metadata: authors: 0: author: Vargas Da Cruz,Marcio Jose
1: author: Moelders,Florian
2: author: Salgado Chavez,Edgar
3: author: Volk,Ariane Tamara
count: Africa
docna: 0: docna: Estimating the Number of Firms in Africa
docty: Policy Research Working Paper
abstracts: cdata!: This paper estimates the number of firms
            in Africa, considering their size and formal status. It
            relies on a novel methodology that combines multiple data
            sources. The results suggest that in 2020, there were 12.7
            million firms with more than one worker, and more than 230
            million own-account businesses, where the proprietor
            constituted the sole employee. Informality is prevalent
            among own-account, micro, and small businesses. The density
            of medium and large firms relative to the population remains
            low across African countries but is positively correlated
            with per capita income, whereas the prevalence of
            own-account businesses decreases as income levels rise. The
            proposed methodology provides valuable insights to
            researchers and policymakers by enabling an assessment of
            the potential market size based on firm characteristics in a
            context of limited information.
display_title: Estimating the Number of Firms in Africa
listing_relative_url: /research/2025/01/34445375/estimating-number-firms-africa
topicv3: Private Sector Development m1326060 1921,Firm Behavior and Jobs m1504204 2873,SME Development m1504603 2651,Market Analysis and Segmentation m1505069 3873,Entrepreneurship m1582903 4424
publishtoextweb_dt: 2025-01-13T00:00:00Z
dois: 10.1596/1813-9450-11032
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 28
author_keys: 0: author_key: 1161834
1: author_key: 1091976
2: author_key: 1238548
3: author_key: 1436482
model_extractions: Policy Research Working Paper 11032 Estimating the Number of Firms in Africa _Marcio Cruz Florian Moelders Edgar Salgado Ariane Volk_ International Finance Corporation January 2025
Policy Research Working Paper 11032

# **Abstract**

This paper estimates the number of firms in Africa, considering their size and formal status. It relies on a novel methodology that combines multiple data sources. The results suggest that in 2020, there were 12.7 million firms with more than one worker, and more than 230 million own-account businesses, where the proprietor constituted the sole employee. Informality is prevalent among own-account, micro, and small businesses. The density of medium and large firms relative to the population remains low across African countries but is positively correlated with per capita income, whereas the prevalence of own-account businesses decreases as income levels rise. The proposed methodology provides valuable insights to researchers and policymakers by enabling an assessment of the potential market size based on firm characteristics in a context of limited information.

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.

_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
# Estimating the Number of Firms in Africa<sup>*</sup>

Marcio Cruz<sup>†</sup> Florian Moelders<sup>‡</sup> Edgar Salgado<sup>§</sup> Ariane Volk<sup>¶</sup>

## **JEL Classification** : D22, L25, L26

**Keywords** : Firms, Entrepreneurship, Africa, Private Sector Development, Market Analysis and Segmentation

*The authors thank Paolo Mauro, Denis Medvedev, Roberto Fattal, Xavier Cirera, Mark Dutz, Mariana Pereira, Justice Mensah, and participants of the authors’ workshop ”Digital Opportunities in African Businesses,” for valuable comments. Lucio Castro actively participated in earlier drafts of this paper and we are indebted to him. Lucien Ahouangbe, Kaleb Abreha, and Ghita Chraibi provided superb research assistance. This paper is a product of the IFC Economic Research Unit. Financial support from the Government of Japan is gratefully acknowledged.

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

- ‡International Finance Corporation, email: fmoelders@ifc.org

- §International Finance Corporation, email: esalgadochavez@ifc.org

- ¶International Finance Corporation, email: avolk@ifc.org
# **1 Introduction**

The number of firms operating in Africa and their characteristics remain challenging to ascertain due to the limited and inconsistent availability of establishment censuses and business statistics across the continent. This paper aims to bridge this knowledge gap by mapping existing sources of firm-level data, integrating employer information from household and labor surveys, and applying a simple statistical model to estimate the total number of firms in African countries, categorized by size and formal status. The scarcity of data on the number of firms in Africa starkly contrasts with the extensive, up-to-date, and comparable firm-level information available in advanced economies. For instance, the OECD’s Structural and Demographic Business Statistics and the European Union’s Business Demography data exemplify the comprehensive nature of firm demographic information available for these regions. Previous studies have emphasized the importance of these estimates for Africa (Teal, 2023b,a; Tsaedu et al., 2023), but often focus on a few particular countries for which firm data is available.

