Wiener Institut für The Vienna Institute for Internationale International Economic Wirtschaftsvergleiche Studies Manufacturing Productivity: Effects of Institutions and Service Sector Innovations Johannes Pöschl , Neil Foster, Robert Stehrer WIOD Final Conference 24-26 April 2012
2 Paper Overview Starting point - Industries can profit from research output of other industries by using their new products as intermediates => this can lead to productivity increase if prices are not fully adjusted for quality improvements Goals - Estimation of productivity effects of spillovers from manufacturing and service industries - Investigation of the influence of institutions on the size of the spillover effect
3 Motivation Size of spillovers important for the worldwide distribution of productivity and GDP Small spillovers ► Divergence - Large spillovers ► Convergence - Real GDP growth 2010
4 Rent Spillovers - Theory The capital good is produced according to the function where χ (m) denotes a capital good of variety m R&D affects production function χ and leads to an increase in the quality or a decrease of price of a product The number of employed varieties M can differ across countries and industries
5 Rent Spillovers - Literature Estimation of spillovers based on R&D expenditures of partner countries / industries and linkages from trade and input-output data - Terleckyj, N. E. (1974). “Effects of R&D on the productivity growth of industries” - Coe, D. T., Helpman, E. (1995). “International R&D Spillovers” - Keller, W. (2002). “Trade and the transmission of technology” Problems of studies at the industry level - I/O tables often not available for all countries - Time dimension missing (vertical integration) Extension of framework
6 EU27 domestic and foreign Input-Output linkages in 2005 production / use 15t16 17t19 20 21t22 23 24 25 26 27t28 29 30t33 34t35 36t37 domestic 15t16 Food, Beverages and Tobacco 17.8 1.9 0.2 0.4 0.2 1.4 0.3 0.2 0.2 0.2 0.2 0.1 0.4 17t19 Textiles, textile products, leather and footwear 0.1 23.4 0.2 0.3 0.1 0.2 0.7 0.2 0.2 0.1 0.1 0.4 1.8 20 Wood and Products of Wood and Cork 0.3 0.1 24.5 0.9 0.0 0.2 0.3 0.7 0.4 0.3 0.2 0.3 10.2 21t22 Pulp, Paper, Paper , Printing and Publishing 1.6 1.1 1.1 22.8 0.2 1.5 1.4 1.2 0.5 0.8 1.0 0.4 2.0 23 Coke, Refined Petroleum and Nuclear Fuel 0.4 0.4 0.7 0.4 7.2 2.9 0.8 1.6 0.9 0.3 0.3 0.2 0.6 24 Chemicals and Chemical Products 0.8 2.8 1.8 2.1 0.8 11.5 9.9 1.9 1.3 0.8 1.1 0.8 1.5 25 Rubber and Plastics 1.3 1.5 0.7 1.0 0.3 1.4 10.1 0.9 0.8 2.2 1.9 3.2 2.7 26 Other Non-Metallic Mineral 0.8 0.3 1.1 0.2 0.1 0.8 0.8 15.7 1.0 0.6 0.9 0.7 0.8 27t28 Basic Metals and Fabricated Metal 1.3 1.0 3.0 0.9 0.5 1.4 3.1 3.0 24.6 16.8 6.9 9.0 10.5 29 Machinery, nec 0.6 0.9 0.9 0.9 0.3 0.8 1.6 1.9 2.0 12.0 1.4 2.7 1.5 30t33 Electrical and Optical Equipment 0.3 0.4 0.3 0.5 0.3 0.6 0.7 0.6 0.9 4.0 11.4 2.8 0.7 34t35 Transport Equipment 0.2 0.3 0.2 0.2 0.1 0.3 0.5 0.4 0.5 1.1 0.5 17.9 0.6 36t37 Manufacturing, nec; Recycling 0.2 0.5 0.6 0.6 0.1 0.2 0.6 0.6 3.0 0.4 0.3 0.5 5.9 60t64 Transport, storage and communications 5.3 5.0 6.1 6.9 2.8 4.6 4.9 8.7 4.1 4.1 3.6 3.0 4.8 71t74 Renting of M&E and Other Business Act. 7.7 6.7 4.3 10.8 2.9 12.0 8.6 8.5 6.2 9.4 10.8 7.5 7.1 foreign 15t16 Food, Beverages and Tobacco 2.4 0.2 0.0 0.1 0.1 0.4 0.2 0.0 0.0 0.0 0.0 0.0 0.1 17t19 Textiles, textile products, leather and footwear 0.1 13.8 0.1 0.2 0.0 0.2 0.8 0.1 0.1 0.1 0.1 0.4 2.2 20 Wood and Products of Wood and Cork 0.1 0.1 9.3 0.3 0.0 0.1 0.1 0.3 0.1 0.1 0.1 0.1 3.8 21t22 Pulp, Paper, Paper , Printing and Publishing 1.2 0.5 0.7 11.0 0.1 0.9 0.8 0.7 0.2 0.3 0.5 0.2 1.0 23 Coke, Refined Petroleum and Nuclear Fuel 0.2 0.3 0.6 0.3 3.6 2.5 1.0 0.9 0.6 0.2 0.2 0.1 0.4 24 Chemicals and Chemical Products 0.9 4.5 3.2 3.2 2.9 22.2 17.1 2.8 1.8 0.9 1.6 1.0 2.3 25 Rubber and Plastics 0.9 0.8 0.6 0.7 0.1 0.9 5.9 0.6 0.5 1.3 1.3 1.9 1.8 26 Other Non-Metallic Mineral 0.2 0.1 0.3 0.0 0.0 0.2 0.3 3.8 0.3 0.1 0.3 0.2 0.2 27t28 Basic Metals and Fabricated Metal 0.4 0.3 1.1 0.4 0.2 0.9 1.8 1.7 21.4 8.8 4.6 5.4 6.6 29 Machinery, nec 0.3 0.4 0.6 0.5 0.2 0.5 0.9 1.1 1.1 7.8 1.1 1.9 0.9 30t33 Electrical and Optical Equipment 0.2 0.2 0.3 0.4 0.2 0.6 0.7 0.5 0.9 5.0 26.5 3.3 0.9 34t35 Transport Equipment 0.1 0.3 0.1 0.1 0.0 0.2 0.4 0.2 0.4 1.1 0.6 18.4 0.5 36t37 Manufacturing, nec; Recycling 0.1 0.4 0.2 0.1 0.0 0.1 0.2 0.1 0.4 0.3 0.3 0.5 2.5 60t64 Transport, storage and communications 0.6 0.5 1.0 1.1 0.5 0.7 0.6 1.1 0.6 0.6 0.7 0.5 0.6 71t74 Renting of M&E and Other Business Act. 1.4 0.8 0.7 2.7 0.4 3.5 1.1 1.2 0.9 1.3 2.7 0.9 1.0 Sum 47.5 69.6 64.8 70.0 24.1 73.9 76.0 61.1 76.0 81.2 81.4 84.5 75.8
