Local R&D and technology transfers: A Comparative Analysis of foreign and local firms in Indian Industries

Aggarwal, Aradhna Working Paper Local R&D and technology transfers: A Comparative Analysis of foreign and local firms in Indian Industries Copenhagen Discussion Papers, No. 2016-58 Provided in Cooperation with: Asia Research Community (ARC), Copenhagen Business School (CBS) Suggested Citation: Aggarwal, Aradhna (2016) : Local R&D and technology transfers: A Comparative Analysis of foreign and local firms in Indian Industries, Copenhagen Discussion Papers, No. 2016-58, Copenhagen Business School (CBS), Asia Research Centre (ARC), Frederiksberg, https://hdl.handle.net/10398/9293 This Version is available at: https://hdl.handle.net/10419/208656 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Editor of the Copenhagen Discussion Papers: Associate Professor Michael Jakobsen Asia Research Centre Copenhagen Business School Porcelænshaven 24 DK-2000 Frederiksberg Denmark Tel.: (+45) 3815 3396 Email: [email protected] www.cbs.dk/arc Local R&D and technology transfers: A Comparative Analysis of foreign and local firms in Indian Industries Aradhna Aggarwal1 Professor Asia Research Centre Department of International Economics and Management Copenhagen Business School Mobile: +45 9145 5565 Email: aa.in[email protected] Abstract This study examines how inter-firm heterogeneities in technology modes and intensities are linked to ownership of firms in India, using a panel dataset of 2000 odd Bombay Stock Exchange listed firms for the period from 2003 to 2014 drawn from the PROWESS database of CMIE. For the analysis, foreign ownership is categorised according to the control exercisable by them as defined under the Companies’ Act of India. A comparative analysis of domestic and different categories of foreign firms was conducted at two time periods: the global boom period of 2004-2008 and post crisis period of 2008-2014. The propensity score matching (PSM) analysis reveals that the majority owned foreign companies spend less on R&D and more on technology transfers than their local counterparts. Overall, threshold equity holding and global conditions matter. A panel data regression analysis on matched sample confirms the findings and validates the PSM findings. A horizontal cluster analysis on 3-digit industry level data shows that foreign firms cluster in high technology industries. JEL classification: G21; G32; K22; L25 Key words: Foreign firms, Majority owned foreign subsidiaries, minority owned subsidiaries, domestic firms, performance 1 I sincerely thank Shweta Jain for her support and assistance in preparing this paper. 1 1. Introduction Rapid advances in new technologies reinforced by the process of globalization have exposed firms in developing countries to intense technological competition both in the domestic and export markets. Conscious efforts towards building technological capabilities are increasingly becoming vital for them to survive. But, building these capabilities is costly, cumulative and evolutionary; it takes time and is uncertain (Lall, 1992). Since MNEs are a major source of cutting edge technology and innovation, developing countries’ governments encourage them to set up local production facilities in the hope that the latter would bring new technologies and help build technological capabilities of the local firms through labour turn overs, imitation, competition and demonstration. While there are direct channels of international technology transfers for local firms (for instance, licensing arrangements and imports of capital goods), FDI is expected to be instrumental in transferring the latest technologies to these countries. There is almost consensus in the literature that the latest and proprietary technologies are transferred through internalization (Dunning, 1993). More than 80% of royalty payments for international technology transfers were made by affiliates to their parent companies (UNCTAD, 1997 and 2005). Thus the presence of MNEs itself is assumed to entail technology transfers to local firms though spillover effects, even if they do not indulge in R&D in the host countries. But, there is also a possibility that MNEs directly undertake R&D activities in the host countries to adapt technologies to suit to local conditions and/or seek new assets to increase group capabilities as a strategic decision (Kuemmerle, 1999; Narula, 2004; Pearce, 1999). These R&D active MNE subsidiaries may provide better access 2 to foreign knowledge, and enable host countries to integrate more advantageously into global innovation networks (Cantwell and Piscitello 2000, Carlsson 2006). Embeddedness of MNEs into the local R&D networks may thus ensure more profound knowledge spillover to local firms. As a result there is growing competition among countries for high value adding R&D active FDI in recent