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Research Paper JPHSR 2014, 5: 89–94 © 2014 Royal Pharmaceutical Society Received August 7, 2013 Accepted December 17, 2013 DOI 10.1111/jphs.12050 ISSN 1759-8885
Effects of Trade Related Intellectual Property Rights on the research and development expenditure of Indian pharmaceutical industry Tannista Banerjeea and Arnab Nayakb a
Department of Economics, Auburn University, Auburn, AL, and bDeloitte Touche LLP, Atlanta, GA, USA
Abstract Objectives This article investigates the factors that affect the research and development (R&D) expenditure of Indian pharmaceutical industry before and after the introduction of Trade Related Intellectual Property Rights, January 1995. Methods The firm level data were obtained from the Prowess database published by Centre for Monitoring Indian Economy. Different econometric modelling techniques are used. Key findings and conclusions Results show that a stricter intellectual property right regime increases the R&D investment of domestic and exporting firms. Results also show that firms with high marketing and advertising expenditure invest more in R&D. This indicates that more active firms performed more R&D. Keywords intellectual property; pharmaceutical; research and development
Introduction
Correspondence: Tannista Banerjee, Department of Economics, Auburn University, 0324 Haley Center, Auburn, AL 36849, USA. E-mail:
[email protected]
Trade Related Intellectual Property Rights (TRIPS) was introduced in January 1995 to generate free flow of goods and intellectual know-how between World Trade Organization (WTO) member countries. According to TRIPS, all WTO member countries have to introduce both product and process patent protection in all the sectors. Before TRIPS, according to the revised Paris Convention 1967, countries were allowed to choose their own intellectual property laws and the extent of patent protections. Countries were free to implement product or process innovation patents. While developed countries implemented product patents ranging from 14 to 20 years, developing countries were more interested towards process protection. Before 1995, India realized that a product and process patent for the pharmaceutical industry would give monopoly power on drug productions and increase the drug price, which would prevent the drug to reach the poor population of the country. Because of political reasons, India implemented only process patent for the pharmaceutical industry. As a result, an Indian pharmaceutical company could produce a generic drug within the patented period. Generic drugs do not need expensive research and development (R&D) investment and only require reverse engineering to produce a drug that is similar to a patented drug. Reverse engineering means any patented drug can be analyzed in a laboratory and the chemical structure of the drug molecules can be reproduced and applied to make similar drugs. It is not difficult and expensive to analyze the chemical structure of the drug molecules. Invention of a new drug is very expensive compared with the reverse engineering and therefore, Indian companies could market generic drugs at a much lower price than patented drugs. Therefore, before 1995, drug prices were low in India but there was a negative effect of this policy. Foreign firms were not interested in outsourcing R&D to India because of the differences in intellectual property right laws and because of the fear of reverse engineering (see http://forbesindia.com/printcontent/29302). In 1995, India signed the TRIPS agreement that introduced both the product and process patents for all the sectors including the pharmaceutical industry. Pharmaceutical industry is a highly R&D-intensive industry, and increased patent protections heighten the R&D activities of pharmaceutical companies. TRIPS allowed product patent for 20 years. TRIPS encourage Indian pharmaceutical firms to invest more in R&D. Product patent reduces the reverse engineering. R&D investment in pharmaceutical industry is expected to increase as a result of TRIPS. However, any company or agency can also apply for pre- or post-grant opposition of a patent. This gives consumers the chance to protest against patenting of
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life-saving drugs in India. TRIPS also allowed compulsory licensing of drugs, which are life saving and important for developing nations. (See Scherer and Watal (2002)[1] and Chandan (2011)[2] for detailed discussion.) Although the TRIPS agreement gives patent protection, the prices for many drugs remain low in developing countries because of the lowpurchasing power and compulsory licensing treaty of the TRIPS agreement (Article 31). But despite this compulsory licensing treaty and low prices, TRIPS agreement is expected to affect the R&D investment of Indian pharmaceutical firms because TRIPS gives some intellectual property right protection to patented drugs. This article investigates the factors that affect the R&D behaviour of Indian pharmaceutical industry in the pre- and post-TRIPS periods. The analysis differentiates between the effects of TRIPS on the R&D expenditures of domestic and exporting firms. Indian pharmaceutical industry draws the attention of many scholars in recent years. Watal[3] shows that as a result of TRIPS agreement, price for Indian drugs increased and the welfare decreased. Thangavelu and Pattnayak[4] use the Centre for Monitoring Indian Economy (CMIE) data for the period of 1989 and 2007 and studied 200 pharmaceutical companies. Using the Olley and Pakes’[5] production function estimation technique, Thangavelu and Pattnayak measure the effect of spillover to domestic firms from foreign firms’ activities. Thangavelu and Pattnayak’s results show that there exist a positive and significant horizontal linkage and a significant negative backward linkage (another article that shows the determinants of R&D investment for firms in different regions is Pammolli et al. (2011)).[6] These results suggest a large technological gap between domestic and foreign pharmaceutical firms in Indian pharmaceutical industry. Chanda[7] uses the CMIE data to show that after the TRIPS, the patenting activities of Indian firms increase significantly. To the best of our knowledge, none of these articles investigate the determinants of R&D intensity for Indian pharmaceutical companies in the pre- and post-TRIPS period. The remainder of the article is organized as follows. The next section introduces the theoretical background of the determinants of R&D investment decision. In second section, we introduce the methodology and the data description. The fourth section discusses the empirical results and we conclude in the last section.
