GENERIC SUBSTITUTION 


AND 


PRESCRIPTION DRUG PRICES: 


Economic Effects of State Drug Product 


Selection Laws 


Alison Masson 


and 


Robert L. Steiner 


Federal Trade Commission 


GENERIC SUBSTITUTION AND PRESCRIPTION 


DRUG PRICES: 


ECONOMIC EFFECTS OF STATE 


DRUG PRODUCT SELECTION LAWS 


by 


Alison Masson 


and 


Robert L. Steiner 


Staff Report 

. of the 

Bureau of Economics 

Federal Trade Commission 

October 1985 
FEDERAL TRADE COMMISSION 


TERRY CALVANI, Acting Chairman 

PATRICIA P. BAILEY, Commissioner 

MARY L. AZCUENAGA, Commissioner 

BUREAU OF ECONOMICS 

DAVID T. SCHEFFMAN, Acting Director and Deputy Director 

for Competition and Antitrust 

JOHN L. PETERMAN, 

Associate Director for Special Projects 

RONALD S. BOND, 

Deputy Director for Operations and Consumer 

Protection 

RICHARD S. HIGGINS, Deputy Director for 

Regulatory and Industry Analysis 

KEITH B. ANDERSON, 

Assistant Director for Regulatory Analysis 

ROBERT D. BROGAN, 

Assistant Director for Competition Analysis 

MARK W. FRANKENA, 

Assistant Director for Consumer Protection 

PAULINE M. IPPOLITO, 

Assistant Director for Industry Analysis 

PAUL A. PAUTLER, Assistant Director for Anti trust 

DISCLAIMER: 

Thia report haa been prepared by the Bureau of Economics of the FTC. It 

haa not been reviewed by, nor does it necessarily reflect the views of, the 

Commission, or any of its members. 
ACKNOWLEDGEMENTS 

The senior author, Alison Masson, wrote the final version 

of the report. She was also responsible for the final specification of all the econometric work, as well as most of the data 

preparation. Robert L. Steiner conceived the idea for the 

study in 1980 and with J. Howard Beales III submitted the original research proposal. He worked initially with Beales and 

then with Masson until he left the Federal Trade Commission in 

September 1983, and he subsequently has served as a consultant 

on the project. 

Gerard Butters also virtually deserves co-authorship. Analytically perceptive always, he questioned, suggested, and 

clarified ideas in progress, in discussions beyond count. His 

assistance on statistical and technical issues was extremely 

valuable. His comments on several earlier drafts substantially 

improved this final report. In addition, his periodic insistence on a plot of the path to the final product has helped us 

reach the end of that path -- this completed report. 

So many others have contributed so much in so many ways to 

this report that it is impossible to name them all. Within the 

Bureau of Economics, Pauline M. Ippolito and Ronald S. Bond 

provided continuing support. John P. Brown, a former Assistant 

Director for Consumer Protection, was instrumental in encouraging the project in its early phases. Michael P. Lynch 

and Charles Smithson, to name but two of numerous helpful 

colleagues, provided counsel on ways to model the problem. 

The enormous data processing task was ably and cheerfully 

programmed by John Hamilton, with the assistance of 

Allen Jefferson and Mead Krim. Many people worked on the 

preparation and digestion of the data, including Barbara 

Battle, Lynn Carpenter, Inez Cerame, Ieva Cucinelli, Ka tina 

Dorton, Alan Emerson, Lawrence Getzoff, Thomas Roney, 

Carolyn Samuels, and Cheryl Williams. Statistical advice was 

provided by generous colleagues. Mark Plummer for a time 

redirected his analytical abilities from his own economic 

research to masterminding, with heroic persistence, the conversion of the entire report from one word-processing system to 

another, creating order (as in tables with straight columns) 

out of chaos. Betsy Zichterman typed the seemingly endless 

tables and put the report into a presentable final format. 
The magnitude and complexities of the data might have overwhelmed the project had not IMS America continued to provide 

patient answers to our multitudinous questions. In particular, 

Steven Schectman, Paul Ryser and Peter Duskey were very helpful. 

We are grateful for information and discussions with many 

other people outside the Federal Trade Commission as well. 

Discussions with Theodore Goldberg, whose lead we were following in studying drug product selection, provided valuable guidance, as did his team's extensive research reports. Peter 

Rodier, Charles Spalding, and Thomas Fulda of the Health Care 

Financing Administration shared insights about the basic data 

and provided the list of leading generic entities which allowed 

all the empirical work to begin to move forward. Among the 

forms of assistance provided by staff at the Food and Drug 

Administration, the drug listings given us by Bruce Byer and 

Jacki Leung of the Drug Listing Branch were especially appreciated. Ty Kelley of the National Association of Chain Drug 

Stores, Thi D. Dao then of the Pharmaceutical Manufacturers 

Association, Richard Penna then of the American Pharmaceutical 

Association, Richard Levy of the National Pharmaceutical 

Council, members of the Generic Pharmaceutical Industry 

Association, and Edward Spierbeck of Drug Fair were among those 

in and around the business of producing and selling prescription drugs who graciously shared their experience and expertise 

with us. Pharmacy board officials across the country patiently 

answered our repeated efforts to understand exactly what state 

statutes and formularies really meant. Professors John Vernon 

of Duke University and Carolee DeVito of the University of 

Miami provided careful reviews of an intermediate draft. 

Conversations with other researchers have provided answers to 

some puzzling questions while often producing yet more intriguing issues, only some of which we have been able to address in 

this report. 

11 
TABLE OF CONTENTS 


CHAPTER 1 

CHAPTER 2 

CHAPTER 3 

CHAPTER 4 

CHAPTER 5 

CHAPTER 6 

CHAPTER 7 

CHAPTER 8 

CHAPTER 9 

APPENDIX A1 

APPENDIX A2 

APPENDIX A3 

APPENDIX A4 

APPENDIX A5 

APPENDIX A6 

INTRODUCTION 

THE INCIDENCE OF 

SUBSTITUTION 19 

WHY ISN'T SUBSTITUTION 

PREVALENT? 35 

VARIATIONS IN SUBSTITUTION 

RATES 49 

THE IMPACT OF DIFFERENCES 

IN STATE LAWS 65 

GENERIC MARKET SHARE 111 

BRAND AND GENERIC PRICES 137 

AVERAGE PRESCRIPTION PRICES 157 

CONCLUSION 183 

THE PROVISIONS OF 

THE STATE LAWS 189 

THE FTC/FDA MODEL STATE 

ACT 219 

FORMULARIES: SOURCES 

AND COVERAGE 223 

AVERAGE STATE SUBSTITUTION 

RATES BY INDIVIDUAL LEGAL 

PROVISION 229 

DATA ON 45 LEADING MULTISOURCE DRUGS 251 

DESCRIPTION OF THE DATA 271 

111 
APPENDIX A 7 ECONOMETRIC ISSUES 301 


BIBLIOGRAPHY 333 


IV 

FIGURE AND TABLES 


Figure 2-1 


Table 2-1 


Table 3-1 


Table 3-2 


Table 3-3 


Table4-l 


Table 4-2 


Table 4-3 


Table 4-4 


Table 4-5 


New Prescriptions, 1980: 

How Prescribed and How Dispensed 26 

Overall Substitution Rates by 

Legal Opportunity to Substitute, 

1980 28 

Brands and Generics: Retail 

Prices, In voice Costs, and Retail 

Dollar Gross Margins, 1980 36 

Retail Dollar Gross Margins on 

Su bstitu ted and Non-substituted 

Prescriptions by Drug for the 10 

Top-Substitution Drugs, 1980 38 

Proportion of Prescriptions Written 

Generically by Insurance 

Coverage, 1980 43 

Substitution Rates by Drug, 1980 50 

Substitution Rates by Insurance 

Coverage and Opportunity to 

Substitute, 1980 54 

Substitution by Insurance Coverage 

by Drug for the 10 TopSubstitution Drugs, 1980 55 

Substitution by Chains and 

Independents by Opportunity 

to Substitute, 1980 60 

Substitution Rates, Distribution 

of State Averages, 1980 62 

v 
Table 5-l 	 Pricing a Substituted Drug Under 

a Full Cost-Pass-Through Law: 

An Example 	 75 

Table 5-2 	 Results of Logit Regressions on 

Substitution Choice by Drug 90 

Table 5-3 	 Summary of Coefficient Signs 

for Individual Legal Provisions 

in the Logit Regressions 

on Su bsti tutio~Choice 96 

Table 5-4 	 Effect of Individual Provisions 

on the Probability of 

Substitution by Drug 102 

Table 5-5 	 Summary of Coefficient Signs 

for Non-Legal Variables in the 

Logit Regressions on 

Substitution Choice 105 

Table 6-1 	 Generic Market Share, Distribution 

of State Averages, 1980 113 

Table 6-2 	 Generic Market Share by Drug and 

Opportunity to Substitute for the 

10 Top-Substitution Drugs, 1980 114 

Table 6-3 	 Sources of Generic Market Share by 

Type of Prescription Written and 

Opportunity to Substitute, 1980 116 

Table 6-4 	 Sources of Generic Market Share by 

Type of Prescription Written 

by Drug, 1980 120 

Table 6-5 	 Proportion of Prescriptions Written 

Generically by Drug and by 

Opportunity to Substitute, 1980 128 

VI 
Table 6-6 


Table 7-1 


Table 7-2 


Table 7-3 


Table 7-4 


Table 8-1 


Table 8-2 


Table 8-3 


Table 8-4 


Table Al-l 

Table A3-1 

Table A3-2 

Proportion of Prescriptions 

Written Generically, Distribution 

of State Averages, 1980 130 

Regression Results for Brand Prices 

by Drug 144 

Regressions Results for Generic 

Prices by Drug 149 

Summary of Coefficient Signs m 

Brand Price Regressions 153 

Summary of Coefficient Signs in 

Generic Price Regressions 154 

Regression Results for Average 

Prices by Drug 160 

Summary of Coefficient Signs in 

Average Price Regressions 172 

Drugs for Which the Generic 

Prescribing Variable Has a 

Significant Effect in the Average 

Price Regressions 174 

Changes in Average Prescription 

Price Due to a DPS Law for the 

10 Top-Substitution Drugs 177 

Taxonomy of State Regulations, 

1980 190 

Sources for State Formularies, 

1980 224 

Number of States in Which 

Substitution was Permitted 

by Drug, 1980 227 

Vll 
Table A4-l 

Table A4-2 

Table A4-3 

Table A4-4 

Table A4-5 

Table A4-6 

Table A5-1 

Table A6-l 

Table A 7-1 


Table A7-2 


Substitution Rates and Other 


Characteristics by State, 1980 232 


Average Substitution Rates in 


States by Legal Provision, 1980 238 


Average State Substitution Rates 


by Type of Mandatory Provision, 


1980 240 


Average State Substitution Rates 


by Format of Physician's 


Prescription Pad, 1980 241 


Average State Substitution Rates 


by Type of Notification 


Requirement, 1980 247 


Average State Substitution Rates 


by Collateral Provisions, 1980 249 


Data on-45 Leading Multi-Source 


Drugs 252 


100 Top Generic Entities and the 


Specification of the Drug Products 


Selected for This Study 280 


Correlations Among Statutory 


Provisions 317 


Correlations between Non-Legal 


and Legal Variables 318 


Vlll 
.CHAPTER 1 

INTRODUCTION 

As of mid-1984 all states have laws allowing pharmacists 

some choice in selecting which brand of drug to dispense in 

filling a prescription that names a specific brand. 1 The 

stated purpose of these drug product selection (DPS) laws is to 

lower the prices consumers pay for prescription drugs through 

substitution of lower-price versions of the drug for the 

higher-price brands typically prescribed by physicians. 2 The 

previous anti-substitution laws required the pharmacist to 

dispense whichever brand the physician named. The newer drug 

product selection laws under certain conditions allow the 

pharmacist to substitute another generically equivalent drug. 

Since most prescriptions are written using the proprietary name 

of a specific brand, rather than the established generic name 

of the drug product, DPS laws in effect shift the choice of 

brand for most prescriptions from the physician to the pharmacist. The premise underlying DPS Ia ws is that the pharmacist has a greater incentive than the physician to identify 

the cheapest source of supply and to pass along at least part 

of the savings to the consumer. 

It is the large price differences between leading brands 

and "generic" versions of the "same" drug that suggest that 

consumers could save substantially from substitution.3 Using 

!/ By 1980 -- the year analyzed in this report -- only three states still 

prohibited substitution. Louisiana's law went into effect in October of 1980, 

Texas' at the beginnning of 1982 and Indiana's in mid-1984. 

For a history of the anti-substitution laws and of their replacement with 

drug product selection laws see Staff Report to the Federal Trade Commission, 

Drug Product Selection, 1979, hereafter cited as FTC Staff Report (1979).

!/ "Generic substitution" is another term often used for drug product selection, and indeed the substitution of unbranded drug products for branded items 

is envisioned by the statutes in that lower-price products typically include 

those sold under the generic name only. The term "drug product selection" 

encompasses a broader range of pharmacist behavior. In filling generically 

written pres·::riptions, pharmacists must always choose a drug product. Also, a 

substitution may involve a second brand rather than a lower-price unbranded 

veraion. 

The type of substitution analyzed in this study its limited to brand interchange within a generic entity (or drug entity), defined as the set of products 

which all have the same (combination of) active chemical ingredient(s).

V In this study, "generics" are defined as being all products other than 

leading brands, thereby including some products sold under a proprietary name 

in addition to products sold under the generic name alone. See Appendix A6 for 

the definitions of "leading brands" and "generics" and Chapter 3 for data on 

leading brand and generic prices. 
CHAPTER 1 

measures of the brand-generic price gap, a number of previous 

studies have attempted to measure the potential savings which 

DPS laws offer. These estimates have been large, on the order 

of hundreds of millions of dollars per year.4 

This study is an empirical analysis of the effects of these 

laws. We measure these effects in terms of substitution rates 

and differences in prices while controlling for influences 

other than the Ia ws. While our primary data are for 1980, we 

also discuss more recent trends. 

I . EQUIVALENCE AMONG DRUG PRODUCTS 

Consumers benefit from substitution only if there is no 

significant offsetting diminution in therapeutic efficacy. One 

important prerequisite for the spread of drug product selection 

Ia ws was the growing acceptance of the view that for many drugs 

various brands could be interchanged without loss of therapeutic efficacy. 

The issue of therapeutic equivalence among generically equivalent products is real. Two same-strength products in the 

same generic entity, containing the same active chemical 

ingredients in identical proportions, may not always have the 

same effects in a patient, because differences in inactive 

ingredients used for binding or coloring may modify the effects 

of the active ingredients or create their own unintended side 

effects. 

However, for many generic entities, there is now substantial agreement that no serious inequivalence problems exist, 

!/ See FTC Staff Report (1979), which reviewa aeveral estimates. These 

eetimatea all a11ume that dollar aavinga are not offset by a diminution in 

therapeutic efficacy. Moat estimates are of the maximum potential benefit•, 

that ie, the savings that would occur if aubatitutiona were in fact made in 

every instance permissible. Of course, the actual amount of "savings" depends 

on the extent to which pharmacists have the opportunity to substitute and on 

whether they actually choose to exercise the substitution option, aa well aa on 

any indirect price effects of the DPS laws. 

2 

INTRODUCTION 

based either on testing or on long cxperience. 5 For some 

others, tests have shown that products arc not eq ui valent and 

that free interchange is not appropriate. 

All state drug product selection laws prohibit substitution 

of products judged to be inequivalent, but they differ in the 

means by which they specify which products are considered to be 

equivalent. In some states reliance is placed entirely on 

independent judgment of the pharmacist, although a criterion 

typically using the terminology of bioequivalence or therapeutic equivalence may be incorporated in the statute. In many 

states (two-thirds of the states in 1980) a formulary lists 

permissible (or, alternatively, impermissible) drug product 

interchanges. 6 The formulary's legal grant of permission to 

substitute is for a particular drug entity very like the broad 

grant provided by the existence of a drug product selection 

statute: without it, substitution is illegal, regardless of 

how much encouragement other provisions of the law give to 

substitution in general. Data for all states permitting substitution in 1980 show that substitution was permitted on 73.6 

~ In a 1979 Federal Register notice, the FDA Commissioner wa.s reported a.s 

being "convinced that only a small fraction of all drugs present bioequivalence 

problema, and that, among those drugs that are currently marketed by more than 

one supplier, the problem drugs have now mostly been identified." 44 Federal 

Register 2942, January 12, 1979. Two products are said to be bioequivalent if 

their absorption into the blood stream and their subsequent excretion into the 

urine occur at the same rate and to the same extent; in practice, bioequivalence is held to imply therapeutic equivalence. Under FDA regulations some but 

not all drugs have been tested for bioavailability. In fact, the lack of bioequivalence does not necessarily lead to a significant difference in therapeutic effect. According to the FTC Staff Report (1979, p. 241), "small differences in bioavailability were likely to produce therapeutic problems for 

drugs with either a steep dose-response curve or a narrow range separating 

effective and toxic levels. Most clinically useful drugs have relatively flat 

dose-response curves; therefore, only large differences in bioavailability were 

likely to alter their therapeutic effect." Members of the expert panel whose 

report wa.s published by the Office of Technology Assessment in 1974 "estimated 

that roughly 85 percent to 90 percent of all prescription drugs were not critical dose drugs for which bioavailability studies were ne-:essary ." FTC Staff 

Report (1979, p. 238, footnote.) 

'if Formularies which list permissible substitutions are called "positive" 

formularies; those that list drugs in which substitution is prohibited are 

"negative" formularies. The effects of this difference are discussed in 

Chapter 5, as are secondary effects of formularies, such as limiting liability 

and simply providing information. 

3 

CHAPTER 1 

percent of all prescriptions for 45 leading multi-source drugs 

studied. 7 

State formularies vary greatly; the proportion of all prescriptions in these 45 drugs on which substitution was permitted in 1980 ranged from 29 to 98 percent.8 This shows that 

there is no single, universally agreed upon list of drugs 

which should be considered in terchangea ble. 9 Since 1980 the 

Food and Drug Administration has published its judgments in a 

periodical list entitled Approved Prescription Drug Products 

with Therapeutic Equivalence Evaluations. 10 Many states use 

the FDA list as a basis for their own formularies or, in the 

absence of a state formulary, recommendations to practicing 

pharmacists. Without such a standard compilation of official 

opinion, drug product selection laws would have been much more 

difficult to implement. 

For the purposes of this study, we use the therapeutic evaluations embodied in state formularies as the standard for 

determining when a substitution can be made with no loss in 

therapeutic effectiveness. We do, however, note the inconsistency of this standard across states for some drugs. 

II. THE ECONOMIC RATIONALE FOR 


SUBSTITUTION LAWS 


In "perfect" markets, consumers choosing between two iden tical products with different prices would choose the lowerprice product, and the price differential could not be maintained. However, in many "real" markets, price differentials 

I./ The data and the selection of the 45-drug sample are described in section III below and in greater detail in Appendix A6. 

§/ See Appendix Table Al-l for 1980 data by state. 

V General agreement on the advisability of prohibiting substitution in a 

particular drug entity is reflected in the fact that for some drugs substitution ia permitted in all states while for others substitution is prohibited in 

moat formulary states. Appendix Table A3-2 shows for each of the 45 drugs 

analyzed in this study the number of states which permitted substitution in 

that entity in 1980. The proportion of prescriptions on which substitution wu 

permissible in 1980 (excluding the three states which prohibited all substitution) ranged from 55 to 100 percent across the 45 drugs.

1!2/ U.S. Department of H<>alth and Human Services (1980 and subsequent editions). 

4 

INTRODUCTION 

persist, and in the market for prescription drugs the legal 

pro hi bitions against substitution have con tri bu ted especially 

to sustained price differentials. 

The difference between the price of the leading brand in a 

prescription drug entity and the price of alternative brands in 

the same entity is typically large: a 1980 average across 

37 leading multi-source drugs, weighted by sales in number of 

prescriptions, was $8.22 for the leading brand and $6.22 for 

the average of other brands, a difference of $2.00, or nearI y 

25 percent of the leading brand price. 11 Despite this broad 

price gap most prescriptions are filled with the leading 

brand. None of 12 leading drugs whose patents expired between 

1970 and 1976 had in 1979 a market share of less than 90 percent in (wholesale) dollar terms, although market shares were 

lower (70 to 90 percent) in terms of units sold. 12 Market 

share erosion is moderate at best in the years following patent 

expira tion. 13 

The institutions of the prescription drug market are markedly different from those in most other product markets. For 

prescription drugs, it has not been the consumer who has made 

the choice among brands; it has been the physician. A physician's prescription is a necessary precondition for the purchase of a prescription drug, and it is the physician who 

designates both the chemical compound and, on four-fifths of 

ll/ Of the 45 drugs selected for study, in only 37 did sales of both brands 

and generics, by our definitions, actually appear in the 1980 data. 

W The analysis of dollar market share is in Statman and Tyebjee (1981). 

The analysis of unit market share is contained in a letter from Mark B. Goodson, Aaaociate Manager, Public Policy Planning, Hoffmann-La Roche Inc. to Robert L. Steiner, January 6, 1982. The computations were based on IMS data and 

covered 6 of the 12 drugs in the Statman/Tyebjee analysis. By mid-1981 the 

unit market shares had fallen to 58 to 84 percent in these drugs.

W Of course, one possible explanation of the persistence of the price 

differential is that leading brands are superior in quality. Despite official 

state formularies stating that certain brands are interchangeable, some consumer~ or their physicians may find that one brand is more effective or confers 

fewer side-effects than another. Even in the absence of laws or institutions 

restricting their options to purchase prescription drugs, some consumers would 

therefore be willing to pay a premium for certain brands of powerful drugs, 

just as they do now for over-the-counter drugs and other products. 

5 

CHAPTER 1 

all prescriptions, a specific brand of the drug. 14 In the 

absence of the opportunity to substitute, consumers have no 

opportunity to exercise an unfettered choice of brand on most 

of the prescriptions received. 

Physicians' behavior reveals not only a marked preference 

for prescribing brand-name drugs but also for specifying the 

first brand marketed in a drug entity. 15 In the absence of 

substitution, this proclivity towards prescribing the pioneer 

brand in effect extends the drug's dominance even after the 

expiration of the patent which conferred the initial legal 

monopoly. One explanation of this pattern is that physicians' 

prescribing habits are formed early in the life of a newlyintroduced drug, at a time when there is only one version of 

the drug, protected by a patent monopoly and promoted hea vii y 

by its manufacturer. These habits are resistant to change, 

even in the face of the lower prices set by post-patent competitors of the leading brand. 

Of course it is possible that physicians' preferences for 

brands accurately reflect consumers' preferences, but there are 

strong arguments to the contrary. First, there is evidence 

that physicians are poorly informed about relative prices of 

drugs. 16 Second, physicians' incentives to choose the most 

cost-effective drug seem weak. A patient buys a bundle of 

services from the physician and may consider the particular 

brand of the drug to be a minor aspect in choice of a physician, giving much greater importance to diagnostic ability 

and overall quality of care. Moreover, to the extent that 

choice of physician is influenced by the cost of the physician's services, the variability in other components of that 

total cost -- such as the cost of the consultation itself and 

the cost of laboratory tests -- may swamp differences in prescription costs. Finally, patiei'ts themselves are not knowIedgea ble about the a vaila bili ty and relative prices of different brands within a generic entity and therefore may not 

!if In 1980, 79.9 percent of multi-source prescriptions specified a brand. 

This figures is a weighted average for 45 multi-source drugs. 

The remaining 20.1 percent were written generically. Regardless of whether 

the law permits substitution on brand-written prescriptions, selection of a 

particular product is necessarily left to the pharmacist and the consumer when 

the prescription has been written by generic name only. 

ll./ Bond and Lean ( 1977).

1!i/ See discussion and references in FTC Staff Report (1979, pp. 64-67). 

6 
INTRODC'CTION 

notice when the physician's prescription 1s not the best 

alternative available. 17 

Since physicians are an unlikely force behind a switch to 

lower-cost brands after the patent period has expired, an 

erosion of the patent-conferred monopoly must depend on others 

who have both the power and the incentive to respond to lower 

prices. That is the role envisioned for the drug product 

selection laws: to transfer some of this power to pharmacists.18 Consumers are the ones most interested in a lower 

price, and pharmacists must respond to consumer demand because 

of direct competition with other pharmacies on prescription 

prices. Also, pharmacists have an immediate incentive to dispense a generic rather than a leading brand because typically 

the retail dollar gross margin on the generic is higher. 19 

Anti-substitution laws, then, prevented pharmacists from dispensing the highest-profit products, and DPS laws can be viewed 

as the removal of a constraint on pharmacists' choices. Under 

the DPS laws, the profit-seeking drug retailer is more likely 

to choose a drug product with a lower (wholesale) cost and LO 

sell it to consumers at a price below that of the leading 

brand. By making use of the pharmacist's interest in higher 

profits, DPS laws offer consumers the benefit of lower prices. 

III. DATA USED IN THIS STUDY 

Our primary data are from the National Prescription Audit 

(NPA) compiled by IMS America, Ltd. and are for 1980; we also 

make use of some more recent data from various sources. In the 

11./ See Chapter 3. 

W Under all state DPS laws, the physician retains the authority explicitly to prohibit substitution on a particular prescription. In almost all 

states consumers also have the right to refuse substitution. 

12/ See Chapter 3. Also, on publicly funded prescription drug programs, 

such as Medicaid, pharmacies may by the regulations be given an incentive dispense low-cost versions, as is done with the Maximum Allowable Cost (MAC) program which sets reimbursement ceilings for some drugs. Private insurers now 

also build into their reimbursement schedules incentives for generic dispensing. 

7 

CHAPTER 1 

1980 NPA, over a million individual retail prescription transactions were recorded from a panel of about 800 retail pharmacies in 48 states and the District of Columbia. For each 

prescription we have information on the drug product prescribed 

and the drug product dispensed, and the retail price. We 

cannot tell, however, whether the physician prohibited substitution. Some data from IMS' 1980 U.S. Drugstore Audit, on drug 

in voice costs at the pharmacy level, are also used. 

From the 1980 data we selected 45 multi-source entities, the 

definition of a generic entity setting the broadest boundary 

within which substitution may be permitted.20 The number of 

brands within an entity ranged from 2 to over 100. These 45 

entities constituted nearly all the multi-source drug entities 

that appeared among the top 100 entities ranked by dollar 

sales to drugstores. Of these 45, only 37 turned out to have 

observations in our sample for both the leading brand and at 

least one generic. When brand-generic comparisons are made, 

these 37 drugs are used rather than the entire 45. In addition 

to cross-tabulations, we used multivariate regressions (generalized least squares for the price analyses and logit for the 

brand choice analyses) to separate out effects of individual 

provisions of the laws and to hold constant other economic 

influences. 

IV. RESULTS OF THE STUDY 

The aim of the drug product selection laws was to reduce 

the prices consumers pay at retail for their prescri;::>tion drugs 

by shifting some market share from higher-price leading brands 

to lower-price versions of the drug, and this aim was accomplished. Substitutions are made and average prescription 

prices do fall. The magnitude of the accomplishment, however, 

was smaller than might have been anticipated, even when the 

upsurge in the last few years is taken into account. 

W Dosage form and strength must also be identical. In some generic entities in some states, substitution is permissible only for selected dosage 

forms. 

8 

INTRODUCTION 

Like others before us, we find that substitution has been 

infrequent. Overall, in 1980 substitution occurred on about 

5.5 percent of prescriptions written for a specific brand for 

which an alternative product was available. 21 When prescriptions for which substitution was legally proscribed are 

removed, this rate rises to 7.3 percent. Even if approximate 

adjustments are made for the fact that physicians sometimes 

prohibit substitution explicitly, the overall substitution rate 

could not have been higher than 10 percent of all prescriptions 

eligible for substitution in 1980.22 

Substitution has been increasing and it is predicted to 

increase even more. The substitution rate in 1984 was double 

that in 1980, occurring still on probably less than a fifth of 

all eligible prescriptions. 23 Consumers, pharmacists, and 

physicians have presumably been gaining experience and knowledge about both the opportunities for substitution and its 

desirability. Moreover, the Hatch/Waxman Act of 1984, which 

makes less costly the introduction of generic products after 

the expiration of the patent of a leading brand, is expected to 

evoke a significant increase in the number of generic products 

on the market. Our overall measures of 1980 behavior, then, 

are an understatement of the role of substitution in 1985 and 

beyond. 

Even in 1984, however, substitution was relatively infrequent. This is puzzling in light of the incentives both consumers and pharmacists have to substitute. The most likely 

explanation is lack of clear and accurate information. In 

particular, it may be that consumers and pharmacists read into 

the fact that physicians specify a brand a strong preference 

on the physician's part for that particular brand, even when 

the physician does not choose to exercise the legal option to 

prohibit substitution explicitly. The physician may not, in 

fact, have that strong a preference, but the consumer's or 

pharmacist's uncertainty deters acceptance of a substitute 

brand. 

ll/ Nearly all (95 percent) substitutions were from leading brands to 

generics.

ll/ See Chapter 2. 

W See Chapter 2. 

9 

CHAPTER 1 

There are many sources of variation in the frequency of 

substitution. Substitution rates are very different from drug 

to drug, from none at all to a maximum, in 1980, of over 20 

percent. Substitution was most common for Medicaid consumers 

and least common for privately insured consumers, with uninsured consumers intermediate. We tested the hypothesis that 

chain drugstores are more likely to substitute than independents but found that the data did not support it. The extent 

of substitution varies also from state to state. 

One possible source of variation is the specific content 

of the state laws. Each DPS law is a composite of individual 

provisions, specifying the design of the physician's prescription pad (making it convenient or inconvenient for the physician to prohibit substitution), stating whether or not cost 

savings due to substitution must be passed through to the 

consumer, and regulating other aspects of the substitution 

process. In 1979 the FTC and the FDA together recommended to 

the states adoption of a set of specific provisions believed to 

promote drug product selection most effectively. The FTC/FDA 

Model Drug Product Selection Act is reproduced in full in 

Appendix A2. 24 

Some of the individual provisions of the state laws are 

shown to make a significant difference in the incidence of 

substitution. The contents of formularies, which prohibit 

substitution on certain drugs, of course have a substantial 

impact on overall substitution. Given that substitution is 

permitted, however, the presence of a (positive) formulary, 

appears to diminish substitution. The design of the physician's prescription pad is shown to be very important, confirming earlier findings and validating the recommendation of the 

Model Act. In particular, if prohibition of substitution is 

made especially convenient for the physician, substitution 

occurs less frequently because, presumably, physicians prohibit 

it more often. States which require pharmacists to discuss 

substitutions with customers have higher rates of substitution, 

suggesting that where consumers' attention is drawn to the 

W The Commission's recommendations were based in part on the FTC Staff 

Report (1979). 

10 
INTRODUCTION 

substitution option, they accept 1t. Where substitution is 

mandated, it occurs more frequently in some drugs, although 

most prescriptions are still dispensed as written. Finally, a 

requirement that cost savings be passed through to the consumer 

surprisingly does not seem to discourage substitution, despite 

the FTC Staff Report's (and our) prediction to the contrary. 

Lower average prescription prices through a higher generic 

market share can result not only from substitution but from 

other mechanisms as well. While for many drugs substitution 

is the primary source of the dispensing of generics, in others 

the dominant source is generically written prescriptions. 

Because many of the latter drugs are especially high-volume 

drugs, generically written prescriptions accounted for over 

two-thirds of all generics dispensed in 1980; of the generic 

market share of 25.1 percent for the U.S. as a whole in 1980, 

generically written prescriptions contributed 18.0 percentage 

points, substitutions 4.1 percentage points, and prescriptions written for specific non-leading brands 3.0 percentage 

points. 25 Therefore, both dispensing choices on generically 

written prescriptions and brand-vs.-generic prescribing choices 

are central in determining generic market share and therefore 

average prescription prices. Both types of decisions may be 

affected by the presence of the opportunity to substitute. 

The opportunity for substitution may increase the probability that a generic will be dispensed on generically-written 

prescriptions, due to consumers' enhanced knowledge and thereby 

acceptance of generics. Another link is that the increased 

possibilities for substitution may induce pharmacies to stock 

generics in drug entities where otherwise they would have 

stocked only leading brands and to use these generic products 

to fill generically written prescriptions that otherwise would 

have been filled with a leading brand. Our data show that the 

laws are associated with a slight increase (about 1.5 percent) 

in the generic share of products dispensed on generically 

written prescriptions.26 

ll/ See Table 6-3. 


W See Chapter 6. 


11 

CHAPTER 1 

Even more vital in determining generic market share are 

variations in generic prescribing, since nearly 90 percent of 

all generically-written prescriptions are filled with generic 

products. Generic prescribing varies a great deal both from 

drug to drug and from state to state. Our data show that the 

existence of a DPS law is associated with a higher incidence of 

generic prescribing, but the interrelationship between DPS laws 

and generic prescribing is complex and we cannot ascertain how 

much of the interstate differences are due to the law and how 

much to other influences. 27 

V. AN ESTIMATE OF THE OVERALL U1PACT OF DPS LAWS 

We have estimated the overall price impact of the DPS laws 

in 1980 by use of multiple regression. 28 Because of our inability to determine how much generic prescribing is influenced 

by the law, we provide two estimates which bracket the possibilities. Across the 45 multi-source drugs analyzed, the 

weighted average price decrease is -$.059 per prescription if 

none of the differences in generic prescribing are attributed 

to the law and -$.103 if all are. For 1980, the total dollar 

saving attributable to DPS laws was between $44 and $80 million 

(1980 dollars), using the two alternate assumptions. This 

estimate refers only to multi-source prescriptions, but we 

found no evidence of offsetting price increases on singlesource drugs due to the DPS 1aws.29 While substantial in 

absolute terms, this is about one-half of 1 percent of total 

purchases of prescription drugs through retail outlets, estimated to be $12 billion in 1980.30 

In light of changes since 1980, our 1980 estimates seriously 

underestimate the effects of the laws in 1985. Given a doubling of the substitution rate and increases in the number 

of prescriptions and in the average price of prescriptions, we 

W See Chapter 6. 


W See Chapter 8. 


W See Appendix A7. 


W IMS Reaearch Group (1981, p. 32). 


12 
I;-.ITRODUCTION 

extrapolate from our 1980 results to an estimated total dollar 

effect of from $130 to $236 million in 1984 (1984 dollars). 

With the future growth of generics augured by the change in 

regulatory requirements implementing the Hatch/Waxman Act of 

1984, these dollar savings are likely to be even greater in the 

future. 

VI. LIA1ITATIONS OF THIS STUDY 

There are some possible effects of drug product selection 

laws which our study canrrot measure. One effect of increased 

substitution may be that manufacturers of leading brands will 

feel pressure to lower their prices (at least relative to what 

they otherwise would have been.) That is, although an analysis 

of pricing by pharmaceutical manufacturers is beyond this 

study, it seems plausible that brand prices will not rise (in 

response to drug product selection) and will probably fall. 

The quantity demanded of leading brands, as measured by pharmacy purchases, will decline or at least not rise as rapidly as 

in the absence of drug product selection. But because this 

pressure on prices will be manifested over time, our study 

which is confined to a single year cannot test for it. 

This study does establish whether or not the preconditions 

for any manufacturer response to DPS exist, in that only if the 

laws do in fact lead to loss of market share will a manufacturer have an incentive to alter pricing policies. Since 

substitution does occur, although not at a great rate, it seems 

likely that manufacturer-level prices, especially on leading 

brands, will decline as a result. Similarly, increased demand 

by pharmacies for generic products may induce manufacturers to 

supply generics sooner and in more drug entities. Evidence 

that the laws have led pharmacies to select within the generic 

category those products with lower costs suggests that even 

among generics there has been room for competitive pressure on 

price.31 To the extent that manufacturers lower their prices, 

retail prescription prices may be lowered even more as a result 

of the drug product selection laws. Since en try in to the 

ll/ See Chapter 7. 

13 

CHAPTER 1 

production of generics is presumably easy, prices for generics 

are unlikely to rise even if demand is higher. 

In fact, the power of drug product selection to lower consumer prices for prescription drugs may be greatest through 

this effect on manufacturer-level prices. Just as a reduction 

in the retailer margin on a drug will benefit all consumers, 

not just those who actually receive substitutions, any reduction in the price of a leading brand as a result of man ufacturer-level price reductions will lower prices even for consumers who choose to stick to that brand. Lower prices would 

be paid not only by consumers in states which permit substitution in the particular drug entity but also by those where the 

formulary excluded the drug. 

We identify two types of possible offsets to the benefits of 

lower prices resulting from the drug product selection Ia ws. 

First, a substituted drug might be inferior to the drug prescribed. While this is undoubtedly an issue for some drugs, it 

is now generally held that for many drugs substitution is 

possible without loss of therapeutic efficacy. Second, it has 

been argued that DPS will diminish manufacturers' incentives 

for research and development and that the consequence will be 

less drug innovation. Thus, it is argued, although consumers 

may pay a lower price for a prescription now, they suffer later 

by the absence of valuable new therapies which the lost profits 

(on brand-name drugs) would have generated. While it is clear 

that enhanced substitution opportunities decrease the expected 

profitability of research aimed at the introduction of new, 

paten table drugs, and therefore decrease or slow down the 

introduction of new drugs, the magnitude of this effect is not 

known. 32 In this study we do not assess the effects of DPS 

laws on drug innovation and, therefore, their effects on total 

welfare. Our aim is to contribute one important component to 

W Grabowski and Vernon (1979) conclude that "the effects of substitution 

laws on innovation incentives are consequential in nature and are highly sensitive to the longevity of patent lives over the ranges considered (i.e., 10 to 

17 years.)" They analyze the effects on the expected rate of return on investment in drug research and development by use of alternative assumptions about 

the length of the effective patent life and the percentage reduction in net 

income after the expiration of the drug's patent. 

14 

INTRODUCTION 

such an overall assessment: a test of whether DPS laws lead to 

lower retail prescription prices. 

Our estimate of the effects of DPS laws on retail prices is 

not intended to be a measure of the impact of DPS laws on 

overall welfare. As the FTC Staff Report points out, most of 

the benefits are "transfer" benefits, from brand-name manufacturers to consumers. Only a small portion would be a real 

welfare gain, in the sense of expanded consumption of prescription drugs, for example, from unfilled prescriptions or underprescribing due to the higher prices that prevailed. 33 

VII. CONTRAST WITH PREVIOUS STUDIES 


OF DRUG PRODUCT SELECTION 


This work is distinguished from previous studies of drug 

product selection in several wa ys.34 Earlier studies typically 

were confined to one or at most several states. Differences in 

states' experiences were sometimes attributed to an individual 

provision in the law even though several other provisions may 

also have differed, as well as economic influences not embodied 

in the laws. With our nationwide data, we are better able to 

generalize about states' experiences with specific provisions 

of the laws. 

We offer a fuller discussion of the economic determinants of 

substitution and of the other effects of drug product selection 

laws. This leads, for example, to the analysis of brand and 

generic prices in addition to the price effect of substitution 

itself. We place substitution in the context of other influences on generic market share and thus on average prescription 

prices by providing data also on other dispensing decisions and 

~/ Of an earlier FTC benefits estimate of between $283 and $469 million 

annually, only $37 to $61 million were not simply transfe~. FTC Staff Report 

(1979, p. 201).

W A number of papers have been published by Theodore Goldberg and his 

associates, based on a major research project centered at Wayne State University. Results of these and other studies are being compiled to be published by 

the National Center for Health SerYices Research in Goldberg and Raskin, eds., 

(forthcoming). We review only briefly some of these earlier results; see Chapter 5. 

15 

CHAPTER 1 

the prescriber's decision as to whether to name a brand or 

to prescribe generically. 

VIII. OUTLINE OF THIS REPORT 

Measures of the overall incidence of substitution in the 

year of our study, 1980, and more recently, 1984, are reported 

in Chapter 2, following a description of the universe of prescriptions for which substitution is possible and permissible. 

The data underlying these and other results are described in 

Appendix A6, and some detail on state formularies is presented 

in Appendix A3. Chapter 3 analyzes why substitution might be 

expected at all and then turns to the puzzle of the surprisingly low incidence of substitution. Chapter 4 reports on several 

sources of variability in substitution rates: drug identity, 

customer insurance coverage, store ownership type (chain and 

independent), and state. Data on the striking differences 

among drugs in substitution rates as well as prescribing patterns and many other attributes are given in Appendix A5, a 

table showing data for each of the 45 multi-source drugs 

included in this study. 

Chapter 5 is about the individual provisions of the DPS 

statutes. First, these regulations are described and hypotheses are developed. The recommendations of the FTC/FDA 

Model Act are also reviewed. A more detailed discussion of the 

statutory provisions and a table summarizing them is presented 

in Appendix A 1, and the FTC/FDA Model Act is reproduced as 

Appendix A2. Also in Chapter 5, a multiple regression logit 

model used to analyze the effects of major legal provisions on 

the probability of substitution is presented and the results 

reported. Some data on the legal provisions classified in 

greater detail are given in Appendix A4, and additional technical discussion of the regression procedures used is found in 

Appendix A7. 

In Chapter 6, variations in overall generic market share and 

its components are discussed. Generic prescribing is emphasized because of its importance in explaining variations in 

generic market share. Multiple regression is used in Chapter 7 

to address the question of whether the DPS laws have affected 

16 

INTRO0 UCTION 

the retail pnces of individual leading brands and generic 

products. 

In Chapter 8, multiple regression is used to estimate the 

DPS laws' effect on the average retail prescription price in 

1980. These estimates are then projected to the total volume 

of multi-source prescriptions. The last chapter, Chapter 9, 

summarizes our findings and returns to the issue of the performance of prescription drug manufacturers and retailers viewed 

as an integrated system. 

17 

18 

CHAPTER 2 

THE INCIDENCE OF SUBSTITUTION 

Substitution occurs on only a small proportion of prescripttons. We report summary data for 1984 and look at 1980, the 

year analyzed in this study, in more detail. One reason is 

that in 1980 only about a third of all prescriptions were 

eligible for substitution, but even for eligible prescriptions, 

the substitution rate was low. Data on the past probably 

understate the future role of substitution, since substitu tion 

rates have been increasing rapidly and the recent passage of 

the Hatch/Waxman Act may lead to more rapid entry of generic 

products. 

I. OVERALL RATES OF SUBSTITUTION IN 1984 

According to IMS' most recent figures, substi tu tion occurred 

on 5.4 percent of all new prescriptions during the first half 

of 1984. 1 When the base is taken as only those new perscriptions which were written for a specific brand of a multi-source 

1/ Chappell (Oct. 1984, p. 31). Chappell is a vice-president of IMS 

America, Ltd. 

It has been suggested that the IMS methodology systematically underestimates 

the extent of substitution. Shopping studies by McKercher (1980) and Morgan 

and Kagan (1984) find much higher rates than those found in studies using an 

auditing methodology, such as that used by IMS. IMS itself alludes to possible 

underreporting: "While the actual volume of substitution may be underreported 

in the NPA, we continue to see a believable trend." IMS America, Ltd., "1983 

Review" (1984, p. 48). Other estimates of overall generic market share, 

obtained from representatives of several major drugstore chains and from 

financial analysts as reported in national publications, when taken in conjunction with patterns of prescribing and dispensing shown by IMS' NPA, are 

consistent only with higher rates of substitution. By a very rough computation, we estimate that with the maximum possible undentatement by IMS would 

require a doubling of their reported substitution rates. Even if there 

is understatement, however, it would not affect the validity of our 1980 crousectional results unleu the understatement occurred in some biased way, and 

the maximum adjustment would raise 1984 substitution rates only to the level of 

"modest" rather than "low". 

19 

CHAPTER 2 

drug -- conditions necessary for substitut io n to be a possibility -- the rate is 9.5 percent. 2 If a ll the criteria fo r 

eligibility for substitutio n could be taken into account, as 

discussed below in the context of ou r 1980 data, the rate would 

be somewhat higher. 

fl. DATA USED FOR 1980 SUBSTITUTION RATES 

Except for the summary figures for 1984, the substitution 

rates in this and subsequent chapters are computed from 

detailed data from IMS' 1980 National Prescription Audit 

(NPA). 

3 Measures of overall substitution in 1980 prov ide the 

context for our detailed cross-section analysis, which utilizes 

the 1980 data. 

We used only a portion of the entire 1980 NPA data set. 

The 45 multi-sou rce drug entities we used were all of the top 

100 entities which were multi-source except for the elimination 

of entities in which there was no si ngle strength (i n miliigrams) tablet o r capsule for oral consumption which had at 

least 500 observations; 5 multi-source entities with data 

problems were also eliminated.4 We further delimited the 

sample b y selecting for each entity a single solid oral dosage 

form, a strength, and the top five prescription sizes in 

numbers of tablets or capsules. (See Appendix A6 for further 

description of t he data and the sample.) 

For this report, substitu tion is defined as the dispensing 

of one manufacturer's or distributor's product when the prescription has named a different firm's version of the same drug 

!/ Choppell, p. 30. 

/ The 1980 and 1984 ratea are therefore subject to two possible sou rces of 

understatement: 1) undermeasurement of substitut ion due to IMS' data collection methodology; and 2) failure to exclude from the base, because of lack of 

data, thoee preacriptions on which physician• p rohibited substitution. See 

diacueeion in footnote 1 and section III below. · 

i/ Drur rank waa determined by total dollar sales t o drugstores , b ased on 

data from IMS' U.S. Drugatore Audit; the liat of t op en tities waa provided to 

the FTC by the Health Care Financing Administration . The identificat ion of 

entitiee which are single-source was baaed on number of supplie" listed in 

IMS' coding manu als for its NPA. 

20 
THE INCIDENCE OF SUBSTITUTION 

entity (in the same dosage form and strength.) 5 This includes 

substitution from one leading brand to another, or from one 

generic brand to another, as well as from a leading brand to a 

generic. Thus the term "substitution" is more inclusive than 

"generic su bsti tu tion," which refers only to those su bsti tutions from a leading brand to a generic. In fact, nearly all 

s u bsti tu tions (94. I percent in I 980) are from a leading brand 

to a generic, where "generic" is used to include all brands 

other than those which are among the top 200 products prescribed or which are named on at least 20 percent of the 

prescriptions in the specific drug entity.6 

The summary substitution rates in this report use as a base 

prescriptions for 45 leading multi-source drugs in all states. 

They are weighted averages of the 1980 substitution rates for 

each of the 45 drugs. Unless otherwise indica ted, the weights 

used are the shares, for each drug, of the number of new and 

refill prescriptions for solid oral dosage forms of the drug in 

the United States in I 980. 7 

By our estimate, in 1980 su bsti tu tion occurred on 5.5 percent of prescriptions written for multi-source drugs for which 

a brand was specified and for which substitution was therefore 

a possibility, for the 45 leading multi-source drugs used in 

our study. IMS' published substitution rates for 1980, based 

also on the NP A, were 5.1 percent of all new multi-source 

brand-written prescriptions, or 2.78 percent of all new 

prescriptions.8 

§_/ Where the product actually dispensed was not recorded, there is no way 

to judge whether a substitution was made. We exclude such observations from 

our analysis of substitution. 

§./ The list of the 200 moat frequently prescribed products is published 

annually in the April issue of Pharmacy Times, based on IMS 's NPA. See Appendix A6 for discussion of these definitions. 

1/ See Appendix A6 for discussion of these weights. 

§./ Chappell {Oct. 1984, p. 30). The difference between our estimate (5.5 

percent) and the published IMS summary estimate (5.1 percent) is due to the 

fact that our computation is based on only a portion of the data used for the 

IMS summary. We also weight the data by each drug's share of U.S. presciption 

sales, weights which may differ from those used by IMS. If instead we use as 

weights the share of dollar retail sales of each of the 45 drugs, the substitution rate is somewhat lower: 4.7 percent as compared to the 5.5 percent. That 

is, of all dollars spent (on these 45 multi-source entities), 4.7 per cent were 

aasociated with substitutions. The difference between these two rates reflects 

21 

CHAPTER 2 

The 1984 figures given above are based on the same Hv1S data 

base and are IMS' own published summaries. Comparing the 1980 

and 1984 figures shows that the rate of substitution approximately doubled over those four years.9 

!II. PRESCRIPTION ELIGIBILITY FOR SUBSTITUTION 

One reason that substitutions constituted only 3 percent of 

all new prescriptions in 1980 is that drug product selection 

laws enlarge the pharmacist's ability to choose which brand of 

a drug to dispense only under very special circumstances. 

There are five criteria for a prescription to be eligible for 

su bsti tu tion. Because each of these requirements shrinks the 

proportion of prescriptions for which substitution can be 

considered, in the end only about a third of all prescriptions 

are candidates for substitution. 

First, no substitutions are possible for single-source products such as new products still under patent protection. In 

1980, the year we studied, multi-source prescriptions -- for 

generic entities in which at least two products were available 

-- accounted for a little over two-thirds of new prescriptions.10 Because of the success of some newly introduced and 

therefore still patent-protected drugs, the single-source 

share of prescriptions is higher in 1984. 11 

the fact that substitution was more frequent on less expensive drugs. Our 

definition also excludes from the denominator those prescriptions for which the 

brand dispensed was not identified, .2 percent of brand-written prescriptions. 

ft./ Another source, Market Measure Inc., confirms the trend. According to 

the Market Measures' National Substitution Audit, the percent of all new retail 

prescriptions on which substitution occurred rose from 0.65 percent in March 

1977 to 4.70 percent in March 1983. See Zeich (1983). 

10/ IMS Research Group, "1980 Review" (1981, p. 21). Of all new prescriptions, 31 percent were for single-source drugs, the remaining 69 percent for 

multi-source. Of the top 100 drug entities in 1980, 41 were offered by only 

one supplier. 

ll/ The share of prescriptions written for single-source products rose 

from 31 percent in 1980 to 35 percent in 1982, and there was a concomitant 

drop in the number of the top 200 leading brands which are in multi-source 

entities, from 152 in 1980 to 144 in 1982. IMS America, Ltd., "1982 Review" 

(1983, pp. 43-45). Chappell comments on the continuation of the same trend 

(Oct. 1984, p. 30). 
THE I!'l"CIDENCE OF SUBSTITUTION 

Second, the prescription for the multi-source drug must name 

a particular brand of a drug. By definition, substitution is 

not possible on a generically written prescription because any 

brand may be used in filling it. 12 In 1980, 80 percent of 

multi-source prescriptions named a particular brand.l 3 

Although until recently there had been a trend towards more 

generic prescribing, the data for 1983 reported by IMS suggest 

a reversal; even for the older antibiotics, which account for 

much of the generic prescribing, 14 a smaller percentage of 

prescriptions were written generically in 1983 than in 1982. 15 

Third, the state in which the prescription is filled must 

not have a general anti-substitution law. As of now, all 

states permit some substitution, but until this year one of the 

major sources of growth of prescriptions for which substitution 

was possible was the spread of additional state drug product 

selection laws. In 1980, three states prohibited all substitution: Indiana, Texas, and, until October when its substitution 

law went into effect, Louisiana. 16 These three states 

accounted for 8.7 percent of the 1980 prescriptions studied. 1; 

Fourth, the state law must not only allow substitution in 

general but substitution on prescriptions for the particular 

drug must be permitted by the formulary. In 1980 almost twoll/ Some state drug product selection laws regulate which products may be 

dispensed on generically written prescriptions, in some cases restricting the 

choice to certain products listed on the drug product selection formulary and 

in 	some cases specifying that the product dispensed must be one with a low 

cost, variously specified. See Chapter 5. 

13/ In the 45 leading multi-source entities we analyzed, :.!0.1 percent of 

prescriptions were written generically. 

When this second criterion waa met, 54 percent of all prescriptions were 

eligible for substitution in 1980. 

14/ Generically written prescriptions are concentrated in certain drug 

categories; over half of all generically written prescriptions were for antibiotics, according to IMS' NPA for January-June 1984. Chappell (Oct. 1984, 

p. 	29).

W Chappell (Oct. 1984).

W Oklahoma's 1961 law can be read to allow substitution within a generic 

entity but has also been sometimes characterized as an anti-substitution law. 

Its language is far less clear in permitting substitution than most of the 

newer laws. We treat Oklahoma as allowing substitution. 

ll/ The exclusion of these three states reduced the 1980 opportunity for 

substitution to 49 percent of all prescriptions in the United States, or 71 

percent of multi-source prescriptions. 

23 

CHAPTER 2 

thirds of the states with drug product scle..::tion laws had formularies which imposed selected restrictions on substitution. 

Formularies vary from allowing substitution on nearly all drugs 

to prohibiting substitution on many. 18 When all states permitting substitution on some or all multi-source drugs are taken 

together, our data for the 45 drug entities studied show substitution permitted on 80.6 percent of all multi-source brandwritten prescriptions in 1980, disallowing substitution on 

the remaining 19.4 percen t. 19 

Fifth, since in every state the physician retains the right 

to prohibit substitution on a prescription, substitution IS 

possible only when the physician has not ruled it out. In many 

states, such prohibitions occur less than 5 percent of the time 

but in others the incidence seems to be as high as 60 percent.20 One reason for this striking difference in physician 

behavior is the mandated design of the physician's prescription pad -- whether prohibition is made easy or difficult. 21 

This seemingly minor factor operates in conjunction with 

habitual behavior on the part of prescribers. There is some 

evidence suggesting that physicians "more often exercise their 

1:§./ See Appendix Table Al-l. 

J.!l/ When formulary restrictions are added to the restrictions previously 

discussed, only 39 percent [80.6 percent of the previous 49 percent] of all 

prescriptions, including those for single-source drugs, were eligible for substitution in 1980. Alternatively, when formulary restrictions are taken into 

account, substitution was possible on 74 percent of all brand multi-source 

prescriptions in 1980 [80.6 percent times the 91.3 percent in DPS states.] 

If all states permitted substitution, as was true by 1984, the comparable 

figures would be 81 percent of brand-written multi-source prescriptions and 43 

percent of all prescriptions. 

W Three reviews of multiple studies are: 1) Goldberg and DeVito (1981); 

2) Gurley and Gagnon (1981); and 3) Traxler and Siegenthaler (forthcoming). 

Another croes-state analysis is an article based on survey data compiled by 

Market Measures (Zeich, 1984). Zeich states that the number! are based on 

prescriptions for 13 varied products in a sample of retail stores (about 100 in 

all) in New York and Pennsylvania -- both two-line signature-pad states -- in 

early 1982. The rate of physician prohibition in those two states had been 

somewhat higher in a 1978-79 survey, when six other states were also surveyed. 

The other states had prescription forms less conducive to physician prohibition 

of substitution. Zeich's estimate of a 5 percent rate in one-line states is 

based on the earlier survey.

ll/ See Chapter 5 and Appendix A4 for discussions and evaluations of prescription pad formats. 

24 
THE INCIDENCE OF SUBSTITUTION 

'veto' because they oppose product selection as an intrusion 

into their professional autonomy than because of possible 

medical concerns about a particular drug product." 22 For 

example, prohibitions occur with about the same frequency 

across all drugs, regardless of their therapeutic category, and 

equally often for single-source drugs, and on genericallywritten prescriptions as well, even though for the latter two 

categories substitution is by definition impossible and therefore a prohibition of substitution makes no sense. 23 If physicians prohibited substitution on 10 percent of prescriptions -· 

a rough figure -- 35 percent of all prescriptions were eligible 

for substitution in 1980, or 67 percent of brand-written 

multi-source prescriptions. 24 

When all of these restrictions are taken into account, substitution was an option available on about two-thirds of all 

brand-written multi-source prescriptions. This is equivalent 

to about one-third of all prescriptions in 1980. 

Figure 2-1 shows how the new 758,000 prescriptions audited 

by IMS in 1980 yielded only 414,000 prescriptions on which 

substitution was possible (disregarding physician prohibitions 

and state laws and f ormularies.) 25 

22/ FTC Staff Report (1979, p. 275). 

23/ Ibid. (1979, pp. 275-278). A recent study of Kentucky's experience 

confirms this pattern. "In fact, the prohibition of substitution was slightly 

greater for prescription orders for single-source entities -- where no product 

choice is possible -- than for multiple-source prescription orders -- where 

some product choice is possible, 6.4% versus 5.3% respectively. Prohibition of 

substitution even occurred to a certain extent (4.6%) on generically written 

prescriptions, where the prescriber had not indicated a product choice and the 

pharmacist had to select a product to dispense." DeVito (1982}.

llJ If all states permitted substitution the comparable figures would be 

36 percent of all prescriptions, or 53 percent of brand-written multi-source 

prescriptions.

1.§./ This IMS ratio is 55 percent of all new prescriptions, higher than oul"8 

because of the several major restrictions on substitution not being taken into 

account. 

25 
CHAPTER 2 

FIGURE 2-1 


New Prescriptions, 1980: 


How Prescribed and How Dispensed 


758 MILLION 


TOTAl 


100% 


/


234 MILLION 524 MILLION 

SINGLE-SOURCE MULTI-SOURCE 

31 ~. 69% 

~/ .....---...\........_.... 


414 MILLION 110 MILLION 

WRITTEN FOR A GENERICALLY 

BRAND WR ITTEN 

55~. 15% 

/ \ 


SOURCE: IMS Re1earch Group, IMS America, Ltd., "The U.S. Pharmaceutical 

Mar.ke& 1980, A Review," Ambler, Pa., 1981, p. 21. 

393 MILLION 

DISPENSED AS 

WRITTEN 

52•1. 

21 MILL IO N 

SUBSTITUTIONS 

OCCURRED 

3% 

26 

THE INCIDENCE OF SUBSTITUTION 

IV. SUBSTITUTION ON ELIGIBLE PRESCRIPTIONS IN 1980 

What is even more striking, then, is that even of the prescriptions which met all five criteria for a prescription to be 

eligible for substitution in 1980, fewer than 10 percent were 

substituted. 26 Based on their own research at about that time, 

Goldberg and DeVito concluded: 

It may be a bit of an exaggeration, but it seems fair 

to conclude that consumers generally do not request substitution, physicians generally do not prohibit substitution, and pharmacists generally do not substitute. 

It seems apparent that relative apathy has prevailed on 

all sides. 27 

Specifically, according to our 1980 data for 45 multi-source 

drugs, 7.3 percent of all prescriptions meeting the first four 

criteria -- multi-source brand-written prescriptions on which 

both state statute and formulary permitted substitution-- were 

substituted. If we make a rough adjustment for the fifth criterion, the incidence of physician prohibitions, for which we 

lack data, the 7.3 percent would imply about a 8.1 percent 

rate if physicians prohibit substitution on 10 percent of 

multi-source brand-written prescriptions. The basic conclusion 

must be that substitution fell far short of the maximum 

potential of 100 percent. 

V. THE EFFECT OF DRUG PRODUCT SELECTION 


LAWS ON SUBSTITUTION 


Table 2-1 summarizes the differences in the overall rates of 

substitution in 1980 according to the legal opportunity to 

26/ See computations in following discunion. 

llf Goldberg anJ DeVito (1981, p. 79). 

27 
CHAPTER 2 

TABLE 2-1 

Overall Substitution Rates, 


by Legal Opportunity to Substitute,l9801 


Substitution illegal 

(According to either 

general statute Substitution 

or formulary) legal Both 

States without 

DPS law 0.4% N/A 0.4% 

States with 

DPS law 1.8 7.3 6.0 

All states 1.4 7.3 5.5 

!/ The base is brand-written multi-source prescriptions. The row and column 

headings define the pool of prescriptions for which the substitution rate is 

computed. For example, the figure in the second row and first column indicates 

that substitution occurred for 1.8 percent of all brand-written multi-source 

prescriptions written in states with DPS laws but for which substitution waa 

nonetheless illegal (because substitution on prescriptions for the drug waa 

prohibited by state formulary.) 

SOURCE: Computed with data for 45 leading multi-source drugs from the 1980 

IMS National Prescription Audit. 

substitute.28 Two types of comparisons are useful. First, all 

prescriptions for multi-source drugs are used as a base. 

Second, only those prescriptions for which substitution is 

permitted according to the state's formulary are used as the 

base. On the one hand, to compare substitution across all 

drugs is to ignore the formulary restrictions and therefore to 

understate the rate of substitution when it is allowed. On 

the other hand, a comparison of substitution on prescriptions 

~/ As explained above, these substitution rates understate substitution 

as a percent of eligible prescriptions because prescriptions on which the 

physician has prohibited substitution cannot be exc:Juded from the denominator 

in the data which we used. For the remainder of the report, however, the 

substitution rates reported will be those based on the IMS 1980 data, the 

overall average of which is 7.3 percent. 

28 

TdE INCIDENCE OF SUBSTITUTION 

in states with formularies (i.e., some of the states with DPS 

laws) and substitution on prescriptions 1n states without 

formularies (including any state prohibiting substitution) 

makes the substitution-prohibited prescriptions appear to have 

substitution rates relative to the substitution-permitted 

prescriptions lower by more than would be true had the formularies been the same in all states. States with formularies 

tend to prohibit substitution on precisely those drugs for 

which substitution would have been relatively infrequent if it 

had been allowed. 

Thus, using as a base prescriptions for only those drugs 

for which substitution is allowed tends to overstate the 

substitution rates that would prevail if substitution were 

allowed on prescriptions for all drugs. 29 

In states with DPS laws substitution occurred on 7.3 percent of all eligible prescriptions as opposed to 1.4 percent of 

perscriptions where substitution was illegal.30 When 

11./ Tests of statistical significance are not reported on this table nor on 

following tables for several reasons. First, in many compariaona, the differencea are large and would obviously meet conventional teats of significance. Second, even for small differences the extremely large number of 

observations underlying each measure means that the estimate of the variance of 

the proportion is extremely small and therefore the value of t for the difference between two proportions is extremely large. For example, the 0.4 

percent estimate in the first column is baaed on 14,774 prescriptions, the 1.8 

percent estimate on 29,903. The value of t for the difference between these 

two proportions (drawn from independent populations) is 17.5. Third, a number 

of the relationships portrayed in cross-tabulations are tested again in the 

context of multiple regression analysis of substitution decisions and of 

prices, reported in Chapters 5, 7 and 8. Statistical testa of all coefficien ta 

are reported there. 

30/ It is evident that substitution, as measured with our data, is not 

totally absent even when statute or formulary forbids it. There are several 

possible explanations for the "apparently illegal" substitutions. One, of 

course, is simple non-compliance with the law. A second is mistakes in coding 

the data, whether at the pharmacy level, in IMS' transcription, or in our 

coding of the formularies. A third pouible explanation is that a physician 

may give selected pharmacists blanket permission to substitute on certain 

drugs, telling the pharmacies in effect to treat certain brand-written prescriptions aa if they were written generically. This permission may be recorded in writing. 

Although a formulary has the appearance of great precision, in practice 

there are some ambiguities. Therefore, for some drugs there may be reasonable 

differences in interpretation as to whether substitution is permitted. In 

seeking clarification of states' formularies with state Boards of Pharmacy, we 

29 

CHAPTER 2 

substitution computed across all 45 drugs are used Js a basis 

for comparison, the resulting 6.0 percent in the 47 states 

where a general DPS statute was in effect in 1980 is an order 

of magnitude larger than the 0.4 percent rate in states where 

the statute prohibited all substitution. There can be no doubt 

that DPS laws have led to more substitution. 

VI. THE UPWARD TREND IN SUBSTITUTION 

Since 1980, the trend in substitution has been steadily and 

strongly upward, with the rate of substitution on all new 

prescriptions rising from 2. 7 8 percent in 1980 to 4.29 percent 

in 1982, to 4.91 percent in 198 3, to 5.4 percent during the 

first half of 1984.31 Some of this increase is due to changes 

were frequently told that since neither complaint nor request for advice had 

been received about the specific drug, no determination had been made and 

therefore the law was not well defined. This was true especially in states 

which tied their formularies to the FDA list of therapeutic equivalents. That 

list left aome ambiguities, since the FDA gave no equivalency ratings for drugs 

marketed prior to 1938 (and therefore not subject to the pre-market clearance 

procedure) for druga marketed between 1938 and 1962 that were approved for 

safety but not effectiveness and were still being reviewed under the administrative procedures of the Drug Efficacy Study Implementation (DESI) program 

(e.g., isosorbide dinitrate, nitroglycerin, dipyrimadole). Thus a state which 

generally allowed substitution on drugs rated "A" in the FDA list and prohibited it on drugs rated "B" had neither rating to guide decisions on unrated 

drugs. The same was apparently true for "old" drugs, i.e., those marketed 

before the imposition of the New Drug Application regulations; these also are 

unrated. (The FTC/FDA Model State Act handles this problem by suggesting that 

states following the FDA "Therapeutic Equivalents" list add to it all drugs not 

subject to FDA approval for safety and efficacy, drugs approved only for safety 

prior to 1962, and drugs marketed prior to 1938.) 

An analysis of actual substitution! in our data which were apparently 

prohibited by formulary suggested that confusion about the formulary may 

have been involved. There were, for example, instances where the formulary and 

prescription referred to slightly different dosage forms, or where one specified a single-entity prescription and the other a combination. Borderline 

misinterpretation! of the formulary cannot, of course, explain the substitutions in the three states prohibiting substitution altogether, but they may be 

one reason why there are more "apparently illegal" substitutions in states 

permitting substitution generally, many with formularies, than in the three 

ba.aeline states. 

ll/ Chappell (Oct. 1984). 

30 
THE INCIDENCE OF SUBSTITUTION 

in the proportion of all prescriptions eligible for substitution, but most of it seems due to an increased exercise of the 

substitution option when it is available. When the base is 

taken as all multi-source brand-written prescriptions, this 

rate has also nearly doubled between 1980 and 1984.32 If, as 

for 1980, we use as a rough adjustment a 10 percent rate of 

physician prohibitions and assume equivalent formulary coverage, the 8.1 percent substitution rate on wholly eligible 

prescriptions estimated for 1980 would be estimated to have 

risen to nearly 15 percent in 1984. 

There is good reason to expect substitution to continue to 

increase. Acceptance by prescribers, dispensers and ultimate 

users can be expected to continue to grow, and local advertising of generics programs by drug chains will spread awareness 

of the advantages of substitution to consumers. Market 

Measures, a marketing research firm, suggests that su bstitu tion 

"will continue to increase at a rate of 15% to 20% yearly for 

the next [few] years." 33 IMS also predicts that substitution 

rates will probably continue to rise and that the absolute 

volume of prescriptions on which substitutions are made is even 

more certain to grow. 34 

A number of important patents are due to expire soon; if new 

entry follows, there will be further opportunity for drug product selection. The Ha tch/Waxman Act, passed in 1984, permits 

more rapid entry of follow-on products, after patent expiration, for drugs approved by the FDA since 1962. 35 At the time 

this bill became law, there were a number of drugs with large 

sales already off patent where no competitor had entered to 

challenge the pioneer brand. The largest of these was Dyazide, 

a brand name for the combination drug triamterene with hydrochlorothiazide, with 1983 sales of over $200 million, off 

patent since 1980.36 There is now a competing version of this 

32/ Chappell (Oct. 1984, p. 31).

W Zeich (1983, p. 73).

W IMS America, Ltd., "1982 Review" (1983, p. 45).

ill Hatch/Waxman Drug Price Competition and Patent Term Restoration Act of 

1984. 

36/ Data for the drugs named in this paragraph are taken from "Pillbox 

War ...," Wall Street Journal (August 13, 1984, p. 1). 

