Supporting Local Economic Development with Business Transaction Data

Project Information

SUPPED

Grant agreement ID: 101062606

DOI

10.3030/101062606

Project closed

EC signature date 12 July 2022

Start date 1 October 2022

End date 30 September 2024

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 199 440,96

Coordinated by

COMPLEXITY SCIENCE HUB VIENNA CSH - VEREIN ZUR FOERDERUNG WISSENSCHAFTLICHER FORSCHUNG IM BEREICH KOMPLEXER SYSTEME
Austria

Periodic Reporting for period 1 - SUPPED (Supporting Local Economic Development with Business Transaction Data)

Reporting period: 2022-10-01 to 2024-09-30

Summary of the context and overall objectives of the project

The general objective of this project is to develop tools and solutions that turn value-added tax (VAT) records into an information system that can support evidence-based policy making and the development of regions, industries and firms in general. In Hungary, firms have to send a monthly report about their business transactions with other companies and the related VAT to the National Tax and Customs Administration. We use this large-scale dataset on VAT records from Hungary to map the nation-wide business transaction network of firms. Combining further financial and geographic data from company registers to the network allows us to analyse business ties at different scales of industrial and spatial aggregation. This makes Hungary an exceptional testing ground for economic theories and a unique case for developing data-driven local economic development policy recommendations.

Industry clusters, the spatial concentration of industries in regions, are at the center of the project. Clusters foster higher innovation and economic performance of firms and they are key for local economic development. Researchers in economic geography and related fields have been studying the factors behind the success of clusters for decades and they stress the role of three different mechanisms: knowledge spillovers, labor market pooling and input-output linkages.

The project aims to enhance the understanding of industry clusters from both scientific and policy perspectives. By aggregating firm-level business transactions, we create industry-region level connections, enabling us to map business relationships between key industries within and across regions. In addition to illustrating these industry-region linkages, we construct a labor flow network using an employer-employee matched dataset to capture labor market dynamics and knowledge transfer across sectors.

The first objective of the project is to build an online, interactive data visualization platform that represent the inter-regional connections of industries in Hungary. This site could provide up-to-date information to policy-makers about the existing capabilities of regional economies, supporting the development of place-based Smart Specialization Strategies.

Beyond agglomeration economies, the success of industry clusters is argued to be due to the cooperation of local firms, related industries and supporting institutions. Network analytical tools are widely used to map collaborative learning and the flow of information and knowledge between organizations in clusters. The second objective of the project is to provide systematic evidence on the connection patterns of firms in successful industry clusters through VAT records and labor flow networks.

The project strives to combine theories, approaches and tools from a diverse set of scientific fields, such as economics, network science, geography and computer science. Moreover, it proposes to use a uniquely detailed dataset beyond academic research and aims to develop tools that can inform policy makers about economic connections.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The project successfully transformed data from VAT records and employer-employee connections into firm-level input-output and labor flow networks. These firm-to-firm connections were then aggregated into industry-region networks, which are now available as an open-access dataset for future research on local and regional economic networks. Additionally, the dataset has been incorporated into an interactive online visualization platform, allowing users to explore connections between industries (at the 3-digit NACE code level) and regions (NUTS3 and NUTS4 levels) within Hungary. This tool provides valuable insights into key industries and dependencies, supporting the development of local economic development plans.

A key achievement of the project is the publication of a peer-reviewed open-access research article, which investigates the determinants of business transactions. The study emphasizes the roles of geographic proximity, industrial relatedness, and ownership ties in shaping buyer-supplier relationships between companies.
The project's most significant scientific output is an open-access preprint that examines the causal mechanisms driving the (spatial) co-location of suppliers. This research utilizes both aggregate and firm-level input-output and labor flow connections. Key variables from the study, such as normalized labor flows and input-output connections across industries are explorable inside NUTS3 and NUTS4 regions in Hungary on the online visualization platform and included in the above mentioned open-access dataset as well.

In addition, the lead researcher has published another peer-reviewed open-access research article and two open-access preprints, which, while less directly related to the project's primary objectives, contribute to the overall body of knowledge.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The scientific outputs of this project represent one of the first efforts to use nation-wide business transaction networks derived from VAT data to study both the forces behind interfirm connection patterns and the co-location of suppliers. The datasets produced through the project have enabled a causal analysis, demonstrating that despite the global nature of supply chains, co-location still plays a significant role due to the ease of knowledge sharing and access to know-how. These findings, published in potentially high impact papers, advance the state of the art in both network science and economic geography.

The published aggregate datasets not only ensure the reproducibility of the core research but also open up new avenues for future research. By making the connections between industries across multiple geographic scales explorable, this data offers valuable insights into the interdependencies of local and regional economies—an aspect that is often difficult to capture in traditional datasets. This holds significant potential for both the research community and policymakers involved in economic analysis and development strategies.

Crucially, the online visualization platform enhances the accessibility and comprehension of the dataset and key scientific results. This cutting-edge approach to communicating complex research outputs significantly broadens the project's potential impact, particularly for policymakers. By simplifying the exploration of economic and industrial connections, the visualization site strengthens the project's relevance in supporting evidence-based regional and local economic policies.