Determinants of research and development expenditure As presented by Grabowski and Vernon[8], and Mahlich and Roediger-Schluga[9], R&D investment decision is determined by the firm’s expected return on investment and cost of capital. We follow Grabowski and Vernon[8], and Mahlich and Roediger-Schluga[9] and modify their modelling in the Indian pharmaceutical industry framework to study the effect of TRIPS on the R&D investment decision of pharmaceutical firms. Following basic economic reasoning, the intersection of the marginal rate of return on investment schedule (mrr) and the marginal cost of capital schedule (mcc) determines the R&D investment decision of a firm. mrr is obtained by arrang-
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New Equity
Cost of funds
90
mrr’ mrr
mcc
Internal fund
mcc’
R*
R**
R&D investment
Figure 1 Determinants of R&D investment. mcc, marginal cost of capital schedule; mrr, marginal rate of return on investment schedule; R&D, research and development. Adapted from Grabowski and Vernon (2000). R* and R** are the R&D investments.
ing all potential investment projects in order of decreasing rates of return. Similarly, mcc is constructed by arranging the opportunity costs of alternative investment in increasing order. R&D investment is unique and should be treated separately than any other investment. The rate of return on R&D investment in the pharmaceutical industry takes a long time to materialize because drug development is a long process. On average, a drug takes 10–15 years to develop. R&D investment also involves investment in scientists, laboratory, research equipment, etc., and knowledge spillover takes place between R&D projects.[10] Therefore, even if drug development projects fail to succeed, not all R&D investments are wasted. Therefore, it is not straightforward how one can account for the marginal rate of return on investment for pharmaceutical R&D. Second, the cost of capital may differ depending on the source of funding.[11] Internal capital is less costly than equity financing and outside financing by debt. Uncertainty in drug development makes outside capital even more expensive. In Figure 1, the lower horizontal segment of the mcc curve shows the lower cost of internal fund, and the upper horizontal segment shows the higher cost of equity financing. The rising part of mcc represents the cost of new debt financing. Marginal rate of return is the downward sloping curve, and the intersection of these two determines the optimal R&D investment R*. Any increase in mrr or any increase in cash flow will shift these curves to the right, and these will increase the optimal R&D investment R*. However, for Indian pharmaceutical firms, abovementioned determinants of R&D investment decision (intersection of mrr and mcc) are only applicable after the introduction of the TRIPS agreement. Before 1995, the effect was reversed. It was more profitable to perform less R&D in those areas where demand was higher. Firms could just copy patented drugs and market generic drugs (because of the absence of effective patent protection). But after 1995, mrr and mcc would determine the optimal mcc. And this becomes
Trade Related Intellectual Property Rights and R&D
.015 R&D/sale .005 .01
Hypothesis 1: Before (after) 1995, R&D investment for Indian pharmaceutical firms is negatively (positively) associated with the rate of return on R&D investment. Hypothesis 2: Before (after) 1995, R&D export for Indian pharmaceutical firms is negatively (positively) associated with the rate of return on R&D investment.
