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Original article
Technological capabilities and firm resources as determinants of export competitiveness—Evidence from Indian pharmaceutical industry using quantile regression approach
Journal of Medical Marketing 0(0) 1–12 ! The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1745790414564262 mmj.sagepub.com
Satyanarayana Rentala1, Bryam Anand2 and Majid Shaban3
Abstract Technological capabilities of firms and other firm resources have been researched by many researchers to assess their impact on export competitiveness of Indian industries. This study reports the findings of the influence of technological capabilities and firm resources on the export performance of Indian pharmaceutical industry. The study employs a panel data of 251 Indian pharmaceutical firms as documented by the Prowess database by Centre for Monitoring Indian Economy. The time period considered for the study was the period 2005–2013 to capture the export performance of Indian pharmaceutical industry post World Trade Organisation (WTO) era starting from 1st January 2005. Of the 11 independent variables considered for the study, 5 variables (size of the firm, import of capital goods, import of raw materials, royalty payments, and choice of technology) were found to have a significant impact on the export competitiveness of Indian pharmaceutical firms using ordinary least squares regression analysis. The study also presents a comparative analysis of the results obtained using ordinary least squares regression method and Quantile regression method. The results indicate that except selling and distribution expenses, all the 11 variables exhibited a significant impact using quantile regression method.
Keywords Export competitiveness, firm resources, Indian pharmaceutical industry, quantile regression, technological capabilities
Introduction Investing in technological capabilities and innovations is considered to be the most important dynamic phenomenon among all the organizational activities of any firm.1 Technological efforts have had a positive impact on the economic development of various nations across the world since many firms were able to leverage their technological capabilities to boost their export activity2 and Indian industries are no exception. Prior research on the role of technological activities in increasing the export competitiveness of Indian firms has been reported by many other researchers.3–8 Past research by many researchers on export competitiveness of Indian industries focused mainly on the information technology industry.2 It needs to be noted that along with information technology industry, other high technology-intensive industries like pharmaceuticals have been found to be highly competitive in the
global markets in the recent times. Research focusing on the technological capabilities of pharmaceutical industry has been limited. A few authors have documented the research carried out on pharmaceutical industry across the world.9 The authors noted that most of the research on pharmaceutical industry was done in the context of USA, UK, and other European countries. Very few studies have focused on emerging economies—more so on issues relating to 1
Department of Management, Pondicherry University, Karaikal, India 2 Department of Management, Pondicherry University, Karaikal, India 3 Department of Commerce, Pondicherry University, Karaikal, India Corresponding author: Byram Anand, Pondicherry University, Karaikal, India. Email:
[email protected]
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technological capabilities, research and development (R&D), and marketing issues. The research work presented in this paper aims to fill this gap and add to the existing domain of knowledge. The main objective of this research is to understand the significance of various technological capabilities and other firm resources that determine the export competitiveness of the Indian pharmaceutical industry. Many ways of acquiring technological capabilities have been documented in the previous studies. Important among them are in-house R&D efforts, import of embodied and disembodied technologies.8 The research presented in this paper investigates the effect of such technological factors combined with other firm resources10 and understand their impact on the export competitiveness of Indian pharmaceutical industry. The Indian pharmaceutical industry has experienced radical shifts owing to India being a signatory to World Trade Organisation (WTO) with effect from 1st January 1995. Indian pharmaceutical industry has been given a window period of 10 years to amend its patents laws. Finally, India had to move from the era of process patents to