metadata: authors: 0: author: Vargas Da Cruz,Marcio Jose
1: author: Pereira Lopez,Mariana De La Paz
2: author: Salgado Chavez,Edgar
count: Africa
docna: 0: docna: Disruptive Technologies and Finance : An
            Analysis of Digital Startups in Africa
docty: Policy Research Working Paper
abstracts: cdata!: This paper investigates the relationship
            between disruptive technologies and access to finance for
            digital tech firms in Africa. Through textual analysis of
            data from Crunchbase and Pitchbook, the study explores how
            firms across different age cohorts incorporate disruptive
            technologies into their offerings in e-commerce, fintech,
            and information technology services. The findings reveal
            three key insights for African digital tech startups. First,
            African startups are less likely to incorporate disruptive
            technologies into their offerings compared to other regions,
            except for mobile payments. Second, incorporating these
            technologies is associated with more funding, but this link
            is weaker in Africa than in other regions. These results
            hold when excluding mobile payments and addressing potential
            endogeneity using instrumental variables. Third, firms that
            do incorporate disruptive technologies tend to secure
            funding earlier, with lower initial amounts, but are more
            likely to succeed in terms of exit or valuation growth than
            their peers.
display_title: Disruptive Technologies and Finance
            : An Analysis of Digital Startups in Africa
listing_relative_url: /research/2023/12/34210179/disruptive-technologies-finance-analysis-digital-startups-africa
topicv3: Private Sector Development m1326060 1921,Early Stage Financing m1326731 1268,Innovation, Technology and
            Entrepreneurship m1327691 376,Digitization m1505678 3078,Venture Capital Fund m1627093 4755
publishtoextweb_dt: 2023-12-07T00:00:00Z
dois: 10.1596/1813-9450-10633
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 45
author_keys: 0: author_key: 1161834
1: author_key: 1349146
2: author_key: 1238548
model_extractions: Policy Research Working Paper 10633 Disruptive Technologies and Finance An Analysis of Digital Startups in Africa _Marcio Cruz Mariana Pereira-Lopez Edgar Salgado_ International Finance Corporation December 2023 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10633

# **Abstract**

This paper investigates the relationship between disruptive technologies and access to finance for digital tech firms in Africa. Through textual analysis of data from Crunchbase and Pitchbook, the study explores how firms across different age cohorts incorporate disruptive technologies into their offerings in e-commerce, fintech, and information technology services. The findings reveal three key insights for African digital tech startups. First, African startups are less likely to incorporate disruptive technologies into their offerings compared to other regions, except for mobile payments. Second, incorporating these technologies is associated with more funding, but this link is weaker in Africa than in other regions. These results hold when excluding mobile payments and addressing potential endogeneity using instrumental variables. Third, firms that do incorporate disruptive technologies tend to secure funding earlier, with lower initial amounts, but are more likely to succeed in terms of exit or valuation growth than their peers.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at marciocruz@ifc.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Disruptive Technologies and Finance: An Analysis of Digital Startups in Africa<sup>*</sup>

Marcio Cruz<sup>†</sup> , Mariana Pereira-Lopez<sup>‡</sup> , Edgar Salgado<sup>§</sup> Originally published in the Policy Research Working Paper Series on _December 2023._ This version is updated on _April 2024._ To obtain the originally published version, please email prwp@worldbank.org.

JEL classifications: D22, O31, O33 Keywords: digital technologies, disruption, financing, startups, entrepreneurship

> *We thank Susan Lund, Paolo Mauro, Denis Medvedev, Samuel Edet, Santiago Reyes, Florian M ̈olders, Georges Houngbonon, Zineb Benkirane, Matthew Saal, Ami Dalal, Verena Wiedemann, Davide Strusani, Stephan Dreyhaupt, and participants of the workshop at IFC for their valuable comments. Lucien Ahoubange provided extraordinary research support. This paper was prepared as a background study for the IFC’s ”Digital Opportunities in African Businesses” project. This paper is a product of the IFC Economic Research Department, in partnership with the Government of Japan.

> †International Finance Corporation, email: marciocruz@ifc.org (Corresponding author)

> ‡International Finance Corporation, email: mpereiralopez@ifc.org

> §International Finance Corporation, email: esalgadochavez@ifc.org
# **1 Introduction**

Digital technologies have been seen as a potential driver of leapfrogging in Africa. The African digital start-up ecosystem, though still nascent, is among the fastest-growing in the world. It is the only region where the median deal size did not decline during 2022 (CB Insights, 2023). Although framed as a footloose sector, digital business providers are widely perceived as an economic activity with the potential and opportunity to bring growth to deprived or lagged regions and enabling technological catching up (Friederici et al., 2017, 2020). The remarkable speed of mobile payment adoption by individuals and firms in Africa are practical examples of the potential implications of disruptive technologies. This process has been driven by leading digital tech platforms and firms, such as M-pesa and Wave, by incorporating innovative and disruptive technological solutions into their offerings, allowing them to disrupt markets through better and cheaper solutions for customers. However, apart from a few well-known successful cases, there is little evidence available on how digital startups in Africa have been incorporating disruptive technologies into their offerings, a key condition to play an enabling role for digital disruptiveness in the continent, or on the role of finance in this process.

This paper analyzes the extent to which digital tech firms in Africa incorporate disruptive technologies and how this relates to access to finance along the firm life cycle. First, it compares the level of disruptive technologies incorporated by digital businesses in Africa with the Latin American market,<sup>1</sup> and a global frontier from which a substantial proportion of digital technologies have emerged, constructed using business data for cities such as Seattle, Palo Alto, Tokyo, and London. Second, the paper documents the variation in the incorporation of disruptive technologies into their offerings along the life cycle of digital tech firms in Africa. Following the literature on firm dynamics and entrepreneurship, which suggests that young firms (startups) are more innovative (Decker et al., 2014), the paper compares the differences in the incorporation of disruptive technologies along the life cycle of tech startups. Third, the paper evaluates to what extent incorporating these disruptive technologies into the firms’ products and services is associated with the amount of funding received. It is expected that these high-potential firms incorporating disruptive technologies should exhibit a higher probability of attracting funding, but there could be different market failures affecting this process.

To address these questions, we use the technologies identified as disruptive by Bloom et al. (2021), based on patents for the U.S., and the frequency with which these technologies are mentioned in earning calls. Using textual analysis, we identify those firms in Crunchbase and Pitchbook<sup>2</sup> that include of the associated with these tech- any bigrams nologies, either in their short or in their long description. We focus on three main digital sectors: Fintech, E-commerce, and Information Technology (IT),<sup>3</sup> which were selected

> 1Latin America represents a good comparison group because it comprises emerging markets and, similar to Africa, it has significantly increased its tech adoption and entrepreneurship in recent years, and these dynamics are relatively inward-related, in sharp contrast with other emerging regions like Asia, where the dynamics tend to be trade and outward-related.

