Production networks are integral to economic dynamics, yet dis-aggregated network data on inter-firm trade is rarely collected and often proprietary. Here we situate company-level production networks within a wider space of networks that are different in nature, but similar in local connectivity structure. Through this lens, we study a regional and a national network of inferred trade relationships reconstructed from Dutch national economic statistics and re-interpret prior empirical findings. We find that company-level production networks have so-called functional structure, as previously identified in protein-protein interaction (PPI) networks. Functional networks are distinctive in their over-representation of closed squares, which we quantify using an existing measure called spectral bipartivity. Shared local connectivity structure lets us ferry insights between domains. PPI networks are shaped by complementarity, rather than homophily, and we use multi-layer directed configuration models to show that this principle explains the emergence of functional structure in production networks. Companies are especially similar to their close competitors, not to their trading partners. Our findings have practical implications for the analysis of production networks and give us precise terms for the local structural features that may be key to understanding their routine function, failure, and growth.

This article discusses the experience and the ideas of National Statistical Institutes from four countries – Portugal, Sweden, Canada, and the Netherlands – in order to build a fully automated data collection system, to provide a system-to-system (S2S) data exchange or Electronic Data Interchange (EDI) between all stakeholders in the production chain. This joint work is a summary of an invited session at the Fifth International Conference on Establishment Surveys, which was devoted to ‘the future of business data collection’. Taken together, the four presentations provide an overview of recent experiences with S2S/EDI data collection for financial business data. The basis for such a system is an integrated unbroken digital information chain that runs from the recording of financial data in computerised administrative systems of individual businesses all the way to publishing economic statistics – the Business Information Chain. This chain can be ‘closed’ and made into a cycle by including a feedback loop, for example by providing benchmark data to businesses. However, to make it happen, technical standardisation, vertical and horizontal conceptual harmonisation between all partners in the chain, and positive business cases for all partners are needed. The article starts by putting EDI developments in historical perspective.

This paper discusses how Statistics Netherlands managed to respond quickly with a range of new outputs to the sudden increase in the need for statistical information following the outbreak of the COVID-19 pandemic. It describes the innovation process already in place, as well as the innovations in response to the pandemic. This is followed by a discussion of what made speedy innovation and implementation possible, after which lessons are drawn in order to maintain the ability to react quickly to future policy questions. One important success factor is the combination of new data sources with already existing statistics for calibration. The developments at Statistics Netherlands can be seen as a case study. Several other NSIs also accelerated innovation after the outbreak of the pandemic, such as the Australian Bureau of Statistics and the British Office for National Statistics.