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  1. Cost-Benefit Approach to Public Support of Private R&D Activity Bettina Peters Centre for European Economic Research (ZEW) b.peters@zew.de DIMETIC Doctoral European Summer School Pecs, July 14, 2010

  2. Motivation • Striking empirical evidence that innovation is a key driver for productivity and employment growth • Concern: Level of R&D investments in the economy might be suboptimal (low) due to three main reasons: • Financial constraints • Market failure due to asymmetric information between borrowers and lenders( especially for young and small firms) • Investment under uncertainty • Uncertainty impedes access to external finance • Firms may delay investment in R&D projects due to outcome uncertainty (real option theory) • Positive external effects of R&D • Market failure due to imperfect appropriability of returns (knowledge spillovers)

  3. Motivation • I will focus only on the third argument: Do such spillovers exist and if yes to what extent?  lecture today: part I: Estimating spillovers • Negative investment effects due to spillovers may be mitigated by innovation policies such as public R&D subsidies or by introducing IPR‘s • Public subsidies reduce private cost of R&D and therefore close the gap between social and private equilibrium

  4. Motivation • Lisbon-Agenda (2010) & EU2020-Agenda • Stimulating growth and competitiveness through R&D and innovation (R&D&I) • Supporting industrial R&D • R&D Tax credits • Technology-specific R&D programmes • Supporting basic research (allocating more funds to universities, introduction of ERC, etc.) • Beyond (traditional) R&D policy: Using other instruments to stimulate R&D and innovation • at EU level: Structural/regional funds & EIB • at national level: stimulating VC investment + technology transfer • EU2020: broader focus, but one aim is still to foster smart growth through R&D&I

  5. R&D Spending (GERD) in % of GDP 2008 or latest available year Source: OECD MSTI 2009-2

  6. GERD financed by Private Sector in % of GDP 2008 or latest available year Source: OECD MSTI 2009-2

  7. GERD financed by Government in % of GDP 2008 or latest available year Source: OECD MSTI 2009-2

  8. Motivation • Problem of public R&D subsidies: crowding-out of privately financed R&D investments may occur! • Evaluation of crowding out effects of R&D subsidies is necessary  lecture tomorrow, part II: Econometrics of Evaluation of Public Funding Programmes • The existence of social returns to R&D, however, are only a necessary but not sufficient condition. Public R&D programmes are always associated with costs which go beyond the pure amount of subsidy.  lecture tomorrow, part III: A Simple Example of a Cost-Benefit Approach to Public Support of Private R&D Activity

  9. Part I:Estimating Spillovers (Social Returns to R&D)

  10. Content • Definition of Knowledge Spillovers • Spillover Literature • Framework: Production Function Approach • Measurement Problems • Empirical Evidence • Example: Social returns to R&D in German firms (Stata) • Conclusion

  11. What a spillover is? • Knowledge as a public good (Nelson 1959, Arrow 1962): • non-excludable • R&D produces knowledge and something intangible as knowledge cannot be kept secret. • Knowledge will always spill over to third parties, so that many more agents will benefit from an R&D investment – not only the initial inventor • no rivalry in consumption • Knowledge can be used by many firms at the same time without getting less (but economic value of knowledge might decrease) • Spillover is a positive externality due to the public nature of knowledge. • Spillovers arise if firm A can benefit from firm‘s B R&D activity without sharing the R&D costs of B (Branstetter 1998)

  12. What a spillover is? • Different channels through which the outcome of R&D can lead to positive external effects. • Griliches (1979): Knowledge spillovers vs. market spillovers • Market (rent) spillovers: • New products or technologies are usually sold at higher prices. However, even if they are protected by patents, these prices often do not fully reflect the quality improvements (e.g. production of a memory chip) • Market spillover: Increase in consumer welfare which arises when new (intermediate) goods are not priced to their user value due to quality improvements

  13. What a spillover is? • Knowledge (technological) spillovers: • View of sender: • The non-appropriable part of knowledge produced by a firm’s innovation effort can be regarded as (outgoing) knowledge spillover. • View of receiver: • “By technological (or R&D) spillovers we mean that a firm can acquire information created by others without paying for that in a market transaction” (Grossman & Helpman, 1991) • Knowledge spillovers can be used to simply imitate new products (if it is not protected), to invent around or as input for further own product or technology developments. • More subtile form of knowledge spillovers: e.g. if a market leader abandon its research in a specific area and therefore signals to others that this research strand is not promising (Jaffe 1998).