> 2Crunchbase (2023) data obtained from www.crunchbase.com and PitchBook (2023) data from https://pitchbook.com/.

> 3It refers to the provision of digital services, such as software and data.

2
given their relevance in the high-growth pattern of startups in Africa.

Comparing the disruptive component among African digital tech firms with the frontier (advanced) markets, defined as cities where important technological hubs are located (Palo Alto, Seattle, London, and Tokyo), we find that firms in Africa are 25% less likely to incorporate disruptive technologies than the average digital tech firm on the frontier. However, the recent wave of adoption of mobile payments in the region underestimates this gap. Excluding mobile payments and Wi-Fi technologies, African firms become 36% less likely to incorporate disruptive technologies than the frontier.

Along the life cycle, we find that African startups are more likely to incorporate disruptive technologies than older digital tech firms. This pattern is similarly observed among their counterparts in Latin America and the frontier. However, not only the whole distribution in Africa is below the frontier markets, which is expected due to different levels of development, but the African curve of disruptiveness along firm age is slightly flatter, indicating that the difference between startups and older firms is smaller than their counterparts in LAC and the frontier. This is particularly relevant for the Fintech sector, where Kenya and Nigeria, which are the leading countries in the region, show more disruption coming from older firms. Moreover, young startups in Africa are less likely to incorporate disruptive technologies than their counterparts in Latin America. This pattern becomes clearer when excluding mobile payments and Wi-Fi technologies.

Our results show that in frontier cities, startups that incorporate any disruptive technologies receive 263% more funding than non-disruptive ones. This funding premium shrinks to 100% in LAC and to 40% in Africa. Excluding mobile payments and Wi-Fi technologies from the disruptive group, the results suggest a similar pattern. Additional evidence suggests that being disruptive is associated with earlier financing but with lower tickets and higher growth in valuation and future funding deal size.

We used an instrumental variable (IV) to test if access to funding is driven by the incorporation of disruptive technologies into African firms’ offerings. This strategy aims to address potential concerns of reverse causality (firms incorporate further disruptive technologies due to more access to finance) and endogeneity driven by omitted variables biases (e.g., unobserved factors may drive both access to finance and disruptive technologies). For this purpose, we instrument the incorporation of disruptive technology into the services provided with the share of firms in a similar sector incorporating these technologies in the frontier markets and the relative supply of skilled human capital in the local labor market, controlling for country fixed effects. Our IV results suggest that firms incorporating disruptive technologies into their offerings are significantly more likely to be funded and receive more funding. The results are robust to incorporating all technologies into the estimations.

Our analysis contributes to a wider literature studying the characteristics of digital Most studies have focused on the of en- entrepreneurship. previous explaining growth trepreneurship in Africa without further characterizing the technological content or the disruptiveness potential of these ventures (Friederici et al., 2020). In this sense, the main contribution of this study is the measurement and systematic analysis of the extent to which these disruptive technologies are incorporated into African entrepreneurship compared to other markets. The second contribution is that we leverage and analyze a large amount of commercial data for Africa, a region that has persistently lacked high-quality 3
datasets, to go beyond previous small-sample qualitative studies and understand the allocation of funding to digital entrepreneurship along the life cycle of firms.

The paper is structured as follows. Section 2 explains the definition and characteristics of disruptive technologies that are used throughout the paper. Section 3 explains the data sources and the methodologies used to identify disruptive technologies and the empirical Section 4 the main results of the the life of strategy. analyzes paper, including cycle disruptive technologies and the differences in securing funding, and section 5 concludes.

# **2 What makes firms disruptive, and where is the frontier?**

Disruptive technologies are those with the potential to create new markets and eventually overtake leading companies in mainstream markets (Bower and Christensen, 1995). The technological architecture embedded in these technologies starts to become a part of established technologies, and thus disruptive technologies show steep increases in performance. As explained by Bower and Christensen (1995) and Christensen (1997), even large established companies that are leaders in the market can be substantially affected by failing to implement these technologies, reducing their market share or, in some cases, even exiting the market. An example of disruptive technologies is artificial intelligence, which was started by creating a new and very specialized market. Furthermore, it has gradually been incorporated into other digital solutions offered to consumers and businesses.

A firm becomes disruptive with innovation or the adoption of new technologies that have the potential to alter the way markets behave. The disruption produced by these new technologies can propagate via input or output markets. For instance, firms with digital financial technologies, usually known as Fintech, use digital technologies to improve their interaction with clients through the use of mobile apps, ultimately expanding coverage because the interaction simplifies. This reduces the cost of market outreach, and the firm eventually expands in the output market. At the same time, the massive collection of data the mobile allows the to the characteristics of through app firm analyze its clients to provide services that are even more tailored to its customers, furthering the impact on the output market. But to analyze these data, the firm needs highly qualified data scientists, which ultimately influences the labor (input) market.

Not all cutting-edge technologies are disruptive technologies. Some products or services with high technological content are too specialized to change markets. On the other hand, there are products for which the technology is not necessarily new, but the potential for creating markets and overtaking existing technologies makes them disruptive. Take, for example, the case of the personal computer and Apple; as explained by Christensen (1997), the technology was not necessarily new but was disruptive because it could bring the existing computer technology to millions of households and businesses. Something similar was observed with the Ford Model T in the U.S., where the car technology was not new, but it was produced in a way that allowed the car to transition from a luxury product to a mass-market good (Eli et al., 2023).

Given the specific characteristics of disruptive technologies, it has been challenging to establish a rigorous classification of disruptive technologies. Bloom et al. (2021) use an innovative and systematic approach. They identify 29 disruptive technologies based on 4
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.

5
in startups in Africa and their importance in startups’ growth in Africa. Fintech is the fastest growing startup industry in Africa. According to Disrupt Africa (2022), Fintech is the power behind the region’s startup revolution. Only in 2022, it raised 43% of the total funding. The second sector in terms of funding was E-commerce and retail tech, with 17% of the region’s total. When we analyze the total number of companies in the African Entrepreneurial Ecosystem, almost 20% of the companies are in Fintech, while 13% are E-commerce firms (Briter Bridges, 2022). The rationale for selecting the IT sector has to do with its characteristics, as it includes technologies like software, IT infrastructure and services, and data storage and management, which are associated with core activities of the digital sector.

For the purpose of this paper, we define startups as digital businesses belonging to these sectors (Fintech, E-Commerce, or IT), younger than ten years of age. The age definition is arbitrary, but follows the convention observed in many regulatory acts aiming to promote innovative and high-growth startups. For example, Tunisia’s Start-Up Act, approved by the parliament in 2018, defines up to eight years as the threshold. This law has been used as a reference by many other African countries (e.g., Senegal). This threshold is used as a reference when comparing the level of disruptiveness between digital startups and older firms.<sup>5</sup> One limitation of the Crunchbase data is that it has only a cross-section. Although it includes very detailed information about the companies, such as the year when the firm was founded, similar firms, the amounts and different rounds of funding, and characteristics of the founders and investors, among others; it is a cross-section and takes the most recent values or attributes of the firm. This does not allow us to analyze the evolution of these firms over time. Therefore, any reference to the life cycle in this paper refers to the comparison of firms across different age groups.

