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Market Structure, Entry Barriers, and Firms’ R&D Intensity: Panel Data Evidence from Electronics Goods Sector in India

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Abstract

This study examines the role of market structure and entry barrier factors on firm-level R&D activity of firms operating in the electronics sector in India. R&D efforts of a firm are distinguished in terms of (a) decision to undertake R&D activities, and (b) firm-level R&D intensity. To analyse (a) we use a Probit model, while the Tobit model is used to explain the inter-firm differences in (b). Using the data from the Prowess database provided by the Centre for Monitoring Indian Economy (CMIE), the analysis was carried out for the time period of 16 years, from 2000 to 2015. The trends point out a large inter-industry variation within this sector in R&D intensity and concentration ratio. For a pooled cross-sectional data of 353 electronic firms, the study finds that lower levels of industry concentration and high set-up cost induce firm-level R&D activity. Further, the study highlights the role of industry size in which the firm operates in decisions related to R&D activities.

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Notes

  1. See Indian ESDM market-analysis of opportunity and growth plan. An IESA - Frost and Sullivan report (2014).

  2. Since Schumpeter (1942) argued that the anticipation of market power is necessary to innovate, the Schumpeterian models therefore laid emphasis on competition for undertaking innovative activity as drivers of dynamic R&D processes (Peneder and Worter 2014). On the other hand, while Arrow (1962) expressed the impossibility of the existence of a perfectly competitive industrial structure, the study considered the presence of a temporary, contestable monopoly as competitive.

  3. For instance, there may be high entry barriers due to innovative efforts undertaken by R&D intensive firms leading to cost reductions, resulting in higher concentration. However, this higher concentration would not imply lower competitive pressure in the market.

  4. As a robustness check, we also constructed measures of industry concentration and market share at the two-digit and five-digit levels. Regression results based on the two-digit industry group level generated qualitatively similar results. Models based on the five-digit level of aggregation also generated much the same results in terms of the sign of the coefficients of the independent variables; however, industry concentration turned out to be statistically insignificant under this (narrow) level of aggregation. It may not have generated the appropriate product market faced by each firms. The reported results and the discussion are therefore based on the three-digit level of aggregation. Also, construction of variables at this level generates a better statistical fit.

  5. One would also use CR4 or CR8 as measures of industry concentration. However, given the nature of the dataset, the construction of these measures did not seem appropriate at the three-digit level.

  6. For analysis purpose, we look at the role of this element of market structure depending on the composition of the industry in term of innovators (R&D undertaking firms) and fringe firms (Ceccagnoli et al. 1998). We therefore consider the number of R&D reporting firms in each industry as a measure of number of competitors. To specify here, “industry” refers to the “industry group” within the Indian electronics goods sector. For example, if the firm belongs to the communication and equipment (C&E) industry group, the output share as well as the ratio of net fixed assets to net sales is computed for the C&E industry. Similar proxy for set-up costs has being used in other studies (see Syverson 2004).

  7. The output share of the industry’s median-sized firm (say, Y) is a measure for the firm’s minimum efficient scale, where the firms’ output is measured by nominal net sales. Assuming firms of different sizes in the same industry have the same capital-output ratio, Y may also be considered “the percentage of the industries fixed capital accounted for by the MES plant” (Orr 1974).

  8. “Marketing expenses” include discounts, rebates, and commission paid to the selling agents. In the present scenario, in order to reap the benefits of a growing industry size, marketing is therefore considered to be a more prominent activity compared with the use of scientific techniques. Marketing intensity as such provides an added incentive to firms towards undertaking in-house R&D activity. The industry invests heavily in marketing as they can help in attracting more demand and thereby create a brand image in order to reap out the benefits of loyalty. On the other hand, “advertising expenses” are payments made towards mass communication in order to impart information, develop attitude, and induce action beneficial to the advertiser.