31 

CHAPTER 2 

combination. Other off-patent drugs are now more likely to 

face competing brands. The patent for chlordiazepoxide 

(Valium), with 1983 sales of $250 million, expired in 1985, as 

did the patent for ibuprofen, sold as Motrin, with 1983 sales 

of $185 million. The patent for chlorpropamide, sold under the 

name of Diabinase, with a $125 million market, expired in 

1984. In addition, a number of the top-selling drugs are due 

to go off patent soon. By one count, there were 44 major drugs 

accounting for $2.5 billion in sales in 1983 due to go off 

patent by 1988.37 The opportunity for generic entry, in 

combination with a less costly FDA approval process, suggests 

that the generic market share will increase substantially. 

VII. CONCLUSION 

By 1984, substitutions were occurring on a madera te proportion -- perhaps 16 percent -- of eligible prescriptions. These 

rates represent both an economically important change from the 

anti-substitution era and, at the same time, a refusal of the 

substitution option on most of the opportunities available. 

Substitution activity has doubled in the four years since 

1980 and there are strong indications that it will continue to 

grow. The Ha tch/Waxman Act of 1984 makes entry of generics 

less costly. The forecast of more generic products on the 

market will mean a broader scope for substitution. 

Substitution is possible on only about one-third of all prescriptions. Formularies and physician directives prohibit 

substitution on some prescriptions, and many prescriptions are 

written either generically or for single-source drugs, in 

either case making substitution impossible. Because of these 

criteria for eligibility for substitution, substitutions were 

made on just over 5 percent of all new prescriptions in 1984. 

W MThe Shift to Generic Drugs," New York Timea (July 23, 1984, p. D5). 

32 
THE INCIDE:'<'CE OF SUBSTITUTION 

The remainder of this study is an analysis of substitution 

patterns in 1980, based on IMS' National Prescription Audit. 

According to our data for 1980, substitution occurred on 7.3 

percent of eligible prescriptions; a reasonable adjustment for 

physician prohibitions raises the rate to 8.1 percent. These 

substitutions constituted about 3 percent of all new prescriptions. 

33 

34 

CHAPTER 3 

WHY ISN'T SUBSTITUTION PREY ALENT? 

The leading brand in a generic entity nearly always is sold 

at a higher price than other versions of the same entity, and 

typically the price difference is large. This opportunity for 

consumer savings is what propelled the movement towards drug 

product selection laws. 1 Nevertheless, substitution is not the 

common practice. In this chapter we address this puzzle. 

Although we do not have direct tests of hypotheses about the 

infrequency of substitution, we do have some suggestive 

indirect evidence. 

I. INCENTIVES FOR SUBSTITUTION 

The retail price differential is clearly large, as Table 3-1 

shows. In all but three of the 37 drug entities summarized in 

the table, the pr;ce of an average prescription of the leading 

brand was greater than the price of an average prescription of 

other brands, and in one of these three the difference is 

reversed when prescriptions of the same size are compared. 2 

Can the infrequency of substitution be explained by financial disincentives to substitute on the part of pharmacists? 

On the contrary, pharmacists too have incentives to substitute. If consumer prefer generics due to their lower 

1./ This discussion is cast in terms of consumers who are not insured for 

out-patient prescription drug costs. The effects of ~1edicaid and other 

private insurance are discussed in Ch:~pter 4. In fact, 80 percent of the 

prescriptions in our sample were for "cash" customers, although some of these 

customers undoubtedly had insurance coverage of which the pharmacist was 

unaware. 

1/ See Appendix A6 for discussion of these three anomalies. 

35 

CHAPTER 3 

TABLE 3-l 


Brands and Generics: 


Retail Prices, Invoice Costs, and Retail Dollar Gross Margins, 


1980 


Brand-Generic 

Brands Generics Difference 

Retail Price $8.22 $6.22 $2.00 

Invoice Cost 4.86 2.65 2.21 

Retail Dollar 

Gross Margin 3.35 3.57 - .22 

1/ The numbers are sales-weighted averages across the 37 drugs where both 

leading brand and generic were dispensed, with the standard weights (solid oral 

dosage form, number of prescriptions} across all states. Data for individual 

drugs are given in Appendix AS. The nature of the data is discussed in 

Appendix A6. 

SOURCE: Computed with data from the 1980 IMS National Prescription Audit 

and the 1980 IMS U.S. Drugstore Audit. 

prices, then retailers who substitute will profit by drawing 

business away from competitors.3 Moreover, the retail dollar 

gross margin earned on a generic version is typically higher 

than that on the leading brand, 4 so that even on the individual 

transaction substitution to a generic is more profitable than 

dispensing the brand as written. As Table 3-1 shows, the 

generic price is, on average, lcwer than the brand price by 

less than the difference between their invoice costs; the 

dollar gross margin was higher on generics than on brands for 

~ One study showed, however, that a higher level of competition between 

retail pharmacies was not associated with increased substitution. Kralewski et 

a!. {1983). 

1./ Retailer dollar gross margin is defined as the difference between the 

retail price and the invoice cost of the product. 

36 
----------------'-\\'-''!._,_1...!.Y~I:..:.;S'-'-~'-'...!.T_;S,;...l;;;_!~B.:;;S~TITCTI 0 ~ P REV AL E~T 7 

23 of the 37 drug entities. 5 We regard these estimates of the 

differences between brand and generic margins as being conservative. As discussed in Appendix 6, we have reason to believe 

that our data overstate invoice costs and moreover overstate 

the invoice costs of generics more than the costs of brands; if 

this is true, the gross margins on generics is understated 

by more than the gross margins on brands. 6 

To investigate the role of margins in the substitution 

decision directly, we compared the retail margins when, first, 

the leading brand named on the prescription was in fact 

dispensed and, second, when a generic version was substituted 

for the designated brand. As shown in Table 3-2, gross 

margins are higher for 7 of the 10 top-substitution drugs. 7 

That gross margins are higher on the generic versions of 

prescription drugs is not surprising. On products or brands on 

which consumers are most sensitive to price differences, gross 

margins can be expected to be lowest. The more price information consumers have and the greater the incentive to patronize 

a low-price store, the greater the competitive pressure on 

prices and therefore on margins. Where a leading brand of a 

prescription drug has large sales, there is simply more 

i nformation about it afloat in the market than on a product 

purchased less frequently. Consumers can compare price more 

easily on a well-known prescription brand: it is easier to ask 

a friend who takes the same brand of medication about the price 

paid; it is easier to find its price from an in-store price 

poster; and it is possible to telephone several stores and 

§) A one-tailed binomial test of the 23:14 split of signs is significant at 

the 10-percent level. This comparison is for prescriptions of a standardized 

size (for each entity.) These computations are across all legal regimes. 

f}j Indeed it is possible, as one chain executive to whom we spoke believes, 

that gross margins are actually greater on generics than on brands in nearly 

all drugs.

1/ The three exceptions are druga where the cost de~ta used (or, in the case 

of amoxicillin, the categorization of brands and generics) are especially 

suapect. 

37 

CHAPTER 3 

TABLE 3-2 

Retail Dollar Gross Margins 1 on Substituted 


and Non-substituted Prescriptions 


for the 10 Top-Substitution Drugs, 2 1980 


Brand Prescribed, Brand Prescribed, 

Drug Brand Dispensed Generic Substituted 

Hydrochlorothiazide 

Chlordiazepoxide 

Amitriptyline 

Penicillin VK 

Amoxicillin 

Atropine sulfate/ 

Diphenoxylate 

Meclizine 

Isosorbide dinitrate 

Hydralazine/Hydrochlorothiazide/Reserpine 

Doxycycline 

$2.98 

2.66 

6.25 

3.52 

4.21 

2.96 

3.39 

2.88 

3.02 

3.54 

$3.55 

3.74 

4.01 

3.60 

3.50 

3.32 

3.79 

3.64 

2.13 

5.32 

l/ For each drug, the gross margins were compared for an average-sized prescription. The substitutions are only those where a generic was dispensed. 

l/ The ten drugs are listed in descending order by substitution rate. 

SOURCE: Computed with data from the 1980 IMS National Prescription Audit. 

specify exactly the product on which the price quotation is 

sought. In contrast, it is both more difficult to ask two 

stores their prices on a generic with a longer and unfamiliar 

name, and it is more difficult to be sure that the two products 

would have the same therapeutic effect. Because price competition is more intense on leading brands, prices are held by 

competition nearer the costs of providing the product, and 

therefore gross margins are lower on leading brands than on the 

38 

WHY ISN'T SUBSTITUTI0;-.1 PREVALENT" 

less well-known generics. 8 Finally, since drawing a customer 

to the store for a particular product is likely to yield 

additional sales of other products, the multi-product nature of 

the retailer magnifies the pressure on the margin of a leading 

brand. 9 

This ranking of gross margins is common in other product 

lines as well. In retail supermarkets, for example, private 

label brands or store brands typically have higher gross 

margins than products which are heavily advertised nationally. 

Well-known products are often featured in retail advertising 

with temporary low prices, because it is these products which 

attract the attention and therefore the patronage of customers. Albion summarizes a large number of studies by Steiner 

and others by saying, "all these studies have shown the 

expected in verse relationship between advertising and gross 

margins, excepting a few of both Borden's and Preston's 

findings in food products." Albion reports his own data to 

'§) This argument is similar to, but not exactly the same as, saying that 

the elasticity of store-level demand is higher for leading brands than for less 

well-known products, and, given the inverse relationship between demand 

elasticity and profit as a percent of sales, gross margin is lower where 

elasticity is higher: MR, marginal revenue, is equal to P(l-[1/e]), where P is 

price and e is price elasticity of demand. Since a profit-maximizing firm sets 

marginal cost (MC) equal to marginal revenue, MC = P(1-[1/e]). Algebraic 

manipulation gives (P-MC)/P = 1/e, w~.ich states that the retailer's gross 

margin is inversely proportional to the elasticity of demand. 

There are two important distin~tiona between this familiar simple model and 

the application to an analysis of the competition between retail pharmacies 

when substitution is permitted. First, it is a comparison of the dollar gross 

margins, not the percentage gross margins, which establishes the pharmacy's 

incentive to substitute. Second, the inverse relationship between elasticity 

and percentage gross margin is strictly accurate only for products with 

independent demands, whereas a central feature of the present problem is the 

interdependence of brand and generic demands, a considerably more complicated 

problem. However, when interdependent demands are taken into account an 

equivalent result can be shown, as in Lynch (Jan. 1983, Sept. 1983): that 

if a retailer sells both brands and generics (actually, high-profile and 

low-profile products), in equilibrium the dollar gross margin of the generic 

is higher than that of the brand. 

~/Holton (1957). 

39 
CHAPTER 3 

"show that, on average, the highly advertised brands sell ror 

gross margins that are 22 percentage points lo\ver than the 

unadvertised brands and 12 percentage points lower than the 

less advertised brands." 10 

!!. WHY THERE IS SO LITTLE SUBSTITUTION 


Why, in the face of the brand-generic differentials in both 

retail prices and gross margins, is there so little substitution? The contrast between the fact that in 1980 generics were 

dispensed on nearly 90 percent of generically written prescriptions, where the doctor had not named a brand, but were 

dispensed on no more than 15 percent of brand-written prescriptions on which substitution is possible and permissible -- two 

situations which are legally nearly identical -- allows us to 

set aside some possible explanations for the infrequency of 

substitution. 11 First, generics are clearly viewed as appropriate for a substantial number of prescriptions. Overall, 

according to our data, consumers bought generics for 25 percent 

of their multi-source prescriptions in 1980, physicians wrote 

20 percent of multi-source prescriptions by the generic name in 

1980, and pharmacists dispensed generics on nearly all of these 

generically written prescriptions. 12 Second, since pharmacists 

routinely dispense generics on generically written prescriptions, the absence of a generic in inventory cannot explain 

lQ/ Albion (1983). 

ll/ State laws frequently impose some extra regulations on substitutions 

(e.g., requiring that the customer be informed of the substitution) and 

occasionally on generically written prescriptions (e.g., requiring that the 

product dispensed be below average wholesale coat in the drug: entity.) Also, 

substitution choices are more commonly constrained by a formu!ary than are 

choices for generically written prescriptions. Therefore the two types of 

transactions are not identical before the law. 

The contrast in dispensing patterns is present when drug~ are analyzed 

individually. See Appendix Table AS-1. 

11/ See Table 6-3. Of course, the proportion of prescriptione written and 

dispensed generically varies from drug to drug, but most multi-source drugs are 

sometimes prescribed generically and thereupon dispensed generically. Similarly, there may be some individual holdouts against sub~titutions among h<!alth 

professionals and among consumers. 

40 

WHY ISN'T SUBSTITUTION PREVALENT? 

the overall infrequency of substitution, even though it may be 

the explanation for failure to substitute in some instances. 

What possible explanations remain? The pharmacist may 

infer from the simple fact that the prescription is not written 

generically that the consumer does not want a generic. 

Alternatively, the simple fact that the physician has named a 

brand rather than written the prescription in generic terms may 

be taken as a signal of the physician's unspoken preference for 

the brand, even when the physician has refrained from indicating that only the named brand is legally acceptable. It could 

be the pharmacist who chooses to honor this unspoken preference, for any of several reasons, regardless of the consumer's wishes, or it could be the consumer's wish to adhere to 

the physician's brand which prevents the pharmacist from 

substituting. We discuss these in turn. 

A. Generically Written Prescriptions 


as a Signal of Consumers' Preferences 


Pharmacists may infer consumers' distaste for substitution 

from the fact that the prescription specifies a brand, on the 

premise that the physician's decision as to whether to write 

the prescription by the generic name reflects the patient's own 

preferences. By this theory, customers with generically 

written prescriptions have already indicated either directly 

(to the physician) or indirectly (through the choice of a 

physician who typically prescribes generically) that a generic 

product is acceptable, and those with brand-written prescriptions have demonstrated their reluctance to pure hase a generic. Pharmacists eager to accede to the custome.r's wishes 

would honor the preference so expressed. The signal need not 

be taken as perfect; if most consumers presenting generically 

written prescriptions prefer the low-cost brand and most 

consumers presenting brand-written prescriptions prefer the 

higher-priced leading brand, the pharmacist will not find it 

worthwhile to risk displeasing the occasional customer whose 

preference is not signalled by the prescription. 

That prescriptions are written generically more often for 

some types of patients than for others suggests that the choice 

41 

CHAPTER 3 

is not unilateral on the physician's part. As Table 3-3 shows, 

customers covered by private insura nee received gene rica ll y 

written prescriptions 16 percent of the time, compared with 21 

percent for "cash" consumers, who had more incentive to buy 

lower-priced products. 

On the other hand, a Food and Drug Administration survey 

showed that only 3 to 6 percent of patients ask their doctors 

about how to take the drug prescribed, precautions, or sideeffects.13 If patients fail to ask even about these behavioral 

and health issues, it seems unlikely that they will challenge 

the doctor's choice of brand. 14 

The evidence is inconclusive as to whether, or how uniformly, a generically written prescription is a guide to 

the patient's preference for a generics. 

B. 	 Pharmacist's Inference that the Physician 

Disapproves of Substitution 

Pharmacists may infer from the fact that a brand was named 

that the physician has a strong preference for that brand. 

Pharmacists' adherence to physicians' implicit wishes is 

shown by some evidence that substitution is more frequent 

when the physician's authorization of substitution is explicit 

rather than implicit. In the period of transition following 

North Carolina's 1980 adoption of a DPS law, physicians in 

that state used prescription pads with various formats. 

Pharmacists substituted much more frequently when the doctor 

was explicit in permitting substitution (13.1 percent substitution on prescriptions written on double-line pads) than on 

prescriptions where substitution was impliedly permitted in 

W Morris et al. (1983).

W The physician might know from experience that the particular patient 

wanta prescriptions written generically. 

42 
WHY ISN'T SUBSTITUTION PREVALENT? 

TABLE 3-3 

Proportion of Prescriptions Written Generically, 

by Insurance Coverage, 1980 

Proportion of prescriptions 

written generically 

20.6% 

Medicaid 20.6 

Private insurance 15.8 

!/ Preeumably aome ineured preacriptione have been recorded by the pharmacist aa unineured, i.e., cash, since the consumer who files for direct reimbursement may not tell the pharmacist about the insurance. 

SOURCE: Computed from data for 45 drugs from IMS' 1980 National Prescription Audit. 

that the physician had not explicitly prohibited it (5.4 

percent substitution on single-line prescriptions.) 15 

Pharmacists may comply with physicians' brand "preference" 

because of a conviction that the wishes of another professional 

ll/ Gurley and Gagnon (1981). Note, however, that our multi-state evidence, 

reported in Appendix At, doea not confirm the hypothesis that explicit authorization by the physician leads to more substitution. Our teet was necessarily 

indirect and may show only that explicitness doee not have an even stronger 

effect than the convenience to the physician in either permitting or prohibitini substitution. 

43 

CHAPTER 3 

should be honored. 16 The satisfaction associated with fulfilling this professional role may be great enough to make up for 

the profits lost from not substituting when consumers would 

otherwise have accepted substitution. However, we doubt that 

the American Pharmaceutical Association would have been as 

active as it was in seeking the repeal of anti-substitution 

laws if most of its constituency were strongly opposed to 

substitution. Also, if consumers sought out those pharmacies 

whose average prices were lower due to substitution, competitive pressures would be felt by those who did not substitute, 

and the financial cost of choosing not to substitute would rise 

beyond that of the foregone higher margin on generics. It 

seems, therefore, that the absence of widespread substitution 

cannot be fully explained by pharmacists' personal preferences. 

It is possible that physicians impose significant financial 

penalties on pharmacists who substitute. However, physicians 

do not always learn whether substitutions have been made, and 

they may not direct patients to one pharmacy rather than 

another; they may therefore not be in a position to inflict 

financial penalties on pharmacies whose practices they dislike. Moreover, the physician can always explicitly prohibit 

su bsti tu tion on the prescription, obviating the necessity of 

trying to ~ersuade the patient to patronize selected retail 

outlets. 

1&./ In addition to professional or financial concerns about the doctor's 

preferences, there are other possible reasons for a pharmacist's unilateral 

decision not to substitute. If dispensing the brand named on the prescription 

ia an ingrained habit, even the time and effort needed to decide whether a 

substitution ia appropriate may eeem to be an unacceptable added cost. Concern 

about liability may inhibit substitution, although the results of the regressions on substitution deciaion8, reported in Chapter 5, did not confirm the 

importance of protection against added liability. 

44 

WHY ISN'T SUBSTITUTION PREVALENT? 

C. Consumer Resistance to Substitution 

While pharmacists' personal views against the appropriateness of substitution may be one explanation for the infrequency 

of substitution, it seems that experience with, or at least a 

belief in, consumer resistance to substitution must be a major 

reason why pharmacists do not substitute. That enterprising 

pharmacists have not substituted extensively strongly suggests 

that consumers either have refused substitutions or, if they 

have accepted them, they have switched to another store for the 

next prescription, thereby penalizing the substituting pharmacist and convincing others not to try. 

The strongest support for the conclusion that consumers have 

a strong and conscious preference for leading brands is that 

retail brand prices are well above retail generic prices for 

nearly all drugs. If instead consumers were unaware or 

uncaring about differences between brands and generics, 

pharmacists would be able to charge as high a price for the 

generic as for the brand. 

Real therapeutic differences among brands of a drug may 

account in some instances for consumers' willingness to pay a 

premium for a leading brand, but also consumers have very 

imperfect information about the alternatives. Consumers report 

being relatively uninformed about either the therapeutic 

equivalence of various versions of a single drug entity or 

about prices. 17 In fact, consumers may not even know that a 

generic alternative for a particular prescription exists. Even 

after use, it is difficult to judge the merits of a drug 

product since the cause of a failure to recover quickly from an 

illness might be the physician's misdiagnosis, or selection of 

11/ According to The CBS Consumer Model, a national survey done in 1983, 

consumens rate themselves as being between "not very informed" and "somewhat 

informed" on the effectivenesa of generic prescription drugs; 45 percent aaid 

they were "not at all informed."The CBS Consumer Model (1984, p. 14). Consumens reported aa "quite important" (4 on a scale of I to 5) the need for 

information on this subject. Consumers considered themselves less than 

somewhat informed (2.78 where "somewhat informed" was 3.00) on brand vs. 

generic prescription costs (p. 14.) They considered it between "somewhat 

important" and "quite important" (3.81) to have such information (p. 15). 

The CBS survey used a national probability sample of households. 

45 

CHAPTER 3 

the wrong chemical entity, or a patient's idiosyncratic 

physiology -- or some problem with the specific brand of the 

drug selected; prescription drugs are to some extent "credence" 

goods. 18 

For many consumers, there is little reason to try to learn 

about alternative brands of a single drug, and search costs may 

be high. Consumers are constrained from making the choice 

un ilaterally; the best they can do is to influence the physician or the pharmacist. Moreover, at least for a short-term 

medical problem, it is usually not worthwhile to gather 

information about the equivalence (or inequivalence) of various 

brands or about the price difi'erence between the brand and the 

generic. If therapeutic equivalence is not understood, price 

differences are less sure to reflect good buys and, conversely, 

if price differences are not noted, there is little incentive 

to investigate the appropriateness of substitution. The dearth 

of prescription advertising directed to consumers raises the 

cost to consumers of learning about drug alternatives and 

prices. Also, except for maintenance drugs, the infrequency of 

purchase means that consumers probably retain little price 

information. 

This incomplete information is likely to bias preferences 

towards the familiar, frequently prescribed brands. Some 

consumers may believe that lesser known brands, or drug 

products sold under the generic name only, are of lower 

quality or that there is greater variation in the quality, 

simp I y because of their lack of prominence. An unknown brand 

may seem to be accompanied by greater riskiness as to both 

efficacy in curing the illness and absence of side-effects. 

The consumer behavior literature strongly suggests that when 

W Nelson (1970) classified consumer goods as "search" goods or "experience" goods. Darby and Karni (1973) added a third category, "credence" goods, 

for goods (or qualities of goods) "which, although worthwhile, cannot be 

evaluated in normal use." 

46 
WHY ISN'T SFBSTITUTION PREVALE:--.IT? 

consumers do not know the quality of different brands, especially in products where there is a high perceived risk, they 

are likely to equate price with quality. 19 

That consumers appear to have a strong preference for the 

leading brand over the generic might explain the low rate of 

substitution, but it cannot explain the acceptance of generics 

on nearly all generically written prescriptions. The contrast 

suggests that consumers make yet another inference: a physician who names a brand has a strong preference for that exact 

brand. To the extent that physicians have named a brand more 

out of habit than as a result of deliberate comparison of the 

alternatives, the consumer's inference is based on a misperception. 

III. CONCLUSION 

In summary, it appears that there is resistance on the part 

of consumers to su bsti tu tion. Apparent! y consumers believe 

that the physician has specifically disapproved of substitution 

on each brand-written prescription, even though this may not 

be the case at all. Consumers may fail to ask physicians or 

pharmacists about substitution, in part because they do not 

know it is possible. Pharmacists, in turn, apparently fear 

consumer resistance in the form of lost sales and therefore do 

not often suggest substitution and may have other personal 

reasons for refraining from su bsti tu tion. Some mutually 

profitable transactions may never be initiated. However, this 

resistance is not immutable. Because substitution is a 

relatively new option, the consumer is likely to be ill-informed about opportunities, limitations, and consequence. 20 

Similarly, pharmacists initially reluctant to substitute may 

over time become more comfortable with the idea as they confirm 

the medical appropriateness of the use of generics and discover 

ill Gardner (1971). In the context of an illness which evokes fear and 

uncertainty, any added risk may be magnified in importance. 

'lSlJ Of consumers surveyed, 44 percent did not know whether substitution was 

permitted in their state. "Patients Loyal ...," Drug Topics (May 7, 1984, 

p. H). 

47 

CHAPTER 3 

the strength of consumers' interest in substitution. The 

evidence suggests that, in addition to legal permissibility, 

some mix of increased awareness of the opportunity for substitution, and a clearer understanding of the physician's judgment 

about the appropriate use of generics on the prescription seems 

to evoke a greater acceptance of generics. 

48 

CHAPTER 4 

VARIATIONS IN SUBSTITUTION RATES 

Substitution rates reveal substantial variation. We 

present data on four sources of variability of substitution 

rates -- drug, state, insurance coverage, and type of pharmacy. By drug, substitution ranged from none whatsoever to 

over 20 percent of eligible prescriptions in 1980. Privately 

insured prescriptions were less like! y to be su bsti tu ted than 

either "cash" or Medicaid prescriptions. Although we hypothesized that pharmacies which were part of chain organizations 

would substitute more frequently than independently owned 

pharmacies, the data showed no significant difference in 

substitution behavior between the two types of pharmacies in 

1980. In some states the rate of substitution was twice or 

three times the national average. (The analysis of state-tostate variation is deferred to Chapter 5, where individual 

provisions of the state DPS statutes are studied in detail.) 

I. INDIVIDUAL DRUGS 

The weighted average across 45 drugs conceals wide variation 

in substitution rates from drug to drug. But, as Table 4-1 

shows, only 9 drugs have rates above 10 percent (using the IMS 

data for 1980): even on the drugs with the highest substitution rates, the option to substitute was exercised relatively 

infrequently. 

Some of the obvious hypotheses about the cross-drug 

differences in substitution were not borne out. For example, 

we expected to find that drugs used on a maintenance regimen 

were more frequently substituted than drugs used for short-term 

therapy, since consumers would have a greater financial 

incentive to seek out and purchase a lower-price version if 

repeat purchases were predictable. However, the correlation 

49 
CHAPTER 4 

TABLE 4-1 


Substitution Rates by Drug, 1980 1 


Percent 

of 

Drug Substitution 

H ydroch1orothiazide 24.1 

Chlordiazepoxide 19.3 

Amitriptyline 18.4 

Penicillin VK 17.3 

Arnoxicillin 17.3 

A tropine sulfa te/Diphenox yla te 17.0 

Meclizine 16.4 

Isosorbide dinitrate 14.9 

H yd ralazi ne /Hydrochlorothiazide/ 

Reserpine 14.2 

Doxycycline 10.4 

H yd roc hlo rot hiazide /Spironolactone 9.2 

Dipyridamole 8.8 

Brompheniramine/Phe nyle phri ne I 

Pseudoephedrine 8.3 

Ampicillin 8.2 

Ch lordiazepoxide/Clidini urn bromide 7.8 

Tolbutamide 7.6 

Conjugated estrogens 7.1 

Chlorthalidone 5.9 

Acetaminophen/Chlorzoxazone 5.9 

Tetracycline 5.6 

Spironolactone 4.6 

Phenytoin 3.8 

Allopurinol 3.6 

De x bromphenira mine/Pseudoephedrine 3.2 

Trimcinolone 3.1 

Chlorpropamide 2.8 

Hydroxyzine 2.3 

Su 1 famethoxazole/Trime thoprim 2.0 

50 
1 

VARIATIONS IN SUBSTITUTION RATES 

TABLE 4-1--Continued 

Percent 

of 

Drug Substitution 

Theophylline 

Terbu ta1ine 

Nitroglycerin 

Metronidazole 

Diethylpropion 

Phentermine 

Minocycline 

Furosemide 

Erythromycine base 

Hydrochlorothiazide/Triemterene 

Amitriptyline/Perphenazine 

Cephalexin 

Erythromycine ethylsuccinate 

Ibuprofen 

Mestranol/Nore thindrone /Placebo 

Hydrogenated ergot alkaloids 

Quinidine sulfate 

1.3 

1.1 

1.1 

1.0 

1.0 

.4 

.4 

.3 

.3 

.2 

.2 

0 

0 

0 

0 

0 

0 

The drugs are listed in descending order by substitution rate. Computed for 

prescriptions on which substitution was permitted. These are the 45 leading 

multi-source drugs used throughout the study. The denominator does not exclude 

those prescriptions on which the physician prohibited substitution. 

SOURCE: Computed with data from the 1980 IMS National Prescription Audit. 

51 
CHAf'TER 4 

between the substitution rate and the proportion of all 

prescriptions for the drug which were refill prescriptions, 

where the refill rate is used as a measure of long-term use, 

was .06.1 Similarly, consumers might be expected to seek or 

accept substitutions on higher-price drugs, but the data show 

instead that lower-price drugs are more frequently substituted; 

the correlation between substitution rate and average prescription price, across 45 drugs, was -.39. This is apparently in 

part because the older antibiotics are both relatively inexpensive and frequently substituted. The frequency of substitution 

in some of the antibiotics in turn reflects confidence in the 

interchangeability of brands, itself due to long and widespread 

usage and (until 1984) the FDA's certification that each batch 

met standards of quality (unlike other drugs, which are not 

batch-certified.) Third, the correlation between the substitution and the difference between brand and generic gross margins 

was very small, -.02. On the other hand, there was some 

support for the hypothesis that substitution was correlated 

with the size of the brand-generic price differential; that 

correlation coefficient was +.27. 2 

Il. CUSTOlo,fER INSURANCE COVERAGE 

Another influence on the extent of substitution is the 

means of payment for the consumer's prescription. Consumers 

whose drug costs are covered by insurance are less likely to be 

concerned with finding a lower-price drug and therefore may be 

expected to purchase the high-price brand prescribed rather 

than to seek or accept a lower-price substitute. In our data, 

9.2 percent of the prescriptions were reported as being paid 

for by private third-party insurance. This estimate is 

undoubtedly understated, for when consumers apply directly to 

lf See Appendix Table AS-I for refill rate and Table 4-1 for substitution 

rates by drug. Data for the other correlations are found in the same tables. 

1/ Of course, product prices themselves might have been changed in response 

to the implementation of a drug product selection law, although this does not 

appear to have happened. See Chapter 7. 

52 

VARIAT::J:-J'S !:-J' SIJBSTITT'TICJN RATES 

their insurance company too btain reimbursement, the pharmacist 

is unlikely to h~ aware of the insurance coverage. An additional ll.l pcr.::ent of prescriptions were recorded as sold to 

customers covered by state Medicaid programs, which unlike 

Medicare do cover outpatient prescription drug costs. This 

estimate is likely to be more accurate, since pharmacists 

receive payment for these sales only by themselves applying to 

the state government for reimbursement. Medicaid consumers 

typically pay a fixed copayment, e.g., $.50 or $1.00, for any 

prescription, while private insurance plans may utilize either 

a flat or a percentage copayment. 

When consumers pay out-of-pocket little or nothing more for 

a more expensive prescription than for a cheaper one, any price 

sensitivity is diminished. From the perspective of consumer 

demand, we would expect the most substitution for "cash" 

consumers and the least for Medicaid consumers. Since those 

pri va tel y-insured consumers with a percentage copaymen t 

have some, albeit limited, incentive to seek low prices, the 

substitution rate for the privately-insured segment would be 

expected to be higher than that for Medicaid. 

However, to base predictions only on the buyer's incentive 

would be to overlook the very important influence of Medicaid 

reimbursement rules, especially for those drugs covered by the 

federal Maximum Allowable Cost (MAC) program, and any additional drugs added by a state's "mini-MAC" program. For these 

drugs, the pharmacist is under a strong incentive to dispense 

low-cost brands to Medicaid patients. The MAC program establishes a maximum reimbursement for the ingredient-cost portion 

of the pharmacy's costs, and if the pharmacy dispenses a more 

expensive brand, it has to absorb the higher cost itself. 

Therefore it can be expected that on MAC drugs the incidence of 

substitution will be highest for Medicaid consumers. 

The pattern is clear with respect to the association 

of payment type and substitution rate: the ranking from most 

to least substitution is Medicaid, cash, and privately 

insured. Table 4-2 gives the summary data over all 45 drugs. 

Regardless of the legal environment, .Medicaid is highest and 

privately insured lowest. 

Since substitution rates vary substantially across drugs, 

it is conceivable that the overall substitution rates for 

53 

CHAPTER 4 

TABLE 4-2 

Substitution Rates by Insurance Coverage and 

by Opportunity to Substitute, 1980 1 

Private 

Medicaid Cash Insurance 

S u bsti tu tion prohibited 2.0% 1.4% 0.5% 

Substitution permitted 10.5 7.3 4.2 

All states 8.1 5.4 3.4 

l/ The substitution rate Cot caah may be understated, since it is likely 

that pharmacists mistakenly recorded aa caah some prescriptions which were 

actually privately insured. 

SOURCE: Computed with data for 45 leading multi-source drugs from the 1980 

IMS N a tiona) Prescription Audit. 

~1edicaid prescriptions, for example, could be high simply 

because frequently substituted drugs are more often prescribed 

to Medicaid patients or are less frequently ineligible for 

Medicaid reimbursement, according to state Medicaid formularies, than infrequently substituted drugs. Indeed, while the 

overall average Medicaid share was 11.1 percent, in 7 of the 10 

top-substitution drugs the Medicaid share of prescriptions was 

at least 20 percent.3 

Despite the variation m Medicaid shares of prescnptions, Table 4-3 shows that the substitution pattern in each of 

the ten most frequently substituted drugs is the same as when 

computed using a 45-drug average: Medicaid is highest and 

private insurance lowest. Of the 11 drugs next highest in 

substitution (not shown), all with substitution rates over 5 

Y While we can identify the drugs in the federal MAC program, we do not 

know which additional drugs, and in which states, are covered by comparable 

reimbursement constraints. 

54 

VARIATIONS IN SUBSTITUTION RATES 

TABLE 4-3 

Substitution by Insurance Coverage by Drug 

for the 10 Top-Substitution Drugs, 1980 1 

Private 

Drug 2 Medicaid Cash Insurance 

H yd rochlo rothiazide 3 56.0% 22.4% 13.6% 

Chlordiazepoxide 3 58.5 18.2 7.6 

Amitriptyline 36.2 16.9 10.3 

Penicillin VK3 33.7 17.0 12.3 

Amoxicillin3 14.1 11.8 11.3 

Atropine sulfate/ 

Diphenoxyla te 3 46.7 15.4 11.7 

Meclizine 26.4 15.8 9.0 

Isosorbide dinitrate 20.8 15.3 7.0 

HydralazinejHydrochlorothiazide /Reserpine 30.1 11.7 2.9 

Doxycycline 12.0 10.8 6.4 

Other federal MAC drugs in 1980: 

Ampicillin 3 4.1 8.9 5.5 

Tetracycline3 7.8 5.5 5.2 

Overall a verage 4 10.4 7.2 4.2 

l/ For prescriptions for which both statute a.nd formulary permit substitution. 

'd./ The ten drugs a.re listed in descending order by substitution rate. In 

addition, data. on two other MAC drugs in our sample of 45 drug entities are 

given.

2./ A drug for which there wa.s a. Medicaid MAC ceiling in 1980. 

1./ The overall average is computed across all 45 drug entities in our 

sample. 

SOURCE: Computed with data. from the 1980 IMS N ationa.l Prescription Audit. 

55 

CHAPTER 4 

percent, Medicaid was highest in 6, and privately insured 

prescriptions were lowest in 9. 

The substitution rate was highest for ·Medicaid consumers 

for almost all MAC drugs; those drugs which were in the federal 

MAC program in 1980 are marked with an asterisk in Table 4-3. 4 

For only one of the 7 MAC drugs, ampicillin, the Medicaid 

rate is not the highest; in this drug, there is no product with 

a large enough market share to be classified as a leading brand 

and there are a number of products with similar costs. 

Because substitution is most frequent for Medicaid consumers 

and least frequent for those with private insurance, the 

magnitude of substitution differences between states may be 

explained in part by differences in the proportions of different kinds of consumers. These proportions do differ from state 

to state. For example, nearly a third (30.6 percent) of 

Michigan's prescriptions were privately insured, many of them 

presumably as part of the United Auto Workers benefits package. This is a much higher proportion than that for the 

country as a whole, 9.2 percent. Similarly, there is variation 

in the size of the Medicaid population. Overall, Medicaid 

prescriptions were 11.1 percent of the total, but there were 8 

states with less than half this proportion5 and 2 states 

reporting over 40 percent. 

A comparison of states with and without general drug product 

selection legislation is probably not confounded by differences 

in the proportion of the population covered by Medicaid. While 

the three states without statutes permitting substitution 

in 1980 had fewer Medicaid prescriptions, on which substitutions are more frequent than on uninsured prescriptions, than 

states which generally permitted substitution, these states 

also had fewer privately insured prescriptions, on which 

substitutions are less frequent. (In the multivariate regression analysis of substitution, variables are included to 

control for insurance coverage of the prescription.) 

if Two of the remaining top ten substitution drugs in our sample are antiobesity drugs with a Medicaid share of less than 5 percent, reflecting the fact 

that many state Medicaid formularies excluded drugs in thie category. 

fUSee Table A4-1. A11 of 1980 two atatea (Arizona and Wyoming) did not participate in the Medicaid program. 

56 

VARIATIONS IN St'BSTITt'TION RATES 

Ill. STORE TYPE (INDEPENDENT A/\1D CHAIN) 

The advantages that chains have interact with the drug 

product selection laws to make substitution even more profitable for chains than for independents.6 This suggests that 

chains might be expected to substitute more frequently than 

independents. However, the data do not support this hypothesis; there is no consistent pattern as to a difference 

between chains and independents in substitution behavior in 

1980. 

Stores which are members of large chains are believed to 

have lower costs than small independents. 7 In particular, 

chains can obtain drug products at lower cost than independents.8 By combining purchases for many large outlets, 

chains' total purchases are large enough to reach the lowest 

prices in a manufacturer's or wholesaler's quantity discount 

schedule. 9 This cost advantage IS probably greater for 

genencs than for branded drugs, since for some drugs generics 

may be dispensed infrequently by a single store and since 

~ Although any ownership group of stores may be called a chain, the term 

often exclude• firms with fewer than 10 units. Accordingly, we use the term 

"independent" to include small chains aa well. 

1/ Chain outlets are typically larger-volume stores than independents' 

and thereby realize more within-store economies o{ scale. According to the 

Lilly Digests, the average dollar prescription sales in 1980 were $212,949 for 

independents and $315,341 for chains. (The '81 Lilly Digest (1981, p.5) and the 

1981 NACDS-Lilly Digest (1981, p. 5). Among chains alone, the NACDS-Lilly 

Digest noted that "the larger volume operations were able to generate almost 

seven times the number of prescriptions with about a 40 percent increase in 

employed pharmacist hours aa compared with data Cor stores in the smallest 

sales category. NACDS-Lilly Digest (1981, p. 12). 

§../ We are not able to measure the size o{ this cost advantage with our 

data. However, a study of invoice costa in a sample o{ Iowa pharmacies found 

that members of chain organizations paid less Cor drug products than independent pharmacies paid. The study also found that drug acquisition costa 

declined aa store prescription volume increased. Stores with the highest 

annual prescription volume purchased Cor less than those with the lowest annual 

prescription volume. Norwood (1977). 

~ These discounts may reflect true cost savings, such as shipment in 

larger units, or avoidance of certain marketing or accounting costa. Moreover, 

chains may perform some warehousing {unctions themselves. The appropriate 

comparison is between the cost to an independent and the cost to the chain 

warehouse with the warehousing and other distribution costs added on. 

57 
CHAPTER 4 

chain-wide needs may be great enough to justify private-label 

arrangements or even vertical integration. Insofar as chains 

get more special deals on generics than on leading brands, drug 

product selection laws offer a greater opportunity for chains 

than for independents. 

Chains can also take advantage of economies of scale in 

advertising. A chain advertisement in a metropolitan newspaper 

reaches many more potential customers for whom an outlet of 

that chain is convenient than does a single independent store's 

advertisement. In addition, the rates newspapers charge for 

advertising typically offer substantial discounts based on the 

total lineage and on frequency or continuity of use. Because 

chains sell a much wider range of merchandise, the discount for 

prescription advertising is increased because of the larger 

amount of non-prescription advertising. 

Chains' cost advantages in advertising, purchasing and other 

areas allows them to charge lower prices than independents. 

Our data show that in virtually every drug entity, medium and 

large stores which are member of chains with 11 or more units 

have lower prices than other stores. The average price, 

computed across all prescriptions in all 45 drug entities 

studied, was in 1980 $7.48 for chains and $8.37 for independen ts. 10 

Chains may combine their scale advantage in advertising, 

the ability to purchase generics cheaply, and their relatively 

low prices in general in featuring a generics program. In 

198 0, for example, shortly after the state of Maryland replaced 

its restrictive drug product selection law with a new statute 

that eliminated the pass-through provision and considerably 

enlarged its formulary, a major chain in the Washington, D.C. 

area ran a series of large-space newspaper ads. 11 Again in 

1984 and 1985 this chain has run full-page ads in the 

Washington Post listing prices by individual brand and 

1Q/ Chain• are shown to have lower average retail prices than independents 

for identically specified prescriptions. On 7 leading brands (for all those 

drugs among the 10 most frequently substituted where there was a single leading 

brand), average chain prices were lower by $.41 to $1.34. 

!.!/Drug Store News (July 21, 1980, p. 3). 

58 

VARIATIONS IN SUBSTITUTION RATES 

generic, 12 and other chains have announced their readiness to 

meet advertised prices. 

Three circumstances argue that instead independents may 

substitute more frequently than chains. First, salaried chain 

employees have a less direct incentive than owner-pharmacists 

to dispense the product with the higher profit. 13 Second, 

independents may be more likely to make arrangements with 

individual physicians that allow substitutions to be made on 

specified brand-name prescriptions, since the smaller scale of 

the independent store may provide greater assurance to the 

physician that specific instructions will be understood by all 

dispensing pharmacists. Third, according to representatives of 

several large chains interviewed in 1981, the pass-through 

and extra information requirements posed particular disincentives for chains to substitute, the former because with their 

high profiles chains are impelled to obey laws that may not 

generally be enforced, and the latter because the extra 

information requirements required them to deviate from their 

chain-wide systems. 

There is no consistent pattern as to differences between 

chain and independent substitution behavior in 1980. 14 Table 

4-4 shows that in our sample where substitution is permitted, 

chains substitute more frequently than independents (7.6 

percent v. 7.2 percent.) But within individual drugs this was 

not always the case; on 4 of the 10 top-substitution drugs it 

was independents that substituted more. Moreover, if formulary 

restrictions are disregarded and substitution is measured for 

all drugs and for all states, independents are shown to have 

substituted more frequently than chains (5.7 percent v. 5.0 

!lf July 17, 1984m p. A-11. 

W For example, according to The New York Times, "Medi-Save Pharmacies, a 

chain with headquarter• in Baton Rouge, La., pays pharmacists in its 100 outleta 50 cents for every generic prescription they fill." "The Shift to Generic 

Druga" (July 23, 1984, p. DS).

!if That substitution behavior of chains and independents in our sample does 

not differ greatly means that results seemingly associated with the lawa are 

unlikely to be explained away in terms of oversampling of chains in some states 

and of independents in other states. 

59 

CHAPTER 4 

TABLE 4-4 

Substitution by Chains and Independents, 

by Opportunity to Substitute, 1980 

Independents and 

Large Chains Small Chains 

(More than ( 10 or fewer 

10 stores) stores) 

Su bsti tu tion pro hi bi ted .9% 1.7% 

Su bsti tu tion permitted 7.6 7.2 

All sta tes 1 5.0 5.7 

!/ A smaller proportion of all chain observations happened to be for 

"substitution permitted" than of all independent observations. This explains 

the seemingly odd result that the overall chain substitution rate is only 5.0 

percent when 7.6 percent of prescriptions were substituted where permitted, 

while the overall independent rate is 5.7 percent although only 7.2 percent of 

prescriptions were substituted where permitted. 

SOURCE: Computed from data for 45 leading multi-source drugs from the 

1980 IMS National Prescription Audit. 

percent). This reversal may be due to greater use by independents of special grants of permission to substitute by prescribing physicians. 

Our evidence that chains did not substitute more frequently 

than independents is consistent with a Market Measures study 

which found that chains substituted on 3.8 percent of prescriptions and independents on 5.3 percent. 15 Indeed, the Market 

Measures estimates suggest the opposite, that independents 

substitute more often. 

ill Zeich (1983, p. 75). 

60 

VARIATIONS IN SUBSTITUTION RATES 

IV. STATE-TO-STATE VARIATION 

The existing empirical work on drug product selection shows 

that experience with drug product selection laws has varied 

widely across states. Within the set of state studies reviewed 

by Gurley and Gagnon, the range of reported su bsti tu tion rates 

was broad, from 2.6 percent to 8.6 percent of brand-written 

multi-source prescriptions on which the physician had not prohibited substitution, and from 6 to 56 percent when prescriptions for which the formulary prohibits substitution were 

excluded. 16 Market Measures reported a range in March 1983 

among seven large states of 4.1 percent (of all new prescriptions) in New York to 10.6 percent in Michigan. 17 

Our data also show wide variations in substitution from 

state to state. 18 Table 4-5 shows the distribution of states 

by substitution rate in 1980. 

The two methods of computing the su bstitu tion rate result 

in substantially different rankings. In particular, states 

with more restrictive formularies rank higher according to the 

method based on only those prescriptions for which the formulary permitted substitution. This difference in ranking is as 

one would expect because, first, as the formulary is made more 

restrictive, a fixed number of substitutions becomes a higher 

percentage of the number of eligible prescriptions and, second, 

the bias towards drugs which invite substitution most frequently is greatest on formularies which are the most restrictive. 

Whichever measure is used, three points stand out. First, 

the variation across states is substantial. Second, even the 

highest state substitution rates are not very high, far short 

of 100 percent. Third, there was a large number of states 

with very little substitution, about a quarter of the states 

being below 3 percent. 

ill Gurley and Gagnon (1981, Appendix C). These percentages are higher than 

the national figures given in Chapter 2, in part because the denominator used 

for the state measures is smaller by the removal of physician prohibitions of 

aubatitution. 

ll/ Zeich (1984).

W See Appendix Table A4-1 for data by state. 

61 

CHAPTER 4 

TABLE 4-5 


Substitution Rates, Distribution of State Averages, 1980 1 


When su bsti tu tion rates When substitution rates 

are calculated across are calculated across 

drugs for which sub­ all drugs 

stitution is permitted 

Over 20% 

I5 - 20 

I 0 - 15 

8 - 10 

6 - 8 

4 - 6 

2 - 4 

I - 2 

0 - 1 

2 

5 

5 

8 

10 

3 

7 

4 

3 

2 

4 

7 

9 

5 

I2 

5 

3 

Average 7.3 6.0 

l/ Only states with DPS laws are included. 

SOURCE: Computed with data for 45 leading multi-source drugs from the 1980 

IMS National Prescription Audit. 

A caution about our estimates by state is necessary, however. The underlying data are not collected in such a way 

as to assure reliability of any individual state estimate. 19 

In this chapter, we have used the state estimates to establish 

the variation in substitution rates and to illustrate the 

effect on state-to-state comparisons of the bias in selection 

of drugs for formularies. We believe our conclusions on these 

issues would not be affected by within-state sampling errors. 

llJ Sampling issues are discussed further in Appendix A6. 

62 
VARIATIONS I~ SUBSTITUTIO~ RATES 

V. COSCLUS!ON 


Substitution activity varies considerably from drug to drug 

and according to other circumstances as well. Whether the 

pharmacy was independently owned or a member of a chain did not 

make much difference in the likelihood of substitution in 

1980. In contrast, whether the customer's prescription 

purchases were insured or not made a substantial difference. 

Medicaid prescriptions were most likely to be substituted and 

prescriptions covered by other private insurance were least 

likely to be substituted. State-to-state variation in substitution is substantial as well; Chapter 5 analyzes the effects 

of differences in state drug product selection laws while 

holding constant some other state characteristics believed to 

affect substitution behavior. 

63 

64 

CHAPTER 5 

THE IMPACT OF DIFFERENCES IN STATE LAWS 

In this chapter we measure the effects of differences in the 

states' drug product selection laws. No two state statutes -­

in actuality, packages of individual provisions -- are the 

same. Each individual provision of a drug product selection 

statute can be analyzed as to its probable effects on substitution via its effects on the pharmacy's costs or demand. 1 

In order to isolate the effects of a single type of provision, the technique of logit regression was used to control for 

the effects of store and state characteristics as well as for 

other provisions of the DPS Ia ws. The present study differs 

from previous studies also in that its use of data across all 

states except Alaska and Hawaii permits broader-based generalizations than were possible in studies of one or a few states. 

Even so, given the myriad provisions of state laws, the 

complications introduced due to the differences among drugs, 

and the relatively small dispersion across states in the amount 

of substitution, it is difficult to reach definite conclusions. We did obtain significant results in our regressions as 

to the effects of some ut-the legal provisions upon substitution, but other provisions were not shown to have any significant effect. (The regression results were for the most part 

consistent with the patterns revealed in cross-tabulations of 

state averages by provision, which are given in Appendix A4.) 

In preview, several results stand out. First, a prescription pad format which makes it easy for physicians to prohibit 

substitution is associated with lower substitution rates. On 

this provision, we confirm a major result of previous 

research. Second, where pharmacies are required to provide 

more information about substitutions to consumers, substitution 

!/ One major prov1s1on of nearly all the laws, the format of the prescriber's prescription pad, affects pharmacy decisions only indirectly, by 

altering the mix of prescriptions presented to pharmacies. However, this 

provision is thought to have a strong impact on prescribing patterns and 

therefore on the overall impact of drug product selection laws. 

65 
CHAPTER 5 

is more common. Third, a positive formulary is associated with 

less substitution. Fourth, contrary to expectations, states 

which require pharmacies to pass drug cost savings through to 

consumers do not have lower substitution rates. Fifth, 

while substitution is more frequent in states where it is 

mandatory, mandatory provisions affect substitution rates only 

modestly. 

We first describe the major types of provisions of the drug 

product selection laws, as well as some collateral regulations, 

and the effect we expect each to have on substitution. Arguments put forward in the FTC Staff Report, in support of provisions included in the FTC/FDA Model Drug Product Selection 

Act, are reviewed. 

Our analysis of the statutory provisions implicitly assumes 

that restrictions on behavior are in fact enforced. To the 

extent that the strength of enforcement differs from state to 

state, our conclusions about the apparent effects (or lack of 

effects) of the statutory provisions themselves are weakened. 

We are told that the intensity of enforcement does vary; it is 

typically the responsibility of the state Board of Pharmacy, 

and interviews with state Board personnel revealed large 

differences in the philosophy of enforcement and the resources 

devoted to it. Overall, enforcement appears to range from 

strict to virtually none, but even this information is difficult to interpret. Among states with roughly equal overall 

emphasis on enforcement, there are considerable differences in 

the attention devoted to particular statutory areas. Enforcement of some provisions may encourage substitution while 

enforcement of others (e.g., formulary) may limit it. Moreover, the basic nature of a drug product selection law is the 

removal of restrictions, so if comparisons were made over time, 

for example, the more relevant measure would be the intensity 

of enforcement of the anti-substitution laws which preceded the 

Ia ws authorizing substitution. 

This chapter's brief discussion of the statutory provisions 

is augmented by Appendix AI, which contains a full description 

of the provisions and a table (Table Al-l) showing the provisions in effect in each state in 1980. The table includes also 

the recommendations which made up the FTC/FDA Model Act. 

The Model Act itself is reproduced as Appendix A2. 

66 
DIFFERENCES IN STATE _LAWS 

I. TYPES OF STATUTORY PROVISIONS 


We use six categories to describe the general nature of the 

different types of major statutory provisions in the drug 

product selection law: 

A. Mandatory or Permissive Substitution 

B. Format of the Physician's Prescription Pad 

C. Formulary 

D. Pharmacist Liability 

E. Cost Pass-through 

F. Notification 

The variations in statutory specifications are many. For 

example, prescription pads are often described as "one-line" or 

"two-line" (for number of signature lines available to the 

physician.) Among two-line prescription pads we found three 

different formats, differing as to the placement of the 

"substitution permitted" and "substitution prohibited" signature lines. Our detailed classifications are presented and 

explained in Appendix A 1. 

A. Mandatory or Permissive Substitution 

Over three-quarters of the states left the substitution 

decision to the pharmacist's option in 1980. 2 (On Medicaid 

prescriptions substitution was mandatory in some states.) 

Other states made substitution mandatory as long as the 

physician or the formulary had not forbidden it; in nearly all 

states, the consumer had the right to refuse substitution as 

well. With some variations, most states that mandated substitution required that the pharmacist substitute a less expensive 

generic equivalent, provided there was one in stock. Some 

states which mandated substitution and used a formulary 

tied the mandated substitution to the formulary but permitted 

V Consumers typically have the right to reject a substitution. This is not 

true in all states; in New York, for example, "[the] law does not g;ve the 

patient the option of choosing the brand name proouct when substitution is 

permitted by the prescriber." New York State Departme::t ')f Health (A;xil 1, 

1980, p. ix). 

67 

CHAPTER 5 

substitution on other drugs as well; others required substitution on formulary drugs and forbade it elsewhere. 

In states with mandatory substitution one might at first 

expect to find a 100 percent substitution rate, but there are 

several reasons why the rate should fall short of 100 percent. 

First, physicians prohibit substitution on some prescriptions, 

although we cannot measure the extent. Second, in most 

states where substitution is mandatory, the requirement is 

empty if the pharmacy has no substitute in inventory. The 

likelihood that the pharmacies' inventories include generics 

thus determines the efficacy of amanda tory provision. Whether 

or not a pharmacy will choose to stock a generic alternative 

depends on the extent of generic prescribing in the market and 

on Medicaid regulations, in combination with the store's 

proportion of Medicaid customers. 

In fact, some have argued that a mandatory provision will 

not lead to higher levels of substitution because other permissive provisions will induce as high a level. The FTC/FDA Model 

State Statute left substitution as optional for the pharmacy 

rather than mandating it. 3 The recommendation was based on the 

belief that mandatory laws were "both unnecessary and unworkable," unnecessary because economic incentives would encourage 

pharmacists to substitute and unworkable in the absence of 

costly enforcement, partly because pharmacists would resent and 

therefore resist intrusive governmental regulation. In 

addition, evidence was cited as to the relatively low rate of 

substitution even in states where it was mandated. 

These arguments might be taken to imply a prediction that a 

mandatory provision will not have any significant effect on 

substitution. However, assuming some enforcement and holding 

other things constant, we expected that where substitution was 

mandatory, the substitution rate would be higher. 

B. Format of the Physician's Prescription Pad 

State statutes require or permit a wide array of formats 

that make it relatively easy or difficult for the doctor to 

~/ FTC Staff Report, pp. 2'74-275. 

68 

DIFFERENCES IN STATE LAWS 

allow substitution on brand-written prescriptions. In part, 

the statutes accomplish this by specifying whether substitution 

is permissible when the physician gives no specific directions 

about substitution. The statutes also specify whether the 

doctor's explicit instructions on the prescription form can be 

preprinted or conveyed by a check in a box or must instead be 

communicated by a handwritten phrase or abbreviation. At one 

extreme, the law in many states permits pharmacists to substitute unless the physician has handwritten "Dispense As Written" 

or similar words. At the other, the physician might have to 

write out several words to permit substitution or can even 

preprint the pad with the words "Do Not Substitute." 

Where the format of the prescription pad makes it easy for 

the physician to prohibit substitution, more brand-written 

prescriptions reaching the pharmacy are likely to carry such 

prohibitions and the pharmacy will have less opportunity for 

substitution. Previous research has suggested that this 

influence on store-level demand affects the substitution 

rate significantly. 

The FTC/FDA Model State Statute specified a single signature 

line prescription pad with the presumption that substitution 

was permitted unless the physician handwrote "medically 

necessary" or words of the same meaning.4 Preprinted instructions were ruled out to ensure that the physician's decision 

to prohibit substitution was made consciously. The FTC Staff 

Report argued that making such designations relatively inconvenient for the physician was appropriate for two reasons. 

First, prohibitions of substitution should be necessary only 

infrequently, especially if a positive formulary were in place 

and kept current. Second, patterns of physicians' actual use 

of their option to prohibit substitution seemed to suggest that 

it was done as much from habit as from careful consideration of 

the circumstances surrounding the particular prescription. 5 

!/FTC Staff Report (1979, pp. 275-278).

Y See Chapter 2. 

69 

CHAPTER 5 

C. Formulary 

A formulary dictates those drug entities within which 

substitutions are permissible, and in some states it lists the 

specific products within the entity which may be considered 

interchangeable as well. The formulary acts like an on-off 

switch as to the legality of substitution, a drug-specific 

equivalent of the general drug product selection (or, alternatively, anti-substitution) statute. 

The general character of the formulary, as well as specific 

inclusions and exclusions, may affect a pharmacist's proclivity 

to substitute. Suppose that for some specified generic entity 

substitution is legal in each of three states. Suppose one 

state to have no formulary, that is, to allow su bsti tu tion in 

all categories; the second state to have included this generic 

entity on a positive formulary; and the third state to have 

excluded it from its negative formulary. The nature of the way 

legality is provided is likely to make a difference in the 

pharmacist's willingness to substitute. 

The presence of a formulary of any sort may encourage 

substitution more than if there is no formulary. Quite apart 

from concern about the legal constraint embodied in the formulary, pharmacists who wish to make accurate and responsible 

professional decisions as to which drugs are interchangeable 

will substitute more frequently if they have a convenient 

source of authoritative information regarding appropriate 

substitutions.6 The list may also be useful in the pharmacy's 

decision as to which brands to carry, especially if the 

formulary itself lists brands or manufacturers. This may have 

some spillover effect for stocking and dispensing decisions for 

generically written prescriptions. 

§} A formulary which is distributed to all pharmacies will have the greatest 

impact. If instead a list is simply maintained at the state Board of Pharmacy, 

the list will be consulted far less frequently. For example, some state 

statutes incorporate an FDA list as the state formulary, but if the at ate 

does not republish the list, the pharmacy must either call the Board of 

Pharmacy for information or take the initiative to acquire its own copy. (The 

FTC/FDA Model Act provides for distribution of the list to all pharmacies and 

prescribers, thereby assuring that the formulary will be most useful. FTC 

Staff Report (1979, p. 285).) 

70 
DIFFERENCES IN STATE LAWS 

The FTC/FDA Model Act includes a formulary and ties it to 

the FDA's list of "Therapeutic Equivalents" in order to assure 

extensive coverage and a basis for automatic updating. 7 The 

recommendation for a formulary was based in part on the 

recognition that some drug products have been shown to have 

serious bioequivalence problems and should therefore not be 

freely interchanged. Moreover, evidence of higher rates of 

substitution in states with formularies was cited. 

A formulary plays a second role in addition to defining what 

the state considers a legal substitution (or a mandatory substitution.) A formulary may provide a pharmacist some protection against liability by private parties. This implicit 

protection against liability is another reason to believe that 

the existence of a formulary encourages substitution. Liability provisions are discussed in the next section. 

There has been considerable controversy as to whether a 

positive or a negative formulary is superior.8 The FTC/FDA 

Model Act contains a positive formulary (tied to the FDA list) 

on the grounds that pharmacists expressed a preference fc.r 

positive formularies and that explicit inclusion of the drug 

provides greater reassurance to the pharmacist and will produce 

the highest rate of substitution. A positive formulary informs 

pharmacists that other experts have deemed these products 

interchangeable, while omission from a negative formulary may 

convey only that bioinequivalence is not established; the 

pharmacist may refrain from substitution if the implication is 

that substitution is not assuredly appropriate. A positive 

formulary which lists brands may further facilitate a pharmacist's selection of low-cost versions to put into inventory. 

There are, however, arguments that a negative formulary may 

be superior to a positive formulary. One drawback of a 

positive formulary is the possibility that bureaucratic delays 

or political pressures may cause states to be slow to add new 

entities and brands to a formulary, even after the drugs have 

appeared on the FDA's "Approved List." The delays may affect 

1/ FTC Staff Report (1979, pp. 281-285). 

§} Two states have both negative state formularies and positive pharmacylevel formularies (Florida and Ohio). 

71 

CHAPTER 5 

the extensiveness as well as the accuracy of the formulary. In 

response to this argument, the FTC/FDA proposal to tie a 

state's formulary automatically to the FDA list assures speed 

in updating and saves the cost of duplicative evaluation of 

drugs. A second advantage of negative formularies, insofar as 

they are brief, is that they may be easier for a pharmacist to 

use and remember -- knowing that substitution is allowed in all 

but a few entities which he must check -- than an extensive 

positive formulary. Thus, omission of a specific generic 

entity from a negative formulary may lead to more substitution 

than its inclusion in a positive formulary. 

We offer no hypothesis as to the relative effects on 

substitution, over all, of a positive or a negative formulary. 

However, given that substitution on a prescription for a particular drug is permitted by the formulary, we expect substitution to be more likely with a positive formulary than with a 

negative formulary, and least likely when there is no formulary 

at all. 

D. Pharmacist Liability 

Somewhat fewer than one-half of the states with DPS laws had 

in 1980 an explicit provision exempting the pharmacist who 

substitutes with the prescriber's permission from any additional liability beyond that which applies in filling a 

generically written prescription. In some other states, 

officials at the state Board of Pharmacy have expressed to us 

their belief that selection of a drug on a positive formulary 

or avoidance of one on a negative formulary provides an implied 

exemption from additional liability. In any case, it seems 

less like I y that the pharmacist would be considered to have 

acted unprofessionally or negligently as a result of substitutions made in accord with the state's formulary. The greatest 

exposure to liability occurs in states with neither a f ormulary 

nor an explicit provision that limits a pharmacist's liability 

when substituting. 

The FTC/FDA Model Act left as optional an express statutory 

protection from greater liability when substituting than when 

72 
DIFFERENCES IN STATE LAWS 

filling generically written prescriptions.9 According to the 

FTC Staff Report, while pharmacists expressed concern about 

liability risks, no actual suits involving substitution were 

identified. 10 Moreover, pharmacists in states where the law 

actually contained an express protection were typically unaware 

of those provisions. Further, the Report states, "most 

liability provisions are more a restatement than a limitation 

of the legal standard likely to be applied by common law," 

although a restatement of those standards in the form of 

statutory provisions may serve to reassure pharmacists. On 

balance, the Report concludes, such a provision is probably a 

good idea but may have little impact. 

Liability is an expected cost and decreases the incentive to 

substitute. In states where there is not as much protection 

when substituting as when filling a generically written 

prescription, there is likely to be less substitution. 

E. Cost Pass-Through 

Prescription drug retailers pay less for generic drugs 

than for the leading branded items in multi-source entities.11 

In nearly two-thirds of the states the pharmacist had in 1980 

the option of passing on all or part of this difference in 

invoice cost on substitutions, with the law requiring only 

that the substitution be less expensive to the consumer. Even 

one penny less generally satisfies this requirement. Additionally, some states specified that a pharmacy could not charge 

more for a drug product when it was dispensed as a substitute 

than the store's normal and customary price for it. 

Other states have enacted laws whose purpose is to ensure 

that most or all of the difference in acquisition cost is 

~/FTC Staff Report (1979, p. 286-288).

lQ/ Since publication of the FTC St aff Report, there has been at least one 

widely publici~ed case involving substitution. However, the pharmacist found 

guilty in this case had substituted a product which had not received FDA 

approval although in his and many other states pharmacists are required to 

dispense only FDA-approved drugs. Despite this distinction, it ia reasonable 

to suppose that pharmacists' fears about liability would have been increased by 

this case. 

llf See Chapter 3. 

73 

CHAPTER 5 

passed through to consumers.12 A pass-th rough prov1ston 

requires pharmacies to lower the price on the substituted 

prescription by the amount of the difference in the pharmacy's 

own cost between the prescribed and the dispensed produc.ts. 

States differ in their methods of computation of the required 

pass-through, and one state (Washington) requires only a 

partial (60 percent) pass-through. Table 5-l shows how a "full 

cost-pass-through" law works. 

Pass-through provisions save consumers money when substitution occurs, but substitutions might occur less often. If a 

pharmacy is required to pass through to the consumer the 

entire difference between the acquisition cost of the prescribed product and the substituted product, the pharmacist's 

incentive to substitute is greatly diminished. As we showed 

in Chapter 3, prescription drug retailers generally make a 

larger dollar margin over their invoice cost on generics than 

on brands in the same entity. Thus, the f ull pass-through 

provision reduces the retail price and dollar margin on 

generics that are dispensed as substitutes on brand-written 

prescriptions. Some incentive to substitute remains, however, 

since dispensing low-price products may draw customers from 

other stores. Furthermore, if prices on substitutions are even 

lower in states requiring a pass-through and if consumers in 

those states either know of the provision or notice the greater 

price diffe rences, consumers in pass-through states may ask for 

generic equivalents more often. 

Emphasizing the usefulness of a profit incentive to induce 

the pharmacist to substitute, the FTC/FDA Model Act required no 

pass-through of the cost savings, requiring only that the price 

of the product dispensed by lower than the price of the product 

llf This concern is reflected in a feature article that appeared in the 

Waahington Post (Sinclair, July 28, 1980) . Sinclair notes that "dr ugstore 

m ark-upa today absorb a substantial portion of the savings origina lly intended 

for consumers. Consumers do pay lese for unbranded prescriptions in most 

cases--but not nearly aa much a.a predicted." 

74 

DIFFERENCES IN STATE LAWS 


TABLE 5-l 


Pricing a Substituted Drug Under 


a Full Cost-Pass-Through Law: An Example 


1. Retail Price of Prescribed Brand $ 8 

2. Invoice Cost of Prescribed Brand 6 

(1-2) 	= 3. Dollar Gross Margin on Prescribed 

Brand 2 

4. Invoice Cost of Substitute Brand 3 

3 = 5. Maximum Allowable Margin on 

Substitute Brand 2 

(4+5) = 6. Maximum Allowable Retail Price of 

Substitute Brand 5 

prescribed.U~ The Staff Report noted also that pass-through 

provisions are difficult to enforce, in part because "an actual 

event (the sale of the dispensed product) must be compared with 

a hypothetical event (the sale of the brand prescribed but not 

dispensed.)" Also, an FTC-sponsored study found that one-third 

to one-half of the pharmacists in states with pass-through 

provisions did not know of the requirement, suggesting that 

compliance cannot be high. The FTC/FDA Model Act is silent on 

another price control provision sometimes used, a requirement 

that the price of the product when substituted be no higher 

than its usual and customary price. Competitive pressures were 

expected to keep the price of a single product from diverging 

in these two different circumstances of dispensing. 

In sum, a pass-through provision is expected to be associated with a lower level of substitution but with a greater 

brand-generic price differential and lower retail prices on 

prescriptions when substitutions. are actually made. However, 

llf FTC Staff Report (1979, pp. 278~279). 

75 

CHAPTER 5 


in light of difficulties in defining and enforcing compliance, the effect might be expected to be modest in magnitude. 

F. Notification 

State drug product selection laws sometimes require that the 

consumer be notified whenever a substitution is made or, in 

some instances, contemplated. Pharmacists are sometimes 

required simply to tell the consumer whenever a substitution 

has been made. In some states, the pharmacist must also state 

the retail prices of the alternative products. Sometimes the 

consumer must be asked to consent explicitly to the substitution, before the drug is packaged. There are extra labelling 

provisions in many states. We have included in this group of 

provisions any extra record-keeping requirements, although such 

a requirement does not constitute direct notification to the 

consumer, because, like the others, it imposes extra costs on 

the pharmacy in conjunction with any substitution. A state may 

have one or several of these notification and record-keeping 

requirements. 