We complement the analysis with data from Pitchbook (https://pitchbook.com/), another VC/PE private repository that provides detailed characteristics and financial and financing history information on more than 3.6 million companies worldwide. Pitchbook focuses on the entire of investments and collects information on life-cycle mostly investment-ready firms.

Figure 1 shows the spatial distribution of the African digital firms analyzed in this paper. As shown, South Africa, Nigeria, and the Arab Republic of Egypt stand out as digital hubs, where most of the firms in our sample are located. Appendix Figure B1 shows the spatial concentration of African digital firms in the main city of each country. In most countries, a large share of the firms is located in the capital city. From the socalled **_Big Four_** countries (Egypt, Nigeria, Kenya, and South Africa), where most of the digital firms operate, it is only in South Africa where the main city concentrates less than 50% of all digital firms operating in the market.

> 5We also test the robustness of our results with a threshold of five years of age for startups.

6
<!-- Start of picture text --> Casablanca, Morocco AlQabirah, Egypt<br>Al Jizah, Egypt<br>Abuja, Nigeria<br>Ikeja, Nigetig { )<br>Addis Ababa, Ethiopia<br>we<br>Kampala, Uganda<br>Lagos; Nigeria<br>Nairobi, Kenya<br>Firm Count < 100<br>@ Firm Count >= 100<br>Firm Count<br>Centurion, South Africa<br>Johannesburg, South Aftica K_LPretotia Pouth Africa<br>750<br>Gauteng, South Africa<br>Durban, South Africa<br>Cape Town, South Africa<br><!-- End of picture text -->
vices of the firm. In practice, we run the search within the short and long descriptions, as well as the industry description and keywords listed by both Crunchbase and Pitchbook. An example of how these exercises work is included in Figure B3 of the Appendix. Importantly, when we capture a disruptive technology into the offering description of the firm, we are not only identifying the adoption of these technologies, but that they are also part of their core services or products, which enable their customers to benefit from these technologies and, therefore, facilitate diffusion across the economy.

Then, we count the number of disruptive technologies a firm has incorporated into its offering. If a firm has many bigrams included in the classification by Bloom et al. (2021), but all of them are associated with the same disruptive technology, the firm obtains a value of one. A firm for which none of these bigrams can be found in its description receives a value of zero in this score.

Figure 2 ranks the most frequent technologies among the three regions we study. Not all technologies are found in the textual analysis. “Lithium Battery”, “Software Defined Radio,” and “Wireless Charging” are non-existent in our sample, while “Drug Conjugates” is part of the technologies found in frontier countries but not in Africa nor LAC. This to the sectoral of our as we are on mostly responds scope analysis only focusing E-commerce, Fintech, and IT. “Cloud Computing” is the most prevalent disruptive technology in the sample, with a higher incidence among Latin American countries. Within frontier countries, the second most frequent is “Machine Learning/AI” but less frequent in Africa and LAC. “Mobile Payments” is the second most frequent technology in Africa, well above the prevalence observed in the frontier countries and in LAC. This technology is associated with the recent Fintech boom in the region. Therefore, to test the robustness of our results, we perform some analyses excluding mobile payments-related technologies as well as Wi-Fi, given that due to the wide adoption of these two technologies among digital companies, they might be taken by the companies as given and, therefore, might not be included in their descriptions.

Figure B2 in the appendix shows the average probability of incorporating a disruptive technology across regions. Panel (a) shows the average probability that a firm uses disruptive technologies (extensive margin) without controlling for any firm-level characteristics, while panel (b) shows the average number of disruptive technologies conditional on reporting at least one (intensive margin). The green bars report both margins after considering any of the 29 technologies discussed above, while the orange bars use only 27 technologies after excluding “Mobile Payments” and “Wi-Fi” from the analysis to account for the Fintech boom in Africa. As shown in the Figure, there are substantial differences against the frontier, but mostly against those firms at the Top of the Crunchbase distribution on the extensive margin. Once firms have implemented at least one technology, they tend to be slightly above one in the number of disruptive technologies with no substantial difference. However, Africa and LAC are slightly below the frontier.

8
<!-- Start of picture text --> Cloud Computing<br>Machine Learning/Al<br>Social Networking<br>Mobile Payment<br>Smart Devices<br>Search Engine<br>Online Streaming<br>Virtual Reality<br>Computer Vision<br>Wifi<br>Electronic Gaming<br>Autonomous Cars<br>3D Printing<br>Hybrid Vehicle/Electric Car<br>GPS<br>Rfid<br>Touch Screen<br>Solar Power<br>Oled Display<br>Drug Conjugates<br>Wireless Charging<br>Software Defined Radio<br>Lithium Battery<br>0 1 2 me) A<br>EE Frontier EE Africa EE LAC<br><!-- End of picture text -->
_Tkri_ = _α_ + _γkr_ ln _agekri_ + **_βXi_** + _εkri_ (2) where _Tkri_ is the number of disruptive technologies of firm _i_ in region _r_ and sector _k_ . _γkr_ is the coefficient associated with the natural log of firm’s age in years. We allow _Tkri_ to be zero for non-disruptive firms, and _Xi_ contains the same set of controls described above (except for age).

# **3.2.3 Funding and disruptive technologies**

Finally, to investigate whether there are differences in the allocation of funding, we estimate the following regression: where _Fi_ is the outcome variable of funding, which could be either a dummy variable indicating that the firm received funding, or the IHS transformation of the amount of funding in U.S. Dollars received (in total or during the last funding round), as reported in February 2023. _Xi_ is the same set of controls outlined above plus a variable that includes the number of similar companies to also control for competition, and _γR_ are region effects (Africa and Latin America).<sup>10</sup>

# **_An Instrumental Variable Approach_**

A crucial feature of the estimates regarding funding and disruptive technologies is that the direction of causality is not clear. It could be the case that firms incorporating disruptive technologies receive more funding or, on the contrary, that having received more funding allows them to incorporate these technologies into their offering.

To identify the effect of disruptive technologies on access to funding, in this section, we propose an Instrumental Variables (IV) approach. The proposed instrument combines country and firm-specific characteristics to create a vector of exposure to the adoption of disruptive technologies. The main assumption behind the instrument is that firms from sectors that are more intensive in disruptive technologies (defined exogenously by what is observed in the frontier) and that are located in countries with better skills should incorporate more disruptive technologies into their offering. The interaction of these two variables should only affect firm-level financing through the capability of the firm to incorporate disruptive technologies.

