Assessing Economic Complexity with Input-Output Based Measures



Markandya 1973; Sonis et al. 1998; Dridi and Hewings 2002; Amaral et al. 2007). For
example, in a more complex economy, the effects of (global) policy measures tend to be
easily and rapidly propagated and more evenly distributed among sectors, and the same goes
for unexpected (desirable or undesirable) shocks of any nature (Sonis et al. 1995,
Dietzenbacher and Los 2002, Steinback 2004, Okuyama 2007).

On the other hand, one might expect the complexity of an economy to be negatively
correlated with the relative weight of its so-called key sectors and this may eventually make
(dominant sectors directed) policy interventions less efficient. (Laumas 1975, Dietzenbacher
1992, Sonis et al. 1995, Muniz et al. 2008).

For understandable reasons, it is also to be expected that, in general, regional economies
will be less complex than national economies, small economies less complex than large
economies and open economies less complex than closed economies, but the exhaustive
study of these comparisons would need careful theoretical and empirical research into areas
that are well beyond the scope of this paper.

It is also predictable that the effects of measurement errors in collecting interindustry data
and the robustness of input-output projections from ESA and SNA Tables are in some sense
related to the complexity of an economy. This may be an important issue for empirical
researchers and statistical units, and so an appropriate measure of sectoral complexity can be
supplemented with these input-output tables, in line with the robustness measure proposed by
Wolff (2005).

The intersectoral measures of complexity analyzed and quantified in this paper can also
be useful in other fields of research, namely for studying the ecological complexity of natural



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