years. Even while most R&D and patenting activities are largely concentrated in the MNEs’ home countries, and in a few developed host countries, there is evidence of relocation of R&D activities by them to developing countries (Dachs et al, 2014; Hall, 2010; Lundan and Dunning,2009; OECD, 2008a). It is against this background that this paper analyses whether MNEs are significantly different from their local counterparts in terms of their technology intensities and forms of technology activities in a developing economy with a special reference to India. We identify three main forms of technological activities: (i) local R&D efforts, (ii) licensing arrangements with foreign (parents for MNE affiliates) firms for technology acquisitions, and (iii) imports of technologies embodied in capital goods. The objective is to investigate whether MNEs undertake larger R&D spending than their local counterparts or they are more likely to acquire technologies from their own global networks through licensing and imports of capital goods. As noted above, the choice of technological spending by them may have different implications for the host developing countries. The analysis is based on the propensity score matching method followed by regression techniques, using the panel data for the period of 2003-04 to 2013-14. The data was partitioned into two periods: the boom period of 2003-04 to 3 2007-08, and the global crisis period of 2008-09 to 2013-14. For each period we conducted a separate analysis to investigate the impact of global conditions on the choices of foreign and local firms and validate our results. Evidence suggests that global relocation of R&D activities has suffered during the global crisis of the post 2008 (Dachs and Zahradnik, 2014; Kinkel and Som, 2012) The distribution of corporate R&D spending is highly skewed across industries. A few high-tech sectors account for the overwhelming share of R&D activity (Hirscheya et al. 2010). It has also been observed that R&D spending in high tech sectors is more effective in closing productivity gaps than that in other sectors (Ortega-Argilés et al, 2009). Thus, the analysis of differential technological behaviour of foreign and local firms is meaningful only if MNEs predominate in R&D intensive sectors. It is likely that MNEs do not even target these sectors. Rather, they concentrate in resource or labour intensive industries seeking access to cheap resources. For our analysis therefore we shall begin by identifying the sectoral distribution of MNEs by technological intensity in India using the cluster analysis. Economic reforms introduced in 1991 marked a transition of the Indian economy from an import substituting to an outward oriented regime. Since then the government of India has carried out massive economic reforms to promote the integration of the Indian economy with the rest of the world (Aggarwal and Kumar, 2012). One of the most important dimensions of these reforms is the lowering of barriers for foreign direct investment. The FDI regime had been fairly restrictive in India between the late 1960s and the early 1990s when ‘technological 4 self-reliance’ was adopted as one of the key objectives of the growth strategy. Numerous restrictions were imposed to attract FDI only in core technology intensive industries where little technological progress had been made in the country. These included, restrictions on the ownership control, entry and growth of foreign companies including, setting up of joint ventures with domestic partners, local content clauses, export obligations, promotion of local R and D and so on. In the open regime of the post 1991 however, the government has directed its policy towards investment liberalization, promotion and facilitation, and has been increasingly amending the investment laws and guidelines to facilitate inflows of FDI. Since 2005, the country has opened nearly all economic sectors for FDI as a step towards FDI-induced growth strategy. The present analysis therefore is expected to have important policy implications for other countries that have adopted a similar path of growth. There have been several studies analyzing the R&D behavior of firms. Most studies include a variable representing foreign ownership as one of the explanatory variables. However, the focus has essentially been on the relationship between local R&D and technology imports (Kumar and Siddharthan, 1997; Sasidharan and Kathuria, 2011 for literature surveys). To the best of my knowledge, there is no systematic analysis of the differential technological behavior of local and foreign firms in the Indian context. Further, while the International Business literature has evolved both, theoretically and empirically to better test and explain the differential behavior of foreign and local firms, most studies for developing countries in particular those in South Asia are