Endogenous innovations in the pharmaceutical industry

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(1)J Evol Econ (2007) 17:473–515 DOI 10.1007/s00191-007-0059-3 REGULAR ARTICLE. Endogenous innovations in the pharmaceutical industry Rodrigo A. Cerda. Published online: 12 May 2007 © Springer-Verlag 2007. Abstract This paper addresses the creation of new products in the US pharmaceutical sector, during the second half of the 20th century. We indicate that the continuous increases in population, and thus in the market size of this sector, play a fundamental role in explaining the large creation of new drugs during that period. We also argue that population and market size can be endogenously determined through the impact of drugs over the mortality rate. Hence, these two effects reinforce each other, producing decrements in the mortality rate and increments in the stock of drugs over time. We obtained the set of new molecular entities approved by the FDA during the second half of the 20th century and we decomposed the data in a panel of 15 therapeutic categories over time. Using this data, we tested our hypotheses using different econometric methods. The results support the hypothesis and are consistent across methods. Keywords Endogenous innovations · Pharmaceutical industry · Population · Market size JEL Classification O31 · I12 · J10 1 Introduction The sustained increase of expenditures in health care over the 20th century brought the cost of health and its finance to the top of the policy agenda. The determinants of health expenditure are thus important topics. As can be seen. R. A. Cerda (B) Department of Economics, Pontificia Universidad Católica de Chile, Casilla 76, Correo 17, Santiago, Chile e-mail: rcerda@faceapuc.cl.

(2) 474. R.A. Cerda. Fig. 1 Health share of GDP and drug expenditure as fraction of total health expenditure. Source: Centers for Medicare and Medicaid Services. in Fig. 1, the US health share of income increased from 9% at the beginning of the 1980s to almost 13% by the year 2000. One of the factors that seems to affect health expenditure is technological change, e.g. new treatments. In fact, Newhouse (1992),using an accounting analysis, attributed a large part of the increase in health expenditures—in the US over the second half of the last century—to technological change. Similarly, Cutler et al. (1998) studied the growth of expenditures on heart attack treatment and found that a large part of the increase was due to new technologies and their diffusion. Technological change in the medical sector includes new procedures such as endoscopies, transplantations and renal dialysis, but also the creation of new products such as drugs. Prescription drugs are important in the cost of health, as they represented almost 10% of total US health expenditure in 2000. Further, prescription drugs represent also a rapid growing item, as they grew from 23.5 million of 1983 dollars in 1991 to 42.7 million in 2000.1 (See Table 1 and Fig. 1). Hence, a topic of considerable importance in explaining the rise in health expenditure is technological change on prescription drugs.. 1 Source:. the Centers for Medicare and Medicaid Services, http://www.hcfa.gov/stats/nhe-oact/..

(3) Endogenous innovations in the pharmaceutical industry. 475. Table 1 Growth rate of health expenditure by use of funds, 1994–2000. Hospital care Physician and clinical services Drug prescription Nursing home Other (prof. services + health care) Administrative costs Dental services Home health care. 1994. 1995. 1996. 1997. 1998. 1999. 2000. 3.9 4.6 6.6 4.0 12.1 9.3 6.6 19.1. 3.4 4.8 11.2 9.1 13.0 3.9 7.4 17.1. 3.6 4.0 10.5 7.2 10.1 0.5 5.2 10.1. 3.3 5.0 12.8 6.4 7.9 −2.7 7.2 2.8. 3.2 6.6 15.1 4.7 7.5 7.5 6.0 −2.8. 3.4 5.2 19.2 0.2 7.1 12.3 6.1 −3.7. 5.1 6.0 17.3 3.3 7.5 13.1 6.3 0.3. Source: Centers for Medicare and Medicaid Services.. A direct measure of technological change in the pharmaceutical sector is the introduction of new drugs. Data on the introduction of new drugs during the second half of the 20th century can be obtained from the US Food and Drug Administration (FDA). Since the implementation of the 1938 Food, Drug and Cosmetic Act, the FDA must approve all new drugs before they are available to the public. This property of US law allows us to reconstruct the history of new drugs in the US health market. Figure 2 shows the evolution of new approvals over the period 1940–1997. The series shows a sustained increased in new drugs over time, except during the period 1955–1965. This unique change of trend might be the result of changes in legislation. In fact between 1938 and 1962, any company seeking approval for a drug from the FDA needed to show that it was safe, while starting on 1962, an additional requirement is to show its efficacy in therapeutical treatment. This paper will provide a hypothesis explaining the long run trend in the creation of new drugs. A novelty of the paper is that it proposes two main interactions that reinforce each other, allowing continuous increases in the stock of drugs and continuous decreases in the mortality rate. The first element playing a role is the relationship between the creation of new drugs and the size of the health market. A larger health market is associated with a larger number of drugs. In fact, a larger market size, holding constant the number of drugs, should be associated with larger profits per drug, which provide incentives for new firms to enter the market throughout the creation of new drugs. A second interaction proposed is a feedback effect of the stock of drugs over the mortality rate. Intuitively, as different drugs are available, a larger number of diseases may be attacked and mortality rates may decrease. In this case, market size is positively affected, because population size increases. This second relationship produces a feedback effect that reinforces any initial increase in the number of drugs. Hence, a virtuous circle or a multiplier effect in the creation of drugs will occur. A characteristic of the paper is the empirical design developed. To analyze the effect of the market size over the creation of new drugs, we examine a panel of 15 therapeutic categories of drugs over the period 1950–1997. We use different methods to disentangle the effects. First, we use the method of.

(4) 476. R.A. Cerda. 60. New Molecular Entities. 50. 40. 30. 20. 10. 1997. 1995. 1993. 1991. 1989. 1987. 1985. 1983. 1981. 1979. 1977. 1975. 1973. 1971. 1969. 1967. 1965. 1963. 1961. 1959. 1957. 1955. 1953. 1951. 1949. 1947. 1945. 1943. 1941. 1939. 0. year. Fig. 2 New molecular entities approved by FDA. Source: FDA. fixed effects (FE) to deal with unobserved heterogeneity among therapeutic categories of drugs and we obtain estimates using within-drug variation. We also provide estimations of between-drug variation by using the GLS method. Later, we estimate using Tobit regressions to deal with the fact that our left hand side variable is censored at zero. Finally, we introduce dynamics lags. We also control for potential simultaneity between market size and drugs by using a measure of expected market size constructed with projected population growth rate in a 10-year horizon. We use this instrument because expected market size should be correlated with observed market size (up to some forecast errors) but uncorrelated with new drugs, since projected mortality rates do not include the information on new drugs. Using this instrument, we provide additional estimates. To test the second hypothesis of the paper, e.g. the effect of the stock of drugs on mortality rates, we estimate a production function for mortality rates across causes of death. The production function depends on inputs and technology, where inputs are approximated by health expenditure, and technology depends on the stock of drugs available in the health market. We provide as above different estimations. We find that the effect of the stock of drugs is negative and significant. The remainder of this paper is developed in the following way. Section 2 provides a review of contributions related with the principal topics of the.

(5) Endogenous innovations in the pharmaceutical industry. 477. paper. Section 3 describes the data, while Section 4 describes the econometric approach and discusses the results. Section 5 concludes. 2 Literature review This paper links inventions in the pharmaceutical sector with population by emphasizing a simultaneity between market size and new drugs. This relationship has not been largely explored in the literature. However, there is a recent contribution to the topic by Geoffard and Philipson (2002), who stressed these effects by analyzing goods for which the extent of consumption affects its duration. They argue that the feedback effect from larger longevity affect research and development (RND) decisions through market size. The literature does not provide empirical estimates of this simultaneous relationship between drugs and market size. However, there are contributions that examine separately (1) the determinants of RND in the pharmaceutical sector and (2) the effect of drugs on life expectancy. As those topics are treated separately in the literature, we initially provide a review of the contributions on the first topic. Later, we indicate the main results of the literature when analyzing the effect of drugs on mortality rates. With the 1962 amendment to the “Food, Drug and Cosmetic Act,” researchers found it necessary to discuss the determinants of inventions in this sector to determine the true impact of the amendment in the structure of pharmaceutical innovations. Among the structural factors that affect research and development, the literature stresses market size, past cash flow of firms, and research productivity. Peltzman (1973) provided a simple model that explained the introduction of drugs as a function of lagged market size. Using a time-series analysis over the period 1948–1962 (annual data), he found a strong positive effect of market size over the introduction of drugs. In his analysis, the 1962 amendment would have had a significant and negative impact over drugs introduced after 1962. Vernon and Gusen (1974) related the introduction of new chemical entities (new drugs) with the size of the firm—rather than the size of the market—and they found that a larger firm would, holding everything constant, introduce more drugs. Grabowski (1968) focused on explaining research and development of the American chemical, drug, and petroleum industries. He used variables (aftertax profits, number of patents per scientist, diversification index) that affect the expected returns from RND rather than market size directly, and found that these variables have a positive and significant impact on RND. Grabowski and Vernon (1981) emphasized the role of past cash flow as inputs on the process of RND in the pharmaceutical sector, and argued that the impact of the 1962 amendment had a dynamically negative effect on a firm’s profits and RND. A more recent study is the one of Lichtenberg (1998a) on the allocation of publicly-funded biomedical research. He found that research funding should increase with disease incidence. Thus diseases affecting a larger number of individuals, a concept linked to market size, should have larger public research funds. More recently, Scherer (2001) examined the.