honoring the product patents starting from 1st January, 2005. It has almost been a decade since new patent laws have come into effect and hence it now becomes pertinent to examine the global competitiveness of the Indian pharmaceutical industry in the post WTO era. This research paper attempts to examine the impact of various technological capabilities and firm resources on the export performance of Indian pharmaceutical industry starting from the year 2005. It can be observed from Table 1 that the Indian pharmaceutical exports have grown at a very healthy compounded annual growth rate (CAGR) of 21.6% during the period 2005–2013. This is attributed to the fact that Indian pharmaceutical firms were able to target the developed health care markets like USA, UK, and other European markets. This was feasible due to the opportunities available through the export of off-patent generics drugs, generic and branded formulations, and bulk drugs. The stringent regulatory requirements of the global markets have impelled the Indian pharmaceutical firms to import high-quality raw materials that increased the value of
Indian pharmaceutical imports. Despite the increase in the imports, the overall balance of trade of Indian pharmaceutical industry stands at US$10.1 billion by the end of 2013 and is expected to grow in the years to come. It needs to be noted that in comparison to the period starting from 2005, pharmaceutical exports grew by only 13.5%, imports by 5.9%, and balance of trade grew by 19.5% during 1995–2004 (authors’ calculations). This indicates that the export competitiveness of Indian pharmaceutical industry is experiencing an increasing trend after 2005. The remaining part of this research paper is structured as follows: after the introduction, a literature review regarding prior studies that dealt with the impact of technological factors and firms resources on export competitiveness of various industries is presented in the second section. The third section presents an explanation of the research methodology used, the data considered for the study, and a note on the independent and dependent variables used in the research. Results and Discussion are presented in the fourth section which is followed by the fifth section that presents the conclusions of this research.
Literature review on determinants of export performance Many studies have been conducted globally by renowned scholars to understand the various determinants of export performance.11–17 Table 2 presents a summary of the most relevant studies on determinants of export performance in the Indian context. It can be observed that many of the studies have been done on a sample of multi-industries, while a few studies focused on one or two industries. Among the various firm resources, size of the firm (measured as sales value or number of employees) was the most commonly used determinant of export performance of Indian industries. Profitability, advertising expenses, selling and distribution expenses, and age of the firms were the other commonly used determinants. R&D expenses, import of capital goods, import of raw materials, and royalties paid were
Table 1. India’s pharmaceutical exports and imports (values in $ billions) Year
2005
2006
2007
2008
2009
2010
2011
2012
2013
CAGR
Exports Imports Balance of trade
2.76 0.94 1.82
3.42 1.18 2.23
4.48 1.62 2.86
5.82 1.87 3.95
5.92 2.05 3.87
7.12 2.43 4.69
9.5 2.74 6.77
10.86 3.07 7.79
13.17 3.06 10.11
21.6 15.9 23.9
Note: CAGR, compounded annual growth rate. Source: Authors’ calculations based on WTO Data sourced WSDBViewData.aspx?Language ¼ E (accessed on 22 November 2014).
from
http://stat.wto.org/StatisticalProgram/
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Table 2. Indian studies on determinants of export performance with export intensity as dependent variable Author
Industry
Sample
Aggarwal (2002)
Multi-industry
916
Bhaduri and Ray (2004)
Electrical and electronics and pharmaceuticals (only pharmaceuticals results reported here)
71
Bhat and Narayanan (2009)
Basic chemicals
131
Chadha (2009)a
Pharmaceuticals (adependent variable: export sales)
131
Franco and Sasidharan (2010)
Multi-industry
3053
Jauhari (2007)
Electronics
164
Kumar and Siddharthan (1994)
Multi-industry (only pharmaceuticals results reported here)
34
Lall and Kumar (1981)
Multi-industry
100
Lall (1986)
Engineering
100
Chemical
45
Independent variables
Results
SIZE IRM ICG RDINT ROY IRM SIZE (LOGSALES) AGE RDINT RDINT ICG ROYINT IRM SIZE (LOGSALES) AGE ADVINT CHOICE OF TECH. SIZE (SALES) PROFIT PATENTS SIZE ROY AGE RDINT SIZE (LOGSALES) RDINT ADVINT ICG ROYINT RDINT ROYINT SIZE (SALES) ADVINT PROFIT SIZE (SALES) PROFIT RD SIZE AGE ADV ROYINT RD SIZE AGE ADV ROYINT RD
