R&Dによる知識資産の資本化と産業連関表の改訂および 全要素生産性測定
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R&Dによる知識資産の資本化と産業連関表の改訂および 全要素生産性測定 - 池内健太 (RIETI/GRIPS/NISTEP)
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R&Dによる知識資産の資本化と産業連関表の改訂および 全要素生産性測定
- 2. Introduction • R&D and Economic Growth • Capitalization of R&D investment • R&D Spillover Effects • Missing Link in capturing Economic impacts of R&D 2
- 3. R&D (≠ Knowledge ≠ Innovation) • R&D {Knowledge Innovation} Economic Growth • R&D activity : Knowledge creation from materials, labor and capital inputs • A part of knowledge published as patents, academic papers, or other format • Success/Failure of R&D projects • Innovation : New product, new process, new organizational and marketing method • Success/Failure of Innovation • Increase in Productivity • Increase in Productive Capacity (and/or Demand) • Economic growth 3
- 4. Number of applications of patents 4 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500,000 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Source: IIP Patent Database 2015
- 5. R&D-Knowledge Linkage • Patents as an indicator for knowledge production • R&D (Knowledge) Patent • R&D performer ≒ Assignee/Applicant of Patent • Matching patent data to R&D survey/Economic Census • e.g., NISTEP-RIETI Joint Project (Leader: Prof. Motohashi) • Cover not only firms but also university/public research institutes. • !Caveat • Not all new knowledge are published as patents. • E.g., trade secrets, human embodied tacit knowledge. 5
- 6. Innovation-Economic Growth Linkage • Measuring Innovations • Subjective measure • Oslo Manual (OECD/Eurostat) • Japanese National Innovation Survey (JNIS): 2003/2009/2012/2015 • Conducted by the National Institute of Science and Technology Policy • Questionnaire to firms • Objective measure • (Indirect) TFP growth, Patent • (Direct) Literature-Based Innovation Output (LBIO): • Count new products on trade journals, press releases*, news papers, … *Project of the NISTEP-GRIPS/SciREX Center using Nikkei Telecom Database (2003-2014) 6
- 7. Questionnaire of JNIS • Technological Innovation • Product Innovation(5), Process Innovation (6) • Non-Technological Innovation • Organizational Innovation (a-c), Marketing Innovation (d-g) 7 Source: Japanese National Innovation Survey 2012 (NISTEP)
- 8. History and the next wave of J-NIS 8 J-NIS 2003 J-NIS 2009 J-NIS 2012 J-NIS 2015 (TBD) Oslo manual Rev. 2 Rev. 3 Rev. 3 Rev. 3 Corresponding CIS CIS 3 (2000/2001) CIS 2008 CIS 2010 CIS 2014 Period 1999-2001 2006-2008 2009-2011 2012-2014 Industries covered Agriculture, mining, manufacturing, some service industries Service industries expanded More expansion in service industries (Same as in J- NIS 2012) Population size 216,585 firms 331,037 412,753 380,226 Sample size 43,174 firms 15,137 20,191 24,825 Response rate 21.4% 30.3% 35.2% (TBD) Product innovative firms % 20% 29% 20% (TBD) International comparison OECD (2003) Innovation in Firms (NISTEP DP 68) OECD STI Scoreboard 2013 (TBD)
- 9. Percentages of firms with innovations (2009-11) 9 14 7 12 22 24 12 6 10 20 23 19 8 17 29 2525 14 25 43 32 0 5 10 15 20 25 30 35 40 45 Product innovation New to market product Process innovation Organizational innovation Marketing innovation % All firms Small firms (10-49 emp.) Medium-size (50-249 emp.) Large firm (250+ emp.) Source: Japanese National Innovation Survey 2012 (NISTEP)
- 10. Example of output indicator: Innovative firms in OECD member countries 10 Manufacturing Services Source: OECD Science, Technology and Industry Scoreboard 2013 Notes: • The rate depends on composition of industry in each country. • Japanese respondents tend to perceive “innovation” in narrower sense than western countries.
