Centre for Economic and Regional Studies Hungarian Academy of Sciences Centre of Excellence

Start: 09/06/2018 14:00

End: 09/06/2018 16:00

Location: MTA Research House, conference room nr. K11-12, ground floor, 4 Tóth Kálmán street, 1097 Budapest

Description

Forthcoming lecture

6 September 2018

Tom Broekel

Measuring technology complexity - a network approach

Department of Spatial Planning and Human Geography, Utrecht University, The Netherlands

Institute of Economic and Cultural Geography, Leibniz University of Hannover, Germany

Extended abstract

The complexity of technologies is seen as crucial explanatory dimension of technological development and economic success (Romer, 1990; Dalmazzo, 2002). Hidalgo and Hausmann (2009) argue that country’s economic development is shaped by its ability to successfully engage in complex economic activities and technologies. Both Sorenson (2005) and Balland and Rigby (2017) show that few cities are capable of mastering complex technologies that lay the foundation for their future growth.

Despite its theoretical relevance and an increasing empirical interest, measuring the complexity of technologies empirically is a complicated issue, as Pintea and Thompson (2007) note: “We do not have any easy way to measure complexity” [p. 280]. The two most prominent approaches are put forward by Fleming and Sorenson (2001) and Balland and Rigby (2017), with the latter transferring the approach of Hidalgo and Hausmann (2009) for approximating economic complexity to the measurement of technological complexity. Both approaches essentially build on the assumption of complexity being scarce. Balland and Rigby (2017) assume technological complexity to be spatially scare, while Fleming and Sorenson (2001) build on the idea of complex knowledge combinations appearing less frequently than simple ones. However, these assumptions are theoretically problematic and induce significant limitations when applying the measures in practice.

The presentation introduces an alternative measure of technological complexity, structural complexity, which avoids these assumptions by applying a network perspective. More precise, the measure of structural complexity quantifies the diversity of technological elements’ embeddedness into the combinatorial knowledge network (aka knowledge space).

In addition to discussing the methodological basis of the measure, the presentation will empirically benchmark the new and the traditional approaches of measuring technological complexity against four stylized facts. The empirical assessment uses patent data for Europe between 1980 to 2015.

References:

Fleming, L. & O. Sorenson (2001). Technology as a complex adaptive system: Evidence from patent data. Research Policy, 30, 1019-1039.

Hidalgo, C. & R. Hausman (2009): The building blocks of economic complexity. PNAS 106(26), 10570-10575.