Average R&D as a percentage of sale
0
even more important for exporting firms who compete in the global market. Before 1995, Indian pharmaceutical firms could export generic drugs but they could not after the TRIPS agreement. Therefore, as export increases, they need to spend more money on R&D to compete in the global market. Therefore, we test the following main hypotheses:
1990
1995
RDSit = f ( Eπi , CFSit −1 ) where RDSit is the average R&D intensity for company i at R & D expenditurei year t is measured as RDit = . Figure 2 Salesi shows the average R&D intensity of pharmaceutical companies for the time period between 1989 and 2007. Table 1 shows that R&D investment increases for Indian firms after the TRIPS period, 1995. From Figure 2, it is clear that the R&D intensity for Indian pharmaceutical companies increased after 1996 with two high picks during 1997–1998 and 2001–2002 windows. Another interesting observation from the data is that after the introduction of TRIPS, the royalty and technical knowhow expenditure for Indian pharmaceutical companies increased at a faster rate (between 1997 and 1998), along with an increase in R&D intensity. The pharmaceutical companies initially increased their royalty and
2000
2005
Year
Methodology and data description In this section, we discuss the econometric techniques used and the data construction methodology. The primary database is the Prowess database published by CMIE. Prowess is the most extensive firm-level panel database covering the Indian firms. Database covers 21 000 firms between 1989 and 2008. Prowess includes firms that are traded on India’s major stock exchanges and the central public sector enterprises and accounts for 75% of all corporate taxes and over 95% of excise duty collected by the Government of India. There are 606 pharmaceutical companies according to the National Industry Classification (NIC) two-digit level. Firms are categorized by the four-digit 1998 NIC code, which is a finer classification than the International Standard Industrial Classification (ISIC) four-digit code. NIC three-digit code matches with ISIC four-digit code. All values are presented in 1 million Indian rupees. The database is an unbalanced panel as the number of firms in Prowess is dynamic and change over time. Prowess gives detailed financial information for each company including their identity, location and contact information. The database also includes information on R&D expenditure, after tax profit, which further breaks down into dividend paid and retained profit, sales, total output, export income, foreign travel expenses, consumption of total and imported raw materials, wages, labour compensation, fixed asset, etc. Using this database, we test the two hypotheses. We estimate the following equation for Indian pharmaceutical firms
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Figure 2 R&D intensity of Indian pharmaceutical companies. CMIE, Centre for Monitoring Indian Economy; R&D, research and development. Source: Company level data are collected from the CMIE database. Table 1
R&D expenditure
Year
Obs
Mean
Std. Dev.
Min
Max
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
63 79 100 118 141 179 226 244 224 238 263 266 260 255 268 299 268 256 214
0.0108 0.0478 0.0475 0.1279 0.3787 0.2889 0.0322 0.3199 0.5852 0.9988 0.5255 0.6989 0.6223 0.6015 0.9867 0.8133 0.1995 0.1376 0.0991
0.0008 0.0025 0.0022 0.0037 0.0155 0.0065 0.0088 0.0078 0.0341 0.0841 0.0296 0.0292 0.1621 0.0115 0.0438 0.0192 0.0302 0.0353 1.1865
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0.6802 0.7173 0.1624 0.2489 0.1761 0.5038 0.8516 0.7386 0.5 1.2857 0.4680 0.3305 2.6048 0.0749 0.5882 0.1336 0.2722 0.2873 17.3636
Table 1 presents the descriptive statistics of R&D expenditure for Indian pharmaceutical companies. Data are from CMIE. Unit is 1 million Indian rupees. CMIE, Centre for Monitoring Indian Economy; R&D, research and development.
technical know-how fees to acquire new molecules. But eventually by increasing their R&D expenditures, they were able to compete in the market without paying high royalty and technical know-how fees. The introduction of TRIPS increased the R&D expenditure of pharmaceutical companies. Table 2 presents the number of firms and total sales per year. Number of firms and total sales both increase after 1995 and remain stable over a period of time. Eπi is the expected return on investment measured by the past sales of pharmaceutical firms in the year t-1 divided by the R&D expenditure in t-1 (return). The second variable, which proxy investment opportunity, is the export income (export). If firms earn more from export income, then they spend more on R&D to compete in
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Table 2 Number of firms and total sales
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Thus, the regression model becomes:
Year
Number of firms
Sales
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
63 79 100 118 141 179 226 244 224 238 263 266 260 255 268 299 268 256 214
48.3849 50.0560 43.5327 45.4554 44.2066 37.9812 38.3609 43.8170 54.9576 59.0559 65.9091 73.3290 75.7822 83.8716 91.5753 98.4695 107.4192 126.7664 181.209
Table 2 presents number of firms and sales per year over the analysis period. Data are from CMIE. CMIE, Centre for Monitoring Indian Economy.
