Innovation in Portugal: What can we learn from the CIS III? Innovation and Productivity Pedro Morais Martins de Faria Globelics Academy May 2005

Introduction The study of the relationship between productivity and innovation is a very active research field. Within it, two topics are crucial in order to understand this relationship: 1) the discussion about the real impact of technological breakthroughs on productivity and; 2) the time period that must be considered to study the real effect of innovation on productivity. Overall, the relationship between innovation and productivity is expected to be positive in the long run and at the macro level (countries and regions). Although, some work indicate that in the short run and at the firm level, the relationship between innovation and productivity growth may be expected to be negative.

In order to contextualize the results, we described three theoretical arguments that justify the negative relationship between productivity growth and innovation in the short run.

Data In order to illustrate the short run relationship between innovation and productivity, we used the CIS III database. The CIS III is a nation-wide firm-level survey that measured directly innovation by asking if firms have introduced any new process or product in the context of the firm. In this context and considering that this survey inquired a representative sample of the Portuguese economy and that provides complete information at the firm level of the period , the database is a good instrument to analyze the relationship between productivity and innovation in the short run.

Model and Methods(1) The model developed 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.

Model and Methods(2) Where: Prdg – Productivity Measure – log (Turnover / nº Workers) Inov – Innovation Dummy Variable Exp – Exports / Turnover NF – Dummy Variable that indicates if the firm was created in 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(3) 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(4) A novelty in this study was the inclusion of a variable that measures 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. The inclusion of this variable can bring some new light to the comprehension of the productivity/innovation relationship. In order to measure the influence of the role of management and investment strategy in productivity, we considered the log of the gross investments in tangible goods (CS), a variable that exposes the investment strategy of the firm. These kinds of investments are seen as an indicator of a firms 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 and Conclusions(1)

Note: * Significant at 10%; ** 5%; *** 1%; Sector Dummies Variables included but not reported Results and Conclusions(2)

In the universe of Portuguese firms enquired by the CIS III, innovative firms have a lower degree of productivity growth when compared with non-innovative firms Results and Conclusions(3) The more productive firms are more innovative – result coherent with the Adjustment Costs theory The inclusion of the variable Gross Investment in Capital Goods gives robustness to the model

Results and Conclusions(4) From these results, some policies implications can be drawn: The evaluation of innovation efficiency and its impact cannot be done immediately: 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 impact: a technology that is inefficient in the short run can raise productivity in the long run. Following this work, I will try to identify the incentives that are behind the decision to innovate, in the Portuguese context

Innovation and Productivity: What can we learn from the CIS 3 Results for Portugal? Pedro Morais Martins de Faria Globelics Academy May 2005