New Seedstars Index: Efficient Tool for Data-Driven Decision Making


New Seedstars Index: Efficient Tool for Data-Driven Decision Making

Konstantin Hapkemeyer

APRIL 22, 2020

The Seedstars Index (SSI) has been fully reworked for the 2020 edition with the aim of achieving a higher quality predictive indicator for the success of an entrepreneurial ecosystem. Why SSI is a powerful tool for data-driven decision making:


  • By better understanding the strengths and weaknesses of entrepreneurial ecosystems, we can tailor our education programs to our clients’ needs.
  • Being a high-growth company with a VC arm, Seedstars makes investment decisions on a daily basis. When evaluating a startup investment, an expansion into a new country, setting up a new Seedspace (co-working) or building a new venture in house, geography is one of the key factors to consider. Therefore, we use the Seedstars Index as an indicator for which emerging markets to invest in.
  • We measure our success in terms of how many high-growth companies we build and invest in. Countries should measure the success of their entrepreneurial ecosystem in the same way. This is why we want the index to explain and predict the number of high-growth companies per ecosystem. Indeed, we plotted this year’s SSI against the number of high-growth companies per ecosystem. The correlation between the SSI and the number of high-growth companies is 0.63 (R-squared = 0.879). This means that you can increase your number of high-growth companies by improving your SSI score. And we know how to improve your score.

Before diving into how the SSI can help to increase the number of high-growth companies in a country, we would like to define what a high-growth company is.

What is a high-growth company?

We based our definition of high-growth companies on a report by Omidyar Network (2018) on enterprise segmentation. According to the report, these companies have “highly innovative business models serving large addressable markets with a rapid growth trajectory”. Moreover, these companies should have more than “$2m in financing needs within a few years and be managed by entrepreneurs who seek to be recognized for achieving disruption at scale.” In other words, we’re talking about typical venture-backed companies. Utilizing Crunchbases’ database, we use companies that raised venture capital in the past 3 years as a proxy for the number of high-growth companies per ecosystem.

If you want to learn why you should use the number of high-growth companies in your ecosystem as a measure of success, please watch this video:

Methodology: A Closer Look Into the Construction

In the past editions of the SSI, the following core pillars have been used to construct the index: Culture, Environment and Opportunity. Following extensive research over the year, we decided to base the SSI 2020 on the more detailed and well established “Domains of an Entrepreneurship Ecosystem” model by Daniel Isenberg:


New Index Structure: 6 domains, 10 subdomains, 21 factors, 35 subfactors

As Isenberg suggests, we constructed the index on the 6 core entrepreneurship domains: Policy, Finance, Culture, Supports, Human Capital, Markets. In addition to that, we implemented 10 out of 12 subdomains suggested by the Isenberg framework: Leadership, Government, Financial Capital, Societal Norms, Non-Governmental Institutions, Support Professions, Infrastructure, Educational Institutions, Labor, Networks, Early Customers. The subdomains Educational Institutions and Success Stories were not included due to a lack of data. Nevertheless, one of the key input factors of the Educational Institutions subdomain, Specific Entrepreneurship Training, was included in the subdomain Labor. While the number of success stories was not included as an input variable, the SSI 2020 was plotted against the number of high-growth companies per ecosystem, which can be seen as a proxy for success per ecosystem.

Sources of Data

While the Isenberg framework was used for the structure, our internal SSI product team sourced data during the year, which was used for the various input factors in the model. The main data sources are WEF, IMF, World Bank and topic-specific databases like Crunchbase & Pitchbook. A growing part of the data is also from our proprietary research including surveys and web-scraping led by our data science team.

Index Weight and Scoring

As can be seen in image 1, a consideration was assigned to every subfactor: low, medium or high. The criterion for the weight assignment is the correlation between the subfactor and the output factor “number of high-growth companies”. A correlation smaller than 0.33 resulted in a low consideration, between 0.33 and 0.66 in a medium consideration, and everything above in high one. The considerations were consequently transformed into weights based on the Fibonacci scale 2 (low), 3 (medium) and 5 (high).

Cities Covered

Cities Covered

While our team travelled to 80+ ecosystems this year, the index includes 58. This is due to the fact that some of those ecosystems are not featured in the WEF’s publications and there were no proxy data points available. We aim to include all ecosystems in our index next year.

Global Results

This was the outcome of the analysis. When looking at the top right corner of Figure 4, we can observe that Singapore (688 high-growth companies) has managed to come up on top of the global ranking - for the fourth time in a row. It is closely followed by Shanghai (661 high-growth companies). Among the top ten, one finds four Asian representatives and three from LATAM, while CEE, MENA and Africa have only one representative. See


SSI vs. Number of high-growth companies (Normal distribution)

In order to have an easier reading of the results, we’ve classified the World Bank three categories (Nascent, Advancing and Mature), see Figure 5. The Ecosystem Area is slightly different from the Isenberg Model, but the rationale remains consistent with our approach.


Categories of Ecosystems

We’ve used three thresholds of the Seedstars Index, as shown in Figure 6 and 7.

Seedstars Index

Results by stage

ssi index

Regional Results SSI 2020

In the following sections, we will look more closely into the single subdomain scores of each region. Each ecosystem has one score between 0 (worst) and 100 (best) per subdomain.



SSI Africa

As in previous years, Nairobi, Cape Town and Johannesburg show a clear advantage compared to their fellow African ecosystems. This year, Nairobi led the group with 97 high-growth companies. Luanda, on the other hand, had only 2. One of the key African ecosystems Lagos was not included in this year's index due to lack of data. On average, the African ecosystem had an SSI score of 34.42, 22.5 high-growth companies and a median of 7 high-growth companies per ecosystem. On paper, Africa was the weakest ecosystem. However, this also means it is the ecosystem where most value can be unlocked.