(6) 478. R.A. Cerda. relationship between the pharmaceutical industry profitability and RND in that sector, and showed that RND expenditures are significantly affected by changes in profitability. Similarly, Grabowski and Vernon (2000) studied the determinants of pharmaceutical RND in the US using data from 1974 to 1994, and found that cash flows and expected returns are the main determinants of RND in that period. Further, Grabowski (2002) extended the analysis to the distribution of returns during the 1990s and found that the rate of return in this sector exceeds modestly the cost-of-capital, supporting the idea that competition in the pharmaceutical sector in which firms focus on innovation is a way of obtaining economic advantages through product differentiation. There is also some evidence on the effect of drugs on the mortality rate and life expectancy. Lichtenberg (1998b) related the introduction of new drugs with an increase in life expectancy. Using cross-section data on diseases for two different periods (1970–1980 and 1980–1991), he found that the introduction of new drugs increased life expectancy by about 0.75–1.0% per annum. Lichtenberg (2002) explained life expectancy by using a times-series analysis over the second half of the 20th century in the US. The long run elasticity of longevity, with respect to the number of new drugs approved, is significant and near 0.03. In Lichtenberg (2001), he analyzed the effects of drugs on mortality from rare diseases and HIV. New drugs, and especially drugs developed through the orphan drug act of 1983, show an important impact on reducing mortality. Further, Lichtenberg (2006) studies the impact of new laboratory procedures and other medical goods on the health of Americans between 1990 and 2003, and found that an increase of almost 6 months of mean age at death is attributable to the use of new lab procedures, which represent almost 42% of the total increase in that period. Thus, the literature links inventions on the pharmaceutical sector with different measures of the rate of return—including market size—and there is also some initial evidence of the positive impact of drugs on life expectancy. Notice that, in general, these studies (1) do not consider the interaction between drugs and population and (2) use only time-series evidence or cross-section evidence with a small data set. This paper will specifically link population and drugs and will provide evidence of those effects in its empirical section. This section uses a panel data on 15 therapeutic categories of drugs over the period 1950–1997 to provide evidence of the effect of market size on the introduction of drugs. It also uses instrumental variables to control for the simultaneity problem, as indicated above. When considering the effect of drugs over the mortality rate, we use a similar panel to disentangle the effects. 3 The data We next provide empirical evidence relating to the effects stressed above: (1) the impact of market size over the creation of drugs and (2) the effect of drugs on the mortality rates. To disentangle these effects, we use information on drugs from 15 different therapeutic categories over time. We also construct alternative measures of.

(7) Endogenous innovations in the pharmaceutical industry. 479. market size per therapeutic category by approximating total purchasing power of individuals buying drugs. We weight different measures of purchasing power by age using prevalence rates. Additionally, we obtain data on government funds used on the RND process of the pharmaceutical sector. This last variable is used as an additional control on the equation determining the effect of market size on drug creation. This section describes this data. 3.1 Drugs The data on drugs was obtained from the FDA by using a freedom of information request. We obtained a list of all new drugs approved from 1939 to 1997. Each record includes the trade name of the drug, the approval date, the chemical type, and the generic name, among other possible categories. The chemical type of a drug has seven possible classifications, which are: 1. New molecular entity, active ingredient never marketed in the US 2. New derivative, a chemical derived from an active ingredient already marketed 3. New formulation, a new dosage or new formulation of an active ingredient already marketed 4. New combination, a drug that contains two or more compounds, the combination of which has not been marketed 5. Already marketed product but a new manufacturer 6. Already marketed product, but a new use 7. Already marketed legally without FDA approval We follow the empirical literature and focus on new molecular entities (NME) only. In fact, the rest of the chemical types correspond to drugs that were derived from molecular entities already on the market. Hence, we will not classify them as new drugs. We found 1,135 NME approved between 1939 and 1997. These are going to be used in our analysis.2 To construct the panel of drugs, we use the generic names. This information allows us to determine therapeutical use, which indicates the type of disease with which the drug deals. The therapeutic use was determined by consulting the 9th edition of the Drug Information Handbook (DIH) published by Lexi-Comp and the American Pharmaceutical Association. This DIH is similar to a pharmaceutical dictionary as it provides an alphabetic list of drugs in the market by generic name (US adopted name). Each drug listed in the DIH has information about its therapeutical category, its use, its contraindications, and adverse reactions, among other fields. We have classified the drugs into 15 different categories (column 1 of Table 2). We chose these categories based on the 1995 National Drug. 2 The. data on NME reported in Peltzman (1973) for the period 1950–1970 contains a few more NME compared to the data in this paper for the same period. However, the differences are not large and they seem to depend on discontinuity of the use of drugs over time..

(8) 480. R.A. Cerda. Table 2 Classification of drugs and International Classification of Diseases (ICD-9) Drug classification. International Classification of Diseases, ICD-9. Antidotes Hematologic Cardiovascular renal Central nervous system Gastrointestinal Skin, mucosae membrane. Poisoning (960–989) Diseases of blood (280–289) Diseases of the circulatory system (390–459) Diseases of the nervous system (320–359) Diseases of the digestive system (520–579) Diseases of the skin and subcutaneous tissue (680–709) Mental disorders (290–319) Neoplasm (140–239) Diseases of the eye and ADNEXA (360–379) Diseases of the ear and mastoid process (380–389) Injury (800–959) Diseases of the respiratory track (460–519) Intestinal infectious diseases (1–9), tuberculosis (10–18), Other bacterial diseases (30–41), HIV infections (042), Poliomyelitis and other (45–49), viral diseases accompanied by exanthem (50–57), arthropod-borne viral diseases (60–66), other diseases due to viruses and chlamydiae (70–79), rickettsioses and other venereal diseases (90–99), other spirochetal diseases (100–104), mycoses (110–118),Zoonotic bacterial diseases (20–27), helminthiases (120–129), other infectious and parasitic diseases (130–136), late effects of infectious and parasitic diseases (137–139) Nutritional deficiencies (260–269), Other metabolic disorders (270–279) Disorders of thyroid gland (240–246), Diseases of other endocrine glands (250–259). Neurologic Oncolytic Ophthalmic Otologic Relief of pain Respiratory tract Antiparasitic, antimicrobial, immunologic. Metabolic, nutrients Hormones, hormonal mechanism. The numbers in parenthesis in column 2 of the table corresponds to the ICD-9 classifications.. Classification (NDC). Among these categories we find antidotes, hematologic drugs, cardiovascular-renal drugs, etc.. To classify the drugs over time, we used the approval date on each record to obtain the year the drug was approved for marketing purposes. Using this information, we obtained a list of drugs classified in 15 different therapeutic categories over the period 1939–1997. The summary statistics concerning NME by drug type can be seen on Tables 3 and 4. 3.2 Mortality rates We constructed a series on mortality rates for each of the 15 therapeutic categories. To do so, we matched each of these therapeutic categories to a set of causes of death. We used the 9th version of the International Classification of Diseases (ICD-9) to determine the matching process between death and drugs. This classification ranges from 1 to 999, where each number is associated.

(9) 1 2 17 11 6 10 1 10 2 0 6 7 4 3 13. 1 6 22 42 20 9 5 6 3 1 7 13 5. 5 23. Antidotes Hematologic Cardiovascular, renal Central nervous system Gastrointestinal Skin, mucosae membrane Neurologic Oncolytic Ophthalmic Otologic Relief of pain Respiratory tract Antiparasitic, antimicrobial, immunologic Metabolic, nutrients Hormones, hormonal mechanism. 1960–1964. 1955–1959. Drug class. Table 3 Creation of drugs by quinquennial, 1955–1999. 1 10. 1 0 3 11 3 2 1 7 2 0 6 2 6. 1965–1969. 2 8. 0 2 8 9 2 2 2 12 5 0 3 1 8. 1970–1974. 7 11. 0 1 9 9 7 1 3 13 9 0 6 3 4. 1975–1979. 2 7. 0 1 19 15 6 1 2 12 1 0 6 3 20. 1980–1984. 4 7. 2 1 18 13 9 1 0 11 5 0 6 6 15. 1985–1989. 7 6. 1 5 14 16 1 2 6 11 4 0 4 8 23. 1990–1994. 13 13. 1 9 18 19 3 4 5 13 6 0 5 11 31. 1995–1997. Endogenous innovations in the pharmaceutical industry 481.

