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Determinants of Firm R&D: The Role of Relationship-Specific Interactions for R&D Spillovers

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Abstract

Research and Development (R&D) is a key component behind technological development and economic growth; therefore, understanding the drivers of R&D is crucial. An interesting question is the role of technology spillovers, transferred by trade, and their impact on firm R&D. Here we analyze not only how international and domestic inter- and intra-industry technology spillovers affect firm R&D but also the relatively unexplored issue of how relationship-specific interactions between buyer and seller affect such spillovers. We find international technology spillovers to be larger and more significant than domestic inter- and intra-industry spillovers. Moreover, relationship-specific interactions between seller and buyer enhance technology spillovers in general and international spillovers in particular.

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Notes

  1. Arguments for localized knowledge are characterized as five ‘stylised facts’ by Dosi (1988) and further developed by Feldman (1994a, b) as well as Baptista and Swann (1998). The spatial dimension of economic growth is highlighted by Amiti (1998) and Hanson (1998). Studies on trade, technology spillovers and R&D include Griliches (1992), Coe and Helpman (1995), Fagerberg (1995), Keller (1997, 2000) and Cohen and Levinthal (1989). For a survey, see Keller (2004).

  2. For example, it may be crucial to control for firm-level heterogeneity, but such a control is difficult when using aggregated data.

  3. To construct the relation-specificity (RS) index, Nunn (2007) builds on Rauch (1999) by using information regarding whether an input is sold on an organized exchange.

  4. Aghion and Howitt (1999) demonstrate how a positive correlation between productivity growth and entry and exit of firms can be established.

  5. See, e.g., Stoneman (1983).

  6. The annual response rate for firms with at least 50 employees in the financial statistics is approximately 97%.

  7. An alternative to the FS R&D data is the bi-annually collected Research Statistics (RS), based on all firms in the FS with at least 200 employees and on a sample of firms with 50–200 employees, and given that these firms report R&D expenditures of at least 200 000 SEK to the FS. Regarding statistical reliability, the bi-annually collected “Research Statistics” is of higher quality but has less coverage. The RS and FS data generate very similar results, but the RS reduces the sample size with more than 50%, and we therefore focus on results from the FS.

  8. Examples of industries not intensive in relationship-specific interactions include poultry processing, flour milling, petroleum refineries and corn milling; conversely, automobile, aircraft and computers are examples of industries intensive in relationship-specific investments.

  9. It might be argued that spillovers are endogenous and/or that spillovers are realized with an impact lag. We therefore follow the assumption of strong exogeniety (Hendry 1995) and apply the spillover variables with one lag. An alternative is to use external instruments, which was not feasible for our research. In addition, as shown by Bound et al. (1995), using weak instruments may amplify the bias.

  10. The significance of both tests for independent equations and the Mills ratio indicates that a selection procedure is appropriate. We find no contradictions between the selection and target equation, though we notice generally lower significance in the selection.

  11. Note that the Inverse Mills Ratio (IMR) is a nonlinear function of the variables included in the first-stage probit and that the target equation can be identified from this nonlinearity alone. The nonlinearity of IMR arises from the assumption of normality. However, identification is aided by adding a variable to the selection equation that is closely related to the decision to undertake R&D. As discussed above, firms’ profit fits these requirements and is therefore applied.

  12. Flowerdew and Aitkin (1982) and Santos Silva and Tenreyro (2006).

  13. The maximum number of observations is 15821, including firms with and without R&D. The selection equation accounts for a slight drop in observations. Results for the OLS model, Heckman target equation and the selection-adjusted FEVD reflects the number of R&D-performers. The Negative binomial model includes firms with zero observations where the fe calculation of the dispersion parameter accounts for loss of observations. See, e.g., Guimarães (2007) and Hilbe (2007).

  14. One explanation for the negative results found regarding particular intra-industry spillovers in interaction intensive industries may be the extent that R&D might be outsourced; however, this is likely to be most pronounced in the home industry where personal interactions are common.

  15. The correlation matrix in Table 7 indicates that though there is no severe multicollinearity, though we cannot exclude that multicollinearity might affect results when all spillover variables are considered.