- 11. Example of a micro-data analysis using J-NIS (2009) • Public financial support enhances R&D intensity. • R&D and research cooperation with business partners and universities lead to innovations. • Product and process innovation accelerate labor productivity growth. 11 R&D per employee (million yen, 2008) Probability of product innovation (2006-2008) Probability of product innovation (2006-2008) Growth rate in labor productivity (2006-2008) Source: Ikeuchi and Okamuro (2013) “R&D, innovation, and business performance of Japanese start-ups: A comparison with established firms” NISTEP DISCUSSION PAPER No.104 Productivity R&D Innovation Collaboration with university Public financial support 0.00 0.05 0.10 0.15 0.20 0.25 No Yes Public financial support Start-up firm Established firm 0.00 0.20 0.40 0.60 0.80 1.00 -3 0 3 R&D intensity in log. Start-up firm Established firm 0.00 0.20 0.40 0.60 0.80 1.00 No Yes University cooperations Start-up firm Established firm -0.40 -0.20 0.00 0.20 0.40 No Yes Product or process innovation Start-up firm Established firm
- 12. Literature-Based Innovation Output Indicators (LBIO) 12 Literature-based Innovation Output Indicators (LBIO) Innovation inputs Innovation outputs Innovation impacts R&D activity Design activity Marketing activity Training Acquisition of machinery, equipment and software Patent Trademark Design New/significantly improved: Products Processes Organizational methods Marketing methods Productivity Profitability Growth Enterprise value Intellectual Property Rights (IPR) Press release Advertisement Public Relations
- 13. #applications of trademarks and design registrations 13 0 20,000 40,000 60,000 80,000 100,000 120,000 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Trademark Design Source: NISTEP Design/Trademark Database
- 14. # of Press Release by Article Types 14 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Others Technological progress Organizational change New product Source: Nikkei Telecom Press Release Database
- 15. Capitalization of R&D and Growth Accounting • Consider a value-added production function • Rather than gross output base. • Before R&D capitalization: • Value-added: 𝑉𝑡 ≡ 𝑄𝑡 − 𝑀𝑡 = 𝐴 𝑡 𝑉 𝑓𝑡 𝑉 𝐿 𝑡, 𝐾𝑡 • Tangible: 𝐾𝑡 = 1 − 𝛿 𝐾 𝐾𝑡−1 + 𝐼𝑡 • Growth Accounting: 𝑉𝑡 𝑉𝑡 = 𝜕𝑓𝑡 𝑉 𝜕𝐿 𝑡 𝐿 𝑡 𝑉𝑡 𝐿 𝑡 𝐿 𝑡 + 𝜕𝑓𝑡 𝑉 𝜕𝐾𝑡 𝐾𝑡 𝑉𝑡 𝐾𝑡 𝐾𝑡 + 𝐴 𝑡 𝑉 𝐴 𝑡 𝑉 • After R&D capitalization: • Value-added: 𝑌𝑡 ≡ 𝑄𝑡 + 𝑅𝑡 − 𝑀𝑡 𝑄 − 𝑀𝑡 𝑅 = 𝑓𝑡 𝑌 𝐿 𝑡, 𝐾𝑡, 𝑁𝑡 • Intangible: 𝑁𝑡 = 1 − 𝛿 𝑁 𝑁𝑡−1 + 𝑅𝑡 • 𝑌𝑡 𝑌𝑡 = 𝜕𝑓𝑡 𝑌 𝜕𝐿 𝑡 𝐿 𝑡 𝑌𝑡 𝐿 𝑡 𝐿 𝑡 + 𝜕𝑓𝑡 𝑌 𝜕𝑀𝑡 𝑀𝑡 𝑌𝑡 𝐾𝑡 𝐾𝑡 + 𝜕𝑓𝑡 𝑌 𝜕𝑁𝑡 𝑁𝑡 𝑌𝑡 𝑁𝑡 𝑁𝑡 + 𝐴 𝑡 𝑌 𝐴 𝑡 𝑌 15 Reference: Miyagawa and Hisa (2013)
- 16. R&D stock as an Intangible Assets • Intangible Assets : Corrado,HultenandSichel（2005） • Computerized information • Custom/package/owner account software • Innovative property • R&D expenditure/Copyrights/Trademark/Design • Economic competency • Brands/Firm-specific human capital/Organizational changes • Measuring Intangible in JIP Database (RIETI) • Miyagawa and Hisa (2013) • 1985-2012 • 108 sectors • Nominal/real investment in intangibles • Intangible capital stock by Perpetual Inventory Method 16
- 17. GDP growth with/without Intangibles (incl. R&D) 17 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 Without Intangibles With Intangibles Source: Presenter’s calculation using JIP database 2015.