When looking at the single subdomains in Africa, we can observe that Africas’ best performance is in “Government” with a score of 54.59. The subdomain government is again influenced by the factors “venture friendliness”, “quality of research institutions” and “regulatory framework incentives”. While there is still a lot of work to be done, it seems like African governments are putting the right policies in place to attract and support startups. However, it seems that the region still has a lot of room for improvement in the field of “support professions”, “networks” and “non-governmental institutions”. Factors that should, therefore, be more strongly considered by policy-makers are increasing the number of technical experts, building startup networks, and attracting NGOs with a focus on entrepreneurship to their countries.



SSI Asia

Historically, Asia has been the most developed ecosystem among the 5 regions we cover. Although there are big differences within the region, strongholds like Singapore and Shanghai have been reliably producing high-growth companies for years. Asia again had the strongest SSI this year with an average score of 49.01. In terms of high-growth companies, the average was 157.18, which is a strongly skewed number by the two outliers. The median number of high-growth companies per ecosystem is 48. The top three ecosystems are Singapore (688), Shanghai (661) and Jakarta (131).

When looking at the 10 subdomains of the index for Asia, it is important to highlight the fact that Asia is one of the most diverse regions in terms of development. While we have highly developed ecosystems like Singapore, at the same time there are nascent ecosystems like Nepal or Cambodia. However, from a regional perspective, Asia is especially strong in 4 areas: “Government”, “Financial Capital”, “Infrastructure” and “Labour”. It seems that on average governments create a venture friendly environment and that public, as well as private capital, is available. Moreover, the infrastructure seems to be well developed in terms of access to energy, logistics and availability of co-working space. Finally, the digital skills of the population are higher than average.

In terms of points of improvement, Asia should focus on the same factors as Africa: increasing the number of technical experts, building startup networks, and attracting NGOs with a focus on entrepreneurship to their countries.




In MENA, this year's average SSI was 41.63. The average number of high-growth companies was 24.5, while the median was 14.5. The top performer in MENA is the Egyptian metropolis Cairo with 89 high-growth companies. Algiers, on the other extreme, has only 2.

In terms of subdomains, MENA performed highest on the early customers’ side. Based on our analysis it seems that MENA also has a high number of early adopters for proof of concept.

On the other hand, in comparison to the other regions, they have a low score on the support professions side. This could be changed by increasing the number of technical experts through, for example, incentive programs, a greater number of higher education facilities with a focus on tech, or incentivising foreigners with a tech background to relocate.




Latin America had one slight abnormality as it had a comparably low SSI and a higher number of high-growth companies. On average, the ecosystems in LATAM had an SSI score of 40.08, and 33.93 high-growth companies with a median of 16. After conducting further analysis we found that this slight abnormality stems from the handful of strong performers and outliers like Mexico City, Santiago and Buenos Aires. The top performer this year was Mexico City with 113 high-growth companies, while Caracas had only 2.

Looking at the subdomains, we can see that LATAM is the strongest in “Government”, “Infrastructure” and “Early Customers”. Here we can also see on average a venture-friendly environment, a good infrastructure in terms of access to technology, energy and logistics, and availability of co-working spaces.

Similar to most of the regions we analysed, LATAM has a weakness when it comes to supporting professionals and networks. Therefore, our recommendation is to focus on policies that increase the number of technical experts, on building startup networks, and on attracting NGOs with a focus on entrepreneurship.

SSW Latam




Last but not least, the CEE and CIS region had the second highest SSI score this year with 44.57. The average number of high-growth companies was 31.22 with a median of 33. This is the only region with a higher median compared to the mean. This year's leader was Warsaw with 80 high-growth companies, while Tbilisi had only 6.

In terms of subdomain scores, CEE is strongest in “Government”, “Infrastructure” and “Early Customers”.

On the other hand, similarly to most regions, there is a lot of room for improvement on the subdomains “Support Professions” and “Networks”.


index - all

One of the key findings of the SSI 2020 is the exponential relationship between the SSI and the number of high-growth companies per ecosystem. From the image above, we can see that increasing SSI triggers an exponential increase in high-growth companies. The reason for this is that the SSI isn’t built out of a single isolated factor but rather various factors that together define an ecosystem. Therefore, an increase in the SSI can be read as an improvement of the whole ecosystem. By definition, an ecosystem is a community of interacting organisms with complex interrelationships. Due to the fact that in an ecosystem everything is connected, an improvement of, for example, entrepreneurship education, will directly affect many other factors. People will be encouraged to start new companies, more VCs will be attracted by the rising number of entrepreneurs, acceleration programs can use this new opportunity to build co-working spaces in the ecosystem, this will trigger information exchange between foreign and local coworkers, etc. The final result will be a rising number of high-growth companies that raise venture capital, create jobs, grow their revenue and might come to a successful exit.

In order to improve the reliability of the SSI, we will focus on improving the following elements for the next SSI edition:

  1. Source enough data to include all of the ecosystems we want to cover (80+).
  2. Source more input data for the domains “Human Capital” and “Culture”. Although both domains were included this year, we expect a higher accuracy of the model once we add high-quality data in these two crucial domains.
  3. Further support the conclusions we draw from the SSI with primary and secondary scientific research.
  4. Allocate weights based on a tailor-made model backed by scientific research.
  5. Find an accurate source for the number of high-growth companies. This year we mainly used data from Crunchbase, Pitchbook and some internal research. In the future, we would like to complement the SSI with more primary research and further internal and external sources.

If you want to learn more about how we support governments, Development Finance Institutions, non-profits and corporates to develop startup ecosystems, please check out the following pages:

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