+VE +VE +VE +VE No. Sig +VE +VE No Sig. +VE +VE +VE No. Sig. +VE +VE VE No Sig. No Sig. +VE +VE +VE VE +VE VE +VE +VE No Sig. No Sig. No Sig. No Sig. No Sig. No Sig. No Sig. +VE +VE +VE VE VE + VE No Sig. +VE No Sig. VE No Sig. No Sig. +VE No Sig. +VE (continued)
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Journal of Medical Marketing 0(0) Table 2. Continued Author
Industry
Sample
Majumdar (2010)
Information technology
112
Pradhan (2007)
Multi-industry
3951
Pulak and Neha (2012)
Multi-industry
264
Rentala et al. (2014) a
Pharmaceuticals (adependent variable: export sales)
23
Automobile
17
Siddharthan and Nollen (2004)
Information technology
145
Singh (2009)a
Multi-industry (adependent variable: export sales)
3542
Independent variables
Results
RD SIZE (LOGSALES) PROFITS AGE SIZE (SALES) RDINT ROYINT ICGINT IRMINT ADVINT RD SDEXP PROFIT SIZE (SALES) RD PROFIT SIZE (SALES) RD PROFIT ROYINT ICGINT IRMINT SIZE (LOGSALES) SIZE (SALES) RD ADV AGE
+VE +VE No Sig. VE +VE +VE No Sig. +VE +VE No Sig. No Sig. +VE No Sig. +VE +VE No Sig. No Sig. No Sig. +VE VE No Sig. + VE No Sig. +VE +VE VE VE
Note: IRMINT: import of raw materials intensity; ICGINT: import of capital goods intensity; ROYINT: royalties intensity; RDINT: R&D intensity; ADVINT: advertising intensity; SDEXPINT: selling and distribution expenses intensity.
among the technological capabilities that were empirically tested as potential determinants of export performance. Number of patents granted and the number of drug master files (DMFs) applied with drug regulatory authorities were considered as determinants in some of the studies. Apart from the multi-industry studies, a few studies18–20 focused on the export competitiveness of Indian pharmaceutical industry. Significant studies on determinants of export competitiveness of Indian industry were contributed by many researchers.4–7,18,21,22
Research methodology, data, and variables Research methodology Analysis for research was done employing the ordinary least squares (OLS) regression method by comparison
of results from the fixed effects model and random effects model. The OLS regression model equation is represented as follows: EXPINT ¼ þ 1 RDINT þ 2 ICGINT þ 3 ROYINT þ 4 IRMINT þ 5 LOGSALES þ 6 AGE þ 7 SDEXPINT þ 8COT þ 9CR þ 10PATINT þ 11DMF þ " In addition to the OLS regression, further analysis was undertaken using quantile regression method.23 Quantile regression helps in providing parameter estimates at designated quantiles. At different quantiles, it can be observed whether the independent variable has a positive or a negative impact on the dependent variable. In comparison to quantile regression, OLS regression method gives the effect of independent
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variables only at the conditional mean of the dependent variable. Hence quantile regression was employed at 11 different quantiles (0.05, 0.15, 0.25, 0.35, 0.45, 0.5, 0.55, 0.65, 0.75, 0.85, and 0.95).
Data Data for the research has been extracted from Prowess data base compiled by Centre for Monitoring Indian Economy. A total of 615 pharmaceutical firms were listed in the Prowess data base. The period of study considered for the research was for nine years (2005– 2013). This is to examine the impact of the independent variables on the export intensity of Indian pharmaceutical industry starting from the year 2005 to assess the post-WTO scenario. Among 615 firms, all the firms that exported at least in one year during 2005–2013 were first considered. The sample was then refined to include only those firms that were established either in 2005 or before 2005 since age was one of the independent variables. As per these criteria, a final sample of 251 firms qualified to be included in this research. Since data was not available for all the variables included in the study, the total number of observations was 27,108 over the nine-year period, which represents an unbalanced panel. Based on authors’ calculations from the database, it was found that the average export intensity of all the 615 firms was 40.2% for nine years. Similarly, the average export intensity of all the sample firms (251) was found to be 41.4%. Though the final sample of 251 firms accounted for only 41% ((251/
615) 100) of all the pharmaceutical firms listed in the Prowess database, the sample firms considered for the study constituted an average of 95% of sales of all the firms and 97.7% of export sales. Hence, it can be inferred that the sample considered for the study is highly representative and inclusive. A total of 364 firms were excluded from the sample since they did not fit the inclusion criteria of export sales in at least one year and that the firm should have been incorporated in the year 2005 or before.
