Knowledge flows and innovation: a pseudo-panel approach

ABSTRACT

This study investigates whether the effect of vertical and horizontal knowledge sources affect technological innovation for a sample of firms in 10 European countries. The empirical analysis is based on a unique dataset extracted from the Community Innovation Survey for the period 2002 to 2012. Results, using a pseudo-panel approach at country-industry level, reveal that vertical knowledge from suppliers strongly affects both product and process innovation, while knowledge flows from clients influence in particular product innovation. Finally, horizontal knowledge flows from competitors appear to have an effect on process innovation. These findings are validated with an instrumental variable approach using Lewbel’s technique to internally generate reliable instruments, when external ones are not suitable. Overall, results seem to suggest the need for ad-hoc policies to foster knowledge flows among firms in different phases of the production process to boost their innovative capacity both for products and processes.

KEYWORDS:

• Vertical knowledge flows
• horizontal knowledge flows
• product innovation
• process innovation
• community innovation survey
• European countries

JEL CLASSIFICATION:

• L6
• O31
• O52

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ In this context, we consider absorptive capacity not as defined by Cohen and Levinthal (1990) who include use of the external knowledge assimilated, but according to the definition proposed by Zahra and George (2002), which distinguishes between ‘potential’ absorptive capacity, related purely to knowledge assimilation, and ‘realized’ absorptive capacity, which includes use of the knowledge (Fosfuri and Tribó 2008).

² CIS is the most widely used dataset in innovation research. It is managed by Eurostat, which has developed a standard questionnaire in accordance with the standard definition in the Oslo Manual (OECD 2005). The CIS is conducted every 2 years in the European Union, EFTA and EU candidate countries. It gives a set of innovation indicators to explore the innovation performance process and its impact on a country’s economy.

³ The new economies are Bulgaria, the Czech Republic, Estonia, Lithuania, Hungary, Romania and Slovakia.

⁴ Czech Republic and Hungary have 13 sectors only for CIS10 and CIS12.

⁵ The name of variables is a minor issue. To solve this, we created a homogeneous classification as set out in Table A3.

⁶ Table A5 reports the summary statistics of the original firm level values from the CIS.