Technological Strategies and their Determinants: A study of Indian Basic Chemical Industry

Transcription

1 Technological Strategies and their Determinants: A study of Indian Basic Chemical Industry K. Narayanan 1 and Savita Bhat 2 Abstract Globalization and the presence of multinational companies (MNCs) in the productive sectors of the economy has compelled the passive entrepreneurs of indigenous firms to adopt different, generally more efficient and productive technological and managerial practices, thereby leading to the growth of the sector in many developing countries. This paper attempts to analyze the differences in the technological strategies adopted by the firms, and how the disparity in the firm characteristics determines the technological behavior of firms drawn from the Indian Basic Chemical industry. The different technological strategies considered are in-house R&D, imports of embodied technology (in the form of import of capital goods), and import of disembodied technology (from the market through lump sum payments, royalties, and technical fees). Using a panel data for the period from 1997 to 2003, an attempt has been made to understand, with the help of cross tabulations and maximum likelihood estimation of the Tobit model, the differences in technological strategies adopted by firms and their determinants. The paper finds that the firms in this industry (excluding Pharmaceuticals) are investing in various combinations of the three modes of technological strategies- R&D, imports of embodied technology, and import of disembodied technology. In particular the firms with foreign equity participation have been found to be investing rigorously on technology efforts than the ones without it. The older firms have been found to be technologically more active than the younger ones. The complementarities between imported technology and in-house R & D appear to be most important only for a subset of firms who are using multiple strategies. 1 Associate Professor of Economics, Department of Humanities and Social Sciences, Indian Institute of Technology, Bombay, Powai Mumbai E mail: knn@hss.iitb.ac.in Fax: Research Scholar, Department of Humanities and Social Sciences, Indian Institute of Technology, Bombay.

2 Technological Strategies and their Determinants: A study of Indian Basic Chemical Industry 1. Introduction It is now widely established that a firm s competitiveness and performance in an industry is greatly affected by the technological strategy adopted by the firm where the technological strategy itself is determined by the technological regime in which the firm operates. Here technological regime consists of various factors, some internal and some external to the firm, that together determine the diverse conditions faced by the firms while operating in the industry. In this context it is important to mention Schumpeter s (1943) process of creative destruction that enthroned technology as an instrument of a firm s competitiveness. According to him, innovation of any kind whether in commodity, technology, source of supply, or organization can give definite cost and quality advantage to any firm in a competitive market. Thus, any firm that is non-innovative may in long run get eliminated from the market. The theory of firm too recognizes a firm as a primary concept that can have active behavior characterized by the continuous efforts put in by the firm to change and/or remove, over a period of time, the constraints in its path of achievement of its objectives (Hay and Morris, 1991). Examples of active behavior are advertising, doing research and development, carrying out product diversification, colluding, merging with or acquiring other firms. There are studies (Kim and Nelson, 2000; Lall, 2001; Siddharthan and Rajan, 2002) that have looked into the role of technology, at both micro and macro level, as a vehicle for development in newly industrializing countries (NIEs) like India. During the protected regime till 1980s, few of the active Indian firms collaborated with foreign firms to import embodied and disembodied technology. After the economic liberalization of 1991, the Indian economic scenario changed substantially with many of the controls and regulations abolished with central role given to the market forces. Subsequently, many firms, that had up till now adopted a passive approach, started importing technology and also doing in-house R&D. The changed economic environment also became conducive for the advent of globalization in India. Many multinational companies (MNCs) have, today, become intrinsic players in various sectors of Indian economy. As the competition is becoming tougher, Indian firms that were so far having a comfortable position in the Indian market have been, after liberalization, forced to review their strategy and choose a different, more appropriate, and feasible one that would aid in improvising on their existing technological knowledgebase so that they can at least survive in the market. Many authors (Lall, 1983; Basant, 1997; Siddharthan and

3 Safarian, 1997; Pandit and Siddharthan, 1998; Siddharthan and Pandit, 1998; Narayanan, 1998; Narayanan, 2004; Narayanan and Banerjee, 2004; Sujit, 2004) have empirically analyzed the technological strategies, competitiveness, and growth of manufacturing firms in India during various policy regimes. These studies have specifically tried to understand how the differences in the firm characteristics may determine the competitiveness of the firms in various Indian manufacturing industries including Chemicals. This paper attempts to contribute to the existing literature by analyzing what differences are seen in the technological strategies adopted by the firms, and how the disparity in the firm characteristics determines the technological behavior of the firms drawn from the Indian Basic Chemical industry. Specifically, the paper tries to investigate, in a liberal economic policy regime, how and what combination of technological strategy a firm deploys in order to stay put in competition and increase its market share. Further, the role of firm characteristics like firm size, age, profitability, vertical integration, and foreign presence in determining its technological efforts is also studied. The different technological strategies considered are inhouse R&D, imports of embodied technology (in the form of import of capital goods), and import of disembodied technology (from the market through lump sum payments, royalties, and technical fees). The second section deals with the literature review giving an overview of the characteristics of the Indian Basic Chemical industry and also looking into theoretical and empirical studies on technological strategies and competitiveness, relevant especially for Indian Chemical industry. The third section deals with the description of the data and the foundation of the methodology used in the study. The fourth section deals with the empirical analysis consisting of cross tabulations as well as Tobit regression analysis. The final section gives the summary of the findings and the conclusions that can be drawn from the study. 2. Background and Review of Literature This section tries to look into various studies on technological strategies adopted by firms for improving their competitiveness. The first subsection briefly looks at the technological characteristics and economic environment prevailing in Indian Chemical industry. The second subsection then looks at various theories and empirical studies dealing with technological strategies, relevant especially for developing countries like India. Though the main focus is on firm characteristics of and strategies used by firms in the Chemical industry of developing countries like India, however studies on other industries in both developed and developing countries have also been touched upon.

