information from millions of U.S. patents and validate this list by analyzing the frequency at which these technologies were mentioned in hundreds of thousands of earnings conference calls. Therefore, this strategy considers both the technology and the potential market components of disruptive technologies. As their exercise is based in the U.S., we consider these technologies a good proxy for the type of technologies that startups and innovative entrepreneurs might decide to incorporate into their products or services.

# **3 Data and methodology**

## **3.1 Data**

The main data source used in this paper is Crunchbase (www.crunchbase.com). Created in 2007, the use of Crunchbase as a primary source of business information and for academic research has gradually increased, with over 55 million users currently. As explained by Dalle et al. (2017), Crunchbase is updated on a daily basis, and the information is obtained from large investor networks, a community of contributors, global investment firms that submit their whole portfolio, as well as entrepreneurs and independent investors that provide their own information. All these data are validated and cross-linked with funders, firm staff, and management profiles. This self-reported information is complemented through Artificial Intelligence and Machine Learning algorithms that web scrape to enrich these profiles.

According to Dalle et al. (2017), comparing Crunchbase with the OECD Entrepreneurship Financing Database, the coverage of Crunchbase appears to be very comprehensive for young firms, especially in the case of the U.S. As this exercise was conducted in 2017, and given the exponential growth of this platform, we can expect that the coverage is even better now. Given the constraints on data availability in Africa, the increasing use of Crunchbase globally, the emergence of other platforms that concentrate information on Africa and the methodology (web scraping and self-reporting) that Crunchbase uses to gather and validate their information, the coverage for Africa appears to be relatively good for these types of exercises.

For this study, we use data from 32,530 digital businesses collected by Crunchbase as of February 2023. While this paper focuses on Africa, we use information from other regions as a benchmark. To define the location, we use information on the firm’s headquarters. We classify 7,785 digital businesses as located in Africa, 12,816 in Latin America (South America and Mexico), and 11,929 in any of the frontier cities (London, Palo Alto, Seattle, and Tokyo). These frontier cities are selected based on the fact that they are locations characterized or known as technological hubs.<sup>4</sup> At the sectoral level, we focus on three sectors: Fintech, E-Commerce, and IT. The selection of these three sectors is directly related to the distribution of recent investments 4We define alternative options for the frontier based on the top Crunchbase ranking and top revenue of the dataset, but we only use them for robustness checks. The main reason for not taking these measures into consideration in the core analysis is that these measures are a combination of technology and funding and, therefore, are already highly correlated with funding. We want a benchmark more related to technology than funding, as this later variable is one of the outcomes or firm characteristics we analyze.

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