Assessing Economic Complexity with Input-Output Based Measures

ASSESSING ECONOMIC COMPLEXITY WITH
INPUT-OUTPUT BASED MEASURES

1. INTRODUCTION

Complexity is a multidimensional phenomenon with several approaches and many
theoretical definitions that we will not discuss in detail here (see Waldrop 1992; Adami
2002). Originating in the physical and biological sciences, the notion of complexity has been
usefully extended to the analysis of social and economic systems (see e.g., Arthur 1999;
Rosser 1999; Durlauf 2003; LeBaron and Tesfatsion 2008).

In the economic context, one interesting dimension of complexity is the level of
interdependence between the component parts of an economy. The Leontief input-output
model is, by its very nature, one of the best theoretical and empirical methodologies for
studying this.

In fact, intersectoral connectedness is the central feature of input-output analysis, and
there are, as expected, many different ways of measuring it, from the earlier and classical
indicators of Chenery and Watanable (1958), Rasmussen (1956) and Hirschman (1958) to
more sophisticated methods, such as the interrelatedness measure of Yan and Ames (1963),
the cycling measure of Finn (1976) and Ulanovicz (1983), the dominant eigenvalue measure
of Dietzenbacher (1992) and many others. Among the more recent examples of
interconnectedness measures, proving the resurgence of interest in this kind of research, are
the average propagation length (weighted or unweighted) proposed by Dietzenbacher and
Romero (2007) and the complexity as interdependence measures of Amaral et al. (2007).

The study of economic complexity in an input-output framework has been an interesting
subject for economic analysis and policy-making purposes (see e.g., Robinson and

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