4 2.1 Indian Basic Chemical industry: characteristics and policy environment To facilitate analysis, studies on Chemical industry (Basant, 1997; Arora et. al., 1998; Arora and Gambardella, 1998; Lall 2001; KPMG India-CHEMTECH Foundation, 2003) in both developed and developing countries, have classified the firms belonging to chemical industry into various types. Going by the classifications used in various studies, the sample in the present study consists of mostly firms belonging to the All-around, Scale-intensive, Basic Chemical industry. The all-around are generally large-scale firms that operate in several stages of the value-chains (Arora et. al., 1998). The major competitive factor in scaleintensive group is the length of production runs (Lall, 2001). Thus innovations in scaleintensive firms generally take the form of complex, capital-intensive processes innovations. However the innovations may not be the cutting edge ones since they are usually an improvement in the process of producing identical or similar products. Basic Chemicals industry could be characterized by high volume since products are standard with low value addition, limited product differentiation across manufacturers, high entry barriers due to high capital investments required for inventory, raw materials and energy, stringent regulations on health, safety and environment, and limited R&D spending that is largely application oriented (KPMG India-CHEMTECH Foundation, 2003). Thus, the firms in the study are mainly upstream Chemical firms that produce organic, inorganic, fine, and specialty chemicals used as inputs in the process of manufacturing of other products by the downstream firms in either Chemicals or other industries such as Textiles, Paints, Paper, Leather, Rubber, and likewise. In this paper we would address the industry as the Indian Basic Chemical industry. During the highly regulated environment India was mainly a producer of Basic Chemicals. In the protected regime, when licensing and import tariffs ensured that the firms did not face much competition from within as well outside India, the firms in India were small scale ones as compared to the world. However, after liberalization and advent of large-scale MNCs in Indian markets, the firms are now experiencing high competitive pressure from within as well as outside India, compelling them to investment on newer and/or better technology so as to remain competitive. Though investing in in-house R&D so as to develop more efficient process technologies for production, has been one of the technological strategies however, in spite of encouragement given to firms, in the form of tax reductions for R&D investments, by the Indian government, the amount spent by Indian firms on R&D is hardly anything compared to the world players (Pandit and Siddharthan, 1998; KPMG India-CHEMTECH Foundation, 2003). As part of liberalization process, the Indian government did away with most of the licensing requirements except for hazardous chemicals and a few specified drugs. Most

5 chemicals and petrochemical products became freely importable and tradable. Also, automatic approval of foreign equity up to 51 percent in most drugs and formulations was made possible (RBI, 2000). Also, in 1993, India enacted a legislation requiring adherence of the Indian firms to both product and process patents when India became a signatory to the general agreement on tariffs and trade (GATT). With all these policy changes, many Indian industries are today witnessing high amount of foreign direct investments (FDIs) in the form of financial collaborations, joint ventures, and technical collaborations. 2.2 Theories and Empirical studies It was in the context of oligopolistic competition that Schumpeter, in the year 1943, put forth his theory of sunrise and sunset industries. According to him an entrepreneur firm could shake the equilibrium existing in the market by introducing a new and/or unique product in the market, which in turn could induce other firms to put in efforts in innovative activities for bringing in other product or process innovations, thereby leading to growth and development of the industry. Nelson and Winter (1977) initiated the development of a framework that could help economies to devise policies that consider the role of innovation in growth and development of the economy. The evolutionary framework that they have proposed recognizes that as the environment of the firm changes over time, the firm tries to adapt by suitably modifying its characteristics. However which of the firms would be successful in surviving the changed scenario would be determined by the efficient strategy chosen by the firm, which in turn would be determined by the firm capabilities. According to studies dealing with evolutionary framework (Nelson and Winter, 1977; Romijn, 1996; Basant, 1997; Pandit and Siddharthan, 1998; Narayanan, 1998; Narayanan, 2004) technological regime or technological paradigm can be considered as design configuration (including policy environment) that acts as a framework for production or operation of a firm in an industry. The trajectories could then be considered as the paths of advancement within the given technological regime/paradigm. The firms, over time, try to achieve different technological sophistication by either shifting to a different trajectory of operation in a given technological paradigm through innovation in existing processes and products or shifting to a trajectory in a totally new technological paradigm through inventions. Though the shift in trajectory or paradigm is determined by the prevailing capabilities of the firm, however, firms often put in extra efforts and time to acquire further capabilities that would help them use a more appropriate trajectory shift in the changing environment.