(10) 482. R.A. Cerda. Table 4 Summary Statistics NME approved by FDA, 1939–1997 Drug class. Mean. St. dev.. Max. Min. Antidotes Hematologic Cardiovascular, renal Central nervous system Gastrointestinal Skin, mucosae membrane Neurologic Oncolytic Ophthalmic Otologic Relief of pain Respiratory tract Antiparasitic, antimicrobial, immunologic Metabolic, nutrients Hormones, hormonal mechanism. 0.2 0.4 2.4 2.5 1.2 0.6 0.5 1.7 0.6 0.0 0.8 1.3 1.9 0.7 1.7. 0.4 0.8 2.1 2.5 1.7 1.1 0.8 1.4 0.9 0.1 1.0 1.5 2.5 1.1 1.8. 1 3 7 12 7 5 4 5 3 1 3 7 14 5 6. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0. with a cause of death. This ICD-9 classification allows us to match each death with one of the drug categories. (To see the way we match deaths and drugs, refer to Table 2). To construct the mortality series, we use the US National Mortality Detail Files (NMDF) publicly available from the Inter-university Consortium for Political and Social Research (ICPSR), for the period 1968–1997. The NMDFs have a record of each death that occurred in the US during each year. The record includes the cause of death, which is determined by the ICD-9. Using Table 5 Summary statistics of mortality rate, 1968–1997 Mortality rate. Mean. St. dev.. Min. Max. Poisoning Blood Diseases Cardiovascular renal Central nervous system Gastrointestinal Skin, mucosae membrane Neurologic Oncolytic Ophthalmic Otologic Injury Respiratory tract Antiparasitic, antimicrobial, immunologic Metabolic disorders, nutritional deficiencies Thyroid gland, endocrine gland. 0.1825 0.0507 7.2221 0.2002 0.5424 0.0204 0.1295 3.1552 0.0004 0.0008 0.9487 1.0854 0.2269 0.0775 0.3133. 0.0241 0.0058 1.3787 0.0604 0.0762 0.0031 0.0609 0.0721 0.0003 0.0005 0.1905 0.1390 0.1127 0.0230 0.0433. 0.1358 0.0428 5.4860 0.1348 0.4530 0.0156 0.0619 3.0211 0.0001 0.0003 0.7226 0.8527 0.1173 0.0493 0.2596. 0.2262 0.0604 9.7140 0.3244 0.6884 0.0263 0.2743 3.2591 0.0009 0.0021 1.2791 1.3172 0.4461 0.1196 0.3775. Note: mortality rates are measured in death per thousands of individuals. The category “Poisoning” correspond to “antidotes” in the drug category, “Injury” to “Pain relief,” “Metabolic disorders, nutritional deficiencies” to “Metabolic, nutrients” and “Thyroid gland, endocrine gland” to “Hormones, hormonal mechanism.”.

(11) Endogenous innovations in the pharmaceutical industry. 483. this information, we are able to construct mortality series by therapeutic category from 1968 to 1997. (Summary statistics of mortality rates are provided in Table 5.) 3.3 Market size To figure out the size of the market for each of the drug categories, we constructed a measure of market size by using population and individual income. We implemented this strategy because a larger population is associated with a larger market size. Also, we used per capita income because a larger per capita income allows a larger expenditure on drugs at the individual level. Thus we linked market size with purchasing power of the population in the economy. To obtain a better approximation of market size, we obtained data on population and income by age-groups over time. We use this strategy because individuals might change their consumption of drugs during their lifecycle. In fact, we expect that older individuals will spend more on drugs and medical procedures than younger individuals, everything else being equal. As a result we give more importance to older individuals when constructing market size. From the U.S statistical abstract, we obtained data on the populations of different age-groups from 1950 to 1997. The groups in this paper are individuals aged 25–34, 35–44, 45–54, 55–64, and 65 and older. In addition to population, we obtained data on the US median income for people in each of these groups at 1997 CPI adjusted dollars from the Current Population Reports of the Census. By multiplying population and median income at each age-group, we obtained the purchasing power in each respective age-group. To construct the size of the market, we add-up the purchasing power of those groups by using appropriate weights. The obvious weights used are prevalence rates. A group with a larger prevalence rate in a given disease has relatively more individuals affected with the disease and should consume more treatments, relative to groups with lower prevalence rates. We obtained a proxy for prevalence rates by using the National Mortality Detail Files. In fact, each record on the NMDF provides the age of the individual who died. This information allows us to classify each death on one of our age groups. Next, to determine the prevalence rate per age-group for a given disease, we use the set of individuals dying of a specific disease. We determine the fraction of individuals dying per age-group and we divide by the total number of deaths:  jit j∈{J}. itJ = . jit. j. where itJ is the weight—proxy for prevalence rate of age-group J on disease i at time t, and jit is an indicator function equal to one if the record indicates that the individual died from disease type i at t and zero otherwise. Consider the following example of prevalence rates for hematologic drugs, drugs that are related directly with diseases of the blood, encoded between 280.

(12) 484. R.A. Cerda. Table 6 Summary statistics of market size by therapeutic category, 1950–1997 (Billions of 1997 US dollars) Market size. Mean. St. dev.. Max. Min. Antidotes Hematologic Cardiovascular, renal Central nervous system Gastrointestinal Skin, mucosae membrane Neurologic Oncolytic Ophthalmic Otologic Relief of pain Respiratory tract Antiparasitic, antimicrobial, immunologic Metabolic, nutrients Hormones, hormonal mechanism. 0.4643 0.0905 11.8918 0.3704 1.1071 0.0328 0.2338 5.7010 0.0008 0.0024 2.3560 1.8319 0.4784 0.1401 0.5526. 0.1359 0.0383 2.6391 0.1994 0.2260 0.0140 0.1761 2.1626 0.0003 0.0010 0.4076 0.7186 0.4137 0.0869 0.1978. 0.6279 0.1675 14.8466 0.8930 1.4036 0.0561 0.7479 9.3639 0.0012 0.0042 3.0511 3.4716 1.5779 0.3373 1.0766. 0.2368 0.0409 6.4861 0.1542 0.6407 0.0141 0.0691 2.5318 0.0003 0.0009 1.4910 0.9129 0.1672 0.0512 0.2797. and 290 on the ICD-9 code. Thus the prevalence rate of individuals aged 65 and older is defined as the total number of individuals aged 65 and older who died of blood diseases, over the total number of individual who died of the same diseases. Using this information, we define a market size per drug-category i at time t as: MKSit =. . itJ popitJ yitJ. (1). J. where MKSit is market size per therapeutic category over time, while popitJ , yitJ are total population and median individual income of age-group J at t. Summary statistics of market size are provided in Table 6. 3.4 Government grants on pharmaceutical research A variable that may have influence on the process of discovering of new drugs is government grants for medical research. We obtain data in each grant from the National Health Institutes (NIH) almanac over the period 1948–1997. The NIH is composed by different institutes which provides funds for research within the pharmaceutical sector. Among them, we find the National Cancer Institute (NCI), the National Hearth, Lung and Blood Institute (NHLBI), the National Institute of Mental Health (NIMH), etc. We compute government expenditure in each of the therapeutic categories by mapping expenditure on a specific institute with a specific therapeutic category. The way we mapped the government expenditures can be seen in Table 7. In the table, there are some centers whose grants were divided between diseases because they could not be directly mapped with a unique.

(13) Endogenous innovations in the pharmaceutical industry. 485. Table 7 Drugs classification and government grant by type of drug DRUG CLASS. Institute. Antidotes. National Center for Research Resources (NCRR), National Institutes of General Medical Sciences (NIGMS) Hematologic National Heart, Lung and Blood Institute (NHLBI) National Heart, Lung and Blood Institute (NHLBI) National Institute of Mental Health (NIMH) National Institute of Diabetes, Digestive and Kidney Disease (NIDDK) National Institute of Arthritis, Musculoskeletal And Skin Diseases (NIAMS) National Institute of Neurologic Disorders And Stroke (NINDS) National Cancer Institute (NCI) National Eye Institute (NEI) National Institute of Deafness And Other Communication Disorders (NIDCD) National Center for Research Resources (NCRR) and National Institutes of General Medical Sciences (NIGMS) Respiratory tract National Heart, Lung and Blood Institute (NHLBI) National Institute of Allergies and Infectious Diseases (NIAID) National Center for Research Resources (NCRR) and National Institutes of General Medical Sciences (NIGMS) National Center for Research Resources (NCRR) and National Institutes of General Medical Sciences (NIGMS). Cardiovascular, renal Central nervous system Gastrointestinal Skin, mucosae membrane Neurologic Oncolytic Ophthalmic Otologic Relief of Pain. Antiparasitic, antimicrobial, immunologic Metabolic, nutrients. Hormones, hormonal mechanism. disease; an example is the case of the NHLBI whose grants were divided among hematologic, cardiovascular-renal and respiratory tract drugs. Summary statistics of government research grants by type of drugs are provided in Table 8. Table 8 Summary statistics of government research grants by institute, 1950–1997 National Institute of Health. Mean. St. dev.. Max. Min. NCRR NIGMS NHLBI NIMH NIDDK NIAMS NINDS NCI NEI NIDCD NIAID. 446,883 699,072 626,720 288,877 460,050 194,596 359,426 612,450 194,529 148,194 357,892. 139,268 147,578 337,380 192,521 189,828 21,522 157,115 336,104 63,504 17,342 237,436. 781,358 966,967 1,100,980 685,150 676,399 229,904 599,770 1,100,490 277,721 167,557 896,510. 291,902 498,130 45,515 4,995 21,605 147,280 20,011 73,457 91,588 111,008 12,333. Note: The amounts are measured in thousands of 1997 dollars..