  14. What a spillover is? • Knowledge spillovers are expected to be higher for basic research (Jaffe 1998) • Knowledge and market spillovers are hard to distinguish in empirics (Nadiri 1993). First attempt: Los and Verspagen 2000

  15. Spillovers: Different Channels • Within industry intra-industrial / horizontal knowledge spillovers • Between industries inter-industrial / vertical knowledge spillovers • Geographical proximity / localized knowledge spillovers: • Anselin, Varga & Acs (2000), Jaffe, Trajtenberg & Henderson (2002); Audretsch & Feldman (2004), Breschi & Lissoni (2001), Thompson & Fox-Kean (2005) • Trade-related & FDI : • Grossman & Helpman (1991), Kinoshita (2001).

  16. Problem associated with Spillovers • Nelson (1959), Arrow (1962) • Research firm cannot appropriate all returns from its initial investment + = • Profit-maximising companies will only account for expected private returns and private cost but not for (higher) expected social return • Socially desirable: all projects for which expected social returns exceed social cost • Thus: underinvestment in R&D from a social point of view Social excess returns to R&D Social returns to R&D Private returns to R&D

  17. Solutions • Solutions, e.g.: • Public subsidies • Tax credits • Fostering formation of R&D cooperations • Reduce private cost of R&D and therefore close the gap between social and private equilibrium

  18. Overview: Spillover Literature • Measuring the existence of (localized) knowledge spillovers • E.g. Jaffe, Trajtenberg & Henderson (2002) • Patents contain geographic information about their inventors • Use citation patterns to test the extent of spillover localization • To the extent that regional localization of spillovers is important, citations should come disproportionately from the same area as the originating patent. • Result: Citations to domestic patents are more likely to be domestic, and more likely to come from the same area as the cited patents. Knowledge spillovers are significant and quite large. But geographic localization fades over time. • Problem: Citations often come from the examiner (esp. in Europe) • Spillovers and agglomeration effects • e.g. Audretsch & Feldman (1996) • Effect of spillovers on cooperation behaviour • D’Aspremont & Jacquemin (1988), AER • Lambertini, Lotti & Santarelli (2004), EINT • Cassiman & Veugelers (2002), AER (incoming and outgoing spillovers)

  19. Overview: Spillover Literature • Effect of spillovers on own R&D / innovation • Non-tornament models: positive (negative) effect if external knowledge is a complement (substitute) to own knowledge (Spence 1984, Levin & Reiss 1988). • Tournament models: Additional uncertainty-resolving effect of spillovers, i.e. spillovers may also function as signals and convey information about the feasibility of undertaking a given R&D project. (Choi 1991) • Multistage patent races in which intermediate results are made known after each stage: If, e.g., a previously lagging firm closes the gap to the leader, the race may “heat up” in the sense that R&D investment will increase. (Grossman & Shapiro 1987, Harris & Vickers 1987). • Empirical studies: • Positive effect of Spillovers on number of patents (elasticity of 1.1%, Jaffe 1986) • Positive effect of Spillovers on own R&D (Jaffe 1988: elasticity of 0.2-0.3, Harhoff 2000: elasticity of 0.01-0.14) • Effect of spillovers on firm performance („social returns to R&D“)

  20. Effect of spillovers on firm performance • Assessing the size, importance and origin of knowledge spillovers due to R&D • Controversial issue, e.g. Krugman (1991, p. 53) „..knowledge flows, by contrast, are invisible; they leave no paper trail by which they may be measured and tracked, and there is nothing to prevent the theorist from assuming anything about them that she likes." • Dominant approaches: Construct a measure of external knowledge („spillover pool“) and include this as an extra term in a firm‘s • Production function (Griliches 1979) • Cost function (e.g. Bernstein 1979, 1997, 1998) • Profit function (Jaffe 1986, Czarnitzki and Kraft 2008) • Lecture focuses mainly on production function approach