  9. The maximum value of HHI at 1 indicates that the sample includes even those firms that appears alone in a particular year and in a particular industry within the electronics sector with no other firms observed in those years for that industry. In order to rule out the possibility of any biasness in our results due to presence of extreme values in the dataset, we re-estimated our regression models by excluding these observations. The results remained qualitatively similar to those reported in this study.

  10. The four-firm concentration ratio CR4 is the sales accounted for by the four largest firms in the industry. Values of CR4 may range from 0 to 1. An improved measure of concentration is the Herfindahl index for concentration. The Herfindahl-Hirschman index (HHI) is measured as the sum of the square of each firm’s market share. This index utilizes the size distribution as well as the total number of firms in the industry and is therefore a more appropriate measure of concentration. The range of the value of HHI is from 1 (monopoly case) to 1/N (for N equal sized firm).With perfect competition when the number of firms is large, the value of HHI is zero.

  11. These ratios are computed using information about firms as per CMIE database.

  12. The figure is arrived at by calculating the percentage change in the concentration indices from 2007 to 2011 and 2011 to 2015, respectively.

  13. We also wanted to estimate the given equations for a balanced panel. However, the entry and exit of firms for major part of time period under consideration has been highly volatile. As a result, estimation for a balanced panel would imply huge loss of degrees of freedom and suffer from robustness checks. The results are therefore not presented.

  14. The Hausman (1978) specification test rejects the null hypothesis that firm specific effects are not correlated with the regressors. This suggests the use of fixed effects estimation rather than random effects estimation. The joint F-test produces a χ2 of 1.04. For best approximation in our result, we therefore consider to demean the dataset. The procedure involves removing the fixed effects by transforming variables to deviations from their firm-specific mean (Hsiao 1986). This algebra is known as the within transformation (Wooldridge 2013). Demeaning each observation by the individual specific mean eliminates the need to create firm-specific dummy variables. It also takes account of all the between-firm variation in R&D intensity but leaves all within-firm variation in R&D intensity available for explanation by the time varying variables.

  15. This bias takes place when a variable that explains cross-sectional variation in R&D intensity is also correlated with some other omitted variable that also explain the variability in the dependent variable (Hausman and Taylor 1981).

  16. Breusch and Pagan’s (1980) LM test for random effects in a linear model is based on pooled OLS residuals (Dougherty 2007).

  17. As we are certain that E(μit| xit) = 0, the pooled approach will lead to unbiased and consistent estimates.

  18. Appendix Table 9 reports the results of this non-linearity test. It is only with respect to marketing intensity as the dependent variable that we find a significant positive coefficient on the R&D firm dummy and a significant negative coefficient on the R&D intensity variable. This pattern indicates a non-linear relationship between marketing intensity and R&D. We therefore include the squared term of marketing intensity in model I. Alternately, we tested the model with the cubic specification of the marketing intensity variable. However, including only the squared term depicting a quadratic functional relationship with R&D gave us the best statistical fit.

  19. The Breusch and Pagan (1979) test suggested heteroscedasticity problem.

  20. It has been argued that the econometric estimates of the impact of various explanatory variables on companies R&D are subject to simultaneity bias (Kumar and Aggarwal 2005).

  21. The econometric models are also estimated after winsorizing the dataset. This exercise is carried out to verify that the empirical findings are not driven by the impact of any outliers. The results after winsorizing the dataset are reported in Appendix Table 11.

  22. We, however, suspect that since the data period of our study is restricted to the second generation of economic reforms during which the Indian electronics sector faced a huge exodus of firms, our sample selection is therefore biased towards the small and young competitive fringes and hence the negative coefficient for the HHI variable.

  23. A commonly used proxy of competition is industry concentration. The more the number of competitors within an industry, the more equally distributed are the market shares, resulting in a lower value of the Herfindahl.

  24. To emphasize here, the nature of the innovative activities in the Indian electronics industry is mostly of non-drastic kind. Hence, there is a greater chance that this improvement in product or process may be found by more number of firms.

  25. It seems reasonable to argue that a pure monopolist would have little incentive to engage in R&D, since it can continue to get economic profits by creating entry barriers and producing old products.