(14) 486. R.A. Cerda. 3.5 Approval time as a measure of regulatory stringency in the pharmaceutical sector A variable that should affect the introduction of drugs is the regulation of the pharmaceutical sector. Some authors (see Baily 1972) have used a dummy variable equal to zero before 1962 and equal to one after to consider the effect of the change in the “Food, Drug and Cosmetic Act” that occurred in that year. However, the introduction of this dummy implicitly assumes that the intensity of the regulation will not vary after 1962 or across therapeutic categories of drugs. Both assumptions are quite restrictive. Grabowski et al. (1978) proposed as a proxy of regulatory stringency the overall average delay that new drugs face to obtain approval for marketing purposes. This variable measures the average number of days since the pharmaceutical firm filed the application form and the date the drug were approved for marketing. We will follow them but, rather than relying in averages computed across all NME approved at a given moment of time, we will compute an average per therapeutical category. We are able to do so because the data we obtained from the FDA has, in each record, information about the date when the application was received and the date approval was obtained. Figure 3 plots the average number of months required to obtain the approval for the industry since 1939. It clearly shows that the approval time. 50 45 40. Number of months. 35 30 25 20 15 10 5. year Average months for approval. Fig. 3 Approval time length. 1996. 1993. 1990. 1987. 1984. 1981. 1978. 1975. 1972. 1969. 1966. 1963. 1960. 1957. 1954. 1951. 1948. 1945. 1942. 1939. 0.

(15) Endogenous innovations in the pharmaceutical industry. 487. after 1962 (when the legislation changed) increased considerably. However, it also shows that after 1962, there have been variations in the number of months required to get approval. 3.6 Expenditure on hospital bed per therapeutic category Finally, when explaining mortality rates across therapeutic categories, we use as a control the expenditure on medical treatments by therapeutic category. The idea of bringing this variables as a control in the mortality rate equation is that we will assume that mortality rates are determined by a production function which depends on technology (number of drugs) and inputs (expenditure in medical treatments). To approximate expenditure in medical treatments by therapeutical category, we obtained data on total hospital bed use at the national level per annum. We compute the data by therapeutic category over the period 1979– 1997 from the National Hospital Discharge Survey (NHDS). Next, to construct real expenditure on hospital beds per therapeutic category, we multiply total hospital beds per category by the price index “hospital and related services” (obtained from the bureau of labor statistics) and divide it by the CPI. This variable will be used as a proxy for real health expenditure per therapeutic category. 3.7 Some initial evidence of the effect of market size The relation between the creation of new drugs and market size seems quite strong. A first approximation to this relationship is to compare the flow of drugs with the size of the population through time. As indicated above, market size depends positively in population size and thus, we would expect that a larger population would be associated with a larger amount of drugs. Figures 4–6 present strong evidence of this relationship. They show the evolution of new molecular entities approved by the FDA compared with the size of the population over the period 1962–1997. (We chose this period to isolate the relationship from the change in legislation in 1962). Figure 4 relies on total population, while Figs. 5 and 6 rely on population between 45 and 64 years old and 65 years and older, respectively. The three figures indicate a very similar pattern, as they show a clear positive association between population and the creation of drugs. Even when the evolution of these series are quite similar, some qualifications seem to be in place. First, the aggregated time-series shown above might not be the most relevant measure of drug inventions when related to market size. Drugs might have different therapeutical uses and thus different drugs might be related to consumers at different stages of their life-cycle. Indeed, drugs used intensively by elderly individuals, such as Alzheimer’s drugs, have a different market size compared to ophthalmic, otologic or skin related drugs. To expand on this concept, we will next focus on analyzing the relationship between market size and flow of drugs across therapeutic categories..

(16) 488. R.A. Cerda. 35. 30. NME. 25. 20. 15. 10. 5 100,000. 110,000. 120,000. 130,000. 140,000. 150,000. 160,000. 170,000. 180,000. Total Population NME approved by FDA. Fig. 4 New molecular entities and population (thousands of individuals). Figures 7–9 present plots of 10-year moving averages of flow of drugs and market size per therapeutic category since 1962. We use these moving averages to emphasize long run trends in the data. A positive relationship between the creation of drugs and the size of the market can be observed. Even when the relationship varies across categories, the positive association is clearly observed in Figs. 7–9. The relationship is clearly stronger in antiparasitic, central nervous system, oncolytic, hormones and cardiovascular-renal drugs. We find three exceptions that were excluded from the figures: ophthalmic, otologic and skin related drugs, which do not present a clear relationship with market size. An explanation for these cases may rely in the way we measure market size. In fact, we use mortality rates as a proxy for prevalence rates, while this might be a good proxy, in the cases of ophthalmic, otologic and skin related drugs, it is not the most adequate since the largest demand for drugs will occur in cases which are not life threatening. However, a second consideration relates to the fact that these categories represent a very low number of drugs and therefore the data show not enough variation to identify, by the use of simple figures, a relationship between market size and drug creation (see Tables 3 and 4). Another variable that seems to be relevant as a measure of demand is government expenditure. Table 8 reports summary statistics concerning government research grants. The table shows that therapeutic categories with a.

(17) Endogenous innovations in the pharmaceutical industry. 489. 35. 30. NME. 25. 20. 15. 10. 5 15,000. 16,000. 17,000. 18,000. 19,000. 20,000. 21,000. 22,000. 23,000. Population 45 to 64 years old NME approved by FDA. Fig. 5 New molecular entities and population 45–64 years old (thousands of individuals). large number of drugs are associated with a larger government expenditure on research grants. This is the case of the National Cancer Institute, which is connected with oncolytic drugs, the National Institute of Mental Health, and the National Health, Lungs and Blood Institute, which are connected to the central nervous system and cardiovascular-renal drugs respectively. Compare this to the cases of the National Institute of Deafness and Other Communications Disorders and the National Institute of Arthritis, Musculoskeletal and Skin Disease that show a weaker association. Finally, it is worthy to notice that the regulatory stringency seems to impact the creation of new drugs. Figure 10 plots two 10-years moving average series corresponding to: (1) NME approved by the FDA and, (2) the average approval time for a new drug application (measured in months). Both series show an increasing trend in the long run but, more interestingly, the series show an opposite behavior when we consider deviations from the trend. Before 1962, new drugs were introduced at a higher rate, while the regulatory system allowed a faster approval of NME (new drugs were approved in 10 months approximately). The 1962 amendment produced a highly significant change in the regulatory stringency, as the number of months required to obtained approval jumped to 30 months in a few years, while NME decreased from its levels of 25 drugs per year to ten drugs per year..

(18) 490. R.A. Cerda. 35. 30. NME. 25. 20. 15. 10. 5 15,000. 17,000. 19,000. 21,000. 23,000. 25,000. 27,000. 29,000. 31,000. 33,000. 35,000. Population, 65 and older. NME approved by FDA. Fig. 6 New molecular entities and population 65 years and older (thousands of. individuals). The next section will provide more evidence and will test formally the predictions of this paper concerning the introduction of new drugs and the effect of market size.. 4 The empirical strategy and the results 4.1 The effect of market size To test the effect of market size on the introduction of drugs, we hypothesize a linear relationship between a firm’s desired stock of drugs and market size. We also include additional control variables, plus the lagged stock of drugs. The relation is: SDit∗ = αi + γt + α1 MKSit∗ + α2 SDit−1 + α3 Govit + α4 Approvalit where i indexes drug categories and t indexes time. The variable SDit∗ denotes a firms’ desired stock of drugs, while MKSit∗ , SDit−1 , Govit , Approvalit denote the market size of drugs, the lagged stock of drugs, government expenditures on drug research, and regulatory stringency (measured as the number of months required to obtain the approval from the FDA for marketing purposes). The variables αi and γt are therapeutic fixed effects and time effects, respectively..

(19) Endogenous innovations in the pharmaceutical industry. 491. 6. 5. Flow of drugs. 4. 3. 2. 1. 0 0. 0.2. 0.4. 0.6. 0.8. 1. 1.2. 1.4. Market size Antiparasitic. Hormones. Central Nervous System. Gastrointestinal. Fig. 7 NME and market size (Billions of 1997 US dollars), I. The time effects plays an important role, as they may capture exogenous changes of legislation or changes in the cost of RND. We include government expenditure to measure government demand for drugs. The lagged stock of drugs is introduced as a way of incorporating dynamics in the creation of drugs. The larger is the regulatory stringency, the lower the expected creation of new drugs. Hence, the expected signs are α1 > 0, α3 > 0, α4 < 0, while the sign of α2 is ambiguous. The flow of drugs may be defined as the change on the stock of drugs as in: F Dit = SDit − SDit−1 Hence, replacing in the above yields: F Dit = αi + γt + α1 MKSit∗ + α2 SDit−1 + α3 Govit + α4 Approvalit + uit. (2). where F Dit is the flow of drugs in the ith therapeutic category at time t, α2 = (α2 − 1), and uit is a random shock that measures the difference between the observed stock of drugs, SDit , and the desired stock of drugs, SDit∗ . The variables flow of drugs, stock of drugs, government expenditures and regulatory stringency (approval) have some values equal to zero, while market size has only positive values. Thus we will take the natural log in the case of market size, while measuring the rest of the variables in levels..