  21. Production Function Approach Y: Output C: Physical capital stock L: Labour KI: Internal knowledge stock KE: External knowledge stock : Exogenous technological change A: Scaling parameter (total factor productivity) u: error term (unsystematic productivity shocks which are not observed)

  22. Production Function Approach • Productivity level (in logs:) Due to log-log specification:  :output elasticity of own knowledge percentage change of output when the own knowledge stock increases by 1 percent „Private rate of return“ Private rate of return: absolute change in output when the own knowledge stock increases by 1 Euro

  23. Production Function Approach Due to log-log specification: θ:output elasticity of external knowledge percentage change of output when the external knowledge stock increases by 1 percent „Social excess rate of return“ Social excess rate of return: absolute change in output when the external knowledge stock increases by 1 Euro

  24. Production Function Approach • Productivity level (per capita) • Assuming constant returns to scale in conventional inputs (e.g. Mairesse und Sassenou 1991 ):  Test on constant returns to scale: • Assuming constant returns to scale in all internal inputs (e.g. Los and Verspagen 2000): • Assuming constant returns to scale in all inputs:

  25. Production Function Approach • Productivity growth rates: • Instead of estimating productivity levels, many authors also use equation in (first) differences, i.e. productivity growth rates (e.g. Harhoff 2000) • Note: So far, output elasticities w.r.t. internal and external knowledge capital are the same for all firms, respectively. • Private and social rates of returns can be indirectly calculated (indirect approach) 25

  26. Production Function Approach • Direct approach I: • Assuming that depreciation rates on knowledge capital are zero: • We approximately get: 26

  27. Production Function Approach • Advantage: • Not necessary not calculate knowledge stock • Direct estimates of private returns and social excess returns to R&D • However, rates of return not directly comparable to indirect approach since depreciation on knowledge has been neglected („gross rate of return“) • Direct approach II (Goto and Suzuki 1989) • Direct estimates of net private returns and net social excess returns to R&D 27

  28. Measurements Problems • Internal and external capital stock: Key ingredients in estimation • How to define these concepts ? • How to measure them ?

  29. Internal Knowledge Stock: Data Issues and Solutions • Nessary to have a long time period • How to get initial kapital stock in t=1? • Permanent growth approximation • Assumptions: • capital accumulation process has been going on for a sufficiently long time • R&D expenditure has been growing at constant rate g in a pre-sample period (many studies simply assume 5 %) • Knowledge has depreciated at a constant rate d • Note: Many studies delete the first observation in regression (e.g. Harhoff 1998, Los and Verspagen 2000)

  30. Internal Knowledge Stock: Data Issues and Solutions • Depreciation rate? • Ideally: Depreciation rate should reflect whether knowledge is measured by inputs (R&D  depreciation on components of R&D such as investments or material) or outputs (patents  loss in value of patent) (Bernstein 1988) • Obviously, depreciation depends on competition (Schumpeter inspired method; Binzer and Stephan 2002) • However, in empirical studies it is common to assume a depreciation rate of 15 percent (Hall and Mairesse 1995) • Sensitivity analysis: results are quite robust to assumed depreciation rate (Bernstein 1988, Hall and Mairesse 1995) • At industry level: knowledge often depreciates faster physical capital (Mansfield 1973, Pakes and Schankerman 1984)

  31. Internal Knowledge Stock: Data Issues and Solutions • Deflator for R&D? • Not available in official statistics • Solutions: • Many papers use GDP deflator (e.g. Los and Verspagen 2000) • Others use deflator for investments (e.g. Harhoff 2000) • Most appropriate: composite index using a deflator for labour costs, material and investments and the (average) share of labour, materials and investments of R&D expenditure (Bernstein 1988, Peters et al. 2009)

  32. Internal Knowledge Stock: Data Issues and Solutions • Double-Counting • R&D expenditure mainly consists of cost for labour and capital which is already counted in L and C (Schankerman 1981, Cuneo und Mairesse 1983) • Solution: Substract from labour the number of R&D employees and from investments the R&D investments • Problem: Many data sets do not have this information • Most studies simply ignore this problem and hence underestimate (!) the private return to R&D • coefficient of internal capital reflects the excess effect of R&D employees/investments • „excess gross private return to R&D“ • Correcting for double-counting lead to a significant increase in the estimated return (Schankermann 1981, Hall and Mairesse 1995, Harhoff 2000, Peters et al. 2009)