6 When Siddharthan and Safarian (1997) used a pooled data on Indian manufacturing industries (including Chemical) for the partially liberalized period (1987 to 1989), age of plant and machinery was found to be an important positive factor in determining import of capital goods for those firms that neither had foreign equity participation nor purchased technology from market. However, Pandit and Siddharthan (1998) found that age of plant and machinery had an adverse impact on technological opportunities stimulated growth in the firms of various Indian manufacturing industries (including Chemicals) for the same period. Schumpeter s (1943) assertion that large firms with monopolistic advantages have far more resources than their smaller counterparts to invest in productive R&D resulted in a number of research articles that have looked into market structure, especially the firm size, as a determinant of innovation (Mansfield, 1963, 1964; Kamein and Schwartz, 1975; Desai, 1985 Acs and Audretsch, 1987; Katrak, 1989; Kamein 1989; Cohen and Levin, 1989; Braga and Willmore 1991; Kumar and Saqib, 1996; Symeonidis, 1996; Basant, 1997; Siddharthan and Safarian, 1997; Sujit, 2004). Some of the arguments in favor of the assertion that innovations are brought about primarily by larger firms include high costs of innovation that only large firms can afford, need for undertaking large scale projects to balance successes and failures, successful commercialization of the innovative ideas that is facilitated when the firm has control over the market which large firms do have (Schumpeter, 1943; Brozen 1951; Mansfield, 1963; Symeonidis, 1996). However, decreasing returns to scale in the production of innovations due to loss of managerial control and bureaucratization of innovative activity, and market power induced passive behavior of larger firms are arguments unfavorable to the viewpoint that larger firms are more innovative (Symeonidis, 1996). There are also others that consider both smaller and larger firms to be equally innovative (Scherer, 1965; Kamien and Schwartz, 1975), the former being the originators and the latter being the developers (Kamien, 1989). However the various empirical studies do not seem to have reached a consensus as to the exact relationship of firm size with the innovative activity of the firm. Some (Mansfield 1964; Basant, 1997; Sujit, 2004) have found that technological activity increases with increase in firm size for firms in most of the industries. In contrast, Deolalikar and Evenson (1989) find firm size having a highly negative effect on patenting intensities in the Indian Chemical industry from Others like Kamien and Schwartz (1975) have found that R&D activity increases more than proportionately with firm size in case of only Chemical industry whereas for all other industries of US considered in the study, the relationship was inverted U shaped, that is, the relative R&D activity appeared to increase with firm size only up to a point after which it either leveled off or declined. But, for a sample consisting of Indian manufacturing firms (including Chemicals) for the period from to 1980-

7 1981, Kumar and Saqib (1996) found that though probability of undertaking R&D activity increased with firm size only up to certain level after which it reduced, the relationship between R&D intensity and firm size was a positive linear one. In more recent studies on Indian manufacturing industries, especially Chemicals, Siddharthan and Safarian (1997) found market share to be unimportant as a determinant of capital goods import in case of foreign affiliates in Indian Chemical and other industries during , and Sujit (2004), in his study on determinants of R&D in Indian manufacturing firms for a sample from , found market share to be unimportant in determining R&D investment intensity in Indian Chemical industry. Brozen (1951) notes, advanced techniques are capital intensive therefore the firm that can invest higher amount will be able to replace older equipments with superior ones. So a firm that will save and reinvest its earnings or profits into innovative activities will remain a technological leader if product marketing and scale problems are absent. Thus, the source and amount of finance for investment may be another aspect that can influence the technological strategy of a firm. A firm may either get finance as a loan from financial institutions like banks or may plough back its profits. But, as the theory of internal financing suggests, taking a loan requires commitment and involves high risk. Therefore firms may prefer internal financing to taking loans. As Kamien and Schwartz s (1975) note, only firms generating a substantial cash flow would be able to support a sizable R&D effort since they may be unwilling or unable to borrow substantial funds to finance a new product or process development. This means that a high current profit, as a source of liquidity, is necessary for in-house R&D. However, the authors also observed that there was no consensus in the empirical literature with respect to the effect of internal financing on innovative efforts since varying and contrasting results were available. In Indian scenario, Narayanan (2004) found that difference in technology acquisitions to be more important as a determinant of growth in the Indian Automobile industry because the firms in the industry, after de-regulation, were heavily reinvesting their profits for establishing themselves in the market. However, Sujit (2004) found profitability to be unimportant as a determinant of R&D intensity for firms belonging to the Indian Chemical industry during the time period from 1994 to There are various modes through which a firm may acquire technology (Link, 1983; Bell and Scott-Kemmis, 1985; Desai, 1985; Pandit and Siddharthan, 1998; Narayanan 1998; Romijn, 1996; Narayanan, Some could use technology imports like arms-length purchase against lump sum or royalty payments; others could use intra-firm transfer of technology through foreign direct investment or foreign equity participation. Still others could use technology transfer through the supply of machinery and equipment where the technology is