The first part of the instrument uses information from countries in the frontier to calculate the share of disruptive firms by industry vertical and impose this on African firms based on their own industry verticals. This strategy based on sectoral intensity follows the idea behind Rajan and Zingales (1998) and has been used recently in the Information Technology (IT) use literature (Bloom et al., 2012; Fernandes et al., 2019; Iacovone et al., 2023). Using only information from the frontier is plausibly exogenous since this excludes country-specific barriers that would prevent the adoption of disruptive technologies in industry verticals that otherwise would be technology-demanding. Crunchbase classifies

> 10In all our estimations, standard errors are clustered at the level of the country.

10
approximately 600 industry verticals, and for each, we calculate the share of disruptive firms listed in these verticals. Then, we match these industry vertical shares to all African firms in the sample. Firms could be linked to one or many verticals. The strategy exploits the fact that most firms are associated with more than one vertical and thus the final exposure of a firm is the combination of all the shares depending on the number of verticals the firm has. The idea is that the exposure to the adoption of disruptive technologies will depend on the industry vertical mix of every firm.

The second component of the instrument, the country characteristic that predicts the adoption of technologies, is the degree of skill mismatch in the country. Bloom et al. (2021) show that the emergence of disruptive technologies is more likely in geographies with a high concentration of skilled labor. Porzio (2023) finds that countries where skill mismatch in the labor force is high, settle in a low equilibrium of low technology adoption. We use skill mismatch data produced by ILO prior to 2010.<sup>11</sup> Mismatch occurs when the level of education of the person in employment does not correspond to the level of education required to perform their job. ILO uses a normative approach to identify mismatched workers. It is based on the educational requirements set out in the International Standard Classification of Occupations (ISCO) for each one-digit ISCO occupational group, and on the level of education of each person in employment. Three categories are possible; the first, are matched individuals whose education level corresponds to the education requirements described in the occupation; the second category is over-educated workers, while the third corresponds to workers with an education below the one required by the occupation. These three groups are computed for employees and self-employed workers. Our instrument uses the share of matched or over-educated workers among employed individuals. The idea is that countries where workers are adequately or over-skilled are more likely to have the conditions for disruptive technology adoption. Being as this variable is defined at the country level, it should be exogenous to the firm.

The instrument, ultimately, is the interaction of these two components: where _Mc_ is the mismatch share in country _c_ , _θk_ is the share of disruptive firms from the frontier in industry vertical _k_ , _Ki_ is the number of industry verticals in firm _i_ .

We calculate this IV for both the full set of disruptive technologies and the set that excludes mobile payments and Wi-Fi. We focus on the sample of firms in the Africa region.

The first stage is defined as: where _Ti_ is the number of disruptive technologies, _IVi_ is the instrument defined in equation 4, and _Xi_ are the same controls included in equation 3.

> 11International Labour Organization (2023), https://ilostat.ilo.org/resources/concepts-anddefinitions/description-education-and-mismatch-indicators/

11
The second stage, similar to equation 3, is defined as: _Fi_ = _λ_ + _θ_ 1 _T_<sup>ˆ</sup> _i_ + **_βXi_** + _εi_ (6) where _T_<sup>ˆ</sup> _i_ is the predicted value from the first stage (equation 5).

# **4 Results**

## **4.1 To what extent are digital entrepreneurs in Africa incorporating disruptive technologies in their offering?**

Figure 3 displays the estimated gap in disruptiveness using equation (1) by region and industry. Panel (a) includes all technologies, while panel (b) excludes mobile payments and Wi-Fi. The left-hand side figures describe the extensive margin (if the firm incorporates any disruptive technology), while the right-hand side figures show the intensive margin (the number of disruptive technology incorporated by the firm, conditional on incorporating disruptive technologies). All figures are normalized to the frontier (zero on the horizontal axis). Overall, when considering all technologies, we observe a large difference across industries, with Fintech being closer to the frontier on the extensive margin. The comparison between panels (a) and (b) shows that the inclusion of “mobile payments” and “Wi-Fi” among the group of disruptive technologies leads to significant differences for African countries, particularly for Fintech. This reflects the recent trends in mobile payments in the region.<sup>12</sup> On the extensive margin, panel (a.1) shows that African firms are, on average, six percentage points less likely to incorporate disruptive technologies. As, in the frontier, 24% of the digital firms report incorporating at least one disruptive technology into their offering; this means that relative to that, African digital firms are 25% less disruptive than the frontier firms in the extensive margin. LAC firms are also less disruptive relative to the frontier by almost seven percentage points, representing a gap of 28%. Excluding mobile payment and WiFi is particularly relevant for Africa as, in the lower panel (panel b.1), the gap against the frontier in Africa represents 36%, while for LAC, it is 32%. This difference is mainly driven by the Fintech sector, which moves from being not statistically different to the frontier (panel a.1) to being about ten percentage points less likely to incorporate other disruptive technologies.

On the intensive margin, analyzing the gap in the number of disruptive technologies conditional on being disruptive, differences against the frontier are observed for all sectors in Africa, except e-commerce (panel a.2). The distance to the frontier on the intensive margin for Africa is larger and more heterogeneous across industries when excluding mobile payments and Wi-Fi (panel b.2). While African firms operating in E-Commerce show a smaller gap against the frontier, Fintech and IT are even less disruptive compared to the frontier.

> 12Given the rapid diffusion of mobile payments in Africa and potential concerns raised by practitioners that some firms under-emphasize the provision of Wi-Fi, we provide results excluding both technologies as robustness.

12
<!-- Start of picture text --> Panel a. Extensive Margin Panel b. Intensive Margin<br>II<br>II<br>ee I —e— I<br>II<br>—o—_ II<br>Africa I —*—_JT-<br>——e——_!<br>a I Africa I<br>II<br>—o— i] —o—_- I<br>II<br>II<br>II<br>II<br>eo I —e— 1<br>II<br>II<br>I I<br>LAC I LAC I<br>—e— | ———o—!_<br>II<br>II<br>I I<br>-14 -12 -1 -08 -.06 04-02 0 02 04 06 08 1 12 14 -14 -12 -1 -.08 --06 04-02 0 02 04 06 08 1 12 14<br>@ Overall @ E-Commerce @ Fintech @ IT @ Overall @ E-Commerce @ Fintech @ IT<br><!-- End of picture text -->

<!-- Start of picture text --> Panel a. Extensive Margin Panel b. Intensive Margin<br>II<br>II<br>oe I -. I<br>I<br>_—_ I re |I<br>Africa I Africa I<br>_e— I __-— I<br>II<br>II<br>—o— i] oe I<br>II<br>II<br>II<br>II<br>eo I eo |<br>II<br>II<br>I I<br>LAC I LAC I<br>—e— | —e—. |<br>II<br>II<br>I I<br>-14 -12 -1 -08 -.06 04-02 0 02 04 06 08 1 12 14 -45 -4 -35 -3 -25 -2 -15 -1 -05 0 05 1 16<br>@ Overall @ E-Commerce @ Fintech @ IT @ Overall @ E-Commerce @ Fintech @ IT<br><!-- End of picture text -->
Figure B7 shows the same analysis for IT firms: while African firms in IT seem to describe themselves more with words like “information technology”, they less frequently use “saas” or “artificial intelligence” in their description than IT firms in the frontier.