still embedded in the classical frameworks and remain beset by the lack of good 5 quality data and weak methodology. It is therefore important to revisit the comparative analysis of technological behavior of firms by ownership. Methodologically, a key problem in evaluating the differential behavior of the local and foreign firms is that the investment decision of foreign firms is not independent from factors determining performance indicators. Better performance of foreign affiliates may for instance simply reflect the fact that they are attracted to high technology industries (Bellak, 2004; Girma et al. 2001;Globerman et al. 1994, p. 144; Howenstine and Zeile 1992, p. 53;). Selection bias can thus be a major problem in such studies. There are recent studies (Damijan et al., 2003; Javorcick and Spatareanu, 2008; Hake,2009) that explicitly address the question of selection bias. However, there is none for India. Furthermore, most existing studies are concerned with foreign ownership; the strategic importance of ownership holding is largely ignored. Finally, the firm-ownership data available from secondary sources which form the basis of most studies in particularly for India is subject to several limitations and that there is a lack of transparency in the identification of foreign firms. The present study contributes to the existing literature by addressing these gaps in the exiting literature. The rest of the study is organized into 6 sections. Section 2 discusses the changing role of FDI in the Indian economy and establishes the need for the present analysis. Section 3 describes the theoretical underpinnings of the analysis. Sections 4 and 5 provide methodological and data related details while Section 6 presents empirical results. Finally, Section 7 concludes the findings. 6 local initiatives. Once again, given the conflicting arguments, we test two competing hypotheses H3: Majority owned subsidiaries with controlling stakes exhibit a greater tendency to embed in local networks and incur larger R&D expenditures than their local counterparts. H4: Majority owned subsidiaries with controlling stakes are more likely to depend on imports of technologies from their parents and other internal network actors. 4. Methodology For empirical analysis, we used a multilevel methodology. In what follows we discuss that briefly. Identifying foreign firms In India, a direct investment enterprise is defined, in keeping with the IMF guidelines, as an incorporated or unincorporated enterprise in which a foreign direct investor owns 10 per cent or more of the ordinary shares or voting power (for an incorporated enterprise) or the equivalent (for an unincorporated). There is however recognition that a numerical guideline of 10% does not capture the essence of FDI for economic analysis. This is adopted essentially for the sake of consistency and cross-country comparability of the FDI statistics, and is based on the premise that a share as low as 10 per cent of voting rights or equity 13 capital allows the investor to ‘influence the management’ and provides the basis for a FDI relationship. The System of National Accounts (SNA) Framework of the UN with an objective of facilitating economic analysis on the impact of FDI uses “controlling stakes” as the basis for defining FDI for which more than 50% ownership is necessary. OECD (2008b) defines companies with 50% or more stake as FDI subsidiaries (controlled enterprises) while those with 10-50% of the stake, FDI associates (influenced enterprises). In line with this approach, for the present analysis of firm level performance, we considered three categories of foreign companies based on the right of the shareholders as defined in the Indian Companies’ Act. The Act defines three threshold levels of shareholding from the perspective of defining “influence” and” control” on the management. These are: 10%, 25% and 50%. Based on this classification and the available data, we identified four types of foreign firms:  Minority holding (10-25%) with minor influence  Dominant minority holding (25-50%) with dominant influence  Majority holding (above 50%) with controlling stake  Experiential foreign firms which are not predominantly foreign firms during the selected period but do have foreign ownership for a short period. Investigating the sectoral distribution of MNEs by technology intensity: Cluster analysis To investigate whether the MNEs target high tech industries in the first place, we clustered the three digit industries by technological orientation and brand value. 