HHL Research Brief

Research Brief: The Relevance of Cluster Economics for the FinTech Industry

by Gabriel Socha | July 23, 2021
While the FinTech industry overall has experienced strong growth in recent years, the geographical presence of FinTechs is not homogeneously distributed. Instead, it can be observed that certain FinTech hotspots are emerging. This paper explores the underlying factors that contribute to the formation of FinTech clusters.

The Relevance of Cluster Economics for the FinTech Industry – Theory and Evidence

By Gabriel Socha I Sparkassen-Finanzgruppe Chair of Macroeconomics

Executive Summary: While the FinTech industry overall has experienced strong growth in recent years, the geographical presence of FinTechs is not homogeneously distributed. Instead, it can be observed that certain FinTech hotspots are emerging. This paper explores the underlying factors that contribute to the formation of FinTech clusters. The results of the study show that besides the availability of positive externalities, such as specialized talent pools, universities, and accelerators, demand factors such as total market size, internet connectivity, and financial literacy are relevant. Furthermore, the results suggest that the presence of competitors and similar firms, as well as the strength of intellectual property protection laws, are related to the size of FinTech clusters.

Introduction

The success of FinTech is multifaceted: innovative products can satisfy previously unmet customer needs, more modern, digital customer interfaces enable a better customer experience, and relatively low regulation, compared to incumbents, gives FinTech a cost and efficiency advantage.

FinTechs are also attracting raising attention from a political perspective, as they entail new regulatory risks, but also contribute to the economic development of regions and cities as drivers of innovation. As FinTechs seem to agglomerate in certain locations, like the famous FinTech hotspots in London and New York, practitioners and policymakers increasingly develop interest in the cluster effects from which FinTechs benefit.

Although the relevance of economic clusters in the FinTech industry is of great importance for various stakeholders such as entrepreneurs, investors, and politicians, research on this topic remains rare. Therefore, the thesis aims to give a current overview of the scientific literature on FinTech and the relevance of cluster economics as well as to provide evidence for this phenomenon. The research question is: “What are the underlying factors that
contribute to the formation of FinTech clusters?”.

Methodology

In order to fulfill the objective, three main steps were performed:

First, a literature review on the intersection between FinTech and cluster economics was conducted in order to provide a theoretical basis for the subsequent analyses.

Second, a qualitative analysis on the underlying factors that contribute to FinTech clustering was conducted by utilizing Porter’s Diamond Model. According to Porter’s Diamond model clusters gain economic competitiveness due to factors that can be distributed into four categories: (1) Factor conditions, (2) demand conditions, (3) related and supporting industries, and (4) strategy, structure, and rivalry. For each of the categories, multiple hypotheses were established.

Third, a statistical analysis of a data set of the European Union was conducted to test the established hypotheses and provide evidence for the depicted underlying factors. The data sample was independently compiled from several sources. For each hypothesis, a variable was identified, for which appropriate data were collected and evaluated using statistical methods.

Main Findings

The most important findings for each category of the Diamond Model are summarized in the following.

Starting with the factor conditions, three variables seem to be particularly crucial for the formation of FinTech clusters: a large existing labour pool in the financial services sector, a large network of nearby universities and the availability of accelerators. These factors illustrate that FinTechs benefit greatly from talent pooling, but also strive to network and participate in initiatives to realize growth opportunities more efficiently by leveraging existing knowledge bases.

Next, demand conditions also contribute significantly to the emergence of FinTech clusters. The results of the analysis suggest that the domestic market size, as well as the level of internet access positively, relates with FinTech formation, while financial literacy has a negative association with the establishment of FinTech clusters. It seems that a healthy, developed, and granular economic environment is important for FinTech agglomeration. Moreover, in countries with low financial literacy, FinTech cluster formation is more likely to occur, possibly due to lower market entry barriers.

Furthermore, in the category “supportive and related industries”, two positive relationships with FinTech cluster formation have been identified: number of local banks and IT firms. This illustrates that FinTechs benefit from collaboration possibilities, knowledge spillovers, and competitive pressure between these players. The analysis also revealed a negative correlation between FinTech agglomeration and insurances, which might be explained by the current low level of collaboration effort between insurances and FinTechs.

In the dimension “strategy, structure, and rivalry”, the analysis shows that intellectual property protection is of significant importance for FinTech cluster formation as it ensures a fair market environment by allowing open communication and encouraging innovation.

Conclusion

Based on the identified factors, political decision-makers can strengthen the agglomeration of FinTechs and the emergence of FinTech clusters with targeted measures. In addition, FinTech executives can use the identified factors to plan location decisions more effectively and in line with the company’s business model.

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