8 embodied in the imported capital good. The firm may then use R&D efforts to locate, adapt, assimilate and develop imported technology (Katrak, 1989; Romijn, 1996; Narayanan, 1998). Thus, another aspect that comes into light is the strategy of import and adapt (IAT) where the firm imports a technology and then invests in some adaptive R&D. Katrak (1989) studied the relationship between R&D and technological imports in newly industrializing countries (NICs) using data from Indian industries for a period of He found that imported technology helped in promoting R&D in the firms. Similarly, Siddharthan (1992) in his study on determinants of in-house R&D, and the impact of technology transfer on R&D expenditure for a sample of firms belonging to the Indian private corporate sector found technology imports and in-house R&D to be complementary. He also noted that the sudden increase in the Indian firms in-house R&D activities was partly related to the liberalization trends in technology imports. Later, Siddharthan and Rajan (2002) on the basis of their survey of literature and various case studies concluded that technology transfer and in-house R&D efforts are by and large complementary and so, a successful strategy for firms would be to have an in-house R&D base along with import of technology. In a more recent study, Sujit (2004) in his study on firms in Indian industries including Chemicals, introduced intensity of import of capital goods and royalty payments as explanatory variables in his regression equations for R&D intensity as explained variable. He found that though royalty payments turned out to be positively determining R&D intensity, import of capital goods did not have any statistically significant effect. Ease of entry and exit from an industry, as the theory of firm suggests, may also determine the competitiveness in an industry. A firm may use backward integration to secure the price of the inputs, especially in case where the input supply is likely to be monopolized in future (Hay and Morris, 1991). High vertical integration in an existing firm can become an entrybarrier for other new firms trying to enter the industry (Brocas, 2003; Narayanan and Banerjee, 2004). This is especially the case when the downstream supplier of a product vertically integrates with an upstream seller of a more efficient, unique, and costly component or technology. Since all aspects of production would be internalized therefore higher vertical integration in the firm may lead to reduction in technology purchase from the market against licenses and royalties and have moderate effect on R&D intensity. Cohen and Levin (1989) assert that a firm s higher degree of vertical integration may actually increase the amount of R&D undertaken especially in the direction where there is possibility of exploiting economies of scope and diversifying. Though Narayanan (1998) found self-production to be more important than sub-contracting in determining the competitiveness of Indian Automobile industry during the liberal regime, Lin (2003) considers specialized small firms, vertically disintegrated industrial system with

9 low entry barriers, and strong social network to be the reason behind the formation of a strong electronics and information industry in Taipei, China. As discussed earlier, with the advent of globalization, a recent trend in India has been the presence of MNCs in various industries, especially in manufacturing sector. Also, after liberalization, there has been an increase in the amount of foreign direct investments. Thus, a factor that has become important in determining the technological investments in Indian industry is foreign equity participation. Siddharthan and Pandit (1998) asserted that, after 1985, firms in Indian drugs and pharmaceuticals, chemicals, and industrial machinery had to compete by creating additional capacities through investments in various technological efforts like R&D, import of capital goods, and technological imports. They found that the multi-national enterprise (MNE) affiliates had advantage in terms of technology, brand names and other intangible assets in the liberal regime and thus they invested more and grew faster. According to Siddharthan (1992) high transaction costs are involved either in transfer of nonstandardized technology that cannot be easily codified to designs and drawings or where a large amount of tacit and firm specific components are present or in cases where there is lot of information asymmetry or where brand names are involved. In all such cases foreign direct investments (FDIs) is preferred to purchases through market. On similar lines, Siddharthan and Safarian (1997) looked into various theories relating to technology transfer in foreign affiliated firms. According to them, in order to avoid the spillover and diffusion of their expensive newly evolved technologies (FDI internalization theory) and to avoid taxes and restrictions related to profit repatriation (transfer pricing or absorption approach), transnational corporations (TNCs) support internalized technology transfer through imports of capital goods. The studies based on product-life cycles and intangible assets suggest that intra-firm transfer of technology through FDI takes place only when the technology is relatively new and still evolving, but when the technology can be standardized and codified, arm s length technological purchases are preferred. Basant (1997) found that foreign equity participation led to high technological dynamism in firms belonging to Indian Chemical industry, in the deregulation period. As Narayanan (2004) also finds that in case of Indian Automobile sector, it was the differences in the technological strategies adopted that determined the differences in the growth of the firms. The firms with foreign equity participation grew faster during the period of strict controls as well as after liberalization, though during the deregulation period import of capital goods turned out to be important. However, Siddharthan and Safarian (1997) found that foreign affiliates, taking advantage of deregulation, imported capital goods and undertook modernization expenditures mainly in the electrical and electronic goods, and automobile industries but not in the chemicals and pharmaceuticals industries.

10 3. The Data and Methodology This section discusses the data and methodology used in the study. The firm characteristics used, their definition, and the analysis techniques used in the study would be highlighted. As indicated in Section 1, this study would use panel data consisting of firm characteristics from the Basic Chemical industry (excluding Pharmaceuticals) for the period from 1997 to The source of the data is the Capitaline-2000 database provided by Capital Market. The number of firms considered in each year falls in the range 151 to 163 with a total of 1086 observations for the seven years. Pooling of data has been done to ensure that any abnormalities specific to certain year do not affect the results. Firm-wise data on sales turnover, gross profits, year of incorporation, expenditure on R&D, expenditure on imports of capital goods, expenditure on lump sum, royalty, and technical fees, value-addition by the firm, and presence or absence of foreign collaborator have been collected for the analysis. In this study an attempt will be made to understand the determinants of the technological investments, especially in-house R&D for the Indian Basic Chemical industry. The modes of technological investments considered in the study are research and development intensity (RDI), capital goods import intensity as a proxy for embodied technology imports intensity (MKI), and disembodied technology (in the form of lump sum, royalty, and technical fee payments) imports intensity (LRI). The methodology consists of empirically analyzing using both- cross-tabulations and Tobit analysis, the determinants of technological strategies, especially in-house R&D. One of the cross-tabulations shows the mean and standard deviations of some of the firm characteristics against various combinations of the three basic technological strategies, namely, in-house R&D, capital goods import, and arm-length purchase of technology. Another cross-tabulation depicts how the mean and standard deviations of the firm characteristics varies with different ranges of R&D intensity used by the firms. In the Tobit analysis RDI is regressed against firm characteristics such as firm size (MS), age of the firm (AGE), profitability (PROFIT), vertical integration (VI) and a dummy variable for foreign equity participation (DFOR) as a determinant. Apart from these firm characteristics, some more dummy variables (D mk, D lr, D mk_lr ) and slope dummy variables (MKI*D mk, LRI*D lr, MKI*LRI*D mk_lr ) have also been introduced for specific analyses. Table 1 describes the variables and their definitions used in the study. Table 1: Variables, symbols, and definitions used in the study Variable Name Symbol Used Definition used in the study Research and Development Intensity RDI (Expenditure on R&D / Sales Turnover of the firm) * 100 Capital Imports Intensity MKI (Expenditure on import

