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Lecture 2: Exporting, Innovation and Productivity

Lecture 2: Exporting, Innovation and Productivity. H. Vandenbussche. Brixen, September 2009. Hylke Vandenbussche Brixen, September 2009. Intro. Research questions : Does Innovation drive exporting? Does Exporting drive innovation?

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Lecture 2: Exporting, Innovation and Productivity

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  1. Lecture 2: Exporting, Innovation and Productivity H. Vandenbussche Brixen, September 2009

  2. Hylke Vandenbussche Brixen, September 2009 Intro • Research questions: • Does Innovation drive exporting? • Does Exporting drive innovation? • How is Innovation measured? • Input measures: R&D expenditures; R&D department; Training • Output measures: Product innovation, Process Innovation • Methodology: Probit models • Empirical Evidence: Mixed

  3. Hylke Vandenbussche Brixen, September 2009 Motivation Early Literature • Melitz, 2003; Bernard and Jensen, 1999: productivity is a random exogenous draw from a Pareto distribution. • Yeaple, 2005; Bustos, 2005; Constantini & Melitz 2007: firms endogenously choose innovation. • Link between innovation and firm growth known in IO Empirical evidence on innovation INPUT measures • Aw et al (2007): no link between R&D and probability to start exporting for Taiwanese firms. • Cassiman & Martinez-Ros(2007): no link between R&D and exporting for Spanish firms. Motivation - Data - Econometric approach - Results - Conclusion

  4. Hylke Vandenbussche Brixen, September 2009 Motivation Empirical literature on innovation OUTPUT measures • Cassiman & Martinez-Ros (2007): Product innovation not process innovation affects exporting for Spanish firms • Caldera (2009): Product innovation ànd Process innovation affect exporting for Spanish firms • Becker and Egger (2007): product innovation matters more than process innovation to exporting for German firms. They do not isolate export starters which may lead to a simultaneity bias • Damijan et al. (2008): no link between product nor process innovation and the decision to start exporting for Slovenian firms Motivation - Data - Econometric approach - Results - Conclusion

  5. Paper 1: Aw, Roberts and Whinston • IO approach • Hopenhayn (’92) and Olley and pakes (1986) assume that a firm’s productivity follows a Markov process and does NOT depend on investment: • More general formulation here: with r: spending on R&D and x: participation in export markets • Data • Taiwanese electronics industry • Largest industrial sector: 25% of exports; 5% of GDP • Firm surveys 1986, 1991, 1996 • Innovation measure • R&D • Training

  6. Transition Matrix of Investment Activities between Years t and t + 1, 1986–1996 Number of firms (row proportion) Investment Activity Year t Year (t + 1) (number of firms in year t) Start R&D/T Stop R&D/T Start Export Stop Export No R&D/WT & No 24 (12.97) – 50 (27.03) – Export (185) Only R&D/WT (82) – 36 (43.90) 42 (51.22) – Only Exporting (276) 73 (26.45) – – 52 (18.84) R&D/WT & Export(530) – 156 (29.43) – 40 (7.55)

  7. Direction of causality not clear • Methodology: bivariate Probit model i.e. takes two independent binary probit models and estimates them together but allows a correlation in the error term. This is to recognize that there may be unobserved variables that affect both binary choices. The model is estimated with maximum likelihood Choice 1: R&D and Exporting Choice 2: only Exporting Choice 3: only R&D

  8. Discrete Investment Activity EquationExporting R&D/WT intercept −3.377 (0.647)* −6.749 (0.626)* year dummy 0.137 (0.108) 0.023 (0.096) entrant dummy 0.647 (0.162)* 0.593 (0.199)* log(age) 0.128 (0.070) −0.209 (0.069)* log(kit) 0.383 (0.038)* 0.496 (0.036)* log(pwageit) −0.319 (0.104)* 0.114 (0.100) multiplant dummy 0.067 (0.127) 0.035 (0.111) productivity (ωit) 1.120 (0.356)* 0.524 (0.283) productivity squared ()ωit 2 −0.631 (0.272)* −0.138 (0.215) lagged Choice 1 dummy Exporting and R&D/WT 1.270 (0.297)* 0.711 (0.251)* lagged Choice 2 dummy Exporting but not R&D/WT 0.921 (0.239)* 0.206 (0.263) lagged Choice 3 dummy R&D/WT but not exporting −0.130 (0.423) 0.329 (0.425) (ωit) * lagged Choice 1 dummy −0.036 (0.652) 0.193 (0.416) (ωit) * lagged Choice 2 dummy 0.829 (0.464) −0.045 (0.415) (ωit) * lagged Choice 3 dummy −0.599 (1.049) 0.246 (0.929) Corr(εxit, εrit) 0.287 (0.059)* Notes: * Statistically significant at the α = 0.05 level.

  9. Conclusion Aw et al. • History of exporting matters • R&D does NOT matter for exporting • But! R&D and exporting together can put a firm on a higher future productivity path!