RDSit = α 0 + α1 Returnit + α 2 Export it + α 3CFSit + α 4 Marketing + α 5Salesit + νi + ρε ti-1 + τ it
(2)
A dynamic model where lagged R&D is used as an additional right-hand side variable may be a more appropriate model specification. Mahlich and Roediger-Schluga (2006)[9] found significant support for this specification in a similar model. Therefore, we consider next a dynamic model specification as follows:
RDSit = α 0 + α1 Returnit + α 2 Export it + α 3CFSit (3) + α 4 Marketing + α 5Salesit + α 6 RDSt -1 + νi + ε ti where RDSt−1 is the lagged value of the R&D intensity. We estimate this model using the Blundell and Bond[13] GMM estimator. This estimator uses lagged levels of variables and lagged differences of the variables as instruments. We use a two-step estimation technique because this is asymptotically more efficient than the one-step procedure. Two separate panel regressions were estimated on two non-overlapping time periods: pre- (1989–1995) and postTRIPS periods (1996–2007).
Results and discussion the global market. We include export earning of firms in the year t-1 as the second proxy for expected return of investment. Next, we include CFSit−1 as the cash flow to sales ratio in period t-1. This is measured by the retained profit in t-1. Retained profit describes the amount of cash flow the Indian pharmaceutical firms have to reinvest in their R&D investment. This is important for Indian firms who rely mostly on internal funds than equity financing or outside debts for R&D investment. Therefore, we proxy cash flow by the past net earnings of the firms. We also include marketing expenditure (marketing) as an additional control. Those firms who spend more on marketing expect more return from the market and spend more on R&D. Following Mahlich and Roediger-Schluga[9], we include firm sales (sales) as an additional control. This controls for the influence of division of R&D by sales. We start our estimation with a basic fixed effect model. We assume that the R&D expenditure of pharmaceutical firms is determined by:
RDSit = α 0 + α1 Returnit + α 2 Export it + α 3CFSit + α 4 Marketing + α 5Salesit + νi + ε ti
(1)
Next, we test and correct for the serial autocorrelation in the model because serial autocorrelation in the error terms is a common problem for most time series model. Following Baltagi and Wu[12], we include an AR(1) in the disturbance term εti and apply feasible generalized least squares technique to remove any first order autocorrelation. The error term εtiis modelled as εti = ρεti−1 + τitwhere ∣ρ∣ < 1 and τit are independent and identically distributed with mean zero and variance σ 2τ . ρ is specified as:ρ = 1 − d/2, where d is the Durbin Watson d statistics.
Results are presented in Tables 3 and 4. Table 3 presents the pre-TRIPS period results, and Table 2 presents the postTRIPS period results. Model 1 is the fixed effect model. Model 2 is the AR(1) model and Model 3 is the dynamic model specification. Dependent variable is the R&D intensity. F tests for all the models show the validity of the models. From the regression results in Table 3, we observe that during the pre-TRIPS period, companies with an increased expected sales from the market (return) invest less in R&D. This is true in all three models, but the effect is highest in the dynamic model. One per cent increase in expected return on investment decreases the R&D intensity by 0.08%. This is because they could just market generic drugs and do not need to invest in R&D. Export earning is not significant in determining R&D investment during this time period in all the three models. This is expected because before the stringent patent law, R&D investment was low and generic export was more prevalent. Internal cash flow weakly influences R&D investment because mostly this is the only variable that might have influenced small R&D investment during this time period. Marketing expenditure is not significant in all three models. Autocorrelation is present and the value of parameter ρ is 0.267. This supports the use of Model 2. In Model 3, the value and significance level of past R&D expenditure variable shows that if pharmaceutical firms spend more in R&D in the past, then they are expected to spend more in R&D in future. The coefficient is 0.24. This maintains the dynamic nature of R&D even in the pre-TRIPS period when the R&D investment is low in general. F values in all three models validate the model specifications. R2, the measure of goodness of fit for Models 1 and 2 are reasonably high. Results in Table 4, post-TRIPS period, show that in all three models expected returns on investment determines
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Table 3 Determinants of R&D expenditure of Indian pharmaceutical companies: pre-TRIPS period 1989 to 1995