Firm demographic indicators are crucial for policymakers as they provide insights into the structure and dynamics of the economy, enabling targeted and effective decision-making. These indicators help identify which sectors and firm sizes drive growth and highlight challenges, such as limited access to technology needed to boost productivity. They are also instrumental in making decisions related to taxation, social security, and policies aimed at fostering job creation. Additionally, firm demographics are vital for assessing economic vulnerabilities, designing tailored crisis responses, and evaluating the long-term impact of policies. A recent article by The Economist (2025) suggests that “Africa has too many businesses, too little business” emphasizing the lack of large enterprises on the continent as a key constraint to economic growth. Better estimates of the distribution of firms by size and formal status, along with the distribution of workers associated with them, can provide greater clarity on the magnitude of this gap.

Estimates on business demographics are also relevant for investors and the private sector. This information can be used to estimate the potential markets for intermediary inputs and the demand for finance, along with population demographics indicators, which are already widely used for these purposes. An example of an application is provided by Cruz, Salgado, and Tran (2024), combining our estimated number of firms by various characteristics across African countries to analyze the potential for digital upgrades by businesses across the continent. Another example of application is the MSME Finance Gap Report (IFC, 2017), which uses estimates on the number of firms to analyze the gap and potential demand for finance by micro, small, and medium enterprises.

This paper estimates the total number of firms in all African countries, distinguished by size and informality status. It contributes to the broad research agenda on firms in developing countries by producing a dataset with the basic demography of African businesses. The dataset is the result of applying statistical methods to information from national censuses and surveys as well as data from the International Labor Organization (ILO) and the World Bank Enterprise Surveys. First, we estimate the number of African firms at the aggregate and country levels based on employers. Second, we differentiate between formal and informal firms. Lastly, we estimate the size distribution for both formal and informal firms. We then validate our results by comparing our 2
estimates with a few countries where establishment censuses were available. Our methodology and estimates aim to address the limited information available on firm demographics in African countries. The results should be interpreted as a first attempt for these estimates that should be improved over time by adding a larger number of harmonized establishment censuses and representative surveys for providing more accurate and rich description of firms in the continent.

Our results suggest that as of 2020, there were about 12.7 million firms with more than one worker and about 232 million own-account businesses in which the proprietor constitutes the sole employee. Ninety-four percent of own-account businesses are informal, totaling 218 million informal own-account businesses. Conversely, 73 percent of firms with more than one worker are informal, amounting to 9.3 million informal firms of this type. The informality rate decreases as firm size increases: from 82 percent among micro-firms (excluding own-account businesses) to 66 percent among small firms, and 36 percent among medium and large firms with twenty or more workers. Distinguishing by formality status and size is instrumental in identifying businesses with growth potential, unlike those driven by necessity.

Our analysis is closely related to various studies emphasizing the relevance of firm characteristics to the economy. One important effort worldwide is the dataset assembled by Bento and Restuccia (2021). This dataset captures the average employment size of establishments in the manufacturing and service sectors, regardless of their formality status. Their findings suggest that manufacturing firms tend to be larger than those in the services sector, and each sector is larger in more advanced economies. They provide estimates of employment size for 42 of the 54 African countries. Another relevant study (Eslava et al., 2024) combines information from harmonized household and labor surveys with firm-level data to analyze the relationship between firms and inequality in Latin America. Informality and self-employment are widespread across Latin America, but even more prevalent in Africa. They are important in driving income inequality and slowing down technology diffusion (Levy, 2024).

This paper also contributes to the broad literature on informality in Africa. Informality varies in several dimensions (e.g., a firm can be formal in some respects and informal in others) and is sparsely captured by few national firm censuses and surveys in the region (Bonnet et al., 2019; Choi et al., 2020). Previous studies have identified key characteristics of informal firms (Loayza, 2016; Porta and Shleifer, 2008), but the literature primarily assesses informality in terms of its contribution to GDP or employment, neglecting the prevalence and significance of informality in micro and small firms (Ohnsorge and Yu, 2022; Quiros-Romero et al., 2021). Informal businesses are also associated with “the missing middle hypothesis” in Africa. Abreha et al. (2022) provide evidence of a missing middle in the size distribution of manufacturing plants in Burkina Faso, Camerron, Ghana, and Rwanda, resulting from the underrepresentation of informal firms. We expand the estimates of number of informal firms by size groups across the continent.