11 Disembodied Technology Import Intensity Firm Size or Market Share Age Profitability Vertical Integration Dummy for Foreign Equity Presence Dummy for Firms doing only R&D along with Embodied Technology imports Dummy for Firms doing only R&D along with Disembodied Technology imports Dummy for Firms doing R&D along with both Embodied and Disembodied Technology imports Slope Dummy for Firms doing only R&D along with Embodied Technology imports Slope Dummy for Firms doing only R&D along with Disembodied Technology imports Slope Dummy for Firms doing R&D along with both Embodied and Disembodied Technology imports LRI MS AGE PROFIT VI DFOR D mk of capital goods / Sales Turnover of the firm) * 100 (Lump sum, royalty, and technical fees payments in foreign currency / Sales Turnover of the firm) * 100 (Sales Turnover of the firm / Sum of the Sales Turnover of all the firms) * 100 One added to the difference between the year of incorporation and the year in the study (Gross profit earned by the firm in the year / Sales Turnover of the firm) *100 (Value Addition by the firm / Sales Turnover of the firm) * 100 DFOR = 1 when foreign equity participation exists DFOR = 0 otherwise D mk = 1 for the firms doing only- RD with MK D mk = 0 otherwise D lr = 1 for the firms D lr doing only- RD with LR D lr = 0 otherwise D mk_lr MKI*D mk LRI*D lr MKI*LRI*D mk_lr D mk_lr = 1 for the firms doing- RD, MK, and LR D mk_lr = 0 otherwise Value equals MKI when D mk = 1 Value equals zero when D mk = 0 Value equals LRI when D lr = 1 Value equals zero when D lr = 0 Value equals LRI when D mk_lr = 1 Value equals zero when D mk_lr = 0 Table 2 shows the mean, variance, minimum, and maximum values of the non-dummy variables for the sample of 1086 firms. Also the number of observation of the total of 1086 observations that take the value of 1 for the dummy variables has also been indicated. As we can observe from Table 2, the Indian Basic Chemical industry sample has average market share of firms at around 0.6 percent with the largest firm having a market share of only 8

12 percent. The mean value of intensity of all the three technology sources for the Indian Basic Chemical industry is less than 0.3 percent and the highest level of intensity in case of all the three sources of technology is below 7 percent. Further, the firms operating in the industry seem to be quite experienced as the mean age of the firms in the industry is around two decades with the oldest firm being 82 years old. The variance in the profitability of the firms is the highest implying that there are firms that are highly loss making along with firms that have high profitability. Also the mean value for profitability is a negative value. Vertical integration closely follows profitability with a high value of variance of Around 25 percent of the firms, that is 269 out of 1086 firms, have foreign equity participation. As is clear from the last three rows of Table 2, less than 7 percent of the total observations use each one of combination strategy of importing embodied and/or disembodied technology with R&D. Table 2: Mean and Variance of Variables used in the analysis Variables Symbol Mean Variance Min. Max. Value Value 1. R&D Intensity RDI Import of Embodied Technology (Capital MKI Goods) Intensity 3. Import of Disembodied LRI Technology Intensity 4. Age of Firm AGE Profitability PROFIT Vertical Integration VI Firm Size (Market Share) MS Dummy for Foreign With Foreign Equity Participation: 269 (24.8%) DFOR Equity Presence Without Foreign Equity Participation: 817 (75.2%) 9. Dummy for Firms doing only R&D along Number of Observations out of 1086 doing only RD D with Embodied mk with MK: 72 (6.6%) Technology imports 10. Dummy for Firms doing only R&D along with Disembodied Technology imports 11. Dummy for Firms doing R&D along with both Embodied and Disembodied Technology imports D lr D mk_lr Number of Observations out of 1086 doing only RD with LR: 42 (3.9%) Number of Observations out of 1086 doing RD with MK and LR: 73 (6.7%) Total Number Of Observations (N) = 1086 Since the data used for the present analysis consists of a large number of observations taking zero values for the explained variable. Therefore, for such censored sample where information on regressand is available only for some observations, Tobit or censored regression model is the most appropriate technique (Siddharthan and Safarian, 1997; Green,