  10. Paper 2: Cassiman&Martinez-Ros • Data • Spanish manufacturing firms • ’90-99 • CIS for Spain: output measure of innovation • One way causality from innovation to exporting • Theory • Vernon (1966) product life cycle i.e. firms invent new product, first sell it at home and than abroad

  11. Table 3a: Past Innovation and Exportst Not Exportt Exportt Total Not Innovate t-1 2807 (58%) 2070 (42%) 4877 (100%) Innovate t-1 389 (16%) 2033 (84%) 2422 (100%) Total 3196 (44%) 4103 (56%) 7299 (100%) Table 3b: Past Product Innovation and Exports Not Exportt Exportt Total No Product Innovation t-1 2799 (51%) 2720 (49%) 5519 (100%) Product Innovation t-1 397 (22%) 1383 (78%) 1780 (100%) Total 3196 (44%) 4103 (56%) 7299 (100%) Table 3c: Past Process Innovation and Exports Not Exportt Exportt Total No Process Innovation t-1 2505 (51%) 2405 (49%) 4910 (100%) Process Innovation t-1 691 (29%) 1698 (71%) 2389 (100%) Total 3196 (44%) 4103 (56%) 7299 (100%)

  12. Table 7: Decision to Export at time t by Non-Exporters in t-1 Small and Medium Firms (<200 workers) Large Firms (> 200 workers) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Prod In (t-1) 0.208** 0.218* 1.112** 1.66** 0.55* 0.696** 0.696** 0.604 0.179 0.059 Proc Inn (t-1) 0.084 0.117 - 0.516 -0.966 -0.107 -0.044 -0.044 0.432 -0.092 -0.2 Size 0.017*** 0.027*** 0.018*** 0.029*** 0.029*** 0.001 0.001 0.001 0.001 0.001 SizeSq -0.100*** -0.163*** -0.104*** -0.173*** -0.172*** -0.001 -0.001 -0.001 -0.0004 -0.0005 Foreign 0.317 0.439 0.250 0.388 0.374 -0.511* -0.511* -0.365 -0.325 -0.325 Cap Int 0.00002 0.00004 0.00003 0.00005 0.00005 0.0001 0.0001 0.0001 0.0001 0.0001 Wage Int -1.248*** -1.478*** -1.210*** -1.522*** -1.467*** 0.1447 0.1447 0.132 0.110 0.138 Low Comp 0.001 0.003 0.001 0.002 0.002 0.010 0.010 0.014 0.011 0.012 Index -0.00003 0.00001 0.00003 0.00002 0.00001 0.00006 0.00006 0.00001 -0.00004 0.00001 Intercept -1.677*** -2.394*** -1.96*** -2.2*** -1.71 -2.50 -2.50 -1.852 -0.76 -1.501 Indy-Time Ds Included Included Included Included Included Included Included Included Included Included Obs 2916 916 2916 2916 2916 140 140 140 140 140

  13. Conclusion Cassiman-Martinez-Ros • Product Innovation explains Exporting ! • Especially in Small firms • Product Innovation suggest firm-specific demand shocks

  14. Paper 3: Damijan et al. • Data -Slovenian firm-level data -CIS community industry survey ‘96-2002 -output measure of innovation • Methodology • Bivariate Probit model on exporting and innovation • Allow for two way causality

  15. Bivariate Probit model A test for correlation between innovation to exporting: Prob(Export t = 1) = f(Exp t-2; Innov t-2;X t-2) A test for correlation between exporting to innovation i.e. “learning”: Prob(Inov t = 1) = f(Inovt-2;Exp t-2;X t-2)

  16. Table 4: Results of bivariate probit regressions (no matching, all exporters) Export status (1) (2) (3) (4) (5) (6) Lagged innovation0.129 0.054 0.096 -0.093 0.191 -0.041 Lagged exportstatus 1.876*** 2.281*** 2.128*** 2.443*** 2.421*** 2.401*** Lagged productivity 0.126* 0.145 -0.076 -0.067 -0.108 -0.050 Lagged employment 0.214*** 0.166*** 0.321*** 0.130* 0.177* 0.145* Lagged capitalintensity 0.144*** -0.108** 0.067 -0.092* -0.029 -0.0640 Lagged R&DInvestment 0.004 0.025 0.009 0.0260 FDI penetration in industry 0.151 0.114 -0.097 -0.079 Industry dummies yes no yes no no no Timedummies yes yes yes yes yes yes N 3812 1551 1428 602 623 623 Rho 0.125 0.139 0.118 0.275 0.423 0.197 Prob rho=0 0.058 0.078 0.092 0.063 0.007 0.132 (1)-(4) Both product and process innovation considered, (5)only produc tinnovation is considered and(6)only process innovation considered