Table 4 Determinants of R&D expenditure of Indian pharmaceutical companies: pre-TRIPS period 1996 to 2007
Variables
Variables
Return Export Cash flow Marketing expenditure Sales
Model 1 −0.0489*** (8.000) −0.0540 (−0.850) 0.3822* (1.80) 0.0293* (1.840) −0.0302 (0.940)
ρ RDSit-1 Constant R-squared within F Prob > F Number of observations
0.0047*** (9.13) 0.93 3.11 0.000 906
Model 2
Model 3
−0.057*** (9.020) −0.0100 (−1.020) 0.0474* (1.800) 0.0018 (0.660) −1.4E−05 (1.180) 0.267
−0.0784*** (10.120) −0.0260 (−0.470) 0.0363* (1.830) 0.8260 (0.930) 0.2400 (0.930)
0.0119*** (10.47) 0.22 1.33 0.002 906
0.2432*** (8.090) 0.0157 (0.04)
321 (Wald) 0.000 612
Return Export Cash flow Marketing expenditure Sales
Model 1 −0.0694*** (8.50) 0.0115*** (10.15) 0.0252* (1.66) 0.0247* (1.68) −0.0137 (−0.90)
ρ RDSit-1 Constant R-squared within F Prob > F Number of observations
0.0179*** (9.18) 0.80 3.14 0.000 3055
Model 2
Model 3
0.0541*** (9.020) 0.0156*** (30.01) 0.0251*** (17.34) 0.0261*** (10.17) −0.0161** (2.29) 0.389
0.0520*** (16.37) 0.2245*** (34.13) 0.0112*** (10.55) 0.0548*** (17.27) −0.0201*** (−33.18)
0.0289** (2.28) 0.66 0.22 0.009 3055
0.1037*** (31.91) 0.0193*** (42.66)
117 (Wald) 0.000 2438
Table 3 presents regression result. Model 1 is the fixed effect model. Model 2 is the AR(1) model and Model 3 is the dynamic model specification. Parenthesis represents the t-values for Models 1 and 2, and z-values for Model 3. Dependent variable is the R&D intensity. Data are from CMIE. F-test shows the validity of the models. ***P < 0.01, *P < 0.1. CMIE, Centre for Monitoring Indian Economy; R&D, research and development; TRIPS, Trade Related Intellectual Property Rights.
Table 4 presents regression result. Model 1 is the fixed effect model. Model 2 is the AR(1) model and Model 3 is the dynamic model specification. Parenthesis represents the t-values for Models 1 and 2 and z-values for Model 3. Dependent variable is the R&D intensity. Data are from CMIE. F-test shows the validity of the models. ***P < 0.01, **P < 0.05, *P < 0.1. CMIE, Centre for Monitoring Indian Economy; R&D, research and development; TRIPS, Trade Related Intellectual Property Rights.
R&D expenditure significantly. One per cent increase in expected return encourages R&D investment by 0.05% to 0.07% during this period of study. Because pharmaceutical firms compete in R&D race, expected return in R&D influences their R&D expenditure positively. For the same reason, export earnings also influence R&D expenditure positively in all the three models. Less reverse engineering influences more R&D spending. As a result, we observe higher cash flow influences R&D expenditure in all three models. One per cent increase in cash flow encourages R&D investment by 0.01% to 0.03% . Marketing expenditure and sales variables also have expected signs respectively. Value of ρ is 0.387 in Model 2, which supports the autocorrelation hypothesis. And in Model 3, the significant coefficient of past R&D again shows the dynamic nature of R&D expenditure in our model. The coefficient is 0.10. The value is less than the pre-TRIPS period. This result is surprising at first glance but after the post-TRIPS period, firms are expected to spend more in R&D so R&D is less sluggish in pre-TRIPS period. F values in all three models validate the model specifications. R2, the measure of goodness of fit for Models 1 and 2 are reasonably high.
duction of TRIPS. In this highly competitive and R&Dintensive industry, results show that a stricter intellectual property right regime increases the R&D investment of domestic competitive firms and the exporting firms. After the introduction of TRIPS, as the reverse engineering of drugs decreased, domestic companies increased their R&D expenditures. After TRIPS, domestic Indian companies compete with international companies that were previously not present in the market but entered the domestic market after receiving intellectual property right protections due to TRIPS. Results also show that firms with high marketing and advertising expenditure invest more in R&D. This indicates that after TRIPS, R&D expenditure of Indian pharmaceutical companies is determined by the expected rate of return on their R&D investment and the availability of cash flows.
Conclusion This article investigates the changes in the nature of R&D intensity for Indian pharmaceutical industry after the intro-
Declarations Conflict of interest The Author(s) declare(s) that they have no conflicts of interest to disclose. Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
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Authors’ contributions All the Authors contributed to the research, design of the study, analysis and the publication. All Authors state that they had complete access to the study data that support the publication.
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