The rest of the paper is organized as follows. Section 2 provides an overview of the data and its limitations. Section 3 describes the methodology used to estimate the number of African firms and their characteristics. Section 4 provides the main results. We then describe the main results and some robustness checks. The final section outlines the main conclusions and further research directions.

3
# **2 Data and the definition of a firm**

The number of firms can be directly determined using business registries, censuses, and surveys. This often requires that a firm has a fixed physical location associated to the firm. Although this is generally regarded as the gold standard for data on firms, these data are rarely available for developing countries, particularly in low-income regions of Africa.

Firms can also be assessed indirectly via information on employment. Following Coase (1937), a firm can be understood as the space where a relational contract occurs. Specifically, it is where someone, an employer, hires workers to perform a number of tasks. Under this characterization, _employers_ , regularly reported in labor and household surveys, can be assumed as a good approximation of a firm.

To put this definition into context, it is worth exploring other definitions. OECD defines a firm as “[...] a legal entity possessing the right to conduct business on its own, for example, to enter into contracts, own property, incur liabilities and establish bank accounts.”<sup>1</sup> The World Bank Enterprise Survey (WBES) defines it as the aggregation of _establishments_ , where the establishment is ”a physical location where business is carried out and where industrial operations take place or services are provided.” A firm may be composed of one or more establishments. In most cases, the WBES focuses on formally registered firms with five or more workers.<sup>2</sup> The World Bank’s Entrepreneurship Database (WBED) provides measures on registered firms across countries, which are defined as formally registered private companies with limited liability.

Formal status is an important dimension for the definition of firms in developing countries. However, to understand the broader universe of firms in the Africa context, we also need to incorporate informal businesses, given they represent a large share of the economy. There are very limited data available across African countries providing comprehensive measures of the formal and informal businesses that remain active.<sup>3</sup> An alternative to address the data limitation on firms is by using information on employer status. This approach allows the use of micro-level datasets (e.g., labor and household surveys) that are more widely available across Africa.

ILO defines employers as those workers who are self-employed and, in this capacity, have engaged one or more persons to work for them as employee(s). There is another group of selfemployed workers defined as own-account, who operates without hiring additional workers.

Our primary emphasis lies on the _employers_ category. This category is our definition of a “firm,” which we group between micro (with less than 5 workers, excluding own-account businesses), small (between 5 and 19 workers), medium (between 20 and 99 workers), and large (with 100 or more workers). In addition, we provide estimates of the number of own-account businesses, both formal and informal.

> 1Source: OECD Data.

> 2See for example “Enterprise Survey 2009-2017, Panel Data Liberia, 2009 - 2017”.

> 3WBED has recently incorporated information on the total number of registered and closed businesses, in addition to the number of new registrations. Comparison with external datasets suggests that business registry sources tend to provide robust estimations of the number of newly registered businesses, but they have more limitations when referring to the total number of active businesses.

4
# **2.1 Employers, own-account, and employees**

ILO offers exhaustive employment and employee statistics, based on nationally representative labor force surveys combined with household surveys and population censuses<sup>4</sup> . ILO data includes the number of employers, employees, and own-account workers by country and year. Specifically, employees who have an explicit or implicit contract of employment with an employer are distinguished from the self-employed, for which remuneration is directly tied to the profits from goods and services that they produce. The self-employed include employers, who continuously have engaged one or more persons to work for them in their business, and own-account workers, who do not employ other workers.

The number of employers, own-account and employees by country constitute the key statistics from which we estimate the number of firms per size and formality status. ILO reports two versions of these variables. First, ILOSTATS is a data repository with information from labor force and household surveys worldwide. The reporting of employment information is harmonized to allow country comparison, however data gaps remain. Not all countries produce labor or household surveys regularly. This creates space for the second version of employment data, stored in a second repository, ILOEST (ILO, 2023). ILOEST produces estimates of labor force variables using a series of cross-validated statistical models selected based on pseudo-out-of-sample comparisons. In some cases, models are country idiosyncratic.