14 There are two different procedures that can be used to cluster data: hierarchical cluster analysis and k-means cluster. Since we did not know the number of clusters in advance, we eliminated the latter and focused on the former. Of the different methods of hierarchical clustering, we used the Wards linkage method. Based on the dendogram and the Calinski and Harabasz (1974) and Duda, Hart and Stork (2001) stopping rules we determined the number of clusters in the sample and then examined the presence of foreign firms by ownership stake in each group. Assessing the difference in technology intensity and activity between the local and foreign owned firms: Propensity Score Matching: As stated earlier, MNEs are likely to exhibit higher technology intensities due to a package of competitive advantages that they possess. But these advantages also provide the reasons of their investing in a host country. It is therefore important to control the effect of these variables before analyzing the impact of foreign ownership. The usual approach in correcting the selection bias is by using instrumental variables. Since most variables that affect foreign acquisition also affect technology intensity, this approach is inappropriate in the case of FDI. We have therefore used propensity score matching (PSM) to control for endogeneity. PSM is a non-parametric estimation method works by creating a comparison group (local firms) with identical distributions of observable characteristics to those in the treatment group (foreign firms). The basic idea is to find, for every 15 foreign firm, a matching local firm in terms of all relevant observable characteristics X. All observable covariates are controlled simultaneously by matching on a single variable, the propensity score. The mean effect of foreign ownership can then be calculated as the average difference in outcomes between the foreign and matched local firms. More specifically, if P=1 for foreign firms and P =0 for the local firms, then the average treatment effect on treated (ATT) on an outcome variable Y (technology spending) is ATT= E (Y1-Y0|P=1), which means, ATT= E (Y1|P=1)-E (Y0|P=1) While data on E (Y1|P=1) i.e. foreign firms are available, estimation of the counterfactual E (Y0|P=1) is the expected value of technology spending of the matched local firms. In all, we identified four broad categories of the foreign firms as stated above, and correspondingly constructed four propensity score models using firm and industry specific attributes. In each case, the balancing property is satisfied. Following the standard practice to limit comparisons to a subset of cases lying on the common support of propensity scores, we dropped local firms with propensity scores that were larger/smaller than the maximum/minimum propensity score. Kernel method was used to identify the local firms that match the foreign firms. This is a type of weighted regression of the outcome on the treatment indicator variable, the kernel weights being a decreasing function of the absolute difference in propensity score between the treated and comparison unit (Smith and Todd, 2005). A Gaussian kernel with bandwidth of 0.06 was 16 used for the analysis. It must however be noted that while matching removes any bias caused by selection on observable variables, it leaves the possibility of bias due to selection on unobservable variables. Thus, perfect matching is not possible. Generalised least square regression on matched sample: The PSM analysis is supplemented by GLS based regression analysis. The variables representing technological activity are regressed on foreign ownership variables after controlling for the scale of operations, age, technological opportunities and demand conditions prevailing in the industry in which the firm operates, technology acquisition through other sources, capital intensity of production methods, outward orientation, internal flows of resources, and government policy, using the matched sample to control for both, the observed and unobservable attributes and produce a robust estimator. 5. Data The Sample We used firm level data from PROWESS. It is a database of the financial performance of over 27,000 listed and unlisted Indian companies from a wide section of manufacturing, utilities, mining and service sectors. The data are collected by the Centre for Monitoring Indian Economy (CMIE) from the balance sheet of companies supplemented by surveys. The database is updated continuously and typically covers the period 1990 on. This data is widely used in 17 firm level studies of India across the world. Along with financials, the database also provides detailed information on shareholding patterns of these companies. The latter however is subject to critical limitations which are often overlooked: first, this information is provided only for the latest year. Most studies use this data assuming that the foreign shareholding of firms remains the same over the years prior to the latest year. This assumption is not reasonable for listed firms because the shares of most these companies are actively traded in the market. Acquisition of shares of the existing firms in the market has become an important mode