Taking all these results together, both the extensive and intensive margins, they point to a general picture of a gap in disruptiveness that, however, disguises important differences by region and industry.

# **4.2 What is the life cycle of disruptiveness in Africa?**

Haltiwanger et al. (2013) suggest that young firms (i.e., start-ups) play a predominant role in job creation, and therefore, their role in innovation and productivity is also of importance. This is also aligned with the ”Silicon Valley” narrative that promotes new firms and young entrepreneurs as positive attributes that lead to transformational and high-growth ventures (Friederici et al., 2020).

Figure 4 shows the average number of disruptive technologies by age estimated using equation (2). These averages are net of size (labor) to allow comparing firms of similar characteristics. The pattern in the frontier firms indicates that the youngest use about twice the number of disruptive technologies than the group of 30-year-old firms. The same pattern emerges in African and Latin American firms, and for both of them, the life cycle of disruptiveness is below the frontier.

Interestingly, young LAC firms are more disruptive than African firms, but this rank reverts for firms seven years old and beyond when we include all disruptive technologies (panel A). We take this as an early indication of further heterogeneity along the life cycle by region and that there is an important degree of action in the right tail of the age distribution in Africa, which could be driven by the characteristics of large sectors, like Fintech, and countries that account for an important proportion of the activity (the big four). This hypothesis is further reinforced by the results shown in panel (b) of Figure 4 where, excluding mobile payments, the differences between firms in LAC and Africa in the number of disruptive technologies incorporated are almost negligible for firms older than 15 years.

Figure 5 looks at the life-cycle patterns by industry. There is a great sectoral heterogeneity as, while the IT sector shows that Africa is below LAC over the whole life-cycle, in E-commerce, younger firms in LAC tend to be more disruptive, but Africa catches up as firms age. The patterns of these two sectors do not show significant differences if we exclude mobile payments and Wi-Fi from the definition of disruptive technologies.

Firms in Fintech, on the other hand, exhibit a life-cycle profile that significantly differs from the other two sectors. In general, the degree of disruptiveness seems relatively constant along the life cycle in the three regions but slightly less so for LAC. Indeed, the estimated coefficient for age is not statistically significant in any of the regions within Fintech. In contrast with E-commerce and IT, excluding mobile payments and Wi-Fi makes a big difference in this sector. As panel (a) of Figure 5 shows, when these technologies are considered in the definition of disruptiveness, Africa is very close to the frontier and even slightly above for young firms, though older firms tend to lag slightly behind. Excluding these technologies significantly increases the distance to the frontier for Africa but less so 14
for LAC, with both regions consistently below the frontier along their whole life cycle. Still, the flatter pattern remains.

At this point, it is clear that the Fintech industry faces a different context than the rest. This could be the reflection of higher entry costs, stricter regulations, or sectoral crossovers (i.e., before becoming Fintech, firms may need to consolidate in adjacent industries). This will be further explored in the following sections.

Figure 6 takes a deeper look into the age profiles by country in Africa. As previously mentioned, South Africa, Nigeria, Egypt, and Kenya are the four largest markets in digital entrepreneurship (the so-called Big Four), and, therefore, these four countries explain the shape of the age profile in the entire Africa. However, some interesting differences in patterns emerge. Older Fintech firms in Kenya seem to be more disruptive than young firms in this sector. South Africa and, to a lower extent, Nigeria show a similar pattern in Fintech, while this is the case for Nigerian IT firms (panel b). Egypt is the only country where excluding mobile payments changes the pattern completely. When mobile payments are considered (panel a), older firms tend to incorporate more of the disruptive technologies, while the exclusion of mobile payments leads to the opposite pattern for this country. This, once again, highlights the critical role that mobile payments have had in the leading African countries.

In the case of Fintech in for this different can be ex- Kenya, example, pattern easily plained using, for example, the high-growth cases or success stories. As explained by Chitavi et al. (2021), there are some key factors associated with these cases in the Fintech sector in Kenya. First of all, there is a preference from consumers for bundled services, as they reduce the information, search, and implementation costs. Secondly, financing entails trust, and though young firms can offer agility and technological architecture, more established and, therefore, older firms can transfer trust from previous related brands. Take, for example, the case of M-Pesa, which, as explained by Chitavi et al. (2021), started in 2007 as a new Fintech product that built upon the trust that Safaricom, the telecommunications giant, already had. On the other hand, the team leader of M-Pesa launched M-Kopa in 2012 (which is among our disruptive companies) and took advantage of the infrastructure and connectivity to M-Pesa.

All these results point to the existence of country and sector differences that produce deviations from what we observe in the frontier.

15
<!-- Start of picture text --> ©<br>\<br>\<br>\<br>iQ \<br>\<br>22oo 4\<br>3 \<br>< \<br>st x<br>2<br>aoO x<br>re) San ~<br>zn Sse,wee ssn eeSS ee .<br>N<br>0 5 10 15 20 25 30<br>Age in Years<br>=== Frontier ms | AfTICa a =| AC<br><!-- End of picture text -->

<!-- Start of picture text --> MOAN<br>\<br>\<br>\<br>\<br>‘<br>+\<br>\<br>2‘<br>8£2c3 as* Ss<br>ee SAL<br>& mse<br>ra ween<br>fo) == ==<br>Zz 0 Se BS =<br>_<br>0 5 10 15 20 25 30<br>Age in Years<br>=== Frontier ms | AfTICa a =| AC<br><!-- End of picture text -->
<!-- Start of picture text --> nN E-Commerce nN Fintech i} IT<br>_t!<br>1<br>e « !<br>i]<br>1<br>eT 4<br>©o\<br>a \<br>)@ \<br>3 “7<br>5] y \<br>2 . \<br>|! \<br>eo}6\\a\\<br>\<br>iN a ac-------- ‘x<br>s, N<br>nN SS ~ ——$Ss$sl___ ° ‘s x<br>-~e N s<br>= = — x ~s *<br>nN<br>0 5 10, 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Age in Years Age in Years Age in Years<br>== == Fronticese Africa LAC == == Frontice Africa = LAC == == Fonte Africa = LAC<br><!-- End of picture text -->

<!-- Start of picture text --> nN E-Commerce nN Fintech i} IT<br>x41<br>1<br>©©l<br>1<br>o+ |<br>i]<br>© © i]<br>2 \<br>D@® \<br>3°32 \<br>S~<br>e3 1 \\<br>°‘*\<br>\N<br>NN<br>Nn xs ° x x<br>sO nN ee eee ee NS<br>em x<br>—. SS<br>- x,<br>_—<_—_—_——_ :<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Age in Years Age in Years Age in Years<br>== == Fronticese Africa LAC === Frontice Africa = LAC == = Frontice Africa = LAC<br><!-- End of picture text -->
<!-- Start of picture text --> E-Commerce Fintech IT<br>©©©<br>.-<br>why5 \<br>~ ~ f / ~<br>no — ws,<br>>8 a / *s . \<br>3S2 .. or<br>@ ” Meee, ” .<br>2><br>a3]. WY<br>Zi]e | ><br>NNN~<br>\<br>05 10 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Age in Years Age in Years Age in Years<br>ee "NG ee NG 8 NG<br>—— EGY ——- KEN ===== Rest — EGY ——- KEN ===== Rest — EGY ——- KEN -==== Rest<br><!-- End of picture text -->