It is not clear whether notification requirements should be 

expected to deter substitution, by imposing costs on the pharmacy, more than they encourage substitution, by increasing 

consumer demand for it. (Record-keeping requirements may 

discourage substitution, since they impose costs but do not 

directly stimulate demand for substitutions.) To discuss with 

a consumer any preference about a possible substitution, or 

even to inform a consumer that a substitution has been made, 

takes additional pharmacist time and imposes a cost. Chains 

report that these requirements are a particular nuisance to 

them and discourage substitution, in part because of the interruption of a well-established dispensing routine. Even if the 

store has been providing the same information by some different 

method, the requirement may compel it to deviate from chainwide standardized operating procedures. Such a routine must 

also be differentiated across chain outlets in states with 

different kinds of requirements. At the same time, any extra 

information provided to the consumer leads to an increased 

awareness of generics and the possibility of substitution. 

Changes in consumers' attitudes would add to pressures for 

76 

DIFFERENCES IN STATE LAWS 

substitution. Ce rtain provisions are more likely to ha ve this 

effect than ot hers. In fact, the requirements for oral 

disclosures, which migh t have the greatest impac t, are also 

probably the most costly for the pharmacy in terms of in terrupting a smooth routine. 

There are three information provisions included in the 

FTC/FDA Model Act. 14 First, the consumer mus t be n otified that 

a substitution is being made and of the righ t to refuse the 

substitution. Second, the prescription label must include the 

identity of the drug product dispensed. Third, t he identity 

of the product dispensed must also be recorded in the pharmacy's file copy of the prescription. The latter two requirements were to apply to all prescriptions, not j ust those 

substituted. The first recommendation was designed to "make 

more meaningful" the consumer's right to refuse substitution 

while at the same time drawing attention to the option so as to 

encourage consumers increasingly to accept substitutions. 

Also, an FTC-sponsored survey indicated that the req uirement 

would not be undul y burdensome to pharmacists. The Model Act 

stops short of requiring a n explanation of possible price 

savings and of requiring discussion prior to filling the 

prescription because both requirements were thought to be 

unduly burdensome and therefore perhaps deter substitution. 

The labeling and recordkeeping requirements were a lso seen as 

unlikely to increase pharmacists' costs significantly. 

The direction of the ef fect of the notification provisions 

is uncertain, since the increase in costs will deter substitution while th e increase in information may lead to stronger 

demand for generic substitution. 

!iJ FTC Staff Report (1979, pp. 279 -281). 

77 

CHAPTER 5 

G. Collateral Regulations 15 

A number of other provisions in this or other state laws may 

interact with drug product selection laws either to encourage 

or to discourage substitution. While these provisions were not 

considered to be so important as to require inclusion in the 

regression analysis, they nevertheless may have some influence. 

1. Advertising Restrictions 

The Supreme Court's 1976 Virginia State Board of Pharmacy v. 

Virginia Citizen's Consumer Council, Inc. decision overturned 

the prohi bition in many state laws against retail price 

advertising of prescription drugs. Since then virtually every 

state that had had price advertising prohibitions has repealed 

its statute. Although no state currently enforces a strict 

prohibition on retail price advertising, nine states have 

restrictive proviSIOns, ostensibly to control potentially 

misleading practices. Examples are provided in Appendix A 1. 

Restrictions against retail price advertising of prescription drugs are not normally part of a state's drug product 

selection statute. Nevertheless, advertising restrictions 

interact with the opportunity to substitute. Even if the 

restrictions affect advertising in general, not advertising of 

generics or the opportunity to substitute in particular, they 

raise the cost of effective advertising and therefore presumably reduce the quantity of any price advertising. 16 The impact 

of drug product selection laws where advertising is restricted 

is likely to be weaker, since advertising increases consumers' 

ll/ The FTC/FDA Model Act includes, in addition to the provisions detailed 

above, sections entitled "Definitions," "Enforcement," and "Effective Date," 

the latter two to be designed by the individual state, and one additional 

section concerning public education and monitoring. FTC Staff Report (1979, 

pp. 285-286). 

16/ For example, prohibitions of cents-off coupons do not have a stronger 

effect on generics, and therefore substitutions, than they do on brands. On 

the other hand, a requirement that the generic name be given whenever a brand 

ia advertised might spur substitution, given that any advertising at all is 

undertaken. 

78 

DIFFERENCES IN STATE LAWS 

information about relative prices both across stores and 

between brands and generics. 

In fact, there has been relatively little advertising of 

prescription drugs at retail, even since Virginia Pharmacy. 

Therefore, the presence or absence of legal restrictions on 

advertising may not translate directly into large differences 

in the actual amount of advertising and therefore into any 

impact on substitution. Nevertheless, executives of some 

chains, and it is chains which have done most of the retail 

prescription drug advertising that has occurred, have told us 

that such restrictions dampen their incentives to substitute 

and to undertake aggressive generic programs. 

2. Prescriber Liability 

A prescriber's decision to allow substitution may increase 

exposure to professional liability should the patient appear to 

have suffered from the substituted drug. Where physicians are 

at greater risk legally, they are less likely to authorize 

substitution by the pharmacist. About one-third of the states 

have enacted a provision exempting physicians from additional 

liability when they permit substitution. 

3. Pharmacy Ownership 

Two quite different types of ownership restrictions have 

been adopted. First, in about a quarter of the states physicians are prohibited from acquiring a controlling interest in a 

pharmacy. Second, in two states partnerships or corporations 

may not own drug stores unless a minimum specified interest is 

held by registered pharmacists. Both restrictions may decrease 

the number of stores in a market, but the restrictions might 

have an even more direct effect on substitution behavior. If 

physicians can direct patients to their own physician-owned 

pharmacies, these stores need stock .only those brands which the 

physician chooses to prescribe, thereby reducing the incidence 

of substitution. It seems unlikely, however, that physicianowned stores would be free from competition from other stores, 

and therefore these restrictions probably have little impact on 

overall substitution patterns in a market. The other ownership 

79 

CHAPTER 5 

restriction, which inhibits the of chain outlets, might 

well have been expected to affect overall substitution had it 

been true that chains substituted more frequently than independents. Since the data do not support that hypothesis, the 

second ownership restriction is also unlikely to alter the 

incidence of substitution in a state. 

4. Posting of Signs 

Some states require the posting of prices. There are two 

kinds of price posting requirements. About one-fifth of the 

states require posting by all pharmacies of the retail prices 

of the top one hundred or so most widely prescribed drugs. 

About one-third of the states provide that the pharmacy 

prominently post a sign informing consumers of the availability 

of generics. Both types of signs are likely to lead to an 

increase in substitution as consumers' attention is drawn to 

price comparisons or to the substitution option. 

5. Mail Order 

Mail order pharmaceutical houses often sell prescription 

drugs at prices lower than available locally. Ten states have 

bans or significant restrictions on intrastate shipments by 

mail order pharmaceutical houses. Stores in these states rna y 

feel somewhat less competitive pressure to substitute and 

therefore substitution may be less frequent. The laws have not 

typically regulated interstate shipments, however, although as 

mail order is increasingly used for prescription sales there is 

corresponding controversy as to the desirability of some 

regulation. 

6. Generically Written Prescriptions 

In most states a pharmacist may fill a generically written 

prescription with any item in the chemical entity and sell it 

for any price he elects. In about one-third of the states, 

such decisions were regulated in 1980. As with mandatory 

substitution laws, most of the statutes dealing with generically written prescriptions required the dispensing of a low 

80 

DIFFERENCES IN STATE LAWS 


cost (va r iously defined) ge neric item, provided one was in 

stock. As reported in Cha pt e r 6, we we re una ble to show that 

such pro v isions have affected the decision as to whether a 

leading bra nd or a generic is d ispensed on generica lly w ritten 

prescriptions, but the regu lation s might have had a separate 

effect on the selection of a specific generic to dispense and 

on the price charged to the consumer. 

fl. MULTIVARIATE LOG!T REGRESSIONS 

To look at the effect of each statutory prov ision while 

holdi ng constant the other aspects of the law, we u se multivariate regressions. The regression technique also allows us 

to hold constant, and to look at separately, the effects of 

non-legal influences. 

Specificall y, we used a legit model, which reflects the fact 

that the probability of substitution must lie between zero and 

one. 

17 The legit model f or the substitution c hoice is: 

Prob (S,) ­ 1 

where P rob(Sr) is the probab ility that a su bstitution is made 

on the rth prescription a nd Zr r eprese nts the va lues of a set 

of independent variables, defined below, for the rth prescriptio n . 

Whe n n a tural logarithms are taken on both si des of the 

eq u ation, 

Prob (S. ) ) log F (Z,) 

( 1 - Prob (5,) 

ll/ In our 1980 d at a, however, moat drugs had very low substitution rates. 

The logit t echnique may not give a good fit at the tails of a distribution. 

See section V of Appendix A7 for a diacueaion of thie p roblem . 

81 

CHAPTER 5 

= + a 2 MA NDt + a3 RXPROt 

+ a• RXANT/t + a POSt + NEGt 5 a6 

+ a7 LIABt + a 8 PASSt + a9 INFOt 

where t = 1, 2, ..., 47 indexes the state; for each t, i • 1, 

2, ..., Nt indexes the stores within that state; and for each 

i and t, r = I, 2, .. . , Rit indexes the prescriptions 

dispensed by tha t store. 

A separate regression is run for each drug. The data for 

each drug are further restricted to a single dosage form, a 

single strength, and the five most frequently dispensed 

prescription sizes in terms of number of tablets or capsules. 

(All five sizes are included in a single regression.) The unit 

of observation is the individual prescription transaction. 

18 

The choice of a brand -- the dependent variable -- IS 

coded as 0 if the prescription was dispensed as written and 

as 1 if instead a substitution was made. 19 

The independent variables include both indicators of the 

presence or absence of specific legal provisions and other 

economic influences on the store's brand choice and pricing 

decisions. 

Appendix A 7 contains a discussion of a number of technical 

econometric issues. 

1!./ See Appendix AS for a description o( the data and Appendix A7 on 

econometric: i11uea, including choice of aggregation.

llJ See our definition of aubatitution in Chapter 2. 

82 
DIFFERENCES IN STATE LAWS 

A. LEGAL VARIABLES 

Eight dummy variables are used to capture the legal provisions discussed above and are coded 1 if the law includes the 

prov ision and 0 ·otherwise. To simplify the analysis, we 

combined some of the most detailed categories of a legal provision into more broadly defined measures. The exact provisions 

fitting into each of our final codes are listed in Appendix 

A1. Data on the substitution effects of some of the more 

detailed subcategories of the provisions are given in Appendix 

A4, based on cross-tabulations. 

The expected sign, based on a priori hypotheses as discussed 

above, is given in parentheses for each variable. A positive 

coefficient therefore means that the presence of the provision 

was associated with a higher probability of substitution. 

MA N D (+) 

Mandatory substitution (on all prescriptions, regardless of 

insurance type.) 

If the state has a formulary that applies to mandatory 

substitution, e.g., substitution is required for drugs listed 

in the formulary applicable in the month when the prescription 

was dispensed (whether or not substitution is permitted on 

all others), MAND is coded 1 for those drugs on which substitution was mandated. 

MAND = 1 for 10 sta tes. MAND = 0 for 37. 

RXPRO (+), RXANTI (-),and RXNEUT (omitted category) 

"Pro-substitution", "anti-substitution", or "neutral" physicians' prescription pad. 

Because there is so much va ria bility in prescription pad 

fo rmats we use three subgroupings: fo rma ts which make it most 

inconvenient for the physician to proh ibit substitution and/ or 

most convenient for the physician to indicate a definite 

decision that substitution is acceptable (RXPRO); formats 

which make it most convenient for the physicia n to prohibit 

83 

CHAFTER 5 


substitution (RXANTJ); and those in between (RXNEUT, which is 

the category omitted in the regression, as being the same as 

having neither RXPRO nor RXANTI.) Variants of both single- and 

double-line formats -- a categorization used by some other 

researchers -- were assigned to each group since we also took 

into account the underlying presumption as to whether substitution was or was not permitted in the absence of a deliberate 

override by the physician. Moreover, this grouping allows us 

to categorize formats not easily labeled simply as single- or 

double-line. (The FTC/FDA proposed format is one of those in 

RXPRO.) 

RXPRO = 1 in 20 states. RXANTl = 1 in 10 states. RXNEUT 

= 1 (or RXPRO and RXANTl both= 0) in 17 states. 

POS (+),NEG(+), and NO FORM (omitted category) 

Positive formulary, negative formulary, or no formulary. 

If the state has a formulary of any sort which limits 

substitution, it is coded either POS or NEG, depending on 

whether the drugs listed are those for which substitution is 

permissible (POS) or prohibited (NEG). 

States coded 0 on both POS and NEG have no formulary and 

therefore allow interchange between any two brands in any 

generic entity, subject to professional judgment. (If the 

state has a formulary that applies only to mandatory substitution, i.e., substitution is required for drugs listed in the 

formulary but permitted on all others as well, both POS and NEG 

are coded 0.) 

Both POS and NEG are expected to have a positive sign not 

only because of a formulary's informational function but also 

because it may provide some protection from liability. POS is 

expected to have a larger coefficient than NEG. 

POS = 1 in 16 states. NEG = 1 in 13 states. In 1 state 

POS = 1 in some months and NEG= 1 in other months. Both POS 

and NEG are 0 in 17 states. 

84 

DIFFERENCES IN STATE LAWS 


LIAB (+) 

Express statutory protection of the pharmacist from greater 

liability when substituting than when filling a generically 

written prescription. 

Some implicit protection against added liability may be 

conferred by the presence of a formulary or by the fact that 

substitution is mandatory. These other effects on liability 

are picked up in the variables POS, NEG and MAND. Therefore 

LIAB is measured against a background wherein many other prescriptions may also present to the pharmacist little added 

liability from substitution. 

LIAB = 1 in 21 states. (Of the 26 states with LIAB = 0, 11 

had either POS = 1, NEG= 1, or MAND = 1.) 

PASS(-) 

Cost pass-through. 

PASS= 1 in 17 states. PASS= 0 in 30 states. 

INFO(?) 

Notification. 

Because the different information provtswns impose different costs and provide different amounts of information to 

consumers, and because states have various combinations of 

information requirements, we coded INFO= 1 any state with at 

least one of a set of provisions seeming to exceed a threshold 

level of information and cost. Our coding scheme is described 

in Appendix AI. The Model Act's requirement would be coded 

1 on the INFO variable. 

INFO = 1 in 27 states. INFO = 0 in 20 states. 

B. NON-LEGAL VARIABLES 

There are six variables designed to capture the most 

important cost and demand influences on prescription brand 

85 
CHAPTER 5 

choices and prices. For most of these variables, the hypothesized signs draw on discussion in Chapter 4. More detailed 

discussion of these variables is found in Appendix A7. 

QUAN (+) 

The number of tablets/capsules in the prescription. 

Purchasers of larger prescriptions have a greater incentive 

to identify and purchase a lower-unit-cost product. 

SS!NDEX (?) 

An index of the store's average price of 18 single-source 

drugs, relative to the average price across all stores. 20 

This single-source price index is a summary measure of those 

demand and cost elements common in a single store's sale of 

both single-source and multi-source drugs. 

CHAIN(+) 

CHAIN = 1 if the store is a member of a chain with 

more than 10 outlets; CHAIN= 0 if the store is independently 

owned or part of a small chain. 

1'1"ED (+), PRIV (-), and CASH (omitted category) 

l\1ode of payment for the prescription. If the prescription 

was paid for through Medicaid, MED = 1, PRIV = 0 and CASH= 0. 

If the prescription was reimbursed through other private 

W Thus a store with an average price across the 18 drugs of $15.00 would 

have a SSINDEX of 1.07 if the overall average, which is set equal to 1.00, were 

$14.00. 

It would be inappropriate to use this variable if the drug product selection 

laws affect the prices of single-source drugs. We are satisfied that this is 

not so, at least to any significant extent. See Appendix A7. 

86 
DIFFERENCES IN STATE LAWS 

insurance, PRIV = 1, MED =0 and CASH= 0. If the prescription 

was paid for out-of-pocket, both the variables included in the 

regression (MED and PRIV) are = 0. The effect of MED is 

expected to be strongest on MAC drugs. The effect of CASH is 

embedded in the CONSTANT term. 

GEN (+) 

The proportion of prescriptions written generically in the 

state, computed across 45 drugs. For each drug, this average 

is adjusted to reflect the drug's U.S. average level of generic 

prescribing. This adjustment makes no difference statistically 

and was done only to make the scale of the coefficient on GEN 

more comprehensible. 

In states where physicians more frequently prescribe generically, consumers are more likely to be familiar with generics 

and stores more likely to carry them in inventory.21 

ll/ The correlation coefficient (across states with a DPS law in 1980) 

between substitution on formulary-permitted drugs and the incidence of generic 

prescriptions on those same drugs was .54 in 1980, which is significant at 

better than the 1 percent level of significance. The data for both measures is 

a weighted average across 45 drugs. There were 47 pain of observations. 

The correlation coefficient between the same substitution measure and the 

incidence of generic prescribing across all drugs (which is closer to the 

measure actually used in the regressions) was not as high, .41, but still 

significant at better than the 1 percent level. 

We have explored the possibility that the DPS laws lead to an increase in 

generic prescribing; see the discussion in Chapter 6. We conclude that much of 

the state-to-state variation in generic prescribing is due to forces other than 

the DPS laws. We suggest in addition that no specific provision of the law has 

a particular effect on generic prescribing and that therefore it is appropriate 

to control for GEN in explaining the effects of the provisions on the incidence 

of substitution. 

87 
CHAPTER 5 

TIME (+) 

The number of months from first implementation of the 

state's drug product selection law to the month of the prescription transaction. 22 

Because information about and acceptance of an innovation 

grow over time, substitution is expected to be more frequent in 

states with longer experience with drug product selection. 

C. RESULTS 

Logit regressions were run for 24 drugs -~ all those of the 

45 multi-source drugs where the number of observations made it 

feasible. Table 5-2 reports the estimates of the coefficients, 

their standard errors, and statistical significance. For 

consistency, the tests reported for statistical significance 

are two-tailed. Where the coefficient is hypothesized to have 

a specific sign, the appropriate one-tailed test is easily 

inferred; a coefficient significant by a two-tailed 10 percent 

test is significant by a one-tailed test at the 5 percent 

level. 

Table 5-3 summarizes the sign and significance patterns for 

the legal variables, first for the 10 drugs on which substitution was most frequent and then for all 24. The pattern among 

the top substitution drugs generally holds up for the other 

drugs, although the proportion of coefficients which are significant is usually lower for the less frequently substituted 

drugs. "Significance" is here measured at the level of 10 

percent or better, using a two-tailed test. Less than half of 

the coefficients are significant and for some variables there 

are several negative and several positive sign ifican t coefficien ts. 23 The last column, "Effect", is a summary measure 

W Oklahoma's law was passed very early, in 1961, more than 200 months 

earlier than 1980. In order to prevent this one observation from dominating 

the estimate on the TIME variable, we arbitarily used 99 months for Oklahoma. 

W The use of individual prescriptions as the unit of observation may 

have led to some instances of false significance, due to the multiple counting 

of what may be a single decision, on the part of the pharmacy, to substitute or 

not to substitute on a certain type of prescription. This statistical issue is 

discussed further in Appendix A7. 

88 
DIFFERENCES IN STATE LAWS 

across all drugs of whether the variable has a significant 

effect. It is based on the use of a binomial signs test (at 

the 10 percent significance level.) 24 By this binomial test, 

only 3 of the provisions have a significant effect: RXANT!, 

INFO and POS. For all three of these (but no others) it is 

also true that there are at least 6 significant coefficients of 

the predominant sign and no more than 2 of the other sign. We 

discuss the results on the individual legal provisions in turn. 

While the recommendations of the FTC/FDA Model State Act 

were not based solely on the criterion of increasing substitution, that was one of the issues analyzed in the FTC Staff 

Report in conjunction with each recommendation. We therefore 

comment, for each type of provision, on the appropriateness, in the limited terms of this single criterion, of each 

recommendation. 

1. 	 Design of the Physician's Prescription Pad 

(RXPRO, RXANTI) 

The strongest result is for RXANTl, confirming the result of 

other analyses that the design of the physicians' prescription 

pad has an important effect on the incidence of substitution. 

In particular, a format which makes it easy for the physician 

to prohibit substitution is associated with less substitution, 

although formats which make it more difficult for the physician 

to insist on a specific brand do not seem to lead to more 

substitution than a "neutral" pad. 

The difference between RXPRO and RXANTI, and its significance were also computed from the regression results. "Anti" 

pads were associated with less substitution than "pro" pads in 

all of the 23 drug regressions where coefficients could be 

24/ 	A pattern of 8 or more signs out of 10 would be significant at the 11 

percent level, using two-tailed tests; 9 out of 10 signs therefore satisfies 

the 	 conventional 10 percent level of significance. Out of 24, 17 must be of 

the 	same sign for etatistical significance to be better than 10 percent when a 

two-tailed teat is used. 

A binomial aigns test is strictly appropriate only if the trials (here, 

regreaaions) are independent. This condition ia not met by our data, since it 

is the same panel of pharmacies represented in each of the individual drug 

regressions. This issue is discussed further in Appendix A7. 

89 

TABLE 5-2 

Results of Logit Regressions or 


Sub11titution Choice by Drug 


CON- RX- RXDRUG STANT MAND PRO ANTI POS NEG LIAB PASS INFO 

Hydrochlorothiazide -2.12 ... 2 

(.60) 3 

.oo•• 

(.11) 

-.28** 

(.10) 

-1.03** 

(.17) 

-.32** 

(.13) 

.HI 

(.18) 

-.39** 

(.09) 

Chlordiazepoxide -3.14 

(.69) 

.sa•• 

(.15) 

.st•• 

(.14) 

-.06 

(.18) 

-.22 

(.16) 

.09 

( .24) 

-.31** 

(.12) 

Amitriptyline -1.76.. 

(.86) 

.27 

(.20) 

.13 

(.18) 

-.11 

(.25) 

-.25 

(.21) 

-.30 

(.27) 

-.18 

(.16) 

1.0 

0 

Penicillin VK 

Amoxicillin 

-3.51*. 

(.38) 

-1.59"' 

(.86) 

.64.. 

(.09) 

.17 

(.18) 

-.10 

(.01) 

.64 .. 

(.16) 

-.41 .. 

(.12) 

.01 

(.21) 

-.35** 

(.09) 

-.18 

(.17) 

-.70*'" 

(.14) 

-.37 

(.24) 

-.46 ... 

(.07) 

.13 

(.14) 

Atropine sulfate/ 

Diphenoxylate 

-1.94 •• 

(.52) 

. 54 .. 

(.13) 

-.37** 

(.10) 

-1.09.. 

(.19) 

-.18 

(.14) 

-.78... 

(.21) 

-.37.. 

(.10) 

Meclizine -5.27** 

(.98) 

.68.. 

(.24) 

.so 

(.20) 

-.27 

(.33) 

-.10 

(.24) 

-.13 

(.37) 

-.08 

(.19) 

.02 

(.13) 

.05 

(.10) 

-.03 

(.16) 

.18 

(.13) 

.09 

(.23) 

10 

(.19) 

-.26*'" 

(.11) 

.46 .. 

(.08) 

.10 

(.20) 

.67•* 

(.16) 

.20 

(.13) 

.49** 

(.12) 

-.48* 

(.27) 

.04 

(.2p 

ll GEN is omitted if there was less than 1 percent generic pre1cribing. For other druga with very little generic prescribing, the 

GEN coefficient may be large but mean little in economic term.. 

?:../ Two-tailed statistical significance ia denoted by u at the 6 percent level, • at the 10 percent level. 

'J./ Asymptotic standard errors are given in parenthe11e1. 

!/ This coefficient could not be eatimated reliably with the data. 

() 

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01 
TABLE 6-2, continued 

SSDRUG QUAN INDEX CHAIN MED PRIV GEN1 TIME N 

Hydrochlorothiazide .005•• 

(.001) 

-.U 

(.43) 

.16 

(.10) 

1.67•• 

(.13) 

-.74.. 

(.14) 

1.94.. 

(.39) 

-.003 

(.003) 

4027 

Chlordiazepoxide .007... 

(.002) 

-.66 

(.61) 

-.21 

(.15) 

1.8a•• 

(.20) 

-1.06.. 

(.26) 

14.03.. 

(2.15) 

.007•• 

(.003) 

2339 

Amitriptyline .004 

(.003) 

-1.35• 

(.77) 

-.06 

(.18) 

1.07•• 

(.20) 

-.6o•• 

(.29) 

7.60.. 

(1.67) 

-.000 

(.004) 

1329 

\0- Penicillin VK -.018... 

(.004) 

1.18.. 

(.33) 

.so•• 

(.08) 

.99'*'" 

(.12) 

-.4s•• 

(.12) 

2.17•• 

(.26) 

.000 

(.002) 

1451 

Arnoxicillin .007 

(.011) 

-1.82 ... 

(.77) 

.119.... 

(.16) 

.23 

(.27) 

-.24 

(.24) 

.70 

(.60) 

.001 

(.003) 

2379 

Atropine sulfate/ Diphenoxylate 

.010... 

(.002) 

-.06 

(.46) 

.48'"'" 

(.11) 

1.72.. 

(.Hi) 

-.64.... 

(.18) 

N/A 

N/A 

-.007.. 

(.003) 

3804 

Meclir.ine .ooe•• 

(.003) 

1.08 

(.81) 

-.09 

(.21) 

.52 ... 

(.22) 

-.57'" 

(.34) 

1s.as•• 

(3.47) 

.ots•• 

(.004) 

1283 

0 

...... 

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ttl 

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tT1 

Ul 

......

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\.0 

N 

TABLE 5-2, continued 

CON- RX- RXDRUG STANT MAND PRO ANTI POS NEG LIAB 

lsosorbide dinitrate 

Hydrala11ine/ 

Hydrochlorothia11ide/ 

Reserpine 

Doxycycline 

Hydrochlorothiazide/ 

Spironolactone 

Dipyridamole 

Brompheniramine/ 

Phenylephrine/ 

Pseudoephedrine 

Ampicillin 

Chlordia11epoxide/ 

Clidinium bromide 

Tolbutamide 

-3.3s•• 

(U3) 

-2.49 

(1.61) 

-1.60 ... 

(.78) 

-2.73 •• 

(1.20) 

-2.21 

(I.75) 

. 49 

(1.13) 

-4.27•• 

(1.28) 

-.09 

(1.06) 

-7.49•• 

(1.84) 

-.77• 

(.43) 

-1.94•• 

(.66) 

1.12.. 

(.18) 

-.01 

(.29) 

1.16 •• 

(.67) 

-.77•• 

(.31) 

-.07 

(.32} 

-1.07•• 

(.43) 

-.07 

(.82) 

.15 

(.31) 

.so•• 

(.32) 

-.60... 

(.16) 

-.04 

(.26) 

.66. 

(.34) 

-.31 

(.24) 

. 16 

(.25) 

.34 

(.23) 

.46 

(.51) 

-.82. 

(.46) 

.10 

(.60) 

-.96·· 

(.29) 

-1.56... 

(.52) 

-2.36•• 

(.94} 

-.34 

(.47) 

-1.19•• 

(.39) 

-.47 

(.'112) 

-.69 

(.67) 

.24 

(.41} 

-.28 

(.39) 

-.24 

(.22) 

.15 

(.36) 

.12 

(.69} 

-1.3t•• 

(.33) 

.08 

(.28) 

-1.12 •• 

(.39) 

-.33 

(.70) 

-1.1o•• 

(.74) 

1.94•• 

(.49) 

-.96•• 

(.30) 

-.70 

(.63} 

-.03 

(.56) 

-.38 

(.31) 

-.62 

(.38) 

-.19 

(.36) 

-1.11 

(1.16) 

.28 

(.36) 

1.35•• 

(.30) 

-.66•• 

(.15) 

.01 

(.23) 

.31 

(.56) 

1.36•• 

(.24) 

-.04 

(.22) 

.13•• 

(.26) 

-.44 

(.69) 

()

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., 

C"1 

;J;1 

c:A PASS INFO 

.72•• -.41 

(.34) (.31) 

.44 .02 

(.40) (.42) 

-.27 1.28"* 

(.27) (.18) 

.36 .41 

(.34) (.30} 

.11 .08 

(.55) (.43) 

-.40 1.53.. 

(.24) (.33) 

-.20 .50 .. 

(.34) ( .25) 

.80.. .06 

(.30) (.30) 

-.22 -.36 

(.74) (.72) 
TABLE !i-2, continued 

SSDRUG QUAN INDEX CHAIN MED PRIV GEN1 TIME N 

Iaosorbide dinitrate .005 

(.004) 

.74 

(1.17) 

.18 

(.26) 

.33 

(.27) 

-.97•• 

(.40) 

-1.71 

(7.66) 

.ou• 

(.001) 

817 

Hydra! azine / 

Hydrochlorothiazide 

Reserpine 

.010... 

(.005) 

-1.82 

(1.40) 

-.79•• 

(.39) 

.41 

(.31) 

-1.33.. 

(.63) 

N/A 

N/A 

.017... 

(.007) 

768 

Doxycycline . 025"' 

(.016) 

-2.40"'" 

(.70) 

-.04 

(.16) 

.23 

(.26) 

-.84.. 

(.25) 

22.50.. 

(6.84) 

-.001 

(.004) 

3242 

\C) 

y., 

Hydrochlorothiazide/ 

Spironolactone 

Dipyridamole 

.oos• 

(.003) 

.007.. 

(.003) 

-1.56 

(1.03) 

-1.32 

(1.46) 

.04 

(.24) 

-.39 

(.37) 

-.03 

(.28) 

-.95"' 

(.49) 

-1.12 .. 

(.40) 

-1.65 .. 

(.63) 

86.12.. 

(39.07) 

5.65 

(21.42) 

.008 

(.007) 

.000 

(.010) 

1455 

958 

Brompheniramine/ 

Phenylephrine/ 

Paeudoephedrine 

.006 

(.006) 

-4.37.. 

(1.00) 

.20 

(.20) 

. so•• 

(.26) 

-.88... 

(.39) 

N/A 

N/A 

.005 

(.006) 

2204 

Ampicillin -.000 

(.014) 

.8! 

( 1.08) 

1.46.. 

(.27) 

-.44 

(.42) 

-.64 

(.41) 

.70 

(.56) 

.009.. 

(.005) 

1738 

Chlordiazepoxide/ 

Clidinium bromide 

.010.. 

{.003) 

-3.71.. 

(1.00) 

-.96•• 

(.29) 

. 52 

(.35) 

-1.53.. 

(.48) 

N/A

N/A 

.011.. 

(.005) 

1744 

Tolbutamide . 015 ... 

(.017) 

3.61•• 

(1.61) 

-.31 

(.48) 

.14 

(.41) 

-1.13 

(.76) 

%5.40 

(17.62) 

-.012 

(.011) 

574 

t:l 

..... 

t%j 

t%j 

tr:l 

:xl 

tr:l 

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0 

tr:l -

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til 

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TABLE 6-2, continued 

CON- RX- RXDRUG STANT .MAND PRO ANTI POS NEG LIAB PASS INFO 

Conjugated edtrogens 

Chlorthalidone 

Acetaminophen/ 

Chlorzoxazone 

Tetracycline \.Q 

~ 

Spironolactone 

Allopurinol 

Dexbrompheniramine/ 

Paeudoephedrine 

Chlorpropamide 

-1.12 

(2.:n) 

.96 

(2.19) 

-.27 

(1.41) 

-4.09•• 

(.92) 

.23 

(2.86) 

-3.77 

(2. 78) 

1.35 

(1.61) 

2.10 

(1.87) 

-4.fi4 

(4.21) 

1.12 

(.72) 

-.as• 

(.52) 

1.03 

(.22) 

.34 

(.88) 

.43 

(.72) 

. 78 

(.60) 

1.44. 

(. 78) 

.oo•• -.34 1.00.. 

(.42) (.97) (.76) 

-.23 .00 1.68... 

(.64) (l.H) (.72) 

.a9•• -1.06 .. -.79 

(.30) (.63) (.51) 

-.50... -1.33.. -. .sou 

(.18) ( .33) (.23) 

.60 4 1.06 

(.69) ( 1.17) 

1.22. .71 -.16 

(.67) (.97) (.96) 

.59 -1.63 • -.36 

(.40) (.91) (.69) 

.12 -2.40.... -1.56• 

(.66) (1.17) (.81) 

-.49 

(. 76) 

.16 

(.68) 

.22 

(.34) 

-.47 

(.34) 

.54 

(.84) 

.39 

(.63) 

-.84 

(.68) 

.02 

(.66) 

2.12 .. 

(.58) 

.49 

(.64) 

.91 .. 

(.32) 

-.32* 

(.17) 

.64 

(.67) 

.00 

(.63) 

.78• 

(.43) 

-.77 

(.67) 

.61 

(.80) 

-.20 

(.60) 

.s6• 

(.35) 

.08 

(.25) 

1.74•• 

(.87) 

.67 

(.79) 

.56 

(.42) 

.94 

(.68) 

L94 .. 

( .61) 

2.15 .. 

(.71) 

-.28 

(.41) 

.96.. 

(.19) 

-.85 

(.74) 

.05 

(. 74) 

.22 

( .43) 

-.91 

(.66) 

::r: 

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t'Jj 

;XI 

c:n 
TABLE S-2, continued 

SSDRUG QUAN INDEX CHAIN MED PRIV GEN1 TTME N 

Conjugated estrOIJIIOI 

Chlorthalidone 

Acetaminophen/ 

Chlorzoxar.one 

\0 

VI 

Tetracycline 

Spironolactone 

Allopurinol Dexbrompheniramine/ 

P•eudoephedrine 

Chlorpropamide 

.008 

(.005) 

. 003 

(.004} 

.oo9• 

(.006} 

-.002 

(.003} 

.003 

(.008) 

.009 

(.007} 

-.003 

(.Oll) 

. 006 

(.006) 

-4.03... ( 1.83) 

-5.80.. (1.55) 

3.15.. 

( 1.39} 

.31 

(.82) 

-4.68° 

(2.49} 

-2.46 

(2.11) 

-5.34.\ •• (1.56) 

-5.63•• 

(1.78} 

.13 

(.31} 

1.48** 

( 45) 

-2.10 .. 

(.55} 

-.89. 

(.50} 

-.10 

(.30} 

-.09 

(.46) 

.1a•• 

(.19} 

.57•• 

(.30} 

-1.00 

(.72) 

.14 

(.61} 

- 2.45 •• 

( 1.06} 

.68 

(.57) 

-.77. 

(.41) 

4 

-9.60 

( 15.86} 

.49 

(.37} 

-.24 

(.52} 

-.35 

(.34} 

-.as• 

(.39} 

-.32 

(.28) 

4 

-.42 

(.66} 

-1.12. 

(.62} 

-2.36.. (1.03) 

-45.41 

(34.25) 

-40.22 

(51.81) 

N/A 


N/A 


.76 


(.49} 


N/A 


N/A 


9.55 


(6.28) 


N/A 


N/A 


29.59 


(79.34} 


-.006 

(.012} 

.007 

(.011) - 010 

(.007} 

-.001 

(.005) 

.003 

(.021) - 009 

(.017} 

-.002 

(.010) 

.000 

(.Oll} 

905 

1487 

1462 

4217 

563 

755 

1862 

1660 

t::l-"'l 

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CHAPTER 5 

TABLE 5-3 

Summary of Coefficient Signs for Individual Legal Provisions 

in the Logit Regressions on Substitution Choice 

(+) and ( -) and 

Signi- Signi- Overall 

Variable (+) ficant 1 (-) ficant Effect3 

MAND 102 

24 

8 

15 

6 

8 

2 

9 

2 

5 

No 

No 

RXPRO 

10 

24 

6 

16 

3 

7 

4 

8 

3 

4 

No 

No 

RXANTI 

10 

24 

2 

3 

0 

0 

8 

21 

5 

12 

No 

POS 10 

24 

1 

7 

0 

2 

9 

17 

2 

6 

NEG 10 

24 

4 

8 

1 

1 

6 

16 

4 

4 

No 

No 

LIAB 10 

24 

3 

12 

1 

5 

7 

12 

5 

7 

No 

No 

PASS 10 

24 

5 

15 

1 

4 

5 

9 

2 

2 

No 

No 

INFO 10 

24 

9 

19 

5 

9 

1 

5 

0 

0 

+ 

+ 

1/ Statistical significance is measured at the 10 percent level, 

tailed test. 

'J../ Regressions for the 10 top-substitution drugs are summarized 

line, the entire 24 drug regressions on the second line. 

~/ According to a 10-percent significance level by a binomial 

pattern of signs. 

by a twoon the first 

test on the 

SOURCE: Table 5-2. 

96 

DIFFERENCES IN STATE LAWS 


meaningfully estimated for these variables, and additional 

tests confirmed the statistical significance of this difference 

for 14 drugs. 

The regression results are in conformity with the recommendation of the Model State Act insofar as the category of 

formats within which the recommended format fits, RXPRO, is 

shown to encourage more substitution than RXANTI formats. 

Moreover, a simple analysis of state substitution rates 

tabulated by specific format, found in Appendix A4, suggests 

that states with the recommended format typically had higher 

rates of substitution than states with most other variants 

within the RXPRO category. However, in that the RXPRO 

formats, as a group, are not associated with significantly more 

substitution than RXNEUTformats, we cannot conclude that most 

formats other than the one endorsed by the Model State Act are 

"worse." 

2. Notification (INFO) 

The requirement that more information be provided to 

consumers leads to more substitution. Before seeing the data, 

we were unsure which of the two opposing effects -- discouraging substitution by raising pharmacists' costs or encouraging 

substitution by providing more information to consumers -­

would dominate. These logit results show the demand-enhancing 

effects to be stronger. Another way of framing the hypothesis 

might have been that pharmacies would less frequently suggest 

substitution in order to avoid the added cost of providing 

information and that therefore the rate of substitution would 

be lower. The data certainly do not support this hypothesis. 

The results on the information requirements suggest that 

consumers are more responsive to the chance to substitute when 

they know more about it. This provides support for our 

argument in Chapter 3 that the low level of substitution 

overall may be due less to a fixed rejection on the part of 

consumers or others than to consumers' lack of information. 

That consumers respond to information is confirmed also by 

reports from chain drugstore executives that their voluntary 

programs seem to have led to growing numbers of inquiries by 

97 
CHAPTER 5 


consumers about the possibility of substitution. In particular, some chains have printed out, with each prescription, 

either the price saving actually realized (when a substitution 

was made) or the saving foregone (when the prescription was 

dispensed as written.) A statement of the immediate saving 

available with substitution is an effective way to capture the 

consumer's attention. In other words, information on the 

opportunity for substitution and its price consequences makes a 

difference. This is not the same as saying that requiring a 

particular form of information dissemination is the only way by 

which this information will reach consumers. 

The Model State Act recommended that consumers be told 

when a substitution is made (and of the right to refuse the 

su bsti tu tion.) The regression results confirm the importance 

of provision of information to consumers in encouraging 

substitution, although the INFO category contains, along with 

the Model Act form of the information requirement, other ways 

in which consumers' attention may be brought to the opportunity 

for or fact of substitution. 

3. Formulary ( POS, NEG) 

The logit results on the presence of a formulary are 

unexpected and odd. Our hypothesis was that a formulary 

would provide reassurance and information and thus lead to more 

frequent substitution is not borne out. Instead, the logit 

results show that a positive formulary has a negative impact 

on substitution (on those prescriptions for which substitution 

was permitted), relative to no formulary. A negative formulary 

is shown to have a less significant effect, although the 

predominance of negative signs on NEG suggests that substitution rna y be discouraged by the presence of any type of formulary. While positive and negative formularies each had 

significant effects on substitution in 10 of 24 regressions, 

when the signs on the differential effect between having a 

positive or a negative formulary are counted, they split 

evenly, suggesting that the real impact on substitution comes 

from having any formulary at all. 

98 

DIFFERENCES IN STATE LAWS 


The FTC/FDA recommendation was for a positive formulary. 

If usefulness to pharmacists is measured by the incidence of 

substitution, this recommendation has not been borne out by 

our study. Our regression results show that positive formularies are associated with less, not more, substitu tion (given 

that substitution in the drug is permissible) than the absence 

of any formulary. However, the recommendation for a formulary 

was based on previous research which was unable to separate out 

the unique effect of a formulary and, moreover, was made not 

only to encourage substitution but also to ensure that products 

with problems of bioinequivalence were reliably excluded from 

the realm ~f possible substitutions. 

4. Mandatory Substitution (MAND) 

Our results on mandating substitution are also peculiar: 

mandatory substitution is not shown generally to lead to 

significant increases tn substitution. Indeed, of the 9 

negative coefficients 5 are significant. While it is possible 

to imagine that a mandatory provision might fail to lead to an 

increase in substitution -- if, for example, the other provisions would have led to just as much substitution -- it is not 

reasonable to expect such a requirement to lead to a decrease 

in substitution. 

The Model Act recommended that substitution not be mandatory, a rule thought both unnecessary and unworkable. Our 

study confirms that mandating substitution does not lead to 

significantly higher levels of substitution. 

5. Cost Pass-through Requirement (PASS) 

We had expected to find that a pass-through requirement 

would be associated with less substitution. However, the data 

show no significant pattern of negative coefficients; a passthrough requirement does not significantly deter substitution. 

In seeking an explanation, we offer the possibility that a 

pass-through provision, expected to deter substitution by 

decreasing the pharmacist's incentive, has a second, countervailing effect on the rate of substitution: that when the 

saving available from substitution is greater, consumers may be 

99 
CHAPTER 5 

more eager to initiate or accept substitution. Similarly, the 

implementation of the pass-through may explicitly draw consumers' attention to the opportunity, and this too would enlarge 

demand. 

The FTC/FDA recommendation was against a cost pass-through 

requirement, in the belief that such a requirement, even if 

workable, would deter substitution. Our results show, to our 

surprise, that the pass-through requirement does not systematically and significantly deter substitution. The Model Act's 

recommendation on this aspect of the law seems less important 

for encouraging substitution than anticipated. 

6. Liability ( LlAB) 

An explicit statutory protection against added liability 

is shown to have no significant effect on substitution. Half 

the coefficients on LIAB are positive; half are negative. 

Perhaps because the specter of liability has not been realized 

in lawsuits, protection against such liability has not assumed 

importance in affecting substitution behavior. Moreover, some 

protection against liability is presumed in conjunction with 

other provisions sometimes present, such as a formulary or 

mandatory substitution. Our results might be read to mean that 

statutory protection does not encourage substitution more than 

do other forms of liability protection. The FTC/FDA Model Act 

left as optional a statutory provision on the subject of 

liability as associated with substitution. Our results confirm 

that statutory protection is not particularly 

determining substitution decisions. 

important in 

7. Magnitude of Effects 

For each of the 10 top-substitution drugs and for the 

average of these 10 and of all 24 drugs, Table 5-4 gives the 

percentage point changes attributable to the presence of each 

proviSIOn. That is, the coefficients from the logit regressions have been transformed to facilitate interpretation. In 

each instance the percentage point change is centered on the 

actual average frequency of substitution in the drug, which is 

implicitly based on the average probability of the presence of 

100 
DIFFERENCES IN STATE LAWS 


each of the other provision. 25 The estimates seem very large. 

Even a 2 percentage poin t cha nge on a n a verage substitution 

rate of 16 percent seems surprising. Despite the insignificance of most of the coefficients, the estimated coefficients 

do provide the best (unbiased) measures of the effects. 

RXANTJ, which was shown above to have the most consistent 

effect across drugs, also had a strong effect on the probability of substitution. Substitution was 18 percentage points 

higher (9 for the top-substitution drugs) where the prescription pad had a "pro"-substitution format rather than an 

"anti"-substitution format. RXPRO is shown to have almost no 

different effect on rates of substitution from neutral prescription pads. When RXANTJ is compared with "neutral" prescription pads, RXANTJ is shown to have led to 16 percentage 

points less substitution (11 for the top-substitution drugs), 

little different from the comparison of RXANTI with RXPRO. 

The two other provisions which showed significant sign 

patterns in the regressions, POS (the presence of a positive 

formulary) and INFO (notification requirements), both had a 

modest impact on the probability of substitution. INFO raised 

substitution by an average of 1 percentage point. In comparison 

with no formulary, a positive formu lary lowered substitution 

rates by 5 percentage points and a negative f ormulary lowered 

them by almost as much (4 percentage points.) Across all 

drugs, positive and negative formularies were shown to have no 

measurably different effects on substitution rates. 

It is surprising that M AND increased substitution only 1 

percentage point overall (3 on top-substitution drugs.) A 

state with otherwise average provisions would therefore still 

be expected to have a substitution rate below 15 percent -- far 

short of the high rate nominally implied by a mandatory proviSlon. 

The cost pass-through provision had no measurable ef fect on 

the rate of substitution. Statu tory protection aga inst 

added liability was associa ted with 2 percen tage points less 

substitution. In ligh t of the mixed sig n and signi fi cance 

patterns a cross the 24 regressions for these two va riables, t he 

?.5/ See Appendix A7 for further detail. 

10 1 
CHAPTER 5 

TABLE 

Effect of Individual Provisions on the 

SUBSTI­ RXANTI RXANTI RXPRO 

TUTION v. v. v. 

RATE MAND RXPRO RXNEUT RXNEUT 

Hydrochlorothiazide 

Chlordiazepoxide 

Amitriptyline 

Penicillin VK 

Amoxicillin 

Atropine sulfate/ 

Diphenoxylate 

Meclizine 

Isosorbide dinitrate 

Hydralazine/ 

Hydrochlorothiazide/ 

Reserpine 

Doxycycline 

Average, 1a drugs 

Average, 24 drugs 

.241 

.193 

.184 

.173 

.119 

.17a 

.164 

.149 

.142 

.104 

.180 

.122 

.as• 

.a3 

.a7* 

.a2 

.as• 

.06* 

-.1a* 

-.31" 

.os• 

.03 

.01 

-.17" 

-.OS* 

-.a3 

-.as• 

-.a4" 

-.14* 

-.07* 

-.13* 

-.04 

-.as 

-.09 

-.183 

-.24" -.07* 

-.a1 .04" 

-.01 .02 

-.a7" -.02 

-.aa .a7* 

-.22* -.a9* 

-.03 .a4 

-.10* .03 

-.ao .a4* 

-.14* -.08 

-.11 -.02 

-.163 .al 

!/ Regessions included only observations for which substitution was permitted by both statute and formulary. The substitution rate in the 

first column is defined in the same way. 

'1./ • means the coefficient was significant at the 1a percent level, by a 

two-tailed test. 

102 

DIFFERENCES IN STATE LAWS 

5-4 

Probability of Substitution By Drug1 

POS POS NEG 

v. v. v. 

NEG NO FORM NO FORM LIAB PASS INFO 

-.09* -.05* .04 -.07* .00 .01 

-.05 -.03 .01 -.05* -.00 .02 

.01 -.05 -.05 -.03 .01 .01 

.06* -.07* -.14* -.07* -.04* .05* 

.02 -.02 -.05 .01 .01 .05* 

.10* -.04 -.14* -.05* .02 .05* 

.00 -.01 -.02 -.01 -.07* .00 

.32* .06 -.27* .03 .06* -.05 

-.10* -.00 .09* .06* .03 .00 

.08 -.04 -.02 

.03 -.03 -.07 -.04 .00 .02 

-.01 -.05 -.02 -.00 .01 

~/ The coefficients necessary for computing this difference could not be 

reliably estimated for one drug, so the averagt! includes only 23 drugs. 

SOURCE: Calculations baaed on Table 5-2. See Appendix A7 for details. 

103 

CHAPTER 5 


estimates of the magnitude of their effects may be best taken 

as underscoring the rejection of our prior hypotheses: 

contrary to expectation, a cost pass-through requirement does 

not lead to significantly less substitution, nor does statutory 

protection from liability lead to more. 

8. Effect of the Non-legal Variables 

Several of the non-legal variables show strong patterns of 

influencing substitution, mostly in accord with our hypotheses. Table 5-5 provides the data underlying these conclusions, based on the logit regressions for 24 drugs. 

Insurance coverage makes a difference. In all drugs, 

substitution is less likely J~or a purchaser covered by private 

insurance; this corroborates the cross-tabulation data reported 

in Chapter 4. The cross-tabulations also showed that substitution is more frequent for Medicaid consumers, and this 

remains true even when other influences are taken into account, 

with positive signs on 18 of the 24 MED coefficients. 

Some of the other variables also show the hypothesized 

effects; on others the pattern is weak or was not predicted. 

Where generic prescribing is more prevalent, substitution is 

more frequent. Substitutions occur more often on larger 

prescriptions. As discussed in Chapter 4, chains and independents are not shown to be significantly different in substitution behavior. The higher the single-source price index, the 

less likely is substitution. To the extent that a high index 

reflects a weaker intensity of competition, this is not 

surpns1ng, but since the SSINDEX measures local cost conditions and other influences as well, the coefficient is difficult to interpret. 

Long experience with the law may lead to more substitution 

but the effect is not strong. Although the sign pattern for 

the length of time since initial implementation of a drug 

product selection law is not sufficiently skewed to meet a 

binomial test for significance, a majority of the signs are 

positive and 6 of the 14 are statistically significant. The 

change in probability associated with a year-older law is 

less than 1 percent. 

104 
DIFFERENCES IN STATE LAWS 


TABLE 5-5 

Summary of Coefficient Signs for Non-Legal Variables 

in the Logit Regressions on Substitution Choice 

(+)and (-) and 

Signi- Signi- Overall 

Variable (+) ficant 1 (-) ficant Effect 2 

QUAN 20 11 4 1 + 

SSINDEX 7 3 17 10 

CHAIN 10 5 14 5 No 

MED 18 9 6 2 + 

PRIV 0 0 24 16 

GEN1 14 7 3 0 + 

TIME 14 6 10 1 No(+) 

!/ Seven drugs had leas than 1 percent generic prescribing, so 

GEN was omitted. 

1/ According to a 10-percent significance level by a binomial 

pattern of signa. 

the variable 

teat on the 

SOURCE: Table 5-2. 

105 

CHAPTER 5 


Iff. RESULTS FROM PREVIOUS STUDIES 

Much of the previous empirical work on the effects of 

individual provisions of the laws has been done by comparing 

results of studies done in individual states. There have been 

quite a few such state studies done. These studies will be 

reviewed in a forthcoming volume to be published by the 

National Center for Health Services Research. 26 We report here 

only on two compilations of cross-state results, each of which 

utilizes data collected in a consistent manner across all the 

states studied. 

A recent summary of results from four states surveyed by 

the Goldberg team draws the following conclusions: 27 

1. The two-signature-line prescription form leads to much 

more frequent physician prohibition of substitution than when 

prescribers are required to write out a phrase such as "Dispense as Written." In Rhode Island, with a two-line form, 

physicians prohibited substitution 38 percent of the time, 

in contrast to a rate of 5 percent or less in states requiring 

a handwritten statement of prohibition (about 5 percent of 

multi-source prescriptions in Michigan, about 1.5 percent in 

Wisconsin, and less than 1.5 percent in Vermont.) 

2. Positive formularies have a significant (positive) 

effect on the rate of substitution. 

3. Manda tory substitution provisions result in higher 

substitution rates; Vermont, which has such a provision, was 

found to have a rate nearly double that of the other states 

studied. 

4. There is no evidence that elimination of a pass-through 

provision will lead to greater substitution. 

A second cross-state analysis has been reported by Richard 

Zeich who uses data from 1000 retail pharmacies nationwide 

sampled in the l\1arket Measures National Substitution Audit. 28 

Some of Zeich's results are at odds with those of Goldberg and 

DeVito: 

W Goldberg and Raskin, ed., (forthcoming).


llf Goldberg and DeVito (1981). 


?&/ Zeich (1984). 


106 
DIFFERENCES IN STATE LAWS 


1. Substitution took place on 6.7 percent of new prescriptions in states with single-line prescription forms but on 

only 4 percent in states with two-line forms. (Substitution 

was also found to be increasing over time in one-line states 

but not as much in two-line states.) This appeared to be the 

result of the difference in the incidence of physician prohibition of substitution: 5 percent of new prescriptions in 

one-line states but 60 to 70 percent in two-line states.29 

2. In the seven states with mandatory substitution, the 

substitution rate has been declining and was lower (4.8 

percent) than in "permissive" states (5.3 percent), where 

substitution was increasing over time. Zeich concludes, 

"substitution rates for the mandatory and permissive states 

were close, suggesting that the legal factors make little 

difference." 

3. On formularies, Zeich states, "States with formularies 

had higher substitution rates than those without them, and 

formularies that were state-generated were more effective in 

encouraging substitution than the Food and Drug Administration's. The highest substitution rates were recorded for 

negative state formularies." 

These studies are in agreement with respect to the effects 

of the two-line prescription pad; they disagree about the 

effects of mandatory provisions and about the superiority of a 

positive formulary. 

Our regression results, then, underscore previous work as 

to the importance of a prescription pad format which demands 

extra attention and effort on the part of a physician deciding 

to prohibit a substitution. On the use of a positive formulary, however, our results show a decrease in substitution, 

whereas others have found an association with higher levels of 

substitution. We note that simple cross-tabulations of our 

data, using average substitution rates by state, suggest this 

positive association, whereas this result is reversed in 

the regressions. This implies that positive formularies are 

frequently found in conjunction with other state characteristics which encourage substitution, but that the presence of a 

llf Measured in a few states only. 

107 

CHAPTER 5 


positive formulary itself cannot be identified as having 

that effect independently. The third provision which proved 

strong in effect in our analysis was the requirement that 

consumers be personally notified, in one or another way, about 

substitution; this type of provision was not the focus of 

study in earlier work. 

Goldberg and DeVito found that mandatory substitution led 

to higher substitution rates, but Zeich did not; our results 

fail to show a strong positive effect of making substitution 

mandatory. On the pass-through provision, our results confirm 

those of Goldberg and DeVito, that the pass-through does not 

seem to deter substitution. 

IV. EVALUATION OF FTC/FDA Jo.fODEL ACT 

Finally, there is no simple, summary evaluation of the 

FTC/FDA Model Act. States have typically adopted some of the 

recommended provisions while modifying others. Conversely, 

there was no state from whose Ia w all traces of the basic 

recommendations were absent. It was impossible, therefore, 

to make a direct empirical comparison of the effects of two 

competing packages of provisions defined in terms of the 

FTC/FDA Model Act. 

What we have attempted to do is to measure the effect of 

each individual provision. As discussed above, some of the 

individual recommendations proved to be in line with encouraging higher rates of substitution, others were less important 

than predicted, and one (positive formulary) turned out to 

be associated with less substitution, rather than more. 

But the recommendations were not based on this single 

purpose. A positive formulary, tied to the FDA's list of 

"Therapeutic Equivalents," for example, was seen to be important also in providing a sound list of drugs for which interchange was medically appropriate, and tying a (positive) 

formulary directly to the FDA list was seen as a way to 

simplify and make less costly the maintenance of an accurate 

and up-to-date list. Similarly, the recommendation that 

consumers be told when substitutions are made, a provision 

which also does appear to encourage substitution, has another 

108 
purpose of ensuring that consumers have the right of refusal. 

Therefore, the value of the Model Act's recommendations should 

not be judged by the recommended provisions' effects on the 

substitution rate alone. 

Moreover, the FTC/FDA Model Act was put forward as a 

package of provisions, likely to have the greatest impact when 

adopted as a package. To the extent that there are interactions within a package -- that each provision does not simply 

have an independent and additive effect-- our logit technique 

fails to pick it up. To introduce even greater complexity into 

the statistical analysis would have demanded even more data 

than we had. 

V. SU/l.1MARY ON PARTICULAR LEGAL PROVISIONS 

Certain provisions of state drug product selection laws 

clearly have strong impact. The presence or absence of a 

formulary is obviously important, primarily because a formulary 

delimits the universe of potential substitutions. However, 

given that permission to substitute has been granted for the 

drug for which the prescription has been written, the presence 

of a formulary, particularly a positive formulary, seems to 

inhibit substitution. The format of the prescriber's prescription pad is very powerful; a format which makes it very 

convenient for the physician to prohibit substitution is 

associated with significantly lower levels of substitution. 

The direct notification to consumers that a substitution has 

been made or is possible seems also to have led to more 

frequent substitutions. 

For other provisions, our results can best be understood 

as showing that the hypothesized effect does not exist to any 

significant extent. For example, the most appropriate statement about the cost pass-through provision is that it did not 

deter substitution, as had been hypothesized, since there was 

no statistically significant negative pattern. Nor did a 

statutory limitation on protection from liability on substitution reveal any systematic and significant effect on the 

incidence of substitution. 

109 
CHAPTER 5 


Finally, a number of other provisions were shown to have 

been less important in their effects on the substitution rate 

than many observers have believed. As the volume of substitution increases, some of the provisions which had not, by 1980, 

demonstrated a significant influence may assume greater 

significance, although, given the 1980 results, the magnitude 

of any effects would probably be small. 

110 

CHAPTER 6 


GENERIC MARKET SHARE 


The drug product selection laws sought to lower average 

prescription prices by increasing the share of lower-priced 

generics, through substitution. In fact, generic market share 

is determined by several types of prescribing and dispensing 

choices, not just substitution. Whether or not all or most of 

state-to-state variations in generic market share should be 

attributed to differences in the DPS law depends on the extent 

to which each type of prescribing and dispensing decision is 

affected by the law. 

In particular, since most generically written prescriptions are filled with generics, an important question is 

whether promulgation of a DPS law leads physicians to write 

more (or fewer) prescriptions generically. If generic prescribing is treated as unaffected by the laws whereas in 

reality the laws lead to more frequent generic prescribing, the 

effects of DPS laws are underestimated, since the added generic 

prescribing would increase the overall generic market share and 

lower the average retail price. Alternatively, if variations 

in generic prescribing should not be attributed to the DPS laws 

but are counted as if they are, the laws' effects are overstated. 

In this chapter we first show the variation in generic 

market share from state to state and, in particular, the difference between states with and without DPS laws. We then 

describe the sources of generic market share in terms of types 

of prescriptions written and, for each type of prescription, 

the choice of brand dispensed. We analyze the effects of DPS 

laws on each of these decisions, paying particular attention to 

the relationship between the DPS laws and generic prescribing. 

Our conclusion is that our data do not discriminate between 

competing hypotheses as to the direction of causation between 

DPS laws and generic prescribing. 

111 

CHAPTER 6 

I. DIFFERENCES IN GENERIC MA RKET SHARE BY 

OPPORTUN! n r TO SUBSTITUTE 

Generic market share va r ies from sta te to state, rang ing 

fr om 12.1 pe rcent to 33.5 percent. 1 Table 6-1 shows the 

distribu tion of state a ve rages. 2 

Where generic market shares differ this much, average 

prescription prices do t oo. In 1980, consumers in a state with 

a 30 percent generic market share would have paid, on average, 

prescription prices which were lower by nearly 4 percent than 

prices paid by consumers in states where the generic market 

share was half as large (individual product prices held 

constan t.) 3 

The generi c market share is much higher in stat es permitting 

substitutio n than in state s prohibiting substitution (26.3 

percent compared to 13.2 percent.)4 This pattern is true for 

1/ G eneric market share is defined here aa a percentage of all prescriptions 

o n which the identity of the product d iapensed waa given. See footnot e 1 to 

Table 6-3 for a comparison with an alternative method of computation. 

1/ See Appendix Table A4-1 for data by state. 

~/ In 1980 the average price of a prescription filled with a leading brand 

was $8.22, compared with $6 .22 for generics. (See Chapter 3, section I-A.) 

The average prescription price would therefore have been $7.92 with a generic 

market share of 15 percent and $7.62 at 30 percent. The $ .30 difference is 

nearly 4 percent of $7.92. 

i/ F or these overall estimates, and for estimates by ind ividual drug, we 

extrapolated the known dispensing patterns, by prescription type, to prescriptions where the identity of the p roduct dispensed was not recorded. Most (98 

percent) of these lat t er prescriptions were generically written, a prescription 

type fo r which the dispensed product was, where r ecorded, typically a generic 

(89 p ercent). If only those prescriptions on which the dispensed brand was 

recorded are included in the computation, the estimated generic market share 

is lower, 23.3 percent for the U.S. in 1980, than the 25.1 percent estimated 

for all prescriptions. 

The generic market sh are is highest where substit ut ion on a dru g is permitted (by bot h statute and formulary). 30.7 perc ent. The generic market share on 

drugs for which substitution is no t permitted (by either t he st atute or the 

formulary) is much lower, 9 .1 percent. (See Table 6- 3 .) This comparison is 

exagge r ated, however, because d r ugs selected for formularies are the very drugs 

in which generic p rescribing is most likely. 

112 
GENERIC MARKET SHARE 

TABLE 6-1 

Generic Market Share, 


Distribution of State Averages, 1980 


Percentage Number of States 

10 - 15% 5 

15 - 20 10 

20- 25 15 

25 - 30 15 

30 - 35 4 

Average: 23.3%1 

!/ This average is computed directly across all prescriptions where the 

product dispensed was identified, for the whole United States sample. Computed 

by taking a simple average of state averages, it is 22.8 percent. 

SOURCE: Appendix Table A4-1. 

most individual drugs, as Table 6-2 shows. Among the ten 

top-substitution drugs there is only one exception (amoxicillin) to the pattern of lowest generic market share where 

substitution is prohibited by statute and highest market share 

where substitution is permitted by both statute and formulary. 

II. OVERVIEW OF SOURCES OF GENERIC MARKET SHARE 

Consumers receive generics sometimes as substitutes on 

brand-written prescriptions, nearly always on generically 

written prescriptions, and occasionally because the physician 

names a particular manufacturer's generic on the prescription. 

Generics are dispensed much more frequently on some drugs than 

on other drugs; 4 large antibiotic entities together accounted 

for 55 percent of all generically dispensed prescriptions in 

our 45-drug sample in 1980, and the 8 drug entities with 

113 
CHAPTER 6 

TABLE 6-2 

Generic Market Share by Drug 


and Opportunity to Substitute 


for the 10 Top-Substitution Drugs, 19801 


Both statute 

Substitution Substitution and formprohibited by prohibited by ularypermit 

Drug statute formulary substitution 

Hydrochlorothiazide 28.2% 55.0% 58.7% 

Chlordiazepoxide 3.3 12.5 29.0 

Amitriptyline 10.4 12.8 34.5 

Penicillin VK 25.9 NA 63.0 

Amoxicillin 27.3 54.3 38.6 

Atropine sulfate/ 3.0 11.0 18.1 

Diphenoxylate 

Meclizine 1.9 6.0 24.5 

Isosorbide dinitrate 1.9 12.4 22.0 

Hydralazine/ 

Hydrochlorothiazide/ 

Reserpine 3.8 10.2 19.6 

Doxycycline 1.1 5.1 14.2 

1/ The ten drugs are listed in order by substitution rate. These percentages are for all prescriptions, with known dispensing patterns extrapolated to 

prescriptions on which the product dispensed was not identified. 

SOURCE: Computed with data from the 1980 IMS National Prescription Audit. 

generic market shares over 30 percent accounted for 81 percent.5 The generic market share therefore is determined both 

by physicians' prescribing choices and by dispensing choices 

for each type of prescription written, as well as by the 

distribution of drugs prescribed. Table 6-3 brings together 

'§../ Computed with data for 45 leading multi-source drugs from the 1980 IMS 

National Prescription Audit. 

114 

GENERIC MARKET SHARE 


data on all sources of generic market share. It shows the 

proport io ns of prescriptions written for lead ing brand, fo r 

specific generics, and unspecified ("generically written"); t h e 

proportion of each type of prescription dispensed with a 

generic; and the contri bution from each prescribing channel to 

the overall generic market share. These are given for a 

weighted average of 45 drugs, by opportunity to substitute. 

The outstanding f eature of the table is the dominance of 

generic prescribing in determining generic market share. For 

the United States as a whole in 1980, nearly three-quarters 

(71.7 percent) of the generic market share was due to generically written prescriptions. Generic substitution on prescriptions written for leading brands accounts for 16.3 percent of 

generic market share, a n d prescribing and dispensing of 

specified versions of generics provides the remaining 12.0 

percent.6 

This dominance of generic prescribing as the source of 

generic market share is due to a few d ru gs which are prescribed 

frequently and usually by the generic name; f or 7 of the 8 

drugs with a generic market share over 30 percent (2 with 100 

percent) generic prescribing is the sou rce of more than half of 

all generically dispensed prescriptions.7 However, of all the 

37 drugs with a generic market sha re of at least 1 percent, in 

o ver ha lf the drugs (21) it was substitu tion f rom a leading 

brand which contributed a majority of generic d ispensing. Only 

10 drugs had generic prescribing as the domi nant channel, and 

for 5 the primary source was prescriptions written for a 

§./ Of the U.S. overall 25.1 percent generic market share of the 45 multi-­

source drugs, generics dispensed on generically written prescriptions contributed 18.0 percentage points, generic aubatitutions 4.1 percent age points, and 

specific generics dispensed at prescribed S.O percentage points.

!/ These 7 drugs (quinidine sulfate, nitroglycerin, tetracycline, ampicillin, penicillin VK, hydrochlorothiazide, and amoxicillin) account for 27.8 

percent of the total number of prescriptions in the 45 drugs studied. 

Looked at from the other aide, of the 11 drugs with at lea,t 10 percent o f 

a ll prescriptions writt en generically, these prescriptions provided a majority 

of the generic market share in 8 drugs. For several drugs ther e is essentially 

no generic prescribing, perhaps because the drug's name is so long as to discourage it, e .g., the combination drug hydrochlorothiazide/reserpine/hydralazine. 

115 

CHAPTER 6 

TABLE 

Sources of Generic Market Share by Type of 

Share of 

All U.S. Percent Prescrigtions Written For: 

Prescriptiona 

Leading 

Brand 

Specified 

"Generics" 

Unspecified2 

Substitution 

prohibited 

26.4% 90.4% 2.1% 6.9% 

Substitution 

permitted 

13.6 71.9 3.1 24.9 

States without 

DPS law 

8.1 85.8 4.0 10.1 

States with 

DPS law 

91.3 75.9 3.0 21.1 

All States 100.0 16.8 3.1 20.1 

!/ For prescriptions on which the identity of the product dispensed was not 

scription written. 

When only prescriptions on which the identity of the product dispensed is 

is the proportion of generically written prescriptions and their contribution 

estimates would be: 

All States 100.0 78.9 3.2 18.0 

Y These are prescriptions that were written generically.


V Numbers in parentheses give percentage of total generic market share 


SOURCE: Computed with data for .45 leading multi-source drugs from the 1980 

116 

GENERIC MARKET SHARE 

6-3 

Prescription Written and by Opportunity to Substitute, 19801 

Percent Generics Dispensed Contribution to Generic 

on Prescriptions Market Share from 

Written for: Prescriptions Written for: Generic 

Leading 

Brand 

Specified 

"Generics" 

Unspecified2 Leading 

Brand 

Specified 

"Generics" 

Unspecified2 Market 

Share 

1.4% 98.2% 73.4% 1.3% 

(14.3%) 3 

2.7% 

(30.0%) 

5.1% 

(56.0%) 

9.1% 

7.2 95.5 90.7 5.2 2.9 22.6 30.7 

(16.9) (9.4) (73.6) 

.4 99.2 87.8 .3 4.0 8.9 13.2 

(2.3) (30.3) (67.4) 

5.9 95.7 89.4 4.5 2.9 18.9 26.3 

(17.1) ( 11.0) (71.9) 

5.4 96.1 89.4 4.1 3.0 18.0 25.1 

(16.3) (12.0) (71.7) 

recorded, we applied the "percent generics dispensed" for the type of preknown are included, the overall generic market share estimate is lower, as 

to the overall generic market share. For All States, for example, t he 

5.4 96.1 89.4 4.2 3.0 16.1 23.3 

(18.0) (12 .9) (69.1) 

attributable to each of the three prescribing channels. 

