Innovation and Productivity: What can we learn from the CIS III Results for Portugal? Pedro Morais Martins de Faria Orientador: Doutor Pedro Filipe Teixeira da Conceição Co-Orientadora: Doutora Elsa Beatriz Padilla 23 December 2004 The research reported in this thesis was partially supported by Observatorio da Ciencia e do Ensino Superior (OCES) [Obervatory of Science and Higher Education, Ministry for Science and Higher Education, Portugal]

1.Introduction 2.Data 3.Model and Methods 4.Results and Conclusions Outline

Introduction Two issues associated with the relationship between productivity and innovation: 1)the impact of technological breakthroughs on productivity; 2) the time frame within which that impact occurs. The relationship between innovation and productivity growth is expected to be positive in the long run and at the macro level (countries and regions). But in the short run and at the firm level, the relationship between innovation and productivity growth may be negative.

Introduction (II) The short run analysis of this relationship is an important issue since the understanding of the effects of the first steps of innovation on the firms’ functioning can explain momentary productivity losses. The revision of the determinants of productivity growth in the short run can prevent precipitated judgments about the effectiveness of a specific innovation, since it supports the idea that innovation effectiveness is only correctly evaluated after a time period.

Three general explanations that predict a negative relationship between productivity growth and innovation in the short run.

Data The CIS III is a nation-wide firm-level survey that measured directly innovation by asking whether firms have introduced any new process or product in the context of the firm. The CIS III inquired a representative sample of the Portuguese firms. The CIS III provides information at the firm level for the period

Advantages of the CIS III survey data: Data (II) 1) Data on innovation and productivity for a two year period ( ); 2) Separation between firms that do not innovate, those that have attempted to innovate and innovative firms; 3) Gathering information, not only about radical innovations linked to patents applications, but also about not radical innovations in the context of the market but new to the firm; 4) Inquiring firms, not only from the manufacturing sector, but also from the service sector, making possible a more complete analysis from the Portuguese economic reality; 5) Existence of information that allows the creation of instruments to correct endogeneity; 6) Differentiation between product and process innovation.

Model and Methods (I) The model was constructed assuming that innovation and productivity change are simultaneously determined in the CIS III sample. Thus, in order to avoid possible biases that result from this fact, the proposed model, that builds on the Conceição et al. (2003) approach, is a system of two equations: one predicting innovation and other predicting productivity growth.

Model and Methods (II) Where: Prdg – Productivity Growth Measure – log (Turnover / nº Workers) Inov – Innovation Dummy Variable Exp – Exports / Turnover NF – Dummy Variable that indicates if the firm is new ( ) GP – Dummy Variable that indicates if the firm is part of a group ED – Share of the Workforce engaged in specialized tasks CS – Gross Investments in Capital Goods S – Sector Dummy Variables Log_Turn_Inic – Critical Identification Variable - log (Turnover 1998)

Model and Methods (III) 1) Endogeneity: Hausman Test OLS – inconsistent 2) Equation System: 3) Covariance Correction: Murphy-Topel Method - two step estimation method for mixed models that include limited dependent variables

Model and Methods (IV) A novelty of this study is the inclusion of a new variable that measures partially the management and strategy of firms. Although the productivity literature states that management and investment strategy influence the level and the dynamics of productivity, these aspects of firm behavior are exceptionally difficult factors to quantify and to measure. This variable can bring new light to the understanding of the productivity/innovation relationship. To measure goods (CS), a variable that exposes the investment strategy of the influence of management and investment strategy in productivity, we considered the log of the gross investments in tangible the firm. This variable is an indicator of a firm’s strategy towards enhancing productivity since productivity growth is often linked to investments in capital goods.

Note: * Significant at 10%; ** 5%; *** 1%; Sector Dummies Variables included but not reported Results (I)

Note: * Significant at 10%; ** 5%; *** 1%; Sector Dummies Variables included but not reported Results (II)

In the sample of Portuguese firms surveyed by the CIS III, innovative firms have a lower degree of productivity growth when compared with non-innovative firms Conclusions (I) The more productive firms are more innovative – this is coherent with the Adjustment Costs Theory The inclusion of the new variable Gross Investment in Capital Goods gives robustness to the model

Conclusions (II) Policy implications: The evaluation of innovation impacts cannot be done only in the short run: technology adoption is a complex process that does not render results instantaneously. Therefore, when evaluating a new technology, decision makers at the firm and state level have to consider this time lag between adoption and productivity growth: a technology that is inefficient in the short run can raise productivity in the long run. Further Work: Case studies could be conducted in Portuguese firms aiming at capturing possible differences between sectors. International comparisons could be done with data from the CIS from other countries.

Innovation and Productivity: What can we learn from the CIS 3 Results for Portugal? Pedro Morais Martins de Faria Orientador: Doutor Pedro Filipe Teixeira da Conceição Co-Orientadora: Doutora Elsa Beatriz Padilla 23 December 2004