An Empirically-Based Taxonomy of Dutch Manufacturing: Innovation Policy Implications

The paper studies the degree of homogeneity of innovative behavior in order to determine empirically an industry classi¯cation of Dutch manufacturing that can be used for policy purposes. We use a two-limit tobit model with sample selection, which explains the decisions by business enterprises to innovate and the impact these decisions have on the share of innovative sales. The model is estimated for eleven industries based on the Dutch Standard Industrial Classification (SBI 1993). A likelihood ratio test (LR) is then performed to test for equality of the parameters across industries. We find that Dutch manufacturing consists of three groups of industries in terms of innovative behavior, a high-tech group, a low-tech group and the industry of wood, where firms seem to have a rather different innovative behavior from the remaining industries. The same pattern shows up in the three Dutch Community Innovation Surveys.mathematical economics and econometrics ;

Productivity effects of innovation modes

Many empirical studies have confirmed the positive impact of innovation on productivity at the firm level. The focus tends to be either on R&D driven techno-logical innovation on the one hand, or on organisational changes complemented by ICT on the other. To investigate the effect of different types of innovations on produc-tivity, we propose a model with two innovation input equations (R&D and ICT) that feed into a knowledge production function consisting of a system of three innovation output equations (product innovation, process innovation and organisational innova-tion), which ultimately feeds into a productivity equation. We find that ICT is an im-portant driver of innovation in both manufacturing and services. Doing more R&D has a positive effect on product innovation in manufacturing. Organisational innova-tion has the strongest productivity effects. We only find positive effects of product and process innovation when combined with an organisational innovation.technological innovation; non-technological innovation; ICT; R&D; productivity; trivariate probit; CDM model;

Product, Process and Organizational Innovation: Drivers, Complementarity and Productivity Effects

We propose a model where both R&D and ICT investment feed into a system of three innovation output equations (product, process and organizational innovation), which ultimately feeds into a productivity equation. We find that ICT investment and usage are important drivers of innovation in both manufacturing and services. Doing more R&D has a positive effect on product innovation in manufacturing. The strongest productivity effects are derived from organizational innovation. We find positive effects of product and process innovation when combined with an organizational innovation. There is evidence that organizational innovation is complementary to process innovation. Nous estimons un modèle dans lequel la recherche-développement (R-D) et l’investissement en technologies de l’information et de la communication (tic) déterminent trois types d’innovation (de produit, de procédé, et organisationnelle), lesquels influencent à leur tour la productivité. Nous trouvons que l’investissement en tic facilite l’innovation tant dans le secteur manufacturier que dans celui des services. Faire de la R-D a un effet positif sur l’innovation en produit dans le secteur manufacturier. L’effet le plus important sur la productivité provient de l’innovation organisationnelle. Les deux autres types d’innovation n’augmentent la productivité que s’ils sont accompagnés d’innovation organisationnelle. Cette dernière est complémentaire à l’innovation de procédé.Innovation, ICT, R&D, productivity , Innovation, ICT, R&D, productivité

Product, Process and Organizational Innovation: Drivers, Complementarity and Productivity Effects

We propose a model where both R&D and ICT investment feed into a system of three innovation output equations (product, process and organizational innovation), which ultimately feeds into a productivity equation. We find that ICT investment and usage are important drivers of innovation in both manufacturing and services. Doing more R&D has a positive effect on product innovation in manufacturing. The strongest productivity effects are derived from organizational innovation. We find positive effects of product and process innovation when combined with an organizational innovation. There is evidence that organizational innovation is complementary to process innovation.Innovation; ICT; R&D; Productivity

An Empirically-Based Taxonomy of Dutch Manufacturing: Innovation Policy Implications

The paper studies the degree of homogeneity of innovative behavior inorder to determine empirically an industry classi¯cation of Dutch manu-facturing that can be used for policy purposes. We use a two-limit tobitmodel with sample selection, which explains the decisions by business en-terprises to innovate and the impact these decisions have on the share ofinnovative sales. The model is estimated for eleven industries based on theDutch Standard Industrial Classi¯cation (SBI 1993). A likelihood ratiotest (LR) is then performed to test for equality of the parameters acrossindustries. We ¯nd that Dutch manufacturing consists of three groups ofindustries in terms of innovative behavior, a high-tech group, a low-techgroup and the industry of wood, where ¯rms seem to have a rather di®er-ent innovative behavior from the remaining industries. The same patternshows up in the three Dutch Community Innovation Surveys.none;

The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models

This paper proposes a method to implement maximum likelihood estimation of the dynamic panel data type 2 and 3 tobit models. The likelihood function involves a two-dimensional indefinite integral evaluated using “two-step” Gauss-Hermite quadrature. A Monte Carlo study shows that the quadrature works well in finite sample for a number of evaluation points as small as two. Incorrectly ignoring the individual effects, or the dependence between the initial conditions and the individual effects results in an overestimation of the coefficients of the lagged dependent variables. An application to incremental and radical product innovations by Dutch business firms illustrates the method.

Persistence of Innovation in Dutch Manufacturing: Is it Spurious?

This paper studies the persistence of innovation and the dynamics of innovation output in Dutch manufacturing using firm data from three waves of the Community Innovation Surveys (CIS), pertaining to the periods 1994-1996, 1996-1998, and 1998-2000. We estimate by maximum likelihood a dynamic panel data type 2 tobit model accounting for individual effects and handling the initial conditions problem. We find that there is no evidence of true persistence in achieving technological product or process innovations, while past shares of innovative sales condition, albeit to a small extent, current shares of innovative sales.Economics ;

Innovative Sales, R&D and Total Innovation Expenditures: Panel Evidence on their Dynamics

This paper studies the dynamic relationship between input and output of innovation in Dutch manufacturing using an unbalanced panel of enterprise data from five waves of the Community Innovation Survey during 1994-2004. We estimate by maximum likelihood a dynamic panel data bivariate tobit with double-index sample selection accounting for individual effects. We find persistence of innovation input and innovation output, a lag effect of the former on the latter and a feedback effect of the latter on the former. The lag effect remains significant in the high-tech sector even after four years. Firm and industry effects are also important.innovation, panel bivariate tobit model, innovation expenditures

Persistence of Innovation in Dutch Manufacturing: Is it Spurious?

This paper studies the persistence of innovation and the dynamics of innovation output in Dutch manufacturing using firm data from three waves of the Community Innovation Surveys (CIS), pertaining to the periods 1994-1996, 1996-1998, and 1998-2000. We estimate by maximum likelihood a dynamic panel data type 2 tobit model accounting for individual effects and handling the initial conditions problem. We find that there is no evidence of true persistence in achieving technological product or process innovations, while past shares of innovative sales condition, albeit to a small extent, current shares of innovative sales.Dynamic panel data type 2 tobit, Innovation, Spurious persistence

Persistence of Innovation in Dutch Manufacturing: Is it Spurious?

This paper studies the persistence of innovation and the dynamics of innovation output in Dutch manufacturing using firm data from three waves of the Community Innovation Surveys (CIS), pertaining to the periods 1994-1996, 1996-1998, and 1998-2000. We estimate by maximum likelihood a dynamic panel data type 2 tobit model accounting for individual effects and handling the initial conditions problem. We find that there is no evidence of true persistence in achieving technological product or process innovations, while past shares of innovative sales condition, albeit to a small extent, current shares of innovative sales.dynamic panel data type 2 tobit, innovation, spurious persistence