  17. Table5: Results of bivariate probit regressions (no matching, all innovators) Innovation status • (1) (2) (3) (4) (5) (6) Lagged inn 1.226*** 1.396*** 0.631*** 0.891*** 0.912*** 0.463*** Lagged exports 0.223*** 0.332*** -0.053 0.536** 0.478** 0.254 Lagged productiv 0.167*** 0.171** 0.199** 0.072 0.092 0.208* Lagged employm 0.224*** 0.256*** 0.178*** 0.130** 0.134** 0.228*** Lagged capital int 0.069* -0.057 0.124* 0.049 -0.042 0.053 Lagged R&D Invest 0.077*** 0.051*** 0.057*** 0.049*** FDI penetration in sector 0.793*** 0.708*** 0.564** 0.651*** Sector dummies yes no yes no no no Time dummies yes yes yes yes yes yes N 3812 1551 1428 602 623 623 (1)-(4) Both product and process innovation considered (5)only product innovation is considered and (6)only process innovation considered

  18. Conclusion Damijan et al. • No evidence that R&D affects exporting • But evidence that Exporting affects innovation (“learning”) for medium and large firms • Results are confirmed with matching techniques

  19. Brixen, September 2009 Innovation, Exports and Productivity: Firm-level evidence for Belgium Ilke Vanbeveren (Lessius, KUL) and Hylke Vandenbussche (CORE-UCL & KUL-LICOS)

  20. Ilke Van Beveren and Hylke Vandenbussche Brixen, September 2009 Intro • Main research Q: Does innovation drive exporting? • Data: Belgium, Community Innovation Survey, 2 waves, starters on export market vs. control group. • Methodology: Probit model. Dependent variable: probability to start exporting. Independent variables: innovation variables and controls. • Main findings: • It is the combination of product and process innovation (not either of two in isolation) that increases firms’ probability to start exporting. • Controlling for endogeneity of the innovation decision: no significant impact of innovation on probability to start exporting.

  21. Ilke Van Beveren and Hylke Vandenbussche Brixen, September 2009 Road map • Motivation & related literature • Data • Econometric approach • Baseline results • Accounting for anticipation effects • Conclusion

  22. Ilke Van Beveren and Hylke Vandenbussche Brixen, September 2009 Motivation Three sources of endogeneity: • Simultaneity: Innovation and export decisions are taken at the same time. • Possible solution: use lagged values of independent variables. • Causality: Past exporting history. • Possible solution: focus on starters versus non-exporters. • Anticipation: Future prospect of exports. • Possible solution: use Instrumental Variable techniques. Motivation - Data - Econometric approach - Results - Conclusion

  23. Ilke Van Beveren and Hylke Vandenbussche Brixen, September 2009 Data • Community Innovation Survey data Belgium. • 2 waves: 2000 (CIS3) and 2004 (CIS4). • Sampling is random in each period: 600 firms have answered both questionnaires. • Information about: • Firm-level innovation • Firm-level exports • All sectors of the economy. • Accounting information of firms: Belfirst (2006). Motivation - Data - Econometric approach - Results - Conclusion

  24. Ilke Van Beveren and Hylke Vandenbussche Brixen, September 2009 Data • Sample selection: 2 restrictions: • Simultaneity bias: we use (four-year) lagged firm-level characteristics in the empirical analysis: we can only include firms that have answered both questionnaires (600 firms). • Causality bias: To rule out the influence of past exporting history: we focus only on starters on the export market and compare these a group of non-exporters (189 firms). • Innovation variables: dummy variables indicating whether firm engaged in a particular innovation activity. Motivation - Data - Econometric approach - Results - Conclusion

  25. Motivation - Data - Econometric approach - Results - Conclusion

  26. Motivation - Data - Econometric approach - Results - Conclusion

  27. Motivation - Data - Econometric approach - Results - Conclusion

  28. Motivation - Data - Econometric approach - Results - Conclusion

  29. Ilke Van Beveren and Hylke Vandenbussche Brixen, September 2009 Econometric approach • Estimation method: probit model. • Dependent variable: probability to start exporting. • Independent variables: • Innovation dummies, • Sector dummies, • Firm-level control variables: Size and productivity. Motivation - Data - Econometric approach - Results - Conclusion

  30. Motivation - Data - Econometric approach - Results - Conclusion

  31. Motivation - Data - Econometric approach - Results - Conclusion

  32. Ilke Van Beveren and Hylke Vandenbussche Brixen, September 2009 Accounting for anticipation effect • How? IV estimation techniques. • Problem: IV probit is not possible when endogenous variable is dummy. • Solution: Linear Probability Model (IV). • Requirements for good instruments: • No direct impact on probability to start exporting. • Significant determinant of endogenous variable, conditional on all other independent variables. Motivation - Data - Econometric approach - Results - Conclusion

  33. Motivation - Data - Econometric approach - Results - Conclusion

  34. Motivation - Data - Econometric approach - Results - Conclusion

  35. Ilke Van Beveren and Hylke Vandenbussche Brixen, September 2009 Conclusion • It is not so much product or process innovation in isolation, but rather the combination of the two, that increases firms’ propensity to start exporting. • After accounting for the potential endogeneity of the innovation decision in firms’ export decision: results suggest that firms self-select into innovation, i.e. they only invest in innovative activities if their future export prospects are good. Motivation- Data - Econometric approach - Results - Conclusion

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