IMS National Prescription Audit. 

I 1 7 

CHAPTER 6 


specific but non-leading brand. Table 6-4 g1ves data for 

individual drugs. 

Ill. GENERIC PRESCRIBING 

The drug product selection laws may alter not only the 

pharmacist's choices but the physician's as well. The laws 

typically specify how the physician can indicate whether or not 

substitution is permitted on a brand-written prescription. 

Whether the laws also affect the choice between naming a brand 

and writing the prescription by the generic name only is more 

difficult to assess. 

A. VARIATION IN GENERIC PRESCRIBING BY 


OPPORTUNITY TO SUBSTITUTE 


Generic prescribing was much more common in 1980 in states 

where substitution was permitted: there was only a little more 

than half as much generic prescribing in states which banned 

substitution altogether in 1980 as in states with DPS laws, 

10.1 percent as compared to 21.1 percent of prescriptions for 

multi-source drugs.8 As Table 6-5 shows, this pattern held 

true for individual drugs. It is clear that in any legal 

regime generically written prescriptions account for most of 

the generic market share but that this effect is even more 

pronounced in states and on drugs where substitution is permitted. 

§./ The 21.1 percent figure is for all 45 drugs in DPS states and does not 

exclude prescriptions for drugs for which brand-written prescriptions would be 

ineligible for substitution by a formulary. For formulary-permitted drugs 24.9 

percent of all prescriptions were written generically; for formulary-prohibited drugs in DPS states, 5.3 percent. There were a few states which allowed 

eubstitution but which had levels of generic prescribing similar to those of 

the three states which forbade substitution (under 15 percent). These states 

had levels of substitution nearly as low as in thoee three states, about 3.3 

percent or lees. 

118 

GENERIC MARKET SHARE 


B. DID THE DPS LAWS LEAD TO 

lvfORE GENERIC PRESCRIBING? 

This pattern raises the question of whether the DPS laws 

themselves lead to an increase in generic prescribing. The 

publicity surrounding passage of a DPS law and the discussion 

generated among physicians about it directs physicians' 

attention to the use of generic drugs, including writing 

prescriptions generically. Similarly, the publication of a 

formulary reduces a physician's cost in learning about the 

equivalence of specific brands. It is even possible that the 

inclusion of a drug on a positive formulary, for example, may 

indirectly confer some additiona! protection against liability; 

a physician could argue that if the official formulary countenances brand interchange, a generically written prescription 

-- which allows the pharmacist the same choices as on a brandwritten prescription for which substitution is permitted -- is 

also appropriate. 

If the rationale behind the DPS Ia ws is true, however, the 

increase in generic prescribing caused by the law must be 

slight; the laws were seen as a way around change-resistant 

physician prescribing patterns. Indeed, the law makes it 

unnecessary for the physician to remember and to write the 

generic name even when the intention is to allow the patient to 

purchase a low-cost brand. This might lead to less generic 

prescribing. 

While the physician's incentive to prescribe generically 

would not seem to be much increased by the passage of a DPS 

law, substitution offers pharmacies a new profit opportunity 

and may thereby direct their greater attention to all aspects 

of a generics program, including the task of convincing 

physicians that generic dispensing is acceptable. For example, 

more than one-quarter of all prescriptions are by telephone,9 

offering an opportunity for the pharmacist to check with the 

prescriber's office as to the acceptability of a generic. If 

the prescriber explicitly approves the generic, the pharmacist 

2,/ 26.3 percent of prescriptions in 45 multi-source drugs. Computation based 

on data from lMS' 1980 NPA. 

119 
TABLE 6-4 

Sources of Generic Market Share by Type of Prescription Written 

by Drug, 1980 1 

TyQ~ Qf Pr~s~riQtiQn Writt~n Overall 

For For Specific Generic- Generic 

Leading Manufacturer's ally Market 

Drug2 Brand Generic Written3 Share 

Hydrochlorothiazide 

0 

N 	 Chlordiazepoxide 

Amitriptyline 

Penicillin VK 

11.9% 1.2% 43.8% 57.0% 

(20.9%) (2.2%) (76.8%) 

15.5 1.0 9.9 26.4 

(58.7) (3.8) (37.4) 

13.0 3.9 14.4 31.2 

(41.5) ( 12.5) (46.0) 

6.2 6.6 47.0 59.8 

(10.2) (11.0) (78.7) 

l/ For each drug entry and type of prescription written, the first line i1 the ahan~ of all preacriptiona (for all types of 

prescription written) accounted for by generically diapenaed pre~~eription111 of that type; the second line is the ahara of the genertc 

market for generically dispensed preacriptiona of that type.

1/ Drugs are listed in deecending order by eubetitution rate in 1980. 


Y Prescriptiona for which no manufacturer i1 1pecified. 

TABLE 6-4, continued 

TyQ~ Qf Pr~~~riQtiQn Written Overall 


For For Specific Generic- Generic 


Leading Manufacturer's ally Market 


Drug2 Brand Generic Written 3 Share 


Amoxicillin 3.6 8.1 25.6 37.3 

(9.6) (21.7) (68.7) 

Atropine sulfate 15.4 1.0 0 16.4 


Diphenoxylate (93.9) ( 6.1) (0) 


,._ 


tv Meclizine 12.9 NA 8.8 21.6 


(59.4) (0) (40.6) 

Isosorbide 11.5 .l 7.5 19.1 

dini tra te (60.1) (.5) (39.3) CJ 

trJ 

z 

Hydralazine 8.7 4.8 .2 13.8 trJ 

;:tl

..... Hydrochlorothiazide/ (63.3) (35.1) (1.6) Q 

Reserpine 2:: 

>

:::0 

Doxycycline 8.5 .3 3.5 12.3 trJ 

(69.2) (2.4) (28.4) >-3 

{/.) 

::r: 

> 

~ 

t"l 
TABLE 6-4, continued 

T~n~ Qf Pres~riQtion Writt~n Overall 

For For Specific Generic- Generic 

Leading Manufacturer's ally Market 

Drug2 Brand Generic Written3 Share 

Hydrochlorothiazide/ 

Spir onolactone 

Dipyridamole 

.... 

N 

N Brompheniramine/ 

Phenylephrine/ 

Pseudoephedri ne 

Ampicillin 

Chlordiazepoxide/ 

Clidinium bromide 

Tolbutamide 

7.7 

(85.6) 

6.8 

(65.9) 

6.6 

(82.0) 

0 

(0) 

6.7 

(82.7) 

5.4 

(57.7) 

.2 1.1 

(2.2) (12.2) 

N A 3.5 

(0) (34. 1) 

1.4 .I 

(16.8) ( 1.2) 

15.2 84.8 

( 15.2) (84.8) 

.8 .6 

(9.9) (7.4) 

NA 4.0 

(0) (42.3) 

9.0 


I 0.4 


8.1 


100.0 

8.1 

9.4 

() 

::I: 

> 

"'t1 

~ 

ttl 

;::o 

m 
TABLE 6-4, continued 

TyQ~ Qf Pr~~~riQtion Writt~n Overall 

For For Specific Generic- Generic 

Drug2 

Leading 

Brand 

Manufacturer's 

Generic 

ally 

Written3 

Market 

Share 

Conjugated 

estrogens 

Chlorthalidone 

..... 

N 

w Acetaminophen/ 

Chlorzaxazone 

Tetracycline 

Spironolactone 

Phenytoin sodium 

4.1 

(62.1) 

4.2 

(85.8) 

3.8 

{96.2) 

1.1 

( 1.4) 

3.7 

(100.0) 

2.7 

(56.4) 

.2 2.3 

(3.0) (34.9) 

NA .7 

(0) (14.2) 

.1 .1 

(2.5) ( 1.3) 

7.1 69.5 

(9.1) (89.4) 

NA 0 

(0) (0) 

NA 2.1 

(0) (43.7) 

6.6 

4.9 

3.9 

77.7 

3.7 

4.8 

0 

tl:j 

z 

tl:j 

;:o 

....... 

Q 

~ >

::d 

~ 

tl:j 

>-3 

(I} >= 


::0 

t?;l 
TABLE 6-4, continued 

TyQ~ Qf Pres~riQtiQn Written Overall 

For For Specific Generic- Generic 

Leading Manufacturer's ally Market 

Drug2 Brand Generic Written 3 Share 

Allopurinol 

-

N 

.t;.. 

Dexbrompheniramine/ 

Pseudoepheddne 

Triamcinolone 

Chlorpropamide 

Hydroxyzine 

Sulfamethoxazole/ 

Trimethoprim 

2.3 

(43.1) 

2.4 

(29.3) 

1.7 

(47.0) 

2.2 

(70. 7) 

1.6 

(88.3) 

.3 

(I 00.0) 

.3 

(5.6) 

5.7 

(70.7) 

.2 

(5.7) 

0 

(0) 

NA 

(0) 

NA 

(0) 

2.8 

(51.3) 

0 

(0) 

1.7 

(4 7.3) 

.9 

(29.3) 

.2 

(1 1.7) 

0 

(0) 

5.4 

8.1 

3.5 

3.1 

1.8 

.3 

() 

::t: 

> 

,

'1j 

tr1 

;::.1 

Ol 
TABLE 6-4, continued 

T~12~ Qf Pr~s~ril2tiQn Writt~n Overall 

For For Specific Generic- Generic 

Leading Manufacturer's ally Market 

Drug Brand Generic Written Share 

Theophylline 

Terbutaline 

sulfate

-

N 

VI 	 Nitroglycerin 

Metronidazole 

Diethylpropion 

Phentermine 

.3 

(1.8) 

.5 

(6.2) 

.4 

(.8) 

.9 

(86.3) 

.8 

(5.0) 

.3 

(5.6) 

13.8 

(98.2) 

5.1 

(62.3) 

.7 

( 1.5) 

.1 

(9.6) 

16.0 

(95.0) 

.6 

(11.8) 

0 

(0) 

2.6 

(31.5) 

44.4 

(97.7) 

.0 

(4.1) 

0 

(0) 

4.2 

(82.6) 

14.1 

8.1 

45.4 

1.0 

16.8 

5.1 

Q 

1:<1 

z 

1:<1 

...... 

~ 

0 

~ >

::u 

~ 

tt:l 

>-i 

en 

:X: >

::0 

tt:l 
TABLE 6-4, continued 

Type of Prescriotion Written Overall 

For For Specific Generic- Generic 

Leading Manufacturer's ally Market 

Drug Brand Generic Written Share 

Minocycline 

-

10 

0\ 

Furosemide 

Erythromycin 

base 

Hydrochlorothiazide/ 

Triamterene 

Amitriptyline/ 

Perphenazine 

0 

(0) 

.4 

(90.7) 

.I 

( 1.2) 

.4 

(1 00.0) 

.l 

(.4) 

1.4 

( 100.0) 

NA 

(0) 

2.1 

(36.9) 

NA 

(0) 

18.6 

(99.6) 

0 

(0) 

.0 

(9.3) 

3.6 

(61.9) 

0 

(0) 

0 

(0) 

1.4 

.4 

5.8 

.4 

18.7 
TABLE 6-4, continued 

Tyn~ Qf Pr~s~rinliQn WriU~n Overall 

For For Specific Generic- Generic 

Leading Manufacturer's ally Market 

Drug Brand Generic Written Share 

Cephalexin 

Erythromycin 

ethylsuccinate ...... 

N 

--...) Ibuprofen 

Mestranol/Norethindrone/Placebo 

Hydrogenated 

ergot alkaloids 

Quinidine sulfate 

0 

(0) 

0 

(0) 

0 

(0) 

0 

(0) 

0 

(0) 

0 

(0) 

NA 

(0) 

NA 

(0) 

NA 

(0) 

NA 

(0) 

NA 

(0) 

.8 

(.8) 

0 

(0) 

0 

(0) 

0 

(0) 

0 

(0) 

0 

(0) 

99.2 

(99.2) 

0 

0 

0 

0 

0 

100.0 

0 

M 

z 

M 

;:<) 

()­

~ >;:<) 

~ 

M 

~ 

Ul 

::r: 

> 

;:<) 

t':l 
CHAPTER 6 

TABLE 6-5 

Proportion of Prescriptions Written Generically 

by Drug and by Opportunity to Substitute, 1980 1 

Both statute 

Substitution Substitution and formprohibited by prohibited by ulary permit 

Drug statute formulary substitution 

Hydrochlorothiazide 

Chlordiazepoxide 

Amitriptyline 

Penicillin VK 

Atropine sulfate/ 

Diphenoxylate 

Meclizine 

Isosorbide dinitrate 

Hydralazine/ 

Hydrochlorothiazide/ 

Reserpine 

Amoxicillin 

Doxycycline 

29.1% 

1.7 

3.7 

20.1 

0.0 

1.9 

1.0 

9.1 

.5 

32.0 

1.1 

50.8% 

9.1 

7.0 

NA 

0.0 

6.0 

7.2 

.3 

80.4 

2.1 

46.1% 

11.0 

17.8 

54.3 

0.0 

10.4 

.1 

52.1 

5.3 

1/ The ten drugs are listed in descending order by substitution rate. 

SOURCE: Computed from data from the 1980 IMS National Prescription Audit. 

128 

GENERIC MARKET SHARE 

may prefer to record the prescription as written generically, 

since under the laws of most states there are fewer regulations 

applicable to generically written prescriptions than to 

substituted prescriptions. In this way, those with the greater 

incentive for dispensing generics -- the pharmacists -- may be 

a link between DPS laws and an increase in generically written 

prescriptions, by first converting brand-written prescriptions 

into generically written prescriptions and, in a longer-term 

sense, educating physicians to write generically. 

Any impact on generic prescribing due to the legal opportunity to substitute would be likely to grow over time, since 

an increase in the familiarity with and acceptance of generics 

would be a gradual process. The data are consistent with this 

hypothesis. The incidence of generic prescribing by state 

(in 1980) is correlated with the length of time since passage 

of a DPS law. The estimated correlation coefficient for 46 

state observations (for all states with a DPS law by 1980) of 

the proportion of prescriptions written generically in 45 

leading multi-source drugs in 1980 and the number of man ths 

since passage of the state's DPS law is .38. 10 This is statistically significant at better than the 1 percent level. 

The correlation between early passage of a DPS law and a 

high level of generic prescribing does not establish the law as 

the cause of the prescribing pattern. It may be instead that 

both are due to an underlying attitude of the medical community 

towards generics. In states where many physicians have a 

favorable view of generics, they are more likely to support (or 

not oppose) legislation authorizing substitution and at the 

same time to prescribe generically more often. One corroborating fact is that early adoption of DPS laws was associ a ted 

with prescription pad formats likely to lead to more substitution, while states with formats which facilitate physician 

prohibitions have newer DPS laws. The average number of 

months since first passage of a DPS law was 38.8 for prescription pads inconvenient for prohibitions, 25.0 months for 

10/ Oklahoma is excluded from these calculationFJ because of its extremely 

early (1961) but ambiguous law. 

129 
CHAPTER 6 

formats making prohibitions easier, and 33.7 months for formats 

between the two extremes.11 

While the correlation between age of the DPS law and the 

incidence of generic prescribing is significant, a great deal 

of variation remains unexplained. Indeed the variation is considerable. Some states have double and, in one case, triple 

the amount of generic prescribing in other states; in 1980 

the United States average was 18.0 percent of multi-source 

prescriptions, with 5 states between 10 and 15 percent and, at 

the other extreme, 2 over 30 percent. Table 6-6 shows the 

distribution. 12 

TABLE 6-6 


Proportion of Prescriptions Written Generically, 


Distribution of State Averages, 1980 


Percentage Number of States 

10 - 15% 6 

15 - 20 16 

20 - 25 18 

25 - 30 7 

Over 30 2 

Average: 18.0%1 

1/ The simple average of state averages (20.7 percent) is higher than the 

U.S. average computed directly across all prescriptions (18.0 percent.) Both 

are computed acrose 45 multi-source drugs. 

SOURCE: Computed from data from IMS' 1980 National Prescription Audit. 

11/ Oklahoma is excluded; see preceding footnote. 

W See Appendix Table A4-1 for data by state. 

130 
GENERIC MARKET SHARE 

One possible ong1n of geographical differences in prescribing might be differences in training. If in pharmacology 

courses at one medical school bioequivalence is emphasized and 

generic prescribing encouraged, while in another medical school 

the subject receives little attention or examples of inequivalence are highlighted, graduates of the two schools may emerge 

with different attitudes about generic prescribing. Differences in training would translate to geographic differences in 

practice style if many graduates of a medical school tend to 

practice in the same state. Moreover, reviews of research on 

physician prescribing decisions emphasize the role of the 

medical community.13 Temin states, "The role of the medical 

community is all important in altering prescribing ha bits." 14 

Nevertheless, it is likely that much of the variation in 

generic prescribing would elude easy explanation. Studies of 

the sometimes startling geographical variation in other medical 

practices have established that the variations cannot be adequately explained even by traditional theories. Researchers 

associated with the Rand Corporation state, 

[R]ates of use of [medical] services (mostly surgical 

procedures) have been found to differ greatly. They 

range up to sixfold between geographic areas, and they 

occur even for seemingly nondiscretionary services such 

as major surgery. Such differential rates have not been 

explained satisfactorily by economic, professional, or 

population characteristics, even age, health status, or 

informed consumer preferences.15 

Therefore, some, but not all, of the interstate differences 

in generic prescribing may be attributed to the DPS laws. As 

long as the law itself had some independent effect, an estimate 

of the law's overall impact is incomplete without that increase 

in generic prescribing, and therefore in generic dispensing, 

ill Miller (1973-1974).

ll/ Temin (1980, p. 113). 

15/ Brook et al. (1984) summarizing work by Wennberg and others, e.g., 

W ennerg and Gittelsohn (1982). 

131 

CHAPTER 6 


and therefore in reduction of average price. Indeed, because 

of the consistent use of generics for filling generically 

written prescriptions, the indirect effect of the law might 

have been of greater economic significant than the direct 

effect of substitution itself. 

IV. BRAND CHOICE ON GENERICALLY 


WRITTEN PRESCRIPTIONS 


Although in all states most generically written prescriptions result in generically dispensed prescriptions, small 

differences in the proportion of generically written prescriptions on which a generic product is dispensed may somewhat 

offset or magnify the effects of prescribing patterns on the 

generic market share and on prices. DPS laws may influence 

brand choice on generically written prescriptions. Substitution may spur consumer interest in generics and increase 

pressure for generic dispensing whenever possible. The 

opportunity to substitute in combination with the need to 

choose a brand on generically written prescriptions may 

together provide enough volume to justify a pharmacy's adding a 

generic version to inventory and thus making it possible to 

dispense a generic on generically written prescriptions. 

However, for 8 of the 10 drugs on which substitution is most 

frequent, generic prescribing occurs on at least 3 percent of 

the prescriptions, suggesting that even in the absence of 

substitution the opportunity to dispense generically might have 

been large enough to warrant stocking a generic. 

Table 6-3 shows that selection of a generic on generically 

written prescriptions was more frequent in our sample where 

substitution is permitted, but noc by much. 16 When formulary 

restrictions are disregarded in states generally permitting 

substitution, allowing comparison across the identical set 

of 45 drugs, generics were chosen slightly more frequently on 

generically written prescriptions in states with DPS laws than 

16/ Comparisons within individual drugs do not yield much useful information because where substitution is prohibited on these usually-substituted 

drugs there are so few observations of generically written prescriptions that 

estimates are imprecise. 

132 
GENERIC MARKET SHARE 

in states without (89.4 percent compared to 87.8 percent, in 

1980).17 

Some of the laws contain one provisiOn which is explicitly 

directed at the choice of brand on generically written prescriptions. In particular, some states require that a low-cost 

brand, specified variously, be dispensed when the prescription 

is written generically. Three states tie this choice to a 

formulary. 18 The average share by state of generics dispensed 

on such prescriptions is not different between states with and 

without any restriction on dispensing of generically written 

prescriptions, about 88 to 89 percent in both cases.19 Therefore, we have found no evidence that the provision regulating 

dispensing on generically written prescriptions makes a 

difference as to what type of product is dispensed. 

In summary, the drug product selection laws have at most a 

very slight indirect effect on generic market share through 

more frequent choice of a generic when the prescription is 

written generically. Because such a high proportion of generically writtent prescriptions are filled generically under 

any conditions (for drugs thought to be reasonably interchangeable), there is little scope for increasing the use of generics. The 10-percent gap may be resistant to further shrinkage 

simply because there are some drugs where some brands seem 

clearly preferable, some drugs where demand is small enough 

that small stores find it unprofitable to stock a second, 

generic version, and some consumers who strongly prefer leading 

brands. 

llf When formulary restrictions are taken into account, the difference is 

much more pronounced (90.7 percent v. 73.4 percent) but the comparison is confounded by the difference in drug coverage. That is, some of the drugs precluded from substitution by formularies are those on which dispensing of a generic 

may be least common everywhere. 

Formulary restrictions typically do not apply to generically written prescriptions. For computations in this section, ·generically written prescriptions are classified by formulary restrictions which would apply to brandwritten prescriptions for the drug whose generically-written prescriptions are 

being analyzed here. 

W See the discussion of these provisions in Appendix Al. 

ill Percentages are computed acroas all 45 drugs. 

133 

CHAPTER 6 


V. PRESCRIPTIONS WRITTEN FOR SPECIFIC GENERICS 

There is one more prescribing channel, prescriptions on 

which the physician designates a specific "generic", whether by 

product name or manufacturer name, whichcontributesanaverage 

of 3 percentage points to the overall generic market share. On 

nearly all of these prescriptions the specified brand is dispensed. The "generic" specified is dispensed slightly less 

frequently where substitution is allowed (95.5 percent) than 

where substitution is prohibited (98.2 percent). However 

prescriptions of this sort are written a little more frequently 

where substitution is permitted (3.1 percent v. 2.7 percent.) 

The net result is that the contribution to generic market share 

from prescriptions written for specific "generics" is slightly 

larger where substitution is allowed (2.9 percentage points v. 

2.7 percentage points.) 

VI. CONCLUSION 

The generic market share in states permitting substitution 

was double that in states prohibiting substitution in 1980, and 

a higher generic market share implies lower average retail prescription prices. Part of this difference was due to substitution itself. Substitution from a leading brand to a generic 

added somewhat more than 4 percentage points to total generic 

market share of multi-source drugs in the United States. In 

states and on drugs where the law authorized substitution, this 

contribution was 5.2 percentage points, in contrast to 1.3 

percentage points where the law or formulary prohibited 

substitution. While substitution leads directly to a decrease 

in average price paid, much of any price difference observed 

between states with different substitution regimes is due to 

differences in generic prescribing. In fact, there was a 

dramatic difference in generic prescribing between states 

without DPS laws (10.1 percent) and those with DPS laws (21.1 

percent) (with market shares of individual drugs held constant.) Thus the size of the increase in generic market share 

cannot necessarily be attributed to the laws alone. The DPS 

laws had a much larger effect on generic market share (and 

presumably on prices) if this difference in generic prescribing 

134 
GENERIC MARKET SHARE 

is attributed to the laws than if generic prescribing was due 

primarily to factors other than the DPS laws themselves. 

The extent to which differences in average price due to 

differences in generic prescribing should be attributed to the 

DPS laws themselves is addressed again in Chapter 8 where 

multiple regression is used to analyze the effects of the laws 

on average prescription prices. Because of the uncertainty 

about the causes of variation in generic prescribing, the 

regressions are run two ways, one holding generic prescribing 

constant, the other allowing state-to-state variations in 

generic prescribing to be attributed to the law. The two 

estimates of the law's effects bracket the magnitude of the 

actual effect. 

135 

136 

CHAPTER 7 


BRAND AND GENERIC PRICES 


Drug product selection laws might lead to changes in the 

prices of individual drug products. We therefore investigated 

the effects of the laws on the prices of brands and the prices 

of generics, using multivariate regressions. 

The decision as to which version of the drug to dispense 

cannot be made independently of the prices set for the brand 

and the generic, and, similarly, the appropriate prices to set 

depends upon predicted dispensing patterns. For example, the 

larger the difference between the price of the brand and the 

generic, the more likely are consumers to seek out or to accept 

substitutions. A second example is that if the introduction of 

the opportunity to substitute spurs retail competition generally, prices of both brands and generics may be pushed down. 

Moreover, the general intent of the law -- to lower retail 

prescription prices -- may be subverted or complemented by 

indirect effects in addition to substitution. The average 

price of a prescription (in a drug entity) is by definition 

determined by the price of the leading brand, the price of the 

generic, and the market shares of each. For example, if the 

law were to lead to a widening of the brand-generic price 

differential, the dollar impact of each substitution would be 

increased, but if this were a result of an increase in brand 

prices, the greater expense of brand-dispensed prescriptions 

would be some offset to the savings due to substitution. 

Differences in retail prices are, in our study, essentially 

differences in retail margins, where the impact of retail 

competition shows up. We interpreted our results as if manufacturer-level or wholesale-level prices were the same across 

all states; if instead pharmacy acquisition costs vary from 

state to state in concert with the laws, our regressions pick 

up this effect as well. Our data did not allow a reliable test 

of the assumption of a single national wholesale price, but the 

relatively low cost of transshipment and the presence of large 

multi-state chains argues that prices would not vary from state 

to state, at least not according to differences in the states' 

137 

CHAPTER 1 

drug product selection laws. With our single-year data, we 

cannot explore changes in prices over time, although not only 

retailers but manufacturers as well may respond to substitution 

with price changes. 

In fact, no simple prediction is possible as to the direction of any price change for either generics or brands. Moreover, the results of our econometric analysis seem to indicate 

that any price effects for individual leading brands or 

generics are not large. 

I. POSSIBLE EFFECTS ON THE RETAIL PRICES 


OF GENERICS AND BRANDS 


The introduction of the possibility of substitution in some 

ways introduces downward pressures on the price of the brand 

and on the price of the generic but in other ways relaxes such 

pressures. A pharmacy wishes to maximize the proportion of 

prescriptions dispensed with a generic, a function of the gap 

between brand and generic prices, while protecting or increasing its share of all prescriptions sold in the community, a 

function of the level of its brand and generic prices relative 

to other stores'. 

Consider first the effects on the generic price induced by a 

legal change that allows substitution to occur. A store may be 

able to sell more generics than before even if the price of the 

generic is raised towards the price of the brand, as long as it 

stays below the brand price. At the same time, a slight 

increase in the price differential will have a higher payoff 

than when substitution is forbidden, since all consumers (not 

just those with generically written prescriptions) are free to 

choose a generic. Moreover, with an increased awareness that 

generics are possible substitutes for brands, consumers will 

pay more attention to the price differential. The opportunity 

for substitution may lead to an increase in retail prescription 

drug price competition in general and interstore competition on 

generics in particular. Advertising of generics programs will 

encourage and facilitate this trend. There are, then, contradictory pressures on the generic price: raise it to make more 

138 
BRAND AND GENERIC PRICES 


money since people will switch even at a smaller price differential, but lower it to induce an even greater switch. 

The net effect of these countervailing pressures on the 

generic price is difficult to assess. Without knowing how 

large each effect is, we do not put forward a hypothesis about 

the effect of the laws on generic prices. 

The authorization of substitution also changes competitive 

pressures on the price of the brand. Customers who come to the 

store with brand-written prescriptions offer a greater profit 

opportunity to the store when substitution is possible than 

when the prescription must always be dispensed as written, 

because they may accept a substitution to a higher-margin 

generic. Thus, a store has a greater incentive to try to draw 

such customers into the store by lowering the price of the 

brand, especially since many consumers with a prescription 

written for a brand may compare stores' prices for that brand 

rather than for a generic equivalent. However, lowering the 

price of the brand has two other effects. First, the store 

loses revenue on purchases by customers who stick to the 

brand. And second, lowering the price of the brand also lowers 

the price differential between the brand and the generic, thus 

reducing the likelihood that a consumer will substitute and 

thereby reducing profits. 

As on the price of the generic, there are contrary pressures 

on the price of the brand created by the opportunity to subsitute. Given the opposing pressure to lower the brand price 

to attract more customers, we are unwilling to hazard a prediction as to the effects on the price of the brand either. 

II. REGRESSION ANALYSIS OF BRAND 


AND GENERIC PRICES 


The regression model used to analyze prices is in many ways 

similar to that described in Chapter 5 for the analysis of 

brand choice. (In fact, the econometric models employ the 

(same) reduced form of underlying relationships explaining both 

brand choice and pricing decisions simultaneously.) Each 

regression contains data for a single drug, specified not only 

as to active chemical ingredients but also dosage form, 

139 

CHAPTER 1 

strength, and frequently dispensed quantities. Non-legal 

influences are measured by the same set of independent variables, with the addition of the variable TIME, the number of 

months since first passage of the state's DPS law. 

One way in which the price regressions differ from the logit 

analysis of substitution is that because the price regressions 

are aimed at answering the broad question of whether the 

general opportunity to substitute affects the prices of 

individual drug products, there is only one legal variable, a 

dummy indicating whether or not substitution was permitted.) 

Since even this broad net failed to produce significant 

results, it did not seem useful to pursue an analysis of 

specific provisions of the state laws. All observations were 

used, including those for stores in states where substitution 

was prohibited by either general statute or formulary. 

A second difference is in the level of aggregation of the 

variables. An average retail price of a prescription is 

computed for each store or for each chain organization (for 

units within a single state), whereas in the logit analysis 

each prescription was a separate observation. Price is taken 

to be the average price for 1980.1 Because the dependent 

variable is a store average, the explanatory variables were 

similarly store-level averages where possible. Of the independent variables, only QUAN varies by drug entity and by type 

of product dispensed. 

Third, the econometric technique is different. For the 

price regres· :ons, we used multivariate linear regression. We 

incorporated a generalized least squares procedure to implement 

an error components model because error terms within each state 

may be correlated. Discussion of the error components model 

and other econometric issues is found in Appendix A7. 

Two sets of regressions were run. One set of regressions 

was for the price of the leading brand in each of 43 leading 

multi-source entities, and the other set was for the price of 

the generic, or rather all generics together, in each of 33 

!/ The exception is where a state's formulary changed mid-year, in which 

case there are occasionally two prices for each store in a state, one for each 

of the two portions of the year. 

140 

BRAND AND GENERIC PRICES 

entities separately. (Regressions were run for every drug with 

enough observations.) In the first set, the only observations 

included were those for prescriptions on which a leading brand 

was dispensed. In the second, only generically-dispensed 

prescriptions were used. Since all generic versions of a drug 

were included together in a single regression, any price effect 

shown may not apply to any single generic product. (This was 

true also for a few drugs in which there were two leading 

brands.) Those regressions cannot distinguish between two 

possible responses to the law: selection of a different 

version from among the available set of generics, or altering 

the price of a specific generic. 

The regression model, then, is 

Pit = + b b2 DPSt+ b3 QUANit + b,. SSINDEXit 1 

+ b5 CHAINit + b6 MEDit + b1 PR/Vit 

where t = 1, 2, ..., 49 indexes the state; and for each t, i 

= 1, 2, .. . , Nt indexes the stores within state t. 

The variables, with hypothesized effects (for each variable, 

common to both brand and generic prices), are: 

DPS2 (?) 

DPS = 1 if the state permitted substitution on the prescription in the month in 1980 when the prescription was sold, 

and DPS = 0 if the state prohibited substitution on the 

prescription in the month in 1980 when the prescription was 

sold. 

For DPS to take the value of 1, two conditions were necessary. First, the state had to have a DPS law in effect in 

!/ A few minor errors in coding were discovered after the regressions were 

run. Their effects on the results are believed to be extremely small. 

See page 10 in Appendix 7 for details. 

141 
CHAPTER 1 

the month the prescription was sold. Second, for states 

with formularies, substitution in the specific drug entity 

had to be permissible according to the formulary in effect at 

the time of the retail transaction. 

QUAN (+) 

The number of tablets/capsules in the prescription. 

The cost of ingredients is higher for a larger prescription. 

SSINDEX (+) 

An index of the store's average price of 18 single-source 

drugs, relative to the average price across all stores. 3 

Cost and demand conditions giving rise to higher prices for 

single-source drugs presumably lead to higher prices on multisource drugs as well. 

CHAIN(-) 

CHAIN= 1 if the store is a member of a chain with more than 

10 outlets; CHAIN = 0 if the store is independently owned 

or part of a small chain. 

Chains have been shown to have generally lower prices, 

reflecting lower costs. 

MED (0), PRIV (0), and CASH (omitted category) 

Mode of payment for the prescription. If the prescription 

was paid for through Medicaid, MED =1, PRIV =0 and CASH= 0. 

If the prescription was reimbursed through other private 

insurance, PRIV = 1, MED = 0 and CASH= 0. If the prescription 

was paid for out-of-pocket, both the variables included in the 

regressions (MED and PRIV are = 0.) The effect of CASH is 

embedded in the CONSTANT term. 

Price discrimination between insurance categories of 

prescriptions is expected to be insignificant. 

V See Appendix A7 for details. 

142 

BRAND AND GENERIC PRICES 

GEN4 (0) for generic prices~ (+) for brand prices 

The proportion of prescriptions written generically in the 

state, computed across 45 drugs. For each drug, this average 

is adjusted to reflect the drug's U.S. average level of generic 

prescribing. This adjustment makes no difference statistically 

and was done only to make the scale of the coefficient on GEN 

more comprehensible. 

In states where physicians more frequently prescribe 

generically, consumers are more likely to be familiar with 

generics, making retail margins (and therefore in this study 

retail prices) lower for generics. For brands, it is unlikely 

that moderate differences in generic prescribing and therefore 

in generic market share would have much effect on individual 

product prices. 

III. RESULTS OF THE REGRESSION ANALYSIS 

Very few of the coefficients on the dummy for the legality 

of substitution were significant even at the 10 percent level. 

Tables 7-1 (brand prices) and 7-2 (generic prices) give the 

estimated coefficients and standard errors, and Tables 7-3 and 

7-4 summarize the sign and significance patterns in the two 

sets of regressions. Of the 43 regressions on the price of the 

leading brand, only 4 DPS coefficients were significant and 

these were split evenly between positive and negative coefficients. Of the 33 regressions on the price of generics, only 

2 of the DPS coefficients were significant, both negative 

coefficients. For the leading brand price, 26 of the 43 DPS 

coefficients were positive, while for the generic price, 23 of 

the 33 had negative coefficients on the dummy for the legality 

of substitution. On the basis of a two-tailed binomial test at 

the 10-percent confidence level, the sign pattern is insignificant for brands but significant (negative) for generics. For 

the leading brands, a weighted average of the 43 coefficients, 

with 1980 U.S. sales in numbers of prescriptions as weights, 

f/ GEN waa excluded in some drug regreesiona becauae there was no generic 

prescribing in the drug. 

143 

.::..

.::.. 

0 

~ 

>"'d

., TABLE 7-1 t".. 

::t1 

Regreuion Reaulte for Brand Pricee by Drug --4

DRUG CON- SS- Adi. 


NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 


Hydrochlorothiazide 

Chlordiazepoxide 

Amitriptyline5 

Penicillin VK 

Amoxicillin 

Atropine sulfate/ 

0 iphenoxylate 

Meclizine5 

-1.13..3 

(.28)4 

-.oo 

(.1!) 

. 07 .... 

(.00) 

lU9,.. 

(.29) 

-.59.. 

(.11) 

.52 

(.34) 

.n• 

(.37) 

-.20 

(.29) 

.74 252 610 

-2.98.. -.17 .10.. 5 . .u•• -.so•• .66 .72 5.55•• .65 147 558 

(.59) (.23) (.00) (.60) (.19) (.59) (.68) (2.22) 

-4.S7.. -.14 .10.. 7.00.. -.45.. -.01 .06 -1.73 .82 309 41l 

(.56) (.17) (.00) (.50) (.16) (.45) (AS) (1.06) 

.06 -.01 .09.. 2.69.. -.u• -.27 .16 -.14 .53 115 707 

(.15) (.11) {.01) (.21) (.09) (.27) (.30) (.19) 

.03 -.15 .17•• 2.77.. -.24 .89 -.95 .21 .38 47 530 

(.40) (.21) (.01) (.48) (.16) (.66) (.60) (.39) 

-2.ss•• -.ss•• .1s•• &.4e•• -.25 .. -.7&·· .26 .76 349 678 

(.37) (.13) (.00) {.36) (.12) (.SS) (.S7) 

-4.12 ... -.00 .11•• 6.29.. .19 -.70 .34 s.ss• .82 299 462 

(.66) (.17) (.00) (.62) (.16) (.44) (.48) (1.87) 

1/ Drugs are listed in deecending order by eub.titution rate. 


V GEN i1 omitted if there wu no generic preacribing for the drug. For other drup with very little ceneric preecribinr the GEN 


coefficient may be large but mean little in economic terml. 

V Two-tailed statistical eilflificance is denoted by •• at the 6 percent level, • at the 10 percent level. 

!/ Standard erron are given in parentheH~. 

§) GLS wae not possible; OLS wu used inetead. 
TABLE 7-1, continued 

DRUG CON- SS- Ad!. 


NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 


lsosorbide dinitrate5 


Hydralazine/ 

Hydrochl9fothiazide/ 

Reserpine 

Doxycycline 

Hydrochlorothiazide/ 

Spironolactone

-

VI
 """ Dipyridamole 

Brompheniramine/ 

Phenylephrine/ 

Pseudoephedrine 

Ch Iordi azepoxide/ 

Clidinium bromide 

Tolbutamide 

Conjugated estrogens 

-3.02'". 

(.76) 

-s.o6• • 

(1.06) 

-:z.:zs•• 

(.63) 

-6.65 •• 

(. 74) 

-.90'" 

(.62) 

-.49•• 

(.19) 

-1.94'". 

(.84) 

-.84. 

(.43) 

-3.51 •• 

(.44) 

-.32 •• 

(.15) 

-.18 

(.19) 

.10 


(.16) 


.32 


(.21) 


-.23 

(.24) 

.06 

(.07) 

.06 

(.12) 

.13 


(.18) 


.12 


(.10) 


. 06 .. 

(.00) .1s•• 

(.00) 

. 86 .. 

(.02) 

.1s•• 

(.00) .12•• 

(.00) 

.12•• 

(.00) 

.1s•• 

(.00) 

.11.. 

(.00) 

.09.. 

(.00) 

6.61 •• -.38.. 

(.66) (.17) 

10.87•• -.7··· 

(.98) (.26) 

4.66 ... -.11 

(.61) (.16) 

s.9a•• .so... (.78) (.24) 

6.19•• -.89.. 

(.76) (.81) 

2.1o•• -.so•• 

(.23) (.10) 

4.79•• -.76•• 

(.42) (.15) 

.-.o.-•• -1.47.. 

(.66) (.23) 

6.o.-•• -1.06.. 

(.44) (.14) 

.63 


(.51) 


.12 


(.69) 


.69 


(.64) 


.20 


(.74) 


-1.76• 

(1.02) 

-.09 

(.80) 

.sa• 

(.49) 

.69 


(.69) 


.66 


(.43) 


.19 


(.54) 


.16 


(.78) 


.03 

(.64) 

-.30 

(.82) 

-2.50... 

(1.26) 

.27 


(.88) 


-.60 

(.66) 

.24 


(.86) 


.42 


(.48) 


4.99 •• 

(2.38) 

-151.44 

(104.85) 

-.86 

(3.70) 

2.6-4 

(23.77) 

1.73 

(8.77) 

80.80 

(69.64) 

-12.61 

(26.86) 

-7.80 

(6.11) 

-4.12 

(6.26) 

.66 


.75 


.75 


.81 


.73 


.71 


.82 


.80 


.84 


101 


191 


280 


303 


161 


233 


400 


194 


355 


368 


439 


639 


503 


418 


671 


627 


347 


485 


ttl 

::0 

> 

z 

t::J 

> 

z 

t::J 

0 

trl 

z 

tr1

;::o 

...... 

0 

'1j 

;::o 

...... 

0 

trl 

Ul 
0 

TABLE 7-1, continued 

DRUG CON- SS- Ad!. 

NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 

Chlorthalidone 

Acetaminophen/ 

ChlorzoxaEone 

Tetracycline5 

Spironolactone 

..... 

A 

0'1 

Phenytoin sodium 

Allopurinol 

Dexbrompheniramine/ 

Pseudoephedrine 

Triamcinolone 

Chlorpropamide5 

Hydroxyzine 

-s.ss•• 

(.63) 

-1.87•• 

(.38) 

-3.02.. 

(.84) 

-4.&2·· 

(.98) 

-t.4o•• 

(.40) 

-6.34•• 

(.77) 

-.1s•• 

(.19) 

-.18 

(.51) 

-7.86 •• 

(.87) 

-l.S4'""' 

(.33) 

.01 

(.14) 

.06 

(.10) 

.40 

(.32) 

-.06 

(.26) 

.08 

(.11) 

-.24 

(.22) 

.08 

(.07) 

.09 

(.16) 

.11 

(.19) 

.06 

(.11) 

. 14 .. 

(.00) 

.13.. 

(.00) 

.03** 

(.00) 

.ts•• 

(.00) 

.o4•• 

(.00) 

.23•• 

(.00) 

. 1s•• 

(.00) 

.27•• 

(.02) 

.t7•• 

(.00) 

.t7•• 

(.00) 

8.47•• -.62•• 

(.60) (.18) 

4.82.. -.10 

(.42) (.13) 

s.s3•• -.21 

(.77) (.24) 

8.28.. -.sa• 

(1.02) (.31) 

4.ss•• -1.24.. 

(.42) (.16) 

9.92•• -.so• 

(.81) (.27) 

3.16.. -.2s•• 

(.24) (.09) 

4.os•• .32 

(.61) (.20) 

lO.W.. -.82.. 

(.80) (.24) 

4.t6•• .32.. 

(.39) (.14) 

-.80 

(.51) 

-.29 

(.41) 

-.66 

(.71) 

-.98 

(.99) 

.07 

(.40) 

-3.26 

(.90) 

.26 

(.29) 

t.oo•• 

(.78) 

-.78 

(.63) 

.70 

(.44) 

.07 

(.56) 

-.90" 

(.47) 

-.38 

(.74) 

1.34 

(1.11) 

t.3o•• 

(.49) 

-.66 

(.94) 

-.12 

(.33) 

-.00 

(.73) 

-.64 

(.73) 

-.14 

(.63) 

3.97 

(10.56) 

17.07 

(115.52) 

-.11 

(.44) 

-8.86 

(6.22) 

-.46 

(1.83j 

-4.44 

(9.27) 

-31.96 

(21.34) 

.50 

(19.92) 

.84 

.77 

.23 

.11 

.53 

.90 

.80 

.56 

.82 

.78 

413 535 

281 594 

24 538 

181 322 

97 600 

584 456 

417 645 

49 263 

347 541 

232 469 

::r: 

>.,., 

~ 

1:"1 

~ 

~ 
TABLE 7-1, 

continued 

DRUG 

NAME1 

CONSTANT DPS QUAN 

SSINDEX CHAIN MED PRIV 

GENl d~. 

R" F N 

A 

-...1 

Su lfamethoxa!ole/ 

Trimethoprim 

Theop hylline5 

T erbutaline aulfa 

te5 

Nitroglycer in 

Met ronidazole 

Diethy

lpropion 

Phentermine Minocycline 

F urosemide 

Erythromycin b a1e 

-1.28*• (.27) 

-2.s3•• 

( .80) 

-3.96 • • 

( .66) 

-.53. (.31) 

-2.1o •• 

( .59) 

-1.71 .. 

( .32) 

-.10 .. 

(.26) 

-3.11.. (1.23) -.62° 0 

(.15) 

-.15 

( .22) 

. 16* 

( .10) .03 (.16) .08 

( .14) 

.04 

(.10) 

-.05 

( .19) . .11 

( .11) .01 

( .12) 

-.67 

( .37) 

.08 

(.07) -.03 

(.09) 

.sa•• 

(.01) 

.u•• 

( .00) 

. 10 .. 

( .00) 

. o t•• 

(.00) 

. 51 .. 

(.01) .2a •• 

(.01) 

.26 • • 

(.01) .43•• 

(.01) 

.11.. 

( .00) 

.12.. 

(.01} 

4.66·· 

(.31) 

4.48• • 

(.77) 

5 .89.. 

( .60) 

3.25.. 

( .38) 

4.66 .. 

(.62) 

4.12.. (.39) 2.a8•• 

( .33) 

1 .28 •• 

(1.34) 

s.43• • 

(.19) 

3.49 .. 

( .29) 

-.32 •• 

(.12) 

-.44" . 

( .20) 

.01 

( .17) 

-.2s•• (.13) -.77•• 

( .2 3) -.s4•• (.14) -.08 (.13) .07 (.41) -.9e•• 

(.09) 

-.22. 

(.12) 

-.16 

(.34) .34 

(.73} 

-2 .02 .. 

(.66) 

.at• 

( .42) 

-.52 (.68) .34 (.49) .03 

( .39) 

- 1.40 

( 1.86) 

. 13 

( .24) .OS 

( .36) 

.01 

(.41) 

-.52 

(.78) 

-.05 

(.61) 

1.31 • • 

(.50) 

-.18 

(.81) -.73 

(.52) 

-.86* 

(.60) 3.14 • 

(1.73) .67 .. (.31) 

.71° 

(.42) 

-165.77 

( 253 .00) 

19.

10 

(79.51 ) 

4.18 .. 

( 1.76) 

.08 

(.24) 

141.60 •• 

(71.67) 

96.01 

(130.07) 

1.23 

(3.61) 2.23 

(9.66 ) 

-26.94 

(31.36) 

-t.oa•• 

(.42 ) 

.68 

.84 

.85 .25 .80 .81 .86 

.79 .85 .47 

250 

227 

290 

Z2 

340 

326 

327 

161 

685 IH 

828 

304 

352 

450 

578 

525 

41 3 296 

818 708 

o:J 

;:tl >z 

0 

> z 

c 

0 

tr.! 

z 

tr.! 

;:tl 

...... 

0 

"0 

~ 

tr.! 

Cll 

0 
0 

...... 

A 

00 

TABLE 7-1, continued 

DRUG CON- SS- Ad!. 


NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 


Hydrochlorotgiazide/ 

Triamterene 

Amitriptyline/ 

Perphenazine 

Cephalexin 

Erythromycin 

ethylsuccinate 

Ibuprofen 

Mestranol/Norethindrone/ 

Placebo 

Hydrogenated ergot 

alkaloid• 

-1.87•• 

(.26) 

-4.92.. 

(.88) 

-3.12.. 

(.48) 

-.33* 

(.19) 

-.62.. 

(.27) 

-.72* 

(.48) 

-4.69•• 

(1.22) 

.04 

(.08) 

.07 

(.20) 

-.10 

(.10) 

.04 

(.00) 

.13 


(.12) 


.25. 

(.17) 

.11 


(.28) 

.11.. 

(.00) 

.1s•• 

(.00) 

.39.. 

{.01) 

.18.. 

(.01) 

.14.. 

(.00) 

.21.. 

(.00) 

. 18 .. 

(.01) 

4.94•• 

(.29) 

8.82** 

(.91) 

6.58.. 

(.41) 

3.23.. 

(.26) 

4.03.. 

(.37) 

. 79 


(.58) 


9.30.. 

(1.21) 

-.94.. 

(.12) 

-.Ui 

(.26) 

-.45•• 

(.14) 

-.19* 

(.10) 

-.9s•• 

(.16) 

-.57.. 

(.18) 

-.02 

(.36) 

.06 

(.30) 

-1.61. 

(.82) 

-.05 

(.39) 

-.25 

(.32) 

1.02 

(.45) 

.95 • 

(.52) 

-3.52.. 

(1.13) 

.13 


(.36) 


1.21 

(.96) 

-1.29.. 

(.41) 

-.37 

(.42) 

.33 


(.56) 


.02 

(.67) 

.26 


(1.31) 

-58.02 

(81.53) 

-1.23 

(5.19) 

.81 


.82 


.70 


.65 


.81 


.87 


.77 


598 


268 


272 


190 


550 


476 


151 


834 


360 


828 


712 


779 


428 


277 


:I: 

> 

'"t! 

~ 

tx:l 

;:tl 

~ 
TABLE 7-2 

Regression Results for Generic Prices by Drug 

DRUG CON-	 SS- Adi. 

NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 

3 Hydrochlorothiuide 	 -.05 -.03 2.85.. . 03 .. -.54•• .28 .05 -.39. .45 75 622 

(.24)4 (.Ui) (.26) (.00) (.11) (.31) (.48) (.26) 

Chlordiazepoxide 	 s.2o•• -.49. .14 .05.. -1.22.. -2.1o•• -1.78. -2.06 .28 32 566 

(.48) (.32) (.49) (.00) (.22) (.79) (.93) (2.72) 

A m1 ·t npty · r me -.91 -.36 s.22•• .Oii .. -2.27.. -3.92 •• 5 	 -.60•• -.62 .46 31 249 

(1.10) (.40) (.98) (.00) (.27) (.88} (.95) (1.94) 

Penicillin VK -.08 -.o2 2.92 •• .os•• -.2s•• .38 .88.. -.10 .41 76 756 

+:>.. (.19) (.14) (.23) (.01) (.09) (.28) (.34) (.20)

\0 

Amoxicillin -.27 -.03 4.61 •• .14•• -.36. .56 z.u•• -.69 .36 36 444. 

(.44) (.27) (.52) (.02) (.20) (.69) (.79) (.45) to 

;:tl 

Atropine sulfate/ .62 -.55 2.84•• .04.. -.39 .45 -.24 	 .28 17 253 >

Diphenoxylate z (1.09) (.59) (.86) (.00) (.27) (.95) (1.06) t:1 

Meclizine 	 -2.94. -.12 5.48•• .o4•• .16 > 1.21 .89 .88 .32 13 174 z 

(1.61) (.90) (1.35) (.01} (.38) (1.02) (1.72) (4.23) 	 0 

0 !/ Drugu are listed in deacending order by aubstitution rate. t<l 

1/ GEN i1 omitted if there wu no generic pre.cribing for the drug. For other drop with very little generic prescribing the GEN z 

t<l coel!icient may be large but mean little in economic terma. ~ 

Two-tailed statistical significance is denoted by •• at the 5 percent level, • at the 10 percent level. ,_, 

!/ Standard errors are given in parenthesu. 0 

't1 GLS wu not possible; OLS was used instead. ;:tl

...... 

0 

tr:! 

TJl 
0 

VI 

0 

TABLE 7-2, continued 

DRUG CON- SS- Ad~. 

NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 


laoaorbide dinitrate 

Hydralasine/ 

Hydrochlorothiuide/ 

Reserpine 

Doxycycline5 

Hydrochlorothiazide/ 

Spironolactone 

Dipyridamole5 

Brompheniramine/ 

Phenylephrine/ 

Pseudoephedrine 

Ampicillin 

Chlordiazepoxide/ 

Clidinium bromide 

Tolbutamide 

.60 -1.27•• 3.60.. .02.. -.19 3.a1•• 1.06 3.02 .32 7 98 

(1.44) (.IH) (1.29) (.01) (.42) (1.25) (2.19) (6.38) 

1.50 .04 1.58 .Oii.. -.18 2.93• -4.19 -272.67 .21 5 110 

(1.26) (.~6) (1.80) (.01) (.57) (1.79) (3.01) (243.11) 

.83 .79 3.73•• .66"" -.55 -1.71 -.54 12.86 .68 45 149 

(1.68) (.70) (1.35) (.04) (.36) (1.41) (1.60) (1l.S4) 

-2.90. .34 s.31•• .10.. -.79 .54 .21 -66.52 .74 36 88 

(1.63) (.66) (1.62) (.01) (.55) ( 1.82) (2.39) (63.02) 

-3.54 -.58 5.85 .o1•• -.07 -.51 2.70 2.76 .37 8 81 

(4.16) (.80) (3.67) (.01) (.90) (4.33) (3.53) (34.13) 

.18 .28 1.ss•• .oo•• -.13•• -.66 -2.6a•• 161.08 .43 12 105 

(.56) (.20) (.68) (.01) (.23) (.80) (1.26) (168.66) 

. 19 -.09 3.22•• .08.. -.15•• -.64•• .06 -.14 .41 88 863 

(.19) (.15) (.25) (.01) (.11) (.30) (.36) (.13) 

.9s•• -.14 1.33•• .11•• -1.07•• .79 -1.07. -51.69. .70 212 643 

(.28) (.14) (.34) (.00) (.19) (.64) (.70) (33.90) 

-.U -.20 3.4a•• .10... -1.56•• .91 .77 -15.22 •• .74 150 370 

(.51) (.21) (.54) (.00) (.26) (.82) (1.00) (5.85) 

::X: 

>'"t1

.., 


t%j 

;xJ 

"""' 
TABLE 7-2, continued 

DRUG 

1 NAME 

CONSTANT DPS QUAN 

SSINDEX CHAIN MED PRIV GEN2 Ad~. 

R F N 

Conjugated eatrogen1 -.19 

(1.01) 

-.09 

(.37) 

2.71 .. 

( 1.17) 

.06"" 

(.01) 

-1.00.. 

(.40) 

2.91 •• 

(1.22) 

2.36 

(2.59) 

-4.08 

(16.25) 

.73 24 61 

Chlorthalidone .17 

(.96) 

-.06 

(.44) 

3.96•• 

(1.16) 

.08.. 

(.01) 

-.96. 

(.64) 

6.34.. 

(1.69) 

.29 

(2.26) 

-23.83 

(38.71) 

.77 22 44 

Acetaminophen' 

Chlorzoxuone 

2.26. 

(2.79) 

-.76 

(.69) 

1.36 

(2.67) 

.07•• 

(.01) 

-.38•• 

(.67) 

1.83 

(2.06) 

.38 

(1.89) 

-956.77 

(778.38) 

.47 8 52 

Tetracycline -.16 

(.21) 

.11 

(.14) 

3.2s•• 

(.22) 

.03.. 

(.00) 

-.63•• 

(.10) 

.04 

(.28) 

.30 

(.33) 

-.27• 

(.16) 

.41 86 841 

..... 

VI- Spironolactone -1.03 

(.73) 

.48 

(.55) 

.60 

(2.10) 

.12.. 

(.02) 

.75 

(.65) 

23.47•• 

(6.99) 

8.44• 

(4.06) 

.85 21 22 

Phenytoin sodium 

Allopurinol5 

Dexbrompheniramine/ 

Pseudoephedrine 

Chlorpropamide 

Theophylline 

1.90 

(1.67) 

-6.40 

(5.03) 

-.23 

(.36) 

-.29 

(4.67) 

-1.04 

(1.12) 

-.25 

(.43) 

.22 

( 1.06) 

.29 

(.20) 

-.91 

(1.42) 

.02 

(.38) 

1.57 

(1.44) 

9.61 •• 

(4.30) 

2.23•• 

(.57) 

2.71 

(4.46) 

3.7o•• 

( 1.64) 

.02•• 

(.01) 

.1s•• 

(.02) 

.14•• 

(.01) 

.os•• 

(.02) 

.14.. 

(.01) 

-1.69•• 

(.48) 

.20 

(1.22) 

-.55•• 

(.23) 

. 17 

(1.19) 

-.96 •• 

(.42) 

-1.44 

(1.74) 

-2.59 

(3.28) 

-1.09.. 

(1.06) 

2.50 

(2.29) 

.50 

(1.69) 

1.14 

(2. 76) 

-4.23 

(5.17) 

.58 

(1.14) 

.99 

(6.96) 

.94 

(1.67) 

-6.99 

(23.24) 

-2.25 

(7.69) 

223.39•• 

(107.17) 

47.24 

(199.38) 

.33 

.79 

.76 

.37 

.84 

5 

23 

83 

66 

53 

43 

156 

40 

86 

t:J:l 

;;t:l >z 

t:t >

z 

t:t 

C} 

tr.l 

z 

tr.l 

~ .... 

Q 

'1:1 

;:z:J -

Q 

tr.l 

Ul 
••• 

0 

.... 

VI 


N 


DRUG CON1
 NAME STANT 

Terbutaline aulfat;e 

Nitroglycerin 

Metroniduo!e5 

Diethylpropion 

Erythromycin baae 

Amitriptyline/ 

Perphenuine 

Quinidine aul{ateli 

.u 

(1.98) 

.n• 

(.42) 

-16.66 •• 

(5.96} 

-.56. 

(.31) 

.83.. 

(.17) 

-3.eo•• 

(1.77) 

-8.2s•• 

{1.53) 

TABLE 7-2, continued 

SS- Ad!. 


DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 


-.76 

(.82) 

-.08 

(.12} 

-3.12.. 

(1.&1) 

.16 


(.13) 


-.16• 

(.00} 

-.26 

(.36) 

-.19 

(.U) 

.10 


(2.36} 


4.0:t•• 

(.45) 

20.99.. 

(4.43) 

3.06.. 

( .34) 

1.1o•• 

(.20) 

1.2o•• 

(1.78) 

9.49•• 

(1.42) 

.10.. 

(.01) 

-.o:t•• 

(.00) 

.31•• 

(.06) 

.2e•• 

(.01) 

.15•• 

(.01) 

.17•• 

(.01) 

.09.. 

(.00) 

-1.08. 

(.69} 

-.29•• 

(.14) 

3.16•• 

(1.&1) 

-.35 

(.16) 

-.44•• 

(.11) 

.48 


(.43) 


-.66• 

(.36} 

-1.58 

(5.56} 

1.11.. 

(.45) 

-1.26 

(3.52) 

-.52 

(.59) 

.1o• 

( .41) 

-3.79•• 

(1.83) 

1.00 

(1.09) 

.36 


(3.06) 

-.23 

(.68) 

-2.54 

(5.79} 

-.71 

(.54) 

.33 


(.43) 


.85 


(1.72) 

.68 


(1.29) 

10.35 

(9.84} 

.23 


(.27) 


430.66 

(589.86} 

-76.88 

(162.26) 

-.so• 

(.45} 

.69 


(.48} 


.10 


.29 


.61 


.77 


.84 


.64 


12 


18 


1 


254 


82 


107 


67 


33 


300 


21 


532 


725 


118 


262 


:X: 

>., 


>1 

1:%.1 

:;.l 

~ 
BRAND AND GENERIC PRICES 


TABLE 7-3 

Summary of Coefficient Signs 

in Brand Price Regressions 

(+)and (-) and 

Variable (+) 

Significant 1 (-) 

Significant 

Overall 

Effect4 

DPS 102 1 0 9 2 

43 26 2 17 2 No 

QUAN 10 10 10 0 0 + 

43 43 43 0 0 + 

SSINDEX 10 10 10 0 0 + 

43 43 42 0 0 + 

CHAIN 10 1 0 9 7 

43 6 2 37 30 

MED 10 6 0 4 0 No 

43 24 3 19 2 No 

PRIV 10 9 1 I 0 + 

43 25 6 18 4 No 

GEN3 9 4 3 5 0 No 

35 17 3 18 1 No 

!/ Statistical significance is meu•1red at the 10 percent level, by a 

two-tailed teat. 

V Regressions for the 10 top substitution drup. 

§} In 8 drup there waa no generic prescribing, eo the variable GEN waa 

omitted. 

!/ Measured by statistical significance at the 10-percent level of a twotailed binomial signa teat. 

SOURCE: Table 7-1. 

153 

CHAPTER 1 

TABLE 7-4 

Summary of Coefficient Signs 

in Generic Price Regressions 

(+)and (-) and 

Signi- Signi- Overall 

Variable (+) ficant 1 (-) ficant Effect4 

DPS 102 2 0 8 2 No 

33 10 0 23 4 

SSINDEX 10 10 8 0 0 + 

33 33 25 0 0 + 

QUAN 10 10 10 0 0 + 

33 32 32 1 1 + 

CHAIN 10 1 0 9 5 

33 6 1 27 20 

MED 10 7 2 3 2 No 

33 20 7 13 5 No 

PRIV 10 5 2 5 1 No 

33 22 3 1 1 3 + 

GEN3 9 3 0 6 2 No 

29 11 1 18 6 No 

!I Statistical significance is measured at the 10 percent level, by a 

two-tailed teat. 

1../ Regressions for the 10 top substitution drugs. 

~/ In 4 drugs there was no generic prescribing, so the variable GEN was 

omitted. 

if Measured by atatiatic:al significance at the 10-percent level of a twotailed binomial oigna test. 

SOURCE: Table 7-2. 

154 

BRAND AND GENERIC PRICES 

was + $.03, less than 1 percent of the average prescription 

price of $8.58 for the leading brands in these 4 3 drugs. For 

the generics, the weighted average of the coefficients was 

$.21, about 3 percent of the 39-drug average price of a generic 

prescription of $6.20. 

One reason that retail prices of generics were lower where 

substitution was permitted is that pharmacies themselves, when 

substitution was an option, selected among availa ble generic 

products those with lower cost The simple average of the 

percent difference between the pharmacy invoice costs in states 

permitting and prohibiting substitution on the drug across the 

10 top-substitution drugs was 11 percent in 1980; in only one 

of these drugs was the cost higher (by 1 percent) where 

substitution was permitted.5 

The effect of the law might be expected to be strongest on 

drugs where substitution actually occurs most frequently. When 

oply the 10 drugs with the highest incidence of substitution 

are examined, the generic price result is strengthened, with 8 

of the 10 coefficients being negative, although the weighted 

average coefficient for the 10 was nearly the same as for the 

33, - $.22. On the other hand, the results on the price of the 

leading brand for these top substitution drugs are inconsistent 

with the result for the 43 regressions taken as a whole: for 

the top 10 substitution drugs, the opportunity· to substitute 

had a negative coefficient in 9 drugs, 2 of them significant, 

and the average coefficient was- $.12. 

Most of the non-legal variables behaved as expected. QUAN 

was positive and highly significant in every brand and generic 

regression. SSINDEX performed nearly as well, being positive 

in all regressions and highly significant in all but one brand 

regression and in three-quarters of the generic regressions. 

CHAIN was negative in 37 of the 43 brand regressions (significant in 30) and in 27 of the 33 generic regressio ns (significant in 20.) The sign patterns on all three va riables are 

statistically significant.6 As expected, the results on 

customer payment type were mixed, revealing no strong pattern. 

Significant coefficients appeared about as frequently with 

§./ Computed with dat a from the 1980 IMS U.S. Drugstore Audit. 


2,/ By a t wo-tailed binomial test at t he 10-percent level. 


155 
CHAPTER 1 

positive signs as with negative s1gns, and the signs themselves 

were split. 

The prevalence of generic prescribing did not have a strong 

impact on individual product prices. The signs on GEN split 

evenly in the brand price regressions and only 4 coefficients 

were statistically significant. The sign pattern for GEN in 

the generic price regressions is stronger, with 18 negative 

signs out of 29, and 6 of the 7 significant coefficients are 

negative as well (although this sign pattern is statistically 

insignificant.) This is consistent with our hypothesis that 

a greater prevalence of generic prescribing will have a 

downward impact on prices of generic products. 7 

IV. CONCLUSION 

Overall, then, we conclude that the opportunity to substitute causes the retail prices of individual generic drug 

products to fall, but not that there is any systematic effect 

on the retail prices of individual leading brands (in both 

cases, given unchanged manufacturer-level prices.) We can, at 

least, rule out the possibility that significant price 

increases on leading brand offset some of the general price 

decline due directly to substitutions. This implies that 

to the extent that substitution occurs, the result is a 

reduction in the average retail prescription price. The 

results for generics show that the direct gains from substitution may be augmented by a decrease in the prices of generics. 

!} Of the 13 significant coefficients of GEN across both sets of regressions, however, 3 are for drugs where generic prescribing occurred on fewer 

than 2 percent of all U.S. prescriptions for the drug. Despite statistical 

significance, there can be little economic significance of (the very small) 

differences in generic prescribing in these drugs. 

156 
CHAPTER 8 


AVERAGE PRESCRIPTION PRICES 


In the end, it is the price and quality of the drug product 

actually dispensed which consumers care about, not whether a 

substitution was made nor whether the prescription was written 

generically or for a brand. The summary measure of the laws' 

immediate effect on consumers is the difference in the average 

price paid for a specified prescription between states where 

substitution was permitted and states where it was not, other 

things held constant. The difference in average price summarizes changes in individual product prices and changes in the 

generic market share. 

We use multivariate regressions, by drug entity, to estimate 

the effect of the Ia w on the price paid for a prescription, 

disregarding whether a brand or a generic was dispensed. The 

results of the individual drug regressions are then aggregated 

into an estimate of total savings in 1980 and then, by extrapolation, savings in 1984. 

Any estimate of the average price effect depends crucially 

on whether increased generic prescribing is due to the law, 

since most of the differences in generic market share are due 

to differences in generic prescribing. At most, only some of 

the difference is properly attributable to the availability of 

the opportunity to substitute, but we do not know how much. We 

therefore offer two estimates, one in which we take as due to 

the law none of the generic prescribing differences, the other 

in which we take all. These two estimates serve as outside 

limits of the actual effects. 

J. MULTIVARIATE REGRESSION ANALYSIS 


OF AVERAGE PRICES 


We estimated the summary effect on the prices of multisource prescriptions by using regressions with the average 

price of all prescriptions in the drug entity, regardless of 

whether they were dispensed with a leading brand or with a 

157 

CHAPTER 8 

generic, as the dependent variable. The model is the same as 

used to analyze the prices of leading brands and of generics, 

as described in Chapter 7 and Appendix 7. 1 The dependent 

variable is a store-average price of all prescriptions for a 

specific drug, dosage form, and strength, for any of the five 

most frequent prescription sizes, in terms of number of tablets 

or capsules, for that specification. All observations for the 

drug were included in a single equation. Because of the 

uncertainty about the causal relationship between generic 

prescribing and the DPS laws, we run each regression equation 

twice, once with a variable for generic prescribing and once 

without. The resulting coefficient estimates on the DPS 

variable bracket the true effects of the law. The equation 

with generic prescribing held constant provides a lower-bound 

estimate of the total effect of a DPS law, while the equation 

omitting the variable provides an upper estimate. It is likely 

that the true effect of the law is somewhere in between, since 

some but not all of the variation in generic prescribing may be 

in response to the DPS law. 

A. HYPOTHESES FOR THE REGRESSIONS 

DPS is expected to have a negative coefficient, since 

substitution shifts market share from the higher-priced brands 

to the lower-priced generics. This coefficient is expected to 

be larger in the regressions which omit GEN since (part of) the 

large difference in generic prescribing between states with and 

without a DPS law is reflected in the coefficient on DPS 

itself. 

The non-legal variables QUAN, SSINDEX and CHAIN are 

expected to behave as they did in the brand price and generic 

price regressions. QUAN is expected to have a positive 

coefficient since it serves to control for the fact that larger 

prescriptions are more expensive. A higher SSINDEX is likely 

to be associated with higher prices and CHAIN with lower 

prices. MED is expected to have a negative coefficient and 

!/ A few minor errors in coding were discovered after the regressions were 

run. Their effects on the results are believed to be extremely small. See 

Appendix 1 for details. 