Dependent and independent variables Table 3 gives an overview of the dependent and independent variables considered for the study. Export intensity which was the most widely used measure of export performance24 was taken as the dependent variable. All the independent variables were chosen after a thorough analysis of the past literature. Size of the firm measured either as sales value or log sales value was the most commonly tested determinant of export performance. Accordingly, in this study, size of the firm measured by natural logarithm of sales was considered since logarithm values represent the relative changes in the actual value and are expected to make the distribution tend toward normality.25 Many of the other independent variables have been adapted from the research reported on Indian basic chemical industry.8 Since the basic chemical industry is closely related to the pharmaceutical industry, the determinants of export competitiveness of basic chemical industry are expected to exhibit a similar impact in the case
Table 3. Definitions of dependent and independent variables S. No. Variables
Description
Dependent variable 1 EXPINT (export intensity) Independent variables 1 RDINT (R&D intensity) 2 ICGINT (import of capital goods intensity) 3 ROYINT (Royalties paid intensity) 4 IRMINT (Import of raw materials intensity) 5 LOGSALES (Natural log of sales) 6 AGE (No. of years) 7 SDEXPINT (selling and distribution expenses intensity) 8 COT (Choice of technology) 9 CR (Current ratio) 10 PATINT (Profitability intensity) 11 DMF (No. of drug master files)
Export earnings/sales RD expenses/sales Import of capital goods/sales Royalties/sales Import of raw materials/sales Natural logarithm of sales Age of the firm from the year of incorporation (Selling and distribution + advertising + marketing expenses)/sales Compensation/net fixed assets Current Assets/Current Liabilities PAT/Sales No. of drug master files (DMFs) filed by the firm with USFDA (United States Food and Drug Administration)
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of Indian pharmaceutical industry. Additionally, three other variables used by other researchers have been included. Profitability, another commonly considered variable was included in the study. Two other variables, which have been very sparsely used by earlier researchers, were employed in this study. The first one is the current ratio used in a study26 on the link between diversification strategies and firm performance among Indian chemicals, electronics, and transportation equipment industries. Current ratio gives an indication of the healthy financial status of a firm in meeting the short-term financial needs. The other variable is number of DMFs filed by Indian pharmaceutical firms with United States Food and Drug Administration (USFDA). In a study on Indian pharmaceutical industry, number of patents filed (in United States Patents and Trademarks Office– USPTO) was included as an independent variable.19 Some researchers27 argued that since majority of the Indian pharmaceutical products are generic in nature, the number of DMFs filed can be a better indicator to assess the technological capabilities of Indian pharmaceutical firms in comparison to the number of patents filed by them. The DMF filings give an account of a firm’s manufacturing facilities and are an indication of future patent filings by the firms.28 Table 4 gives an overview of the average values of the dependent variable and all the independent variables considered for the research. It can be observed that the average export intensity of all the sample firms increased from 39.1% in 2005 to 55.4% in 2013. This indicates that the Indian pharmaceutical firms have consciously capitalized on the opportunity to export their products to various countries across the world.