- 18. TFP growth with/without Intangibles (incl. R&D) 18 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 Without Intangibles With Intangibles Source: Presenter’s calculation using JIP database 2015.
- 19. Growth Accounting with Intangible Assets (incl. R&D) 19 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 TFP Intan-ECom Intan-Innov Intan-IT Tangible Labor VA Source: Presenter’s calculation using JIP database 2015.
- 20. R&D Spillovers and Knowledge Flow • R&D Spillovers • Pecuniary spillovers • Static/Dynamic Unit TFP : Kuroda and Nomura (2004) – not standard but interesting approach • Licensing (partially captured in IO-table/SNA) • Technological Spillovers (Knowledge Flow) • Appropriatability, Proximity (agglomeration economy), Absorptive capacity • Public/Academic R&D • Roles of Academic or Science Activities in Economy • Science-Industry Linkage • Data sources • Patent data (citation, joint application, joint invention) • Journal publication data • Scientists/Researchers’ CV • Governmental Research Funding (such as KAKEN) 20
- 21. Framework of Knowledge Flows (Spillovers) 21 海外企業学術研究機関 研究費 イノベーション 生産性 スピル オーバー 論文の共著関係 特許→論文引用 特許の共同出願 雇用特許 意匠・商標 国際競争力 論文☓特許 書誌情報 スピル オーバー 研究費 特許 学術論文 共同研究 の状況 技術的 近接性 研究者の 異動状況 著者・発明者情報 特許 学術論文 所属学会・団体 個人的 ネットワーク 出身校 勤務先履歴 SNS その他成果 • 共同利用設備 • ソフトウェア等 その他成果 • 共同利用設備 • ソフトウェア等 スピルオーバーの経路
- 22. Available Data-sources and Required Linkages 22 PATSTAT Scopus (WoS) IIP-PD 発明者・日 NISTEP 企業名辞書 著者・英 所属機関・英 NISTEP 機関名辞書 JST-FMDB (TR-DWPI) NEDO KAKEN JST (e-Rad) Researchmap 出願人・日 科調統計 （大学・公的機関） 経済センサス （企業） 個人名辞書 引用文献 論文ID 引用文献 引用文献 住所辞書 経済センサス 学校基本調査 産学連携調査 科調統計 企活統計 イノベ調査 企業財務DB 産連DB（~02’） Person table 電話帳 OECD /KUL HAN-DB Patent Funding Academic Paper Orbis+Patent Orbis
- 23. Productivity Effects of R&D Spillovers • Ikeuchi, Belderbos, Fukao, Kwon and Kim, (2013) NIPTEP DP#93 • “Sources of Private and Public R&D Spillovers: Technological, Geographic and Relational Proximity” • Data: • Census for Manufactures (plant-level microdata) • R&D Survey (firm-level microdata) • Specification: 𝑄𝑖𝑡 = 𝑓 𝐿𝑖𝑡, 𝐾𝑖𝑡, 𝑀𝑖𝑡 𝑔 𝑅𝑖𝑡−1, 𝑆𝑖𝑡−1, 𝑃𝑖𝑡−1, X 𝑖𝑡 𝑈𝑖𝑡 • Where: • 𝑄𝑖𝑡: Gross output of the plant • 𝐿𝑖𝑡, 𝐾𝑖𝑡, 𝑀𝑖𝑡: Inputs of plant 𝑖 in year 𝑡 • 𝑅𝑖𝑡−1: Firm-level R&D stock • 𝑆𝑖𝑡−1: Private R&D stock • 𝑃𝑖𝑡−1: Public R&D stock • X 𝑖𝑡: a vector of other observable factors (control variables) affecting plant productivity • 𝑈𝑖𝑡: plant-year specific unobserved efficiency. 23
- 24. Spillovers: Private R&D Stocks • Firms’ R&D distinguished by 30 fields: mapped into 25 (2- digit) industries • Other firms’ R&D by field weighted by technological proximities between industries, allowing for geographic decay in the effectiveness of spillovers 𝑆𝑖𝑓𝑠𝑡 = 𝑓′≠𝑓 𝑠′ 𝐾 𝑓′ 𝑠′ 𝑡 𝑇 𝑠𝑠′ 𝑒 𝜏𝑑 𝑖𝑓′ 𝑠′ 𝑡 • where: • 𝑑𝑖𝑓′ 𝑠′ 𝑡: Minimum geographic distance between plant 𝑖 and the plant of firm 𝑓′ in the field 𝑠′ in year 𝑡; • 𝑇 𝑠𝑠′: the technological proximity weight; • 𝑒 𝜏𝑑 𝑖𝑓′ 𝑠′ 𝑡: Weight for geographic proximity of plant 𝑖 to R&D stock firm 𝑓′ for field 𝑠′ ; • 𝜏: an exponential decay parameter, with 𝜏 < 0 (e.g. Lykachin et al. , 2010) 24