(20) 492. R.A. Cerda. 4.5. 4. 3.5. Flow of drugs. 3. 2.5. 2. 1.5. 1. 0.5. 0 0. 2. 4. 6. 8. 10. 12. 14. 16. Market size Cardiovascular-Renal. Oncolytic. Relief of Pain. Respiratory track. Fig. 8 NME and market size (Billions of 1997 US dollars), II. Equation 2 will be estimated by different methods, allowing us to identify the parameter of interest. First, we use within-drugs information to disentangle the parameter of interest through the fixed effect method. Second, we add-up between drugs variation by using the GLS method . Later, we provide estimates of a Tobit regression to address a potential problem of censoring in the flow of drug data. To address potential simultaneity between drugs and market size, we construct a series of expected market size and report new estimations for the methods described above, using this variable as an instrument. Finally, we include dynamic lags in our empirical design, as explained below. 4.2 The instrument The instrumental variable procedure requires an instrument correlated with market size but uncorrelated with the flow of drugs, except through the variables that are included in the flow of drugs’ equation. As an instrument, we will use expected market size, which will be constructed by using forecasted population, where forecasted population depends on the expected mortality rate. The reason is the following. The problem of simultaneity arises because population, which is a component of market size, is endogenous to drugs throughout the mortality rate. We decided to construct a measure of expected market size which depends on forecasted mortality rate, where these forecasts.

(21) Endogenous innovations in the pharmaceutical industry. 493. 1.8. 1.6. 1.4. Flow of drugs. 1.2. 1. 0.8. 0.6. 0.4. 0.2. 0 0. 0.1. 0.2. 0.3. 0.4. 0.5. 0.6. 0.7. Market size Antidotes. Hematologic. Neurologic. Metabolic. Fig. 9 NME and market size (Billions of 1997 US dollars), III 45. 40. 35. Month and NME. 30. 25. 20. 15. 10. 5. year Average Approval Time (Moving Average). Fig. 10 NME and average approval time. NME Approved by FDA (Moving Average). 1996. 1993. 1990. 1987. 1984. 1981. 1978. 1975. 1972. 1969. 1966. 1963. 1960. 1957. 1954. 1951. 1948. 1945. 1942. 1939. 0.

(22) 494. R.A. Cerda. are constructed with no information on the creation of new drugs. The way we a− j proceed is as follows. We take population at time t − j of age a − j, Popt− j , where j > 0. These individuals and their mortality rate during the next j a− j periods, d, will determine Popat , as in Popt− j (1 − d) = Popat . Note that the flow of new drugs between t − j and t might affect the mortality rate, d. This is the potential simultaneity problem. Rather than use the effective mortality rate, d, we will use d∗ , where d∗ is the expected fraction of individuals dying in the next j periods—the expected mortality rate. The characteristic of d∗ is that it uses information available until t − j. We will define expected population for this group of individuals at time t as a− j a,∗ Popt− j (1 − d∗ ) = Popa,∗ t . This population, Popt , should be the one observed in the absence of any shock that alters the mortality rate. However, the observed mortality rate is altered by new drugs introduced. This expected population is then uncorrelated with new drugs and can be used in the construction of our instrument. To construct expected population size, we use the life-tables published in the US statistical abstract. These life tables provide expected mortality rates by age, constructed by using current population and current death rates. The procedure uses the death of individuals aged 1–84, obtained from the National Mortality Detail Files, while in the case of individuals aged 85 and older, mortality rates are constructed by using medicare data (see Anderson and DeTurk 2002). We project these mortality rates into a 10-years horizon and construct a 10-year forecast of the population for different age groups. Thus we define Pop∗jt =Pop j(t−10) ∗ (1 − d∗ ), where d∗ is the expected mortality rate in a 10-year horizon and we next construct expected market size: EMKit =. .  jti Pop∗jt y jt. (3). j. This construction is analogous to the construction of market size developed above, but we replace population by expected population, Pop∗jt . Expected market size is uncorrelated with the flow of new drugs, except through variables that are included in the flow of drugs’ equation as argued above, but also. Table 9 Effect of market size over flow of new drugs 1950–1997 (No instruments) Variable. ln(market size). ln(market size). ln(expected market size). 1.022336b (0.0105962) yes RE 0.99 750. 1.022122b (0.0216673) yes FE 0.99 750. Year dummies Method R-sq Observations. Standard errors in parenthesis. a Indicate the variable is significant at 0.05. b Indicate the variable is significant at 0.01..

(23) Endogenous innovations in the pharmaceutical industry. 495. 26. 25.7. 25. Millions. 24. 23.7. 23. 22.8 22.5. 22. 21.8 21.1. 21. 20 45 to 54. 55 to 64. 65 and over. Age Projection 1980. Observed 1980. Fig. 11 Observed versus projected population, 1980. this measure must be highly correlated with effective market size, as the only deviation in the construction of expected market size compared to effective market size is the use of d∗ instead of the effective mortality rate, d. Table 9 presents the result of running market size on expected market size using FE and GLS methods, and controlling for time effects. There a significant and positive relationship between the variables, and expected market size is highly explicative of the variation in market size, as expected. Figures 11 and 12 provide some evidence indicating that expected death rates do not include information about the development of new technology. These two figures uses expected death rates to project future population in a 10-year horizon. The projection is done by using expected death rates from the US statistical abstract; the projection of death rates for 1980 uses information published in 1970 while the projection relating to 2000 uses information published in 1990. The figures compare observed and projected population by different age-groups in the US in the year 1980 and 2000, respectively. It is quite interesting to note that, in all the cases considered by the figures, projected population overestimates realized population. This difference might be due to either (1) differences between the projection of the mortality rate and its realized value or (2) immigration shocks. As the groups plotted in the figures (individuals older than 45 years old) have relatively low immigration (immigrants are generally younger individuals), we might conclude that there.

(24) 496. R.A. Cerda. 19. 18.4. 17. 16.1. Millions of individuals. 15 13.5 13. 12.0. 11.9. 12.4. 10.8. 11 9.3 9. 7. 5 3.8. 4.2. 3 55 to 59. 60 to 64. 65 to 74. 75 to 84. 85 and over. Age Predicted 2000. Observed 2000. Fig. 12 Observed versus projected population, 2000. exists a sustained difference, across time and across groups, between projected death rates and observed death rates, the latter always smaller than the former. Thus there is clearly some relevant information not included in the information set used to forecast death rates, and one of the possible elements are new technologies, which includes drugs. 4.3 The dynamics of the impact of market size on NME A topic to be addressed is the average duration of the creation of a NME (from the initial human testing to the FDA approval), which during the 1960s and the 1970s was estimated at 6 years (Hansen 1979) and about 9 years during the 1980s (DiMasi et al. 1991). Further by the early 1990s, this process took as long as 15 years (see DiMasi et al. 1994). Therefore, there is a large lag between the moment a pharmaceutical firm discovers a NME and the moment the NME is approved by the FDA. This long duration might be a caveat to the empirical analysis here used. To approach the problem, we follow two strategies. On the one hand, we include Approvalit as an explanatory variable which allows us to control for the potential impacts of the regulatory stringency on the creation of new drugs. However, the average FDA review time as an additional control is not enough. The reason is that FDA review time is just a piece of the drug creation process, which ranges from a new drug’s discovery to FDA approval..

(25) Endogenous innovations in the pharmaceutical industry. 497. Therefore, using this variable may not control for the time ranging from the new drug’s discovery to the moment the application is submitted to the FDA. As we do not have a direct measure of this last variable, we decide to use dynamic lags on market size and government research grants to take into account this factor. We modify Eq. 2 to include dynamic lags on market size time: F Dit = αi + γt + A(L)MKSit∗ + B(L)Govit + α2 SDit−1 + α4 Approvalit + uit. (4). where A(L) = (β0 + β1 L + β2 L2 + .... + β p L p ), B(L) = ( γ0 + γ1 L + γ2 L2 + q .... + γq L ) are lag operators of order p and q, respectively. The problem in estimating Eq. 4 is the collinearity between market size lags and government expenditure lags. To consider this problem, we will use two different methods. First, to avoid collinearity, we follow the Almon-lag methodology (see Almon 1965) and we restrict the functional form of the coefficients on the A(L) and B(L) polynom. In fact, we assume that these coefficients have the form β j = ψ0 + ψ1 j, γj = δ0 + δ1 j, which implies that Eq. 4 can be written as in:3 F Dit = αi + γt + ψ0. p . ∗ MKSit− j + ψ1. j=0.  q. + δ0. j=0. p . ∗ j ∗ MKSit− j. j=0.  q. Govit− j +δ1. j ∗ Govit− j + α2 SDit−1 +α4 Approvalit +uit (5). j=0. From the estimation of Eq. 5, we may obtain the underlying parameters of the A(L) and B(L) polynomials. A second approach to avoid collinearity is the use of principal components (see Mundlack 1981). The idea in this case is to decompose the polynomial A(L)MKSit∗ + B(L)Govit to its associated principal components, which are by definition orthogonals. Let X = [MKSit∗ Govit ] and [A(L) B(L)] = β, then the use of principal components allows us to write (4) as: F Dit = αi + γt + X A A β + α2 SDit−1 + α4 Approvalit + uit = αi + γt + Z δ + α2 SDit−1 + α4 Approvalit + uit where A is the eigenvector matrix associated with X, and Z = X A and A β = δ, where Z is the matrix of principal components of X. If we partition Z on [Z I Z I I ], Z I being the set of significant principal components of the above regression and Z I I being the set of non significant principal components. Eliminating the non significant principal components, we finally obtain: F Dit = αi + γt + Z I δ I + α2 SDit−1 + α4 Approvalit + uit. 3 In. (6). fact, the form of the coefficient polynomials could be of higher order than the one considered on the text. However, in our specifications, we tested other possible specifications and we chose the linear specification here considered..