  16. Similar results are obtained in the research of Gustavsson and Kokko (2003) and Tingvall Gustavsson (2004).

References

  • Aghion P, Howitt P (1999) Endogenous growth theory. MIT, Cambridge

    Google Scholar 

  • Altomonte C, Békés G (2010) Trade complexity and productivity. Center for Firms in the Global Economy (CeFiG) WP No. 12

  • Alvarez R (2006) Explaining export success: firm characteristics and spillover effects. World Dev 35(3):377–393

    Article  Google Scholar 

  • Amiti M (1998) New trade theories and industrial location in the EU: a survey of evidence. Oxf Rev Econ Policy 14:13–31

    Article  Google Scholar 

  • Anderson J, Wincoop EV (2004) Trade costs. J Econ Lit 42(3):691–751

    Article  Google Scholar 

  • Archarya RC, Keller W (2008) Technology transfer through imports. Can J Econ 42(4):1411–1448

    Google Scholar 

  • Arrow K (1962) Economic welfare and the allocation of resources of innovations. In: Nelson R (eds) The rate and direction of innovative activity. Princeton University Press, Princeton

  • Baptista R, Swann P (1998) Do firms in clusters innovate more? Res Policy 27:525–540

    Article  Google Scholar 

  • Bartel A, Lach S, Sicherman N (2009) Outsourcing and technological change. IZA DP No. 4678

  • Bound J, Jaeger D, Baker R (1995) Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variables is weak. J Am Stat Assoc 90(430):443–450

    Google Scholar 

  • Breusch T, Kompas T, Nguyes H, Ward MB (2010) On the fixed-effects vector decomposition. Munich Personal RePEc Archive MPRA Paper No. 21452, 2010

  • Brooks EL (2006) Why don’t firms export more? Product quality and Colombian plants. J Dev Econ 80(1):160–178

    Article  Google Scholar 

  • Burger M, van-Oort F, Linders GJ (2009) On the specification of the gravity model of trade: excess zeros and zero inflated estimation. Spatial Economic Analysis 4(2):167–190

    Google Scholar 

  • Casaburi L, Gattai V (2009) Why FDI? An empirical assessment based on contractual incompleteness and dissipation of intangible assets. University of Milano-Bicocca, Department of Economics WP No. 164

  • Coe D, Helpman E (1995) International R&D spillovers. Eur Econ Rev 39(5):859–887

    Article  Google Scholar 

  • Coe D, Helpman E, Hoffmaister A (1997) North-South spillovers. Econ J 107:134–149

    Article  Google Scholar 

  • Cohen WM, Levinthal DA (1989) Innovation and learning: two faces of R&D. Econ J 99:569–596

    Article  Google Scholar 

  • Dasgupta P, Stiglitz S (1980) Industrial structure and the nature of innovative activity. Econ J 90:266–293

    Google Scholar 

  • DeLong BJ, Summers LH (1991) Equipment investment and economic growth. Q J Econ 106(2):445–502

    Google Scholar 

  • Dosi G (1988) The nature of the innovative process. In: Dosi G et al (eds) Technical change and economic theory. Pinter Publishers, London

    Google Scholar 

  • Fagerberg J (1995) User-producer interaction, learning and comparative advantage. Camb J Econ 19(1):243–256

    Google Scholar 

  • Feldman MP (1994a) The geography of innovation. Kluwer, Boston

    Google Scholar 

  • Feldman MP (1994b) Knowledge complementarities and innovation. Small Bus Econ 6:363–372

    Article  Google Scholar 

  • Ferguson S, Formai S (2010) Institution-driven comparative advantage, complex goods and organizational choice. Research Papers in Economics 2011:10, Department of Economics, Stockholm University

  • Fernandes AM (2007) Trade policy, trade volumes and plant-level productivity in Colombian manufacturing industries. J Int Econ 7(1):52–71

    Article  Google Scholar 

  • Flowerdew R, Aitkin M (1982) A method of fitting the gravity model based on the poisson distribution. J Reg Sci 22:191–202

    Article  Google Scholar 

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

    Google Scholar 

  • Greene W (2010) Fixed effects vector decomposition: a magical solution to the problem of time invariant variables in fixed effects models? Department of Economics, Stern School of Business, New York University

  • Griliches Z (1992) The search for R&D spillovers. Scand J Econ 94:29–47

    Article  Google Scholar 

  • Guimarães P (2007) The fixed effects negative binomial model revisited. Econ Lett 99(1):63–66

    Article  Google Scholar 

  • Gustavsson P, Kokko A (2003) Sveriges konkurrensfördelar för export och multinationell produktion. Bilaga 6 till Långtidsutredningen 2003, Finansdepartementet

  • Hanson GH (1998) North American economic integration and industry location. Oxf Rev Econ Policy 14:30–43

    Article  Google Scholar 

  • Hendry D (1995) Dynamic econometrics. Oxford University Press

  • Hilbe J (2007) Negative binomial regression. Cambridge University Press

  • Keller W (1997) Technology flows between industries: identification and productivity effects. Econ Syst Res 9(2):213–220

    Article  Google Scholar 

  • Keller W (2000) Do trade patterns and technology flows affect productivity. World Bank Econ Rev 14(1):17–47

    Article  Google Scholar 

  • Keller W (2002a) Geographic localization of international technology diffusion. Am Econ Rev 92(1):120–142

    Article  Google Scholar 

  • Keller W (2002b) Trade and the transmission of technology. J Econ Growth 7(1):5–24