- 25. Technological Proximity between Industry • Technological relatedness between firms are derived from patent citation data and based on Leten et al. (2007). • Patent citation data are available at the 4-digit IPC level. • (IPC: International Patent Classification) • The IPC codes can subsequently be mapped onto industries using the industry-technology concordance table developed by Schmoch et al. (2003) in which each technology field is uniquely linked to its corresponding NACE two-digit industry. • Weights for the own industry normalized at 1. 25
- 26. Knowledge Flow (Technological proximity) between industries (# of citing patents / # of total patents) Spillovers sources (cited) Focal industries (citing) [04] [05] [06] [07] [08] [09] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [04] Food products 1.000 .003 .006 .000 .125 .359 .041 .001 .000 .004 .001 .001 .001 .094 .021 .001 .003 .002 .000 .026 .026 [05] Textile mill products .007 1.000 .045 .024 .631 .065 .104 .001 .002 .172 .007 .006 .023 .243 .026 .013 .033 .019 .005 .148 .114 [06] Pulp and paper products .022 .073 1.000 .126 .415 .049 .089 .002 .000 .100 .003 .003 .043 .301 .009 .008 .190 .004 .001 .123 .083 [07] Printing .000 .011 .042 1.000 .270 .021 .095 .000 .000 .028 .008 .011 .020 .085 .003 .003 .181 .002 .000 .087 .017 [08] Chemical fertilizers and industrial chemicals .009 .020 .008 .015 1.000 .147 .050 .012 .004 .039 .007 .007 .005 .070 .005 .010 .032 .006 .001 .041 .027 [09] Drugs and medicine .026 .002 .001 .001 .147 1.000 .013 .000 .000 .002 .000 .000 .000 .010 .001 .000 .005 .000 .000 .076 .001 [10] Miscellaneous chemicals .031 .032 .012 .035 .488 .128 1.000 .020 .000 .038 .008 .007 .010 .093 .010 .006 .057 .014 .003 .055 .036 [11] Petroleum and coal products .004 .004 .002 .001 .763 .031 .143 1.000 .000 .008 .006 .005 .014 .209 .003 .036 .074 .030 .004 .130 .014 [12] Rubber products .000 .008 .001 .001 .400 .002 .006 .000 1.000 .008 .014 .011 .004 .030 .001 .005 .028 .064 .002 .050 .116 [13] Ceramic, stone and clay products .003 .064 .026 .021 .439 .015 .047 .001 .001 1.000 .030 .027 .073 .225 .020 .022 .108 .032 .008 .112 .197 [14] Iron and steel .001 .006 .002 .013 .248 .011 .028 .004 .007 .120 1.000 .580 .069 .410 .030 .059 .152 .036 .008 .065 .048 [15] Non-ferrous metals and products .001 .009 .003 .030 .392 .020 .042 .004 .010 .187 1.000 .978 .108 .486 .034 .111 .233 .052 .009 .097 .075 [16] Fabricated metal products .001 .009 .012 .015 .066 .006 .016 .004 .000 .104 .025 .024 1.000 .259 .027 .050 .082 .081 .025 .070 .102 [17] General-purpose machinery .010 .012 .008 .007 .114 .019 .018 .005 .001 .040 .019 .013 .033 1.000 .018 .020 .059 .078 .014 .082 .058 [18] Household appliances .022 .015 .003 .004 .091 .012 .022 .001 .000 .039 .014 .010 .039 .188 1.000 .057 .121 .056 .004 .079 .106 [19] Electrical machinery .000 .003 .001 .001 .080 .003 .004 .003 .000 .019 .013 .015 .026 .084 .022 1.000 .244 .082 .009 .127 .031 [20] Info.