country dummy variables used to control for

# **4.2 Product innovation and spillovers between**

Our second set of estimations builds on previous research on spillovers by Jaffe et al. (1993), Branstetter (2001) and Griffith et al. (2006). This literature argues that knowledge spillovers between two firms should be proportional to the similarity and intensity of their research programs and related to geographical distance between the source and the receiver of the spillover. To capture these effects, these papers construct a measure of technological proximity between firms or split sources of spillovers between local, national and international. For technological similarity they use the uncentered correlation coefficient between firms’ vectors of R&D expenditures. Additionally, all these papers use patent citation data to identify the direction of knowledge flows.

Unfortunately, we are not able to follow completely Branstetter (2001) and Griffith et al. (2006) as we do not have information about the way firms allocate R&D expenditures across technological categories. To overcome this drawback, we work under the assumption that if two firms operating in the same country and industry are equally productive, they are involved in similar research projects. Therefore, we measure technological proximity between two firms by comparing their productivities, although we do not compute productivity per se but work with a set of productivity correlates. Many studies show that more than one factor determines the productivity of a firm. Clerides et al. (1998) and Bernard and Bradford (1999) provide evidence that exporters are more productive. Feenstra and Hanson (1997) show that affiliates of multinational corporations outperform domestic firms in recipient countries; Almeida and Fernandes (2008) report that majority foreign-owned firms are less likely to engage in technological innovations than minority foreign-owned ones, and Brambilla (2006) finds that firms with more than 50 percent of foreign ownership introduce on average more than twice as many more new products as private domestic companies. Finally, Cohen and Kelpper (1996) report that the likelihood of a firm reporting positive R&D efforts rises with firm size.

Thus, we consider several correlates of productivity to construct a proxy of technological distance between firms. More precisely, the index is the first principal component of the following variables: a dummy that takes value 1 if the firm is an exporter and 0 otherwise, a dummy that takes value 1 if the firm is foreign owned and 0 otherwise, a dummy that takes value 1 if the firm invests in R&D and 0 otherwise, a dummy that takes value 1 if the firm holds technological licenses and 0 otherwise, and the logarithm of the firm’s labor force (since since size and productivity are expected to be correlated). The index is defined as the predicted value for the first component in the principal component analysis of these variables. Table 1 displays the loadings corresponding to each input. In turn, the technological distance between firm _i_ and _j_ , _dij_ , is the absolute value of the difference between their predicted productivities.

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