of entry for foreign firms in India. Second, the data for shareholding patters is based on convenience sampling which means that it is subject to availability. For the listed firms it is available only for those firms that are actively traded in the market and for unlisted firms it is subject to their permission. Clearly, the studies using this data are subject to selectivity bias. For different periods, the results may vary depending upon the availability of ownership data and firms’ ownership stakes in that year. There is evidence that the distributional properties of samples drawn from PROWESS are not consistent over different periods (Chaudhury, 2002). To address this limitation, we procured the month-wise ownership data of 5109 listed firms from 2000-1 to 2013-14. Interestingly, this database is also provided by PROWESS separately but gets largely ignored in the firm level analysis of FDI. We focused on this data as of March 31 of each year. It was matched with the SEBI data to validate it. It was observed that the data pertains only to actively traded firms. Therefore we cleaned this data to compare apples with apples following the two truncation rules. Firstly, we included only those firms for which the information was available for each of the 11 years. The rest were dropped. We were thus left with 2004 firms. Secondly, 18 we dropped those firms reporting zero or negative netsales. After the cleaning process, our final data set consisted of a balanced panel of 1781 firms belonging to 280 three-digit manufacturing industries based on the National Industrial Classification (NIC) 1998 and spanning 11 years, from 2003 to 2014. Variables used in the analysis are defined in the Appendix Table A1. 6. Empirical analysis Sectoral distribution of MNEs by technology intensity For the analysis, the firm level data was aggregated into 280 three digit level industry data for the period 2003-04 to 2007-08. This exercise was done only for the boom period. The crisis period was excluded from the cluster analysis to avoid any type of bias that might have affected technological efforts of firms asymmetrically across industries. The analysis was conducted using the sectoral, technology and product differentiation variables: R&D spending (RD_INT), royalty payments (ROY_INT), and capital goods imports (CAPIMP_INT), branding intensity (ADINT) and a dummy for manufacturing sector (MFG). All these variables were converted into a binary form and the ‘matching dissimilarity matrix’ method was used in the clustering procedure. Based on the standard rules as mentioned above, we identified 5 clusters of industries. All these clusters are well populated. None of them dominates in terms of the number of observations, and none of them represents a mere 19 residual category. This confirms that each cluster is substantive. Table 1 gives mean values on the variables in the five principal clusters. The main dividing line runs between principally manufacturing and service industries on the one hand, and between industries that score high and low on the technology and product differentiation variables on the other hand. Based on the mean values of the cluster variables we distinguished them into 5 categories as shown in Table 1. Table 1: Mean Values by clusters Manufacturing Service Variable High Tech and high product differentiation Medium technology and high product differentiation Low technology and low product differentiation High Tech and high product differentiation Medium tech but high product differentiation No. of industries 83 67 53 25 52 RD_INT 0.65 0.30 0.00 0.39 0.00 ROY_INT 0.35 0.04 0.00 0.10 0.05 CAPIMP_INT 1.79 2.54 1.19 2.32 3.50 ADINT 1.50 2.17 0.91 0.98 3.02 Note: See, Appendix table A1 for the definition of variables Source: Estimated by the author Table 2 reports clustering results by ownership of the firms (appendix table A1 for definitions), which is of primary interest to us. It may be seen that the majority owned companies are by far the most advanced ones as over 62% of them during 2009-2014 belonged to the high-tech manufacturing cluster and almost one-fourth of them concentrated in the high-tech services cluster. Only about 15% of them are classified as low-tech either in manufacturing or services. As stated above, the technological or brand superiority of MNEs is the primary reason why they venture into investing abroad in the first place. In India, this 20 pattern can also be attributed to the legal framework prior to 1991 which sought to channelise the activities of FDI into high technology production by setting higher FDI caps in these sectors. During 2004-08, the distribution of foreign companies was highly skewed in the favour of high tech manufacturing industries. As more policy reforms were introduced, services started becoming more promising in the late 2000s, and the distribution became somewhat