158 

AVERAGE PRESCRIPTION PRICES 

PRIV a pos1t1vc coefficient because of the differences in 

substitution and in generic prescribing as well. 

GEN raises generic market share both by affecting the 

su bst.i tu tion rate and directly through dispensing on generically written prescriptions. It therefore is expected to have 

a negative effect on average price. 

B. RESULTS 

The results of these pairs of regressions for each of the 45 

drugs are shown in Table 8-1. Sign patterns and significance 

levels, for both sets of regressions, are summarized in Table 

8-2. 

Taken individually, the results are mostly insignificant; 

in both models, in only 8 or 9 of the regressions is the coefficient on DPS statistically significant. In one, sense, the 

insignificance is surprising, since l) substitution is definitely higher where it is permitted; 2) substitution must 

decrease average price since nearly all substitution is from a 

leading brand to a generic; 3) there do not seem to be offsetting increases in individual product prices; 2 and 4) individual 

generic product prices are lower. 3 In another sense, the 

insignificance is consistent with the low substitution rates 

observed for most drugs in 1980. The rate of substitution on 

eligible prescriptions was less than 2 percent in 17 drugs and 

over 10 percent in only 10 drugs. Moreover, less than threequarters (on average) of the prescriptions for these drugs were 

eligible for substitution. 

Taken overall, the results do verify that a DPS law led to 

lower prices in 1980, but only because of its effect in some 

selected drugs. The signs on the DPS coefficient split quite 

evenly; the sign pattern for the 45 regressions fails to reach 

statistical significance. 4 However, of the 8 significant 

coefficients (in the with-GEN regressions), 6 are negative; in 

the without-GEN regressions, 9 of the DPS coefficients are 

significant, of which 7 are negative. Also, in each set of 

V According to the regression results reported in Chapter 7. 


~/ According to the regression resuits reported in Chapter 7. 


1} By a two-tailed binomial test at the 10-percent level of significance. 


159 

....... 


0\ 

0 

TABLE 8-1 


Regrea11ion Result• for Average Prieet~ by Drug 


DRUG CON- SS- Ad~. 

NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 


Hydrochlorothiazide 

Chlordiazepoxide 

Amitriptyline 

.26 

(.24)3 ("06.14) 

3.13.. 2 

(.27) 

.o4•• 

(.00) 

-.64 •• 

(.12) 

-.s2• 

(.34) 

1.2s•• 

(.42} 

-1.1s•• 

(.28) 

.45 98 816 

-.05 -.21 3.08.. .o4•• -.s3•• -.66"' 1.4s•• .45 114 816 

(.20) (.13) (.26) (.00) (.12) (.34) (.43) 

.16 -.so• 4.09•• .01'" .. -.1s•• -1.42 •• .70 -3.95. .40 63 657 

(.55) (.30) (.61) (.00) (.26) (.70} (.90) (2.67} 

-.06 -.ss•• 4.o4•• .o1•• .1s•• -1.4&•• .82 .4.0 73 657 

(.52) (.28) (.61) (.00) (.25) (.70) (.90) 

-.61 -.4s•• 4.7:z•• .osu -.67•• -1.07• 1.08. -3.47•• .63 140 567 

(.60} (.22) (.54) (.00} (.19) (.55) (.66} (1.28) 

-.62 -.s:z•• 4.37•• . 08 .. -.70... -1.09. 1.23. .63 164 567 

(.43} (.20) (.61) (.00) (.19) (.55} (.68) 

!I The first regression reported for each drug include• the variable GEN for generic preacribing; the second excludes GEN. 


!I Two-tailed statistical significance is denoted by •• at the 5 percent level, • at the 10 percent level. 


~~ Standard errors are given in parentheeet~ 

1.1 GEN is omitted if there wu no aeneric pret~cribins for the drua. For other drup with very little aeneric prescribing the GEN 

coefficient 	may be large but mean little in economic tenna. 

§) GLS was not possible; OLS wu used instead. 

0 

:z:: 

., 


~ 

tzj 

::0 

00 
TABLE 8-1, continued 

DRUG CON- SS- Ad~. 

NAME1 STANT DPS QUAN INDEX CHAIN MED PRJV GEN2 R F N 


Penicillin VK . 29 •• 

(.15) 

-.02 

(.11) 

2.00.. 

(.19) 

.o6•• 

(.01) 

-.2s•• 

(.08) 

-.06 

(.22) 

.s5•• 

(.28) 

-.48.. 

(.16) 

A5 109 918 

.16 

(.13) 

-.15 

(.10) 

2.00.. 

(.19) 

. 06 .. 

(.01) 

-.28.. 

(.08) 

-.06 

(.22) 

.74•• 

(.28) 

.45 126 918 

Amoxicillin -.0 

(.32) 

-.13 

(.19) 

4.08.. 

(.39) 

. 14 .. 

(.01) 

-.24 • 

(.15) 

.79• 

(.H) 

.73 

(.54) 

.03 

(.33) 

.37 63 729 

- -.41 

(.32) 

-.12 

(.18) 

4.09.. 

(.39) 

.14•• 

(.01) 

-.24 

(.15) 

.so• 

(.H) 

.72 

(.54) 

.37 74 729 

0'\- Atropine sulfate/ 

Diphenoxylate 

-.36 

(.30) 

-.50.. 

(.17) 

4.02•• 

(.34) 

.11 .. 

(.00) 

-.46.. 

(.15) 

-1.41 •• 

(.42) 

.48 

(.50) 

.58 167 733 

-.36 

(.30) 

-.5o•• 

(.17) 

4.02.. 

(.34) 

.n•• 

(.00) 

-.46.. 

(.15) 

-1.41 .. 

(.42) 

.48 

(.50) 

.58 167 733 

Meclizine .51 

(.51) 

-As• 

(.26) 

3.23•• 

(.57) 

.08.. 

(.00) 

-.10 

(.22) 

1.54•• 

(.63) 

1.18. 

(.82) 

-1.59 

(2.60) 

.50 78 544 

.43 

(.49) 

-.5s•• 

(.23) 

3.2o•• 

(.57) 

.08.. 

(.00) 

-.10 

(.22) 

-1.56. 

(.63) 

1.23 

(.82) 

.50 91 544 

> <

t".l 

~ C) 

t".l 

'"tJ 

::0 

t".l 

t/) 

0 

::0 

....... 


'"tJ 

~ ....... 


0 

z 

'"tJ 

::0..... 

0 

t".l 

t/) 
....... 


0\ 

N 

TABLE 8-1, continued 

DRUG CON- SSNAME1 STANT DPS QUAN INDEX CHAIN 

Isosorbide dinitrate 

Hydralazine/ 

Hydrochlorothiazide/ 

Reserpine 

Doxycycline 

Hydrochlorothiazide/ 

Spironolactone 

.27 

(.58) 

.28 

(.5.) 

-1.70• 

(.85) 

-1.81 .. 

.78) 

-1.·5.. 

(.H) 

-1.55 •• 

(.•5) 

-3.27•• 

(.66) 

-3.•9.. 

(.62) 

-.1o•• 

(.22) 

-.'10.. 

(.22) 

-.26 

(.26) 

-.32 

(.26) 

-.10 

(.17) 

-.12 

(.16) 

.16 

(.23) 

. 15 

(.22) 

•.67•• 

(.68) 

•.sa•• 

(.68) 

6.•0.. 

(1.01) 

6.08.. 

(.99) 

4.07•• 

(.•7) 

•.os•• 

(.•7) 

7.3··· 

(.70) 

7.•3.. 

(.70) 

.o••• 

(.00) 

.o••• 

(.00) 

.11•• 

(.01) 

.11.. 

(.01) 

.s••• 

(.02) 

.s••• 

(.02) 

.15•• 

(.00) 

.11) .. 

(.00) 

-.37 

(.26) 

-.37 

(.26) 

-.s••• 

(.35) 

-.sa• 

(.35) 

-.23 

(.17) 

-.23 

(.17) 

-.80.. 

(.26) 

-.79•• 

(.26) 

MED PRIY GEN2 Ad~. R F N 

0 

>==

'1:l

, 

ttl 

::0 

OD 

.41 

(.7.) 

2.17•• 

(.93) 

.23 

(3.H) 

.41 41 404 

.•1 

{.7•) 

2.15•• 

(.92) 

.41 47 404 

-.63 

(.!19) 

1.•s• 

(1.27) 

-245.56. 

(H3.63) 

.52 76 481 

-.6. 

(.99) 

1.19 

(1.28) 

.52 89 451 

.31 

(.56) 

.32 

(.58) 

-2.•8 

(3.8•) 

.73 264 669 

.32 

(.56) 

.3. 

(.58) 

.73 307 669 

.38 

(.80) 

.26 

(.92) 

-11.61 

(25.25) 

.76 242 525 

.38 

(.80) 

.29 

(.91) 

.76 283 525 
TABLE 8-1, continued 

DRUG CON- SS- Ad~. 

NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 


Dipyridamole -.89 

(.82) 

-.62. 

(.32) 

7.39.. 

(1.06) 

.09•• 

(.01) 

-.so• 

(.40) 

-.52 

(1.35) 

-.02 

(1.64) 

-1.98 

(11.64) 

.49 63 ly.i 

-.98 

(.75) 

-.as• 

(.32) 

7.41 •• 

(1.06) 

.09•• 

(.01) 

-.59 

(.40) 

-.53 

(1.35) 

.00 

(1.64) 

.48 73 463 

-

0"1 

w 

Brompheniramine/ 

Phenylephrine/ 

Pseudoephedrine 

-.26. 

(.18) 

-.26 

(.16) 

.06 

(.08) 

.05 

(.08) 

2.67•• 

(.24) 

2.ss•• 

(.24) 

.12•• 

(.00) 

.n•• 

(.00) 

-.ss•• 

(.11) 

-.38"" 

(.11) 

-.19 

(.32) 

-.19 

(.32) 

.11 

(.42) 

.11 

(.42) 

5.93 

(64.00) 

.66 

.65 

186 

217 

688 

688 

Ampicillin .16 

(.19) 

-.10 

(.U) 

3.26... 

(.26) 

.o1•• 

(.01) 

-.77"• 

(.11) 

-.6o• 

(.29) 

-.01 

(.36) 

-.12 

(.13) 

.42 91 885 

.11 

(.18) 

-.15 

(.13) 

3.28'"'" 

(.26) 

.07... 

(.01) 

-.76... 

(.11) 

-.50* 

(.29) 

.00 

(.36) 

.42 106 889 

Chlordinepoxide/ 

Clidinium bromide 

-1.2o•• 

(.37) 

-.06 

(.14) 

4.37•• 

(.47) 

.12.. 

(.00) 

-.74... 

(.18) 

.19 

(.67) 

-.47 

(.67) 

-39.16 

(30.82) 

.74 259 650 

-1.ss•• 

(.36) 

-.07 

(.U) 

4.31•• 

(.47) 

.n•• 

(.00) 

-.76 •• 

(.18) 

.17 

(.&7) 

-.50 

(.67) 

.74 301 650 

><ttl 

;;o >

G1 

ttl 

'\:! 

;;o 

trl 

(/) 

() 

;;Ij

..... 

'1j 

-


z 

'1j 

;;Ij 

..... 

() 

ttl 

(/) 

0 
TABLE 8·1, continued 

DRUG CON- ss- Adl 

NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 

Tolbutamide -.78 

(.61) 

.01 

(.21) 

4.2o•• 

(.70} 

.to•• 

(.00} 

-1.42•• 

(.26} 

. 59 

(.77) 

.40 

(.95) 

-12.33 • 

(5.80) 

.71 129 367 

-1.14. 

(.51) 

-.01 

(.21) 

4.07•• 

(.68} 

.to•• 

(.00) 

-1.44•• 

(.26} 

.54 

(.78} 

.41 

(.97) 

.71 151 367 

Conjugated estrogens -3.87.. 

(.46} 

.06 

(.10} 

6.39•• 

(.46) 

.09.. 

(.00) 

-.96•• 

(.15) 

.48 

(.43) 

.42 

(.48) 

-3.11 

(5.32) 

.83 343 502 

..... 

0\ 

~ 

Chlorthalidone 

-3.89•• 

(.44) 

-6.17.. 

(.69) 

.Oil 

(.10) 

.02 

(.16} 

6.311 •• 

(.411} 

a.ae•• 

(.611) 

. 09 .. 

(.00) 

.13•• 

(.00) 

-.96.. 

(.111) 

-.47.. 

(.20) 

.47 

(.43} 

.sa• 

(.1111) 

.43 

(.48) 

-.12 

(.119) 

-4.12 

(11.27) 

.83 

.81 

400 

342 

502 

546 

-6.43•• 

(.68) 

. 01 

(.111) 

8.99•• 

(.611} 

.13.. 

(.00} 

-.411 • 

(.20) 

-.88 

(.1111} 

-.25 

(.58} 

.82 402 546 

Acet&minophen/ 

Chlorsaxuone 

-1.26•• 

(.39) 

-.03 

(.11) 

4.118.. 

(.411) 

.13•• 

(.00) 

-.17 

(.111) 

-.49 

(.45) 

-.94. 

(.53) 

-113.26 

(127.72) 

.71 214 606 

-1.34•• 

(.39} 

-.03 

(.11) 

4.62•• 

(.411) 

.ts•• 

(.00} 

-.17 

(.U} 

-.48 

(.411} 

-.98. 

(.62} 

.11 249 606 

0 

::::: 

>'tl 

1-i 

t"1 

~ 

Oil 
TABLE 8-1, continued 

DRUG 

1 NAME 

CONSTANT DPS QUAN ssINDEX CHAIN MED PRIV GEN2 Adi. 

R F N 

Tetracycline -.06 

(.19) 

.11 

(.14) 

3.27.. 

(.22) 

.03.. 

(.00) 

-.48•• 

(.10) 

-.11 

(.28) 

.57 

(.35) 

-.33•• 

(.15) 

.40 84 882 

-.16 

(.17) 

-.02 

(.15) 

3.28•• 

(.21) 

.03.. 

(.00) 

-.48•• 

(.10) 

-.12 

(.28) 

.44 

(.34) 

.40 98 882 

Spironolactone -3.33•• 

(.85) 

-.15 

(.27) 

7.49•• 

(.96) 

.14•• 

(.00) 

-.72• 

(.32) 

-.50 

(1.05) 

1.52 

(1.20) 

.75 165 330 

...... 

0\ 

V\ 

Phenytoin sodium 

Allopurinol 

-3.33 •• 

(.85) 

-1.37•• 

(.42) 

-1.33 •• 

(.37) 

-7.18.. 

(.29) 

-1.1o•• 

(.94) 

-.15 

(.27) 

.03 

(.11) 

.02 

(.11) 

-.21 

(.23) 

-.22 

(.23) 

7.49•• 

(.96) 

4.6o•• 

(.43) 

4.39•• 

(.42) 

11.53.. 

(.95) 

11.80.. 

(.96) 

.14•• 

(.00) .o4•• 

(.00) 

.04•• 

(.00) 

.22.. 

(.00) 

.22.. 

(.00) 

-.72. 

(.32) 

-1.23•• 

(.16) 

-1.24 •• 

(.16) 

-.17 

(.29) 

-.14 

(.29) 

-.50 

(1.05) 

.05 

(.41) 

.03 

(.41) 

-3.29•• 

(.94) 

-3.3t•• 

(.93) 

1.52 

(1.20) 

1.47•• 

(.51) 

1.48•• 

(.52) 

-.42 

(.98) 

-.40 

(.96) 

-9.82. 

(6.39) 

-1.27 

(1.93 

.75 

.50 

.61 

.88 

.88 

165 

89 

106 

494 

580 

330 

615 

615 

471 

471 

>

< 

tr.l 

~ 

> 

C) 

tr.l 

'l:J 

~ 

trl 

Ul 

0 

~ ..... 

'i:l

..,-

0 

z 

'l:J 

::<1 -

0 

tr.l 

Ul 
TABLE 8-1, continued 

DRUG 1 CON- SS- Ad~. 

NAME STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 


Dexbromphenirarnine/ 

Peeudoephedrine 

-.&s•• 

(.16} 

.09 

(.07} 

2.sa•• 

(.23) 

.1a•• 

(.00) 

-.27•• 

(.09} 

.22 

(.29} 

-.21 

(.34) 

.79 409 665 

-.as•• 

(.18} 

.OQ 

(.07} 

2.8a•• 

(.23) 

.1e•• 

(.00) 

-.27•• 

(.09} 

.22 

(.29} 

-.21 

(.34} 

.79 409 665 

Triamcinolone -1.14 

(.96} 

-.03 

(.19} 

5.16.. 

(.87} 

.26•• 

(.02) 

.53•• 

(.24} 

1.84. 

(.92) 

.40 

(.81} 

-8.89. 

(10.73} 

.37 24 269 

- -1.27 

(.96) 

-.07 

(.19} 

5.13.. 

(.87} 

.26•• 

(.02} 

.52. 

(.24} 

1.85 • 

(.92) 

.36 

(.81} 

.37 28 269 

0\ 

0\ 

Chlorpropamide -6.98•• 

(1.01) 

.10 

(.22} 

10.61 •• 

(.93} 

.16•• 

(.00) 

-.67•• 

(.28) 

-1.61 •• 

(.72} 

-.60 

(.86} 

-22.27 

(24.85} 

.74 227 553 

5 -7.09.. 

(1.00) 

-.12 

(.22} 

10.52.. 

(.92} 

.1a•• 

(.00) 

-.67•• 

(.28) 

-1.53•• 

(.72} 

-.55 

(.85} 

.74 265 553 

Hydroxyeine -1.67•• 

(.36} 

.06 

(.11) 

4.4o•• 

(.42} 

.17.. 

(.01} 

.4o•• 

(.14) 

.78• 

(.45} 

-.01 

(.56} 

-8.56 

(20.64} 

.76 209 469 

-1.715•• 

(.lUI} 

.06 

(.11) 

4.41 •• 

(.41} 

.17•• 

(.01) 

.41•• 

(.14) 

.77• 

(.415} 

-.01 

(.1515} 

.76 243 469 

0 

::r:: 

> 

1-i "' 
 trl 

;:t! 

00 
TABLE 8-1, continued 

DRUG 

NAME1 

Sulfamethoxar.ole/ 

Trimethoprim 

CONSTANT 

-1.29•• 

(.26) 

-l.s1•• 

(.27) 

DPS 

.16. 

(.10) 

.16. 

(.10) 

QUAN 

4.64.•• 

(.31) 

4.66•• 

(.31) 

SSINDEX 

.36.. 

(.01) 

.36.. 

(.01) 

CHAIN 

-.82•• 

(.12) 

-.s2•• 

(.12) 

MED 

-.19 

(.34) 

-.20 

(.34) 

PRIV 

.02 

(.41) 

.04 

(.40) 

GEN2 

-164.68 

(262.07) 

Ad~. 

R 

.68 

.68 

F 

254 

294 

N 

828 

828 

0\ 

--l 

Theophylline5 

Terbutaline eulfate5 

Nitroglycerin 

-1.1s• • (.8~.) 

-1.13•• 

(.80) 

-3.69.. 

(.66) 

-3.39•• 

(.66) 

-.20 

(.18) 

-.17 

(.16) 

.02 

(.17) 

.OS 

(.16) .06 

(.14) 

. 16 

(.14) 

-.02 

(.07) 

-.02 

(.07) 

4.07•• 

(.77) 

4.07•• 

(.76) 

5.63•• 

(.60) 

6.68*"' 

(.60) 

3.02•• 

(.25) 

s.os•• 

(.25) 

. 12 .. 

(.00) 

.12•• 

(.00) 

.1o•• 

(.00) 

.10.. 

(.00) 

.00.. 

(.00) 

.oo•• 

(.00) 

-.64 •• 

(.21) 

-.54.. 

(.21) 

-.11 

(.17) 

-.11 

(.17) 

.38.. 

(.10) 

-.38.. 

(.10) 

.42 

(.72) 

.43 

(.72) 

-1.96 •• 

(.64) 

-1.82•• 

(.64) 

.76•• 

(.29) 

.77.. 

(.29) 

.76 

(.76) 

.76 

(.75) 

-.22 

(.68) 

-.42 

(.67) 

1.28•• 

(.38) 

1.28•• 

(.38) 

3.69 

(83.00) 

3.90.. 

(1.73) 

.06 

(.17) 

.81 

.81 

.85 

.85 

.ss 

.33 

214 

251 

289 

333 

44 

51 

351 

351 

364 

364 

628 

628 

><

trj 

;:tl 

> 

0 

trj 

"' ;:tl 

trj 

en 

a 

;:tl

"CI -

~ ....... 

0 

z 

'"0 

::ua 

trl 

Ul 
TABLE 8-1, continued 

DRUG CON- ss- Aqj. 


NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 


Metronidazole -s.oo•• 

(.70) 

-.11 

(.20) 

5.94•• 

(.70) 

.49•• 

(.01) 

-.as•• 

(.24) 

-1.19 

(.72) 

-.07 

(.86) 

156.42•• 

(76.04) 

.78 288 580 

-2.07•• 

(.60) 

-.06 

(.20) 

6.61.. 

{.66) 

.49•• 

(.01) 

-.68•• 

{.24) 

-1.23. 

(.72) 

-.20 

(.86) 

.78 339 580 

Diethylpropion -2.22 •• 

(.39) 

.ts• 

(.11) 

4.5e•• 

(.42) 

.26•• 

(.01) 

-.25• 

(.15) 

-.27 

(.47) 

-.41 

(.49) 

65.83 

(137.40) 

.77 284 581 

,_. 

0\ 

00 

Phentermine 

-2.22•• 

(.37) 

-.59.. 

(.SO) 

.19. 

(.11) 

.09 

(.14) 

4.59•• 

(.42) 

2.58.. 

(.38) 

.26•• 

(.01) 

.2s•• 

(.01) 

-.2s•• 

(.15) 

-.06 

(.14) 

-.26 

(.47) 

-.02 

{.44) 

-.44 

(.49) 

-.93• 

(.56) 

-1.25 

{3.77) 

.77 

.81 

330 

250 

581 

413 

-.62•• 

(.28) 

.09 

(.13) 

2.ss•• 

{.37) 

.2s•• 

(.01) 

-.07 

(.14) 

-.02 

(.44) 

-.94• 

(.56) 

.81 292 413 

Minocycline -6.2s•• 

(1.56) 

-.62. 

(.37) 

9.st•• 

(1.63) 

.44•• 

(.01) 

.42 

(.42) 

-1.01 

(1.82) 

2.12 

(1.57) 

3.92 

(9.51) 

.79 161 299 

-6.4s•• 

(1.66) 

-.58 

(.36) 

10.11.. 

(1.53) 

.44•• 

(.01) 

.4e•• 

(.41) 

-.89 

(1.81) 

2.o4•• 

(1.56) 

.79 100 299 

() 

:I: 

> 

'"0 

1-:3 

txl 

::x:l 

00 
DRUG 

1 NAME 

Furosemide 

CONSTANT 

-.sou 

(.16) 

-.68.. 

(.13) 

DPS 

.08 

(.07) 

.01 

(.07) 

QUAN 

:uso•• 

(.19) 

s.28•• 

(.Hl) 

TABLE 8-1, continued 

ssINDEX CHAIN 

.11.. -.94•• 

(.00) (.09) 

.n•• -.96.. 

(.00) (.09) 

MED 

.18 

(.25) 

.18 

(.26) 

PRIV 

.sa• 

(.31) 

.67• 

(.31) 

GEN2 

-35.93 

(31.61) 

A~j. 

R 

.85 

.85 

F 

653 

761 

N 

820 

820 

0'1 -

\0 

Erythromycin ba.ae 

Hydrochlorothiuide/ 

Triamterene 

Amitriptyline/ 

Perphenasine 

-.13 

(.19) 

-.31• 

(.16) 

-1.99•• 

(.27) 

-1.99•• 

(.27) 

-5.ao•• 

(.91) 

-s.so•• 

(.91) 

.00 

(.08) 

-.05 

(.08) 

.04 

(.08) 

.04 

(.08) 

-.09 

(.19) 

-.09 

(.19) 

3.:u~.. 

(.26) 

3.24•• 

(.26) 

6.02•• 

(.SO) 

6.02.. 

(.30) 

9.22•• 

(.88) 

9.22 •• 

(.88) 

.12.. 

(.01) 

.12.. 

(.01) 

.11.. 

(.00) 

.11 .. 

(.00) 

.1a•• 

(.00) 

.1a•• 

(.00) 

-.26•• 

(.11) 

-.26. 

(.11) 

-.9s•• 

(.12) 

-.9s•• 

(.12) 

-.02 

(.25) 

-.02 

(.25) 

-.21 

(.32) 

-.24 

(.33) 

.09 

(.30) 

.09 

(.30) 

-2.27•• 

(.77) 

-2.27•• 

(.77) 

.72. 

(.38) 

.74• 

(.38) 

.16 

(.36) 

.16 

(.36) 

.36 

(.86) 

.35 

(.86) 

-1.01 •• 

(.39) 

.50 

.50 

.81 

.81 

.82 

. 82 

112 

131 

594 

594 

306 

306 

768 

768 

834 

834 

405 

405 

>

< 

t%l 

~ (.,"') 

t%l 

"' ;:tj 

tlj 

(f.l 

0 

;:tj

.... 

"' >-3

.... 

0 

z 

"' ;:tj 

...... 

() 

tr.l 

CIJ 
TABLE 8-1, continued 

DRUG CON- SS- Aqj. 


NAME1 STANT DPS QUAN INDEX CHAIN MED PRIV GEN2 R F N 


Cephalexin 

--...) 

0 

Erythromycin 

ethylauc:cinate 

Ibuprofen 

Meatranol/ 

Norethindrone/P lac:ebo 

-s.1s•• 

(.48) 

.10 

(.10) 

6.69.. 

(.41) 

.39.. 

(.01) 

-.46 •• 

(.14) 

-.06 

(.39) 

-1.29•• 

(.41) 

-67.97 

(81.62) 

.70 272 828 

-3.16•• 

(.47) 

-.11 

(.10) 

6.67". 

(.41) 

.39.. 

(.01) 

-.46 •• 

(.14) 

-.06 

(.39) 

-1.28•• 

(.41) 

.70 317 828 

-.s3• 

(.20) 

.04 

(.08) 

s.s1•• 

(.26) 

.17.. 

(.01) 

-.17• 

(.11) 

-.27 

(.33) 

-.34 

(.42) 

-1.36 

(6.20) 

.64 183 716 

-.sa• 

(.18) 

.04 

(.08) 

3.30.. 

(.26) 

.17•• 

(.01) 

-.17 

(.11) 

-.27 

(.33) 

-.35 

(.42) 

.64 213 716 

.62. 

(.27) 

.13 

(.12) 

4.os•• 

(.37) 

.14.. 

(.00) 

.96.. 

(.16) 

1.02 • 

(.41i) 

.33 

(.li6) 

.81 550 779 

.62 •• 

(.27) 

.13 

(.12) 

4.03.. 

(.37) 

.14•• 

(.00) 

-.96.. 

(.16) 

1.02.. 

(.46) 

.33 

(.66) 

.81 550 779 

-.72 

(.48) 

. 25 

(.17) 

.79 

(.68) 

.:n.. (.00) 

-.67•• 

(.18) 

.96. 

(.U) 

.02 

(.67) 

.87 476 428 

-.72 

(.48) 

. 26 

(.11) 

.79 

(.58) 

.21 •• 

(.00) 

-.57•• 

(.18) 

.9&· 

(.62) 

.02 

(.67) 

.87 476 428 

0 

::X: 

>'tl 

~ 

tr1 

:::tt 

00 
TABLE 8-1, continued 

DRUG 

NAME1 

CONSTANT DPS QUAN 

SSINDEX CHAIN MED PRIV GEN2 A~j. 

R F N 

--...) -

Hydrogenated ergot 

alkaloide 

Quinidine sulfate 

-4.69** 

(1.22) 

-4.69** 

(1.22) 

-5.4o•• 

(1.25) 

-5.20'"* 

( 1.24) 

.11 

(.28) 

.11 

(.28) 

.01 

(.38) 

. 23 

(.35) 

9.3o•• 

(1.21) 

9.so•• 

(1.21) 

6.87** 

(1.19) 

7.14.. 

(1.17) 

.18"'* 

(.01) 

.18** 

(.01) 

.o9•• 

(.00) 

.09.. 

(.00) 

.02 

(.36) 

.02 

(.36) 

-.64* 

(.32) 

-.61 • 

(.32) 

-3.52 •• 

(1.13) 

-3.62** 

(1.13) 

1.74* 

( 1.04) 

1.80* 

( 1.04) 

.26 

(1.31) 

.26 

(1.32) 

1.61'" 

(1.07) 

1.43 

(1.07) 

.64* 

(.42) 

.77 

.77 

.60 

.60 

151 

151 

71 

82 

277 

277 

325 

329 

>

<! 

t'l 

:=t:l 

> 

C) 

t'l 

"'

:=t:l 

t'l 

Ul 

0 

:=t:l -

"'

.,-

0 

z 

"'

:=t:l -

0 

t'l 

Ul 
CHAPTER 8 

TABLE 8-2 

Summary of Coefficient Signs 

in Average Price Regressions 

No. (+)and (-) and 

of Signi- Signi- Overall 

Variable Drugs (+) ficant 1 (-) ficant Effect 

DPS 102 

45 

0/03 

24/19 

0/0 

2/2 

10/10 

21/26 

5/5 

7/6 No 

QUAN 10 

45 

10/10 

45/45 

10/10 

45/45 

0/0 

0/0 

0/0 

0/0 

+ 

+ 

SSINDEX 

10 

45 

10/10 

45/45 

10/10 

44/44 

0/0 

0/0 

0/0 

0/0 

+ 

+ 

CHAIN 10 

45 

0/1 

6/1 

0/1 

4/4 

10/9 

39/38 

7/5 

31/29 

MED 10 

45 

4/3 

18/18 

2/1 

1/7 

6/7 

27/27 

4/4 

12/12 

No 

No 

PRIV 10 

45 

10/9 

30/32 

6/4 

11/9 

0/1 

15/13 

0/1 

3/3 

+ 

+ 

GEN4 9 

37 

2 

10 

0 

3 

7 

27 

5 

10 

No 

!/ Statistical significance measured at the 10 percent level. 

Y Regressions for the 10 top substitution drugs. 

~ The number before the slash ia for the model including GEN; the number 

after the alash ia for the model excluding GEN. 

!I In 8 drugs there was no generic preacribing, so the variable GEN was 

omitted. 

SOURCE: Table 8-1. 

172 

AVERAGE PRESCRIPTION PRICES 

regressions a weighted sum of the coefficients on the DPS 

variable is statistically significant at better than the 5 

percent level of si gnificance.5 (As discussed in Appendix 7, 

the assumption necessary for this test -- that the individual 

drug regressions are independent of one another -- is not 

strictly appropriate. Nevertheless, we believe that if it were 

possible to take fully into account any interdependence, the 

results would not differ by much.) 

These regressions provide us with our best estimates of the 

magnitude of the price effect of a DPS Ia w. In the regressions 

with GEN included, the weighted average coefficient on DPS is 

-$.059. When GENis excluded, the weighted average coefficient 

. 6 IS -$.103. 

In many ways, the results from the two models are very 

similar. While the law is shown to have a larger effect in 

decreasing average prescription prices paid when the effects of 

variations in generic prescribing are attributed to the DPS 

law, in only 10 of 37 drugs does GEN have a significant 

negative effect on average price and in 3 regressions the GEN 

coefficient was positive and significant. 7 However, 27 of the 

37 coefficients are negative, which is statistically significant.8 A comparison by drug of the results shown in Table 

8-1 shows that the exclusion of the GEN variable changed 

the sign on the DPS coefficient in only 2 drugs, in both 

instances changing it from positive to negative. For most 

drugs, the size of the DPS coefficient was changed very little 

in the second regression model; in 23 of the 37 regressions 

which included GEN the difference was no greater than .02. In 

the 13 regressions where GEN was statistically significant, the 

weighted average change in the DPS coefficient was -$.096; more 

frequent generic prescribing lowered average prescription 

prices, as expected. As shown in Table 8-3, the drugs for 

which GEN has a significant negative effect almost all have 

W In the with-GEN regression!.!, t = 2.31. In the without-GEN regressions, 


t = 4.10. 


§} The (weighted) average of only the 9 significant coefficients is -$.42 


for the with-GEN regres!lions and -$.48 for the without-GEN regressions. 


'1} In 8 drugs there waa no generic prescribing at all, so the with-GEN model 


wu not applicable. 


§} See footnote 4. 


173 
CHAPTER 8 

TABLE 8-3 


Drugs for which the Generic Prescribing Variable 


has a Significant Effect 


m the Average Price Regression 


Proportion of Brand-Generic 

All Prescriptions Price 

Drug Written Generically D ifferen tial 

Negative Significant Coefficient on GEN: 

Hydrochlorothiazide 45.4% $2.63 

Chlordiazepoxide 10.2 3.83 

Amitriptyline 16.0 2.44 

Penicillin VK 51.6 1.09 

Hydralazine/Hydrochlorothiazide/ 

Reserpine .3 4.98 

Tolbutamide 5.2 2.69 

Tetracycline 69.6 .45 

Phenytoin 2.5 2.00 

Triamcinolone 2.7 4.30 

Erythromycin base 28.4 .18 

Positive Significant Coefficient on GEN: 

Metronidazole .3 4.01 

Terbu taline 10.9 1.3 7 

Quinidine sulfate 99.2 N/A1 

45-Drug Average 20.1 

37-Drug Average 2.00 

!/ While there are many brands of quinidine sulfate, sold at different 

prices, all were classified as generics by our definition, so no brand-generic 

price differential can be computed. 

SOURCES: Tables 8-1 and AS-1. 

174 

AVERAGE PRESCRIPTION PRICES 

either high average generic prescribing or a large brandgeneric price differential (or both) such that variations 

around the average level of generic prescribing may be significant in size and a shift in generic market share will have a 

noticeable effect on average price.9 

The results on the other variables are generally as hypothesized. The coefficients on SSJNDEX and QUAN are all positive 

and all significant (with the exception of one SSINDEX coefficient.) Nearly all of the CHAIN coefficients are negative 

and most are significant. 

The coefficients on insurance type are sometimes but by no 

means always significant. Two-thirds of the coefficients on 

PRJV are positive, significantly so in 11 drugs in the withGEN model and 9 in the without-GEN model, indicating that for 

many drugs customers with private prescription drug insurance 

pay more than cash customers. 10 The results for MED are even 

more mixed and support no conclusion about the relative prices 

paid for Medicaid prescriptions.ll This is surprising given 

the consistent pattern of higher substitution rates for people 

with Medicaid coverage, but of course even those rates were 

often low. On the 7 MAC drugs in the sample, Medicaid prescriptions were significantly less expensive in 4, significantly more expensive in 1; the remaining 2 had negative signs 

which were not significant. 

It is apparent from these results that the effects of the 

law on prescription prices differ from drug to drug and are 

important for some drugs and unimportant for others. Even 

among the 10 top-substitution drugs the reduction in price 

varied considerably from drug to drug, ranging from 2 cents 

(penicillin VK) to 70 cents (isosorbide dinitrate) (using the 

with-GEN regressions.) Four of the decreases were about 50 

cents per prescription. Table 8-4 shows the estimated price 

~ Of the 3 significant positive coefficients on GEN, 2 occur in drugs where 


there is very slight state-to-state variation in GEN since nearly all prescrip­


tions (quinidine sulfate) or almost none (metronidazole) are written generical­


ly. We therefore do not place great reliance on the significance of these GEN 


coefficients (nor in the GEN coefficient for hydrochlorothiazide/hydrala­


zine/reserpine, for the same reason.) We have no explanation for the third 


drug, terbutaline. 


lQ/ The sign pattern on PRIV is statiatically significant. See footnote 4. 


ll/ The sign pattern on MED is not atatistically significant. See footnote 4. 


175 
CHAPTER 8 

change due to the Ia w for each of the ten drugs analyzed, 

both in dollar terms and in percent of average prescription 

price. In percentage terms, the price declines ranged from 1.2 

percent to 9.4 percent. Variations among drugs reflect 

differences in the brand-generic price differential, determining the potential savings from an increase in the generic 

market share; the extent of substitution; and, in the withoutGEN model, the prevalence of generic prescri bing. 12 

Ill. ESTIMATE OF OVERALL SAVINGS 

We have estimated the dollar difference in total retail 

expenditures for prescription drugs between two sets of 

comparable prescriptions, differing by the permissibility or 

prohibition of substitution. The estimates are rough, intended 

to provide a sense of the general magnitude of the effects of 

the DPS Ia ws, for 1980 and, by extrapolation, also for 1984. 

Based on our average-price regressions, our rough estimate 

of the savings due to a drug product selection law for the 45 

drugs we studied was between $21 and 38 million per year in 

1980. The $21 million estimate utilizes the per-prescription price difference estimated for each drug based on the 

regressions which held constant the proportion of prescriptions 

written generically, multiplied by the number of prescriptions 

for that drug (all dosage forms) which were eligible for 

substitution in 1980. 13 The $38 million estimate allows for 

12/ See Appendix Table A6 for data by drug.

W The number of new prescriptions {all dosage forms) in each of the 45 

drug entitiea in IMS' 1980 NPA sample waa taken from IMS' 1980 Basic Data 

Report and was expanded to an estimate of a national total for the drug entity 

by using the ratio of the whole NPA aample {1,113,486, as reported in the 

Basic Data Report) to the IMS estimate of the universe of U.S. new preacriptiona in 1980 {750.289 million (Pharmacy Times, April 1982, p. 25)), a ratio 

of 674.114. The national estimate of new prescriptions for the drug was then 

adjusted by an estimate of the drug's refill rate, based on unpublished 

(1983) data provided by IMS on new and refill prescriptions by drug product, to 

produce a figure for total (new plus refill) prescriptions for that drug in 

1980. 

176 

AVERAGE PRESCRIPTION PRICES 


TABLE 8-4 

Changes in Average Prescription Price Due to a DPS Law 

for the 10 Top-Substitution Drugs• 19801 

with-GEN withQyt-GEN 

Drug 

Average 

Prescription 

Change Change 

In as a 

Change Change 

in as a 

Price Price Percent Price Percent 

Hydrochlorothiazide $4.71 -$.06 -1.3% -$.21 -4.5% 

Chlordiazepoxide 7.13 -.50 -7.0 - .65 -9.1 

Amitriptyline 7.08 - .45 -6.4 - .62 -8.8 

Penicillin VK 4.48 - .02 -.4 - .15 -3.3 

Amoxicillin 6.56 - .13 -2.0 - .12 -1.8 

Atropine sulfate/ 

Diphenoxylate 6.41 -.50 -7.8 

Meclizine 6.90 - .48 -:7.0 - .55 -8.0 

Isosorbide 

dinitra te 8.21 - .70 -8.5 - .70 -8. 

Hydralazine/ 

Hydrochlorothiazide/ 

Reserpine 10.58 - .26 -2.4 - .32 -3.0 

Doxycycline 8.21 - .10 -1.2 - .10 -1.5 

!/ The ten drugs are listed in descending order by substitution rate. 

SOURCES: Tables 8-1 and A5-1. 

177 

CHAPTERS 

both estimates, the per-prescription price differences estimated for solid oral dosage form products were extended to 

prescriptions for other dosage form products as well. 

Extrapolating from these 45 drugs, which accounted for a 

little less than half (333.0 million) of all multi-so\irce 

prescriptions eligible for substitution (696.9 million) in 

1980,14 we estimate that total retail expenditures on multisource drugs were $44 to $80 million less in 1980 (in 1980 

dollars) than they would have been had all substitution been 

prohibited. While substantial in absolute terms, this is 

about half of 1 percent of total purchases of prescription 

drugs through retail outlets, estimated to be $12 billion in 

1980. 16 This simple extrapolation from the top-selling drugs 

to all prescriptions is probably an underestimate of the total 

effect for all drugs. By definition, the other one-third of 

prescriptions occur in less frequently prescribed drugs. Based 

on our sample of 45 drug entities, price effects and indeed 

substitution itself appear to be higher for less common drug 

entities. The weighted average substitution rate (on eligible 

prescriptions) was 6.1 percent for the 22 drugs in our sample 

with U.S. sales ranks in 1980 above 50 and 9.0 percent for the 

23 drugs ranked between 51 and 100. Similarly, the weighted 

average coefficient from the average price regressions was 

-$.038 in the with-GEN regressions (-$.065 in the without-GEN 

regressions) for the higher-ranked drugs and more than twice 

that, -$.084 ( -$.139), for the lower-ranked drugs. Our 

ll/ Of the total number of prescriptions dispensed in 1980 of 1,394.308 million, new prescriptions were 63.9 percent, or 750.289 million (Pharmacy Times, 

April 1982, p. 25). According to IMS, 69 percent of new prescriptions in 1980 

were multi-source (IMS Research Group, 1981, p. 21); thus there were 517.699 

million new multi-source prescriptions in 1980. Based on refill ratios 

computed from unpublished (1983) data provided by IMS, new prescriptions 

accounted for 54.66 percent of all prescriptions for our 46 drugs; assuming the 

same refill ratio for all other multi-source drugs, we multiplied the 617.699 

by 1.829 (= 1/.5466). We thus arrived at an estimate of all multi-source 

prescriptions in the U.S. in 1980 of 946.871 million. Finally, we multiplied 

by the fraction of prescriptions for our 45 drugs which were eligible for 

substitution, 73.6 percent, for our estimate of the number of multi-source 

prescriptions eligible for substitution in 1980: of 696.897 million. Dollar 

savings for all multi-source drugs are, then, a multiple, 696.9/333.0 = 2.093, 

of the savings for the 45 drugs alone. 

l§.j Trapnell et al. (1983, p. 8). 

178 
AVERAGE PRESCRIPTION PRICES 

estimate of the effects of DPS laws in 1980 is therefore 

probably conservative. 

Since 1980 consumer savings due to the drug product selection laws have substantially increased. Since we had neither 

retail price nor substitution data on a drug-by-drug basis for 

more recent years, we combined aggregate data with several 

simplifying assumptions, discussed below, to project our 1980 

estimates forward to 1984. Four factors have clearly worked 

to increase the savings from substitution: the addition of 9.5 

percent more prescriptions to the pool of those eligible for 

substitution because of the passage of DPS laws in the holdout 

states; 16 a 5.3 percent increase in the number of multi-source 

prescriptions written,17 a near doubling (86 percent increase) 

in the percentage of brand-written prescriptions on which 

substitutions were made, 18 and inflation in prescription drug 

prices of 38 percent from 1980 to 1984.19 If the prices of 

branded and generic drugs are assumed to have risen at the same 

rate, the differential between brand and generic prices has 

also risen by 38 percent. If also the pattern of substitution 

across drugs is assumed not to have changed significantly, the 

average saving per substitution has increased by 38 percent as 

well. Combining the increased number of substitutions with the 

increased savings per substitution, we project that savings 

from substitution may have tripled, in nominal terms, to a 

!§./ In 1980, states allowing aubstitution accounted for 91.3 percent of the 

prescriptions in our sample.

ll/ 996.8 million is our estimate of the number of all multi-source prescriptions dispensed in 1984. This is 65 percent of the total number of preacriptiona dispensed in 1984; according to IMS' NPA, 1,533.6 million prescriptions 

were sold in 1984, an increaae of 11 percent since 1980. "Top 200 Drugs of 

1984," Pharmacy Times (April, 1985, p. 25). The multi-source share used is 65 

percent, a rough correction to reflect the continuing trend towards a larger 

single-source share, from 31 percent of new prescriptions in 1980 to 33 percent 

in 1983; see footnote 11 in Chapter 2. 

W As a percent of potentially substitutable prescriptions, substitution 

increased from 5.1 percent in 1980 to 9.5 percent in 1984, according to !MS. 

Chappell (Oct. 1984).

12/ The prescription drug component of the Consumer Price Index rose from 

154.8 in 1980 to 213.8 in 1984, an increase of 38.1 percent. U.S. Department 

of Commerce, Bureau of Labor Statistics, "Consumer Price Indexes, Detailed 

Report" (monthly issues, 1980 and 1984). This reflects in part an increase in 

the size of the average prescription. 

179 

CHAFTER 8 

range of $130 to $236 million in 1984;20 in constant dollars, 

this is more than a doubling. 

The first assumption is that the prices of brands and 

generics rose at the same rate. Suppose instead, for example, 

that brand prices rose less than generic prices. Then the 

price differential would have shrunk and our estimate of 1984 

savings would be upwardly biased. We believe that if there is 

any bias, it is in the other direction: that our estimates may 

be low. Limited data indicate that neither broad changes in 

the relative prices of brands and generics at the manufacturer 

level nor changes in retail dollar gross margins, which 

together make up retail price changes, have led to a diminution 

in the brand-generic price differential, and indeed that 

differential may well have widened. 

We checked on the assumption that manufacturer-level brand 

and generic prices rose at the same rate by comparing 1980 and 

1984 invoice data for individual drug products.21 For the 45 

drugs studied in this report, brand prices typically rose by 

about the same amount as retail prescription drug prices 

overall, while generic prices often fell, even in absolute 

terms. As to changes in retail dollar gross margins, our 

regression results in Chapter 7 suggest that an increase in 

substitution does not have any significant effect on the dollar 

gross margin at retail of brands but does depress prices of 

ill 1.15 {increase in number of pre~criptions) X 1.86 (increase in substitution rate on eligible prescriptions) X 1.38 {increase in prices) = 2.95. 

Formulary coverage is assumed to remain the same on average.

ll/ The compilations, baaed on IMS' U.S. Drugstore Audit data, were obtained 

from the U.S. Department of Health and Human Services, Health and Human 

Services, which they had purchased under their contract number 500-81-0057. 

The data were for October/November 1980 and August 1984. The 1980-1984 

change in the price for the leading brand(a) could be computed for 42 of the 45 

entities studied. The sales-weighted average of these changes was 35.5 

percent. This wu little different from the 37.5 percent increase in the 

producer price index for drugs and pharmaceuticals. U.S. Department of 

Commerce, Bureau of Labor Statistics, "Producer Price Indexes," (1980 and 

1984). For generic prices, the comparison was between simple averages of all 

prices in 1980 and in 1984, since we did not have market shares to use for 

weighted averages. A sales-weighted average of the 29 entity averages available waa -10.8 percent. 

180 
AVERAGE PRESCRIPTION PRICES 

generics, thereby widening the brand-generic price differential.22 Both of these patterns imply an increase in the 

brand-generic price differential over 1980, which is also 

the result generated by the application of a single rate of 

price increase to both brand and generic prices, although we do 

not know the accuracy of our simple 38 percent estimate. 

The second assumption is. that the pattern of substitutions 

across drugs in 1984 was essentially unchanged from 1980. 

However, if, for example, a larger proportion of substitutions 

now take place in drugs where the saving per substituted 

prescription is larger than average, the average saving, on all 

prescriptions whether substituted or not, would be greater than 

if the 1980 distribution of substitutions across drugs had been 

maintained. 

We suggest that the increase in substitution has probably 

occurred primarily in two types of drugs: those in which 

substitution was already frequent, and those which have 

recently moved from single-source to multi-source status. 

If much of the substitution occurred in 1984 in the very 

drugs identified as top-substitution drugs in 1980, our use of 

the 1980 estimates is appropriate. Newly multi-source drugs 

are likely to have an even larger price differential, since 

there has been little time for competition at the manufacturer 

level to bring down the price of the leading brand. We infer 

that the average saving, as measured by the brand-generic price 

differential for substitutions, is likely to be at least as 

large (in constant dollars) in 1984 as in 1980. 

Thus it seems that if our assumptions bias our projections, 

we probably understate the increase in consumer savings since 

1980. Moreover, the 1984 estimates leave out some addi tiona! 

possibilities of saving. If manufacturers have responded to 

the growth of substitution by lowering prices or by entering 

llf In fact, increased levels of substitution may have produced larger perprescription price effects, regardless of changes in the brand-generic price 

differential, because of downward pressure on the prices of individual brands 

and generics as well as the direct price effect of the switch from brand to 

generic: the regressions reported in Tables 7-1 and 7-2 show that for both 

brands and generics the price decreases associated with a DPS law were much 

larger for the top-substitution drugs than for other drugs. 

181 

CHAPTER 8 

the generic segment more aggressively, consumers may be 

enjoying additional savings. 

An annual decrease in consumers' retail expenditures for 

prescription drugs due to the DPS laws of between $130 and $236 

million, as of I984, is still only about I percent of all 

retail sales of prescription drugs through drugstores. 23 Of 

course, a large share of total sales cannot be reduced by DPS 

Iaws -- not only the single-source share but also the necessary 

costs of producing and marketing multi-source drugs. 

IV. CONCLUSION 

On average, the price per prescription was 6 to I 0 cents 

lower for multi-source prescriptions on which substitution was 

permitted in I980 than on prescriptions where substitution was 

forbidden. This range reflects our uncertainty as to how much 

of observed differences in generic prescribing should be 

attributed to the workings of a drug product selection law. 

For consumers as a group, average multi-source prescription 

prices were less than I percent lower in I980 because of the 

law. On prescriptions for some drugs, consumers saved little 

while for other drugs consumers saved considerably, 50 cents or 

more per prescription. 

Total savings due to the DPS laws is estimated to have been 

$44 to $80 million in I980 (in 1980 dollars.) Given the 

increase in substitution and the expansion of the prescription 

market in both numbers of prescriptions and price increases, 

the savings for I984 are believed to be between $130 and $236 

million per year (in 1984 dollars.) 

W A rough estimate of 1984 retail sales of prescription drugs is $18 

billion, based on an IMS eatimate of the average retail prescription price 

($11.99) multiplied by the total number of prescriptions dispensed. "Top 200 

Drugs for 1984", Pharmacy Timea (April 1985). We have not attempted to refine 

estimates of total retail aales since we want only to suggest that, relative to 

total retail prescription drug sales, the savings from substitution are very small. 

I82 
CHAPTER 9 

CONCLUSION 

Drug product selection laws promoted a modest shift from 

leading brands to lower-priced generics. The total reduction 

in consumer expenditures was $44 to $80 million in 1980 and 

perhaps three times that in 1984. 

In 1980 substitution occurred on 7.3 percent of all prescriptions for 45 leading multi-source drugs on which substitution was legally permissible. 1 This was 5.5 percent of all 

multi-source brand-written prescriptions. Substitution alone 

accounted for slightly more than 4 percentage points of generic 

market share. The drug product selection laws may have 

increased the generic market share indirectly as well; the 

proportion of generically written prescriptions on which a 

generic product was dispensed was perhaps 1-1.5 percentage 

points higher where substitution was permitted, and more 

prescriptions may have been written generically. The average 

retail price of a prescription ($8.1 0 in 1980) was at least 

$.059 lower in 1980 where substitution was permitted, and lower 

by more (between $.059 and $.1 09) if some of the increase in 

generic prescribing is attributed to the effects of the law. 

All our 1980 results are based upon nationwide retail price 

data for 45 leading multi-source drugs, drawn from IMS' 

National Prescription Audit. Because our savings estimates are 

based on retail price data, they take into account the fact 

that while manufacturer-level prices are typically lower for 

generics than for brands, retailer dollar gross margins are 

higher for generics than for brands. 2 Because individual drug 

!/ Prescriptions on which the prescriber prohibited substitution could not 

be excluded from the data. If physicians prohibited substitution on 10 percent 

of the multi-source brand-written prescriptions, the 7.3 percent figure would 

be raised to 8.1 percent of eligible prescriptions.

Y An estimate (inappropriately) based on comparing manufacturers' or wholesale prices for brands and generics would overstate the savings since it would 

fail to take into account the higher retailer margins on generics. 

183 

CHAPTER9 


entities are analyzed separately and then summarized, the 

estimates reflect differences in both substitution rates and 

brand-generic price differentials, and therefore savings, 

across drugs. 

By combining aggregate data with several simplifying 

assumptions 3 we projected our 1980 estimates of consumer 

savings forward to 1984. We estimate that these savings have 

more than doubled in real terms to a range of about $130 to 

$236 million in 1984 dollars. The growth in savings reflects 

an increase in the number of multi-source prescriptions (about 

5 percent), a near doubling of the percentage of brand-written 

prescriptions on which substitutions were made (from 5.1 

percent to 9.3 percent of all brand-written multi-source 

prescriptions, according to IMS), the implementation of drug 

product selection Ia ws in the remaining states, and general 

increases in drug prices (51 percent). We expect savings to 

continue to increase due to the continued spread of the 

acceptability of substitution and the entry of more generic 

products as a result of the 1984 Hatch/Waxman Act. 

The incidence of substitution varies a great deal. In some 

drugs, there was essentially no substitution at all in 1980, 

while in others the rate was over 20 percent of eligible 

prescriptions. Substitution occurred more frequently on 

Medicaid prescriptions than on cash prescriptions and noticeably less frequently on privately insured prescriptions. Chain 

and independent stores, however, did not appear to differ 

significantly in their substitution behavior in 1980. 

One source of variation from state to state was differences 

in the legal provisions of states' drug product selection 

laws. Of course, states with drug product selection laws had 

more substitution than states with antisubstitution laws. 

State formularies played a similar role on a drug-by-drug 

basis. On the four-fifths of all prescriptions for which a 

state formulary did not preclude brand interchange, the 

substitution rate was much higher than on the remaining one­

!/ For the purpose of the eetimate, we assumed that there were no major 

changes in the pattern of substitutions across drugs nor in the response of 

generic prescribing to the laws. We assumed also that brand and generic prices 

rose at the same rate, in terms of both manufacturer-level prices and retail 

margins. 

184 

CONCLUSION 

fifth. Three other provisions were also shown to have affected 

the rate of substitution significantly. First, a prescription 

pad format making it easy for physicians to prohibit substitution lowered substitution; second, a requirement that pharmacists consult or discuss with customers any substitutions 

increased substitution; and third, the presence of a formulary, 

especially a positive formulary, unexpectedly led to less 

substitution, even when substitution was permissible on 

prescriptions for a given drug. Mandatory substitution led to 

higher rates of substitution in some drugs but never as high as 

the mandate might suggest, and moreover did not even show a 

significant systematic effect of increasing substitution across 

the board. Finally, a requirement that consumers be given the 

difference in invoice cost between the prescribed brand and the 

substituted generic was not shown to deter substitution. Even 

the particular provisions which most promote substitution 

failed to push it to high levels. Of course, since the general 

grant of authority to substitute produced relatively weak 

results everywhere, small adjustments within the Ia w were 

unlikely to cause the substitution rate to jump substantially. 

As substitution becomes more common, the consequences of 

differences in detailed provisions may increase. 

Since both consumers and pharmacists have economic incentives that favor substitution, we regard the relative infrequency of substitution as somewhat of a puzzle; for consumers, 

a marked brand-generic differential in retail prices and, for 

pharmacists, a higher dollar gross margin on generics than on 

brands could be expected to encourage substitution. Part of 

the explanation is simply the newness of the institutional 

change. As consumers and health professionals continue to 

garner experience with generics they are likely to find 

substitution increasingly acceptable. Consumers or pharmacists 

appear to believe that the very fact that a prescription is 

written for a brand name, even when the physician has not 

explicitly prohibited substitution, means that the physician 

strongly prefers the branded product for the patient. Our 

inference comes from the strong contrast in 1980 between 

dispensing behavior on brand-written prescriptions eligible for 

substitution and on generically written prescriptions: while 

substitution on brand-written prescriptions was infrequent, 

185 

CHAPTER 9 

generically written prescriptions were almost always filled 

with generics. Most consumers probably do not know whether in 

the prescriber's expert opinion a substitution is acceptable; 

being unsure, they may not feel confident making the medical 

judgment on their own. We hypothesize that if consumers were 

to understand their physicians' considered opinions, they might 

choose substitution more frequently. 

As the market share of generics increases, competitive 

pressures may depress manufacturers' prices, a potential effect 

of substitution and the DPS laws overall which goes beyond 

those measured in this study. While lower prices provide an 

immediate saving for consumers, inroads on the high market 

shares and (manufacturing-level) price premiums for leading 

brands diminish the incentives for research and development of 

new drugs. 4 In the long run these effects could be of greater 

importance than the direct savings from substitution. We 

therefore examine briefly some historical data on changes in 

manufacturers' prices as a guide to the future. 

Dramatic declines in prices characterized the major antibiotics in the 1960s and 1970s; by 1980 manufacturers' prices in 

the four largest antibiotic entities 5 had fallen by threequarters or more in nominal prices -- more in real terms -­

since the 1960s.6 During this time there was a large increase 

in the market share of generics and secondary brands. The 

increase in generic market share predated the introduction of 

DPS laws and was instead a response primarily to an upsurge in 

generic prescribing, now ranging above 50 percent in three of 

these drugs. 7 

if Although by extending the patent life of new drugs the 1984 Hatch/Waxman 

Act provides additional incentive for these activities. 

§_/ Tetracycline, ampicillin, penicillin VK, and erythromycin base. These 

four entities together accounted for 21 percent of all prescriptions in our 45 

multi-source drugs in 1980. 

fll The information on drug price competition in the 1960s and 1970s used in 

this and the following paragraph is taken from Schwartzman (1976, 

pp. 255-292.). 

!/ Generic prescribing rose between 1966 and 1973 from 8.9 percent to 17.4 

percent on erythromycin base prescriptions; from 4.1 percent to 49.7 percent on 

ampicillin prescriptions; from none to 14.2 percent on penicillin VK prescriptions; and from 29.8 percent to 40.7 percent on tetracycline prescriptions 

186 
CONCLUSION 

Can we predict a similar drop in prices due to the DPS Ia ws' 

leading to a substantial increase in the use of generics? It is 

not clear, for three reasons. First, the price decline in 

antibiotics may have been caused by factors other than increases in generic market share. The early price decreases in 

antibiotics occurred in an environment which also included 

strong competition between different chemical compounds used 

for similar therapeutic purposes. There were substantial 

promotional efforts, often mounted in conjunction with entries 

of competing products by other major manufacturers, not just 

small "generic" producers. In fact, even during the patent 

period there were competing brands marketed by other large 

pharmaceutical firms which presumably inhibited the development of a deep-rooted brand loyalty to the first product. 

Additional factors (such as MAC regulations) have also affected 

the course of competition in these and other drugs. Second, 

some major non-antibiotic drugs showed no such dramatic price 

decline by 1977, despite substantial market share incursions by 

generics and secondary brands. Third, in the 10 top-substitution drugs in 1980 -- drugs in which substitution was causing a 

significant increase in the use of lower-price generic products 

-- manufacturers' prices of leading brands did not fall between 

1980 and 1984. Instead, they rose by about the same amount 

(36.2 percent) as the average across all drugs.

8 Thus, whether 

an increase in generic market share in itself can force down 

the manufacturer's price of the leading brand and whether the 

increase in generic market shares in individual drug entities 

brought about by substitution will be enough to accomplish this 

is still an open question. 

Finally, whether substitution is the most useful vehicle for 

lowering the prescription drug prices paid by consumers (and 

the goal itself is controversial) is a question which reaches 

much further than the bounds of this study. We point out two 

(Schwartzman (1976, p. 269}.) In 1980, generically written prescriptions were 

28.4 percent of all erythromycin baae prescriptions; 84.2 percent of ampicillin 

preacriptions; 51.6 percent of ampicillin prescriptions; and 69.6 percent of 

tetracycline prescriptions. 

§./ See footnot e 21 in Chapter 8 (or the methodology. On the other hand, 

prices of generics in the 10 top-substitution drugs fell more (-18.9 percent) 

between 1980 and 1984 than the average across all d rugs (-10.8 percent). 

187 
CHAPTER 9 

other mechanisms. One, generic prescribing, stands out 

in our data. In 1980 generic prescribing accounted for about 

two-thirds of the overall generic market share. Although in 

many drugs substitutions account for most of the generic 

dispensing, in others nearly all prescriptions are written 

generically and thereupon dispensed generically. Moreover, 

there is striking variation from state to state in the incidence of generic prescribing: the range is from 15 to 30 

percent of multi-source prescriptions. In most drugs, generically written prescriptions are almost always filled with 

generics. Since a change in the amount of generic prescribing 

translates almost directly into generic market share, whatever 

determines differences in generic prescribing is responsible 

for a good portion of any differences in average retail 

prescription drug prices. A second price-reducing mechanism is 

the often over-looked arena of competition among retailers,9 of 

considerable importance in that retail gross margins constituted 40 percent, on average, of the retail prices of the 45 

leading multi-source drugs we studied. The recent trend toward 

more aggressive retailer advertising of generic lines is a 

development that tends to invigorate inter-pharmacy competition. So too, would the introduction of consumer advertising 

by drug manufacturers, whatever its other implications. 10 If 

retail competition is sharpened, the search for competitive 

advantage may lead to greater efficiency and lower consumer 

pnces. 

We conclude that drug product selection laws have reduced 

retail prescription prices by increasing the market share of 

generics, but not at the scale hoped for by some advocates of 

the laws. While differences among state laws did affect rates 

of substitution in 1980, the more vivid result is the very 

modest amount of substitution occurring in most states and most 

drugs. In the past four years, however, substitution has 

become much more frequent, and there is good reason to expect 

it to play an even larger role in prescription drug markets in 

the future. 

2/ For a general discussion of the relationships between retailers' and 

manufacturers' margins, including evidence that they tend to be inversely correlated, see Steiner (1973, 1978, 1984). 

10/ See Masson and Rubin (1985}. 

188 
APPENDIX Al 

THE PROVISIONS OF THE 


STATE LAWS 


Table Al-l shows for each state the individual provisions of 

its drug product selection law. Because our data are for 

1980, we concentrated on the coding of the law and formulary in 

effect in 1980. The primary provisions used in the analysis 

are shown in Columns 2 through 7. Columns 8 through 12 contain 

information on other regulations which may less directly have 

affected su bstitu tion decisions. (Because these provisions 

were not as central to our analysis, we were not as thorough in 

reviewing our information. Our tabulation should therefore not 

be considered definitive.) Column 13 shows which states 

regulated the choice of products to dispense on generically 

written prescriptions. In addition, we show pre-1980 provisions and some, but not all, post-1980 amendments. We do not 

show post-1980 changes in the formularies. The recommendations 

of the FTC/FDA Model Act are shown at the bottom of the table. 

In the text following the table, we provide a key to the 

classifications of the provisions, with explanations where 

necessary. 

We recognize that a statute may be open to several interpretations. The refore, our classification of the provisions 

may appear to have greater precision than is true. Even the 

employees of state Boards of Pharmacy had some difficulties 

specifying the exact meaning of some provisions, since in many 

instances there have been no challenges which would have led to 

legal clarification. While we have been diligent in our 

attempt to understand the statutes and have sought interpretations from every state's Board of Pharmacy (or equivalent 

agency), errors in classification (or differences in judgment) 

may remain. 

189 

APPENDIX Al 

TABLE Al-l" 


Taxonomy of State Regulations, 19801 


(1) (2) (3) (4) (5) (6) 

Man- Formulary 

datory Type and PharEffec­ or Per- Format Extensive­ macist Cost 

tive missive of Dr's ness in Lia- PassState Date Substit. 

Mo/Day/Yr 

Rx Pad 1980 

a b2 

bility through 

Alabama 1/1/80 PERM N2 X 100% HI NO 

Alaska3 

Arizona 1/1/79 PERM P2 + 36% LO-STAT NO 

(Amnd) 8/1/794 

Arkansas 8/14/75 PERM Pl-a 84% LO-FORM UC-3 

Calif. 5/1/76 PERM Nl-b x5 100% LO-STAT YES-3 

(Amnd) 1/1/80 

(Amnd) 1/1/81 N0-3 

Colorado 4/26/76 PERM Nl-b 81% LO-STAT YES-3 

Connect. 10/1/76 PERM Pl-b X 100% HI YES-3 

(Amnd) 12/7/77 

Delaware 12/20/76 PERM N2 89% HI YES 

(Amnd) 7/1/81 

D.C. 9/10/76 PERM Pl-a + 37% LO-STAT N0-3 

Florida 5/31/74 PERM Nl-a TT 91%6 HI NO 

(Amnd) 6/3/76 MAND-LO Pl-b LO-STAT uc 

Georgia 1/1/78 PERM-LO A2 X 100% HI uc 

• See footnotes at end of table. 

190 

THE PROVISIONS OF THE STATE LAWS 

TABLE Al-l--Continued 

a 

(7) 

Notification 

b c d e f g 

(8) 

Advertising 

(9) 

Prescriber 

Liability 

(10) 

Pharmacy 

Ownership 

a b 

(11) 

Price 

Posting 

and 

Signs 

a b 

(12) 

Mail 

Order 

(13) 

Generically 

Written 

Rx's 

- - - - - - - HI + ANY 

+-+-+-­ LO + ANY 

+ - -

-

-

-

+ 

- + -

HI 

LO + 

+ 

+ 

ANY 

ANY 

+----+­

+ + + - + 

+-+-+-­

+ HI 

HI 

HI 

+ 

+ 

+ 

ANY 

ANY 

ANY 

- + + -

+ 

LO 

HI 

LO 

HI 

+ + 

+ 

+ 

LO 

ANY 

ANY 

LO 

191 

APPENDIX Al 

TABLE Al-l, continued 

(1) (2) (3) (4) (5) (6) 

Man- Formulary 

datory Type and PharEffec­ or Per- Format Extensive­ macist Cost 

tive missive of Dr's ness in Lia- PassState Date Substit. 

Mo/Day/Yr 

Rx Pad 1980 

a b2 

bility through 

Hawaii7 

Idaho 7/1/78 PERM N2 X 100% HI YES 

Illinois 10/1/77 PERM N2 + 42% LO-STAT NO 

(Amnd) 8/19/81 Nl-a 

Indiana 7/1/84 

Iowa 7/1/76 PERM Nl-I X 100% HI YES 

Kansas 7/1/78 PERM Nl,2 81% HI uc 

Kentucky 6/15/72 MAND-LO Pl-a + 46%8 MAND-FORM NO 

Louisiana 9/14/80 PERM NN - 0/81% HI uc 

Maine 1/1/76 PERM Nl-a X 100% HI NO 

(Amnd) 1/1/79 

Maryland 5/1/73 PERM Nl-b + LO-FORM YES 

(Amnd) 1/1/78 Pl-c LO-FORM YES-3 

(Amnd) 1/1/79 -I+ 75% N0-3 

Mass. 1/20/77 MAND N2 + 41% MAND-FORM NO 

Michigan 4/1/75 PERM Pl-a X 100% HI YES-3 

Minnesota 8/1/75 PERM Pl-a X 100% HI YES 

Miss. 7/1/79 PERM P2 94% LO-STAT NO 

Missouri 1/1/79 PERM A2 84% LO-STAT NO 

Montana 4/18/77 PERM Nl-b X 100% LO-STAT YES 

Nebraska 9/2/77 PERM Pl-a 81% LO-STAT NO 

192 

THE PROVISIONS OF THE STATE LAWS 

TABLE Al-l--Continued 

(7) (8) (9) (10) ( 11) (12) (13) 

Prescri- Price Genericber Pharmacy Posting ally 

Adver- Lia- Owner- and Mail Written 

Notification tising bility ship Signs Order Rx's 

a b c d e f g a b a b 

+ - - - - HI + + ANY 

+ - + LO + ANY 

ANY 

- + + - + HI + + ANY 

- - - HI ANY 

+ - - - - + HI + ANY 

- - - - - + - + HI + ANY 

+ - - + - HI + ANY 

+ - -

- - - - - + + HI + ANY 

+ + 

+ - - - - HI ANY 

+ + - + HI + + + + ANY 

+ - - + - HI ANY 

+ - ­ - + LO ANY 

+ HI ANY 

+ - LO + + LO 

LO + LO 

193 

APPENDIX Al 

TABLE Al-l, Continued 

(1) (2) (3) (4) (5) (6) 

Man- Formulary 

datory Type and PharEffec­ or Per- Format Extensive­ macist Cost 

tive missive of Dr's ness in Lia- PassState Date Substit. 