    Article  Google Scholar 

  • Keller W (2004) International technology diffusion. J Econ Lit XLII:752–782

    Article  Google Scholar 

  • Kukenova M, Strieborny M (2009) Investment in relationship-specific assets: does finance matter? University Library of Munich, Germany, MPRA Paper No 15229

    Google Scholar 

  • Lopez RA (2006) Imports of intermediate inputs and plant survival. Econ Lett 92(1):58–62

    Article  Google Scholar 

  • Lopez RA (2008) Foreign technology licensing, productivity, and spillovers. World Dev 36(4):560–574

    Article  Google Scholar 

  • Lumega-Neso O, Olarreaga S (2005) On indirect trade related R&D spillovers. Eur Econ Rev 49(7):1785–1797

    Article  Google Scholar 

  • Malerba F, Mancusi LM, Montobbio F (2007) Innovation, international R&D spillovers and the sectoral heterogeneity of knowledge flows. Centro di Ricerca sui Processi di Innovazione e Internazionalizzazione (CESPRI) Università Commerciale “Luigi Bocconi WP, No. 204

  • Mancusi LM (2008) International spillovers and absorptive capacity: a cross country cross-sector analysis based on patents and citations. J Int Econ 76(2):155–165

    Article  Google Scholar 

  • Markusen JR, Trofimenko N (2009) Teaching locals new tricks: foreign experts as a channel of knowledge transfers. J Dev Econ 88(1):120–131

    Article  Google Scholar 

  • Nunn N (2007) Relationship-specificity, incomplete contracts, and the pattern of trade. Q J Econ 122(2):569–600

    Article  Google Scholar 

  • Pavcnik N (2002) Trade liberalization, exit, and productivity improvements: evidence from chilean plants. Rev Econ Stud 69(1):245–276

    Article  Google Scholar 

  • Plumper T, Troeger EV (2007) Efficient estimation of time-invariant and rarely changing variables in finite sample panel analyses with unit fixed effects. Polit Anal 15(2):124–139

    Article  Google Scholar 

  • Plümper T, Troeger VE (2011) Fixed effects vector decomposition: response. University of Essex, Department of Government, Colchester

    Google Scholar 

  • Portugal-Perez A, Wilson JS (2009) Why trade facilitation matters to Africa. World Trade Rev, Cambridge University Press 8(3):379–416

    Article  Google Scholar 

  • Rauch JE (1999) Networks versus markets in international trade. J Int Econ 48:7–35

    Article  Google Scholar 

  • Santos Silva JCM, Tenreyro S (2006) The log of gravity. Rev Econ Stat 88:641–658

    Article  Google Scholar 

  • Schiff M, Wand Y, Olarreaga M (2002) Trade-related technology diffusion and the dynamics of north-south and south-south integration. The World Bank, Policy Research Working Paper Series, No. 2861

  • Schumpeter JA (1934) The theory of economic development. Harvard University Press, Cambridge

    Google Scholar 

  • Stoneman P (1983) The economic analysis of technological change. Oxford University Press, Oxford

    Google Scholar 

  • Stoneman P (1995) Handbook of the economics of innovation and technological change. Blackwell Publicers Ltd, Oxford

  • Tingvall Gustavsson P (2004) Effekter av näringslivets internationalisering på forskning och utveckling. Kap. 5 i Näringslivets internationalisering: Effekter på sysselsättning, produktivitet och FoU, ITPS A2004:14

  • Tybout J (2000) Manufacturing firms in developing countries: how well they do, and why. J Econ Lit 38(1):11–44

    Article  Google Scholar 

  • Westerlund J, Wilhelmsson F (2009) Estimating the gravity model without gravity using panel data. Appl Econ 43(6):641–649

    Google Scholar 

  • Xu B, Wang J (1999) Capital goods trade and R&D spillovers in the OECD. Can J Econ 32(5):1258–1274

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Centre of Excellence for Science and Innovation Studies (CESIS) and Stockholm School of Economics, 113 83, Stockholm, Sweden

    Patrik Gustavsson Tingvall

  2. Statistics Sweden (SCB), 701 89, Örebro, Sweden

    Andreas Poldahl

Authors
  1. Patrik Gustavsson Tingvall
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  2. Andreas Poldahl
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Correspondence to Andreas Poldahl.

Appendix

Appendix

Table 4 The effect of R&D spillovers. Estimations with full set of industry dummies at the 2-digit level included. Dependent variable: natural log of firms’ R&D expenditures
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Table 5 Summary statistics
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Table 6 Variance decomposition and correlation between RS-index and spillover variables
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Table 7 Correlation matrix, variables
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Table 8 Summary statistics, variables
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Tingvall, P.G., Poldahl, A. Determinants of Firm R&D: The Role of Relationship-Specific Interactions for R&D Spillovers. J Ind Compet Trade 12, 395–411 (2012). https://doi.org/10.1007/s10842-011-0112-7

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