diffused (as shown by standard deviations). But the changes in the sample were marginal rather than substantive. Table 2: Classification of firms by technological orientation of industries (%) 2003-04 to 2007-08 Majority owned Domina nt__min ority Minority owned Experie ntial_FF Domestic firms High Tech and high product differentiation mfg 64.6 62.3 62.2 36.7 37.7 Medium technology and high product differentiation mfg 6.3 7.5 2.7 10.0 13.2 Low technology and low product differentiation mfg 1.3 9.4 13.5 0.0 5.5 High Tech and high product differentiation services 19.0 18.9 13.5 46.7 32.2 Medium tech high product differentiation services 8.9 1.9 8.1 6.7 11.4 Total 100 100 100 100 100 Standard deviation 25.7 24.4 24.0 20.4 14.1 2008-09 to 2013-14 Majority owned Dominant_ _min Minority owned Experie ntial_FF Domestic firms High Tech and high product differentiation mfg 62.4 56.0 50.0 46.2 37.7 Medium technology and high product differentiation mfg 7.3 12.0 9.3 19.2 12.9 Low technology and low product differentiation mfg 0.9 4.0 11.1 7.7 5.4 High Tech and high product differentiation services 22.9 22.0 18.5 26.9 32.0 Medium tech high product differentiation services 6.4 6.0 11.1 0.0 11.9 Total 100 100 100 100 100 Standard deviation 25.1 21.3 17.1 17.9 14.0 21 There also appears to be substantial restructuring of firms in terms of ownership holding across the first 4 categories in the late 2000s. Within services, the share of experiential firms has declined sharply while that of other foreign firms has increased significantly across both high and low tech categories. In the manufacturing sector, on the contrary, the share of experiential firms has shown an upward movement. This reflects a clear shift of FDI from manufacturing to services. Within manufacturing, there is a visible shift of foreign firms in favour of the medium tech consumer goods. However, they continue to predominate in high tech sectors. A critical question is whether foreign firms are also more R&D active than their local counterparts or they continue to embed in internal knowledge networks. In the PSM and regression analyses we shall control for the effect of these sectors to control for the sectoral endogeneity. These groups are represented as HTECH_MFG, MTECH_MFG, LTECH_MFG, HTECH_SER, and MTECH_SER in the subsequent analysis. Propensity score matching While applying the PSM, we first estimate the predicted probability of receiving foreign investment, given several firm and industry characteristics. As discussed above, we have four treatment groups: majority owned foreign firms, dominant minority owned firms, and minority owned firms and experiential firms. For 22 DFOR10 DFOR_EX HTECH-MFG MTECH-MFG LTECH-MFG HTECH-SER MTECH-SER TAX)…..(2) It must however be noted that the two modes of technology activity namely R&D and technology acquisition may not be alternative to each other. Technology imports by firms are likely to influence their R&D efforts but the intensity of technology imports may itself depend on R&D efforts. There is thus an issue to simultaneity between 1 and 2. Further, with respect to most explanatory variables in (1) and (2), there could be a problem of two-way causality. To address these issues we assume that both, technology choice and intensity are strategic decisions and have a long term orientation. They are not spontaneously determined by the firms on the basis of their current performances. Rather these strategic decisions are influenced by their past, current and planned behavior and performances. Therefore, the performance and behavioural explanatory variables: CAPINT ROY_INT TECHEM EX_INT PCM in model 1 and CAPINT RDS TECHEM EX_INT PCM in model 2 are converted into moving average of three years: lagged year, current year and lead year. TAX is a lagged variable. Inclusion of lagged and lead variables has addressed the issue of causality and simultaneity, and has allowed us to estimate the two models separately to explore the impact of foreign ownership on them. A panel data approach is employed to control for the unobserved firm and time specific characteristics. Using the propensity scores, the firms that are off the common support are dropped to include only those in the common support region and are matched. A fixed effect specification of the model would be an 29 ideal choice but is ruled out, as it does not return estimates of the main variables which are dummy variables. The only feasible way to estimate the model, therefore, is to use the random effect-specification. While estimating the model, we also took into account the year dummies to capture fixed effects of intertemporal shifts and corrected the estimates for heteroscedasticity for ensuring robustness of the estimates. The regression analysis is thus expected to produce doubly robust results. The GLS estimates of model are presented in Table 5. It may be seen