Mo/Day/Yr 

Rx Pad 1980 

a b2 

bility through 

Nevada 1/17/79 PERM A2 + 49% LO-STAT NO 

(Amnd) 10/1/79 

New Hamp. 8/27/73 PERM Al-b + 98% LO-STAT NO 

(Amnd) 7/1/81 Pl-b 

New Jer. 9/29/77 MAND Nl-a M l00%9 MAND-FORM uc 

New Mex. 5/14/76 PERM Pl-b + 29% LO-FORM NO 

(Amnd) 1/1/82 

New York 4/1/78 MAND-ELSE P2 M 100%9 MAND-FORM NO 

N.C. 1/1/80 PERM A2 X 100% LO-STAT NO 

N.D. 7/1/79 PERM A2 X 100% LO-STAT NO 

(Amnd) 1/1/80 

Ohio 1/1/78 PERM Pl-a TT 81%10 HI YES-3 

Oklahoma 4/1/61 PERM NN X 100% HI NO 

Oregon 9/12/75 PERM Pl-a X 100% LO-STAT NO 

Penn. 6/25/77 MAND P2 + 49% LO-STAT UC-3 

R.I. 7/1/76 PERM P2 + 43% LO-STAT NO 

(Amnd) 1/1/79 MAND-ELSE YES 

(Amnd) 7/1/81 

S.C. 1/1/79 PERM P2 X 100% HI NO 

194 

THE PROVISIONS OF THE STATE LAWS 

TABLE Al-l--Continued 

(7) (8) (9) (10) (11) (12) (13) 

Prescri- Price Genericber Pharmacy Posting ally 

Adver- Lia- Owner- and Mail Written 

Notification tising bility ship Signs Order Rx's 

a b c d e f g a b a b 

+ - + - + - - HI + + + ANY 

-----+­ HI + ANY 

-

+ 

+ 

-

- + - + HI 

HI 

+ ANY 

ANY 

+ -

+ -

+ -

+ + 

-

+ 

+ 

+ 

-

+ 

-

-

-

+ 

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

+ 

+ 

HI 

LO 

HI 

LO 

HI 

LO 

LO 

LO + 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

ANY 

ANY 

ANY 

ANY 

ANY 

LO 

ANY 

ANY 

+-++--­ + HI ANY 

195 

APPENDIX Al 

TABLE Al-l, Continued 

(1) (2) (3) (4) (5) (6) 

Man- Formulary 

datory Type and PharEffec­ or Per- Format Extensive­ macist Cost 

tive missive of Dr's ness in Lia- PassState Date Substit. 

Mo/Day/Yr 

Rx Pad 1980 

a b2 bility through 

S.D. 7/1/78 PERM A2 X 100% HI NO 

Tenn. 6/1/77 PERM N2 + 29% LO-STAT YES 

Texas 1/1/82 PERM N2 0 LO-STAT NO 

Utah 5/10/77 PERM Nl-b 84% LO-FORM YES 

Vermont 2/23/78 MAND-LO Pl-b + 49% MAND-FORM uc 

Virginia 7/1/76 PERM Al-a + 47% LO-FORM YES 

(Amnd) 7/1/77 

(Amnd) 1/1/79 A2 

(Amnd) 7/1/81 NO 

Wash. 9/19/77 MAND A2 X 100% LO-STAT YES 

W. Va 7/1/78 MAND-LO P2 86% LO-STAT YES 

Wisconsin 3/20/76 PERM Pl-a + 37% LO-FORM YES 

Wyoming 7/1/79 PERM A2 81% LO-STAT uc 

FTC/FDA Model PERM Pl-b + 49% LO-STAT NO

(Optional) 

196 

THE PROVISIONS OF THE STATE LAWS 

TABLE Al-l--Continued 

a 

(7) 

Notification 

b c d e f g 

(8) 

Advertising 

(9) 

Prescriber 

Liability 

(10) 

Pharmacy 

Ownership 

a b 

( 11) 

Price 

Posting 

and 

Signs 

a b 

( 12) 

Mail 

Order 

(13) 

Generically 

Written 

Rx's 

- - - HI ANY 

- - - - - - - HI + ANY 

+ + - - + LO + ANY 

- + - LO ANY 

+ - - + - - ­ HI + + + LO 

+ - ­ - - - ­ HI + ANY 

+ 

LO 

- + - - - LO + ANY 

+ - + LO + + + LO 

- - - - + - - HI + + LO-ELSE 

- - - HI LO 

- + ­

197 

APPENDIX Al 

Footnotes to Table Al-l 

1/ Some amendments after 1980 are shown but not necessarily all. 

l/ Percentage of prescriptions, for 45 leading multi-source drugs, on which 

substitution was permitted in 1980. 

V Laws for Alaska and Hawaii were not researched because the IMS data used 

do not include those two states. 

i/ Where an amendment did not change the code, the column is left blank. 

(Details of other provisions or other aspects of the law may have been changed.) 

§./ California nominally has a negative formulary but there were not drugs 

listed on it. 

§../ Florida and Ohio have negative state formularies but require each 

pharmacy to establish its own positive formulary. The percentages given are 

for the state formulary. 

While the provision for pharmacy formularies would not appear to constrain 

pharmacists' choices, a study done in Florida showed that the application of 

pharmacy formularies removed from possible substitution an additional 25 

percent of all prescriptions written. Vuturo, Krischer and McCormick (1980). 

Z/ See footnote 3. 

V Kentucky's law is ambiguous as to whether it controls all substitution or 

only mandatory substitution. We use the interpretation provided by state 

officials: that it controls all substitution. 

2,/ New Jersey and New York have positive formularies listing drugs for which 

substitution is mandatory; substitution is permitted, however, in all other 

drugs, making these states in one sense "no formulary" states; the extensiveness of permissible substitution is therefore given as 100 percent. (New 

Jersey mandates substitution on about 39 percent of multi-source prescriptions 

and New York 76 percent.) 

In the other states with mandatory substitution, the formulary controls all 

substitution, not just mandatory substitution. 

lQ/ See footnote 6. 

SOURCE: Compiled by the Bureau of Economics, Federal Trade Commission. 

198 

THE PROVISIONS OF THE STATE LAWS 


COLUMN 1: EFFECTIVE DATE OF DPS STATUTE 

OR AMENDMENT 

The first column in Table A 1-1 gives the date on which the 

state's initial DPS law, or subsequent amendment, became 

effective. The effective date is typically some months after 

the date it became law. However, not every provision of the 

law may have become effective on that date. For instance, 

physicians often were given a grace period to change over to a 

new type of prescription pad. Also, it sometimes takes months 

before a formulary is published. And, of course, actual 

conduct adapts gradually after the introduction of the law, 

especially when the statute mandates a radical departure from 

previous behavior. 

COLUMN 2: MANDATORY OR PERMISSIVE 

SUBSTITUTION ("MAND") 

PERM: 	 The pharmacist is permitted to substitute, once the physician has allowed 

it. 

PER/1,1-LO: 	 PERM condition plus the pharmacist 

must use the least expensive generic 

in stock or one with a below-average 

wholesale cost when substituting. 

MAND: 	 The pharmacist must substitute, once 

the physician has allowed, if the 

pharmacy has a lower-price genenc 

equivalent in stock. 

MAND-LO: 	 /lfAND condition plus the pharmacist 

must use the least expensive generic in 

stock or one with a below-average 

wholesale cost when substituting. 

199 

APPENDIX Al 

MAND-ELSE: 	 The pharmacist must substitute, once 

the physician has allowed it but if a 

lower-price generic equivalent is not 

in stock may not fill prescription. 

Most of the states left the su bsti tu tion decision to the 

pharmacist (and consumer) (PERM and PERM-LO). 

There are two basic classifications for states requiring 

that, in the absence of a prohibition by the prescribing physician, a pharmacist must substitute an equivalent, less expensive drug product for the prescribed brand. In some states the 

pharmacist had to substitute a less expensive product only if 

there was one in inventory (MAND). Other states not only 

mandated substitution but also required that if the pharmacy 

did not stock a less expensive generic equivalent (New York) or 

a less than average wholesale cost equivalent (Wisconsin) the 

pharmacist could not fill the prescription at all (MAND-ELSE). 

Some states, such as Iowa, made substitution mandatory if 

public funds were used to pay for the prescription. We believe 

this requirement to be relatively common; we have not coded 

it. On MAC drugs the reimbursement formula provides a 

powerful incentive for the pharmacist to substitute a below-MAC 

drug. 

States which both mandated substitution and had a positive 

formulary used the formulary in two different ways. Most 

required substitution for drugs on the formulary and forbade 

all other substitution. Two states (New Jersey and New 

York) required substitution for formulary drugs but left 

substitution to the discretion of the pharmacist for all other 

drugs. 

In 4 states (F~orida, Georgia, Kentucky and West Virginia) 

the pharmacist when substituting was required to dispense the 

least expensive gentric equivalent in stock. This is designated PER/11-LO or AfAND-LO on the table. 

The variable ,\1AND in the regressions is coded 1 for the 

MAND codes and 0 for the PERAl codes. 

200 

THE PROVISIONS OF THE STATE LAWS 

COLUMN 3: FORMAT OF PHYSICIAN'S PRESCRIPTION 

PAD ("RXPRO." "RXANTI," AND "RXNEUT") 

RXPRO formats: 

PI-a: 

PI-b: 

P2: 

RXNEUT formats: 

NI-a: 

NI-b: 

NI-l: 

Single signature line. A handwritten abbreviation is required 

to prevent su bsti tu tion. 

Single signature line. A handwritten designation of two or more 

words is required to prevent 

su bsti tu tion. 

Two signature lines. The bottom 

right line denotes "substitution 

permitted". 

Single signature line. A handwritten designation is required to 

prevent substitution. A preprinted line(s) or box(es) ts 

(are) required. 

Single signature line. A handwritten designation is required to 

prevent substitution. A preprinted line(s) or box(es) IS 

(are) permitted. 

Single signature line with physician's choice as to presumption 

(substitution permitted or 

prohibited.) (Iowa only.) 

201 

APPENDIX Al 

N2: 	 Two signature lines. Physician's 

option as to whether to designate 

right or left line as substitution 

permitted. 

N1.2: 	 Physician's choice of single or 

double signature line. (Kansas 

only.) 

NN: 	 No regulations dealing with 

format, with respect to substitution. 

RXANTI 	formats: 

Al-a: 	 Single signature line. A handwritten abbreviation is required 

to permit su bsti tu tion. (No 

longer used; formerly used in 

Virginia.) 

Al-b: 	 Single signature line. A handwritten designation of two or more 

words is required to permit 

substitution. (In New Hampshire 

only, formerly; replaced in 1981.) 

A2 : 	 Two signature lines. The bottom 

left line denotes "substitution 

permitted". 

We categorized physicians' prescription pad formats as to 

the likelihood that the physician would permit or, alternatively, prohibit substitution. Accordingly, they are labeled 

RXPRO, RXNEUT, and RXANTI. Table A4-4 in Appendix A4 

indicates that while most of our a priori judgments about 

likely effects appear to be borne out, some were not. 

The DPS literature has generally characterized physicians' 

prescription pads as "single-" or "double- [signature] line". 

In the first case, the doctor merely signs the prescription 

202 

THE PROVISIONS OF THE STATE LAWS 


and, in the absence of any designation to the contrary on the 

prescription, substitution is permitted. (Prior to 1981, 

one state's single-line pad carried the opposite presumption, 

that without special instructions substitution was prohibited.) 

With the second format, one signature line bears a printed 

annotation that substitution is permitted, the other line that 

substitution is not permitted, and the physician designates a 

preference by choosing which line to sign. We chose an 

alternate categorization in order to summarize the effects of 

the many variants mandated or permitted by the state laws and 

to incorporate our belief that some double-line formats 

are much less conducive to physician prohibition of substitution than others. 

Some states left to the physician's option features which 

make it either difficult or easy to permit or prohibit substitution. Without knowing the proportions of physicians in a 

state who exercise each option, we cannot know the overall 

effect of the prescription pads chosen. Several such states 

are assigned to the RXNEUT category. 

A. RXPRO FORMATS 

Two of the three prescription pad formats in the RXPRO 

category are single-line pads. On these, either a handwritten 

abbreviation (PI-a) or two or more words (Pl-b) are necessary 

for the physician to prohibit substitution. Examples of the 

first are the initials "DAW" (meaning "Dispense as Written") in 

Minnesota and "B.N." (meaning "Brand Necessary") in the 

District of Columbia. The second type of prescription pad 

requires that the phrase be written out in full. States with 

either of these two formats did not allow a preprinted box 

to be checked or a line to be initialed for the purpose of 

preventing substitution. One of the two-signature line pads is 

classified as RXPRO. On P2 pads, the "substitution permitted" 

signature line is on the right, the place where physicians are 

accustomed to signing the prescription. It has been suggested 

that physicians would continue to sign in that location simply 

out of habit. Therefore, if the "substitution permitted" 

203 

APPENDIX Al 

signature line is in the bottom right corner, most prescnptions would allow substitution, while if the "substitution 

prohibited" line is there, more prescriptions would disallow 

substitution. The latter formats are classified as RXANTI. 

B. RXANTJ FORMATS 

The "most anti" RXANTI pad is a single-line format with the 

presumption that substitution is prohibited unless the physician has written out a statement that substitution is permitted 

(Al-a). The New Hampshire law prior to its amendment in 1981 

made it especially difficult for the physician desiring to 

permit substitution by specifying that the following phrase had 

to be written out: "or its generic equivalent drug listed in 

the New Hampshire Drug Formulary." Somewhat less of a burden 

is the requirement that an abbreviation be written on a 

single-line prescription pad if su bstitu tion is to be permitted 

(Al-b). 

The third RXANT! format is the two-line pad with the 

"su bsti tu tion permitted" line on the left, leaving the "su bstitution prohibited" line in the location where physicians may be 

accustomed to signing automatically (A2). 

C. RXNEUT FORMATS 

Finally, there are six categories of RXNEUT prescription 

pad formats, several of which leave major elements of the 

design to the physician's own option. Therefore, both the 

physician who generally opposes substitution and the physician 

who generally allows it can have a prescription form printed in 

such a way that it is simple to designate the usual decision. 

Several states required two signature lines but left to the 

physician the decision of which instruction was to appear on 

the right (N2). We have included with the N2 pads theMassachusetts format that required the "substitution permitted" 

line to be on the lower left if a left-right format was used 

but also permitted an alternative arrangement of both signature 

lines in the lower right corner whereby the "substitution 

permitted" line had to be printed above the other line. 

204 
THE PROVISIONS OF THE STATE LAWS 


Some states with single-line pads and the presumption that 

substitution was permitted unless the physician indicated 

otherwise required the form to contain preprinted line(s) or 

box(es) which the physician could check or initial, thus 

facilitating a physician's designation for overriding the 

presumption (N 1-a). 

Another category, N 1-b, also contains single-line formats 

where the presumption is that substitution is permitted, but 

the states with this format permitted (but did not mandate) the 

pre-printing of a box to be initialed by hand to prohibit 

substitution. Physicians who elect to print such a box may be 

expected to forbid substitution more frequently than in the 

absence of the preprinted reminder; even when the box is not 

initialed, their selection of that format may suggest a disapproval of substitution. These formats are considered "neutral" 

because the convenience with which most prescribers can 

prohibit substitution is determined by the formats they 

choose. In a state permitting such a choice, the proportion of 

"pro" and "anti" pads depends on the proportions of physicians 

with predispositions for or against substitution in general. 

Two other regulations, each unique to a single state, also 

allowed physicians to choose a format in accord with a preformed opinion about the general desirability of substitution. 

The Iowa statute (N 1-I) was one such example, permitting a 

physician to have the form preprinted with either the words 

"su bstitu tion permitted" or "do not su bstitu te," depending upon 

usual prescribing preferences. Kansas allowed physicians to 

use either a single-line or a double-line form (Nl,2). If a 

single-line form was used, substitution was permitted unless 

"dispense as written" was handwritten on the prescription. On 

a double-line form, the "first line" had to be labeled "dispense as written." 

Finally, the two states with no regulations as to prescription pad format (NN) obviously allowed physicians to use 

whatever design made their usual decisions easiest to communicate. 

205 

APPENDIX Al 

D. TELEPHONE PRESCRIPTIONS 

Our coding classifies states according to their statutory 

provisions for written prescriptions. However, more than a 

quarter of all prescriptions are conveyed by telephone. 1 In 

general, we would expect that when the physician (or someone in 

the physician's office) speaks with the pharmacist there is 

established a clear understanding of what will be dispensed. 

That is, the pharmacist can inquire as to whether a specified 

version of the drug will be acceptable to the physician. If 

so, the prescription as recorded will rna tch the brand dispensed 

and no substitution need be recorded. 

However, in some states telephoned prescriptions were 

treated by the law differently from written prescriptions. In 

Louisiana and New York, for example, while the presumption on 

written prescriptions was that substitution was permitted, the 

presumption was the opposite for telephoned prescriptions: 

unless the physician explicitly stated that substitution was 

permitted, the pharmacist could not substitute. To the extent 

that pharmacists typically mention a proposed substitution 

anyway, in the course of receiving a telephoned prescription, 

such a I a w would not have much force. 

COLUMN 4: TYPE AND EXTENSIVENESS OF FORMULARY 

IN 1980 (''POS," "NEG," AND "NO FORM") 

4(a) Type: 

+ : 	 Positive formulary. Substitution is 

permitted if the drug is on the 

formulary. (Positive formularies 

used to regulate mandatory substitution 

are coded M, rather than +.) 

!/ See footnote 9 in Chapter 6. 

206 
THE PROVISIONS OF THE STATE LAWS 

!vf: 	 Mandatory formulary. A positive 

formulary controlling mandatory 

substitution only. Substitution IS 

permitted (but not mandated) on 

non-formulary drugs. 

Negative formulary. Substitution is 

permitted if drug IS not on the 

formulary. 

-/+ : 	 Negative formulary at beginning of 

1980, changed to positive formulary in 

mid-1980. 

TT: 	 Two-tier. Negative state formulary and 

positive individual pharmacy formularies. 

X: 	 No formulary. Pharmacist makes 

independent judgments regarding 

substitution. 

4(b) 	 Extensiveness: The extensiveness of the opportunity to 	substitute is measured by the proportion of prescriptions in 45 leading multisource drugs on which 

substitution was permitted (by formulary) in 1980. The 

computation was based upon state formularies and information obtained from state Boards of Pharmacy. Each drug (in 

the specified dosage form) was coded, by state and month, 

as to whether substitution was permitted or not. Both 

the proportion of the year during which substitution 

was permitted on the drug in the state (since some formularies were changed during 1980) and each drug's share of 

the total number of prescriptions dispensed in the U.S. in 

1980 (for the 45 drugs studied) were taken into account. 

(Data for the 45 drugs were weighted by the scheme 

discussed in Appendix A6, Section V.) 

207 

APPENDIX Al 

Because a number of state formularies have been amended since 

1980, the table should not be read to reflect the current 

extensiveness of the state formularies. 

In the regressions, POS is coded 1 for + and M states. NEG 

is coded 1 for - and IT states. Both POS and NEG are coded 0 

for X states. 

COLUMN 5: PHARMACIST LIABILITY ("L!AB") 

HI: 	 By engaging in substitution, a pharma· 

cist is exposed to a patential increase 

in professional liability. 

LO-STAT: 	 The statute expressly protects a 

pharmacist from increased liability 

when substituting. 

LO-FORM: 	 The pharmacist is impliedly protected 

when selecting from drug products on a 

positive formulary (or, by interpretation in a few states, by avoiding those 

on a negative formulary.) 

MAND-FORM: 	 The pharmacist is impliedly protected 

when substituting as required in accord 

with a formulary established for the 

purpose of regulating the mandatory 

substitution. 

Some states have decided that a pharmacist should not face 

a more stringent standard of liability when substituting than 

when filling a generically written prescription, when by 

definition one or another brand must be selected. There 

are three forms of protection against added liability: first, 

an express statutory provision (LO-STAn; second, an implicit 

protection as long as substitutions are made in accord with the 

state's formulary (LO-FORM); and third, an enhanced form of 

the second, in states where substitution is mandated according 

to a formulary. (In some mandatory states, the statute 

208 
THE PROVISIONS OF THE STATE LAWS 

itself provided protection.) In a few (non-mandatory) states 

with a formulary, the Board of Pharmacy did not confirm that 

the formulary provided such protection. These few states are 

coded as having no protection against added liability (H!), as 

are no-formulary states which had no express statutory language 

to limit liability on substitutions. 

The variable used in the regressions, LIAB, is coded 1 if 

there was statutory protection (LO-STAD and 0 otherwise. 

Some state officials expressed the view that no extra 

liability is incurred when a pharmacist substitutes within a 

generic class as long as the drug dispensed has an FDA approval. We know of no judicial test of this view. Were it true, 

the state-to-state differences in liability provisions would 

have little effect. 

COLUMN 6: COST PASS-THROUGH ("PASS") 

NO: 	 A substituted product need only have a 

retail price lower than the prescribed 

brand's. 

UC: 	 NO provision plus the restriction that 

the su bsti tu ted product be sold at a 

price no higher than the usual and 

customary retail price. 

YES: 	 The pharmacist must pass through into 

the substituted item's retail price 

most or all of the difference between 

its wholesale cost and that of the 

prescribed i tern. 

...-3: 	 The state statute or regulation 

includes a provisiOn that 3rd party 

payers must receive the same savings as 

uninsured private consumers. 

209 

APPENDIX Al 

If a state did not regulate the retail price or the pharmacist's margin on substituted products, the state was coded NO. 

The same code is used when the state required that the retail 

price of the substituted product simply be less than the price 

of the originally prescribed product. This requirement is 

unlikely to be restrictive, for pharmacists nearly always 

charge somewhat less for generic equivalents of branded drugs 

even when they are not legally obliged to do so. A different 

code (UC) is used if the state required not only that the 

su bstitu ted product be sold at a price lower than the one 

prescribed but also that its price when substituted not be 

higher than its usual and customary price, that is, the price 

charged when it was dispensed to fill a generically written 

prescription or a prescription on which the i tern itself was 

prescribed. 

The code YES is used when the state had a pass-through 

requirement, as described in Chapter 5. Most of these states 

required that the full cost savings be passed through. The 

state of Washington is an exception in that pharmacists were 

required to pass through only 60 percent of the difference 

between the wholesale prices of the prescribed and the substituted products. Our conversations with state officials 

indicated that there is wide variation in the enforcement of 

pass-through provisions across states, but we did not incorporate these differences in our coding scheme. 

The addition of a 3 to any of the codes in Column 6 

indicates that the state statute or regulation specifically 

provides that third-party payers (both private and governmen tal) receive the same savings as cash-paying customers. 

The variable PASS used in the regressions is coded 0 (i.e., 

no pass-through requirement) for either of the two codes NO and 

UC, and is coded 1 only when column 6 is coded YES. 

COLUMN 7: NOTIFICATION ("INFO") 

7(a): + Extra labeling is required. 

7(b): + Extra recordkeeping is required. 

210 

THE PROVISIONS OF THE STATE LAWS 

7(c): + 	 The pharmacist is required to make an oral 

comparative price disclosure. 

7(d): + 	 The pharmacist must receive ad vance consent 

or orally notify consumer of the right to 

refuse. 

7(e): + 	 The pharmacist must orally inform the 

consumer in ad vance. 

7(f): + 	 The pharmacist must orally notify the 

consumer that a substitution was made. 

7(g): + Notification of the physician is required. 

The coding of the notification provisions was particularly 

difficult. Even after discussions with Boards of Pharmacy 

officials, it was not always clear what the state statutes or 

regulations meant, for example, whether the information had to 

be given when the prescription was presented or after it was 

filled (when it was picked up.) Moreover, in order to designate a provision as requiring an extra procedure, we had to 

understand what types of information pharmacists in a state 

were required to provide on non-substituted prescriptions. 

We have classified requirements for providing extra 

information when substituting into seven types. 

1. Extra Labeling (Column 7(a)). 

For many years all states have required the pharmacy to 

label a prescription drug container with certain information. 

Typically the label includes the prescriber's name, the name of 

the pharmacy, the name of the product dispensed, the patient's 

name, dosage instructions, and (more recently and not necessarily by requirement) drug and food interaction warnings. 

Pharmacists may not routinely indicate on the label that a 

substitution has taken place, nor generally record on the label 

the name of the manufacturer of a generic drug they are 

dispensing. 

211 

APPF.NDIX Al 

Extra labeling requirements when a substitution is made are 

of several sorts. For example, Wisconsin specified that both 

the generic name and the manufacturer's name of the dispensed 

product be listed, and the name of the prescribed brand as 

well. In Vermont pharmacists had to note on the label that a 

substitution was made. Missouri required that the pharmacist 

print the product code of the substituted product on the label. 

2. Extra Recordkeeping (Column 7(b)). 

Similarly, every state has some form of recordkeeping 

requirement that antedates its drug product selection statute. 

Even in the absence of statutory provisions, good pharmacy 

practice obliges the pharmacist, either on the original 

prescription or elsewhere, to record the name of the brand 

prescribed and the name and manufacturer of the brand dispensed. Provisions which do no more than require that the 

foregoing procedures be followed when the pharmacist substitutes are not considered to be extra recordkeeping requirements. But certain jurisdictions required that additional (and 

sometimes redundant) information be recorded, for instance, a 

separate notation that a substitution was made. 

3. Disclosures to the Consumer (Column 7(c)- (/)). 

There are several variants on what a pharmacist is required 

to say to a consumer whenever a substitution is being made. 

The most extensive notification is the requirement to 

inform the customer, in advance, of the difference in retail 

price between the prescribed branded product and the generic 

equivalent that the pharmacist is preparing to substitute 

(Column 7(c)). This provision is likely to be the most costly 

to the pharmacy of any of the information provisions since 

calculating and explaining the price comparison is time 

consuming. On the other hand, the information provided is 

arguably the most useful to the consumer. 

In some states the pharmacist had to, before filling the 

prescription, notify the customer of the right to refuse a 

substitution or get consent for the substitution (7(d)). 

Although not all statutes coded with a check in Column 7(d) 

212 

THE PROVISIONS OF THE STATE LAWS 


were explict about "in advance", we were told that these 

conversations were held in advance because it would be too 

costly for the pharmacist to risk a refusal after already 

incurring the expense of filling the bottle and labeling the 

prescription. 

A similar but slightly weaker requirement is to inform the 

customer, in advance, that substitution will occur but without 

necessarily acquiring the customer's explicit concurrence 

(Column 7(e)). States with this requirement include those that 

mandated oral price disclosure (see discussion of 7(c)) 

but without requiring consent or notification of right to 

refuse. 

A yet weaker version (Column 7(f)) is the requirement that 

the pharmacist orally inform the:·customer at the time the 

prescription is picked up that a substitution has been made. 

4. Notification of the Physician (Column 7(g)). 

No state any longer requires the pharmacist to notify the 

prescribing physician that a substitution has taken place. 

5. Definition of the INFO Variable in the Regressions 

The definition of the variable INFO used in the regressions 

was coded 1 if any one of the following four provisions was in 

effect: 

(7(c)), the pharmacist was required to make a comparative 

price disclosure, OR 

(7( d)), the pharmacist was required to receive advance 

consent or to notify the consumer of the right to refuse 

substitution, OR 

(7( e)), the pharmacist was required to inform the consumer 

orally in advance, OR 

(7(f)), the pharmacist was required to notify the consumer 

whenever a substitution was made. 

213 

APPENDIX Al 

Column 8: ADVERTISING RESTRICTIONS 

+ : 	 There are significant restrictions on 

the pharmacy's ability freely to 

advertise prices. 

While retail advertising of prescription drugs is permitted 

in all states, some states impose some restrictions. Restrictions on advertising are varied. For example, New York and New 

Jersey do not permit the use of a coupon with an amount off the 

regular price of a drug. Colorado requires that the product's 

generic name be displayed in any ad that contains the product's 

brand name. Ohio requires that information concerning the 

drug's usage and counterindications be published in the ad. 

Louisiana had not repealed its provisions on price advertising, 

and, although it was not enforcing its statute, a Board of 

Pharmacy official advised us that druggists had hesitated to 

advertise prescription drug prices. Some types of regulations 

may affect the profitability and likelihood of substitution 

more than others. 

COLUMN 9: PRESCRIBER LIABILITY 

HI: 	 The statute exposes the physician to a 

potential increase in professional 

liability when the physician permits 

substitution. 

LO: 	 The statute expressly protects the 

physician from increased liability when 

the physician permits substitution. 

214 

THE PROVISIONS OF THE STATE LAWS 


.......· 


COLUMN . 10: PHAR!vfACY OWNERSHIP 

10(a): + 

JO{b ) : + 

There are regulations prohibiting 

physicians from having a controlling 

ownership in a pharmacy. 

Statutory restrictions bar ownership of 

drug stores by partnerships or corporations unless a mmtmum specified 

percent of the interest or stock 1s 

i 

I 

I 

held by registered pharmacists. 

Included among states which had some provision restricting 

a physician from acquiring a controlling interest in a pharmacy 

(a + in Column 10(a)) are states which do not have a specific 

provision regarding prescription drugs, physicians and pharmacists but which do have a general conflict of interest law 

enforced by the attorney general's office. It has been 

suggested that physician ownership of a pharmacy sets up a 

potential conflict of interest. This is particularly evident 

if the physician can direct patients to a pharmacy located 

in the same building as the physician's office. 

The second type of restriction on pharmacist ownership (a + 

in Column 10( b ) ) apparently still exists in only two states, 

North Dakota and Michigan, the North Dakota statute being far 

more restrictive. Laws in these two states bar ownership of a 

drugstore by a partnership or corporation unless a certain 

percentage is held by pharmacists. These statutes could 

clearly interfere with entry of chain outlets. 

COLU!v!N 11: PRICE POSTING REQUIREMENTS 

11( a ) : + 	 The pharmacy must post prices of top-selling 

(100 or so) drugs. 

215 
APPENDIX Al 

11( b ): + 	 The pharmacy must post general information 

about the availability of generic drugs. As 

an example: "Pennsylvania law permits 

pharmacists to substitute a less expensi· 

ve generically equivalent drug for a brand 

name drug unless you or your physician 

direct otherwise." 

Such displays are likely to increase the demand for lower· 

price drugs generally, whether these are dispensed on generi· 

cally written prescriptions or as substitutes for branded 

prescriptions. Neither posting requirement imposes a special 

cost in conjunction with a substitution and therefore is 

unlikely to be any deterrent to substitution. The posting of 

prices, in particular, may increase interpharmacy competition. 

COLUMN 12: MAIL ORDER 

+ 	 : There are regulations restricting 

pharmaceutical houses from operating a 

mail order prescription drug business 

within the state. 

Those states having an ou tright ban or significant restrictions on mail order dispensing by firms located within the 

state are checked in Column 12. However, the restrictions do 

not prevent out-of-state mail order firms from shipping 

prescription drugs into the state, although there is ongoing 

controversy about the role of interstate mail order sh ipments 

of prescription drugs. 

COLUMN 13: GENERICALLY WRITTEN PRESCRIPTIONS 

ANY: 	 The pharmacist rna y fill a generically 

written prescription with any genen· 

cally equiva lent product. 

2 16 

. ~\ 
THE PROVISIONS OF THE STATE LAWS 


LO 	 The pharmacist must fill a generically 

written prescription with the lowestprice generically equivalent product 

that he has in stock. 

LO-ELSE 	 The pharmacist must dispense a product 

of below-average wholesale cost of the 

items in formulary or forgo filling the 

prescription. 

About three-quarters of the states left the decision as to 

what to dispense on generically written prescriptions entirely 

to the pharmacist (NO). In the remaining states, the pharmacist had to fill the prescription with "the lowest price 

generic product that the pharmacy has in stock" or with a 

below-average-cost product defined in some other way ( LO ). 2 In 

D.C. and Vermont, the pharmacist was required to dispense the 

least expensive product in inventory which is listed in the 

formulary. Wisconsin required that the drug product chosen 

when filling a generically written prescription be one whose 

cost (to the pharmacist) was below the average for those listed 

in the formulary. Wisconsin's provision implies that if the 

pharmacy did not carry in inventory a product meeting this cost 

criterion, it had to forgo filling the prescription ( LO-ELSE). 

1/ Some of the officials contacted in these states said that they considered the "in stock" language to be a large loop-hole which allowed pharmacists to carry only relatively expensive branded products in a multi-source 

entity. Only if competition among retail pharmacies is weak would this occur. 

217 

218 

APPENDIX A2 

THE FTC/FDA MODEL STATE ACT 

Section 1. [DEFINITIONS.] 

(a) "Established name" has the meaning given in section 

502(e)(3) of the federal food, drug and cosmetic act (21 U.S.C. 

352(e)(3)). 

(b) "Equivalent drug product" means a drug product 

with the same established name, active ingredient strength, 

quantity and dosage form as the drug product identified in the 

prescription, and listed as therapeutically equivalent in the 

current [name of state] drug formulary. 

(c) "Prescriber" means a person licensed by the state to 

prescribe drug products. 

Section 2. [DRUG PRODUCT SELECTION.] 

(a) Unless instructed otherwise by the person receiving 

the drug pursuant to the prescription, a pharmacist filling a 

prescription for a drug product prescribed by its trade or 

brand name may select an equivalent drug product listed in the 

current [name of state] drug formulary. 

(b) The pharmacist shall not select an equivalent drug 

product if the prescriber handwrites "medically necessary" 

or words of the same meaning on the written prescription, or 

when ordering a prescription orally, the prescriber specifies 

that the prescribed drug product is medically necessary. The 

designation of medical necessity shall not be preprinted or 

stamped on the prescription. This subsection does not preclude 

a reminder of the procedure required to prohibit selection of 

an equivalent drug product from being preprinted on the 

prescription. 

219 

APPENDIX A2 


(c) The pharmacist shall not select an equivalent drug 

product unless its price to the purchaser is less than the 

price of the prescribed drug product. 

(d) The pharmacist, or the pharmacist's agent, assistant or 

employee shall inform the person receiving the drug pursuant 

to the prescription of the selection of a lower-cost equivalent 

drug product and of the person's right to refuse the product 

selected. 

Section 3. [PRESCRIPTION LABEL.] 

Unless the prescriber instructs otherwise, the label for 

every drug product dispensed shall include the product's 

trade or brand name, if any, or its established name and the 

name of the manufacturer, packer or distributor, using 

abbreviations if necessary. 

Section 4. [PRESCRIPTION RECORD.] 

The pharmacy file copy of every prescription shall include 

the trade or brand name, if any, or the name ofthe manufacturer, packer or distributor of the drug product dispensed. 

Section 5. [DRUG FORMULARY.] 

(a} The [state health department, board of pharmacy or drug 

formulary commission] shall establish and maintain by regulation a [name of state] drug formulary of equivalent drug 

products. The formulary shall list all drug products that the 

commissioner of food and drugs, United States food and drug 

administration, has approved as safe and effective, and 

has determined to be therapeutically equivalent. The formulary 

shall list all drug products that were not subject to premarketing approval for safety and effectiveness· under the 

federal food, drug and cosmetic act, that are manufactured by 

220 

THE FTC/FDA MODEL STATE ACT 

firms meeting the requirements of that act, are subject to 

pharmacopeia! standards adequate to assure product quality, 

and have been determined by the commissioner of food and drugs 

to meet any other requirements necessary to assure therapeutic 

equivalence. The formulary may list additional drug products 

that are determined by the [department, board or commission] to 

meet requirements adequate to assure product quality and 

therapeutic equivalence. 

(b) The [department, board or commission] shall provide for 

revision of the formulary as necessary but not less than 

annually. 

(c) The [department, board or commission] shall provide 

for distribution of the formulary and revisions to all pharmacies and prescribers licensed in this state and to other 

appropriate individuals. 

(d) The [department, board or commission] shall assess the 

need and if appropriate provide for public education regarding 

the provisions of this act and from time to time shall monitor 

the effects of the act. 

Section 6. [PHARMACIST LIABILITY.] (Optional) 

A pharmacist who selects an equivalent drug product pursuant 

to this act assumes no greater liability for selecting the 

dispensed drug product than would be incurred in filling a 

prescription for a drug product prescribed by its established 

name. 

Section 7. [ENFORCEMENT.] 

Section 8. [EFFECTIVE DATE.] 

221 

222 

APPENDIX A3 

FORMULARIES: SOURCES AND COVERAGE 

This appendix contains some additional material on formularies. Table A3-1 shows the sources relied upon by the states 

in developing their formularies. The frequency with which 

states' formularies were updated, as of 1980, is also shown in 

Table A3-l by the dates of the formularies in effect during 

1980. Table A3-2 gives the number of states for which substitution was permissible in each of the 45 multi-source entities 

used in this study. The precise coding of each state's 

formularies for 1980, by drug and month, is available upon 

request. 

l. SOURCES OF FORMULARIES 

Table A3-l lists those states which, to the best of our 

knowledge, relied on some established listing for formularies 

in effect in 1980. In some instances this reliance is indicated in the statute but no listing is actually mailed to the 

state's pharmacists. Some states adopt a list but make minor 

modifications. Others perhaps rely on the list but publish 

what appears to be the state's own compilation. For this 

reason, the table underestimates the effect of the FDA's lists 

in that presumably even those states which produce a unique 

formulary look to the FDA's list for guidance. Further, it may 

well be that the FDA list is used as a basic guide even in 

states with no formulary. Finally, our list of sources is sure 

to contain errors, since neither we nor officials in the state 

were able to reconstruct some of the history. 

223 

APPENDIX A3 


TABLE A3-l 

Sources for State Formularies, 1980 

A. 	 FDA list of "Therapeutic Equivalents", used as positive 

formulary. The first edition was published in October 

1980 with monthly supplements and annual editions 

thereafter. However, a draft of the first list was in 

circulation from about January 1979. 

Arizona (from 7/80) 


Maryland (from 12/80) 


Nevada 


B. 	 FDA list of "Therapeutic Equivalents" used but converted to negative formulary 

Kansas 


Louisiana (from 9/80) 


Nebraska 


Ohio 


West Virginia (from 12/80) (modified) 


Wyoming 


C. 	 FDA's earlier list (January 1977) of drugs with known or 

potential bioequivalency problems, used as a negative 

formulary 

Arkansas 


Colorado 


Maryland (modified) (to 5/80) 


Utah 


West Virginia (modified) 


224 

FORMULARIES: SOURCES AND COVERAGE 

TABLE A3-l, Continued 

D. 	 Maximum Allowable Cost list. The list applicable for most 

of 1980 was the October 1979 list; an update was issued in 

December 1980. 

New Mexico 


Tennessee 


E. 	 List of 200 Leading Products. This is based on an IMS 

compilation which is published annually in the April issue 

of Pharmacy Times. 

New Hampshire 

F. New York's formulary (precursor of FDA list) 

Maryland (from 5/80 until adoption of the FDA list m 

12/80) 

G. State's own list 

Arizona (5/79 list used until 7/80; FDA list used thereafter) 

District of Columbia (7 /79) 

Delaware (2/79, 2/80, 6/ 80) 

Florida (7 /79) 

Illinois (8/79, 12/80) 

Kentucky (1/78, 2/80, 6/80) 

Massachusetts (9/79) 

MISSOUri (ll/79, 10/80) 

New Jersey (10/79, ll/79, 12/79, 2/80, 8/80) 

New York (4/79, 4/80) 

Pennsylvania ( l/80) 

Rhode Island ( l /80, 7/80) 

Vermont (3/79) 

Virginia (7 /79, 4/80) 

Wisconsin (7 /79) 

225 

APPENDIX A3 


II. PERMISSIBILITY OF SUBSTITUTION BY DRUG IN 1980 

Table A3-2 shows, for each of the 45 multi-source drugs 

analyzed in this study, the number of states in which substitution was permitted in 1980. This is not the same as the number 

of states whose formularies listed the drug, since states with 

no formulary are included as permitting substitution in every 

drug. Where a mid-year formulary change caused a change in the 

classification of the drug in a state, that state is counted 

twice (once in each classification.) 

226 

FORMULARIES: SOURCES AND COVERAGE 

TABLE A3-2 

Number of States in Which Substitution was Permitted, 

by Drug, 19801 

Number of States Number of States 

Permitting Prohibiting 

Drug S u bsti tu tion Substitution 

Hydrochlorothiazide 45 3 

Chlordiazepoxide 45 2 

Amitriptyline 45 4 

Penicillin VK 47 0 

Amoxicillin 46 1 

Atropine sulfate/ 45 2 

Diphenoxyla te 

Meclizine 44 3 

Isosorbide dinitrate 37 1 1 

Hydralazine/ 25 24 

Hydrochlorothiazide/ 

Reserpine 

Doxycycline 43 6 

Hydrochlorothiazide/ 37 13 

Spironolactone 

Dipyridamole 34 14 

Brompheniramine/ 27 20 

Phenylephrine/ 

Pseudoephedrine 

Ampicillin 47 0 

Chlordiazepoxide/ 32 16 

Clidinium bromide 

Tolbutamide 35 17 

Conjugated estrogens 24 24 

Chlorthalidone 34 14 

Acetaminophen/ 34 14 

Chlorzoxazone 

!/ The base number of states, including D.C., is 47. There are no observations for Alaska and Hawaii. Indiana and Texas are not included because they 

227 
APPENDIX A3 


TABLE A3-2--Con tin ued 

Number of States Number of States 

Permitting Prohibiting 

Drug Substitution Substitution 

Dexbrompheniramine/ 

Pseudoephedrine 

Triamcinolone 

Chlorpropamide 

Hydroxyzine 

Sulfamethoxazole/ 

Trimethoprim 

Theophylline 

Terbutaline 

Nitrog1 ycerin 

Metronidazole 

Diethylpropion 

Phentermine 

Minocyc1ine 

Furosemide 

Erythromycin base 

Hydroch1orothiazde/ 

Triamterene 

Amitriptyline/ 

Perphenazine 

Cephalexin 

Erythromycin 

ethly1succina te 

Ibuprofen 

27 21 

26 21 

35 11 

34 13 

41 8 

26 22 

32 16 

34 14 

34 13 

27 20 

39 9 

38 9 

34 14 

29 19 

33 14 

27 21 

37 12 

35 13 

34 11 

Mestrano1/Norethindrone/ 42 6 

Placebo 

Hydrogenated ergot 34 13 

alkaloids 

Quinidine sulfate 45 6 

!/ (continued) 


are no-substitution states. Because some states changed formularies duriang 


1980, those states are counted twice if part of the year belongs in the "Yes" 


column and part in the "No." The following states changed their formularies 


during 1980: Arizona, Maryland, Illinois, Kentucky, and Virginia. 


228 
APPENDIX A4 


AVERAGE STATE SUBSTITUTION RATES 


BY INDIVIDUAL LEGAL PROVISION 


One method of looking at the effects of each proviSion is 

the method used by previous researchers: computing the 

substitution rate by state and then comparing states. Starting 

with substitution rates by individual states, we have computed 

a simple average across those states in which the provision 

was in effect. 

I. METHODOLOGICAL PROBLEMS 

There are at least two serious drawbacks to this method. 

One is that the estimates of individual state averages made 

with our data set cannot be taken as statistically reliable 

measures. A second is that an individual provision of the law 

does not operate in isolation; other provisions and non-legal 

influences affect the measured substitution rate. For these 

reasons, we rely more heavily on the results of our logit 

regressions. In fact, however, the patterns which appear in 

the simple tabulations reported in this appendix are, for the 

most part, qualitatively borne out by the more elaborate 

statistical techniques. 

A. The IMS Data Sample Is Not Random Within a State 

As discussed in Appendix A6, the sample used by IMS for 

their National Prescription Audit is not designed to assure 

good estimates at the state level, since IMS stratifies their 

sample by region and by store ownership type, but not by 

state. Statistical tests based on these state averages are 

therefore not appropriate. (Some of the other studies have 

sought to design a sample appropriate at the state level and 

have therefore been more able to rei y on state-level averages.) 

229 

APPENDIX A4 


Using the logit regression technique circumvents this 

sampling problem, since we take the sample as a whole to be 

representative of the United States as a whole. Also, we use 

the individual prescription as the unit of observation, making 

it possible to control for a number of other influences on 

substitution. (That the IMS sample has elements of a "sample 

of convenience," since non-cooperating stores must of necessity 

be excluded, introduces some non-randomness which is not 

removed by our statistical techniques.) 

One obvious source of possible bias is oversampling, in a 

state, of one or another type of store. However, according to 

the logit regression analysis reported in the text, chains and 

independents do not appear to differ significantly in substitution behavior. Therefore a state's substitution rate is 

unlikely to be exaggerated due to a sampling imbalance of this 

sort. 

While these data by state cannot support statistical tests, 

they do suggest wide interstate variation, on several measures. Moreover, averages across groups of states, whereby any 

bias in an individual state's estimate is diluted, provide a 

useful preliminary view of patterns according to individual 

provisions of the Ia w. 

B. Averages by Provision Reflect Other 


Influences As Well 


Any inference that the presence or absence of a single 

provision is the cause of the difference in substitution rates 

may be incorrect if whenever that provision is inc! uded in a 

statute, some other selected provision tends also to be 

present. It could be, in such a case, that the first provision 

had no effect -- that it was instead the second provision 

which, even in the absence of the first, would have led to 

higher substitution rates. This was a second reason for our 

use of multivariate logit regressions, reported in the text. 

230 

SUBSTITUTION BY INDIVIDUAL LEGAL PROVISION 

II. SUBSTITUTION RATES AND OTHER 


CHARACTERISTICS BY STATE 


Table A4-l presents data on substitution rates, legal 

provisions, and other characteristics by individual state. 

A comparison of the two (the second and fourth) columns 

giving substitution rates, one computed for the prescriptions 

for which the formulary permitted substitution and one for all 

prescriptions shows how the choice of definition affects not 

only the magnitude of the measures but also the ranking of the 

states. In general, the smaller the proportion of prescriptions for which the formulary permits substitution (% Rxs 

Eligible for Substitution), the higher is the formulary-restricted substitution rate (% Substit. on Form-Eligible Rxs) 

relative to the all-prescription rate (% Substit. on All 

Drugs). 

Table A4-2 shows for a weighted average of our sample of 45 

multi-source drugs, first for only those prescriptions on which 

the formulary permits substitution and then across all prescriptions, the simple average of state su bstitution rates for 

states with and without each of the broadly-defined provisions 

used in the logi t regressions. 

According to Table A4-2, formularies cut into the universe 

of substitution opportunities and therefore are associated with 

lower rates of substitution measured across all drugs, but 

given that substitution is permitted for a drug, a formulary 

leads to more substitution. That states with positive formularies typically allow substitution on fewer drugs than states 

with negative formularies is one reason that when substitution 

is measured across all drugs, the rate is higher in negativeformulary states than in positive-formulary states. 

Mandatory substitution leads to higher substitution rates, 

although not as much higher as the outright mandate would lead 

many to expect. The cost pass-through, notification requirements, and a prescription pad format which is more conducive to 

permitting the pharmacist to substitute also are each shown to 

be associated with more substitution. Statutory protection 

against additional liability when substituting is not associated with higher substitution rates, but protection from 

231 

%Subatit. 

DPS Law: on FormMo/Yr Elicible 

State Effective Rxl 

Alab~a 

Aluka 

Arir.ona 

Arkanau 

California. 

Colorado 

Connecticut 

N Delaware 

1,.,..) D.C. N Florida 

Geor!P! 

Hawaii 

Idaho 

Illinoi1 

Indiana 

Iowa 

Kanau 

Kentucky 

Louiliana 

Maine 

Maryland 

Mauachuaeua 

Michipn 

1/80 

1/79 

8/7'6 

5/76 

4/76 

10/76 

10/76 

9/76 

5/74 

1/78 

7/78 

10/77 

7/84 

1/77 

7/78 

6/76 

1/81 

1/76 

1/78 

1/77 

1/77 

4.0 

6.9 

1.7 

9.1 

7.0 

9.3 

liU 

10.2 

11.3 

s.s 

8.6 

7.8 

NA 

6.6 

1.9 

11.1 

3.0 

15.4 

4..6 

24.0 

IU 

TABLEA4-1 

Subetitution Ratu and Other 


Characterutiee by State, 1980 


%Rxt 

Elicible %Subetit. lndividyal P[2vision! of DPS Law1 

for on All RX- RXSubetit. Drup MAND PRO ANTI POS NEG LIAB PASS lNFO 

100.0 

!6.3 

83.8 

100.0 

81.2 

100.0 

81U 

S7.;.s

90. 

100.0 

100.0 

42.1 

0 

100.0 

81.2 

46.06 

33.3 

100.0 

76.4 

41.2 

100.0 

4.9 0 0 

4.8 0 1 

u 0 1 

9.1 0 0 

6.5 0 0 

9.3 0 1 

17.0 0 0 

:us 0 1 

10.2 1 1 

3.3 0 0 

8.8 0 0 

3.4 0 0 

.7 NA NA 

8.8 0 0 

u 0 0 

10.45 1 1 

1.3 0 0 

16.4 0 0 

4.8 0 0 

8.0 1 0 

IU 0 1 

0 0 0 0 

0 1 0 1 

0 0 1 0 

0 0 0 1 

0 0 1 1 

0 0 0 0 

0 0 1 0 

0 1 0 1 

0 0 1 1 

1 0 0 0 

0 0 0 0 

0 1 0 1 

NA NA NA NA 

0 0 0 0 

0 0 14 0 

0 1 0 0 

0 0 1 0 

0 06 06 0 

0 1 1 0 

0 1 0 0 

0 0 0 0 

0 0 

0 l 

0 1 

1 1 

1 1 

1 1 

1 1 

0 0 

0 1 

0 0 

1 0 

0 1 

NA NA 

1 1 

0 0 

0 0 

0 1 

0 l 

0 0 

0 0 

1 

>'"0 

'"0 

ttj 

z 

~ ..... 

>< 

> 

""' 
TABLE A4-1--Continued 

State 

DPS Law: 

Mo/Yr 

Effective 

%Subetit. 

on FormEligible 

Rxa 

%RxtJ 

Eligible 

for 

Subetit. 

% Sub1tit. 

on All 

Drup MAND 

RXPRO 

Individual Provisions of DPS Law1 

RXANTI POS NEG UAB PASS INFO 

N 

w 

w 

Minneaoh. 8/76 

Mieaiuippi 7/79 

Mi111ouri 1/79 

Montana 4/77 

Nebraska 1/78 

Nevada 1/79 

New Hampshire 8/73 

New Jereey 9/77 

New Mexieo 5/76 

New York 4/78 

North Carolina 1/80 

North Dakota 7/79 

Ohio 1/78 

Oklahoma 7/76 

Oregon 9/76 

Penneylvania 8/77 

Rhode leland 7/76 

South Carolina 1/79 

South Dakota 7/78 

Tenneuee 6/77 

Texu 1/82 

Utah 6/77 

Vermont 2/78 

Virginia 7/76 

Waehington 9/77 

Weet Virginia 7/78 

Wieconein 3/76 

Wyomin1 7/79 

Ave. of State Averagee 

Ave. Across All Prescriptions 

7.1 

7.7 

4.1 

14.7 

2.7 

0.3 

9.7 

9.9 

18.2 

7.3 

3.7 

1.4 

7.3 

0.2 

8.0 

8.6 

13.7 

3.4 

0.6 

7.9 

NA 

2.6 

16.3 

8.6 

1.6 

14.3 

26.1 

2.8 

8.3 

7.3 

100.0 

94.0 

83.8 

100.0 

81.2 

49.4 

97.6 

100.0 

28.8 

100.0 

100.0 

100.0 

81.23 

100.0 

100.0 

49.1 

43.0 

100.0 

100.0 

28.8 

0 

83.8 

48.7 

46.6 

100.0 

86.7 

37.1 

81.2 

73.6 

7.1 

7.3 

3.6 

14.7 

2.1 

.1 

9.4 

9.2 

2.4 

7.3 

3.7 

1.4 

6.4 

.2 

8.0 

4.3 

4.4 

3.4 

.6 

1.9 

.3 

2.4 

6.6 

3.8 

1.6 

12.3 

6.3 

u 

5.8 

5.6 

0 

0 

0 

0 

0 

0 

0 

1 

0 

1 

0 

0 

0 

0 

0 

1 

1 

0 

0 

0 

NA 

0 

1 

0 

1 

1 

0 

0 

1 

1 

0 

0 

1 

0 

0 

0 

1 

1 

0 

0 

1 

0 

1 

1 

1 

1 

0 

0 

NA 

0 

1 

0 

0 

1 

1 

0 

0 

0 

1 

0 

0 

1 

1 

0 

0 

0 

1 

1 

0 

0 

0 

0 

0 

0 

1 

0 

NA 

0 

0 

1 

1 

0 

0 

1 

0 

0 

0 

0 

0 

1 

1 

1 

1 

1 

0 

0 

0 

0 

0 

1 

1 

0 

0 

1 

NA 

0 

1 

1 

0 

0 

1 

0 

0 

1 

1 

0 

1 

0 

0 

0 

0 

0 

0 

0 

1 

0 

0 

0 

0 

0 

0 

0 

NA 

1 

0 

0 

0 

l 

0 

1 

0 

1 

1 

1 

1 

1 

1 

0 

0 

0 

1 

1 

0 

0 

1 

1 

1 

0 

0 

1 

NA 

0 

0 

0 

1 

1 

0 

1 

1 

0 

0 

1 

0 

0 

0 

0 

0 

0 

0 

0 

1 

0 

0 

0 

1 

0 

0 

1 

NA 

0 

0 

1 

1 

1 

1 

0 

1 

1 

0 

1 

0 

1 

07 

1 

0 

1 

0 

1 

1 

0 

0 

1 

0 

1 

0 

0 

NA 

1 

1 

1 

0 

1 

0 

0 

Ul 

c::: 

tJ:j 

Ul 

....,

-...., 

c::: 

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0 

%Rxl 

Insurance Covera1e 

CASH% MED% PRIV% 

Chain Share of R:x~ 

NPA Census 

SSINDEX9 

Preacribed 

Generically 

83.6 15.3 1.1 40.0 36.3 .96 16.7 

96.7 .1 4.2 77.3 61.6 .92 20.2 

84.2 13.7 2.2 38.0 16.4 .97 16.9 

74.2 19.7 6.2 33.8 39.0 1.13 26.7 

77.3 16.7 7.0 0 21.3 1.02 22.8 

78.3 11.2 10.6 3.7 19.8 .92 23.8 

82.4 9.9 7.7 0 60.3 .92 21.9 

88.2 8.2 3.6 44.9 66.1 1.10 28.2 

82.5 14.0 3.6 24.0 49.2 1.03 21.5 

80.8 16.9 2.3 26.3 41.4 1.01 13.9 

91.2 2.2 6.6 27.3 16.0 .80 21.1 

84.9 7.4 7.7 37.9 31.3 .97 17.8 

90.6 2.7 6.7 67.1 68.1 11.1 

79.0 8.4 12.6 10.8 22.2 1.04 21.1 

96.3 3.6 1.2 0 13.4 1.03 20.4 

86.3 10.0 4.8 13.7 36.6 1.08 17.6 

68.9 26.9 4.2 0 33.4 .99 10.3 

66.6 41.1 2.4 0 44.3 1.01 26.2 

69.8 16.3 13.9 68.3 67.7 .98 24.2 

78.8 19.8 1.3 14.7 23.9 1.02 30.8 

66.9 12.6 30.6 18.4 33.2 .99 15.4 

Alab~a 

Alaaka 

Arisona 

Arkansaa 

California 

Colorado 

Connecticut 

N Delawant 

V.J D.C. ~ Florida 

Georgi~ 

Hawaii 

Idaho 

Illinoia 

Indiana 

Iowa 

Kanaaa 

Kentucky 

Louisiana 

Maine 

Maryland 

Maauchusetts 

Michican 

TABLE A4-1--Continued 

% o( Rxa 

Prescribed 

Generically 

which were 

Dispensed 

Generically 

90.5 

96.2 

88.3 

82.0 

96.4 

89.1 

98.9 

79.1 

93.9 

89.0 

86.1 

86.7 

87.6 

86.4 

78.8 

86.6 

76.1 

91.9 

83.6 

88.9 

90.6 

Generic 

Market 

Share 10 

22.0 

20.5 

16.9 

27.8 

26.1 

26.7 

27.5 

24.2 

26.5 

18.5 

25.3 

19.4 

13.1 

25.2 

15.6 

24.0 

12.1 

37.5 

25.1 

32.1 

21.1 

>'"tl 

'"tl 

tr:l 

z 

..... 

>< 

>

• 
TABLE A4-1--Continued 

Inaurance Cove[&le 

CASH% MED% PRIV% 

Cbain §hare of nx. 

8 NPA Cenaua 

SSINDEX9 

%R.x. 

Preacribed 

Generically 

%o( R.x. 

Preacribed 

Generically 

which were 

Dispenaed 

Generic ally 

Generic 

Markelo 

Share 

N 

w 

Vl 

Minnesota 

Miesiuippi 

Miasouri 

Montana 

Nebraska 

Nevada 

New Hampshire 

New Jeney 

New Mexico 

New York 

North Carolina 

North Dakota 

Ohio 

Oklahoma 

Oregon 

Pennsylvania 

Rhode Island 

South Carolina 

South Dakota 

Tennessee 

Texas 

Utah 

Vermont 

Virginia 

Waahin(ton 

West Virginia 

Wisconsin 

Wyoming 

87.8 

41.6 

85.1 

94.1 

77.6 

89.6 

87.6 

77.7 

76.6 

71.6 

87.1 

82.2 

73.2 

92.1 

87.1 

78.4 

79.4 

73.3 

88.5 

78.8 

86.4 

95.7 

81.2 

90.2 

81.1 

67.1 

81.1 

96.2 

5.6 

67.6 

9.8 

6.1 

6.7 

7.4 

10.8 

6.4 

16.0 

10.7 

10.6 

16.9 

6.6 

6.6 

10.6 

10.1 

16.1 

24.1 

9.1 

18.5 

10.4 

2.0 

17.3 

7.4 

13.1 

10.9 

9.6 

2.6 

6.6 

.7 

5.0 

.7 

16.7 

3.0 

1.7 

15.9 

7.3 

17.8 

2.4 

1.9 

20.3 

2.4 

2.3 

11.6 

4.5 

2.6 

2.6 

2.7 

3.1 

2.3 

1.4 

2.4 

5.8 

22.1 

9.4 

1.2 

62.1 

0 

27.9 

79.1 

0 

0 

0 

13.6 

3.6 

23.9 

14.3 

0 

46.2 

14.8 

0 

26.2 

38.4 

0 

0 

24.3 

54.3 

42.7 

15.9 

59.2 

63.7 

16.1 

6.6 

14.6 

28.1 

27.0 

.32.6 

20.9 

12.7 

49.5 

18.4 

26.7 

30.1 

28.6 

48.3 

20.7 

60.8 

21.3 

4.6 

41.3 

39.2 

45.9 

12.7 

33.4 

38.5 

27.0 

16.4 

58.4 

27.1 

47.3 

20.4 

14.2 

.89 

1.00 

.96 

.96 

.99 

1.02 

1.02 

.99 

107 

.99 

1.02 

1.08 

.94 

lOS 

1.02 .95 

.90 

1.00 

.91 

.99 

.88 

.94 

.96 

1.07 

.92 

101 

.99 

23.1 

17.6 

19.1 

27.0 

18.6 

19.9 

29.9 

17.7 

24.9 

21.2 

16.6 

12.4 

16.8 

13.0 

23.3 

17.4 

23.7 

16.8 

20.7 

18.0 

1U 

24.5 

30.9 

22.9 

29.9 

17.1 

26.5 

22.0 

83.3 

82.8 

85.3 

86.0 

74.9 

73.4 

68.1 

86.6 

76.1 

91.1 

92.7 

71.4 

86.4 

80.1 

91.6 

81.8 

90.5 

89.7 

97.8 

88.4 

85.5 

82.7 

90.1 

86.9 

82.9 

91.4 

87.0 

81.4 

24.3 

27.8 

19.0 

33.5 

18.5 

17.0 

26.6 

23.8 

20.6 

25.8 

20.2 

13.3 

21.8 

14.3 

28.7 

19.3 

24.8 

21.7 

19.2 

19.4 

14.7 

22.2 

31.5 

25.1 

23.0 

25.4 

26.4 

21.3 

en 

c::: 

ttl 

en 

>-:l 

..... 

>-:l 

c::: 

>-:l 

..... 

0 

z 

t:C 

><: 

..... 

z 

t::l 

..... 

<!

.... 

t::l 

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t'"' 

t'"' 

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en

.... 

0 

z 
APPENDIX A4 

Footnotes for Table A4-1 

1/ As coded in the regressions. "1" means provision is present.

'£/ IMS' NPA does not include data for Alaska or Hawaii. 

V Each store's percentage varies according to its own formulary. This 

percentage reflects only the state negative formulary. 

!/ Kentucky's formulary was ambiguous, containing both a positive mandatory 

formulary, implying that substitution on other prescriptions was forbidden, but 

also a negative formulary, suggesting that for drugs not on the negative 

formulary substitution was mandatory on some drugs, permitted on the rest. We 

accepted the interpretation of state officals: that where substitution was not 

mandatory, it was prohibited. 

§) For the 4 months when the DPS law was in effect, there was 3.0 percent 

substitution; 81 percent of prescriptions were eligible for substitution. 

§./ Maryland's formulary was negative from January to May 1980, positive 

thereafter. 

1/ New Hampshire was coded "0" on INFO in the regressions by error. 

§.f The computation used data from IMS, from the 1981 Lilly and NACDS-Lilly 

Digests, and from the 1977 Census of Retail Trade. Details are available upon 

request. 

2,/ These state averages of the single-source price index used in the 

regressions are taken from the data set for the top substitution drug (hydrochlorothiazide). 

1Q/ Generic market share is computed as the percentage of all prescriptions 

on which the identity of the product dispensed was given. The percentages 

would be 1 or 2 points higher, on average, if estimates were made also for 

prescriptions on which the product dispensed was not recorded. See footnote 1 

in Table 6-3. 

SOURCES: State statutes and formularies; computations with data for 45 

leading multi-source drugs from the 1980 IMS National Prescription Audit; the 

1981 Lilly and NACDS-Lilly Digests; U.S. Department of Commerce, Census of 

Retail Trade, 1977. 

236 

SUBSTITUTION BY INDIVIDUAL LEGAL PROVISION 


liability is often conferred by a formulary (especially a 

mandatory formulary). 

Most of the patterns shown in Table A4-2 are consistent with 

the results of the multivariate logit regressions reported in 

Chapter 5. Some differences stand out. In particular, the 

regressions show that, when other influences are taken into 

account, a formulary leads to less, not more substitution, and 

notification requirements to more. Also, some results which 

look quite clear from the cross-tabulations are not significant 

in the regressions, even when the sign patterns are consistent. This is true of MAND and PASS. The comparison of the 

two sets of results suggests that the specific legal provisions 

are correlated with other influences on substitution. 

Ill. AVERAGE SUBSTITUTION RATES 


BY PROVISIONS IN DETAIL 


Because the exact specification of some of the particular 

legal provisions have been the subject of controversy, we 

report the average state substitution rates by more detailed 

subcategories of the provisions. The data on the detailed 

provisions should be treated even more tentatively than 

the cross-tabulations of the more broadly defined provisions. 

Not only are there the methodological problems discussed above 

but also many of the detailed provisions were present in only a 

very few states. However, since more elaborate techniques 

tended to confirm most of the broad patterns seen in simple 

tabulations, these patterns too may be indicative, at least for 

provisions which are present across more than a few states. 

A. Mandatory or Permissive Substitution 

There are three ways in which statutes mandate substitution. In 4 states, the statute requires either that the 

pharmacist dispense the lowest cost version of the drug 

entity in inventory or that the product dispensed be one whose 

wholesale cost is below average. In two states, the pharmacy 

is held to a more stringent requirement: if the pharmacy does 

not have a lower cost version, the prescription cannot be 

237 

APPENDIX A4 

TABLE A4-2 


Average Substitution Rates in States by 


Legal Provision, 19801 


Average Substitution Rate2 

Across drugs for 

Number which substitution Across 

Provision of States is permitted all drugs 

MAND 3 

PERM 

RXPRO 

RXNEUT 

RXANTI 

POS 

NEG 

NONE 

LO-STAT 

OTHER 

YES 

NO 

YES 

NO 

I. Mandatory or Permissive Substitution 

10 

37 

12.4% 

7.7 

7.4 

5.3 

II. Format of Physician's Prescription Pad 

20 

17 

10 

10.2 

9.0 

3.6 

6.2 

6.7 

.9 

III. Formulary 

17 

13 

17 

11.6 

6.6 

6.2 

5.3 

5.7 

6.2 

IV. Pharmacist Liability 

21 

26 

7.3 

9.1 

5.4 

6.0 

v. Cost Pass-Through 

17 

30 

10.1 

7.2 

7.4 

4.8 

VI. Notification 

28 

19 

7.9 

8.8 

6.6 

4.4 

Average of State 

Averages 47 8.3 5.8 

Average Across All 

Prescriptions 7.3 6.0 

1/ There were in 1980 47 states plus the District of Columbia in which 

substitution was permitted at least part of the year.

'J../ Averages are computed as simple averages across states, where each 

state's substitution rate is a weighted average across drugs. 

~/All row titles are explained in Chapter 5 and Appendix Al. 

SOURCE: Substitution rates computed with data for 45 leading multi-source 

drugs from the 1980 IMS National Prescription Audit. 

238 
SUBSTITUTION BY INDIVIDUAL LEGAL PROVISION 

filled, and the customer must go to another pharmacy. The 

remaining 4 states which mandate substitution impose neither of 

these specific requirements. (One of the states which does not 

mandate substitution nevertheless requires that any substitution made must be to a below-average-cost product. This state 

(Georgia) has a lower substitution rate (3.3 percent) than 

other "permissive" states.) 

One might have expected the "substitute or else lose the 

prescription" regulation to lead to the most su bsti tu tion. 

Instead, as Table A4-3 shows, it is associated with the least 

substitution of the three variants of the mandatory regulation 

(although the two states with this provision may be different 

in other ways as well.) The requirement that the lowest-costproduct-in-inventory be used led to somewhat more substitution 

than the general provision. 

Mandating substitution may encourage substitution 

indirectly by making it less likely that pharmacists will be 

found liable for substitutions (in accord with any formulary 

restrictions) since the state has left them no option. It is 

difficult to separate any effect of liability protection from 

the direct mandate. We discuss this further in section D 

below, on Pharmacist Liability. 