(26) 498. R.A. Cerda. But note that, from δ I , we obtain β, since β = A I δ I , while its associated variance is Var(β) = A I Var(δ I )AI . Hence, from Eq. 6, we obtain our parameter of interest and its associated standard errors. 4.4 The results We now analyze the results obtained by different methods. Tables 10 and 11 present the initial results. Neither table includes dynamics. Table 10 reports results with market size not accounted for simultaneity, while Table 11 reports similar results but accounting for potential simultaneity. The first three columns of Table 10 are the results of estimations excluding year dummies, while the latter three columns are the results when we include year dummies. In that table, the coefficient of the initial stock of drugs is significant in some of the estimations, with a range of values between 0 and 0.02. These values are associated with elasticities, evaluated at the means of both variables, ranging from 0 to 0.36.4 The effect of government research grants is also positive and significant in the specifications, including the time effect. Evaluating at the mean of variables, as above, we obtain elasticities of government expenditure ranging from 0.14 to 0.38. The effect of the regulatory stringency is significant and negative, as expected in all the specifications. In general, the associated elasticity of this variable vis-á-vis the creation of new drugs is between −0.12 and −0.44.5 The effect of market size is positive and significant in all the cases, as expected. The elasticity of the flow of drugs with respect to market size is in the range 0.08–0.34.6 The results in Table 11 are quite similar, the coefficient of market size being slightly smaller. Tables 12 and 13 provide estimates for the inclusion of dynamics lags on market size and government grants.7 As can be seen in the tables, there is no significant change in the estimates of the impact of the lagged stock of drugs, while the regulatory stringency (the approval variable) loses significance. More interestingly, in the Almon lag methodology (Table 12), the coefficients of both polynomials are significant. The first coefficient is positive, while the second is negative in both polynomials. Both set of coefficient are significant. These estimations indicate that the impacts of market size and government research grants are initially larger, but decreasing over time. Table 13 provides a complementary result. As in Table 12, the impacts of lagged variables are smaller than the impact of contemporary variables. However, these estimates, as we use the principal component methodology, show that the impact of market size is significant until the 5th lag, while thereafter the impact is not. 4 The mean flow of drugs and the mean stock of drugs in our sample are 1.33 and 24.06, respectively. 5 The. mean regulatory stringency is approx. 23 months. we evaluate again at the mean flow of drugs. 7 We include ten lags of market size and government research grants. We proved other specifications, but there was no significative improvement on the estimates. 6 Where.

(27) 0.0056803 (0.0042049) 0.4498393 b (0.1770587) 1.05e–07 (5.33e–07) −0.016592b (0.0036433) no 0.12 750. Stock of drugs at t-1. 0.0181507b (0.0059016) 0.2869606b (0.0711844) −2.64e–08 (7.15e–07) −.0254137b (0.0058393) no. 0.020205b (0.0031107) 0.1482955b (0.0361784) −4.23e–07 (3.86e–07) −0.0186069b (0.0033531) no 0.21 750 750 415 335. New drugs Tobit. New drugs RE 0.0178916b (0.0032944) 0.1101755b (0.0388334) 8.13e–07a (4.81e–07) −0.0088844b (0.0038833) yes 0.31 750. New drugs RE. 0.006202 (0.0063892) 0.2601123b (0.0724092) 2.21e–06b (8.63e–07) −0.0092045 (0.0063748) yes. −0.0009426 (0.0043843) 0.4652314b (0.1761705) 1.47e–06b (6.02e–07) −0.0072619a (0.0040201) yes 0.18 750. 750 415 335. New drugs Tobit. New drugs FE. Standard errors in parenthesis. a Indicate the variable is significant at 0.05. b Indicate the variable is significant at 0.01. The variable “stock of drugs” is measured at the end of the last period. The variable “flow of gov. exp.” correspond to the flow of government expenditure in research per therapeutic category. The variables are on levels with the exception of market size, which is measured in natural logs.. Year dummies R-sq Observations Uncensored Censored at zero. Approval length. Flow of gov. exp. ln(market size). New drugs FE. Variable Method. Table 10 Effect of market size over flow of new drugs 1950–1997 (no instruments). Endogenous innovations in the pharmaceutical industry 499.

(28) 0.0057461 (0.0042691) 0.4401132b (0.2079106) 1.17e–07 (5.49e–07) −0.01655b (0.0036736) no 0.13 750. 0.0203141b (0.003112) 0.14319b (0.0364117 ) −4.12e–07 (3.87e–07) −0.0185954b (0.0033532) no 0.22 750. Stock of drugs at t-1. 750 415 335. 0.0182533b (0.0058925) 0.273923b (0.0733536) 6.40e–09 (7.14e–07) −0.0254071b (0.0058445) no. New drugs Tobit 0.0180629b (0.0032969) 0.1037494b (0.0391167) 8.42e–07a (4.81e–07) −0.0087724b (0.0038842) yes 0.31 750. New drugs RE. 0.0064427 (0.0063689) 0.242051b (0.0744515) 2.25e–06b (8.63e–07) −0.0091681 (0.0063818) yes. −0.0009158 (0.004411) 0.4593519b (0.2057618) 1.48e–06b (6.07e–07) −0.0072512a (0.0040248) yes 0.18 750. 750 415 335. New drugs Tobit. New drugs FE. Standard errors in parenthesis. a Indicate the variable is significant at 0.05. b Indicate the variable is significant at 0.01. The variable “stock of drugs” is measured at the end of the last period. The variable “flow of gov. exp.” correspond to the flow of government expenditure in research per therapeutic category. The variables are on levels with the exception of market size, which is measured in natural logs.. Year dummies R-sq Observations Uncensored Censored at zero. Approval length. Flow of gov. exp. ln(market size). New drugs FE. New drugs RE. Variable Method. Table 11 Effect of market size over flow of new drugs 1950–1997 (market size instrumented). 500 R.A. Cerda.

(29) 0.0224566b (0.0033638) 0.2595775b (0.1010545) −0.0512647b (0.0202935) 4.93e–07b (2.07e–07) −8.86e–08b (4.04e–08) −0.0066927a (0.0036578) yes 0.37 600. Stock of drugs at t-1. 0.0043862 (0.005806) 0.2318349b (0.11073) −0.0362928 (0.0234684) 6.00e–07b (2.25e–07) −9.24e–08b (4.07e–08) −0.0033727 (0.0038478) yes 0.19 600. New drugs FE 0.022569b (0.0033371) 0.3698032b (0.1104801) −0.073583b (0.0221702) 4.39e–07b (2.08e–07) −7.89e–08b (4.06e–08) −0.007246 b (0.0036539) yes 0.38 600. 0.0169116b (0.0075716) 0.3574059b (0.1661573) −0.0691774b (0.0334587) 6.23e–07a (3.29e–07) −8.52e–08 (6.20e–08) −0.0044302 (0.0059495) yes 600 336 264. New drugs RE-IV. New drugs Tobit 0.0055015 (0.0058026) 0.4342871b (0.1242144) −0.0871401b (0.0273603) 5.20e–07b (2.29e–07) −7.52e–08a (4.15e–08) −0.0038941 (0.0038527) yes 0.28 600. New drugs FE-IV. 600 336 264. 0.0168757b (0.0074313) 0.510313b (0.1821405) −0.1004759b (0.0366317) 5.27e–07 (3.33e–07) −6.81e–08 (6.26e–08) −0.0052423 (0.0059531) yes. New drugs Tobit-IV. Standard errors in parenthesis. a Indicate the variable is significant at 0.05. b Indicate the variable is significant at 0.01. The variable “stock of drugs” is measured at the end of the last period. The variable “flow of gov. exp.” correspond to the flow of government expenditure in research per therapeutic category. The variables are on levels with the exception of market size, which is measured in natural logs.. Year dummies R-sq Observations Uncensored Censored at zero. Approval length. δ1. δ0. ψ1. ψ0. New drugs RE. Variable Method. Table 12 Effect of market size over flow of new drugs 1950–1997, Almon lags, market size instrumented. Endogenous innovations in the pharmaceutical industry 501.