&com. electronics .000 .001 .003 .008 .024 .003 .005 .001 .000 .008 .003 .003 .005 .027 .005 .026 1.000 .010 .001 .068 .009 [21] Motor vehicles, parts and accessories .000 .003 .001 .001 .028 .001 .008 .002 .003 .017 .004 .004 .029 .183 .012 .046 .055 1.000 .022 .076 .041 [22] Other transportation equipment .000 .004 .001 .001 .032 .002 .012 .003 .000 .031 .006 .005 .064 .260 .008 .043 .041 .197 1.000 .060 .064 [23] Precision instruments and machinery .003 .009 .004 .007 .070 .129 .011 .003 .001 .019 .003 .003 .009 .078 .007 .030 .151 .030 .003 1.000 .035 [24] Miscellaneous manufacturing .011 .019 .009 .007 .180 .007 .024 .001 .008 .106 .007 .006 .042 .184 .034 .023 .076 .048 .009 .117 1.000 26 Source: Leten et al. (2007) and Ikeuchi et. al (2013)
- 27. Public R&D Spillover Pools • R&D expenditures by Universities from R&D survey (~100% resp. rate) • Calculate R&D stocks with 15 percent depreciation rate • Allocated to science fields based on # of researchers per science field • Public R&D spillovers: • R&D per science field weighted by its relevance for specific industries • Allow for decaying effects due to geographic distance (plant-institution) 𝑃𝑖𝑡𝑠 = ℎ 𝑚 𝐴ℎ𝑚𝑡 𝑇𝑠𝑚 𝑒 𝜃 𝑑 𝑖ℎ • 𝐴ℎ𝑚𝑡: R&D stock of public institutes in location ℎ for academic field 𝑚 in year 𝑡; • 𝑇𝑠𝑚: Knowledge flow matrix between industry/R&D field 𝑠 and science field 𝑚; • 𝑑𝑖ℎ: geographic distance between plant 𝑖 and institution location ℎ; • 𝜃: the geographic decay parameter, 𝜃 < 0. • Technological proximity: • Citations in patents to scientific literature by academic field ( Van Looy et al, 2004): gives concordance between science fields and IPC/technology classes • Technology class to industry: IPC/technology class to industry concordance (Smoch et al. 2003) 27
- 28. Knowledge flow matrix from science to industry (# of patents citing paper / # of total papers * 1000) Science field (of cited papers) [01] [02] [03] [04] [05] [06] [07] [08] [09] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] Industry (of citing patent) 農 学 生 物 学 医 学 ・薬 学 看 護 学 歯 学 化 学 応 用 化 学 物 理 学 地 学 そ の 他 工 学 （機 械 ・船 舶 ・土 木 ・ 建 築 ） 電 気 ・通 信 原 子 力 （エ ネ ル ギ ー ） 材 料 科 学 数 学 教 育 学 そ の 他 人 文 ・社 会 科 学 商 学 ・経 済 学 史 学 ・政 治 ・法 学 哲 学 食料品製造業 1.47 .47 .11 .18 .04 .10 .63 .01 .00 .01 .01 .03 .01 .00 .03 .02 .00 .00 .00 繊維工業 .01 .01 .00 .00 .00 .02 .08 .00 .00 .00 .00 .00 .03 .00 .00 .00 .00 .00 .00 パルプ・紙・紙加工品製造業 .02 .02 .00 .00 .01 .02 .06 .01 .00 .00 .01 .00 .11 .00 .00 .00 .00 .00 .00 印刷業 .00 .00 .00 .00 .00 .05 .02 .05 .00 .00 .04 .01 .03 .00 .00 .00 .00 .00 .00 化学肥料・無機・有機化学工業製 品製造業 1.75 3.90 1.17 .41 .69 4.51 3.21 .29 .13 .24 .12 .55 1.30 .01 .03 .04 .01 .00 .00 医薬品製造業 3.43 15.55 5.80 2.33 2.10 7.04 3.15 .27 .06 .22 .34 .35 .31 .04 .07 .20 .02 .01 .04 その他化学工業 .17 .07 .01 .01 .02 .24 .51 .10 .02 .04 .06 .09 .25 .00 .02 .00 .00 .00 .00 石油・石炭製品製造業 .01 .05 .01 .00 .02 .05 .15 .02 .02 .02 .02 .05 .03 .00 .00 .00 .00 .00 .00 ゴム製品製造業 .02 .02 .02 .00 .01 .09 .16 .04 .00 .06 .05 .04 .16 .00 .01 .00 .00 .00 .00 窯業・土石製品製造業 .06 .07 .03 .01 .03 .32 .36 .19 .03 .13 .11 .07 .96 .00 .01 .00 .00 .00 .00 鉄鋼業 .02 .03 .01 .00 .01 .17 .18 .23 .03 .05 .17 .12 .94 .00 .00 .00 .00 .00 .00 非鉄金属製造業 .02 .03 .01 .00 .01 .17 .18 .23 .03 .05 .17 .12 .94 .00 .00 .00 .00 .00 .00 金属製品製造業 .01 .02 .00 .00 .01 .11 .06 .04 .00 .04 .05 .06 .23 .00 .00 .00 .00 .00 .00 一般機械器具製造業 1.49 1.45 .37 .15 .13 1.14 1.77 .55 .15 .51 .38 .51 1.65 .01 .01 .05 .00 .00 .00 家庭電気機械製造業 .04 .01 .00 .00 .00 .05 .10 .05 .00 .05 .03 .04 .08 .00 .00 .01 .00 .00 .00 電気機械器具製造業 .02 .04 .02 .01 .00 .31 .08 .62 .02 .26 1.00 .36 .69 .01 .06 .02 .00 .00 .00 情報通信機械器具製造業 .14 .39 .18 .08 .13 .89 .43 2.50 .17 1.23 12.48 .80 1.96 .26 2.24 .12 .31 .03 .04 自動車製造業 .01 .07 .03 .01 .07 .07 .10 .05 .02 .12 .18 .08 .05 .00 .00 .02 .00 .00 .00 その他輸送機械器具製造業 .00 .00 .00 .00 .00 .00 .01 .01 .03 .03 .01 .01 .01 .00 .00 .00 .00 .00 .00 精密工業製品製造業 .67 3.67 2.38 .90 1.72 2.87 1.20 1.51 .30 .56 1.88 .66 .71 .04 .10 .13 .03 .02 .02 その他製造業 .01 .02 .01 .00 .01 .03 .05 .02 .00 .03 .08 .01 .03 .00 .01 .02 .00 .00 .00 電気・ガス .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 28 Source: Ikeuchi et. al (2013). Based on PATSTAT (EPO), van Looy et.al (2004).
- 29. Relational proximity • R&D stocks of supplier and customer industries • Identifying the importance of supplier and customer transactions from Input-Output tables for 58 JIP industries. • Supplier industry proximity weights 𝑆𝑈𝑃 𝑠𝑠′ and customer proximity weights 𝐶𝑈𝑆 𝑠𝑠′, with: • Supplier industry weights: 𝑆𝑈𝑃 𝑠𝑠′ 𝑡 = 𝑄 𝑠′ 𝑠𝑡 𝑗 𝑄 𝑗𝑠𝑡 • Share of industry 𝑠′sales to industry s in the sum of sales by all industries j to industry s (input share). • Customer industry weights: 𝐶𝑈𝑆 𝑠𝑠′ 𝑡 = 𝑄 𝑠𝑠′ 𝑡 𝐸𝑋𝑠𝑡+𝑄 𝑠𝑡 • Weights for customer R&D stocks are the shares of sales by industry s to industry 𝑠′ in total sales (domestic sales + exports) industry s (output share). 29
- 30. TFP growth decomposition (Balanced panel) 30 0.390 0.347 0.336 0.334 0.869 0.381 0.247 0.175 0.143 0.178 0.147 0.090 0.001 0.784 0.314 0.400 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 1987-19921992-19971997-20022002-2007 Other factors Public R&D spillovers Inter-firm private R&D spillovers Intra-firm R&D effects (incl. R&D>0 dummy) TFP growth rate Source: Ikeuchi et. al (2013)
- 31. Decomposition of the inter-firm R&D spillovers effects: technological vs. relational proximity 31 0.306 0.140 0.069 0.059 0.287 0.132 0.099 0.071 0.276 0.108 0.078 0.044 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1987-1992 1992-1997 1997-2002 2002-2007 Customer industry R&D spillovers Supplier industry R&D spillovers Tech-related industry R&D spillovers (Balanced panel) Source: Ikeuchi et. al (2013)
- 32. Decomposition of the inter-firm private R&D spillovers effects: entry and exit 32 (Balanced panel) -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 Entries of plants with R&D Surviving plants R&D effects Exits of plants with R&D Total Source: Ikeuchi et. al (2013)
- 33. Inter-firm R&D spillovers effects by prefectures (1997-2007) 33 (Balanced panel) -0.10 -0.05 0.00 0.05 0.10 0.15 Aichi Kanagawa Tochigi Mie Chiba Shiga Hiroshima Fukuoka Ibaraki Gumma Fukushima Nagano Saitama Kumamoto Oita Tokushima Iwate Yamaguchi Nara Ehime Kyoto Hokkaido Gifu Hyogo Miyagi Yamanashi Osaka Shizuoka Tokyo Entry Survival Exit Total Source: Ikeuchi et. al (2013)