B. Format of the Physician's Prescription Pad 

Table A4-4 gives, for 12 different formats of prescription 

pads, the simple average substitution rate across states for 

each format. For some formats there are very few states, or 

even only one, so it is very likely that other provisions in 

those states or other non-legal characteristics strongly color 

the substitution rates reported. 

The format which stands out as being different is the 

two-line form on which a signature on the left-hand line 

permits substitution. This is one of those categorized as 

RXANTI ( A2). The 9 states with this format have a very low 

average substitution rate, under 3 percent. At the other 

extreme, among the RXPROs, a presumption that substitution 

is permitted in the absence of a handwritten abbreviation 

( P 1 -a) is associated, across 9 states, with a high average substitution rate of 9.8 percent (for substitution-permitted 

239 

APPENDIX A4 

TABLE A4-S 


Average State Substitution Rates by 


Type of Mandatory Provision, 1980 


Average Substitution Rate 1 

Across drugs for 

Number which substitution Across 

Provision of States is permitted all drugs 

MAND: 

Substitution is 

mandatory, in 

general terms 11.0% 5.8% 

MAND-LO: 

Pharmacy must 

substitute lowest 

cost product in 

inventory 14.8 9.8 

MAND-ELSE: 

If pharmacy does not 

have lower cost 

product in inventory, 

prescription cannot 

be filled 2 10.5 5.9 

Any Mandatory 

Provision 10 12.4 7.4 

ALL DPS STATES 47 8.1 5.8 

JJ Averages are computed as simple averages across states, where each 

state's substitution rate is a weighted average across drugs. 

SOURCE: Computed with data for 45 leading multi-source drugs from the 1980 

IMS National Prescription Audit. 

240 

TABLE A.(-.( 


Average State Substitution Rates by Format of Physician's Prescription Pad, 1980 


Required 

to Override Average Substitution Rate 

Number of Presumption Presumption Across drugs for 

Number Signature Subst. OK or Side for which substitution Across 

Format of States Lines or not OK OK signature is permitted all drugs 

RXPRO: 20 10.2% 6.7% 

P1-a 

Pl-b 

P2 1 

9 

4 

7 

1 

1 

2 

OK 

OK 

N/A 

abbreviation 

word 

right 

9.8 

13.8 

8.8 

5.8 

7.1 

6.2 

RXNEUT: 17 9.0 6.7 

N 

J:>. 

N1-a 

N1-b 

N1-l 

N2 1 

N1,2 

NN 

3 

5 

1 

5 

1 

2 

1 

1 

1 

2 

1 or 2 

Not OK 

OK 

opt. 2 

N/A 

opt. 

no regulations 

req. line or box 

opt. line or box 

opt. 

opt. 

opt. 

11.0 

8.7 

6.6 

12.8 

1.9 

2.0 

9.3 

7.4 

6.6 

8.1 

1.5 

.8 

RXANTI: 

A1-b1 

A21 

10 

1 

9 

1 

2 

Not OK 

N/A 

word 

left 

3.6 

9.7 

2.4 

3.2 

9.4 

2.2 

ALL DPS 

STATES 47 5.8 8.3 

!/ Physician made explicit decision that substitution is allowed. 

'1./ "Opt." means at the physician's option. 

SOURCE: Computed with data for 45 leading multi-source drugs from the 1980 IMS National Prescription Audit. 
APPENDIX A4 


drugs), and when one or more words must be written out instead 

of an abbreviation (Pi-b), the 3-state average rate is even 

higher, 13.8 percent (7 .1 percent across all drugs}. Another 

of the formats included in RXPRO also yields a higher-thanaverage substitution rate, 8.8 percent, in the 7 states whose 

double-line formats reserve the right-side signature line for 

"substitution permitted" ( P 2 ). 

The RXNEUT formats, with an average state rate of 9 

percent on substitution-permitted prescriptions, included some 

with rates well above some of the RXPRO pads. For example, the 

most frequently adoptedRXNEUTformat had ana verage state substitution rate of 12.8 percent in the 5 states which allowed 

the physician to decide whether to put the "substitution 

permitted" signature line on the right or the left ( N2). Where 

a line or box was required for any handwritten prohibition of 

substitution ( N 1-a), making it easy to countermand the presumption that substitution was permissible, the 3-state average was 

11 percent. A third RXNEUT format used the opposite presumption, that substitution was not permitted but a box for 

checking permission was allowed to be pre-printed ( N 1-b ); the 

average substitution rate for 5 states with· this format was 

also above average, 8.7 percent. Two of the other 3 "neutral" 

prescription pad formats had very low substitution rates but 

they occurred in only one or two states so it is even less 

appropriate to infer any association between any of those 

formats and the observed substitution rate. It is difficult to 

know what to infer from the results on the "neutral" pads, 

since the choice of prescription pad format is itself is a 

rna tter of choice for the physician and must therefore reflect 

any underlying predilection towards or against substitution. 

The remaining RXANTI format (Al-b) had an above-average 

substitution rate but it was present in only one state. 

Other analysts have labeled prescription pads as "one-line" 

or "two-line." It is interesting to note that the two two-line 

pads perform very differently, depending on whether the 

substitution-permitted signature is on the right (P2, with 8.8 

percent) or on the left (A2, with 2.9 percent.) We sought to 

make a similar comparison within the one-line forms which 

differ as to the presumption about the permissibility of 

substitution. Where the physician must take an active role to 

242 

SUBSTITUTION BY INDIVIDUAL LEGAL PROVISION 


prohibit substitution (PJ-a and PJ-b), the rate of substitution 

is high, 9 percent or above. However, although the substitution rate was also high (9.7 percent) where a handwritten 

designation was required to overcome the presumption of 

prohibition, the fact that there was only one state means 

we cannot infer much from the high rate. 

Another way of categorizing prescription pads is as 

to whether the physician's intent is expressed explicitly. A 

pharmacist may be more likely to substitute if the physician 

has taken some action on the prescription to indicate that 

substitution is appropriate, than if the prescription implicitly permits substitution in the absence of physician override. 

We cannot test this proposition directly since we cannot 

disentangle the physician's and the pharmacist's decisions. We 

can look only at the first condition (the prescription pad) and 

the final result (the substitution rate.) Only if "explicitness" were to have a stronger effect than "convenience" would 

the connection be clear. 

Our data do not confirm the hypothesis that explicit 

physician approval is associated with more frequent substitution, a result shown by Gurley and Gagnon, although our data 

cannot be understood to refute the hypothesis, either. 1 The 

formats identified by an asterisk in the table above convey to 

the pharmacist that an explicit decision was made by the 

physician. Explicit indications are absent when the presumption is that substitution is permitted, that is, on prescription forms where the physician must exert additional effort to 

prohibit substitution (Pi-a and Pi-b), and these formats are 

associated with higher substitution rates (on formulary drugs, 

9.8 and 13.8 percent) than when explicit permission is given, 

as by format P2 (8.8 percent). The first two formats are, 

after all, the ones which make it most inconvenient for the 

physician to prohibit substitution. Format P2 is more convenient for prohibitions as well as more explicit for pharmacists; 

the two characteristics must be somewhat offsetting in their 

!/ Gurley and Gagnon (1981). See the summary of their results in conjunction with the discussion of the pharmacist's interpretation of the physician's 

wishes in Chapter 3. 

243 

APPENDIX A4 


effect on the final substitution rate. Also, although two-line 

formats are equally explicit regardless of the side on which 

the "substitution-permitted" signature line appears, that 

detail appears to make a substantial difference, reflecting the 

customary physician behavior of signing on the right. While 

ease and custom seem to make a difference in physician prescription writing and therefore in the incidence of substitution, our data give no support to the contention that physicians' explicit permission to substitute makes it more likely 

that a substitution will occur given that it is permitted. On 

the other hand, our evidence cannot be taken to refute that 

hypothesis, either, in that the appropriate interpretation of 

these figures is that explicitness is not more important than 

convenience, a much less likely proposition. 

C. Formulary 

A formulary cuts into the universe of prescriptions 

on which substitution is permitted. Therefore the substitution 

rate on formulary-eligible prescriptions is higher than on all 

prescriptions in part simply because the denominator is 

smaller. Similarly, states with a formulary have a lower 

substitution rate on all drugs (5.5 percent) than states with 

no formulary (6.2 percent.) Negative formularies prohibit 

substitution on prescriptions in relatively few drugs and 

therefore tend to permits substitution on a larger proportion 

of prescriptions (85 percent, on average) than do positive 

formularies (45 percent, on average). 2 It is therefore not 

surprising that states with negative formularies have a higher 

average substitution rate across all drugs (5.7 percent) than 

states with positive formularies (5.3 percent.) 

1/ These averages omit New Jersey and New York, where the formularies 

regulate only mandatory substitution, and Maryland, since the "% Rxs Eligible 

for Substitution" reported in Table A4-l is based on both negative and positive 

formularies. 

244 

SUBSTITUTION BY INDIVIDUAL LEGAL PROVISION 


Given that substitution is permitted on a prescription, the 

presence of a formulary of any kind is associated with more 

substitution (on average, 9.4 percent) than states with no 

formulary (6.2 percent.) Positive formulary states have higher 

average substitution rates (11.6 percent) than negative 

formulary states (6.6 percent.) 

However, these cross-tabulation results are not borne 

out by the regressions which take into account other influences 

on substitution. The logit regressions show that the presence 

of a formulary is associated with lower rates of substitution. 

It is still true, in the regressions, that a positive formulary 

encourages substitution more than does a negative formulary; 

the coefficients on POS are less negative than those on NEG. 

This seeming contradiction between the two methods of 

analysis impies that the presence of a formulary is correlated 

with some other conditions which encourage substitution. One 

obvious complication is the fact that states which mandate 

substitution use a formulary (with one exception), and the 

mandate itself leads to more substitution. (On the other hand, 

if high average state substitution rates in formulary states 

are due in part to a formulary's providing some protection 

against liability when substituting, this effect is not 

separated out in the regressions.) Table A 7-1 shows the 

correlations between POS and NEG and other variables used in 

the regression. 

D. Pharmacist Liability 

Explicit statutory protection from additional liability 

when substituting is associated with somewhat higher average 

rates of substitution than the absence of any protection (7.3 

percent vs. 6.6 percent, for formulary-eligible prescriptions.) 

However, other forms of liability protection may be equally as 

effective or even superior, as suggested by the 9.1 percent 

average substitution rate for ails tates without express 

statutory protection on liability. Where there is no statutory 

provision but substitution is mandatory, rates are double (14.9 

percent), and when there is a formulary guiding (permissive) 

substitution the rate is also higher (10.3 percent.) It is 

especially difficult to make a strong inference about the role 

245 
APPENDIX A4 


of liability protection, however, since states in these last 

two categories have, by definition, other provisions which seem 

to encourage substitution. 

E. Cost Pass-Through 

The average substitution rate in states requiring a 

cost pass-through (10.1 percent on formulary-eligible prescriptions) was higher than in states with no pass-through requirement (7.2 percent). 

Some states (9) did not require a cost pass-through 

but did insist that the price charged on a substitution be the 

"usual and customary" price (as when it is dispensed on a 

generically written prescription.) Substitution in these 9 

states was somewhat lower (6.6 percent on formulary-eligible 

prescriptions) than in the 21 states which had no price or 

margin regulation whatever (7.5 percent.) 

Not all of the 17 states which require that the pharmacy's 

acquisition cost savings on a generic substitute be passed 

through to the consumer require that the full savings be 

passed on. Washington state specifies that 60 percent be 

passed on. Its substitution rate was very low, under 2 

percent, but this single observation is not sufficient reason 

to believe that a full pass-through leads to more substitution 

than a partial pass-through. 

F. Notification 

Based on the cross-tabulation data in Table A4-5, one 

might conclude that some of the information provisions increased substitution and some diminished it, although not by much. 

Obtaining a customer's consent in advance of the transaction 

was associated with an average substitution rate a bit above 

average (9.1 percent on formulary-eligible prescriptions) but 

providing a price comparison was not (8.0). A general provision about advance notification, which may impose somewhat 

less cost on pharmacists than the first two while still 

providing information to consumers directly, is associated with 

a higher average level of substitution (10.2 percent) but an 

246 

SUBSTITUTION BY INDIVIDUAL LEGAL PROVISION 

TABLE A4-5 


Average State Substitution Rates by 


Type of Notification Requirement, 1980 


Average Substitution Rate1 

Across drugs for 

Type of Number which substitution Across 

Notification of States is permitted all drugs 

a. Extra labeling 

required 24 9.7% 6.9% 

b. Extra recordkeeping 

required 5 7.2 6.2 

c. Pharmacist required 

to make oral 

comparative price 

disclosure 10 8.0 6.5 

d. Pharmacist must 

receive advance 

consent or orally 

notify cust.0mer of 

right to refuse 8 9.1 7.1 

e. Pharmacist must 

orally inform 

customer in advance 12 10.2 7.5 

f. Pharmacist must 

orally inform 

customer that 

substitution was 

made 7 7.0 5.8 

INF0=1: 

At least one 

of (c) through 

(f) 28 9.0 7.0 

INFO=O: 

None of (c) 

through (f) 19 7.3 4.1 

ALL DPS STATES 47 8.1 5.8 

!/ Averages are computed as simple averages acrose states, where each 

state's substitution rate is a weighted average across drugs. 

SOURCE: Computed with data for 45 leading multi-source drugs from the 1980 

IMS National Prescription Audit. 

247 

APPENDIX A4 


even more general notification requirement, not specifying 

advance notification, has a below-average level of substitution 

(7.0 percent.) Additional labeling requirements, which may 

usefully inform consumers, does have an above-average rate (9.7 

percent.) The additional recordkeeping requirement, which 

imposes costs but does not convey information to consumers, has 

a lower average substitution rate (7.2 percent.) The presence 

of at least one direct notification provision is shown to lead 

to an above-average level of substitution (9.0 percent), and 

the logit regression confirm this. 

G. Collateral Provisions 

Table A4-6 provides substitution data on other regul:l r ions 

not included in the logit regression analysis. Based on these 

data, advertising restrictions do not appear to discourage 

substitution, nor do restrictions on physician ownership of 

pharmacies have any apparent effect on substitution. The 2 

states which in 1980 required some form of pharmacist ownership 

of drugstores had a lower substitution rate than other states. 

Both sign requirements, for prices and to inform consumers that 

substitution was possible, were associated with higher levels 

of substitution. This is consistent with an active role by 'he 

consumer in determining the extent of substitution activity. 

Finally, restrictions on mail order activity within a state 

seemed to have no effect on substitution rates. 

248 

SUBSTITUTION BY INDIVIDUAL LEGAL PROVISION 

TABLE A4-6 


Average State Substitution Rates by 


Collateral Provisions, 1980 


Average Substitution Rate1 

Across drugs for 

Number which substitution Across 

Provision or States is permitted all drugs 

I. Advertising Restrictions 

Yea 8 8.0% 6.6% 

No 39 8.3 6.6 

II. Pharmacy Ownership Restrictions 

A. Physician Yes 13 8.6 6.2 

Ownership No 34 8.1 6.6 

B. Pharmacist Yea 2 4.7 4.7 

Ownership No 46 8.4 6.8 

Ill. Prescriber Liability 

Low 17 8.1 5.9 

High 30 8.3 6.6 

IV. Posting of Signs 

A. Prices Yes 10 12.1 7.1 

No 37 7.2 5.4 

B. Availability 

of Generics/ Yes 16 10.4 7.1 

Substitution No 31 7.2 6.0 

V. Mail Order Restrictions 

Yes 10 8.0 6.0 

No 37 8.3 6.9 

!/ Averages are computed as simple averages across states, where each 

state's substitution rate is a weighted average across drugs. 

SOURCE: Computed with data for 46 leading multi-source drugs from the 1980 

IMS National Prescription Audit. 

249 

250 

APPENDIX AS 

DATA ON 45 LEADING MULTI-SOURCE DRUGS 

Appendix A5 consists of a single table showing, for each of 

the 45 leading multi-source drugs analyzed in this study, 

price, cost, and gross margin data, for leading brands, 

generics, and the drug entity as a whole. Also shown are the 

proportions of all prescriptions written for the leading brand, 

written generically, and dispensed generically. Some additional data are included as well. 

Definitions, data sources and methods of computation are 

described in Appendix A6. Computations are across all U.S. 

prescriptions in 1980. The source of the data is IMS' 1980 

National Prescription Audit. 

251 

TABLEA5-1 

Leadin1 Multi-Source Drup: 

Characteriatic:a, and Pr•cribin1 and Dilpenain1 Pattenu, by Type of Brand, 1980 

Generic N arne Ibuprofen Cephalexin Furoaemide Isoaorbide Chlorpropamide 

dinitrat 

Rank• 3 6 9 12 14 

Therapeutic Cate1ory Antiarthritic Antibiotic Diuretic Cardiovucular Antidiabetic 

Refill Rate 60.096 16.096 66.0% 78.0% 69.0% 

Leading Branda Motrin Keflex Luix Iaordil, Sorbitrate Diabinese 

Number of Manufacturen 2 2 7 34 8 

% Rxs Prescribed for Leading Branda 100.0% 99.9% 99.7% 91.8% 98.8% 

% Rxs Prescribed Generically 0.0% 0.1% 0.3% 8.1% 1.2% 

% Rxll Dispensed with Generics of 

Those Prescribed Generically 0.0% 13.6% 92.7% 75.0% 

N % Rxa Substituted 0.096 0.0% 0.4% 12.6% 2.2% 

VI 

N % Rxll Dispenaed with Generic• 

Cost per Rx 

0.0% 0.096 0.4% 18.4% S.O% 

Average $8.16 $9.29 $4.54 $5.08 $10.67 

Leading Brand(•) (LB) $8.16 $9.29 $4.55 $6.05 $10.87 

Generics (G) $2.81 $1.06 $4.94 

Cost Difference per Rx 

LB-G $1.74 $4.99 $5.93 

(LB-G)/G 61.9% 470.8% 120.0% 

Price per Rx 

Average $l1.71 $13.27 $7.42 $8.21 $14.06 

LB $l1.71 $13.27 $7.43 $9.01 $14.25 

G $6.97 $4.92 $8.57 

Price Difference per Rx 

LB-G U.46 $4.00 $5.68 

(L~-G}LG Jj.§2fi 8~·12fi 66.S2fi 
TABLE A6-1--Continued 

Generic N arne Ibuprofen Cephalex.in Furoumide Iaoeorbide Chlorpropamide 

dinitrat 

Gross Margin per Rx ($) 

Average 

LB 

G 

Gross Margin Difference per Rx ($) 

G-LB 

(G-LB)/G 

% Gross Margin 


Average 


LB 


N G 

~ Number of Obaervations 

Proportion of Preacriptions on 

Which Substitution was Permitted 

in 1980 

Proportion of Prescription• by 

Customer Payment Type: 

Cash 

Medicaid 

Private Insurance 

$3.66 

$3.66 

30.3% 

30.3% 

5,326 

66.3% 

74.9% 

13.6% 

11.5% 

$3.98 

$3.98 

30.0% 

30.0% 

6,843 

63.7% 

84.9% 

6.5% 

8.6% 

$2.88 

$2.88 

$3.16 

$0.28 

8.9% 

38.8% 

38.8% 

62.9% 

7,368 

66.6% 

71.9% 

19.3% 

8.8% 

$3.14 

$2.97 

$3.87 

$0.90 

23.3% 

38.3% 

33.0% 

78.7% 

1,144 

78.8% 

72.6% 

16.7% 

u.s% 

$3.39 

$3.38 

$3.63 

$0.25 

6.9% 

24.1% 

23.7% 

42.4% 

2,346 

70.1% 

64.7% 

23.2% 

12.1% 

• Rank i• determined by dollar sale• to druptores bued on data from IMS' U.S. Druptore Audit for 1980. The list of top entities 

was provided by the Health Care Financing Administration. 
TABLE A&-1--Continued 

Generic Name 

Rank• 

Hydrochloro- Sulfamethoxuole/ 

thiazide/ trimethoprim 

triamte[!ne 

16 19 

Doxycycline 

20 

Theophylline 

21 

Amitriptyline/ 

Perphenazine 

22 

Therapeutic Category 

Refill Rate 

Diuretic 

71.0% 

Antibiotic 

21.0% 

Antibiotic 

17.0% 

Bronchodilator 

62.0% 

Antidepressant 

62.0% 

Leading Branda 

Number of Manufacturen 

Dyuide 

4 

Septra OS, Bactrim DS 

6 

Vibramycin 

26 

Theo-Dur 

4 

Triavi! 

3 

% R.xa Prescribed for Leading Branda 

% Rxa Prescribed Generically 

% R.xa Dispensed with Generica of 

Those Preacribed Generically 

% R.xa Subatituted 

100.0% 

0.0% 

0.4% 

100.0% 

0.0% 

1.7% 

94.9% 

4.8% 

73.0% 

8.9% 

85.1% 

0.2% 

0.0% 

1.1% 

81.4% 

0.0% 

0.1% 

N 

Vl 

.t:.. 

% R.xa Dispensed with Generics 

Cost per R.x 

Average 

Leading Brand(a) (LB) 

Generica (G) 

Cost Difference per Rx 

LB-G 

0.4% 

$6.21 

$6.21 

0.3% 

$6.64 

$6.65 

$3.25 

$3.40 

12.0% 

$7.05 

$7.50 

$3.93 

$3.57 

14.0% 

$6.44 

$6.31 

$7.42 

-$1.11 

18.7% 

$9.96 

$9.80 

$10.65 

-$0.85 

(LB-G)/G 

Price per R.x 

Average 

LB 

$8.87 

$8.87 

104.6% 

$10.39 

$10.39 

90.8% 

$11.06 

$11.27 

-15.0% 

$10.06 

$9.78 

-8.0% 

$14.17 

$14.02 

G 

Price Difference per R.x 

LB-G 

$8.60 

$1.79 

$9.49 

$1.78 

$12.14 

-$2.36 

$14.82 

-$0.80 

(LB-G}lG 20.8! 18.8! -19.4! -5.4~ 

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TABLE A6-1--Continued 

N 

Vl 

Vl 

Generic Name 

Grona Margin per Rx ($) 

Average 

LB 

G 

Grol8 Margin Difference per Rx ($) 

G-LB 

(G-LB)/G 

% Gross Margin 

Average 

LB 

G 

Number of Observation& 

Proportion of Prescriptions on 

Which Substitution waa Permitted 

in 1980 

Proportion of Prescription& by 

Customer Payment Type: 

Caah 

Medicaid 

Private Insurance 

Hydrochlorothiuide/ 

triamterene 

$2.66 

$2.66 

30.0% 

30.0% 

8,998 

58.1% 

78.1% 

12.3% 

9.6% 

Sulfamethoxa&ole/ 

trimethoprim 

$3.76 

$3.74 

$6.36 

$1.62 

30.2% 

36.1% 

36.0% 

62.3% 

6,425 

73.8% 

84.4% 

8.7% 

6.8% 

Doxycycline 

$4.00 

$3.78 

$5.56 

$1.78 

32.0% 

36.2% 

33.5% 

58.6% 

4,065 

84.5% 

86.3% 

6.1% 

8.6% 

Theophylline 

$3.61 

$3.47 

$4.72 

$1.25 

26.5% 

35.9% 

35.5% 

38.9% 

825 

57.7% 

79.2% 

12.4% 

8.5% 

Amitriptyline/ 

Perphenazine 

$4.21 

$4.21 

$4.17 

-$0.04 

-1.0% 

29.7% 

30.0% 

28.1% 

964 

54.7% 

75.8% 

14.4% 

9.8% 

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0 

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G) 

00 
TABLE A5-1--Continued 

Generic Name Allopurinol Chlorthalidone Dipyridamole Erythromycin Amoxicillin 

ethii•yccinate 

Rank* 

Therapeutic Cate1ory 

Refill Rate 

Leadin1 Brande 

Number of Manufaduren 

% RX8 Preacribed for Leadinl' Branda 

%RX8 Prescribed Generically 

%Rn Dispensed with Generics of 

Tho11e Preacribed Generically 

% RX8 Sub11tituted 

% Rxs Di11pen11ed with Generic11 N 

Vt Coat per Rx 

0\ 	 Average 

Leading Brand(a) (LB) 

Generic• (G) 

Coat Difference per Rx 


LB-G 


(LB-G)/G 


Price per Rx 


Avera1e 


LB 


G 


Price Difference per Rx 

LB-G 

{L~-G}lg 

25 


Anticout 


72.0% 


Zyloprim 


6 


83.1% 


16.7% 


16.6% 

2.8% 

5.2% 

$12.84 


$12.93 


$11.27 


$1.66 

14.7% 

$16.64 


$16.85 


$13.29 


$3.56 

J§.§! 

28 

Diure~ic 

71.0% 

Hygro~on 

27 

98.1% 

1.9% 

29 

Cardiovucular 

74.0% 

Penantine 

21 

96.1% 

3.9% 

36.8% 

4.3% 

4.9% 

90.7% 

7.1% 

9.4% 

$6.43 

$6.60 

$3.35 

$10.36 

$11.20 

$2.90 

$3.25 

97.0% 

$8.30 

286.2% 

$9.42 

$9.65 

$7.08 

$14.70 

$15.37 

$8.81 

$2.47 

3t.al 

$6.56 

7~.§21i 

30 

Antibio~ic 

12.0% 

E.E.S. 

2 

97.9% 

2.1% 

0.0% 

0.0% 

0.0% 

$3.62 

$3.62 

$7.42 

$7.42 

35 

Antibiotic 

9.0% 

Amoxil, Larotid 

29 

41.6% 

50.0% 

51.2% 

10.1% 

36.8% 

$2.32 

$2.29 

$2.38 

-$0.09 

-3.8% 

$6.56 

$6.55 

$6.57 

-$0.02 

-0.031 

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0

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TABLE A6-1--Continued 

Generic Name Allopurinol Chlorihalidone Dipyridamole Erythromycin Amoxicillin 

eth;yJsuccinate 

Gross Marrin per Rx ($) 

Average 

LB 

$3.80 

$3.1U 

$2.9Q 

$2.96 

$4.37 

$4.20 

$3.80 

$3.80 

'$4.24 

$4.27 

G $2.02 $3.74 $6.96 $4.19 

Grou Marrin Difference per Rx (8) 

G-LB -U.89 $0.79 $1.76 -$0.08 

N 

VI 

-..l 

(G-LB)/G 

% Grou Marrin 

Average 

LB 

-93.6% 

22.8% 

23.2% 

21.1% 

31.7% 

30.9% 

29.4% 

29.7% 

27.3% 

61.2% 

61.2% 

-1.9% 

64.6% 

66.2% 

G 16.2% 62.8% 67.6% 63.8% 

Number of Observations 

Proporiion of Prescriptions on 

Which Substitution waa Permitted 

1,346 2,250 1,630 6,997 6,690 

in 1980 66.6% 66.8% 62.0% 68.7% 87.5% 

Proport;ion of Prescriptions by 

Cuatomer Payment Type: 

Caah 

Medicaid 

fnvat! Insurance 

76.691) 

11.1% 

1:!.326 

76.4% 

11.3% 

13-~~ 

76.6% 

12.0% 

11.326 

83.3% 

6.1% 

7.726 

87.9% 

4.8% 

7.326 

0 

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0 

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C! 

G) 

en 
TABLE A6-l--Continued 

Generic Name ConjuJfated 

81troafena 

Nitroalycerin Metroniduole M•tranol/ 

Norethindrone/ 

Placebo 

Hydrochlorothiuide 

Rank• 36 39 41 42 44 

Therapeutic Category 

Refill Rate 

Eltrogen 

82.0" 

Cardiovucular 

49.0" 

Trichomonacide 

8.0" 

Oral Contraceptive 

78.0" 

Diuretic 

73.0% 

Leading Branda 

Number of Manufacturen 

Premarin 

51 

Nitroatat 

10 

Flagyl 

8 

Ortho-Novum, 

Norinyl 

2 

Hydro-Diuril, 

Eaidrix 

62 

N 

VI 

00 

% Rxl Preacribed for Leading Branda 

% Rx1 Preacribed Generically 

% Rxl Diapenaed with Generic• of 

Thou Prncribed Generically 

% Rxl Subatituted 

% Rxl Diapenaed with Generic• 

Coat per Rx 

Average 

Leadina Brand(•) (LB) 

Generia (G) 

Coat Difference per Rx 

LB-G 

97.1" 

2.7" 

84.8" 

4.2" 

8.5" 

$8.11 

$8.24 

$4.19 

$2.05 

39.2" 

80.1" 

73.8" 

0.9" 

40.1" 

$1.39 

$1.41 

$1.36 

$0.06 

99.7" 

0.3" 

14.3" 

0.9% 

1.0% 

$9.29 

$9.30 

$7.75 

$1.55 

100.0% 

0.0% 

0.09(1 

0.0" 

$8.18 

$8.18 

63.3% 

46.4% 

98.6% 

22.2% 

55.0% 

$1.68 

$3.31 

$0.51 

$2.80 

(LB-G)/G 

Price per Rx 

Average 

LB 

48.9" 

$8.30 

$8.39 

4.4" 

$3.10 

$3.39 

20.0" 

$13.52 

$13.58 

$7.99 

$7.99 

549.0" 

$4.71 

$8.24 

G $7.09 $2.71 $9.55 $3.81 

Price Difrerence/Rx 

LB-G $1.30 $0.88 $4.01 $2.83 

{LI!-G}£2 uu~ al·l~ jJ.Q~ 7J.226 

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TABLE Al-l--Continued 

Generic Name Conjugated 

utropna 

Nitro,tycerin MetroniduoJe M•tranoJ/ 

Norethindrone/ 

Placebo 

Hydrochlorothiuide 

Groaa Margin per Rx ($) 

Average 

LB 

$2.20 

$2.11 

$1.73 

$2.00 

$4.23 

$4.26 

-$0.17 

-$0.17 

$3.04 

$2.94 

G $2.90 $1.37 $1.81 $3.11 

Groaa MUJin Difference per Rx ($) 

G-LB $0.71 -$0.63 -$2.46 $0.17 

N 

VI 

\0 

(G-LB)/G 

9li Groa Marsin 

Avenge 

LB 

21.9,., 

26.6,., 

26.6\lfi 

-46.091i 

66.8,., 

69.0\lfi 

-136.4,., 

31.391i 

31.4\lfi 

-1.9% 

-1.9% 

6.6% 

64.6% 

47.1% 

G 40.9\lfi 60.6% 19.0% 86.2% 

Number of Obeervationa 

Proportion of Prescriptions on 

Which Substitution waa Permitted 

1,761 2,867 2,836 1,669 8,140 

in 1980 

Proportion of Prescriptions by 

Customer Payment Type: 

Caah 

61.99(1 

86.3% 

68.7\lfi 

80.3% 

68.2\lfi 

81.8% 

76.9\lfi 

83.0% 

92.5% 

76.9% 

Medicaid 

Private Insurance 

6.9\lfi 

§.§1 

12.8,., 

6.21 

11.0\lfi 

7.11 

14.1,., 

;.926 

12.9% 

10.11 

t::l 

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> 

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0 
Generic Name Phenytoin 

TABLE Al-l--Continued 

Erythromycin Amitriptyline 

b 

Ampicillin Penicillin VK 

>< 

><:1' 

Rank· 41 48 54 55 58 

Therapeutic Cateaory 

Refill Rate 

AnticonvulADt 

14.0" 

Antibiotic Antidepreaant 

13.0" 62.0" 

Antibiotic 

13.0" 

Antibiotic 

12.0" 

IAadinJ Branda 

Number of Manufacturers 

Dilantin 

28 

E-Mycin ElavU 

7 S6 

(none) 

62 

V-Cillin K, Pen-VEE-K 

69 

" Rxt Prescribed for LeadinJ Brandt 

% Rxt Preacribed Generically 

87.5" 

2.5" 

68.4" 80.0" 

28.4" 18.0" 

0 .0" 

84.2" 

41.3% 

51.6% 

N 

0\ 

0 

" Rxa Diapenud with Generica of 

Tho.. Prescribed Generically 

% Rxt Sub.\ituted 

%Rxt Diaperaed with Generica 

Coat per R.x 

Averap 

Lead.in1 Brand(a) (LB) 

Generica (G) 

Coet Difference per R.x 

84.8" 

2.8" 

4.6% 

$3.70 

$3.82 

$1.12 

12.~ 89.8" 

0 .2" 15.9" 

4.4" S0.3" 

U .18 $1.74 

U.81 $1 .72 

$1.97 $1.78 

100.0" 

7.2" 

100.0% 

$1.26 

$1 .26 

91.1% 

1&.2% 

58.0% 

$1.16 

$1.68 

$0.80 

LB-G $2.70 -$0.09 -$0.08 $0.88 

(LB-G)/G 

Price per R.x 

Averap 

LB 

G 

Price DitfeNnce per R.x 

LB-G 

(LI-Sill!J 

241.1" 

$6.57 

$6.66 

$4.66 

$2.00 

~a.il 

-4.6" -3.4" 

$6.96 $7.08 

$6.97 $7.85 

U .70 $5.41 

$0.18 $2 .44 

I·II ~'·II 

S4J~2 

4.82 

110.0% 

$4.48 

$5.1S 

h .O.C 

. U .09 

27.01 

,.. 

TABLE Ali-I--Continued 

Generic Name Phenytoin Erythromycin Amitriptyline Ampicillin Penicillin VK 

bue 

Grou Margin per Rx ($) 

Average 

LB 

G 

Grosa Margin Difference per Rx ($) 

G-LB 

(G-LB)/G 

% Gross Margin N 

0\ Average 

LB

- G 

Number of Observations 

Proportion of Prescriptions on 

Which Substitution wu Permitted 

in 1980 

Proportion of Preacriptions by 

Cu1tomer Payment Type: 

Cash 

Medicaid 

Private Insurance 

U.87 

$2.84 

$3.54 

$0.70 

19.8% 

43.7% 

42.6% 

76.0% 

2,683 

58.6% 

66.9% 

25.2% 

7.926 

$4.10 $5.36 

$4.11 $6.16 

$3.83 $3.64 

-$0.28 -$2.52 

-7.3% -69.2% 

68.8% 75.7% 

68.8% 78.5% 

66.2% 67.3% 

7,123 1,866 

67.2% 86.7% 

StU% 78.2% 

5.9% 11.7% 

9.1" 10.1" 

$3.67 

$3.67 

14.6% 

14.6% 

11,862 

93.0% 

83.5% 

8.9% 

7.7" 

$3.33 

$3.46 

$3.24 

-$0.22 

-6.8% 

14.3% 

67.5% 

80.2% 

18,214 

92.0% 

86.3% 

6.2% 

7.5% 

t:l 

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c 

0 

en 
TABLE Ai-l--Continued 

Generic Name Hydrochlorothiuide/ 

Sl!ironolactont 

Hydro1enated 

·~t 

alkaloisl! 

Terbutaline Minocycline Quinidine 

1ulfate 

Rank• 57 58 60 61 62 

Therapeutic Cate10ry 

Refill Rate 

Leadin1 Brandl 

Number of Manufacturen 

Diuretic 

77.096 

Aldactuide 

21 

Cardiovucular 

73.096 

Hyde!'line 

3 

Bronchodilator 

89.096 

Brethine 

2 

Antibiotic 

40.0" 

Minocin 

2 

Cardiovucular 

79.09(; 

(none) 

59 

N 

0"1 

N 

9(; RD Prucribed for Leadins Brandl 

9(; RD Prucribed Generically 

9(; RD Dilpen•ed with Generic• of 

Tho•• Prescribed Generically 

9(; RD Substituted 

9(; RD Di•pen1ed with Generia 

Coat per Rx 

Averqe 

Leadins Brand(•) (LB) 

Generia (G) 

Colt Difference per Rx 

LB-G 

98.896 

1.29fi 

100.09(; 

7.89fi 

8.996 

$9.29 

$9.75 

$5.05 

$4.70 

100.096 

0.096 

0.096 

0.096 

$14.37 

$14.37 

83.99fi 

10.996 

23.59(; 

0.79(; 

8.19fi 

$6.38 

$6.41 

$5.98 

$0.45 

92.99fi 

5.596 

0.09fi 

0.396 

1.4911 

$12.94 

$12.99 

$9.06 

$3.93 

0.09(; 

99.2% 

100.09(; 

0.09(; 

100.09(; 

$6.98 

$6.98 

(LB-G)/G 

Price per Rx 

Averase 

LB 

G 

Price Difference per Rx 

LB-G 

93.196 

$12.50 

$12.83 

$9.44 

$3.39 

$18.86 

$18.86 

7.896 

$9.72 

$9.84 

$8.47 

U.37 

43.496 

$17.33 

$17.37 

$14.16 

$3.21 

$10.93 

U0.93 

£LI!-~ll~ ~§.92fi !SUI 2J.'f% 

>'1:1 

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z 

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TABLE A6-1--Continued 

Generic Name 

Groaa Margin per Rx ($) 

Average 

LB 

Hydroc:hlorothia&ide/ 

SJ;!ironolactone 

$3.21 

$3.08 

Hydrorenated 

errot 

alkaloid!! 

$4.49 

$4.49 

Terbutaline 

$3.36 

$3.42 

Minocycline 

$4.39 

$4.38 

Quinidine 

sulfate 

$3.96 

G $4.39 $2.60 $6.11 $3.95 

Groaa Margin Difference per Rx ($) 

G-LB $1.31 -$0.92 $0.73 

N 

0'1 

V.l 

(G-LB)/G 

% Groaa Margin 

Average 

LB 

G 

29.8% 

26.7% 

24.0% 

46.6% 

23.8% 

23.8% 

-36.8% 

34.6% 

34.8% 

29.6% 

14.3% 

26.3% 

26.2% 

36.1% 

36.1% 

36.1% 

Number of Observations 

Proportion of Prescriptions on 

Which Substitution waa Permitted 

1,915 592 800 770 777 

in 1980 

Proportion of Preacriptiona by 

Customer Payment Type: 

Cash 

Medicaid 

Private Immrance 

74.5% 

76.9% 

12.S% 

11.8$ 

67.9% 

69.1% 

25.0% 

6.9% 

67.9% 

71.9% 

17.7% 

10.4% 

77.6% 

8S.8% 

4.7% 

11.6$ 

89.6% 

77.2% 

12.9% 

9.9% 

t:l 

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CJ 

(/) 
TABLE A6-l--Continued 

Generic Name Hydralazine/ Brompheniramine/ Chlordiazepoxide Chlordiazepoxide/ Hydroxyzine 

Hydrochlorothiazide/ Phenylephrine/ Clidinium 

Reaerpine Pseudoephedrine bromide 

Rank• 

Therapeutic Category 

Refill Rate 

Lea.dinc Branda 

Number of Manufacturer~ 

%Rxa Prescribed for Leading Branda 

%Rxa Prescribed Generically 

%Rxa Dispensed with Generics of 

Those Prescribed Generically 

% Rxa Substituted 

%R.xa Dispensed with Generics 

Coat per Rx 

Average 


Leadinc Brand(s) (LB) 


Generics (G) 


Coat Difference per Rx 

LB-G 

(LB-G)/G 

Price per Rx 

Average 

LB 

G 

Price Difference per Rx 

LB-G 

(LB-Gl/G 

65 

Cardiovucular 

79.0% 

Ser-Ap-Es 

22 

94.8% 

0.3% 

76.0% 

8.9% 

14.0% 

$7.88 

$8.68 

$3.88 

$4.70 

121.1% 

$10.68 

$11.32 

$6.34 

$4.98 

78.61 

66 67 


Cold preparation Tranquilizer, minor 


38.0% 62.0% 


Dimetapp Librium 


26 41 


98.5% 88.8% 


0.2% 10.2% 

100.0% 97.0% 

6.6% 17.3% 

8.1% 26.6% 

$2.61 $4.26 

$2.82 $6.63 

$0.43 $0.83 

$2.39 $4.70 

667.8% 666.3% 

$6.18 $7.13 

$6.33 $8.16 

$3.67 $4.33 

$1.68 $3.83 

16.21 §8.61 

71 73 

Antiapumodic Tranquilizer 

66.0% 40.0% 

Librax Atarax, Vistaril 

20 21 

98.6% 99.3% 

0.6% 0.1% 

100.0% 30.0% 

6.8% 1.6% 

8.0% 1.8% 

$6.86 $4.99 

$6.27 $5.04 

$1.61 $1.98 

$4.66 $3.06 

289.4% 156.6% 

$8.81 $8.51 

$9.10 $8.61 

$5.82 $6.83 

$3.28 $1.78 

66.41 26.1% 

> "t! 

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TABLE AI5-1--Continued 

Generic Name Hydraluine/ Brompheniramine/ Chlordiuepoxide Chlordiuepoxide/ Hydroxyzine 

Hydrochlorothiuide/ Phenylephrine/ Clidinium 

Reuroine Pseudoephedrine bromide 

Groaa Margin per Rx (8) 

Average 

LB 

G 

Groll Margin Difference per Rx (8) 

G-LB 

(G-LB)/G 

% Groaa Margin 

N Ave 

0'1 LB 

G 

Number of Observations 

Proportion of Prescriptions on 

Which Substitution wu Permitted 

in 1980 

Proportion of Prescriptions by 

Customer Payment Type: 

Caah 

Medicaid 

Private Insurance 

VI 

82.70 

82.74 

82.47 

-$0.27 

10.9% 

25.5% 

24.2% 

39.0% 

1,583 

48.5% 

14.4% 

17.0% 

8.7" 

82.157 

82.151 

83.24 

80.73 

22.15% 

49.6% 

47.1% 

88.3% 

4,079 

52.2% 

82.2% 

7.2% 

10.7" 

82.87 

82.64 

$3.151 

$0.87 

24.8% 

40.3% 

32.4% 

81.1% 

2,964 

88.9% 

83.5% 

7.6% 

8.9" 

82.915 

82.83 

$4.21 

$1.38 

32.8% 

33.5% 

31.1% 

72.3% 

2,904 

59.2% 

83.4% 

7.1% 

9.5" 

$3.58 

$3.56 

84.85 

$1.29 

26.6% 

41.8% 

41.4% 

71.0% 

1,354 

61.7% 

80.1% 

8.6% 

11.2% 

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r./} 
TABLE A6-1--Continued 

Generic Jiame 	 TriamcinoiQne Phentermine hfeclizint TetracicJine SQironolactone 

Rank• 

Therapeutic Category 

Refill Rate 

Leading Branda 

Number of Manufacturere 

% Rxs Prescribed for Leadins Branda 

% Rxs Prescribed Generically 

% Rxs Dispensed with Generics of 

Those Prescribed Generically 

% Rxs Substituted 

% Rxs Dispensed with Generics 

Coat per Rx 

N Averase 0\ 

0\ 	 Leadins Brand(a) (LB) 

Generics (G) 

Coat Difference per Rx 

LB-G 

(LB-G)/G 

Price per Rx 


Averase 


LB 


G 


Price Difference per Rx 

LB-G 

(1&-G}lQ 

80 

Cortic011teroid 

19.0% 

Ariatocort•• 

21 

97.1% 

2.7% 

61.6% 

1.7% 

3.3% 

$4.88 

$6.06 

$1.04 

$4.01 

386.6% 

$8.70 

$8.87 

$4.67 

$4.30 

9~.126 

82 83 

Anti-Obesity Antinauaeant 

26.0% 63.0% 

Futin Antivert 

11 31 

96.1% 90.6% 

4.3% 9.4% 

97.7% 93.4% 

0.3% 14.2!11i 

3.6% 20.8% 

$6.22 $3.60 

$6.38 $4.40 

$1.16 $0.68 

$4.22 $3.72 

363.1% 647.1% 

$9.03 $6.00 

$9.22 $7.78 

$6.14 $4.12 

$4.08 $3.66 

72.~1 88.~2£ 

86 


Antibiotic 


26.0% 


Achromycin-V 


136 


23.1" 


69.6% 


99.9% 


6.0% 


77.8% 


$0.73 

$0.79 

$0.71 

$0.08 

11.3% 

$3.76 

$4.12 

$3.67 

$0.46 

13.326 

87 


Diuretic 


77.0% 


Aldactone 


18 


100.0% 


0.0% 


3.7% 


3.7% 


$9.96 

$10.16 

$4.96 

$5.19 

104.6% 

$13.62 

$13.66 

$9.91 

$3.76 

37.82£ 

>

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> 

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TABLE A5-1--Continued 

Generic Name Triamcinolone Pbentermine Meslisint Tetragcline Spironolactone 

Groa Marlin per Rx ($) 

Averap $3.81 $3.82 $3.39 $3.03 $3.56 

LB 83.82 $3.84 $3.37 $3.33 $3.51 

G 83.53 83.98 $3.44 $2.95 $4.95 

Groa Margin Difference per Rx ($) 

G-LB -$0.29 $0.14 $0.07 -$0.38 $1.44 

(G-LB)/G -8.2" 3.4% 2.0% -12.9% 29.1% 

" Groa Margin Ave 43.8% 42.2% 49.1% 80.6% 26.3% 

N 

0\ 

...._J 

LB 

G 

43.1% 

77.2% 

41.2" 

77.5% 

43.3% 

83.5% 

80.8% 

80.4911 

25.7% 

50.0% 

Number of Observations 551 1,590 1,666 13,104 811 

Proportion of Prescriptions on 

Which Substitution was Permitted 

in 1980 48.5% 14.2% 87.3% 93.0% 64.5% 

Proportion of Prescriptions by 

Customer Payment Type: 

Cash 89.8% 91.3% 79.2% 85.5% 75.3% 

Medicaid 2.4% 1.8% 12.4% 7.3% 15.9% 

Privatt Insurance 7.8% 6.9% 8.3% 7.2% 8.8% 

•• No' among 200 leading brands prescribed, bu' baa high proportion of prescriptions in this entity. 
TABLE A5-1--Continued 

Generic N arne Dexbrompheniramine/ 

PseudoeQhedri ne 

Rank• 88 

Atropine sulfate/ 

Diphenoxylate 

93 

Diethylpropion 

96 

Acetaminophen/ 

Chlonoxuone 

96 

Tolbutamide 

98 

Therapeutic Category 

Refill Rate 

Cold Preparation 

47.0% 

Antidiarrheal 

26.0% 

Anti-Obeaity 

33.0% 

Muecle Relaxant 

36.0% 

Antidiabetic 

78.0% 

Leading Branda 

Number of Manufacturers 

Drixora.l 

IS 

Lomotil 

40 

Tenuate 

6 

Parafon Forte 

16 

Orinaae 

25 

% R.xll Prescribed for Leading Brands 

% Rxa Prescribed Generically 

% Rxa Diepenaed with Generics of 

Thoae Prescribed Generically 

%Rxa Substituted 

94.3% 

0.0% 

2.4% 

98.9% 

0.0% 

16.6% 

83.7% 

0.1% 

0.0% 

1.0% 

99.8% 

0.1% 

50.0% 

3.8% 

94.8% 

5.2% 

76.2% 

5.7% 

tv 

01 

co 

% Rxs Diapenaed with Generics 

Coat per Rx 

Average 

Leading Brand(a) (LB) 

Generia (G) 

Coat Difference per Rx 

LB-G 

8.1% 

$3.44 

$3.43 

$3.60 

-$0.17 

16.6% 

$3.34 

$3.93 

$0.80 

$3.13 

17.0% 

$6.42 

$6.31 

$6.96 

-$0.64 

4.0% 

$5.07 

$5.25 

$0.88 

$4.37 

8.7% 

$6.11 

$6.47 

$2.75 

$3.72 

(LB-G)/G 

Price per Rx 

Average 

LB 

G 

Price Difference per Rx 

LB-G 

[LB-G}lQ 

-4.7% 

$6.!!1 

$6.31 

$6.30 

$0.01 

o.a! 

391.3% 

$6.41 

$6.95 

$4.12 

$2.83 

68.7~ 

-9.2% 

$10.16 

$10.14 

$10.19 

-$0.06 

-0.52§ 

486.2% 

$8.64 

$8.67 

$5.62 

$3.16 

57.12§ 

135.3% 

$9.65 

$9.91 

$7.22 

$2.69 

37.3! 

>­

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z 

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...... 

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TABLE A&-1--Continued 

Generic Name 

Grou Margin per Rx ($) 

Average 

LB 

Dexbrompheniramine/ 

f!!Yd2JQhedrine 

$2.81 

$:1.88 

Atropine aulfate/ 

Diphenoxy)ate 

$3.08 

$3.02 

Diethylpropion 

$3.73 

$3.83 

Acetaminophen/ 

ChJonoxuone 

$3.47 

$8.4% 

Tolbutamide 

$3.55 

$3.45 

G 

Grou Margin Difference per Rx ($) 

G-LB 

$:1.70 

-$0.18 

$3.32 

$0.30 

$3.24 

-$0.59 

$4.64 

$1.%% 

$4.49 

$1.04 

N 

01 

1.0 

(G-LB)/G 

% G rosa Margin 

Average 

LB 

-6.6% 

46.5% 

54.6% 

9.0% 

48.1% 

4!U% 

-18.2% 

36.891) 

37.8% 

26.3% 

40.6% 

39.5% 

23.2% 

36.8% 

34.8% 

G 42.9% 80.6% 31.8911 84.1% 62.2% 

Number o( Obaervationa 

Proportion o( Prescriptions on 

Which Substitution was Permitted 

3,424 4,211 2,420 2,636 1,015 

in 1980 

Proportion of Prescriptions by 

Cuatomer Payment Type: 

Caah 

Medicaid 

Private Insyrance 

62.3% 

83.7% 

4.9% 

11.4% 

88.8% 

86.4% 

5.5% 

8.1% 

56.6% 

89.1% 

1.0% 

9.9% 

54.6% 

80.1% 

6.8% 

1S.2% 

59.8% 

72.3% 

18.8% 

8.9% 

a 

>>-1 

> 

0 

z 

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t) 

;:.:l 

c::: 

GJ 

en 
270 

APPENDIX A6 

DESCRIPTION OF THE DATA 

Two kinds of data were necessary for this analysis: first, 

a compilation of the provisions of the state laws, including 

formulary restrictions; and second, data on prescription 

transactions, as to the brand prescribed, the brand dispensed, 

the cost to the pharmacy, and the retail price. In this 

appendix, these data are described and problems noted. 

Most of the analysis utilized data for 45 leading multisource drug entities. The criteria for the selection of the 

data are specified. 

I. STATE LAWS AND FORMULARIES 

In addition to collecting the state statutes and amendments 

pertaining to drug product selection and all formularies in 

effect in 1980, we sought out compilations of statutory 

provisions already prepared by other researchers and trade 

organizations. We consulted by telephone with Board of 

Pharmacy or other officials in each state to confirm our 

understanding of the statute and to seek clarification when 

statutes or formularies appeared ambiguous or were silent on 

some important issue. In some instances, state officials 

themselves expressed uncertainty as to the most appropriate 

interpretation of an ambiguous provision. 

The results of our compilations are summarized in severa 1 

tables. Table Al-l in Appendix Al shows the statutory provisions in effect in each state in 1980. Information on formularies is given in Appendix Tables Al-l, A3-l and A3-2. 

271 ' 
APPENDIX A6 


fl. DATA ON DRUG TRANSACTIONS. PRICES AND COSTS 

The Federal Trade Commission purchased from IMS America, 

Ltd. data from two of its services. The National Prescription 

Audit (NPA) gathers data on the nature and prices of prescriptions dispensed through retail drugstores. The U.S. Drugstore 

Audit (USD) compiles data on the quantities and costs of 

prescription drugs purchased by drugstores for resale to 

consumers. The data used in this analysis are from the 1980 

audits. 

A. National Prescription Audit (NPA) 

The NPA uses a panel of about 800 retail drugstores in 

which, on two days each month, each prescription transaction is 

recorded. In 1980, the NPA included over one million prescription transactions. 

1. Sample Design 

The sample of stores is selected to be representative of 

both region of the country and type of store within each 

region. Stores are classified as having independent or chain 

ownership, by whether the chain has eleven or more units, and 

by the volume of the individual outlet. In the end, the sample 

must be based to some extent on convenience, since some stores 

decline to participate. 

Since the NPA sample is balanced across each of five regions 

of the continental United States rather than across individual 

states, the data may over-represent some states within a region 

and under-represent others. State shares of all U.S. prescriptions as measured by the NPA are often quite different from 

shares based on Census data, although positively correlated. 1 

Similarly, the NPA sample is not designed to provide a 

random sample of chains and of independents within each state, 

nor even to reflect the chain and independent market shares 

within an individual state. A comparison of NPA chain market 

!/ A comparative tabulation is available upon request. 

272 

DESCRJPTION OF DATA 

shares by state with market shares as measured by Census and 

other data show marked discrepancies in many states. 2 Were 

chains and independents to respond differently to drug product 

selection laws, inferences about differences between states' 

laws might be clouded by this sampling problem. However, our 

results show no strong difference in the behavior of chains and 

independents. 

Because most of the analysis uses data for a group of states 

at a time, rather than each state singly, the problem of 

inappropriate samples in individual states may be alleviated to 

some extent, as, for example, oversampling of chains in one 

state in the group is balanced by undersampling of chains in 

another state. Estimates for individual states are less 

reliable. 

2. Data on Individual Prescriptions 

The information recorded for each prescription includes, for 

both the drug product prescribed and the drug product dispensed, the name of the brand or drug entity, the manufacturer's 

name where specified, and the dosage form and strength; the 

number of units (e.g., tablets); the type of prescriber; 

whether payment is through Medicaid, other insurance, or cash; 

and the retail price. Retailers are instructed to record the 

full price received for a prescription. For insured prescriptions, including Medicaid, this price is the sum of any 

copayment paid by the customer plus the amount the pharmacy is 

reimbursed directly by the state or insurer. However, we 

observed in the NPA data prices of $.50 or $1.00, common 

amounts for a copayment, suggesting that in some instances only 

the copayment was recorded. Observations with a price of $.50 

or $1.00 were omitted from the regressions.3 Other extreme 

prices were judged to be coding errors and were excluded as 

well. 4 

'1./ See Table A4-l. 

~ All observations were included in the computation of the descriptive 

cross-tabulation statistics. 

!/ Details are available upon request. 

273 
APPENDIX A6 

We also reviewed IMS' coding on dosage form and strength, 

filling it in where missing, correcting it where necessary, and 

making the coding consistent within a drug entity. Our 

analysis required using a generic entity as the unit of 

analysis and this information was added wherever missing or 

incorrect in the IMS data. Observations where the product 

dispensed did not match the product prescribed in terms of 

generic entity, dosage form, strength, or number of tablets 

were excluded from the analysis. 

A major deficiency in the data for our purposes is that the 

record does not show whether the physician permitted or 

prohibited substitution. We therefore cannot wholly distinguish between physician and pharmacist behavior on substitution. 

B. U.S. Drugstore Audit ( USD) 

IMS' U.S. Drugstore Audit (USD) gathers data from pharmacy 

invoices as to the quantities purchased from and prices paid to 

manufacturers and wholesalers. Each month IMS collects data on 

purchases by the 1600 drugstores which participate in this 

audit. 5 IMS either microfilms all invoices received in a 

drugstore or, for outlets of large chains, accepts computer 

tapes covering warehouse withdrawals for specified outlets. 

For the purposes of our analysis, the methodology used in 

collecting the USD cost data has two major dra whacks: there 

was no adjustment for discounts based on overall volume, and 

chain costs were not recorded in a way appropriate for our 

use. In addition, costs were unavailable for many of the 

products in our study, even for some leading brands. Cost data 

for comparable brands in the same entity were used for interpolation of costs where missing. 

Only a single national average cost was computed for each 

drug. The cost estimate for any individual brand is therefore 

the same for states permitting and states prohibiting substitution. 

§../ The USD and NPA panels have little overlap. 

274 

DESCRIPTION OF DATA 


1. Not All Discounts Subtracted 

The USD fails to capture all discounts, especially total 

volume discounts. If the manufacturer or wholesaler gives a 

discount on the particular product and if that discount is 

recorded in the invoice line pertaining to the product, then 

the USD cost reflects the discount. In contrast, if a discount 

is given for the total purchase, across many products, and is 

not tied to a particular product, then the discount is not 

allocated among the purchases and is in fact not recorded at 

all. There are many discounts of the latter type. For 

example, a discount may be granted on the basis of total 

purchases for the quarter or for the year. Purchasers of 

larger quantities effectively pay lower prices than those 

described in the USD. 

The result of this omission is that the prices paid by 

pharmacies shown in the USD are higher than the actual transactions prices. 

2. Treatment of Chain Purchases 

The cost data reported for chains do not measure what the 

chain as a large buying organization actually paid. The costs 

reported are those which the chain warehouse charges to the 

chain's individual outlets. If the chain has its own warehouses, it may add to the price it paid the manufacturer or 

wholesaler a warehousing fee. Of course, this might restore 

comparability of chain costs to independents' costs where 

wholesalers have performed the warehousing function. However, 

we do not know what a chain actually adds to its own purchase 

costs. A chain may even add a non-uniform up-charge if it 

wishes to juggle its retail outlets' incentives to sell various 

products. We do know that, for a few drug products, the cost 

reported in the USD was substantially (and unbelievably) higher 

for chains than for independents. 

Because of these problems, and the fact that data are 

collected from only a few of the major chains, we eliminated 

the chain observations altogether. We estimated only a single 

average cost for each drug product, using data from independent 

stores only. 

275 

APPENDIX A6 


3. Greater Overstatement of Generic Costs Than of Brand Costs 

We believe that firms marketing generics are more likely 

than sellers of leading brands to grant large across-the-line 

discounts in order to secure a sale, but because bottom-line 

discounts are not recorded in this audit, comparisons of brand 

and generic costs are biased. In addition, the generic cost 

estimates we use are overstated relative to brand costs because 

we excluded from the computations all observations from large 

chains. Bias in the brand-generic comparison exists if chains 

purchase leading brands at lower costs than independents but 

receive even greater discounts on generics, relative to 

independents.6 

Overall, while the USD cost data have some deficiencies, 

they do support broad conclusions about the relative costs and 

retail gross margins of leading brands and generics. Indeed, 

the identifiable biases imply that some results would be even 

stronger with data better suited to our purposes. 

Ill. SAMPLE OF GENERIC ENTITIES 

A. Leading Entities in Terms of Dollar Sales 

For much of the analysis, we used 45 multi-source entities 

found among the top 100 entities ranked in order of total 

dollar sales to retail drugstores in 1980. The ranking of 

generic entities was provided by the Health Care Financing 

Administration and was based on IMS' USD data. The top 100 

entities accounted for about 900,000 of the million and a half 

prescriptions in the 1980 NPA. Of the 100 top entities, 41 

were single-source and 59 multi-source. 

fjj The trade press reports that chains purchase generics at especially 

advantageous prices. Drug Store News, July 21, 1980. 

At our request, an executive of a large drug chain checked the prices his 

firm had paid in 1980 for several pairs of products for which our data showed 

the cost of the generic to be higher than the cost of the brand or the retail 

gross margin of the generic to be lower. He found the cost to his chain to 

be lower on all the drugs discussed and the cost of the generic, in particular, 

to be much lower than our data showed. 

276 

DESCRIPTION OF DATA 


The products actually analyzed in this study are not the 100 

most frequently dispensed drug products in terms of numbers of 

prescriptions. Relatively low-cost prescriptions appear in the 

list ranked by dollar sales only if they are high in number of 

prescriptions. 

B. Solid Oral Dosage Forms 

We restricted the sample to tablets and capsules only. 

These dosage forms constitute about three-quarters of all 

prescriptions.7 Of the top 100 drug entities, 6 were not sold 

m tablet or capsule form. 

C. Most Frequent Dosage Form/Strength Combination 

The data for each drug entity were further broken down into 

unique dosage form/strength combinations, e.g., 100 milligram 

tablets, 50 milligram time-release capsules. In 11 entities 

there was no single oral dosage form/strength combination with 

at least 500 observations, an arbitrarily set minimum. Of 

these 11, 8 were single-source drugs. The most frequently 

dispensed single oral dosage form/strength combination for the 

sample as a whole was selected. 

D. Modal Prescription Sizes 

Finally, only those prescriptions which were among the five 

most frequently dispensed prescription sizes within that dosage 

form/strength combination (again, for the U.S. as a whole) were 

retained (e.g., 20, 30, 50, 60 and 100 capsules). Our reason 

was that price information spreads more readily on purchases of 

typical size than on odd sizes. 

1/ De Nuzzo (1981). 

277 

APPENDIX A6 

E. Multi-source and Single-source Entities 

We next identified which of these entities were singlesource and which multi-source. If the IMS coding system 

identified two or more suppliers, or if either of two FDA 

listings did, the drug was classified as multi-source. 8 

Otherwise, the drug was listed as available from a single 

source only. 

This criterion produced some anomalies. For 4 drug entities, 9 despite the apparent availability of alternatives to 

the leading brand, on none of the prescriptions in our sample 

was anything other than the single leading brand dispensed. 

Indeed, several drugs which we classified as multi-source 

appear to be have been still under patent protection in 1980. 

By our criterion, ibuprofen was a multi-source drug (although 

our sample of prescriptions showed only Motrin dispensed), 

whereas in 1980 the Boots brand Rufen had not yet been introduced in competition with the leading brand Motrin. Although 

the patent on Dyazide did not expire until 1981, .4 percent 

of our sample prescriptions of triamterene/hydrocholorothiazide 

from the 1980 NPA were filled with some other product, as were 

3 percent of the prescriptions for chlorpropamide although the 

Diabinese patent remained in force until 1984. Whether these 

oddities in the data were due to recording or processing 

errors, or whether there were sales of products by firms other 

than the patent holder, we cannot say. Reverse classification 

errors rna y also exist. Because we did not study patents or 

patent licenses, our definition makes possible the listing of a 

drug entity as multi-source when the second seller shares a 

patent or is licensed by the first and therefore may not set 

prices independently. 

Of the 83 drug entities meeting the other criteria, 50 were 

multi-source. Of these, one was dropped because by 1980 it was 

§./ The two FDA listings were the list of "Approved Prescription Drug 

Products" and a listing by ingredients provided by the Drug Listing Branch 

("Ingredient Search").

Y Ibuprofen, cephalexin, erythromycin ethylsuccinate, and hydrogenated 

ergot alkaloids, in the dosage forms and strengths specified for this study. 

278 

DESCRIPTION OF DATA 


(in this dosage form) an over-the-counter drug and 4 others 

because of data problems. Most of our analysis, therefore, is 

based on 45 multi-source drugs. 

In some of the analysis we use only 37 of the 45 entities. 

The 8 entities excluded are those in which either only leading 

brands or only generics were in fact dispensed on prescriptions 

in our sample. Only brands were dispensed for the 4 listed in 

footnote 8, although generics appeared to be a vail able. For 2 

entities (ampicillin and quinidine sulfate) all products were 

classified as generics, while for norethindrone/mestranol/ 

placebo both products were classified as leading brands. 

Finally, for triamterene/hydrocholorothiazide we were missing 

some of the data necessary for making comparisons between 

Dyazide and the rarely dispensed alternative. 

F. List of Drugs Studied 

Table A6-l lists the top 100 entities, the exact specification of the observations selected, and the number of observations for these. 

G. The Representativeness of the 45 Drugs Studied 

The 45 drugs studied cannot be taken as the "45 most 

frequently prescribed drugs." Two reasons have been given 

already. Single-source drugs are necessarily excluded, and the 

ranking by dollar sales to drugstores probably does not match a 

ranking by numbers of prescriptions sold. In addition, some of 

the drugs which we excluded because of too few observations in 

a selected dosage form/strength combination were sold most 

frequently in non-oral dosage forms. In others, sales were 

spread over many dosage form/strength combinations rather than 

concentrated in one or a few; the entity as a whole was large 

even though no specification was appropriate for our analysis. 

Finally, a few large drug entities were excluded because of 

data problems. 

Nor are these 45 drugs strictly representative of the whole 

set of multi-source drugs. Less frequently sold drugs may well 

be less frequently substituted, both because pharmacies may 

find it unprofitable to stock multiple brands and because 

279 

APPENDIX A6 


TABLE A6-1 

100 Top Generic Entities and the Specification 

of the Drug Products Selected for this Study 

Multior 

Single Dosage 

Rank Generic Entity Source Form Selected 

1 Cimetidine 

2 Diazepam 

3 Ibuprofen 

4 Propranolol 

5 Methyldopa 

6 Cephalexin 

7 Sulindac 

8 Potassi urn Chloride 

9 Furosemide 

10 Metoprolol 

11 Ethinyl Estradiol/ 

Norgestrel 

12 Isosorbide Dinitrate 

13 Naproxen 

14 Chlorpropamide 

15 Indomethacin 

16 Hydrochlorothiazide/ 

Triamterene 

17 Mestranol/ 

Norethindrone 

18 Acetaminophen/ 

Codeine 

19 Sulfamethoxazole/ 

Trimethoprim 

20 Doxycycline 

s 

s 

M 

s 

s 

M 

s 

M 


M 


s 

s 

M 

s 

M 

s 

M 

M 

M 

M 

M 

tab 

tab 

tab 

tab 

tab 

cap 

tab 

Data problems; 

not used 

tab 

tab 

tab 

tab 

tab 

tab 

cap 

cap 

Data problems; 

not used 

Data problems; 

not used 

tab 

cap 

280 

DESCRIPTION OF DAIA 


TABLE A6-1, Continued 

Number 

of PreStrength Prescription scriptions 

Selected Sizes Selected in Sample 

300 mg 

5 mg 

400 mg 

40 mg 

250 mg 

250 mg 

200 mg 

40 mg 


50 mg 


500 UGM/ 

50/UGM 

10 mg 

450 mg 

250 mg 

25 mg 

25 mg/ 

50 mg 

16 mg/ 

800 mg 

100 mg 

100,60,30,50,40 

30,1 00,50,60,20 

100,30,60,50,40 

100,60,120,50,30 

1 00,60,30,50,90 

20,30,28,40,24 

60,30,20, 100,50 

30,1 00,60,50,20 

100,60,30,50,40 

21/63/42/126/147 

100,120,60,200,50 

60,30,1 00,50,20 

100,30,60,50,90 

30,1 00,50,60,40 

100,30,60,50,20 

20,30, 14,1 0,28 

1 0,8, 12,7,20 

6,688 

12,913 

5,326 

3,288 

3,980 

6,843 

2,339 

7,358 

1,974 

2,135 

1,144 

3,090 

2,347 

3,437 

8,998 

6,425 

4,065 

281 

AfPENQIX A6 

TABLE A6-1, Continued 

Multior 

Single Dosage 

Rank Generic Entity Source Form Selected 

21 Theophylline 

22 Amitriptyline/ 

Perphenazine 

23 Hydrochlorothiazide/ 

Methylodopa 

24 Timolol Maleate 

25 Allopurinol 

26 Clorazepae Dipotass. 

27 Prazosin 

28 Chlorthalidone 

29 Dipyridamole 

30 Erythromcin Ethylsuccinate 

31 Zomepirac 

32 Acetaminophen/ 

Propoxyphene 

Napsylate 

33 Thioridazine 

34 Flurazepam 

35 Amoxicillin 

36 Conjugated Estrogens 

37 Fenoprofen 

38 Nadolol 

39 Nitroglycerin 

40 Doxepin 

41 Metronidazole 

M 


M 


s 

M 

s 

s 

M 

M 

M 

s 

s 

s 

s 

M 

M 

s 

s 

M 

M 

M 

time-release 

tab 

tab 

Not used 

No solid oral 

dosage forms 

tab 

cap 

Not used 

tab 

tab 

tab 

Not used 

tab 

Not used 

cap 

cap 

tab 

Not used 

sub-lingual 

tab 

Data problems; 

not used 

tab 

282 

300 

DESCRIPTION OF DATA 


TABLE A6-l, Continued 

Number 

of PreStrength Prescription scriptions 

Selected Sizes Selected in Sample 

25 mg/ 

2 mg 

300 mg 

7.5 mg 

50 mg 

25 mg 

400 mg 

100 mg 

30 mg 

250 mg 

125 mg 

.3/.4 mg 

250 mg 

1 00,60,30,50,20 

100,30,60,50,90 

100,30,50,60,34 

30,50,100,60,40 

30,100,50,60,40 

100,120,200,60,90 

20,30,40,24,28 

30,20,50, 100,24 

30,20, 15,50,10 

30,15,21,20,18 

100,30,50,21 ,60 

100,25,30,50,200 

21 ,30,8,42, 16 

825 

964 

1,345 

2,203 

2,250 

1,630 

5,997 

6,739 

5,596 

5,690 

1 '751 

2,657 

2,836 

283 

APPENDIX A§ 

TABLE A6-l, Continued 

Multior 

Single Dosage 

Rank Generic Entity Source Form Selected 

42 Mestranol/ M 

Norethindrone/ 

Placebo 

43 Lorazepam s 

44 Hyd roch toroth iazide M 

45 Disopyramide s 

46 Ethinyl Estradiol/ s 

Norgestrel/Placebo 

47 Phenytoin Sodium M 

48 Erythromycin Base M 

49 Carbidopa/Levodopa M 

50 Tolazamide s 

51 Ethinyl Estradiol/ s 

Norethindrone Acetate 

52 Quinidine Gluconta M 

53 Tolmetin s 

54 Amitriptyline M 

55 Ampicillin M 

56 Penicillin VK M 

57 Hydrochlorotiazide/ M 

Spironolactone 

58 Hydrogenated Ergot M 

Alkaloids 

tab 

tab 

tab 

Fewer than 

500 obs.; 

not used 

tab 

cap 

tab 

Fewer than 

500 obs.; 

not used 

Not used 

Fewer than 

500 obs.; 

not used 

Fewer than 

500 obs.; 

not used 

Not used 

tab 

cap 

tab 

tab 

tab 

284 

DESCRIPTION OF DATA 


TABLE A6-1, Continued 

Number 

of PreStrength Prescription scriptions 

Selected Sizes Selected in Sample 

1 mg/ 


40 UGM 


1 mg 


50 mg 


300 UGM/ 

30 UGM 

100 mg 

250 mg 

25 mg 

250 mg 

250 mg 

25 mg/ 

25 mg 

1 mg 

28,84,56, 112,168 

30,60,50, 100,20 

100,30,60,50,20 

28,84,56, 168, 112 

100,90,60,120,200 

20,40,30,28,24 

30,100,50,60,20 

20,40,30,28,24 

40,20,30,28,34. 