According to ILO (2023), the initial modeling predicts country-level labor force participation rate using economic and demographic variables across nine estimation groups split based on economic and geographical proximity. Relevant to our setting, ILO further models the distribution of employment by status. In a similar procedure, first, the shares of each category (employer, employee, own account) are modeled against a set of country characteristics such as per capita income, economic structure, and model-specific variables such as the ”work for an employer” index from the Gallup World Poll. Second, the models estimate the evolution of these employment category shares based on the economic cycle and population demographics. The application of these shares on the estimated number of people employed gives the estimated number of individuals engaged in each employment category, which is the main input we use. Among other limitations of these data to estimate the number of businesses by country is that it does not provide information on firm characteristics, such as size distribution, which is available in firm-level establishment censuses and representative surveys.

# **2.2 Establishment censuses, economic censuses, and surveys**

Economic censuses are statistical surveys conducted on the full set of economic units belonging to a given population. In the US, the Economic Census serves as the statistical benchmark for current economic activity, feeding into the calculation of GDP and the Producer Price Index (PPI). The Census provides information on employment, payroll, revenue, sales, and industry classification. Economic censuses are also commonly used to update business registries.

> 4See table A1 in the Appendix for the underlying data sources used by ILO.

5
Establishment census data collected by national registries, statistical offices, and ministries are the gold standard for information on establishments and firms and serve as a sample frame for firm surveys. They provide information on the number of firms by size, age, sector, and often, formality status. Most African countries carry out such censuses with considerable time gaps between rounds, because of the time and effort required to survey the entire universe of establishments.

While some form of economic censuses exists for Botswana, Burkina Faso, Cameroon, the Democratic Republic of Congo, the Arab Republic of Egypt, The Gambia, Ghana, Mauritius, Namibia, Rwanda, Sierra Leona. Tunisia, Senegal, and Uganda, it should be noted that access to most of this census information is constrained or aggregated in statistical reports.<sup>5</sup> Comprehensive micro-data for this exercise was available only for Kenya, Burkina Faso, Cameron, Rwanda, and Ghana.

Although census data are available for some countries, coverage on economic censuses or firm registries varies considerably, compromising harmonization of the collected data across countries. For example, among the available micro-datasets, Cameroon’s registry excludes the taxi industry, while Ghana’s registry includes modern agriculture, albeit it states that the agriculture sector is not covered. In general, most of these registries and censuses focus on the non-agriculture sector, but to varying degrees.

We extract information about firm characteristics from World Bank Enterprise Surveys (WBES). WBES are firm-level surveys aiming to provide data that are comparable across countries for a representative sample of firms with 5 or more workers. The surveys cover a broad range of business environment topics, including access to finance, corruption, infrastructure, competition, and performance measures. WBES provides a comprehensive cross-country database for 194,000 manufacturing and services firms in 155 countries. The surveys, however, include mostly formal establishments with five or more workers, and therefore exclude micro-enterprises and informal firms, with a few exceptions. This focus on small to large formal firms might lead to a potential bias in the representation of the demographics of the firm in Africa (Li and Rama, 2015).

Another source of data on firms is the World Bank Entrepreneurship Database (WBED), which was not used in this analysis. WBED provides information on the number of private formal companies with limited liability for developing and developed economies on a yearly basis. This data would, hence, form a lower threshold for the number of firms in these countries. The database also provides information on firm entry and exit for selected economies derived from national business registries. A comparison between WBED and OECD’s _Structural and Demographic Business Statistics database_<sup>6</sup> , where available for both, reveals that firm exit may not be accurately captured because it requires firms to file the legal closing of the establishment with government entities to be included in the number of exiting firms. This might cause an upward bias in the number of formal firms, the extent of which is unclear. WBED also lacks information about informal or unregistered establishments. For these reasons, we exclude the WBED from this analysis.

> 5Aggregate numbers also prevent breaking down by size category because of idiosyncratic thresholds used in every context. Harmonization was attempted but the proposed analysis ultimately required microdata, restricting further the number of countries with census microdata at our disposal.