  26. However, gradually “these costs of adjustment become so large … that they swamp the firm’s revenue, and lead to large negative returns.” (Sargent 1979).

  27. While the average R&D intensity (year-wise) is relatively low among the dominant incumbents compared with their smaller counterparts, it does not necessarily imply lower levels of R&D efforts by these firms. This is due to the substantial economies of scale enjoyed by these large dominant firms.

  28. We get qualitatively similar results when industry size is measured at constant prices (taking 2004–2005 as the base year). These results are provided in Appendix Table 10.

  29. The communication and broadcasting equipment segment contributed approximately 31% of the total output of electronic products for the financial year 2013 while the second largest expanding industry, the consumer electronics segment contributed around 23% in the same year.

References

  • Acs ZJ, Audretsch DB (1987) Innovation in large and small firms. Econ Lett 23:109–112

    Google Scholar 

  • Acs ZJ, Audretsch DB (1988a) Innovation and firm size in manufacturing. Technovation 7(3):197–210

    Google Scholar 

  • Acs ZJ, Audretsch DB (1988b) Innovation in large and small firms: an empirical analysis. Am Econ Rev 78(4):678–690

    Google Scholar 

  • Aghion P, Bond S, Klemm A, Marinescu I (2004) Technology and financial structure: are innovative firms different? J Eur Econ Assoc 2(2–3):277–288

    Google Scholar 

  • Aghion P, Bloom N, Blundell R, Griffith R, Howitt P (2005) Competition and innovation: an inverted-U relationship. Q J Econ 120(2):701–728

    Google Scholar 

  • Angelmar R (1985) Market structure and research intensity in high-technological-opportunity industries. J Ind Econ 34(1):69–79

    Google Scholar 

  • Arrow KJ (1962) Economic welfare and the allocation of resources for invention. In: Nelson RR (ed) The rate and direction of inventive activity: economic and social factors. Princeton University Press, Princeton, pp 609–625

    Google Scholar 

  • Bain JS (1956) Barriers to new competition. Harvard University Press, Cambridge

    Google Scholar 

  • Barthwal RR (1984) Industrial economics: an introductory text book. Wiley Eastern Limited

  • Beneito P, Coscolla-Girona P, Rochina-Barrachina ME, Sanchis A (2015) Competitive pressure and innovation at the firm level. J Ind Econ 63(3):422–457

    Google Scholar 

  • Blundell R, Griffith R, Reenen JV (1995) Dynamic count data models of technological innovation. Econ J 105(429):333–344

    Google Scholar 

  • Blundell R, Griffith R, Reenen JV (1999) Market share, market value and innovation in a panel of British manufacturing firms. Rev Econ Stud 66(3):529–554

    Google Scholar 

  • Breusch TS, Pagan AR (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47(5):1287–1294

    Google Scholar 

  • Breusch TS, Pagan AR (1980) The Lagrange multiplier test and its applications to model specification in econometrics. Rev Econ Stud 47(1):239–253

    Google Scholar 

  • Brown JR, Petersen BC (2011) Cash holdings and R&D smoothing. J Corp Finan 17(3):694–709

    Google Scholar 

  • Cabral LM (2000) Introduction to industrial organization. The MIT Press, Cambridge

    Google Scholar 

  • Ceccagnoli M, Arora A, Cohen W, Vogt W (1998) R&D, knowledge spillovers, and competition among firms with asymmetric technological capabilities. Working Paper, Heinz School of public policy and management, Carnegie Mellon University

  • Comanor WS (1967) Market structure, product differentiation and industrial research. Q J Econ 81(4):639–657

    Google Scholar 

  • Comanor WS, Wilson TA (1972) Advertising as a source of monopoly power. Working Paper No. 108, Stanford Graduate School of Business

  • Czarnitzki D, Etro F, Kraft K (2011) Endogenous market structures and innovation by leaders: an empirical test. Economica 81(321):117–139