  33. External Knowledge Stock: Data Issues and Solutions • Measurement of external knowledge stock is quite heterogenous in literature (which knowledge is included?) • Same measurement problems as for internal knowledge stocks arise for external knowledge stocks + …. • Simple measures for spillover pools: • Sum of all R&D expenditure of all other firms • Sum of R&D expenditure of all other firms in the same industry (except firm i!), e.g. Bernstein (1988) • Intra-industrial knowledge spillovers / horizontal spillovers • Sum of R&D expenditure of all other firms in all other industries • Inter-industry spillovers / vertical spillovers • Sum of R&D expenditure of all other firms in the same region/country (e.g. Bernstein 1998) • Localized knowledge spillovers

  34. External Knowledge Stock: Data Issues and Solutions • Griliches (1979): Firms that are closely related benefit more from each other than firms that are more distant. • Use proximity or similarity metrics to weight the R&D expenditure of other firms/industries/countries in the first place (spilloverpool) • Use Spilloverpool S to construct external knowledge capital stock KE using the perpetual inventory method

  35. External Knowledge Stock: Data Issues and Solutions • Several weighting schemes have been used in the literature: • Input-output flows (industry matrix): • Idea: traded goods transfer knowledge between industries • Terleckyi (1974), Sveikauskas (1981), Wolff &Nadiri (1987), Odagiri (1985) • Patent flows across industries • Industry matrix in which patents are classified according to their industry of manufacture and industry of use • Scherer (1982), Griliches & Lichtenberg (1984), Sterlaccini (1989), Mohnen & Lepine (1991), Putnam & Evenson (1994), Los & Verspagen (2000) • Import shares, esp. for international spillovers • Coe & Helpman (1995), Lichtenberg & van Pottelsberghe (1998), Naidiri & Kim (1996), Jacobs, Nahuis & Tang (2002)

  36. External Knowledge Stock: Data Issues and Solutions • Technology distance measured by patents • Jaffe (1986), Guellec & van Pottelsberghe (2001, 2004), Park (1995, 2004), Hanel & St. Pierre (2002), Bloom, Schankerman & van Reenen (2007), Capron & Cincera (2001), Aldieri & Cincera (2009), Peters et al. (2009) • Technology distance measured by research fields • Many R&D surveys include information on the devision of R&D expenditure according to product areas • Goto and Suzuki (1997), Harhoff (2000), Adams (1997), Peters et al. (2009)

  37. Technological Distance (Jaffe, 1986) • Characterizes the technological position of firms. • Suppose there are K “technological areas” in which firms can invest in R&D / patent. • Then firm’s i position in the technological space can be summarized as where PAik is either • Share of patents in a specific IPC class k • Share of R&D budget allocated to a specific product area / technology field k 37

  38. Technological Distance (Jaffe, 1986) • Example (k=2) • Jaffe technology distance index (uncentered correlation of Fi and Fj): 1 Orthogonal firms Close firms 1 Nice properties: 1 for identical firms 0 for orthogonal firms Not directly affected by length of F 38

  39. Empirical Evidence of Spillovers • Studies at the country level • Coefficient of national capital stock: • Can be used to derive the social domestic rate of return: Sum of private rate of return of researching firm and social excess returns to other domestic firms (within and between industries) • Coefficient of international capital stock: • Can be used to derive the foreign social excess rate of returns: profit gains by domestic firms due to knowledge generated by foreign firms • In many studies social domestic rate of return is smaller than international social rate of return, but not in all e.g. Coe & Helpman (1995), Guellec & van Pottelsberghe (2001, 2004) / Nadiri & Kim (1996), Park (1995, 2004) • Social domestic rate of return varies in these studies between 20% and 145% (Coe & Helpman 1994, Lederman & Maloney 2003, Lichtenberg & van Pottelsberghe 1998, Park 1995, 2004) • Social domestic rate of return is larger in G7 countries (Coe & Helpman 1995) • International spillovers are larger in more open countries (Coe & Helpman 1995) • Evidence for spillovers from public R&D: mixed (Park 1995, Guellec & v. Pott. 2001)