that the regression results confirm the PSM based results. Firms with ownership stakes higher than the threshold of 25% are more likely to import technologies than their local counterparts. Table 5: GLS estimates of R&D and technology transfers: 2004-2008 and 2009-2014 on matched sample 2004-08 2009-14 RD_INT ROY_INT RD_INT ROY_INT VARIABLES Model 1 Model 5 Model 9 Model 13 SIZE -0.0149 0.0272* 0.0489 0.0314** (-0.741) (1.716) (1.430) (2.113) SIZE2 0.00480 -0.000495 -0.00402 -0.00198 (1.512) (-0.307) (-0.925) (-1.508) AGE -0.00381** 7.14e-05 -0.00428** 0.000673 (-2.082) (0.0387) (-2.057) (0.489) ROY_INT# -0.00330 -0.0911 (-0.770) (-1.112) RDS # -0.000169 -0.00667 (-0.452) (-1.177) CAPINT # -4.93e-06 4.01e-06* -5.58e-07 1.15e-07 (-0.672) (1.722) (-0.759) (0.340) CAPIMP# 0.00831 -0.000104 0.000207 2.30e-05 (0.845) (-0.234) (0.935) (0.210) EXINT # 0.0198*** 0.000595 0.0169*** -0.000569 (2.638) (0.617) (3.018) (-0.886) 30 IMPR_INT# -0.00911*** -0.000325 0.00111 0.00123* (-2.648) (-0.636) (0.422) (1.700) PCM# -2.89e-06 5.48e-07 1.42e-05 1.84e-07 (-1.276) (0.419) (1.261) (0.298) MTECH_SER -0.0332 -0.0791 -0.109* 0.0292* (-0.655) (-0.726) (-1.769) (1.688) HTECH_SER 0.179 0.110 0.133 0.208*** (1.290) (0.679) (0.947) (2.823) HTECH_MFG 0.448*** -0.0469 0.454*** 0.0961*** (5.014) (-0.342) (4.708) (4.815) MTECH_MFG -0.0311 -0.125 -0.159*** 0.0130 (-0.754) (-1.047) (-2.805) (0.874) DFOR50 -0.296*** 0.450*** 0.0565 0.520*** (-3.517) (3.948) (0.401) (4.883) DFOR25 -0.109 0.482** -0.0490 0.242* (-0.908) (2.228) (-0.420) (1.771) DFOR10 -0.268*** 0.00817 -0.0835 0.0377 (-3.321) (0.180) (-0.720) (0.894) DFOR_EXP -0.189* -0.0735 0.583 0.186 (-1.761) (-1.192) (0.721) (1.067) TAX## -8.71e-07 -9.07e-07 -1.16e-06 2.11e-06 (-0.265) (-1.006) (-0.518) (1.168) Constant 0.122 -0.0659 0.204** -0.175*** (1.037) (-0.716) (2.113) (-3.033) Year dummies YES YES YES YES Observations 4,800 4,800 5,229 5,229 Number of code 1,690 1,690 1,615 1,615 Note: # represents Three years; moving average; ## represent lagged variable *** Significant at 1%; **: significant at 5%*: significant at 10% DFOR50 and DFOR25 turn out to be significant in all specifications for ROY_INT both the periods. Our results thus indicate that the majority owned and dominant minority owned firms spend a considerable more amount of money on R&D performed abroad (in their parent companies) than the local firms. On the other hand, the DFOR50 and DFOR25 exhibit ambiguous results in the RD_INT specifications. As a matter of fact, R&D spending of foreign firms across all categories is less than that of local firms during the first period. It was only during the crisis that these firms increased R&D relocation to India. But the change has been marginal. On an average, foreign firms are not technologically embedded in India. They are more likely to depend on their parent labs. 31 Among the control variables, it is interesting to note that the R&D and royalty intensities are affected differently by the strategic explanatory variables. High tech sectors in both manufacturing and services attract significant technology transfers; but those in manufacturing alone induce significantly higher R&D intensities. Thus promoting manufacturing is more likely to accelerate R&D efforts in Indian industries. Further, exporting is significantly associated with local R&D efforts; its relationship with technology imports turns out to be insignificant. Younger but large sized firms are more likely to undertake R&D; relatively smaller firms exhibit a greater tendency to import technologies. Finally, global conditions seem to affect R&D and technology transfers differently. However, the relationship between them is not found to be significant in any case. Thus technology transfers may not positively influence local R&D efforts. It is important to identify the triggers for the latter to augment technological capabilities of firms. 7. Conclusion The majority owned and dominant minority owned firms are considered as conduits of technology transfers but evidently, their local R&D efforts are not significantly different from those of their local counterparts in the Indian context. The activities of technology generation are still concentrated in the home countries of MNEs located in India. A mere presence of MNEs as has been hypothesized in the literature may not be sufficient to generate knowledge spillover effects. The role of FDI in technology diffusion in this case depends on 32 the strengths and weaknesses of the local innovation systems and firms’ competitiveness. In general, MNEs’ R&D efforts, by cultivating closer ties to host companies and research institutes are an important source of technology spillovers. But their decision to relocate R&D to a host country itself depends in part on the strengths and weaknesses of the local R&D infrastructure. 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