    Google Scholar 

  • Desai AV (1988) Technology absorption in Indian industry. Wiley Eastern Limited

  • Dougherty, C. (2007). Introduction to econometrics. Third edition. Oxford University Press

  • Drummond H (1992) The quality movement – what total quality management is really all about! Kogan Page, London

    Google Scholar 

  • Erickson GM, Jacobson R, Johansson JK (1992) Competition for market share in the presence of strategic invisible assets: the US automobile market, 1971-1981. Int J Res Mark 9(1):23–37

    Google Scholar 

  • Geroski PA (1990) Innovation, technological opportunity, and market structure. Oxf Econ Pap, New Series 42(3):586–602

    Google Scholar 

  • Hausman JA (1978) Specification tests in econometrics. Econometrica 46(6):1251–1271

    Google Scholar 

  • Hausman JA, Taylor WE (1981) Panel data and unobservable individual effects. Econometrica 49(6):1377–1398

    Google Scholar 

  • He Z, Wintoki MB (2016) The cost of innovation: R&D and high cash holdings in U.S. firms. J Corp Finan 41:280–303

    Google Scholar 

  • Hsiao C (1986) Analysis of panel data. Cambridge University Press, Cambridge

    Google Scholar 

  • IBEF Report, Electronics (2015). Website Source: https://www.ibef.org/download/Electronics-March-2015.pdf. Accessed 24 March 2018.

  • Indian ESDM Market-Analysis of Opportunity and Growth Plan. (2014). An IESA - Frost and Sullivan report. Website Source: http://www.iesaonline.org/downloads/IESA-FS-report-Indian-ESDM-Market.pdf, retrieved on 15 Dec 2017

  • Jovanovic B, Gilbert RJ (1993) The diversification of production. Brookings Papers on Economic Activity Microeconomics, vol. 1993, no. 1, pp. 197–247

    Google Scholar 

  • Kamien MI, Schwartz NL (1982) Market structure and innovation. Cambridge surveys of economic literature. Cambridge University Press

  • Kraft K (1989) Market structure, firm characteristics and innovative activity. J Ind Econ 37(3):329–336

    Google Scholar 

  • Kumar N (1987) Technology imports and local R&D in Indian manufacturing. Developing Economies 25(3):220–233

    Google Scholar 

  • Kumar N, Aggarwal A (2005) Liberalisation, outward orientation and in-house R&D activity of multinational and local firms. Res Policy 34(4):441–460

    Google Scholar 

  • Kumar N, Saqib M (1996) Firm size, opportunities for adaptation, and in-house R&D activity in developing countries: the case of Indian manufacturing. Res Policy 25(5):712–722

    Google Scholar 

  • Lall S (1983) Determinants of R&D in an LDC: the Indian engineering industry. Econ Lett 13(4):379–383

    Google Scholar 

  • Lee CY (2003) Firm density and industry R&D intensity: theory and evidence. Rev Ind Organ 22(2):139–158

    Google Scholar 

  • Mansfield E (1963) Size of firm, market structure, and innovation. J Polit Econ 71(6):556–576

    Google Scholar 

  • Nickell SJ (1996) Competition and corporate performance. J Polit Econ 104(4):724–746

    Google Scholar 

  • Orr D (1974) An index of entry barriers and its application to the market structure performance relationship. J Ind Econ 23(1):39–49

    Google Scholar 

  • Peneder M, Worter M (2014) Competition, R&D and innovation: testing the inverted-U in a simultaneous system. J Evol Econ 24(3):653–687

    Google Scholar 

  • Sargent T (1979) Macroeconomic theory. Academic Press, New York

    Google Scholar 

  • Scherer FM (1965) Firm size, market structure, opportunity, and the output of patented inventions. Am Econ Rev 55(5):1097–1125

    Google Scholar 

  • Scherer FM (1967) Market structure and the employment of scientists and engineers. Am Econ Rev 57(3):524–531

