1. About the Project

Implisense is a leading German B2B sales intelligence and marketing automation provider. Currently, we are working on an open source engine called European Company Explorer Platform (ECEP). With ECEP one will be able to search for arbitrary properties and statistics on company websites in Europe. Our goal is to create a comprehensive European company index, similar to our existing company index for Germany. Our German company index offers a new perspective to investigate the impact of economic shocks and current trends by analyzing 100 million data points for 2.2 million German companies. In this article we want to demonstrate the power of our existing German engine to show what ECEP could be used for by analyzing the consequences of the Brexit for the German economy. ECEP is funded by ODINE, Europe‘s first incubator for open data businesses.


2. The United Kingdom: A Pillar of German Trade

UK’s decision to leave the European Union is a looming threat to the British, German, and European economy. Although, it is hard to foresee the actual consequences of this economic shock, several predictions concerning the long term impacts have already been made. One common prediction is that the access to the British market will probably get more difficult, endangering exports and imports for German companies (see DIW). Furthermore, business and research cooperations are also threatened by the Brexit. This means that in particular highly innovative companies, such as biotech and medical companies, which depend on cooperations with British universities, might also be affected (see The Guardian). We have taken a non-conventional approach and searched the websites of companies in Germany to identify those companies that mention business relationships to the United Kingdom. We checked for a list of geographic terms, including the German and English names of the United Kingdom, Great Britain, the member states of the UK and the capital London. Our engine enables us to identify the relevance of business relationships or the importance of fundamental economic processes (such as the digitalization) faster than most conventional economic research institutes. Furthermore, the engine enables us to limit the sample to very specific industries and derive common properties that individual companies from these industries share with each other. Hence, we can determine the risks of the Brexit for individual subgroups, such as the subgroup of highly innovative companies.


Figure 1: International Business Relationships of German Companies


Figure 2: Number of Companies with Relationships to EU Countries

Our analysis confirms that the UK belongs to the group of the most important business partners (Figure 1). In the end our search engine identified 76511 companies associated with the United Kingdom (Figure 2), which makes it the third most important market within the European Union for German companies, behind Austria and France, but far ahead of Italy and the Netherlands. It is important to mention that the numbers do not reflect the volume of German exports and imports. Thus, the importance of each country is not measured as a fraction of German gross domestic product here. However, our approach provides a specific number of companies potentially endangered by the Brexit. We are able to break the trade effects down on spatial regions and industries. We can confirm the significant importance of the UK market for the German economy, consistent with data from the Statistisches Bundesamt, which also ranks the United Kingdom third by total trade turnover with 127.6 billion Euros in 2015, behind France (169.9 billion Euros) and the Netherlands (167.4 billion Euros) (see Statistisches Bundesamt).


3. The Brexit: Which industries and regions might be affected?


Figure 3: Distribution of German Companies by State

Using our engine we can easily break down the analysis from the country-wide aggregation to a regional level. The big picture one receives shows that most of the West German states are overrepresented in the UK sample whereas all East German states (excluding Berlin) are underrepresented. By “UK sample” we denote the subsample of German companies that have an identified business relationship to the UK. In particular, companies from Berlin and Hesse have strong relations to the UK (Figure 3) and therefore face an increased risk. The red bars depict the distribution of the total population of our entire German company index as a benchmark for comparison.


Figure 4: Distribution of German Companies by Industry

In addition, we compared the distribution of the most important industries within the UK sample (a selection is given in Figure 4). Again, as a benchmark the red bars denote the industry distribution of all German companies in our index. Classification of industries is done using the European NACE standard (see NACE). It is important to know that many companies have up to three NACE codes assigned, because often companies’ industry sectors are not uniquely classifiable. In our UK sample, manufacturing trade companies (C), retailers (G) and IT companies (J) are overrepresented, which in turn means that companies in these industries might be endangered above average by the Brexit. The overrepresentation of Berlin as a region might be caused by the high share of IT companies (J) in the UK sample. The second most important industry by size is the sector “Provision of professional , scientific and technical services” (M). The four most important industries (Figure 4) together represent 68 percent of all companies in the UK sample and about 50 percent of the complete index. Sector M is not only relevant by size, it also includes the most innovative companies, in particular M72 (“Scientific research and development”).  Companies from this sector develop the technologies of tomorrow and thus shape the future of the German economy. Therefore, we focus in the following on this important industry sector to further investigate the impacts of the Brexit.


4. Innovation at Risk: German High-Tech Industries


Figure 5: Topics and Terms

In our subsample of high-tech companies (industry sector M72) related to the United Kingdom, the majority consists of medical and biotech companies. In total we were able to identify over 900 relevant German companies from this industry sector. The Implisense engine detects highly characteristic topics and terms on each single company website. This enables us to determine a common profile that companies share within a given subsample.  Many of these companies are market leaders in their respective field. These companies are characterized by the following properties (Figure 5): they are highly innovative and they specialize in high quality products. Biotech companies with business relations to the UK tend to be expanding and more internationally oriented than the average German company. This can be explained by the fact that larger companies are overrepresented in the UK sample. Out of the tech companies we identified, 50% have a revenue of at least one million Euros (compared to 20% in the complete company index) and 21% have more than 50 employees (compared to 4% in the complete company index).

Many of them are engaged in research projects in cooperation with universities, as the term cloud indicates. With our engine we can detect details in the links between German tech companies and British universities. For one sixth (154 companies) of the high-tech companies we can identify relationships to one of the four leading British universities (see Complete University Guide). One example how young European startups and British universities are connected is the ODINE project itself, in which the University of Southampton is an important partner. In this respect, the Brexit might cut off or at least restrict the degree of cooperations between German high-tech companies and British universities. Considering the excellent reputation of British scientists, this would be a huge loss.


Figure 6: German High-Tech Companies with Business Relationships to the UK

One of our distinctive competencies is the ability to identify the location of the affected biotech and medical research companies. The major hubs are the bigger metropolitan areas, such as Munich, Hamburg, Stuttgart, the Ruhr, the Rhein-Main, and the Rhein-Neckar region (Figure 6). However, the highest density can be found in Berlin, with 161 companies out of the over 900 identified high-tech companies being located here. Our engine allows us not only to receive a detailed statistical profile of biotech companies that are potentially affected by the Brexit, but also allows to identify each individual company with exact name and location. The results show that German high-tech companies are massively engaged in business relationships with the United Kingdom. The looming Brexit could hit Germany’s high-tech industries disproportionately strong, causing a competitive disadvantage against competitors from the United States and Asia.


5. Conclusions

Of course, our analysis covers only a few aspects of the threat the Brexit poses to the German economy. It is not meant to substitute the work of conventional economic research institutes. Instead, we provide data about additional aspects that are usually not covered in macroeconomic databases. A distinguished feature of our engine is that it provides a comprehensive list of potentially affected companies. With our ability to build exact profiles of company topics and terms, conditional on revenue, number of employees, industries or regions, we showed that the Brexit can be regarded as a threat to highly innovative industries, especially in Berlin.

If you liked this article, we would be happy to hear from you and we encourage you to share our report. You can easily do so by using the buttons below. In the future we will continue to write similar reports to demonstrate the capabilities of our ever improving engine. One topic that we will likely cover soon is the current state and potential of digitalization of German companies.

Written by Alexander Pankratov