<!-- Start of picture text --> E-Commerce Fintech IT<br>©©©<br>x.<br>+ «4:<br>2gv<br>2”<br>3=2<br>z \<br>a .<br>cs}z _ — — —_——, N *. \ *<br>~ teea -_— Ts_aee, = n ~~ —_ —<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Age in Years Age in Years Age in Years<br>ee "NG ee NG 8 NG<br>—— EGY ——- KEN ===== Rest — EGY ——- KEN ===== Rest — EGY ——- KEN -==== Rest<br><!-- End of picture text -->
# **4.3 The Allocation of funding**

How does the capital market allocate funding to disruptive firms? Being disruptive entails risks but also the promise of future revenue. Since the definition of disruptiveness adopted by Bloom et al. (2021) implies that these disruptive technologies are being increasingly discussed in earnings conference calls, it is possible to expect a funding premium or return for disruptive firms. We explore this with equation (3) and show the results in Tables 1, 2, and 3.

In the frontier countries, firms incorporating disruptive technologies into their offers are 16 percentage points more likely to receive funding at some point in their life cycle. Table 1 shows that these results contrast with digital tech firms in LAC, where this probability is only seven percentage points higher, and Africa, where it is even lower, three percentage points larger than firms with no disruptive technologies. The magnitudes are slightly lower when we only consider the last round of funding (column 2), but the differences between the frontier, LAC, and Africa have the same magnitudes. The results do not change much when we exclude mobile payments and Wi-Fi from the disruptive technologies (columns 3 and 4).<sup>13</sup> Table 2 shows the elasticities of funding (measured as the IHS of funding value) on being disruptive (incorporating at least one disruptive technology into the firm’s offering), considering both the extensive and the intensive margins of funding. The reference category consists of firms with no disruptive technologies. Panel (a) focuses on all firms. Being disruptive in the frontier has a funding premium of 263% compared to nondisruptive. This means that while non-disruptive firms in the frontier receive funding of about US$ 6 million, disruptive firms will get funded by US$ 21.8 million. Being disruptive in Africa also attracts funding, but to a lesser degree: the disruptiveness premium is 40%, while in LAC it is 100%. Once again, as shown in column (2), the magnitudes are lower when we consider the last funding, but the conclusions are the same. Even considering the importance of mobile payments, the elasticities do not change much when we exclude these technologies along with WiFi from the classification of disruptiveness.

Panel (b) turns the attention to startups, here classified as not older than ten years. Among start-ups in the frontier, being disruptive has a premium of 217%, as indicated in column (1). This coefficient is qualitatively smaller than the overall coefficient from column (1) in panel (a), suggesting that, at least in the frontier, the attraction of funding among disruptive firms occurs more among older firms. Panel (c) confirms this by showing that the disruptiveness premium among firms older than ten years is 294%. In the case of Africa, younger firms have a premium to disruptiveness of 28%, while older firms have a premium of more than twice (60%). Once again, this suggests that older firms exhibit a higher correlation between disruptive technologies and funding. In sharp contrast with Africa, the premium in LAC is almost the same between startups and older firms and, as previously explained, larger than the one observed in Africa.

These results, in terms of the lower return to disruptiveness observed in Africa, are not

> 13Appendix Figure B4 explores some of the technologies individually and finds that the positive association is statistically dominated by a few technologies: although most associations are positive, only a few are statistically significant. This, however, could be the result of a lack of statistical power given the constraint to every specific technology.

19
associated with the lower amount of funding (in absolute terms) that these countries receive. Initially, the lower level of funding, which is indeed observed, is already accounted for by the regional terms and the intercept. Secondly, when we estimate split regressions for each we still observe that have a and re- region, disruptive firms higher probability ceive a higher amount of funding than non-disruptive firms in the same region, even in specifications where we control for country effects. The lower return to disruptiveness observed in these regions has, therefore, to do with other factors. One of them could be related to a potentially higher perception of risk compared to frontier countries, where there are lower information failures and risks can be more thoroughly evaluated.

When we restrict our sample to firms obtaining funding, that is, the intensive margin of funding, in Table 3, the differences between disruptive and non-disruptive firms in the frontier and Africa become very small and not significant. This points to a critical role of the extensive margin of funding. Once firms obtain financing, there are no important differences in the value of funding according to disruptiveness. However, in the case of LAC, the premium, conditional on receiving funding, becomes negative, particularly for startups. A potential explanation for this result is that these firms associated with disruptive technologies might be perceived as relatively riskier and, therefore, obtain relatively lower tickets (around 27% lower).<sup>14</sup> Table 1: Disruptiveness and Probability of Receiving Funding

||Any Tec|hnology|Excludin<br>Payments|g Mobile<br>and Wi-Fi|
|---|---|---|---|---|
||Ever Funded|Last Funded|Ever Funded|Last Funded|
||(1)|(2)|(3)|(4)|
|Disruptive|0.164***|0.135***|0.179***|0.149***|
||(0.016)|(0.011)|(0.017)|(0.013)|
|Disruptive_×_Africa|-0.134***|-0.103***|-0.152***|-0.114***|
||(0.018)|(0.013)|(0.020)|(0.016)|
|Disruptive_×_LAC|-0.093***|-0.072***|-0.101***|-0.079***|
||(0.024)|(0.016)|(0.030)|(0.021)|
|_R_<sup>2</sup>|0.214|0.185|0.215|0.186|
|Observations|25464|25464|25464|25464|
|Countries|33|33|33|33|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, and the number of similar firms (in IHS transformation), dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). The dependent variable is a dummy variable that takes a value of one if the firm received funding. Standard errors clustered at the country level.

14Tables A2 and A3 in the appendix re-estimates the tables 2 and 3 using a different definition of start-up: firm no older than 5 years. Results suggest again that most of the financial gain among disruptive firms accrues for older firms.