> 6Source: https://www.oecd.org/sdd/business-stats/structuralanddemographicbusinessstatisticssdbsoecd.htm

6
We collect information on firm demography from censuses and surveys for more than 25 African countries from all regions and income levels between 2010 and 2020. We extract information on firm size based on the number of employees, which forms the distinction between micro, small, medium, and large firms. Further, where available, we add information on firms’ formality status - whether a firm is registered or at least licensed in the country or province, as well as sector.

We also identify where national censuses and surveys do not cover key sectors (e.g. agriculture), or explicitly exclude informal or household-based firms. We discard outdated censuses and surveys collected before 2010 as well as censuses and surveys that lack information about how data was collected. We restrict ourselves to censuses and surveys up to the year 2020, as evidence shows that the COVID-19 pandemic caused much volatility in firm entry and exit, and potentially large fluctuations in the number of firms (Decker and Haltiwanger, 2022). Evidence from rapid surveys suggests that up to 90 percent of firms in selected Sub-Saharan African countries closed temporarily (Aga and Maemir, 2021). Many firms had to lay off workers in the short term only, with consequences for a firm’s employee count. While recent surveys and censuses may give a more up-to-date assessment of the number and characteristics of firms, they may also introduce comparability issues with censuses and surveys collected pre-pandemic.

Collected data varies across countries in the definitions of size strata, and coverage of large and informal firms and sectors. As mentioned above, this compromises the harmonization of the datasets. We find meaningful differences, in particular, in how countries define a microenterprise. For instance, only entrepreneurs with no employees are considered micro-enterprises in the Gambia, but in three countries, establishments with up to ten employees are identified as micro-enterprises. Small firms are defined as anywhere from 1-4 employees to 10-100 employees (Morocco). Few countries follow the standardized WBES definitions of firm size: 5 to 19 (small), 20 to 99 (medium), and 100 or more (large). In the absence of available micro-data for censuses and surveys, this heterogeneity, unfortunately, cannot be resolved.

Further, the definition of informality (if included in the documentation) differs widely across countries. Some countries inherently include activities of households as employers as informal activities; others classify any micro-enterprise as informal, independent of firm registration. Some countries draw the line between formal and informal firms according to firm registration, firm licensing, or both. Other establishment censuses and surveysexplicitly exclude businesses without fixed premises from the stock of businesses, and others classify itinerant traders, taxis, or market stands as informal solely based on the lack of fixed premises. Where possible, we avoid relying on countries’ definition of formality, but instead extract information on the number of registered firms, and the number of licensed firms as a second-best measure of formal firms.

# **3 Methodology**

This section describes the methodology we followed to calculate the number of firms by country and the subsequent distinction by size group and formality status. Because harmonization across establishment censuses or registries is not currently feasible or available, based on ILO data, we 7
adopt an approach that favors having the same definition of a firm from the perspective of the employer across the whole economy and across countries. We then combine this information with the moments of the distribution of firms across sizes from other data sources.

# **3.1 Estimating the total number of firms**

The simplest calculation of the total number of firms is the sum of people reporting to be em. ployers<sup>7</sup> This is facilitated by the availability of data on employers, employees, and own-account workers provided by ILO (2023). The latest available data point is for 2020 and indicates that there are about 12.7 million employers and 232 million own-account businesses on the African continent. There is large variation on the number of firms across countries (see figure 1.a). These numbers are strongly associated with population size in these countries, with a correlation of 0.74. Regarding density, North and South Africa are the regions with a larger number of firms relative to the working age population (see figure 1.b). There is low association between the density of firms and the total population, with a correlation of 0.13. Yet, as discussed in the next chapter, firm density is positively and strongly associated with per capita income.

> 7Conceptually, this excludes own-account from the group of employers since an employer hires at least one worker. In our conceptual framework, the group of sole proprietors that would usually be covered in an economic census, particularly if they are formal, is within the own-account category.

8
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# **3.2 Breakdown by size**

To break down the number of firms by employment size, we develop a method that leverages complementary information from the WBES and census microdata from Ghana, Burkina Faso, Cameroon, and Rwanda. We also use a representative survey for Kenya. These data sources are combined in a framework that links the proportion of different firm sizes to key moments of the firm size distribution.

