,205,681

1993 $46.79 $12,061,384,335 $20,723,703,874

1994 $50.03 $13,021,977,945 $20,910,450,971

1995 $54.36 $14,286,584,751 $21,440,311,361

1996 $59.89 $15,881,839,547 $22,275,101,543

1997 $67.23 $18,003,459,448 $23,598,862,810

1998 $79.11 $21,375,526,811 $26,185,939,492

1999 $97.97 $26,716,982,518 $30,588,273,285

2000 $115.32 $31,747,355,082 $33,969,669,938

TOTAL $206,173,326,166 $319,469,587,040
 18 

Exhibit 4 

Future Value of the Consumer Surplus 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 Billion 
 19 

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