This was possible due to the acceptance of Indian pharmaceutical products owing to their price competitiveness and adherence to global quality standards certified by drug regulatory agencies in various countries. The R&D intensity of Indian pharmaceutical firms was initially higher (in the years 2005, 2006, and 2007), but gradually stabilized at around 7%. The number of DMFs stayed around 1.0% for all the sample firms considered for the study. Table 5 gives an account of the number of firms considered for the study in each year considered for the study during the period 2005–2013. It needs to be noted here that the number of sample firms for the dependent and independent variables varied across years since the data is an unbalanced panel. It is interesting to observe that though the export intensity of the sample firms increased, the number of firms that exported has drastically reduced from 193 firms in 2005 to 112 firms in 2013. This indicates that there is a consolidation phenomenon happening with fewer exporting firms but with increased exports per firm. It can be observed that among the entire sample firms, only about 40 firms have been able to register at least one DMF with USFDA during the period considered for the study. The number of firms that paid royalties to acquire new technology has decreased from 36 firms to 16 firms by the end of 2013. This possibly indicates that the Indian pharmaceutical firms were able to achieve a self-sufficient status in terms of technological capabilities over a period of time after India became a signatory to WTO in 1995. Similarly, the number of firms that imported capital goods reduced from 90 firms in 2005 to 69 in 2013. Import of raw materials experienced a decline in the number of firms from 167
Table 4. Average values of the dependent and independent variables (2005–2013) Variable
2005
2006
2007
2008
2009
2010
2011
2012
2013
CAGR
EXPINT RDINT ICGINT ROYINT IRMINT SALES AGE SDEXPINT COT CR PATINT DMF
39.1 8.2 4.9 0.3 16.5 1629.6 23.5 8.4 24.2 292.9 10.9 0.9
38.5 9.6 3.8 0.4 17.9 1924.4 24.5 8.1 22.8 275.9 12.3 1.0
42.6 8.4 3.5 0.3 18.9 2391.6 25.5 7.7 23.5 262.4 14.6 1.0
43.5 7.4 3.2 0.4 17.8 2751.2 26.5 7.7 23.5 297.2 13.9 1.1
45.6 6.9 4.1 0.5 19.0 3199.0 27.5 8.1 23.7 281.9 9.3 0.9
46.4 7.2 2.6 0.5 17.5 3561.5 28.5 7.5 25.5 262.7 13.0 0.9
45.0 6.2 2.5 0.3 16.7 3819.5 29.5 7.6 27.3 217.0 27.0 1.0
50.0 7.2 2.8 0.2 15.6 4007.3 30.5 9.1 27.3 190.3 8.0 0.8
55.4 7.7 2.4 0.3 15.8 3983.3 31.5 7.6 29.7 157.5 10.7 0.8
4.5 0.7 8.5 2.7 0.5 11.8 3.7 1.2 2.6 7.5 0.2 2.5
Note: CAGR, compounded annual growth rate. Source: Authors’ calculations based on data from Prowess data base.
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Table 5. Year-wise sample size Variable
2005
2006
2007
2008
2009
2010
2011
2012
2013
EXPINT RDINT ICGINT ROYINT IRMINT LOGSALES AGE SDEXPINT COT CR PATINT DMF
193 102 90 36 167 227 251 223 228 231 229 37
194 104 109 34 172 223 251 221 223 224 224 37
197 108 117 41 166 225 251 221 227 228 227 40
191 108 109 32 170 218 251 214 221 223 223 41
197 112 102 29 165 220 251 215 221 222 222 43
182 110 109 31 159 210 251 207 212 211 213 43
149 96 96 24 135 166 251 159 167 167 167 46
137 85 81 23 124 149 251 143 150 150 150 44
112 78 69 16 105 122 251 119 123 123 123 36
Source: Authors’ calculations based on data from Prowess data base.