(30) 502. R.A. Cerda. Table 13 Effect of market size over flow of new drugs 1950–1997, principal components, market size instrumented Variable Method. New drugs RE. stock of drugs at t-1 0.019b (0.004) Approval length −0.004 (0.003) Market size, t 0.038b (0.011) Market size, t-1 0.037b (0.011) Market size, t-2 0.031b (0.009) Market size, t-3 0.023b (0.007) Market size, t-4 0.016b (0.007) Market size, t-5 0.013a (0.007) Market size, t-6 0.012(0.007) Market size, t-7 0.010(0.008) Market size, t-8 0.005(0.009) Market size, t-9 0.0005(0.01) Market size, t-10 −0.001(0.01) Flow of gov. exp, t 0.29b (0.11) Flow of gov. exp, t-1 0.17b (0.05) Flow of gov. exp, t-2 −0.01(0.05) Flow of gov. exp, t-3 −0.15(0.11) Flow of gov. exp, t-4 −0.15(0.10) Flow of gov. exp, t-5 −0.008(0.01) Flow of gov. exp, t-6 0.16a (0.09) Flow of gov. exp, t-7 0.21(0.12) Flow of gov. exp, t-8 0.08(0.07) Flow of gov. exp, t-9 −0.12a (0.05) Flow of gov. exp, t-10 −0.27a (0.11) Year dummies yes R-sq 0.35 Observations 600. New drugs FE 0.006 (0.005) −0.002 (0.003) 0.069b (0.017) 0.069b (0.017) 0.062b (0.016) 0.053b (0.014) 0.046b (0.014) 0.043b (0.014) 0.043b (0.014) 0.040b (0.014) 0.035b (0.015) 0.030b (0.015) 0.028(0.016) 0.33b (0.11) 0.21b (0.059) 0.02(0.058) −0.12(0.11) −0.12(0.10) 0.02(0.02) 0.19b (0.09) 0.23b (0.12) 0.11(0.07) −0.10b (0.05) −0.26b (0.11) yes 0.25 600. New drugs RE-IV 0.019b (0.004) −0.004 (0.003) 0.038b (0.012) 0.033b (0.010) 0.026b (0.008) 0.018b (0.007) 0.014b (0.007) 0.014b (0.007) 0.015b (0.007) 0.013a (0.007) 0.007(0.007) −0.001(0.01) −0.006(0.01) 0.30b (0.11) 0.17b (0.05) −0.01(0.05) −0.15 (0.11) −0.15(0.10) −0.009(0.017) 0.16a (0.09) 0.21(0.12) 0.08(0.07) −0.13b (0.05) −0.28b (0.11) yes 0.35 600. New drugs FE-IV 0.007 (0.005) −0.0024 (0.0038) 0.065b (0.017) 0.060b (0.016) 0.053b (0.015) 0.046b (0.012) 0.041b (0.014) 0.041b (0.014) 0.041b (0.014) 0.040b (0.014) 0.033a (0.014) 0.024(0.016) 0.019(0.017) 0.33b (0.11) 0.21b (0.06) 0.15(0.06) −0.13(0.12) −0.13(0.11) 0.014(0.020) 0.19(0.09) 0.23(0.12) 0.10(0.07) −0.11b (0.05) −0.26a (0.11) yes 0.19 600. Standard errors in parenthesis. a Indicate the variable is significant at 0.05. b Indicate the variable is significant at 0.01. The variable “stock of drugs” is measured at the end of the last period. The variable “flow of gov. exp.” correspond to the flow of government expenditure in research per therapeutic category. The variables are on levels with the exception of market size, which is measured in natural logs.. significant, although positive as expected. A similar result emerges in the case of government research grants. However, in that case, the impact is significant until the 1st lag only. As in Table 12, the regulatory stringency becomes not significant, indicating that the dynamics of market size and government research grants may contain all the relevant information to the process of drug creation. 4.5 The effect of drugs over mortality rate We next estimate the feedback effect of drugs on the mortality rate and thus on market size. In our specification, we assume the mortality rate is determined by a production function that is dependent on technology (drugs in our case),.

(31) Endogenous innovations in the pharmaceutical industry. 503. inputs (health expenditure) and some additional variables that might shift the production function. To determine the effect of drugs over the mortality rate, we use a linear specification of a mortality rate function: λit = μi + ηt + γ1 SDit + γ2 hit +. it. (7). where λit is the mortality rate, SDit is the number of drugs available for public purchase, and hit is total health expenditure. All these variables are measured per therapeutic category and time period. Lastly, μi and ηt are therapeutic fixed effects and time fixed effects, respectively, while it is a random shock. The expected signs are γ1 < 0, γ2 < 0. Equation 7 will be estimated by FE and GLS methods plus the dynamics lags methodology sketched above. We initially report results for the mortality rate equation using only the stock of drugs and year dummies as controls—we omit hit . We follow this strategy, as it allows us to report results over the period 1968–1997. When we include the measure of expenditure per therapeutic category, we can report results only over the period 1980–1997. We use three different left hand side variables: (1) overall mortality rate, (2) mortality rate of individuals aged 55–64 and (3) mortality rate of individuals aged 65 and older. These variables are measured as number of dead individuals per thousand inhabitants. Tables 14 and 15 report the GLS, FE plus the Almon lag estimations. The stock of drugs is highly significant in all the estimations and the impact is larger on older individuals. Further, the use of lags indicates that the impact of the stock of drugs is spread out. This result is in line with results by Steffensen et al. (1999); Lichtenberg (2003) and Mansfield (1963, 1969, 1985) in the sense that there is a diffusion process in the acceptance and utilization of new pharmaceuticals.8 Further, the contemporary stock of drugs has a larger effect on new drug creation compared with lags of the stock of drugs. Tables 16 and 17 provide similar results, as we introduce the expenditure on hospital beds as an additional covariate. Finally, Table 18 provides the results of the estimations when we include lags of the stock of drugs using the principal component methodology. In that case, and similarly to the result obtained with the Almon lag methodology, the impact of the stock of drugs on the mortality rate is spread out over time and the impact is generally decreasing over time. In the tables, as we include the proxy for health expenditure(columns 4–6 on the tables), we obtain similar results . The results also show that the coefficient on health expenditure is negative, as we expected, but not significant. This results show that the effect of the stock of drugs on the mortality rate measured as death per thousands of individuals is approximately equal to −0.013. Thus an increase of 10% in the stock of new molecular entities (an increase of almost 100 NME) would produce a decrease of 1.3 points in mortality rate per thousand individuals, bringing mortality rate from 12.7 per thousand individuals (1997 level in our sample) to 11.4. Also the effect of the stock of drugs over mortality rates for individuals aged 65 years and older 8I. thank a referee for pointing this out..

(32) −0.017b (0.0019). −0.014b (0.0018). yes 0.14 450. FE. FE. yes 0.18 450. Mortality 55–64 yrs. Mortality overall. yes 0.18 450. −0.057b (0.007). FE. Mortality 65+ yrs. −0.0086173b (0.0017793) 0.0015355b (0.000375) yes 0.22 450. Almon lags, FE. Mortality overall. −0.0105004b (0.0019091) 0.0018733b (0.0004024) yes 0.19 450. Almon lags, FE. Mortality 55–64 yrs. −0.0392919b (0.0072974) 0.0071964b (0.001538) yes 0.22 450. Almon lags, FE. Mortality 65+ yrs. Standard errors in parenthesis. a Indicate the variable is significant at 0.05. b Indicate the variable is significant at 0.01. Mortality rate is measured as death per thousand individuals, while the stock of drugs is measured in levels. Both variables are measured across the 15 therapeutic categories.. Year dummies R-sq Observations. ψ1. ψ0. Number of drugs. Variable. Table 14 Effect of drugs over mortality rates (1968–1997). 504 R.A. Cerda.

(33) −0.016b (0.0019). −0.013b (0.0018). yes 0.13 450. GLS. GLS. yes 0.18 450. Mortality 55–64 yrs. Mortality overall. yes 0.17 450. −0.054b (0.007). GLS. Mortality 65+ yrs. −0.0084728b (0.0017952) 0.0015209b (0.0003784) yes 0.22 450. Almon lags, GLS. Mortality overall. −0.0102746b (0.0019301) 0.0018471b (0.0004068) yes 0.19 450. Almon lags, GLS. Mortality 55–64 yrs. −0.0387634b (0.0073567) 0.007148b (0.0015506) yes 0.23 450. Almon lags, GLS. Mortality 65+yrs. Standard errors in parenthesis. a Indicate the variable is significant at 0.05. b Indicate the variable is significant at 0.01. Mortality rate is measured as death per thousand individuals, while the stock of drugs is measured in levels. Both variables are measured across the 15 therapeutic categories.. Year dummies R-sq Observations. ψ1. ψ0. Number of drugs. Variable. Table 15 Effect of drugs over mortality rates (1968–1997). Endogenous innovations in the pharmaceutical industry 505.