7 R&D expenditures in the EU R&D expenditures 1995 growth of R&D exp. 1995-2005 58% 15t16 - Food, Beverages and Tobacco 77% 17t19 - Textiles, textile products, leather and … 29% 20 - Wood and Products of Wood and Cork 42% 21t22 - Pulp, Paper, Paper , Printing and … 0% 23 - Coke, Refined Petroleum and Nuclear Fuel 62% 24 - Chemicals and Chemical Products 90% 25 - Rubber and Plastics 38% 26 - Other Non-Metallic Mineral 30% 27t28 - Basic Metals and Fabricated Metal 63% 29 - Machinery, nec 31% 30t33 - Electrical and Optical Equipment 77% 34t35 - Transport Equipment 44% 36t37 - Manufacturing, nec; Recycling 262% 60t64 - Transport, storage and communications 176% 71t74 - Renting of Machinery & Equ. and … 0.0E+00 1.0E+10 2.0E+10 3.0E+10 Included: BEL, CZE, DEU, DNK, ESP, FIN, FRA, GBR, HUN, IRL, ITA, NLD, PRT, SVN, SWE
8 Influence of Institutions Human capital and absorptive capacity - Engelbrecht, H.-J. (1997). “International R&D spillovers human capital and productivity in OECD economies” Patent protection - Czarnitzki, D., Toole, A. (2008). “Patent Protection, Market Uncertainty and R&D Investment” Ease of doing business, quality of tertiary education - Coe, D. T., Helpman, E., Hoffmaister A. W. (2009). “International R&D Spillovers and Institutions”
9 Institutional quality Quality of contract enforcement, property rights and courts law 1996 law 1996-2005 2.50 2.00 1.50 1.00 0.50 0.00 -0.50 -1.00 CZE HUN ITA SVN PRT ESP JPN BEL FRA USA IRL DEU GBR NLD AUS SWE DNK FIN
10 Data Sources WIOD: Input/Output linkages for 40 countries and 35 ind. EU Klems: Productivity Data (TFP gross output) STAN Anberd: R&D stocks calculated from R&D expenditures using perpetual inventory method (comparability: PPP USD / deflated) Worldwide Governance Indicators: Institutional data on quality of contract enforcement, property rights Barro and Lee dataset : Secondary school completion ratios Final sample contains 18 countries from 1995/1996 to 2005 AUS, BEL, CZE, DEU, DNK, ESP, FIN, FRA, GBR, HUN, IRL, ITA, JPN, NLD, PRT, SVN, SWE, USA
11 Empirical specification R&D spillovers from Same country Other countries RD s RD fs Same industry RD o Other industries Service industries RD serv LAW ct … Law enforcement: contracts, property rights, patents EDU ct … Secondary school completion ratio
12 Variable Construction RD ict … R&D spillover Effect to industry i in country c at time t R ict … R&D stock of industry i in country c ω icjdt … industry i ‘s (located in country c ) share of intermediate inputs from ind. j in country d at time t
13 Unit root and cointegration tests Panel unit root test Variable log(MFP) log(RDs) log(RDfs) log(RDo) log(RDserv) IPS 1.63 2.34 -1.56* -2.15** -0.75 The values represent W-t-bar statistics of the one-sided Im-Pesaran-Shin test (2003). The number of lags included in respective tests is chosen using the Akaike information criterion. . ***, ** and * denote tests being significant at a 1, 5 and 10% level, respectively. Dynamic OLS Westerlund ECM panel cointegration tests log(MFP) log(RDs) log(RDfs) log(RDo) log(RDserv) log(RDs) 1987 Gt - 5.42*** 1.30 -2.43*** -0.05 -1.32*** Ga 0.46 -0.97 -1.12 -0.17 -1.70 Pt - 7.98 -10.21*** -9.28** -2.98 -14.11*** Pa 0.82 -1.20 -1.40** -0.15 -1.70*** A rejection of H0 for the Ga and Gt test-statistics should be taken as evidence of cointegration of at least one cross-sectional unit. The Pa and Pt test statistics pool information over all the cross-sectional units and a rejection of H0 provides evidence for cointegration for the panel as a whole. ***, ** and * denote tests being significant at a 1, 5 and 10% level, respectively.
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