Table 6. Descriptive statistics (2005–2013) values in Rs. millions Variable
No. of observations
M
SD
Minimum
Maximum
EXPORT EARNINGS SALES PAT ICG IRM AGE (years) SDEXPINT RDEXP ROYALTIES DMF COT
251 251 251 251 251 251 251 251 251 251 251
1252.7 3029.8 413.2 48.3 409.6 27.6 235.6 121.1 0.1 0.9 5.6
4023.8 6845.8 1502.1 161.5 1190.3 16.6 694.2 491.1 1.0 2.8 16.2
0.0 1.4 873.1 0.0 0.0 9.0 0.0 0.0 0.0 0.0 0.0
31795.5 51356.9 15938.1 1474.7 11336.9 108.0 7704.3 4317.5 11.5 18.0 229.6
Source: Authors’ calculations based on data from Prowess data base.
in 2005 to only 105 in 2013. All these indicate the progress achieved by the Indian pharmaceutical firms in developing indigenous capabilities and reduce dependence on imports. Table 6 provides an account of the descriptive statistics of all the variables considered for the study. Analysis of the descriptive statistics indicate that among all the pharmaceutical firms included in the sample (251 firms), Dr. Reddy’s Laboratories had the highest average export value over a period of nine years (2005–2013). In terms of sales value, Cipla had the highest average value. Piramal Enterprises Limited (Piramal Health Care) scored the highest average value in terms of profitability. This is due to the fact that Piramal Enterprises Limited has sold its formulations
business to Abbot Laboratories in the year 2011. The average value of imported capital goods was registered by Claris Life Sciences. Aurobindo Pharma registered the highest average value of import of raw materials. Aurobindo Pharma also ranked the highest in terms of DMFs registered with USFDA. Ranbaxy Limited invested the maximum resources in terms of R&D expenses and sales and distributions expenses. Merck Limited has paid the highest average royalties and Dr. Agarwal’s Pharma ranked the highest in terms of average value of choice of technology (measured as compensation divided by net fixed assets). Gufic Biosciences was listed as the oldest pharmaceutical firm among all the sample firms considered for the research.
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Journal of Medical Marketing 0(0) Table 7. Regression results—fixed effects and random effects models. FE (fixed effects)
RE (random effects)
Variable
Coefficient
t-Statistic
Coefficient
t-Statistic
VIF
C RDINT ICGINT IRMINT AGE ROYINT COT SDEXPINT LOGSALES PATINT CR DMF R2 Adjusted R2 F statistic Durbin–Watson statistics
3.7372 0.0003 0.1647 0.4378 0.0591 1.6900 0.0094 0.0330 3.5988 0.0002 0.0009 0.0761 0.7823 0.7539 27.4963 1.1131
1.0872 1.7826 2.0647* 9.6544** 0.5201 6.2663** 2.5956** 0.4751 20.8163** 0.5765 0.6375 0.4871
3.6582 0.0004 0.1729 0.4735 0.1894 1.6809 0.0115 0.0141 3.4592 0.0002 0.0011 0.0534 0.3420 0.3388 106.1700 0.3601
1.7480 1.9222 2.2166* 11.1248** 3.3067** 6.2859** 3.2355** 0.2126 21.0801** 0.4714 0.7899 0.3677
1.166 1.018 1.297 1.089 1.168 1.100 1.288 1.850 1.038 1.199 1.104
Note: VIF, variance inflation factor. * and **Indicate statistical significance at 5% and 1%, respectively.
Results and discussion Table 7 presents a comparative analysis of the regression results using fixed effects model and random effects model. The results from the fixed effects model and random effects model, indicate that only five independent variables (import of capital goods, import of raw materials, royalties paid, choice of technology, and size of the firm) have shown a significant effect on export intensity of Indian pharmaceutical industry using the fixed effects model. In comparison to the fixed effects model, results from the random effects model indicate that six independent variables (import of capital goods, import of raw materials, age, royalties paid, choice of technology, and size of the firm) have exhibited a significant impact on export intensity of Indian pharmaceutical industry. Based on the results from the Hausman test it was decided that the fixed effects model be accepted for further discussion of the results. The data has been checked for stationarity using the panel unit root test29 and the data was found to be stationary. The data was also checked for multicollinearity by calculating the variance inflation factor (VIF) values. It was found that all the VIF values are