(34) −0.0126b (0.0015). −0.0087b (0.0015). −0.027 (0.041) yes 0.26 285. FE. FE. −0.002 (0.040) yes 0.29 285. Mortality 55–64 yrs. Mortality overall. −0.0028 (0.15) yes 0.29 285. −0.036b (0.006). FE. Mortality 65+ yrs. −0.0028b (0.00146) 0.00039 (0.00031) 0.0005 (0.0432) yes 0.29 285. Almon, FE. Mortality overall. −0.0034b (0.0014) 0.0004 (0.0003) −0.032 (0.16) yes 0.26 285. Almon, FE. Mortality 55–64 yrs. −0.0196b (0.0056) 0.0033b (0.0012) 0.095 (0.166) yes 0.27 285. Almon, FE. Mortality 65+ yrs. Standard errors in parenthesis. a Indicate the variable is significant at 0.05. b Indicate the variable is significant at 0.01. Mortality rate is measured as death per thousand individuals, while the stock of drugs is measured in levels. Both variables are measured across the 15 therapeutic categories.. Year dummies R-sq Observations. ln(exp. on hospital beds). ψ1. ψ0. Number of drugs. Variable. Table 16 Effect of drugs over mortality rates (1968–1997), including hospital beds. 506 R.A. Cerda.

(35) −0.0118b (0.0015). −0.008b (0.0015). −0.006 (0.042) yes 0.25 285. GLS. GLS. 0.014 (0.041) yes 0.26 285. Mortality 55–64 yrs. Mortality overall. 0.054 (0.15) yes 0.27 285. −0.034b (0.006). GLS. Mortality 65+ yrs. −0.003b (0.0014) 0.0004 (0.0003) 0.020 (0.043) yes 0.26 285. Almon, GLS. Mortality overall. −0.0036b (0.0015) 0.0004 (0.0003) −0.006 (0.0443) yes 0.25 285. Almon, GLS. Mortality 55–64 yrs. −0.0201b (0.0057) 0.0034b (0.0012) 0.160 (0.1680) yes 0.26 285. Almon, GLS. Mortality 65+ yrs. Standard errors in parenthesis. a Indicate the variable is significant at 0.05. b Indicate the variable is significant at 0.01. Mortality rate is measured as death per thousand individuals, while the stock of drugs is measured in levels. Both variables are measured across the 15 therapeutic categories.. Year dummies R-sq Observations. ln(exp. on hospital beds). ψ1. ψ0. Number of drugs. Variable. Table 17 Effect of drugs over mortality rates (1968–1997), including hospital beds. Endogenous innovations in the pharmaceutical industry 507.

(36) −0.00112b (0.00013) −0.00109b (0.00013) −0.00106b (0.00012) −0.00102b (0.00012) −0.00099b (0.00011) −0.00096b (0.00011) −0.00093b (0.00011) −0.00089b (0.00011) −0.00086b (0.00010). −0.00092b (0.000125) −0.00089b (0.00012) −0.00087b (0.00011) −0.00084b (0.00011) −0.00081b (0.00011) −0.00079b (0.00010) −0.00076b (0.00010) −0.00074b (0.00010) −0.00071b (0.00009). Number of drugs, t. Number of drugs, t-8. Number of drugs, t-7. Number of drugs, t-6. Number of drugs, t-5. Number of drugs, t-4. Number of drugs, t-3. Number of drugs, t-2. Number of drugs, t-1. Mortality 55–64 yrs. Mortality overall. Variable −0.0039b (0.00051) −0.0037b (0.00050) −0.0036b (0.00048) −0.0035b (0.00047) −0.0034b (0.00046) −0.0033b (0.00044) −0.0032b (0.00043) −0.0031b (0.00041) −0.0030b (0.00040). Mortality 65+ yrs. Table 18 Effect of drugs over mortality rates (1968–1997), principal component analysis. −0.00036b (0.00013) −0.00035b (0.00012) −0.00034b (0.00012) −0.00033b (0.00012) −0.00032b (0.00011) −0.00031b (0.00011) −0.00030b (0.00011) −0.00029b (0.00010) −0.00028b (0.00010). Mortality overall. −0.00041b (0.00014) −0.00040b (0.00013) −0.00038b (0.00013) −0.00037b (0.00013) −0.00036b (0.00012) −0.00035b (0.00012) −0.00034b (0.00011) −0.00033b (0.00011) −0.00031b (0.00011). Mortality 55–64 yrs. −0.00139b (0.00053) −0.00135b (0.00052) −0.00131b (0.00050) −0.00127b (0.00049) −0.00123b (0.00047) −0.00119b (0.00045) −0.00115b (0.00044) −0.00111b (0.00042) −0.00107b (0.00041). Mortality 65+ yrs. 508 R.A. Cerda.

(37) yes −0.01055b (0.0012) 0.27 360. (0.0011) 0.21 360. −0.00083b (0.00009) −0.00080b (0.00009). yes −0.0087b. −0.00068b (0.00009) −0.00066b (0.00008). (0.0048) 0.18 360. yes −0.0362b. −0.0028b (0.00038) −0.0027b (0.00037). (0.0012) 0.08 228. −0.00027b (0.00009) −0.00026b (0.00009) 0.0358 (0.0595) yes −0.00341b (0.0013) 0.15 228. −0.00030b (0.00010) −0.00029b (0.00010) 0.0088 (0.0637) yes −0.00384b (0.0050) 0.05 228. −0.00103b (0.00039) −0.00099b (0.00038) 0.1964 (0.240) yes −0.01309b. Standard errors in parenthesis. a Indicate the variable is significant at 0.05. b Indicate the variable is significant at 0.01. Mortality rate is measured as death per thousand individuals, while the stock of drugs is measured in levels. Both variables are measured across the 15 therapeutic categories.. R-sq Observations. Year dummies Long run impact, Number of drugs. ln(exp. on hospital beds). Number of drugs, t-10. Number of drugs, t-9. Endogenous innovations in the pharmaceutical industry 509.

Figure

Fig. 1 Health share of GDP and drug expenditure as fraction of total health expenditure
Fig. 1 Health share of GDP and drug expenditure as fraction of total health expenditure p.2
Table 1 Growth rate of health expenditure by use of funds, 1994–2000

Table 1

Growth rate of health expenditure by use of funds, 1994–2000 p.3
Fig. 2 New molecular entities approved by FDA. Source: FDA
Fig. 2 New molecular entities approved by FDA. Source: FDA p.4
Table 2 Classification of drugs and International Classification of Diseases (ICD-9)

Table 2

Classification of drugs and International Classification of Diseases (ICD-9) p.8
Table 5 Summary statistics of mortality rate, 1968–1997

Table 5

Summary statistics of mortality rate, 1968–1997 p.10
Table 4 Summary Statistics NME approved by FDA, 1939–1997

Table 4

Summary Statistics NME approved by FDA, 1939–1997 p.10
Table 6 Summary statistics of market size by therapeutic category, 1950–1997 (Billions of 1997 US dollars)

Table 6

Summary statistics of market size by therapeutic category, 1950–1997 (Billions of 1997 US dollars) p.12
Table 8 Summary statistics of government research grants by institute, 1950–1997

Table 8

Summary statistics of government research grants by institute, 1950–1997 p.13
Table 7 Drugs classification and government grant by type of drug

Table 7

Drugs classification and government grant by type of drug p.13
Fig. 3 Approval time length
Fig. 3 Approval time length p.14
Fig. 4 New molecular entities and population (thousands of individuals)
Fig. 4 New molecular entities and population (thousands of individuals) p.16
Fig. 5 New molecular entities and population 45–64 years old (thousands of individuals)
Fig. 5 New molecular entities and population 45–64 years old (thousands of individuals) p.17
Fig. 6 New molecular entities and population 65 years and older (thousands of individuals)
Fig. 6 New molecular entities and population 65 years and older (thousands of individuals) p.18
Fig. 7 NME and market size (Billions of 1997 US dollars), I
Fig. 7 NME and market size (Billions of 1997 US dollars), I p.19
Fig. 8 NME and market size (Billions of 1997 US dollars), II
Fig. 8 NME and market size (Billions of 1997 US dollars), II p.20
Fig. 9 NME and market size (Billions of 1997 US dollars), III
Fig. 9 NME and market size (Billions of 1997 US dollars), III p.21
Fig. 10 NME and average approval time
Fig. 10 NME and average approval time p.21
Table 9 Effect of market size over flow of new drugs 1950–1997 (No instruments)

Table 9

Effect of market size over flow of new drugs 1950–1997 (No instruments) p.22
Fig. 11 Observed versus projected population, 1980
Fig. 11 Observed versus projected population, 1980 p.23
Fig. 12 Observed versus projected population, 2000
Fig. 12 Observed versus projected population, 2000 p.24
Table 13 Effect of market size over flow of new drugs 1950–1997, principal components, market size instrumented

Table 13

Effect of market size over flow of new drugs 1950–1997, principal components, market size instrumented p.30

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