    Google Scholar 

  • Scherer FM (1980) Industrial market structure and economic performance, 2nd edn. Houghton Mifflin, Boston

    Google Scholar 

  • Schumpeter JA (1942) Capitalism, socialism, and democracy. Harper and Row, New York

    Google Scholar 

  • Scott J (1984) Firm versus industry variability in R&D intensity. In: Griliches Z (ed) R&D, patents, and productivity. University of Chicago Press, pp 233–248

  • Shepherd WG (1972) The elements of market structure. Rev Econ Stat 54(1):25–37

    Google Scholar 

  • Shrieves RM (1978) Market structure and innovation: a new perspective. J Ind Econ 26(4):329–347

    Google Scholar 

  • Stigler G (1968) Barriers to entry, economies of scale, and firm size, the organization of industry. Irwin, Homewood

    Google Scholar 

  • Sutton J (2007) Market structure: theory and evidence. In: Armstrong M, Porter R (eds) Handbook of industrial organization, vol 3. North-Holland, Amsterdam

    Google Scholar 

  • Syverson C (2004) Product substitutability and productivity dispersion. Rev Econ Stat 86(2):534–550

    Google Scholar 

  • Tingvall PG, Poldahl A (2006) Is there really an inverted U-shaped relation between competition and R&D? Econ Innov New Technol 15(2):101–118

    Google Scholar 

  • Tirole J (1995) The theory of industrial organization. In: The MIT press. Cambridge, USA

    Google Scholar 

  • Vossen RW (1999) Market power, industrial concentration and innovative activity. Rev Ind Organ 15(4):367–378

    Google Scholar 

  • Wooldridge JM (2013) Introductory econometrics: a modern approach, fifth edition. Cengage Learning, Delhi

    Google Scholar 

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Acknowledgments

I acknowledge the research support received at the Department of Humanities and Social Sciences, IIT Bombay, while conducting this work. I am grateful to my supervisors, Prof K. Narayanan and Prof. S. Bhattacharyya, for their valuable guidance, and their critical and detailed comments at every stage of this study. All remaining errors are my responsibility.

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  1. Department of Economics, Birla Institute of Technology and Science-Pilani, K.K. Birla Goa Campus, Goa, India

    Richa Shukla

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Appendices

Appendix 1

Table 8 The independent variables used in this study
Full size table

Appendix 2

1.1 Testing for Non-linearity

Table 9 reports a basic specification in which the explanatory variables are a zero/one dummy that identifies observations on firms that report positive R&D expenditure and the firm’s R&D intensity measured as the ratio of R&D expenditure over (nominal) net sales. Industry group dummies and the year dummies are included, to control for industry effects and trend effects, respectively.

Table 9 Regression results testing for the non-linear relationship between independent variables and R&D
Full size table

Appendix 3

Table 10 Regression results of model specification IV with industry size measured at constant prices (base year 2004–2005)
Full size table

Appendix 4

Table 11 Regression results with variables winsorised at levels 1% and 99%
Full size table

Appendix 5

The Centre for Monitoring Indian Economy (CMIE) is a leading business information company, headquartered at Mumbai. The CMIE database consists of annual company-level data supplied by over 49,000 Indian companies, listed, and others from both the private and public sectors. It was established in 1976, primarily as an independent think tank. CMIE has built India’s largest database on the financial performance of individual companies: it conducts the largest survey to estimate household incomes and pattern of spending and savings; it runs a unique monitoring of new investment projects on hand; and it has created the largest integrated database of the Indian economy.

CMIE has a presence over the entire information food chain, from large-scale primary data collection and information product development through analytics and forecasting. CMIE produces economic and business databases and develops specialised analytical tools to deliver these to its customers for decision-making and for research (for details see http://www.cmie.com).

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Shukla, R. Market Structure, Entry Barriers, and Firms’ R&D Intensity: Panel Data Evidence from Electronics Goods Sector in India. J Ind Compet Trade 20, 115–137 (2020). https://doi.org/10.1007/s10842-019-00308-1

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