Ben Davies Blog Research Vita

I recently wrote an article for Asymmetric Information summarising my paper with Dave Maré on the relatedness and complexity of economic activities in New Zealand. The full text for that article is quoted below.

Introduction

Current European regional policy encourages regions to build on their strengths by diversifying into activities that draw upon existing knowledge bases. This “smart specialisation” approach encourages entrepreneurship, innovation and long-term growth by fostering local interactions between workers with complementary knowledge and skills.

Balland et al. (2018) define a framework for analysing smart specialisation using the ideas of relatedness and complexity. Expanding into activities that are related to existing specialisations carries low growth risk because local workers already possess the knowledge and skills needed to conduct those activities. Expanding into complex activities delivers the highest expected economic returns because such activities “form the basis for long-run competitive advantage.” Balland et al.‘s framework identifies low-risk, high-return development opportunities as locally under-represented activities with high local relatedness and high complexity.

We examine the contribution of relatedness and complexity to urban employment growth in New Zealand. This allows us to evaluate the efficacy of implementing smart specialisation policies in New Zealand by identifying whether the associated mechanisms appear to influence employment dynamics.

Data and methods

Our analysis uses historical New Zealand census data aligned to current industry, occupation and urban area codes. We select 50 “cities” (urban areas) and 200 “activities” (industry-occupation pairs) with persistently high employment in census years 1981, 1991, 2001 and 2013. Our selected activities span 61 industries and nine occupations.

We recognise activities as being “related” if they require similar inputs. We infer such similarities from employee co-location patterns. These patterns reveal firms’ shared preferences for using spatially heterogeneous resources, which encourage firms engaged in related activities to co-locate in order to benefit from agglomeration economies.

We measure activities’ relatedness using weighted correlations of local employment shares. Our approach extends discrete measures used in previous studies by recognising variation in the extent of local specialisation and by adjusting for differences in employment data quality between geographic areas.

We recognise activities as being “complex” if they rely on specialised combinations of complementary inputs. For example, consulting is more complex than lecturing because consultants need local clients while lecturers do not rely as much on other activities being present locally.

We define activity complexity using the second eigenvector of the row-standardised activity relatedness matrix. Our approach generalises Calderelli et al.‘s (2012) eigenvector approximation of Hidalgo and Hausmann’s (2009) Method of Reflections. We use a similar approach, applied to the transpose of the city-activity employment matrix, to estimate city complexity.

Mapping relatedness

We define an “activity space” that captures the network structure of activities based on our relatedness estimates. We describe activity space by a weighted network in which nodes correspond to activities and in which edges have weight equal to the relatedness between pairs of activities. The subnetwork induced by the 500 edges of largest weight is shown below, with nodes coloured by occupation.

At the centre of our map is a tightly connected, nest-shaped cluster of low-skill occupations in the distributive services sector. To the right of this cluster is a group of medium- to low-skill occupations in the construction, retail and healthcare sectors. These activities are ubiquitous and appear together as local relative specialisations in smaller, less diverse cities. In contrast, the lower wing of our network map comprises a cluster of high-skill occupations in the professional and information service sectors, which tend to concentrate in large cities and to have higher levels of complexity.

Do relatedness and complexity predict employment growth?

More complex activities grew faster during our period of study. On average and holding local relatedness constant at its weighted mean value, a one standard deviation increase in activity complexity is associated with a 0.89 percentage point increase in local employment growth per year. This effect rises to 0.98 percentage points when we control for city complexity. More locally related activities experienced slower growth, especially in complex cities.

Balland et al.‘s (2018) framework suggests that complex activities with high local relatedness offer the strongest prospects for future growth. If this were true then we would expect a strong positive coefficient on the interaction of local relatedness and activity complexity. Our estimates show only a weak and insignificant interaction.

Relatedness appears to promote growth only in the largest and most complex cities. This result is consistent with the idea that cities are dense networks of interacting activities: the benefits of such interaction are more apparent in larger cities, where workers and firms engaged in related activities interact more frequently.

Conclusion

Complex activities grew faster during our period of study, especially in complex cities. However, this growth was not significantly stronger in cities more dense with related activities. Overall, we do not identify strong effects of relatedness and complexity on growth in local activity employment. It remains an open question whether the effects do not operate or whether New Zealand cities lack the scale for such operation.

Further details are available in Motu Working Paper 19-01.