  There is an emerging consensus in the literature that locally embedded
capabilities and industrial know-how are key determinants of growth and
diversification processes. In order to model these dynamics as a branching
process, whereby industries grow as a function of the availability of related
or relevant skills, industry networks are typically employed. These networks,
sometimes referred to as industry spaces, describe the complex structure of the
capability or skill overlap between industry pairs, measured here via
inter-industry labour flows. Existing models typically deploy a local or
'nearest neighbour' approach to capture the size of the labour pool available
to an industry in related sectors. This approach, however, ignores higher order
interactions in the network, and the presence of industry clusters or groups of
industries which exhibit high internal skill overlap. We argue that these
clusters represent skill basins in which workers circulate and diffuse
knowledge, and delineate the size of the skilled labour force available to an
industry. By applying a multi-scale community detection algorithm to this
network of flows, we identify industry clusters on a range of scales, from many
small clusters to few large groupings. We construct a new variable, cluster
employment, which captures the workforce available to an industry within its
own cluster. Using UK data we show that this variable is predictive of
industry-city employment growth and, exploiting the multi-scale nature of the
industrial clusters detected, propose a methodology to uncover the optimal
scale at which labour pooling operates.