20
Table 2: Disruptiveness and Elasticity of Funding

||Any Tec|hnology|Excludin<br>Payments|g Mobile<br>and Wi-Fi|
|---|---|---|---|---|
||Ever Funded|Last Funded|Ever Funded|Last Funded|
||(1)|(2)|(3)|(4)|
||Panel|a. All Firms|||
|Disruptive|2.634***|2.090***|2.847***|2.297***|
||(0.284)|(0.195)|(0.257)|(0.178)|
|Disruptive_×_Africa|-2.239***|-1.683***|-2.505***|-1.878***|
||(0.300)|(0.204)|(0.294)|(0.210)|
|Disruptive_×_LAC|-1.635***|-1.245***|-1.776***|-1.374***|
||(0.387)|(0.249)|(0.456)|(0.300)|
|_R_<sup>2</sup>|0.241|0.208|0.242|0.209|
|Observations|25464|25464|25464|25464|
|Countries|33|33|33|33|
|P|anel b. Start-u|ps (10 years old|or less)||
|Disruptive|<br>2.172***|<br>1.795***|<br>2.417***|2.029***|
||(0.107)|(0.080)|(0.074)|(0.100)|
|Disruptive_×_Africa|-1.891***|-1.451***|-2.150***|-1.609***|
||(0.173)|(0.126)|(0.144)|(0.145)|
|Disruptive_×_LAC|-1.297***|-1.024***|-1.407***|-1.114***|
||(0.225)|(0.173)|(0.364)|(0.260)|
|_R_<sup>2</sup>|0.229|0.193|0.230|0.195|
|Observations|15634|15634|15634|15634|
|Countries|33|33|33|33|
|Pa|nel c. Older fr|ms (11 years ol|d or more)||
|Disruptive|2.944***|2.161***|3.095***|2.328***|
||(0.397)|(0.135)|(0.411)|(0.151)|
|Disruptive_×_Africa|-2.339***|-1.616***|-2.526***|-1.840***|
||(0.489)|(0.241)|(0.485)|(0.246)|
|Disruptive_×_LAC|-2.137***|-1.461***|-2.294***|-1.643***|
||(0.504)|(0.243)|(0.533)|(0.284)|
|_R_<sup>2</sup>|0.162|0.128|0.163|0.129|
|Observations|9830|9830|9830|9830|
|Countries|33|33|33|33|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, and the number of similar firms (in IHS transformation), dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). The dependent variable is the IHS transformation of the amount of funding in US Dollars received in the last funding round. Standard errors clustered at the country level.

21
Table 3: Disruptiveness and elasticity of funding conditional on receiving funding

||Any Tec|hnology|Excludin<br>Payments|g Mobile<br>and Wi-Fi|
|---|---|---|---|---|
||Ever Funded|Last Funded|Ever Funded|Last Funded|
||(1)|(2)|(3)|(4)|
||Panel|a. All Firms|||
|Disruptive|0.036|0.024|0.016|0.043|
||(0.127)|(0.119)|(0.117)|(0.134)|
|Disruptive_×_Africa|-0.023|-0.077|-0.077|-0.190|
||(0.217)|(0.194)|(0.275)|(0.290)|
|Disruptive_×_LAC|-0.306**|-0.344**|-0.352**|-0.387**|
||(0.131)|(0.128)|(0.129)|(0.150)|
|_R_<sup>2</sup>|0.524|0.476|0.524|0.521|
|Observations|6519|5791|6519|5791|
|Countries|33|32|33|32|
|P|anel b. Start-u|ps (10 years old|or less)||
|Disruptive|<br>0.035|<br>0.063|<br>0.025|0.054|
||(0.130)|(0.133)|(0.120)|(0.136)|
|Disruptive_×_Africa|-0.035|-0.086|-0.059|-0.175|
||(0.200)|(0.199)|(0.254)|(0.261)|
|Disruptive_×_LAC|-0.324**|-0.374**|-0.374***|-0.398***|
||(0.143)|(0.143)|(0.126)|(0.144)|
|_R_<sup>2</sup>|0.531|0.485|0.531|0.527|
|Observations|5520|4907|5520|4907|
|Countries|33|32|33|32|
|Pa|nel c. Older fr|ms (11 years ol|d or more)||
|Disruptive|0.036|-0.138|-0.009|0.010|
||(0.198)|(0.181)|(0.206)|(0.223)|
|Disruptive_×_Africa|0.184|-0.058|-0.270|-0.317|
||(0.663)|(0.681)|(0.602)|(0.666)|
|Disruptive_×_LAC|-0.134|-0.177|-0.150|-0.259|
||(0.301)|(0.215)|(0.302)|(0.298)|
|_R_<sup>2</sup>|0.458|0.428|0.458|0.461|
|Observations|999|884|999|884|
|Countries|24|22|24|22|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, number of similar firms (in IHS transformation), dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). The dependent variable is the IHS transformation of the amount of funding in US Dollars received in the last funding round. Standard errors clustered at the country level.

22
# **4.3.1 Instrumental variable approach**

Table A4 in the Appendix shows the first stage results of our specification using the instrument described in subsection 3.2.3. The coefficient associated with the IV is statistically significant at the 1% level, and the F-stat in every specification is above the rule of thumb of ten.

We implement the IV estimation on the sample of African firms born after 2010 to ensure that the IV represents characteristics at baseline. This results in a drop in the number of observations mainly due to a lack of ILO information on the skill mismatch for African countries.

Table 4 presents the results<sup>15</sup> for the probability of receiving funding, the IHS transformation of the amount received, and the IHS transformation of the amount received conditional on being funded among African firms.<sup>16</sup> Regarding the probability of receiving funding, it is worth recalling that the overall coefficient for Africa derived from column 1 in table 1 is three percentage points (0.164 - 0.134). Column 1 from panel (a) in Table 4 suggests a higher coefficient (7 percentage points). Results are similar for the probability of receiving recent funding (last funded) or if we use the alternative definition of disruptive technologies that excludes mobile payments and Wi-Fi.

Similarly, the estimated elasticity of funding in panel (b) of Table 4 is 1.05 p.p., which is higher than the elasticity estimated in table 2, 0.40 (2.634-2.239). The conditional elasticity shown in panel (c) becomes statistically significant for the disruptive measure that considers all technologies (columns 1 and 2). Disruptive firms with access to finance receive more funding than non-disruptive firms with access to finance.

Overall, the IV results confirm the findings of Tables 1 to 3 but tend to be larger in magnitude. Similar to the case of Iacovone et al. (2023), the larger magnitudes obtained in the IV specifications could be related to heterogeneity of treatment, in this case, in the returns to disruptiveness.<sup>17</sup> The results are robust to including country fixed effects and, as shown in Appendix Table A5, the results are qualitatively similar.<sup>18</sup>

> 15The number of countries and observation reduces significantly for this IV regression due to the fact that we are restricting the sample to firms that were in operation before 2010 and to the data availability of mismatch indicators from ILO. Yet, the original OLS results for this subsample are similar to those reported in Tables 1 and 2.

> 16Results for LAC are reported in Appendix tables A6 and A7.

> 17Card (2001) presents a discussion of the cases in which OLS estimators are lower than IV in the context of the returns to education.

> 18We also used a measure of mismatch with only the matched skills –i.e., excluding over-skilled workers, and results (available upon request) were similar.