- 34. Comprehensive Framework for Public R&D Spillovers (On going RIETI-NISTEP joint projects) 34 Higher Education Institutes & Public Research Institutes Industry (Firm) Paper publication Patent application Patent application Direct collaboration 1. Non-Patent Literature Citation +2. Academic Patent Citation +3. Joint Patent invention “Science intensity by industry by linking dataset of science, technology and industry” (with Kazuyuki Ikeuchi, Ryuichi Tamura and Naoshi Tsukada), presented at OECD BlueSky Conference Sept. 2016
- 35. Database Construction 35 IIP-PD • Inventor Part (1995-2013 application) Disambiguation of inventor (Li et. al, 2014) • using information of name, address, applicant, IPC4, co-inventor • Telephone directory rare name as training set • KAKEN info as correct reference info to decide threshold value English inventor name (PATSTAT link) and inventor affiliate (by single applicant patent) • Applicant Part (all IIP patent) Standardized applicant name Standardized address Scopus • Author ID information (1999-2012 publication) Author name Author affiliateNISTEP Institution ID Economic Census • Firm and institution ID information (2001, 2004, 2006, 2009 and 2012) Name Address Match by name and location (using establishment level info)
- 36. 36 Academic involvement in patenting Source: Motohashi et. al (2016)
- 37. Scientific intensity within academic inventors 37 Original Data : # of research papers by science field “s” (Rs) # of patents by technology field “t” (Pt) for researcher “i” Step 1: Construct W matrix, 𝑊𝑠𝑡 Paper = 𝑖 𝑅𝑖𝑠 × 𝑃𝑖𝑡 𝑡 𝑃𝑖𝑡 Step 2: Normalize by patent counts of “i” 𝐿 𝑡 Paper = 𝑠 𝑊𝑠𝑡 𝑃𝑎𝑝𝑒𝑟 𝑖𝑡 𝑃𝑖𝑡 Step 3: S-T Linkage by technology class “t” is calculated by 𝑆𝐼𝑡 = 𝐿 𝑡 Paper ∗ 𝐴𝑐𝑎𝑑𝑒𝑚𝑖𝑐_𝑆ℎ𝑎𝑟𝑒𝑡
- 38. Science Intensity by Industry, 2000-2003 (papers per employees*1000) 38Source: Motohashi et. al (2016)
- 39. Science Intensity by Industry, 2008-2011 (papers per employees*1000) 39Source: Motohashi et. al (2016)
- 40. Conclusions • R&D and Economic Growth • Knowledge and Innovation • R&D-Knowledge Linkages • Innovation-Economic Growth Linkages • Capitalization of R&D investment • Effects on TFP measures and Growth Accounting • R&D Spillover Effects • Pecuniary spillovers (Static/Dynamic Unit TFP) • Technological Spillovers and Knowledge Flow • Academic or Science Activities in Economy • Missing Link • Knowledge-Innovation linkages Knowledge flow matrix • Liking with IO-table? 40
- 41. Thank you for your attentions. Kenta Ikeuchi (RIETI/GRIPS/NISTEP) ks.ikuc@gmail.com 41
Editor's Notes
- The last slide in page 6 shows the history of our survey and the new next research. We start this survey from 2003 and we have conducted the survey three times by now. We plan to conduct next survey in this year. Response rate have been gradually improved but not so much high. Also, you can see the percentage of the firms with a product innovation has not been in a steady trend. That rate fallen between JNIS 2009 and 2012 from 29% to 20%. Between 2009 and 2011, Japan economy had been influenced by two economic shocks. One is the Global financial crisis occurred in the fall of 2008 and the another is the big earthquake in the east side of Japan. These shocks seems to negatively influence on innovation activities in firms. This is all of my presentation. Thank you very much for your attention.