13 2002, Gujarati, 2003). The underlying methodology in estimation of Tobit model is the maximum likelihood estimation technique and not the least-square estimation technique. So the technique is free from many of the necessary conditions of the least square estimation technique. Statistically, a Tobit model can be expressed as: Y i = β 0 + β 1 X 1i +...+β n X ni + u i, Y * i = 0 if Y i 0, = Y i if Y i > (1) where Y * i is the regressand and X 1i to X ni are the n regressors. The present study would also use a Tobit model for the econometric analysis. 4. Empirical Analysis This section would use both cross tabulations and maximum likelihood estimation of the Tobit model, for understanding the determinants of technological strategies in Indian Basic Chemical industry. The first subsection would deal with the cross tabulations and the second subsection would discuss the econometric (Tobit) model used and the hypotheses formed. The third subsection then discusses the results of the Tobit model. The definitions of the firm characteristics considered for the analyses have been already explained in the previous section. 4.1 Cross Tabulations Table 3 shows the exhaustive combinations of three of the basic technological strategies, namely, in-house research and development, embodied technological imports, and disembodied technological imports. Based on which of the three strategies, a firm is using at a given time the firm can be considered to be belonging to one of the eight combinational strategies. For example, a firm would be using RDI&MKI strategy if and only if the firm is investing on only- in-house R&D and capital goods (embodied technology) imports. The degree of the sub-strategy has also been given for ease of analysis. Here degree of the strategy depicts the number of the basic strategies that have been used together. For example MKI is of degree 1 since it represents firms using only import of capital goods strategy, RDI&LRI is of degree 2 since it represents the combination of both R&D and disembodied technology imports, and RDI&MKI&LRI is of degree 3 since it represents the firms using all the three basic strategies at the same time. As can be observed from Table 3, more than half of the firms belong to NONE strategy implying that most of the firms in Basic Chemical industry are passive. However of the active firms around ¼ prefer to use a strategy of doing only in-house R&D followed by

14 Serial No. import of capital goods alone at 17 percent. From the last but one column of Table 3, one can clearly notice that import of disembodied technology whether alone or with either of the other two basic technological strategies is not favored by the firms in the Basic Chemical industry as compared to the other two basic strategies, though amongst the active firms in the industry, the strategy of using all the three basic technological strategies (14.93%), that includes disembodied technology imports, is as popular as doing in-house R&D with import of capital goods (14.73%). Sub- Strategy Table 3: R&D, Import of Capital Goods, and Import of Technology Symbol Degree of the substrategy Does the firm invest in any of the following? R&D Import of Capital Goods Import of Technology 1. None NONE 0 No No No 2. Only R&D RD 1 Yes No No Only 3. Import of Capital MK 1 No Yes No Goods Only 4. Import of LR 1 No No Yes Technology R&D with 5. Import of Capital RD&MK 2 Yes Yes No Goods R&D with 6. Import of RD&LR 2 Yes No Yes Technology Import of Capital 7. Goods with MK&LR 2 No Yes Yes Imports of Technology 8. All Together RD&MK&LR 3 Yes Yes Yes No. Of Obs. (Out of 1086) In the Sample Using The Sub- Strategy 604 (55.62%) 121 (11.14%) 84 (7.74%) 32 (2.95%) 71 (6.54%) 41 (3.78%) 61 (5.62%) 72 (6.63%) Percentage Of Active Firms (Out of 482) using the Substrategy Percentage of firms using the Substrategy that have foreign presence Again, one can observe from the last column of Table 3 that the percentage of foreign affiliated firms of the ones not using any of the technological strategies is at around 13 percent. As we go down the column the percentage of foreign affiliated firms amongst the firms using higher degree of combination strategies increases. In other words, for the firms using all three technological strategies (RD&MK&LR), percentage of foreign affiliated firms is the highest (63.89%), but for the firms using only two of the three basic strategies at a time the percentage of foreign affiliation is lesser at around 50 percent. Similarly, of the firms that

15 use only one of the technological strategies, still lesser, that is, less than 40 percent are foreign affiliated. Table 4: Mean with Std. Deviation in the bracket Number of MS AGE PROFIT Observations (%) (%) (%) NONE 604 (0.385) (11.32) ( ) RD 121 (1.223) (12.69) (14.736) MK 84 (0.886) (13.25) (21.033) LR 32 (0.463) (15.66) (16.125) RD&MK 71 (1.328) (15.25) (8.990) RD&LR 41 (0.827) (20.61) (7.168) MK&LR 61 (0.985) (15.32) (10.555) RD&MK&LR 72 (1.505) (14.03) (6.775) VI (%) ( ) (14.925) (17.948) (14.730) (10.384) (7.040) (15.613) (8.539) Table 4 gives the mean and standard deviation of some of the firm characteristics, for the various combination strategies. As can be seen from Table 4 the least mean size (MS = percent) is for the firms doing nothing. The mean size for the firms using the combination of two or more strategies is more than that for the firms using only one of the basic strategies. Similarly in case of AGE too, the mean age of the firms using none of the strategies is the least at around 19 years. The mean age of the firms using only one of the basic strategies at a time is less than that of those using more than one combination strategies. However when we look at the column depicting mean profitability for the various strategic combinations, we observe that the average firm using none of the strategies is making loss, though the variance of this category is very high. Again, even though no clear pattern emerges with respect to profitability, however one can notice that the least mean profitability amongst the active firms can be observed for the firms using only in-house R&D (1.26 percent) and for all other strategies that involve import of technology (either embodied or disembodied or both) are having higher mean profitability. With respect to the mean values of vertical integration for the various strategies, one can only say that the average integration of the firms using none of the technological strategies is very high ( %) and for all the active firms the average VI is in thirties. Table 5 shows the distribution of the mean and variances of the firm characteristics against the various intensity ranges of one of the important technological strategies, that is, in-house R&D. As can be observed from Table 5, with increasing ranges of R&D intensity the average capital goods import intensity decreases. This suggests a substitution relationship between