23
Table 4: IV Estimates of the Effect of Disruptiveness on the Probability and Elasticity of Funding

||Any Tec|hnology|Excludin<br>Payments|g Mobile<br>and Wi-Fi|
|---|---|---|---|---|
||Ever Funded<br>(1)|Last Funded<br>(2)|Ever Funded<br>(3)|Last Funded<br>(4)|
||Pa|nel a. Probabil|ity of Being Fund|ed|
|Disruptive|0.070**|0.076***|0.061*|0.066**|
||(0.030)|(0.024)|(0.035)|(0.029)|
|_R_<sup>2</sup>|0.144|0.123|0.145|0.125|
|Observations|2428|2428|2428|2428|
|Countries|9|9|9|9|
|||Panel b. Elast|icity of Funding||
|Disruptive|1.049**|1.091***|0.922**|0.965***|
||(0.412)|(0.301)|(0.454)|(0.337)|
|_R_<sup>2</sup>|0.163|0.142|0.164|0.144|
|Observations|2428|2428|2428|2428|
|Countries|9|9|9|9|
||Panel c. Elasti|city of Fundin|g Conditional on|Being Funded|
|Disruptive|0.422*|0.343|0.278|0.216|
||(0.231)|(0.288)|(0.320)|(0.493)|
|_R_<sup>2</sup>|0.437|0.436|0.439|0.436|
|Observations|548|481|548|481|
|Countries|9|9|9|9|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, number of similar firms (in IHS transformation), dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). The dependent variable is either a dummy variable indicating if the firm received funding (panel a), or the IHS transformation of the amount of funding in US Dollars received in the last funding round (panels b and c). Standard errors clustered at the country level.

# **4.4 Disruptiveness and performance**

To analyze the correlation between the incorporation of disruptive technologies in firms’ products and services offering and firm-level performance, we rely on Pitchbook data, which includes more detailed information in terms of valuation and growth. Due to data restrictions, in this case, we are only able to compare Africa with LAC.

In Table 5, we estimate similar models to the ones shown in previous tables but using some performance-related indicators as dependent variables. As shown in panel (a) of the table, disruptive firms in Africa receive funding 0.66 years earlier than non-disruptive 24
firms. Still, the value of funding is 15% lower, while the first valuation of the firm is 12% lower. Once again, this could potentially be explained by some risk considerations. On the other hand, these firms exhibit 4.7 percentage points higher probability of success and an average 46% larger valuation growth over the life cycle of the firm. Furthermore, they tend to receive deals later on. The results do not much if we con- larger change only sider those firms that were born after the technology; that is, those firms that potentially were created with these disruptive technologies embedded in their offering rather than adopted them afterward.<sup>19</sup> The results for LAC do not differ significantly against Africa, except for the case of the probability of success, which, though still larger than non-disruptive firms, is lower than the one observed for disruptive firms in Africa. Still, when we consider only those firms that started operations after the technology was created, we see that the ticket value, though still lower for disruptive firms, is not as low as the one observed in Africa.

Table 5: Disruptiveness and Performance

||Years Until<br>First Deal|First<br>Deal Value|First<br> Valuation|Success<br> Probability|Value<br> Growth|Value Annual<br>Growth|Deal<br>Growth|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|||Pa|nel a. All Fi|rms||||
|Disruptive|-0.655***|-0.153***|-0.116***|4.662***|0.455**|0.053|0.039*|
||(0.190)|(0.031)|(0.030)|(1.035)|(0.168)|(0.041)|(0.023)|
|Disruptive_×_LAC|0.304|0.071|0.052|-2.990*|-0.142|-0.116**|-0.004|
||(0.275)|(0.054)|(0.049)|(1.760)|(0.167)|(0.056)|(0.032)|
|_R_<sup>2</sup>|0.286|0.172|0.088|0.235|0.231|0.106|0.152|
|Observations|2065|2327|2327|2327|365|344|1851|
|Countries|51|53|53|53|34|34|47|
||P|anel b. All F|irms born a|fter technol|ogy|||
|Disruptive|-0.917***|<br>-0.172***|<br>-0.129***|<br>5.517***|<br>0.477***|0.055|0.049***|
||(0.277)|(0.042)|(0.031)|(0.795)|(0.169)|(0.044)|(0.018)|
|Disruptive_×_LAC|0.160|0.089|0.047|-3.287**|-0.118|-0.118*|0.003|
||(0.346)|(0.058)|(0.053)|(1.482)|(0.164)|(0.061)|(0.027)|
|_R_<sup>2</sup>|0.266|0.170|0.089|0.239|0.234|0.105|0.158|
|Observations|2023|2273|2273|2273|354|334|1813|
|Countries|51|53|53|53|34|34|47|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, number of similar firms (in IHS transformation, dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). Standard errors clustered at the country level.

> 19’Success probability’ is based on the PitchBook VC Exit Predictor model, which leverages machine learning and our vast database of information about VC-backed companies, financing rounds, and investors to assess a startup’s prospect of a successful exit. ’Value growth’ is defined as the ratio between the last and the first valuation.

25
# **5 Conclusion**

This paper analyzed the level of disruptiveness of digital businesses in Africa along their life cycle and its association with funding. Using data from Crunchbase and through textual analysis, we identified which firms incorporate into their products and services technologies considered disruptive according to Bloom et al. (2021) classification, based on earning conference calls and patents information. To produce meaningful comparisons, we collected data on Africa, Latin America, and a group of cities at the technological frontier like Seattle, Palo Alto, London, and Tokyo.

Our results indicate that African digital firms incorporate a lower level of disruptive technologies into their offering than the frontier. Still, there is a great deal of sectoral heterogeneity, especially when we take into account mobile payments, particularly in the case of Fintech, where Africa is significantly closer to the frontier, particularly on the extensive margin (probability of incorporating at least one of these technologies). The region is significantly below the frontier on the intensive margin (number of different technologies incorporated) in all the subsectors analyzed.

Although startups in Africa tend to be more disruptive than older firms, the level is much lower than the frontier. Still, we observe a great level of heterogeneity in the lifecycle of disruptiveness by sector, with Fintech showing a different pattern, particularly for Kenya, South Africa, and Nigeria, which exhibit more disruption from older firms in this sector. These results are consistent with different characteristics of the sector, where the level of trust, technology infrastructure, and the possibility of bundling different products, along with potential regulatory frictions, can be critical factors.

We also find that, although disruptiveness positively correlates with funding, this relationship is weaker compared to the frontier. In addition, we find that even though being disruptive helps attract more funding compared to not being disruptive, this is preeminently occurring among older firms and mostly on the extensive margin of funding. These results are consistent with frictions affecting both the life cycle and the allocation of financing in the African market. Additional results indicate that being disruptive is associated with earlier financing but with lower tickets and higher growth in valuation and future funding deal size. Once again, these results point to some market imperfections (e.g., information failures) related to the assessment of risk among these potentially disruptive and high-growth firms, which limits their funding.

Our analyses can inform the intervention of multilateral development institutions in terms of their role in creating markets, contributing to de-risking some of these investments, and, therefore, promoting growth in underdeveloped markets.

26