  40. Empirical Evidence of Spillovers • Studies at the industry level • Coefficient of own industry capital stock: • Can be used to derive the industry‘s private rate of returns to R&D: Sum of private rate of return of researching firm and social excess returns to other domestic firms within the industry industry (intra-industry spillovers) • Coefficient of capital stock by other industries: • Can be used to derive the social excess rate of returns to other industries (inter-industry knowledge spillovers) • Range of private rate of returns at industry level: 10% to 80% Goto & Suzuki 1989, Bernstein & Nadiri 1991, Griffith et al 2003, Hanel 1988, Jacobs et al 2002, Link 1978, Mohnen & Lepine 1988, Mohnen et al. 1986, Odagiri 1985, Rouvinen 2002b, Scherer 1982, 1984, Sterlacchini 1989, Sveikauskaus 1981, Terleckyi 1974, Wolff & Nadiri 1987. • Range of social rate of returns: 15% to 150% and in most studies larger than the private rate (exception: Mohnen & Lepine 1991) • High variation (5% to 200%) of private rates to return across industries (Mohnen & Lepine 1991) • Industries with higher R&D intensities benefit more from intraindustrial spillovers (Bernstein 1988)

  41. Empirical Evidence of Spillovers • Studies at the firm level • Coefficient of own capital stock: • Can be used to derive the private domestic rate of return • Coefficient of external capital stock: • Can be used to derive the social excess rate of return (national, horizontal, vertical, international depending on the measurement of the external capital stock)

  42. Example: Social Returns to R&D in Germany • Peters, Kladroba, Licht & Crass (2009) • Data: German R&D survey 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005 • In contrast to CIS data R&D surveys (ideally) cover the total population of all R&D doing firms • Firm-level data • Production Function Framework • Weights to measure technological distance • Based on research fields (40 categories) • Based on patents classifications (30 IPC categories)

  43. OLS and Fixed Effects Estimates Supports absorptive capacity hypothesis, cf. Cohen Levinthal 1989, Klette 1994, Harhoff 2000 Stata: OLS: reg lnlp lnCpe lnKEpe lnKIpe lnL east time, robust cluster(id) FE: xtreg lnlp lnCpe lnKEpe lnKIpe lnL east time, fe robust cluster(id)

  44. OLS and Fixed Effects Estimates (II) Heterogenous effects, more dispersed for external knowledge

  45. First Difference Estimates

  46. First Difference Estimates (II)

  47. Knowledge Spillovers by Group of Firms Not significant Imprecisely estimated

  48. Social Rate of Return in Germany 1991-2005 • Social rate of return of an additional Euro in R&D: sum of private rate of return and social excess rate of returns

  49. Impact of Technology Distance Measure • For comparison: Using Jaffe‘s index with patents (instead of research categories): • Output elasticity of own knowledge: 0.081 (similar) • Output elasticiy of external knowledge: 0.331 (nearly 10 times higher!) • Most empirical studies using patents for measuring technological distance comparably find higher effects (e.g. Cincera 2001, Los & Verspagen 2000) • Explanation: • Only patenting firms, i.e. mainly large firms (selection bias) • Patenting firms may use patents by other firms more often and more efficiently • Firms may be more diversified in terms of research fields than in terms of patent fields (many firms with only one or a few patents). Jaffe index: more diversified firms benefit less from spillovers (Jaffe 1988).

  50. Some More Econometric Issues • Potential Endogeneity of R&D and capital: shocks (e.g. unobserved “management skills”) may both lead to higher productivity and to higher investments and/or R&D expenditure. • Panel data econometrics: Traditional way to overcome this problem • IV or GMM estimator using lagged endogenous variables as instruments (stata: ivreg or xtabond) e.g. Aldieri and Cincera (2009) • Productivity literature: estimator of Olley and Pakes (1996) or Petrin and Levinson (2003) • Yet no application of estimating knowledge spillovers using this estimator