16 R&D and import of capital goods strategies. However when we look at how the mean of intensity of disembodied technological imports varies with increasing R&D intensity ranges, the trend is not very systematic since initially the mean arms length imports show increasing trend (though the variance is also very high for them compared to later R&D intensity ranges), then suddenly there is a drop in the arms length imports intensity for the R&D intensity range from above 2 to 3 percent, and then the intensity rises little bit to around 0.02 percent for later ranges. Table 5: Average intensity of the Firm Characteristics, with Std. Deviation in the bracket, for various ranges of RDI Firm Character istics NOB MKI LRI AGE PROFIT VI MS RDI Range Equal To (0.093) (0.352) (12.37) ( ) ( ) (0.588) Above till 1 (0.019) (0.405) (15.300) (12.57) (12.382) (0.991) Above till 2 (0.008) (0.477) (15.137) (6.565) (10.024) (1.696) Above till 3 (0.007) (0.0003) (12.563) (8.954) (9.126) (1.722) Above till 4 Above 4 till 5 5 Above 5 8 (0.004) (0.002) (0.001) (0.048) (0.054) (0.044) (11.810) (20.47) (15.236) (8.273) (9.122) (4.356) (9.329) (7.874) (10.539) (0.956) (0.624) (1.277) The column depicting mean age for the various R&D intensity ranges clearly shows that on an average the younger firms (around 20 years old) are passive with respect to R&D. The mean age of the firms doing R&D in higher rages then increases up to- above 3 till 4 percentrange, after which the mean age of the firms investing in still higher ranges of R&D shows decreasing trend. This suggests that the oldest firms are more middle range R&D investors, maybe because they use a combination strategy where they have to invest on importing of technology too apart from on in-house R&D. Again the trend could also suggest that the oldest firms try to just maintain their position in the market by undertaking minimal R&D that is required for introducing new products and processes occasionally. As is obvious, the average firm that is not investing on R&D is incurring losses. It should be noted that the variance in the profitability for RDI equal to 0 is very high ( ) compared to other ranges of R&D intensity. In general, the mean profitability of firms investing on higher R&D intensity ranges increases with increase in ranges though the mean

17 profitability of firms in the highest R&D intensity range (9.317 percent for above 5 percent R&D intensity range) is lesser than that of the lower rages of R&D intensity. Again, the firms that are not spending on R&D are highly vertically integrated. But on an average the firms that are spending differing amounts on R&D are similar to each other with respect to vertical integration since mean vertical integration is close to thirty percent for all the R&D investment ranges (excluding zero investment). The firms that are not investing on R&D have the least market share (0.377 percent) in the Basic Chemical industry. Till one percent R&D intensity, the mean market share of the firms increases to around 0.89 percent which further increases, in general, to above 2 percent for higher ranges of R&D intensities. 4.2 Correlation Matrix Table 6: Correlation Matrix between the Variables Variables RDI LRI MKI AGE PROFIT VI MS RDI 1.00 LRI MKI AGE 0.265** 0.182** 0.070* 1.00 PROFIT VI ** 1.00 MS 0.521** 0.182** ** ** and * represent 1% and 5% significance level respectively NOB = 1086 Table 6 shows the correlation matrix between some of the variables used in the study. As can be seen in the table the age is positively correlated to intensities of all the three technological strategies, namely, intensity of R&D, intensity of import of technology, and intensity of import of capital goods. This implies that the older firms are the ones having higher intensities of technological investments. Market share is also positively correlated to R&D and import of disembodied technology intensities, which means that larger firms are more intensive with respect to R&D and disembodied technology investments. Another aspect to be noted is that the explanatory variable market share is positively correlated to age implying that the firms that are large are also the ones who are older and experienced. Also, vertical integration is negatively correlated with significant value to profitability. This means that the firms that are highly sub-contracting type are the ones that are also highly profitable. 4.3 Tobit Model and Hypotheses The Tobit model that has been used to analyze the determinants of R&D in the Indian Basic Chemical industry is defined in equation 2.