100,30,60,50,40 

100,90,60,50,340 

1,559 

1,856 

8,140 

799 

2,683 

7,123 

1,866 

11,962 

18,214 

1,915 

592 

285 

APPENDIX A6 


TABLE A6-l, Continued 

Multior 

Single Dosage 

Rank Generic Entity Source Form Selected 

59 Cefaclor 

60 Terbutaline Sulfate 

61 Minocycline 

62 Quinidine Sulfate 

63 Tamoxifen 

64 Fluocinonide 

65 Hydralazine/ 

Hydrochlorothiazide/ 

Reserpine 

66 Brompheniramine/ 

Phenylephrine/ 

Pseudoephedrine 

67 Chlordiazepoxide 

68 Miconazole 

69 Haloperidol 

70 Nystatin 

71 Chlordiazepoxide/ 

Clidinium Bromide 

72 Clotrimazole 

73 Hydroxyzine 

74 Beclomethasone 

Dipropiona te 

s 

M 

M 

M 

s 

M 

M 

M 

s 

M 

M 

M 

Not used 

tab 

cap 

tab 

Fewer than 

500 obs.; 

not used 

No solid oral 

dosage form 

tab 

time-release 

tab 

cap 

Not solid oral 

dosage form 

Not used 

Fewer than 500 

obs.; not 


used 


cap 


No solid oral 

dosage form 

tab 

No solid oral 

dosage form 

286 

DESCRIPTION OF DATA 


TABLE A6-l, Continued 

Number 

of PreStrength Prescription scriptions 

Selected Sizes Selected in Sample 

2.5 mg 

50 mg 

200 mg 

100 UGM/ 

25 mg/ 

25 mg 

12 mg/ 

15 mg/ 

15 mg 

10 mg 

5 mg/ 

2.5 mg 

25 mg 

1 00,30,60, 50,90 

30,20,60,24,50 

l 00,120,200,60,50 

100,30,60,50,90 

20,30, 12,60,50 

l 00,30,50,60,40 

100,30,50,60,40 

30,20,50,60,40 

800 

770 

777 

1,583 

4,079 

2,964 

2,905 

1,354 

287 

APPENDIX A6 

TABLE A6-l, Continued 

Multior 

Single Dosage 

Rank Generic Entity Source Form Selected 

75 Nitrofurantoin s 

Macrocrystalline 

76 Potasium Bicarbonate s 

77 Procainamide M 

78 Oxycodone/APC s 

79 Ethinyl Estradiol/ s 

Acetate/ 

Placebo 

80 Triamcinolone M 

81 Gramicidin/Neomycin/ 

Nysta tin/Trimcinolone 

82 Phentermine M 

83 Meclizine M 

84 Carbamazepine s 

85 Ethynodiol Diacetate/ s 

Ethyinyl Estradiol 

86 Tetracycline M 

87 Spironolactone M 

88 Dexbrompheniramine/ M 

Pseudoephedrine 

89 Danazol s 

90 Pseudoephedrine M 

91 Amitriptyline/ s 

Chlordiazepoxide 

cap 

tab 

Fewer than 500 

obs.; not 

used 

tab 

Fewer than 500 

obs.; not 

used 

tab 

Not solid oral 

dosage form 

cap 

tab 

Not used 

tab 

cap 

tap 

time-release 

tab 

Fewer than 

500 obs.; 

not used 

OTC in this 

dosage form; 

not used 

Not used 

288 

DESCRIPTION OF DAIA 


TABLE A6-l, Continued 

Number 

of PreStrength Prescription scriptions 

Selected Sizes Selected in Sample 

SO mg 

2S mg 

S mg 

4 mg 

30 mg 

2S mg 

1 mg/ 


SO UGM 


2SO mg 

2S mg 

20 mg 

40,30,28,60,20 

30,60,90, 1 oo,so 

30,20, 12,1 oo,so 

16,30,20, 12, 1s 

30,60,20, IS, 14 

30,SO, 100,20,60 

21 ,63,42, 126,84 

30,20,100,40,24 

1 00,60,30,40,120 

20,30, 12,60,SO 

1,796 

1;022 

4,S46 

SS1 

l,S90 

1,666 

1,093 

13,1 OS 

811 

3,424 

289 

APPENDIX A6 


TABLE A6-1, Continued 

Multior 

Single Dosage 

Rank Generic Entity Source Form Selected 

92 Hydrochlorothiazide/ s 

Propranolol 

93 Atropine Sulfate/ M 

Diphenoxylate 

94 Cyclobenzaprine s 

95 Diethylpropion M 

96 Acetaminophen/ M 

Chlorzoxazone 

97 Meclofenamic Acid s 

98 Tolbutamide M 

99 Oxazepam s 

100 Hydralazine/ s 

Hydrochlorothiazide 

Fewer than 

500 obs.; 

not used 

tab 

Not used 

time-released 

tab 

tab 

Fewer than 

500 obs.; 

not used 

tab 

Not used 

Not used 

290 

DESCRIPTION OF DATA 


TABLE A6-1, Continued 

Number 

of PreStrength Prescription scriptions 

Selected Sizes Selected in Sample 

2.50 mg/ 

25 UGM 

715 mg 

250 mg/ 

300 mg 

500 mg 

30,20,50,24, 100 4,211 

30,60,50, 15,20 2,420 

40,30,50,60, 100 2,636 

1 00,60,30,50,120 1,015 

291 

AfPENDIXA6 

consumers may not learn comparative price information as 

easily. Therefore our quantitative estimates of substitution 

and price effects may be somewhat higher than would be true 

for all multi-source drugs (and are certainly higher than the 

appropriate figures for the entire universe of drugs including single-source drugs.) 

IV. CLASSIFICATION BY TYPE OF BRAND 

While the terms "brand" and "generic" may seem to be clear 

without further definition, they are in fact subject to 

considerable ambiguity. For example, many products of small, 

non-research-intensive firms (commonly labeled as "generic 

firms") are marketed with unique names, not just the names of 

the generic entities. For this study we have used definitions 

of brand type intended to capture the functional distinction 

associated with physician, pharmacist and consumer familiarity 

with and loyalty to a brand, to distinguish between a brand 

which can command a premium price because of strong loyalty, 

especially among prescribing physicians, and an alternative 

product which relies on low prices as its primary selling 

feature. 

A. Definitions 

The definitions are as follows: 

Leading Brand. A leading brand is defined in terms of 

the number of prescriptions as written 

(not as dispensed) and is any brand either 

a) included i=t the list of the 200 products 

most frequently prescribed, or b) named by 

the physician on at least 20 percent of all 

prescriptions in its entity. 

We used the list for 1980 compiled (annually) by IMS from 

its National Prescription Audit and published in the April 1981 

issue of Pharmacy Times. Among the 200 leaders are a number of 

292 
DESCRIPTION OF DATA 


generic names, reflecting the high incidence of generically 

written prescriptions in some large entities; these are 

excluded from our definition of leading brand. We added the 

second criterion because even the dominant brand in a small 

drug entity may not appear on the list of the top 200 leading 

brands. 10 

In most but not all instances a leading brand was marketed 

by one of the pharmaceutical firms characterized by large size 

and a strategy of heavy investment in research and development 

and in marketing. Of the 49 leading brands found in the 45 

multi-source entities selected for analysis, 43 were marketed 

by one of the top 30 firms, ranked by dollar sales in 1980.11 

For these firms, a premier strategy is the discovery of a new 

chemical entity. The newly discovered drug is typically 

marketed intensively; "detail men" visit physicians to describe 

the new drug and to try to persuade them to prescribe it. 

During the period of patent protection the firm is free from 

competition from other versions of the same drug entity and 

usually strong physician loyalty to the pioneer brand is 

developed which continues even after the expiration of the 

patent. 

Generic. All non-leading brands are termed "generics," whether 

or not the product is marketed solely under the 

genenc name. 

The term "generic" as applied to a particular version of a 

drug entity, sold by an identified firm, can be and has 

been -- defined in several different ways. One definition is 

whether the product is sold under the name of the generic 

entity only, without any proprietary name. Another is whether 

the product is sold by one of the so-called "generic firms," 

i.e., a firm other than one of those characterized as a large 

research-intensive firm. These firms typically avoid the 

lQ/ In only one of the 45 multi-source entities selected for analysis did 

this supplementary criterion lead to the reclassification of a drug product as 

a leading brand {the Aristocort brand of trimcinolone.)

W The IMS Research Group (1981) ranked companies in order of 1980 dollar 

purchases by drugstores of ethical pharmaceutical products. 

293 
APPENDIX A6 


expenses of research and development and of brand-name marketing and specialize instead in offering low-price versions of 

drugs whose patents have expired. 

There is, of course, some overlap in firms' marketing and 

research strategies. Small firms specializing in generics may 

also market one or a few leading brands. Also, as mentioned 

above, many of the non-leading brands sold by generic houses 

carry proprietary names, even though few prescriptions are 

written for these products by name. Similarly, some researchintensive firms have complemented their leading-brand 

offerings by selling other products either under the generic 

name alone or with a name which combines a part of the firm's 

name with the generic name, e.g., SmithKline French's SKAmpicillin. If a firm sells a line of generics under this type 

of naming system the products are called "branded generics." 

For some purposes we wish to distinguish "branded generics" 

from other "generics." The criterion is simply the identity of 

the firm marketing the product. Non-leading brands sold by the 

30 largest pharmaceutical firms are classified as branded 

generics, and non-leading brands sold by all other (smaller) 

firms are classifed as generics. This definition accords with 

the characterization of a branded generic as a product without 

strong brand loyalty marketed under the repu ta tional umbrella 

of a major research firm. 

In summary, it is not accurate to describe a drug entity as 

typically containing one brand and one or more products sold 

only under the generic name. For the purpose of this study, 

the two-part classification of "leading brand" and "generic" 

captures the distinction in function between a high-price 

frequently prescribed brand and all others. The term "generics" will be used interchangeably with "all other brands", as 

distinguished from "leading brauds." 

B. The Effect of Alternative Definitions 

Some of our results would probably be altered if different 

definitions were used. Generic market share, for example, is 

wholly dependent upon this definition. Categorizing products 

which command perhaps 8 or 15 percent but fewer than 20 percent 

of the prescriptions in an entity (our cutoff) as "brands" 

294 

DESCRIPTION OF DATA 

would substantially reduce our measures of the generic market 

share in a number of drugs. The largest reductions would occur 

for several older antibiotics in which a handful of brands each 

have a significant but small market share. One example will 

illustrate this. In ampicillin there are three brands which 

each account for over 10 percent of prescriptions dispensed and 

another three with over 5 percent apiece. Together these 6 

account for 70 percent of all prescriptions dispensed, and all 

but SK-Ampicillin carry recognizable brand names that do not 

include the generic term "ampicillin." Because none of them 

commands more than 5 percent of prescriptions written for 

ampicillin, we infer that pharmacies may not feel constrained 

to carry them in inventory for the purpose of filling "dispense 

as written" brand-written prescriptions. For other purposes, 

however, these products might better be categorized "brands", 

rather than "generics." If that were done, the share of 

ampicillin prescriptions dispensed with a generic (as redefined) would fall from 100 percent to 26 percent. Since 

antibiotics account for about half of all generic prescribing, 

our numbers are very dependent on the definition of "brand" and 

"generic" for antibiotics. 12 

C. Anomalies in Brand-Generic Classification: 


Retail Price of Generic Higher 


Than Price 0 f Brand 


Three drugs failed to follow the pattern of the brand 

selling for a higher retail price than the generic. The three 

exceptions were theophylline sustained-release tablets, 

perphenazine and amoxicillin. These exceptions illustrate two 

difficulties in finding a single coding system for brand type 

which captures the functional meaning of "leading brand" versus 

"generic". First, some of the "generics" are brands with small 

market shares which nevertheless are preferred to the leading 

brand by some consumers or physicians, as is apparently the 

case for theophylline and perphenazine. Second, some of our 

ll/ See Chapter 6 for discussion of choice of product to be dispensed on 

generically-written prescriptions and generic market share in general. 

295 

APPENDIX A6 


"leading brands" belong to a generic entity for which consumers 

and physicians apparently believe all brands are essentially 

the same; amoxicillin is an example. The three drugs are 

discussed in turn. 

I. Theophylline, a bronchodilator 

We have identified only Theodur as a leading brand. 13 It 

accounts for 85.1 percent of prescriptions written and about 

the same (85.9 percent) of prescriptions dispensed. A 

secondary brand, Sustaire accounts for essentially all the rest 

of the prescribing (14.7 percent) and dispensing (13.9 percent.) (For .1 percent of theophylline prescriptions dispensed 

the brand or manufacturer was not recorded. There is little 

substitution (1.09 percent) and almost no generic prescribing 

(.24 percent).) Sustaire sold at higher prices; the retail 

price per prescription, averaged across all states, was $9.78 

for Theodur and $12.14 for Sustaire. (This price disparity is 

more striking because the size of the average Theodur prescription was one-quarter larger.) The higher retail price of 

Sustaire is consistent with the higher pharmacy-level cost 

shown in our data, $.0956 per tablet for Theodur, $.1123 for 

Sustaire. 

2. Perphenazine, an antidepressant 

The leading brand, Triavil, accounts for about 81 percent 

of all prescriptions, both as written and as dispensed. A 

second brand Etrafon accounts for essentially all the rest 

(.1 percent of prescriptions dispensed do not have the 

manufacturer recorded.) The retail prices, across all states, 

Jdl Slophyllin Gyro-caps is also listed among the top 200 prescribed 

drugs in 1980, which would classify it an a leading brand. However, no 

prescriptions for this brand in the 300 mg. strength we had selected appeared 

in our data. Products named Theodur obviously confront stronger competition, 

aa with Slophyllin, in related dosage forms and strengths, a.nd it ma.y be that 

the overall competition faced by a brand-name product is reflected in a price 

schedule which appears to have some internal consistency, and that the relatively low price seen in this dosage form and strength is due to the wider 

picture. 

296 

DESCRIPTION OF DATA 


were $9.80 for Triavil and $10.65 for Etrafon (same sized 

prescriptions); the wholesale costs were ordered consistently, 

$.1468 and $.1595 per tablet. 

3. A.moxicillin, an anti-infective (antibiotic) 

This is an entity where there are several brands with 

nearly the same market shares, nearly the same prices, and 

nearly the same costs to pharmacies. There are two leading 

brands, Amoxil and Larotid, but the two together account for 

only 42 percent of all prescriptions written but 63 percent of 

all prescriptions dispensed. There are several other brands 

with 5 percent to 10 percent of the market. The prices of 

these two leading brands are slightly lower than the prices of 

other brands, $6.55 compared with $6.57 per prescription. 

However, the fact that the average prescription sold for 

the leading brand is one capsule smaller than the average for 

other brands is enough to reverse this result. The cost per 

tablet of the leading brands ($.1004) is slightly higher than 

the average cost for other products (products of other large 

manufacturers, $.1032; products of small manufacturers, 

$.0980.) 

V. A-1ETHODS OF GENERALIZING 


ACROSS DRUGS14 


In order to isolate the effects of the substitution laws by 

minimizing variation due to other causes, the analysis proceeded drug by drug. But patterns of substitution are very 

different from drug to drug, as are costs, prices, gross 

margins, refill rates, the incidence of generic prescribing -­

all important variables. Any answers to questions about the 

overall effects of the laws require a method of generalizing 

across drugs. 

Uf The way in which differences in formulary restrictions makes comparisons 

more difficult for data aggregated across drugs is discussed in Chapter 2. 

297 
APPENDIX A6 


We have used different methods for different purposes. The 

methods discussed here apply to cross-tabulations of data. For 

generalizing across regressions run for each drug separately, 

other techniques were used. 15 

One natural way of summing up effects across different 

drugs is to take a simple average of the averages for the 

individual drugs. This is used in Chapter 3 to summarize 

brand-generic price, cost and gross margin differences. This 

method, however, weights drugs with small sales too heavily 

relative to their actual importance in the economy. 

Another way to aggregate across drugs is to weight each 

drug by the number of prescriptions. This is the method used 

most frequently in reporting results of cross-tabulations a bout 

prescribing and dispensing-patterns. Any estimates of proportions of prescriptions thus reflect a proportion of all 

transactions, or of all decisions. 

Where not otherwise indicated, the weights used are shares 

of total (new and refill) prescriptions for solid oral dosage 

forms. While we used only data for new prescriptions in our 

analysis, for purposes of generalizing across drugs we wanted 

to take in to account the fact that prescriptions for some drugs 

are refilled far more frequently than for other drugs. We 

assumed that the substitution rate for a drug would be the same 

for refills as for new prescriptions; we are told that it is 

usual for a pharmacist to dispense on a refill the same product 

dispensed on the new prescription and there is sometimes even a 

requirement to that effect. We therefore incorporated refill 

ratios in the weights: 16 each drug entity's weight was its 

share in the total number (new plus refill) of prescriptions 

for the 45 leading multi-source drugs studied. The total number 

of new (but not refill) prescriptions in each drug entity was 

calculated by summing the numbers of prescriptions for each 

brand and generic version within the drug entity given in the 

1980 Basic Data Report published by IMS, based on its National 

Prescription Audit. After computing a refill rate for each 

entity using unpublished materials from IMS, we were able to 

!§./ See Chapter 5 and Appendix A7. 

W Refill rates va.ry widely from drug to drug, as shown in Appendix Table 

AS-1. 

298 

DESCRIPTION OF DATA 


estimate the total number of prescriptions for each entity in 

1980. 

An alternative set of weights is the share of dollar sales, 

useful in that it reflects the fact that a substitution on an 

expensive prescription is likely to save consumers more than a 

substitution in a low-priced entity. This weighting scheme was 

computed in an analogous manner from the same data sources. In 

general, the results from the two weighting schemes were 

similar. 

Another issue in designing a weighting system is the selection of prescriptions to be included in the universe. Because 

this study uses data for solid oral dosage forms, the weights 

are based on a total for only these dosage forms. An alternative would be each drug's share of all prescriptions regardless 

of dosage form. This would be appropriate if brand choice and 

pricing behavior is indeed the same across dosage forms. 

Estimates using this alternative weighting scheme were computed 

by an analogous method. This yielded, for example, a slightly 

higher substitution rate, implying that drugs which are sold 

also in other forms are substituted slightly more frequently 

(when in tablet or capsule form) than other drugs. However, 

since we lack estimates of actual substitution rates for 

liquids or ointments, for example, using all-form weights 

assumes that the substitution rate is the same across all 

dosage forms. While there is no obvious reason to doubt the 

appropriateness of this assumption, weights which are defined 

in the same terms as the underlying data eliminate a possible 

source of unreliability, and the weights which match the data 

most closely are used throughout most of the report. We 

believe our estimates to be reasonably accurate measures of the 

whole universe of (multi-source) prescriptions. In any case, 

tablets and capsules constitut~ 75 percent of all prescriptions. 

The estimation of total dollar savings due to the DPS laws 

required somewhat different treatment. Instead of using data 

only for solid oral dosage form prescriptions, we used data for 

all prescriptions (all dosage forms). The methodology and 

numbers used are presented in section III of Chapter 8. 

299 

300 

APPENDIX A7 

ECONOMETRIC ISSUES 

This appendix supplements the discussions in Chapters 5, 7 

and 8 about the econometric models estimated. The models are 

presented in greater detail than in the text and modeling 

choices are explained. In particular, the choices made about 

the level of aggregation have statistical consequences for 

which in some cases we are able to make adjustments. 

I. STATEMENT OF THE MODELS 

A. The Logit Model for Brand Choice 

The logit model for the substitution choice is: 

Prob (5.) ... 1 

where Prob(Sr) is the probability that a substitution is made 

on the rth prescription and Zr represents the values of a set 

of independent variables, defined below, for the rth prescription. 

When natural logarithms are taken on both sides of the 

equation, 

Prob (5,) ) "" F(Z.) log ( 1 - Prob (5.) 

301 

APPENPIX A7 


+ a4RXANTlt. + a6POS + a 6NEG 

+ a1LlABt + a8PASSt + a9lNFOt 

where t • 1, 2, .. . , 47 indexes the state; for each t, i 

• 1, 2, . . . , Nt indexes the stores within that state; and for 

each i and t, r = 1, 2, .. . , R" indexes the prescriptions 

dispensed by that store. 

The choice of a brand -- the dependent variable -- is coded 

as 0 if the prescription was dispensed as written and as a 1 if 

instead a substitution was made. 

T he fi rst 8 variables, representing provisions of the 

state' s drug product selection law, are dummy variables, coded 

1 if the provision is present and 0 if not. They are discussed 

in Cha pter 5 and Appendix AI. The remaining variables are 

discussed at greater length in this appendix. 

MAND 

Mandatory substitution (on all prescriptions, regardless of 

insurance type.) 

RXPRO. RXANTl, and RXNEUT 

"P co-substitution", "anti-substitution" or "neutral" 

physicians' prescription pad, respectively. (RXNEUT is the 

omitted category.) 

302 

ECONOMETRIC ISSUES 


POS, NEG. and NO FORM 

Positive formulary, negative formulary, or no formulary, 

respectively. (NO FORM is the omitted category.) 

L!AB 

Express statutory protection of the pharmacist from greater 

liability when substituting than when filling a generically 

written prescription. 

PASS 

Cost pass-through. 

INFO 

Notification. 

QUAN 

The number of tablets/capsules in the prescription. 

SS!NDEX 

An index of the store's average price of 18 single-source 

drugs, relative to the average price across all stores. 

CHAIN 

CHAIN= 1 if the store is a member of a chain with more than 

10 outlets; CHAIN= 0 if the store is independently owned or 

part of a small chain. 

MED, PRIV. and CASH 

Mode of payment for the prescription: Medicaid, private 

insurance, and out-of-pocket. (CASH is the omitted category.) 

303 

APPENDIX A7 


GEN 

The proportion of prescriptions written generically In the 

state, computed across 45 drugs. 

TIME 

The number of months from first implementation of the 

state's drug product selection law to the month of the prescription transaction. 

B. The Generalized Least Squares Model for Prices 

For explaining (brand, generic and average) prices we used 

an error components model estimated with a generalized least 

squares procedure: 

Pit = + b2DPSt + b3QUANit + b b 4SSINDEXit 1 

+ b5CHA!Nit + b6MEDit + b7PR!Vit + b8GENt 

+ ERRORit 

where t = 1, 2, ..., 49 indexes the state; for each t, i = 1, 

2, ..., Nt indexes the stores within that state; and for 

each i and t, r = 1, 2, ..., Rit indexes the prescriptions 

dispensed by that store. 

The price is a store average. There are three regressions 

run for each drug. In one, the only observations included are 

those for prescriptions on which a leading brand was dispensed. In the second, only generically-dispensed prescriptions are used. The third uses all observations for prescriptions in the drug entity, regardless of whether a brand or a 

generic was dispensed. Thus, both intercepts and slopes can 

vary both with the drug entity and with the type of drug 

product (brand or generic) being analyzed. 

The non-legal variables are defined as in the logi t regressions except that, like the price, several are store-level 

averages instead of pertaining to an individual prescription. 

The DPS variable is a dummy indicating whether substitution 

304 
ECONOMETRIC ISSUES 


was permitted by statute and formulary (DPS = 1) or not (DPS = 

0). Of the right-hand variables, only QUAN varies by drug 

entity and by type of product dispensed. 

ll. ECONOMETRIC STRATEGY 

A. Reduced Form of a Simultaneous System 

The models are essentially in reduced form and contain 

determinants of both costs and demand. We are not attempting 

to estimate the demand and cost functions which form the underlying structural system. 

A pharmacy's decisions on choice of brand to dispense and on 

prices for brands and generics are made simultaneously. For ( 

example, whatever influences pricing decisions affects also 

dispensing decisions, since the prices determine the gross 

margins and therefore the pharmacy's incentive to dispense 

one product rather than another product. Thus, the statistical 

analysis of one decision must include the determinants of all 

decisions. Each of the regressions contains the same set of 

independent variables, although the level of aggregation varies 

to match that of the dependent variable. 

Not only are the several decisions made simultaneously 

within a single pharmacy; those decisions are made at the same 

time as, and with an eye to, the comparable decisions in every 

other pharmacy in the market. There is, then, (conceptually) a 

system of simultaneous equations of multiple decisions made by 

each individual pharmacy, which in turn reflect its underlying 

cost and demand functions, and this system is embedded in the 

larger system which includes the same set of decision problems 

for each competitor in the market. Each store includes in its 

calculus the probable effects of its prices, for example, on 

the decisions of the other stores with which it competes. Each 

pharmacy's decisions therefore depend not only on its own cost 

and demand conditions but on those of its rivals as well. 

The model of monopolistic competition offers a useful 

paradigm for describing the operation of a local prescription 

drug market. Typically there are many sellers (at least in an 

urban area) and entry into retailing IS not difficult. 

305 

APPENDIX A1 

Retailers are differentia ted from one another in the eyes 

of consumers by location, services, and selection of other 

merchandise offered. Because stores are differentiated in 

these ways, a retailer has some discretion over the price in 

that a price set slightly higher than a nearby competitor's 

will not lead to the loss of all customers to that rival; some 

consumers will prefer to stay even if they must pay a slight 

premium. 

Other models are also plausible. Differences among consumers might be emphasized; some consumers find it worthwhile 

to seek comparative price information and thereupon find their 

way to the lowest-price stores, while others find it too costly 

to search, and purchase at the most convenient store without 

knowing how its prices compare to those elsewhere. Yet a third 

description is that of local oligopolies, linked in a chain 

through space. 1 That is, each retailer takes into account the 

response of only a few other retailers loca--red nearest, but 

each of these competitors competes not only with the first 

retailer but with a few others located at a slightly greater 

distance from the first retailer. 

The general result of any of these plausible models is that 

a store need not simply accept some price dicta ted by the 

market but instead knows that a slight price increase will 

cause it to lose some (but not all) of its customers, while a 

slight decrease will bring some but not all customers in the 

market. In other words, each store faces a downward sloping 

demand curve and all sellers determine their prices simultaneously, taking into account the probable response of at 

least some of the other competitors in the market. We envision 

this model as approximately applicable in each of the local 

markets in our sample. 

B. Short-Run Analysis 

We also recognize that we are analyzing the short run, not 

the long run. That is, we are not testing the hypotheses that 

drug product selection Ia ws lead to changes in the retail 

!/ See also Rossiter (1982) for a duopoly model. 

306 

ECONOMETRIC ISSUES 

market structure nor that manufacturers will lower their 

prices. 

C. Separate Regression for Each Drug 

In every case (logit and GLS), the regression is run for 

each drug entity separately, allowing both intercept and slopes 

to vary from drug to drug. Drugs differ greatly not only as 

to wholesale cost but also in demand patterns. The number of 

people afflicted with a disease for which the drug is useful 

directly affects the level of demand for the drug. Also, drug 

products differ as to the intensity of competition with other 

drugs used for the same therapeutic purpose and, even more 

immediately, with other brands of the same drug. The level of 

confidence about bioequivalence and the (related) incidence of 

generically written prescriptions affect the likelihood that 

pharmacies will dispense generic versions of the drug. All of 

these factors affect consumers' interest in shopping around for 

a good price and their willingness to accept generics. 

Within each entity the five leading prescription sizes were 

selected and observations on all five included in a single 

regression. This was done in order to obtain enough observations but to restrict the selection to the sizes on which 

consumers were likely to have the most price information. 

The pharmacy is modeled as seeking to maximize profit in 

each drug entity. That is, each entity is treated as independent of every other entity and of other non-prescription drug 

products sold by the store. This simplification is reasonable 

in that prescriptions for different drug entities are not 

interchangeable. Of course, interrelationships on both the 

demand and the cost sides surely exist. It is likely that a 

consumer's choice of store depends in part on the prices paid 

in the past for an array of prescriptions, and it is obvious 

that prescription department costs and store overhead are 

shared by all drugs. 

Some have argued that prices on single-source drugs -- where 

no substitutions are possible -- would be higher where substitution on multi-source drugs is permitted, that pharmacies will 

"make up" on single-source drugs for lower prices charged on 

multi-source drugs. This seems unlikely for at least two 

307 
APPENDIX A7 


reasons: if higher prices on single-source drugs were more 

profitable, the pharmacy would have charged those higher prices 

regardless of substitution possibilities. Moreover, the margin 

on substituted generics is higher, meaning that there are no 

lost profits to make up. 

A second possibility for higher single-source prices depends 

on a longer term response. That is, if the opportunity to 

substitute were to lead to lower profits for the pharmacy as a 

whole, some stores would exit. Especially if too many stores 

exited, the movement towards a new equilibrium might involve 

some increases in all prices, including those of single-source 

drugs. 

A contrary prediction -- that single-source prices will be 

lower where substitution is encouraged -- also depends on a 

longer-run story. That is, if competition were generally 

enhanced by substitution opportunities, such that price information on prescription drugs circulates more actively than 

before, the new environment might spur increased competition on 

single-source drugs as well. Further, if substitution opportunities led to a more rapid increase in the market share of 

low-cost retailers, more consumers would be paying lower prices 

on single-source drugs. 

We have tested whether prices of single-source drugs are 

affected by the drug product selection laws. For 19 singlesource drugs2 we ran GLS regressions with the following model: 

Pit = b1 + b2DPSt + b3QUANit + b 4CHA!Nit + b5MEDit 

+ b6PR!Vt + ERRORit 

where i indexes the store and t indexes the state. This model 

is identical to the one used for the multi -so urce prices except 

for the omission of the single-source price index (SSINDEX), of 

which the price being estimated is a part, and the variable for 

generic prescribing (GEN), since · generic prescribing of a 

V The 19 entities used are given in the list oC the top 100 entitiu in 

Table A6-l. 

308 
ECONOMETRIC ISSUES 

single-source drug cannot in fact affect stocking or dispensing 

decisions. The same GLS procedure used for the multi-source 

drug regressions was used for the single-source drug regressions.3 

The results of the 19 single-source price regressions do not 

show that the laws have any systematic effect on the prices of 

single-source drugs. Of the 19 coefficients on DPS, 12 were 

positive and 7 negative but none were significant even at the 

10 percent level. (The 12:7 split is not significant at the 

10 percent level by a binomial signs test.) 

That the Ia ws appear not to lead to substantial complementary or offsetting price changes in single-source drugs means 

that any estimate of consumer savings in multi-source drugs 

alone can be taken as an estimate of the total savings associated with the law. Had single-source prices risen a great 

deal, as a result of the law, consumers might have been worse 

off even if prices paid for multi-source drugs fell. Alternatively, the estimate would have been understated had singlesource prices fallen alongside multi-source pnces. The 

independence of the single- and multi-source drug markets 

precludes the need for any such adjustment. 

III. THE INDEPENDENT VARIABLES 

A. Detailed Definitions 

1. The Laws 

The inclusion of the dummy variables for the laws (DPS) or 

provisions of the laws (MAND, RXANTI. RXPRO, POS, NEG, 

PASS, LIAB, INFO) represents another way in which we are 

exploring only part of a system. That is, we do not attempt to 

explain why certain states adopted certain laws. In fact, we 

(implicitly) assume that the nature of the Ia w is not causally 

related to any of the variables we also use to explain price 

§../ See Section IV below. The specification of the single-source drug 

price data is described below. 

309 

APPENDIX A7 

and brand choice differences. This implies, for example, that 

states where chains have an especially high market share are 

not more likely to have very liberal (or very conservative) 

drug product selection laws than other states. 4 

Suppose instead that the laws are endogenous, that both the 

nature of the law and market behavior could be explained by 

some third set of (state) characteristics. If so, the performancf! of the retail prescription drug market is not a direct 

response to the specific provisions of the DPS law but rather 

reflects some underlying environment in the state which colored 

the drafting of the law as well as subsequent market behavior. 

That is, both the nature of the law and market behavior may be 

explained by some third set of state characteristics. In 

Chapter 6, we made a similar argument: that the prevailing 

attitude towards generic drugs among physicians in a state 

might explain the correlation between early adoption of a DPS 

law and a higher incidence of generic prescribing. 

We did not seek to model or test hypotheses about the lawmaking process. While an analysis of the actual evolution of 

each state's law would be valuable, it lies beyond this 

study. 

Our codification of the laws is described in Appendix A 1 and 

discussed further in Chapter 5. 

2. Quantity 

Because the ingredient cost to the pharmacy is higher with 

larger prescriptions and because prices for the five most 

popular quantities dispensed are all included in the same 

regression, the quantity (QUAN) variable is included in the 

!/ Oster (1980) analyzed the patterns of. adoption of several types of state 

consumer protection laws including the permissibility of retail advertising of 

prescription drugs.) The determinants of the nature of state drug product 

selection laws might be expected to be similar. However, the signs on the 

explanatory variables used in Oster's analysis (intended to measure consumer 

pressure, industry pressure, and ease of coordination among pharmacies) were as 

predicted but the coefficients were insignificant. 

310 

ECONOMETRIC ISSUES 


price regressions. (The pnce per tablet declines with prescription size, presumably reflecting the fixed cost of 

dispensing.) QUAN also picks up part of the differential cost 

of carrying inventory which is, or course, higher for highercost drugs; part is picked up by the constant term. 

It is possible that there is some interaction between the 

size of the prescription and the effect of drug product 

selection laws; this argues for the inclusion of QUAN in the 

substitution regressions as well. If large dollar expenditures 

cause buyers to search more, those with larger prescriptions may have more price-elastic demands. There are other 

demand-related factors associated with prescription size, too. 

Medicaid purchases are on average for larger quantities than 

cash purchases. This may reflect Medicaid regulations which in 

some states limit the number of prescriptions which may 

be purchased per month. Also, in many entities prescriptions 

on which a generic is dispensed are on average larger than 

those on which a leading brand is dispensed. And these, then, 

are also correlated: the generic market share is larger for 

Medicaid buyers than for others. These factors suggest that 

QUAN could have been included as a slope shifter on the 

DPS law variables (with an interaction term) as well as 

included to shift the intercept. We chose to avoid this 

additional complexity. 

3. Index of Single-Source Drug Prices 

The single-source index (SSINDEX) is an index of the average 

price, over 18 single-source drugs (on which no substitution is 

possible), relative to all stores in the sample. This index 

captures both demand and cost elements which are common to both 

single-source and multi-source drugs. 

On the cost side, differences in costs other than the 

acquisition cost and extra inventory costs of the drug are 

reflected in the single-source price index. The cost of 

dispensing a prescription, including overhead, is essentially 

the same regardless of the brand name on the tablet and 

therefore does not affect a pharmacist's choice of brand to 

311 

AfPENDIX A7 

dispense.6 However, differences m local retailing costs 

must be taken into account when comparing prices across 

locations. Store economies of scale in operating costs and in 

quantity discounts on purchases are also reflected, as are any 

chain-wide economies of scale. 

The level of demand is set primarily by the number and 

distribution of prescriptions written by physicians and disease 

patterns, other determinants of demand for physician services 

(such as incomes and extent of insurance), the availability and 

price of physician services, and factors affecting physician· 

prescribing choices which vary from place to place.6 The extent 

of Medicaid or private drug insurance coverage affects not only 

overall demand but demand for particular drugs. 

The willingness of consumers to accept or seek substitutions 

varies. Some types of consumers may simply find generics more 

acceptable than others. The age and income of the population, 

as well as the prevalence of insurance, affect the propensity 

to search. In markets with more price dispersion search is 

more valuable to consumers. Information is more readily 

available where there is newspaper advertising of retail 

prescription drug prices or ads featuring generics generally, 

and where populations are more homogeneous. 

Finally, while the nature of competition in each local 

market can be generally characterized as monopolistically 

competitive, the actual market structure varies in terms of 

numbers and types of competitors. A large store is further 

encouraged to keep prices low because of complementarities on 

the demand side, since the attraction (by low price) of an 

§} Recordkeeping costa and delay in payment make the coat per prescription 

higher for inaured prescriptions than for others. Therefore, the proportion of 

a atore'• cualomera whose insurance (private or Medicaid) covers prescription 

druga will alao affect the average coat of dispensing. Theee coat differences 

are picked up by the variables for customer payment type, MED and PRIV. 

Some drug product selection laws impose a special cost if a substitution 

ia made . This is discussed in Chapter 5. 

~ Some prescriptions written by physicians are not filled. All that is 

neeeaaary t o eliminate this problem from a single pharmacy's decision problem 

ia that it is the average price in the market that determines how many preacriptiona are left unfilled and that each store believes itself too small to 

have any significant effect on the overall price level in the market. 

312 

ECONOMETRIC ISSUES 


additional prescription customer will result in greater sales 

of non-drug items. 

The SSINDEX variable was constructed in the following way. 

Of the original list of the top 100 drug entities, 33 had only 

one provider. For each of these 33 single-source drug entities 

we selected the dosage form/strength combination with the 

largest number of prescriptions in our data, and within this 

specification the quantity (e.g., number of tablets) most 

frequently dispensed. We then used the 18 with the largest 

number of prescriptions to form the single-source price index. 

These are listed in Table A6-l. For each store, an annual 

average price for each of the 18 drugs was calculated. An 

index was formed by the ratio of this price to the mean store 

price of that drug, across all stores. Finally, for each store 

an unweighted mean of the 18 indices for the store was calculated. Each store, therefore, has one summary single-source 

price index. If for any of the single-source drugs included in 

the index the data set contained no observations for a store, 

the store's index was composed of an unweighted average of its 

indices on the remaining drugs. 

We believe that it is appropriate to use the SSINDEX 

variable in the regression since the drug product selection 

laws do not seem to affect the prices of single-source drugs; 

see section II.C above. (If instead the law in fact did affect 

single-source drug prices, the inclusion of both SSINDEX and 

dummies for the law would still not bias the coefficients. The 

multicollinearity might make it difficult to establish statistical significance, however.) 

4. Store Type 

While the single-source price index reflects most economies 

of scale enjoyed by large chains, we posit addi tiona I chain 

economies specific to the sale of multi-source drugs. Two are 

the possibility of especially favorable quantity discounts in 

buying generic drugs, or special contracting, and economies of 

scale in advertising a generics program or prices of specific 

generics. We therefore also include a dummy variable for type 

of ownership of the store (CHAIN). Chain market share is 

313 

APPENDIX A7 


treated as exogenous in this model; this choice would be less 

appropriate in a longer-run model. 

5. Customer Payment Type 

As discussed in Chapter 4, differences in insurance coverage 

affect the propensity of consumers to seek out the lowest price 

among stores or among brands. Also, reimbursement regulations, 

including MAC ceilings, influence pharmacists' brand choices 

and therefore pricing decisions. Differences in the proportion 

of the population covered by Medicaid (MED) and private 

insurance (PR!V), rather than paying out-of-pocket (CASH) will, 

then, cause differences in the numbers and types of prescriptions written, in the propensity of consumers to search out the 

lowest price and, for MAC drugs, the pharmacy's incentive to 

dispense low-cost products. Like the effects of chain ownership, some of the effects of insurance are picked up by the 

SSINDEX. However, the SSINDEXis a very summary and therefore imprecise measure of any single aspect of demand. Inclusion of the payment-type variables highlights this specific 

influence, especially for drugs where insurance could be 

expected to play a particularly important role. 

The variables representing customer payment type are treated 

somewhat differently in the brand-choice and price regressions, 

reflecting different choices as to the appropriate level of 

aggregation in the two models. The aggregation choices are 

discussed in Section IV below. In both models, the omitted 

category, incorporated in the constant term, is CASH. 

6. The Incidence of Generic Prescribing 

The effect of generic prescribing (GEN) on substitution in 

particular and on generic market share and prices in general 

was discussed in Chapter 6. As explained in Chapter 8, the 

average price regressions were run twice, once with and once 

without the GEN variable. 

The best measure would be the incidence of generic prescribing in each store for the individual drug entity which is 

the subject of the regression. However, there are many 

instances when there were so few observations of a drug even in 

314 

ECONOMETRIC ISSUES 

a whole state that the measured incidence of generic prescribing was zero, even though the national incidence was moderately high. Ratios specific to both drug and state therefore 

seemed prone to substantial misestimation. We used instead the 

incidence of generic prescribing measured across a weighted 

average of 45 drugs in the state. A similar sparseness of data 

at the store level led us to use a single state average rather 

than store averages. Therefore, the measure of generic 

prescribing varies only by state but not by store. This 

average is adjusted for each drug by multiplying it by a ratio 

of that drug's U.S.-wide proportion of generically written 

prescriptions to the same proportion computed across all 45 

drugs. This adjustment makes no statistical difference in the 

regressions but puts the magnitude of the coefficient on the 

variable into a more appropriate range. 

7. Pharmacy Cost of the Drug 

There is one variable, the cost of the drug product, which 

is present in a theoretical model but which is not included in 

the regression. Because a pharmacy's choice as to whether or 

not to substitute will depend largely on a comparison of the 

gross margins, the theoretical model clearly includes the costs 

of both brand and generic as explanatory variables. However, 

the cost data available did not allow this, and cost is not 

included in the regression analysis. For the reasons discussed 

in Appendix 4, we have a single per-tablet cost estimate for 

all stores for each drug product. Therefore the inclusion of 

cost as an independent variable would simply be the introduction of a constant.7 

The price regressions are, for this analysis, essentially 

equivalent to regressions with dollar gross margin as the 

dependent variable, since invoice cost is constant across 

states and thus gross margin would equal the price less a 

constant. This is a result of our method of computation but is 

probably true in actuality as well. That is, manufacturers 

!/ Insofar as a generic entity incl'udes several generic versions at varying 

costs, the cost measure would not be a constant in the regressions on generic prices. 

315 

APPENDIX A7 


probably do not vary their prices according to the nature of a 

state's drug product selection Ia w; it would be impossible to 

do so in selling to a multi-state chain. We reiterate that the 

single-period analysis cannot capture the effects of changes 

over time in manufacturers' prices on gross margin or price. 

8. Constant 

The constant can be interpreted as a measure of both the 

average acquisition cost of the drug plus the average dispensing cost. 

B. Correlations Among Variables 

An individual provision occurs sometimes in conjunction with 

another (or other) provision(s), and other characteristics of 

the retail prescription drug market are also intercorrelated. 

Whether this reflects some underlying structure which led to 

the passage of certain laws or whether it is simply a random 

pattern, it causes multicollinearity which in turn makes the 

estimation inefficient. That is, multicollinearity increases 

the difficulty of statistically identifying the effects of 

individual provisions or, indeed, of any independent variable 

which is correlated with others in the regression; when 

two influences (for example) typically occur in tandem neither 

can be identified as the one with greater force. Specifically, 

multicollinearity causes the estimates of the standard errors 

of the coefficients to be large, making it difficult to find 

statistical significance even where there is economic significance. 

Table A 7-1 shows pair-wise correlations among the eight 

individual statutory provisions. The correlations are based on 

the states as the units of observations. The correlations for 

prescription-level data used in the logit regressions or for 

store-level data used for the brand-choice regressions would be 

similar although not exactly the same due to the fact that 

there might be relatively many or few observations in those 

states whose statutes contain any pair of provisions. (In 

addition to pair-wise correlations there may also be more 

316 

ECONOMETRIC ISSUES 


TABLE A7-1 


Correlations among Statutory Provisions 


RX- RXMAND PRO ANTI POS NEG LIAB PASS INFO 

MAND 1 +.191 -.011 +.371 -.081 +.061 -.058 +.017 

RXPRO 1 -.434 +.169 +.151 +.021 +.258 +.114 

RXANTI 1 -.058 +.034 +.375 -.165 -.087 

POS 1 -.451 +.049 -.184 -.081 

NEG 1 +.124 +.039 +.135 

LIAB 1 -.038 -.021 

PASS 1 +.184 

INFO 1 

complex patterns among several provisions which would give rise 

to the same type of statistical problem.) 

RXPRO and RXANTI are of course highly correlated; if the 

prescription pad is not RXPRO, it can only be RXNEUT or 

RXANTI. For similar reasons, POS and NEG are correlated. 

The correlation between MAND and FORM reflects the fact that 

most states which mandate substitution have a formulary, 

typically a positive formulary. Other combinations also happen 

to occur. LlAB and RXANT/ occur together more than one-third of 

the time, and PASS and RXPRO about one-fourth of the time. 

There are also correlations between other economic measures 

and the presence or absence of a DPS law in 1980. Table A 7-2 

shows these for store-level data for the drug tetracyclinr· 

317 

APPENDIXA7 


TABLE A7-2 


Correlations between Non-Legal and Legal Variables 


MED PRIV GEN CHAIN 


DPS +.056 +.084 +.408 -.063 


(picked because of the large number of observations.) As 

discussed in Chapter 6, generic prescribing is much more 

frequent where DPS laws were in place. 

IV. AGGREGATION 

Whether to use the individual prescription as the unit of 

observation, or instead an average, was a major issue. Several 

types of averages were possible: store level, chain (within or 

across states), state, and any of these could be monthly or 

annual averages. We elected to use the individual prescription 

data for the brand-choice (logit) regressions and to use an 

annual store average (with a single state average across 

outlets for each chain) for the price regressions. The reasons 

for and implications of these choices are discussed in the 

context of each of the two types of models. 

A. Aggregation Levels Chosen 

1. The Logil Models: Individual Prescription Data 

The unit of observation used in the logit regressions is the 

individual prescription. The pharmacy may not adhere to a 

uniform policy of substituting either always or never. The 

decision may reflect individual consumers' willingness to 

accept a generic, or, alternatively, resistance to a higherpriced brand. In particular, the probability of substitution 

will reflect whether the customer pays with cash or is covered 

318 

ECONOMETRIC ISSUES 


by either Medicaid or some other form of insurance. For 

example, in some states the law requires substitution for 

Medicaid customers. (We include in the logit regressions 

dummy variables for the customer's insurance type.) 

An alternative specification would be the proportion of 

prescriptions on which substitutions were made by a store (over 

some specified time period.) However, because of the fewness 

of observations in many stores, the sample proportion for many 

stores would have been 100 percent (e.g., su bsti tu tion occurred 

on the only prescription in the sample) and for others 0 percent (e.g., no substitution was made on the one or two prescriptions in the sample), even though those stores might well 

have substituted, on average, some intermediate percent of 

the time. Technically, the logit program cannot use observations with 100 percent substitution because the denominator of 

the fraction is then zero, and zero cannot be used as the 

divisor. Even had the store-level aggregation been feasible, 

it would have wasted the information as to the insurance 

coverage of the specific prescription. 

We rejected also the state level of aggregation. To use 

state averages would have meant that a great deal of informa· 

tion about the variation in the data, due to differences among 

stores and among prescriptions within the store, would be 

lost. Less of the overall variance could be explain<:d, 

making it more difficult to achieve statistically significant 

results. 

2. The Price Regressions: Annual Store Al'erages 

Rather than the price for each individual prescription, an 

average price is used for each store or for each chain within a 

sta te.8 There are several reasons for this choice. 

l/ Price-per-tablet was also rejected as the unit of observation. A 

consumer makes a single decision ae to what and where to buy for the preec:ription aa a unit and presumably comparee prices for the prescription rather than 

the pric:e per tablet. Moreover, most of the pharmacy'& costa exc:ludinar 

the coat of the drug itself are per-prescription costa. The paperwork for 

reimbursement on an inaured prescription, for example, or the time needed to 

discuss substitution with a customer does not vary with the size of the 

prescription. 

319 

APPENDIXA7 


A store is modeled as setting a single price (or at least 

establishing a pricing policy) for a drug product (specific as 

to brand, dosage form, strength and quantity) rather than 

varying the price from customer to customer. One reason to 

believe that a pharmacy would set a single price is simplicity 

and avoidance of recomputation. 

Similarly, there is likely to be a chain-wide pricing policy 

which makes the prices of the different outlets more similar to 

each other than to prices at other chains or independents. · A 

chain's prices are averaged but only within a single state 

since Iaws vary from state to state. 

An alternative would have been to use three separate prices 

for the three insurance types, in recognition of the cost and 

demand differences among the three. However, even a single 

price set by a store reflects the mix of its customer types. 

For example, a store with a high proportion of privately 

insured customers may set a higher price than a store with 

mostly cash customers who are more attuned to price comparisons. Our regression model controls for customer mix through 

the two independent variables measuring the proportions of the 

store's sales which are Medicaid and other private insurance 

(with cash payment the omitted type). Because there are few 

observations per drug per store, this proportion would be 

poorly estimated if the proportion were computed over a single 

drug. Therefore, the payment-type proportions were calculated 

for each store or chain over all multi-source drugs together. 

The time period selected for the aggregation is the entire 

year 1980, except where the state's formulary (or, for 

Louisiana, the law) changed mid-year. In such instances the 

two portions of the year are kept separate and there are two 

"store averages" for a single store. 

The alternative to the formulary-period average is a monthly 

average. The primary benefit of using monthly data would be to 

correct for shifts in demand or in costs. While demand is not 

likely to have varied from month to month,9 costs may well 

have. However, inflation does not confound the results as to 

2./ Certain drugs may have seasonal patterns, for example, heavy use of 

cough and cold medications in winter. 

320 

ECONOMETRIC ISSUES 


the effects of the drug product selection laws as long as the 

samples of observations is balanced across the year in the 

same way across states, and this is very likely. Moreover, we 

found that month dummies included in some early regressions 

showed no consistent significant patterns, and the coefficients on month dummies bore little relationship to changes in 

invoice costs, the largest element of the prescription's cost. 

As in the logits, we rejected further aggregation, to state 

averages, because of the loss of store-specific information 

useful in explaining the overall variation in prices and 

therefore in isolating significant influences. 

Because aggregate data are used for the price variables, 

independent variables are aggregated also for the price 

regressions. For example, QUANtity is the average number of 

tablets or capsules per prescription for the store or chain.10 

For chains, SSINDEX is averaged over that chain's outlets in 

the state, with each outlet's index weighted by the number of 

transactions for the selected drug. The payment-type variables 

are the proportion of the store's or chain's multi-source 

prescriptions paid for by Medicaid or other third-party 

insurance, respectively. 

B. Correlated Error Terms Within Regressions 

While we have taken into account both the laws and other 

major cost and demand influences, it is possible that there are 

other conditions which affect pharmacist response to the 

opportunity to substitute and which vary by store or by state. 

If this is so, the error terms in the regression are correlated 

and the estimation is inefficient. The extent of the problem 

is different for the brand-choice regressions and for the price 

regressions because the level of aggregation of the variables 

differs. Our options for making corrections to the estimation 

procedure also differ because the logit regressions are 

lQ/ For the selected drug and time period. For regressions involving a 

single brand or brand type, the average quantity is for that brand or brand 

type only. The SSINDEX is averaged in the same way. 

321 

APPENDIX A1 


estimated by a maximum likelihood procedure while the price 

regressions employ least squares. 

1. Store-Level Correlated Errors 

At the store level, despite some intrastore price variability due to price discimination or accident or reporting error, 

there is likely to be a strong commonality among a single 

store's prices, stronger than any similarity of prices among 

stores. The data confirmed that there were many stores in 

which several observations were identical, and the same was 

true across units of the same chain. Even when store-specific 

influences (such as local cost conditions) have been controlled 

for in the model, some store-level idiosyncracy in pricing or 

brand-choice behavior may prevent the observations from being 

truly independent of one 1nother. An example of an omitted 

store characteristic which might lead to such behavior is a 

personal commitment, on the part of an owner-pharmacist, either 

for or against substitution. The existence of store idiosyncracies means that when a substitution is made on one transaction in a store, su bsti tu tion is more (or less) likely on a 

second prescription in the same store than in another otherwise 

identical store. 

2. Statistical Implications of Correlated Errors 

In technical terms, the errors -- the portion of the 

dependent variable not explained by the model -- would be 

correlated for observations from a single store, that is, would 

be more similar among observations from a single store than 

across stores. The statistical implication of correlated error 

terms is that the estimates of the standard errors of the 

coefficients are not unbiased and the tests of statistical significance are not entirely reliable. In a sense, the inclusion in the regression of several observations from a single 

store gives the false appearance of a larger number of independent observations than is really true; a second observation 

from a store gives less "real" new information than an observation from a different store. Therefore it is likely that the 

standard errors of the coefficients are understated, making 

322 
ECONOMETRIC ISSUES 


it possible that statistical tests of significance show more 

coefficients to be significant than would be true if the data 

had been drawn from a sample designed differently. 

3. State-Level Correlated Errors 

Similarly, the many stores within a single state might share 

some characteristic not captured by the regression's independent variables. For example, there may be differences in the 

training of pharmacists or physicians. If most pharmacists in 

a state are trained at a single school of pharmacy, they may 

all have learned a particular approach to certain decisions. 

If, for example, in one school certain drug entities are held 

up as examples of potential bioequivalence problems, pharmacist 

graduates of that school may decline to substitute in those 

entities more frequently than pharmacy graduates whose attention was drawn to a different set of entities. 

If there is some such omitted state-wide characteristic (or 

a complex of them), when substitutions are made frequently by 

one store (or when prices are high), then substitutions are 

more likely (or prices higher) in other stores of the same 

state. This would cause correlation among the error terms 

within each state with the attendant statistical problems. 

4. 	 Types of Problems Present in Price and Brand-Choice 

Regressions 

Correlated error terms are a potential problem in both types 

of regressions. In the price regressions, while the possibility of within-store correlations is eliminated by using store 

averages, correlations seem likely between stores within a 

single state. In the logit regressions, both sources of 

possible error correlation exist since there may be several 

observations in a single store. 

5. 	 Results Using Alternative Levels of Aggregation 

a. Price Regressions. To check the effects of different 

levels of aggregation in the price regressions, we ran a number 

323 

AfPENDIXAI 

of models with individual prescription data, monthly store 

averages, and state averages by month and year. The results of 

regressions run with aggregated data were not much different 

from those for disaggregated data. As expected, fewer coefficients were significant when aggregated data were used. An 

occasional (insignificant) sign was switched but never when the 

coefficient was significant in either the aggregated or 

disaggregated regression. 

b. Brand-Choice Regressions. In order to see whether the 

results were very sensitive, we ran an alternative specification of the brand-choice regressions for one drug and compared 

the results with those of the individual-prescription logit 

regression. We ran OLS regressions using state-level data, 

with the "log-odds" of substitution as the dependent variable, 

In [(P/(1-p)], where p is the proportion of prescriptions on 

which a substitution was made. 

In the regression for hydrochlorothiazide, the top-substitution drug, the signs on 7 of the 8 legal dummies were the same 

as in the individual-prescription logit regression; LlAB became 

negative instead of positive. None of the coefficients were 

significant, while in the individual-prescription logit 

regression 6 of the 8 were significant. 

While this loss of statistical significance may have been 

due partly to the disappearance of false significance attributable to the correlated error terms, aggregation to the state 

level also removes a great deal of valuable information which 

is useful in explaining variation and therefore in determining appropriately significant relationships. For example, 

no longer is a particular prescription identified as being a 

Medicaid prescription. "True" significance lies somewhere 

between the results of the individual-prescription logits and 

the state aggregate "log-odds" OLS regressions. 

We ran two other such experimental "log-odds" regressions, 

one for a weighted average of all 45 drugs where substitution 

was permitted on the specific drug and one for a weighted 

average of the 45 drugs disregarding formulary restrictions. 

Again, none of the coefficients were statistically significant. The signs corresponded to the general sign patterns 

324 

ECONOMETRIC ISSUES 


reported across the individual logit regressions for 24 drugs 

(except for RXPRO, which showed no pattern.) 

6. Error Components Model 

For the price regressions, we used a generalized least 

squares (GLS) procedure, based on an error component model 

designed to capture the presence of state-by-state influences, 

~ince ordinary least squares (OLS) is inappropriate when error 

terms are correlated. Specifically, we assume that the error 

term is composed of two parts: 

ERROR.~ = m. + e.~ h 1 h 

where mi is the state component and eit is the random component. We assume that the state component shifts the intercept but not the slopes. We assume that mi is a random 

variable rather than being fixed. We therefore rule out 

using dummy variables for each state. In fact, use of state 

dummy variables would not have been feasible since the variables in which we are most interested also are state-level 

variables and the inclusion of both sets of dummies would have 

produced a singular matrix, making estimation mathematically impossible. 

The model we use is that described in Judge et al. 11 The 

resulting variance-covariance matrix is block-diagonal in form. 

The GLS procedure based on this model adjusts each observation for the fact that any observation (more accurately, the 

error term) is more similar to other observations in the same 

state than to observations in other states. (Whatever "average 

state component" exists is added into the CONSTANT term.) 

This reduces the importance, in the estimation procedure, of 

the extreme observations which a large state with an aboveaverage state error component would contribute to the pool of 

all observations. The GLS correction, then, improves the 

validity of the statistical tests on the coefficients. 

1!/ Judge et al. (1980). 

325 

APPENDJXAT 


The GLS procedure used in the price regressions is not 

applicable in the logit regressions because they use a maximum 

likelihood estimation technique, not ordinary least squares 

(OLS). 

V. ESTIMATION PROCEDURES 

A. Logit Estimation Procedures 

We use a logit model to explain or predict the pharmacy's 

choice of brand to use in filling a prescription, or rather the 

probability of a given brand choice. We cast the choice on 

brand-written prescriptions as binary: whether a substitution 

is made, or not. (For generically written prescriptions, the 

choice is also treated as binary: whether a generic is 

dispensed or not (i.e., a brand is dispensed), where generic 

and brand are defined as explained in Appendix 6.) The 

logistic function used in the logit model keeps the predicted 

probabilities between 0 and 1, as is appropriate; a linear 

probability model, for which OLS could be used, does not. 

One drawback of the logit model for our problem is that 

the model works best when the sample observations are split 

relatively evenly between the 0 and 1 choices, whereas substitution rates tend to be quite low, so that for a number of the 

drugs the split is 90 percent/ 10 percent or even more extreme. 

To provide a better fit on the tails of the distribution it 

would be necessary to use a model based on a different distribution of the error terms, such as the Poisson distribution. 

In the absence of computer software available for this alternative model, we used the logit. 

B. GLS Estimation Procedure 

The GLS estimation procedure was adapted by Gerard Butters 

from Fuller and Battese.12 Two alterations were needed. Our 

model has only one component instead of two, and instead of a 

W Fuller and Batteae (1974). 

326 
ECONOMETRIC ISSUM 


fixed number of observations for each state (component), the 

number was variable. Three regressions are run: first, a 

regression in which each observation is the difference from the 

respective state mean; second, the ordinary least squares 

regression with uncorrected data; and third, ·a regression in 

which each observation is adjusted by subtracting some fraction 

of the appropriate state mean, where the proportion depends on 

the amount of variation within a state. 

The procedure is as follows: 

1. Using ordinary least squares (OLS), regress the dependent 

variable on the k-1 (here, 6) independent variables plus a 

constant, using the original (untransformed) data. · Retain the 

sum of squared residuals (SSR) from this regression. Compute 

an adjusted mean square error: 

SSR • s•J(N - k) 

V(X*'X•> ) N • tr SSRI(N • k) ( 

where V is the variance-covariance matrix from the OLS regression using original values, and tr is the trace of the matrix. 

2. Run a regression where the value of each variable, dependent and independent, is the difference between the original 

value and the mean for that variable in the appropriate state. 

Independent variables which are state-level dummies (DPS) or 

state-level averages (GEN) fall out of this regression, since 

the difference from the state mean is zero for each observation. The constant term is also omitted. 

Let k* be the number of independent variables in this 

regression (4). This number k* is smaller than k in the 

original regression, by the number of state dummies plus one 

(the constant.) 

Compute the sum of squared residuals from this second 

regression, SSR*, and calculate an estimated mean square error: 

327 

APPENDIX A'T 


where N t is the number of observations in the tth state and T 

is the number of states, here 49. 

Retain the (X'X)* matrix from this regression, to be used 

below. 

4. Transform each dependent variable by replacing the original 

value Pit with: 

Transform each independent variable similarly. 

5. Regress the transformed dependent variable on the transformed independent variables. This regression provides the 

final results. 

In some regressions the calculations produced a negative 

estimate of the variance in step 2. In those instances, OLS 

was used in place of GLS. 

VI. INDEPENDENCE ACROSS REGRESSIONS 

An assumption underlying the running of separate regressions 

for each drug is that the error terms in each regressions are 

independent of those in all other regressions. We recognize 

that this assumption is probably over-strong for our analysis; 

there is in fact probably some interdependence. The same panel 

328 
ECONOMETRIC ISSUES 


of pharmacies is represented in each of the regressions, and 

even when most store-level influences are held constant by the 

inclusion of the SSINDEX and other variables, there may be 

idiosyncratic behavior consistent across a store's decisions on 

all multi-source drugs, not just across all prescriptions of a 

single drug. A pharmacy which prices high on one multi-source 

drug may well price high on another multi-source drug. To run 

each drug separately, as we have done, is to ignore some 

store-specific information that is available, that is, pricing 

(or brand-choice) decisions on other drugs. 

A. Seemingly Unrelated Regressions 

A procedure superior to ordinary least squares is the 

estimation of the system of seemingly unrelated regressions, 

using GLS with estimates of the components of the variancecovariance matrix. The processing requirements for our price 

analysis, given a system of 45 equations (or rather 45 times 2., 

if both brand and generic prices are included), ruled this 

out. Also, it was unclear as to how -- or whether -- the GLS 

correction in the price regressions could be incorpora ted along 

with the seemingly unrelated system. 

Under certain conditions, OLS results are the same as those 

from the estimation of seemingly unrelated regressions. If all 

the independent variables are the same in all the equations, 

OLS is efficient. In our regressions, all regressions have the 

same set of named variables, of which only one (QUAN) varies by 

drug. If all regressions had observations from exactly the 

same set of stores, therefore, there would be only one variable 

which differed from equation to equation, and the gain from 

moving from OLS to GLS estimation of seemingly unrelated 

regressions would be slight. 

In fact, the subset of stores represented in one drug 

regression is not exactly the same as in all other drug 

regressions, since observations were missing for some stores in 

some drugs. The tables in Chapters 7 and 8 show the number of 

observations in each regression. For example, the average 

price regression for meclizine has 548 observations, while that 

for isosorbide dinitrate has 409. The total number of stores 

in the panel was much greater, as indicated by the fact that 

329 
APPENDIX A1 

the penicillin VK regression has 959 observations. This makes 

it quite likely that the stores represented in the meclizine 

and isosorbide dinitrate regressions do not wholly overlap. 

B. Binomial Tests of Sign Patterns Across Regressions 

Our ability to generalize across drugs by summarizing sign 

patterns in the regressions is also conditional on the independence of the individual drug regressions. In the text we 

report, for example, that in the logit regressions on substitution 21 of 24 coefficients on RXANTI were negative and that 

by a binomial test (based on a .5 probability of a positive 

sign) this is a significant pattern. This test applies only if 

the observations counted up are independent. If instead all 24 

regressions represent essentially the same information, little 

is gained and a count of signs is meaningless. We believe that 

the problem of correlated error terms among regression equations is probably not extreme and that therefore the binomial 

signs test is useful in summarizing the regression results. 

VIII. TRANSFORMATION OF LOGIT COEFFICIENTS 

INTO ESTIMATED CHANGES IN PROBABILITY 


OF SUBSTITUTION 


The coefficients estimated by the logit regressions are 

not estimates of the changes in the probability of substitution 

associated with each independent variable since the dependent 

variable is the logarithm of the odds of choice, not the actual 

probability. To state the effects of the independent variables 

in terms of changes in the probability of substitution, the 

coefficients must be transformed. 

Our method of transforming the logit coefficients is as 

follows. 13 What we are seeking is the change in probability 

due to a move from the absence of a legal provision (dummy 

equal to zero) to the presence of the provision (dummy equal to 

ill See also Pindyck and Rubinfeld (1981, pp. 299-300). 

330 

ECONOMETRIC ISSUES 

one.) For each drug, we evaluated the estimated equation using 

the mean values of the independent variables; let us term the 

value obtained A. We computed an average probability of 

substitution 

p = 

1 + eA 

Since the estimated equation included all dummies equal to one, 

this probability corresponds to the presence of each provision (P1). To find the probability of substitution when a 

particular provision was absent, it was necessary to subtract 

from A its contribution, which is its estimated coefficient, 

and calculate the resulting P (iP0 where i indexes the 

provisions). We then took the difference in the two probabilities (P1 - ipo) as the estimate of the provision's effect on 

the proba bility of substitu tion. 14 

!if The computation of differences in probability associated with RXNEUT 

required removing the effects of both RXPRO and RXANTI from the equation, 

to find the probability of substitution when the prescription pad had a 

"neutral" format. The same was true for differencea involving no formulary. 

331 
332 

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340