- Process technologies that are relevant to the improvement of processes known to the market but new to the firm.

- Process technologies that are relevant to the improvement of the existing process and that are new both to the firm and to the market.

In terms of **innovation sales** , the Chilean survey (the Colombian survey does not provide this type of information) asks the firms to report their innovation sales as a percentage of total sales. These percentages are divided in different groups and ranked from 1 to 5, varying from 0 to 100 percent of the total sales.<sup>16</sup> An increase in the percentage of a firm’s innovation sales classifies the firm as innovative. This measure relies on the ability of the firms to correctly and consistently report such percentages and they are, in principle, a better assessment of past, rather than current, innovative activities.

The last measure of outcome innovation is in terms of **patents** . This measure combines patents held by the firms for technologies developed by the firms themselves, developed outside the firms, or simply technologies prepared and presented. In this broad sense patents, or simply grants for a new technology presented, represent a “new” advance on existing knowledge and can be regarded as proxy for innovative output. In the Colombian survey, data on six different types of IPRs are also considered.<sup>17</sup> A firm is considered **IPR innovative** if any of the six statements (please refer to footnote 17) is higher than zero. Initially the measure was restricted to only encounter patents, but the figure was simply too low to provide any variance in the empirical models.

# **4.2 EXPLANATORY VARIABLES**

The explanatory variables of the model have been divided in four groups: (i) characteristics of the firms, (ii) human capital and knowledge embedded in the organization, (iii) absorptive capacity, and (iv) collaboration (knowledge diffusion or linkages with centers of knowledge production or sharing). A complete list of variables and a brief description is reported in Annex 2, while some descriptive statistics are provided in Annex 3 (tables A.3.1 and A.3.2). Below the rationale for using the selected groups of variables as determinants of innovation is presented:

- (i) **Firm’s characteristics:** according to Schumpeter (1942), they play an important role in determining firms’ innovative activity, and therefore it is important to control for them.<sup>18</sup> While we are not trying to test Schumpeter’s hypothesis that innovation activity is promoted by large firms and by imperfect competition, it is

> 16 With this type of dependent variable (in a ranked order), it is not possible to use the ordinary probit model. It is however possible to use an ordered probit model, which makes it possible to fully explore the information available in the survey. It is possible to show that the ordered probit model is a special case of the multinomial probit model showed in Chapter 3. Please refer to Daisuke Nagakura (2004) for a complete proof.

> 17 The six types of IPRs answers to the following questions:

> 1. Number of patents the firm applied for during 1996 – 2004.

> 2. Number of registers of utility models the firm applied for during 2003 - 2004.

> 3. Number of registers of industrial design the firm applied for during 2003 - 2004.

> 4. Number of distinguishing signs and marks the firm applied for during 2003 – 2004.

5. Number of authors rights the firm applied for during 2003 – 2004.

> 6. Number of software registers the firm applied for during 2003 – 2004

> 18 And also Galbraith (1952) and Arrow (1962).

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