Using the five-worker threshold for micro firms, as per WBES size classification, figure 2 suggests that the proportion of micro firms will vary depending on the shape of the distribution. The economy of Cameron will have more micro firms (88%) than Ghana (80%) but fewer than Rwanda (93%). It is natural, then, that Ghana has a larger share of small firms (those employing between 5 and 20 workers),which is reflected in the larger grey shaded area between the reference point 5 and 20 in figure 2. In general, a firm size distribution that looks more like Ghana’s will yield a higher proportion of small firms than a firm size distribution that looks more like Rwanda.

Figure 2: Firm Size Distribution and Size Brackets: from establishment census data <!-- Start of picture text --> Firm Size Distribution and Size Brackets<br>Burkina Faso (2016) Cameroon (2009) Ghana (2014)<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>5 20 100 5 20 100 5 20 100<br>Average firm size Average firm size Average firm size<br>Rwanda (2014) Rwanda (2017) Rwanda (2020)<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>5 20 100 5 20 100 5 20 100<br>Average firm size Average firm size Average firm size<br>All firms Informal firms<br>Fraction of firms Fraction of firms Fraction of firms<br>Fraction of firms Fraction of firms Fraction of firms<br><!-- End of picture text --> _Source_ : Own calculations based on census data from Burkina Faso (2016), Cameroon (2009), Ghana (2014) and Rwanda (2014, 2017 and 2020). The x-axis reports log firm size to ease visibility. Informality definition according to each country’s criteria. Red lines are size thresholds of 5, 20, and 100 workers.

Since data on the whole firm size distribution is scarce, our strategy first defines a statistical 10
model that relates each size share on key moments of the firm size distribution. The idea is to get an empirical source to predict the share of micro, small, medium and large firms based on just a few moments of the firm size distribution, reducing notably the data requirements.

The strategy also helps with the harmonization of the resulting dataset. It is not uncommon that countries have their own size shares, corresponding to idiosyncratic interests. While that could be relevant for the country itself, it prevents comparability. Our approach defines one set of thresholds that is applicable to all countries. To further leverage information from the WBES, we define the same size thresholds at 5, 20 and 100 employees.

This also follows the suggestion in Li and Rama (2015) that correcting the biases arising from the exclusion or under-representation of micro- and small firms in enterprise surveys is necessary, using household and labor force surveys to re-calibrate the firm size distribution.

The first goal is to map the relationship between the shape of the distribution and each size proportion through these four moments. Ideally, we would observe several firm size distributions of different shapes that will inform a statistical model that connects size shares and the shape of the firm size distribution. However, we have only 5 countries with microdata that allow this: Burkina . Faso (2016), Cameroon (2009), Ghana (2014) and Rwanda (2014, 2017 and 2020) and Kenya (2017)<sup>8</sup> To increase the number of distributions, we can observe, for which we can derive key moments and firm size shares, we break each country’s microdata into age groups from 0 to 61 to create more distributions with different shapes. This leaves us with 62 firm size distributions by country, totaling 434 distributions from which we can extract size shares and information on the shape of the distribution.

We compute the four size proportions from each firm size distribution and the mean, p25, p50, p75, and p90. We have 434 observations to elucidate their relationship to the firms’ size shape accounted for by these four moments. In practice, this implies estimating<sup>9</sup> the following regression for each of the four size proportions: where _zki_ is the proportion _k_ = _{micro_ , _small_ , _medium_ , _large}_ coming from the firm size distribution of all firms aged _i_ = _{_ 0, ..., 61 _}_ . The algorithm uses the estimated _β_ s to predict all remaining countries based on the four moments we reconstruct in the following step.

It is important to estimate equation 1 using the information of several distributions of different shapes so the _β_ s carry the precise connection between the moments and the size shares. Figure 3 shows the correlation between the actual size shares and the predicted ones in every age group. The model produces accurate estimates of the size shares based on these five moments, mean, p25, p50, p75, and p90.

The next step consists of obtaining the five moments for every country so we can use the _β_ s from equation 1 to predict the size shares. For this step, we rely on ILO and WBES data. With ILO data, it is possible to recover at least the nationwide average firm size for every country

> 8Results of this section, and the overall estimations are robust to excluding Kenya survey data and using only census data. Results available upon request.

> 9STATA implements the maximum likelihood estimator developed by Papke and Wooldridge (1996).

11
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firms. This is done in two steps.