- Next, I’d like to show several results of our survey. First, the figures in page 2 are derived from OECD’s Science, Technology and Industry Scoreboard 2013. We provided the results of Japanese Innovation Survey to OECD and OECD collect the data and compare the results among countries. The figure shows the percentage of the firms which introduce each type of innovations in each countries. If we focus on product and process innovation, you can see that the share of innovative firms in Japan is relatively low.
- We also have conducted data analysis using the micro data of Japanese National Innovation Survey. Figure in page 4 is an example. In this research, we use the micro data of J-NIS 2012 and analyze and compare the determinants of R&D, innovation and productivity between start-up firms and established firms. As the results of this analysis, we found public financial support significantly enhances R&D intensity of the firms and R&D expenditure and research cooperation with university increase the probability of firms to introduce an innovation. Also, we found the introduction of product and process innovation accelerate labor productivity growth.
- Next, I’d like to explain about empirical model and data. First, we adopt the standard knowledge stock augmented production function framework. Qit denotes the gross output of the plant L denotes labor inputs, K denotes capital and M denotes intermediate inputs. Rit-1 is the one-year lagged firm-level R&D stock Sit-1 is the private R&D stock P is the public R&D stock X is a vector of other observable factors affecting plant productivity Finally, Uit denotes the plant-year specific unobserved efficiency.
- Using the firm/field-level R&D stock, next, we construct the private R&D spillover variables. We define the technologically relevant private R&D stock as a spillover pool: as the sum total of other firms’ R&D, K, assigned to their plants on the basis of the industry, weighted by the technological relatedness, T, with the field of the plant and geographic decay in the effectiveness of spillovers. We model an exponential decay function in the effectiveness of spillovers with the distance d between plants and the parameter, tau, to be estimated.
- Our technological relatedness measure between fields is derived from patent citation data and based on a previous study b Laten et al. The relatedness between technologies will be reflected in the intensity with which technologies in a field build on prior art in a different field. Weights for the own industry normalized at 1.
- This table shows the technological relatedness index between industries used for the construction of technologically relevant R&D stocks. I’d like to skip the detailed explanation for time saving.
- We also calculate the public R&D stock as another source of R&D spillovers Public R&D expenditure data is also derived from the R&D surveys as well. We differentiated public R&D by location and academic field. We apply fifteen percent (15%) depreciation rate. Same as the private R&D stock, we consider the technological relatedness, T tilde, between plant industry and each science fields of public R&D stock and the decay in the geographic distance, d tilde. Applied technological relatedness measure is also based on the data on patent citation to science literature.
- We also measure relationally proximate R&D stocks by the R&D stocks of supplier and customer industries. To calculate the supplier and customer R&D stocks, we use supplier industry weights and customer industry weights instead of the technological proximity between industries. We identify the importance of supplier and customer transactions from Input-Output tables. Supplier weights are measured as the input share. Customer industry weights are measured as the output share.
- Given these regression results, we can decompose the actual TFP growth in Japan into several factors: Firm internal R&D effects, private R&D spillovers effects, and public R&D spillovers effects. We can see that; declining R&D spillovers, in particular private R&D spillovers, play an important role in the decline in TFP in Japan.
- Moreover, we can decompose changes in private R&D spillovers; To the tech-related industry R&D, and customer and supplier industry R&D spillovers. Results show that; declining spillovers is generally due to the observed declining growth of private R&D stocks. In addition, we can see that; supplier spillovers are relatively persistent compared to technological proximate R&D spillovers.
- We can further decompose the changing private R&D spillovers into effects; due to exit of plants, entry of plants, and surviving plants. This exercise shows that; in particular, the effects of exit of plants operated by R&D intensive parent firms has a mostly important role in the spillovers decline.
- Finally, we can also decompose these effects into the regional level. Results show the most of these exit effects appear in the major industrial agglomerations in Japan; Such as Tokyo area including Kanagawa and Saitama prefectrue And Aichi prefecture which is the home town of Toyota automobile.