18 RDI = α 0 + α 1 MKI + α 2 LRI + α 3 AGE + α 4 PROFIT + α 5 VI + α 6 MS + α 7 MS 2 + α 8 DFOR + α 9 D mk + α 10 D lr + α 11 D mk_lr + α 12 [ΜΚΙ D mk ] + α 13 [LRI*D lr ] + α 14 [MKI*LRI*D mk_lr ] + u 1 RDI * = 0 if RDI (above) 0 = RDI if RDI > (2) Following the studies based on evolutionary framework (Nelson and Winter, 1977; Romijn, 1996; Basant, 1997; Pandit and Siddharthan, 1997; Narayanan, 1998; Narayanan, 2004), age of the firm (AGE), that acts as a proxy for experience or learning over time, has been included in the model. As discussed in sub-section 4.1, in general, the mean age of the firm using the combination strategies increases as the degree of the combination strategy increases. Since the average age of the firms in the industry is around twenty years, which means that the firms must have acquired sufficient investment capabilities, also the correlation coefficient between age and R&D intensity is positive and statistically significant, it is hypothesized that age of the firm would positively influence R&D investments in the industry. Since, on an average the firms in the industry are making losses (Table 2), the industry, as a whole must be finding it difficult to get finance from outside. Therefore the active firms must be reinvesting the profits earned for technology improvements in order to further improve its position in the market. However, it should also be noted that profitability was found to be unimportant as a determinant of R&D intensity for firms belonging to the Indian Chemical industry in a study by Sujit (2004) for the time period from 1994 to As discussed in previous sub-section, the mean profitability of the firms is least for the active firms using R&D alone than other combination strategies. However the trend of mean profitability of firms with respect to various ranges of R&D intensity shows an increasing relationship till 5 percent intensity. So the effect of profitability (PROFIT) of the firm on the technology related investments is more likely to be positive in the industry. As discussed earlier, following Cohen and Levin (1989) one car argue that vertical integration can have a positive effect on the adaptive R&D. However, though on an average the firms in the industry are highly vertically integrated (Table 2, mean VI is 106 percent), the number of observations doing R&D along with importing embodied and/or disembodied technology is quite low (the observations in the last three rows, that is, 72, 42, and 73, of Table 2 add up to 187 of 1086, that is around 17 percent). Again, as discussed in sub-section 4.1, the highest vertically integrated firms are passive and all the active ones are moderately vertically integrated. Therefore one may postulate that higher vertical integration would have

19 a negative impact on the technological investments like in-house R&D in the Indian Basic Chemical industry. To investigate how far the theory of import and adapt strategy (Katrak, 1989), that is common to the developing countries like India, applies to the Indian Basic Chemical industry, intensity of import of embodied technology (MKI) and intensity of import of disembodied technology (LRI) have been included as explanatory variables. Basant (1997) had noted that for chemical industry in India where there is a weak patent regime, import and adapt strategy may be highly relevant; however the results of his empirical analysis had showed that R&D and foreign technology licensing were neither perfect complements nor perfect substitutes. Therefore to explore whether there is any impact and if so then how significant is the impact of the technology import variables (MKI and LRI) on the intensity of R&D for the firms who are explicitly using the combination strategies, dummy variables representing the particular subsets of technological combinations have been also included in the equation. Studies (Basant, 1997; Siddharthan and Safarian, 1997; Narayanan, 2004) have found foreign equity participation to be important in determining the technological activity and competitiveness of Indian manufacturing firms. Therefore a dummy variable (DFOR) that explores the effect of foreign equity participation on the intensity of R&D investments has been included in the equation. Since in the sub-section 4.1 it was found that more foreign affiliated firms as compared to those that are not use higher degree of combination strategies, it is hypothesized that foreign presence would have positive effect on the R&D intensity. Finally, to investigate the effect of firm size on the R&D investments in the firms of Indian Basic Chemicals industry, market share of the firm (MS) has also been included as an explanatory variable. The correlation matrix shows a positive and significant positive correlation between market share and R&D intensity therefore one can postulate that size of firm would have positive effect on in-house R&D intensity. However, as was discussed earlier, there has not be consensus in the literature regarding the exact effect of the firm size on technological investments, therefore the square of market share (MS 2 ) has also been included in the equation as explanatory variable to explore the possibility of non-linear relationship between size and R&D investment intensity. 4.4 Tobit Results and Interpretation Table 7 shows the results of the econometric (Tobit) model of equation 2 for the Indian Basic Chemical industry. As can be observed from Table 7, the log-likelihood is high and Chisquare is also significant there the results can be interpreted meaningfully. The coefficient of firm size is positive and significant implying that firms with larger market share are investing

20 more on R&D. However, the coefficient of the square of market share is also significant with a negative sign. This implies that the relationship between firm size and R&D investment is non-linear one that takes the form of inverted U shape. In other words the medium sized firms in the Indian Basic Chemical industry invest relatively (to their own size) more on R&D than the firms that are larger or smaller sized. Table 7: Results of Tobit analysis for R&D intensity as explained variable Variables Symbols Coefficient Estimates 1. Constant (-11.39)*** 2. Intensity of Capital Goods Imports MKI (-2.45)** 3. Intensity of Disembodied Technology LRI Imports (-4.22)*** 4. Age of the Firm AGE (3.48)*** 5. Profitability PROFIT (0.47) 6. Vertical Integration VI (-1.58) 7. Market Share MS (4.73)*** 8. Square of Market Share MS (-1.80)* 9. Dummy for Foreign Presence DFOR (0.48) 10. Dummy for Firms doing only R&D along with Embodied Technology imports 11. Dummy for Firms doing only R&D along with Disembodied Technology imports 12. Dummy for Firms doing R&D along with both Embodied and Disembodied Technology imports 13. Slope Dummy for Firms doing only R&D along with Embodied Technology imports 14. Slope Dummy for Firms doing only R&D along with Disembodied Technology imports 15. Slope Dummy for Firms doing R&D along with both Embodied and Disembodied Technology imports D mk (10.17)*** D lr (5.86)*** D mk_lr (11.49)*** MKI*D mk (1.11) LRI*D lr (2.45)** MKI*LRI*D mk_lr (1.87)* NOB 1086 LR chi2(14) = Prob > chi2 = Log likelihood = Pseudo R2 = left-censored obs. at RDI <= uncensored obs. ***, **, * are respectively 1%, 5% and 10% significance level Values in bracket are t-statistics for the coefficient estimates It should be noted that though the signs of coefficient estimates of variables depicting profitability and vertical integration are as expected, the results are not statistically