First, we calculate the five moments for formal firms as described above: based on the average firm size among formal employers, own-account workers, and employees, the other three percentiles are calculated by imposing the relative position of each percentile to the mean as found in the WBES<sup>10</sup> .

Second, we produce the same calculation for the mean and percentiles of informal firms based on the average firm size of informal firms and then re-scale the percentiles of the informal distribution concerning the same percentiles of the formal size distribution. For this second re-scaling, we use microdata from Burkina Faso, Cameroon, Ghana, Rwanda and Kenya to extract the mean and four percentiles for both formal and informal and obtain the relative size of each percentile from the informal firm size distribution with respect to the same percentile from the formal firm size distribution. For instance, the p25 of the formal firms’ size distribution is 2 in Ghana, and 3 in Kenya, while the same percentile among informal firms is 1 both in Ghana and Kenya. This means that the p25 of the informal firms in Ghana is 1/2 of the p25 of formal firms and 1/3 in Kenya. We produce this calculation also for percentiles p50, p75, and p90 and keep the mean across the five micro-datasets. In the example, this means using 1/3 as the adjustment scalar for the p25 of . informal firms<sup>11</sup> The method we followed has several limitations. First, estimates of the number of firms using employer statistics might not be consistent with estimates of the number of firms from establishment censuses. Second, we are using only five countries and thus imposing the shape of the firm size distribution based on limited information. In further revisions, we expect to include more countries and characterize the differences in the percentiles between formal and informal.

# **3.4 Number of workers by size and formality**

By dividing the number of employees by the number of firms, it is possible to estimate the average firm size in the economy (26 workers per firm in Africa, 33 in the rest of the world). For all the countries for which this calculation is possible, the elasticity to income measured with GDP per capita is 0.13. Bento and Restuccia (2021) estimate an elasticity of 0.3 for an average firm size that includes all people involved in business activities (but only manufacturing and services). In our setting, this would be equivalent to redefining people working for a business as the sum of employees and own-account, and also redefining firms as the sum of employers and own-account. This second definition yields and average firm size of 1.9 workers for the African continent and 7.7 elsewhere. The elasticity to income is 0.48, higher than the one initially estimated, and conceptually closer to the one estimated by Bento and Restuccia (2021).

We employ this framework to impute average firm size by size group and formality status using a model that links firm size in any of these groups to log GDP per capita, log GDP, firm

> 10For countries with no WBES data we use the average relative positions across countries covered by WBES.

> 11The broad assumption is that all countries are re-scaled with the same scaling parameter (momentspecific). It is possible to expect that moment differences between formal and informal vary by country. A further refinement to explore is making the scaling parameter dependent on country’s income level.

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informality rate, the average firm size, the number of own-account workers, employees, and micro firms.

The number of people working in each size category (small, medium, and large) and formality is calculated as the result of multiplying the estimated firm size by the number of firms. To ensure consistency, the number of workers for the category of micro firms in each country, both formal and informal, is calculated as the difference between the total number of workers in the formal or informal markets minus the estimated number of workers obtained by the previous multiplication. Because we rely on the WBES distribution for formal small, medium, and large firms in countries for which there are no establishment censuses, we are likely to underestimate the number of workers associated with large firms.<sup>12</sup>

# **4 Results**

## **4.1 Total number of firms and share of workers associated with them**

Our analysis estimates that Africa had approximately 244 million businesses in 2020. The vast majority of these—around 232 million—were own-account businesses, while the remaining 12.7 million were firms employing workers. Table 1 provides a detailed breakdown by size. Excluding own-account businesses, there were 7.7 million micro firms in Africa in 2020, employing 16% of the labor force. Small firms accounted for 4 million businesses, contributing 7% of employment, while medium and large firms totaled 1.1 million and collectively absorbed 15% of the labor force. Overall, the 12.7 million firms employing workers accounted for 38% of the labor force, with the majority — 62% — engaged in own-account businesses.

Table 1: Estimated number of businesses (in thousands) and labor absortion

||(1)<br>Number of Firms|(2)<br>Labor Share(%)|
|---|---|---|
|Micro (less than 5)|7,683|16|
|Small (5-19)|3,997|7|
|Medium (20-100)|933|7|
|Large (100+)|127|8|
|Total Firms|12,741|38|
|Own-account|231,689|62|
|Total Firms + Own-account|244,429|100|

Source: Own calculations.

Note: Micro firms exclude single proprietor and places it within the own-account category

> 12The average size of large firms in WBES is smaller than those obtained from establishment censuses in countries for which data were available.

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Table 2 shows the estimated number of firms by employment size by country. The number of firms increases with population. The correlation between the log number of firms and the log population is 0.9, while the correlation between the log number of own-account businesses and the log population is 0.91. Hence, Egypt, Nigeria and South Africa are the countries with the largest number of firms. The combined number of firms in these three countries alone constitutes 36% of all firms in Africa. This is higher than the population share of these three countries, which is 26%.

# **4.2 By formality status**

Using the methodology described above, we break down the number of firms by formality status. Table 3 suggests that 9.3 million firms operate in the informal market. This represents an informality rate of 73%. Although high, the informality rate among firms is still lower than the informality rate estimated for own-account businesses, where 94% of the 232 million own-account businesses operate informally. By size, firm informality is driven by micro firms, of which 82% are informal. Informality among small firms is estimated to be 66%, while 38% is the rate of informality among medium-sized firms and 20% among large firms.

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Table 2: Estimated number of firms in 2020 (thousands)

||Micro<br>|Small<br>|Medium<br>|Large<br>|Total<br>|Own-account<br>|Total+Own-account<br>|Population<br>|
|---|---|---|---|---|---|---|---|---|
|AGO<br>BDI|96.9<br>46.8|355.5<br>11.6|105.9<br>1.7|7.3<br>0.3|565.6<br>60.4|7,051.1<br>2,862.8|7,616.7<br>2,923.2|33,428.5<br>12,220.2|
|BEN|44.3|15.5|2.7|0.3|62.8|3,318.2|3,381.0|12,643.1|
|BFA|14.2|24.2|5.9|0.6|44.9|3,620.0|3,664.9|21,522.6|
|BWA|7.7|7.5|2.9|0.9|19.1|197.4|216.5|2,546.4|
|CAF|10.8|3.8|0.6|0.1|15.4|1043.0|1058.4|5343.0|
|CIV|89.7|50.8|9.0|1.9|151.4|,<br>5,834.3|,<br>5,985.7|,<br>26,811.8|
|CMR|205.0|77.3|14.5|4.0|300.8|6,339.6|6,640.3|26,491.1|
|COD|320.5|330.4|54.1|5.2|710.2|19,287.7|19,998.0|92,853.2|
|COG|4.3|8.2|4.1|0.3|16.9|1,242.5|1,259.4|5,702.2|
|COM|0.2|0.9|0.7|0.0|1.9|107.4|109.2|806.2|
|CPV|4.1|3.2|0.8|0.2|8.2|50.7|59.0|582.6|
|DJI|4.0|2.1|0.3|0.1|6.6|78.1|84.7|1,090.2|
|DZA|241.6|166.6|44.8|9.8|462.9|2,568.1|3,031.0|43,451.7|
|EGY|1,593.7|696.0|106.6|20.8|2,417.2|3,338.2|5,755.4|107,465.1|
|ERI|9.3|7.6|1.8|0.2|18.9|847.5|866.4|3,555.9|
|ETH<br>|97.0|103.7|47.5|10.7|258.9|26,581.9|26,840.8|117,190.9|
|GAB|5.1|5.4|1.9|0.6|13.0|162.4|175.4|2,292.6|
|GHA|604.7|64.5|13.0|2.0|684.2|8,200.8|8,885.0|32,180.4|
|GIN|53.4|7.4|0.8|0.2|61.7|2,516.1|2,577.8|13,205.2|
|GMB|7.1|5.3|1.2|0.2|13.9|519.0|532.9|2,574.0|
|GNB|2.7|3.5|0.6|0.1|6.8|362.4|369.2|2,015.8|
|GNQ|40.1|0.5|0.4|0.0|40.9|351.2|392.2|1,596.0|
|KEN<br>|48.3<br>|180.9<br>|87.5<br>|7.1<br>|323.8<br>|7,835.4<br>|8,159.2<br>|51,985.8<br>|
|LBR|13.5|14.6|4.6|0.3|33.0|1,544.2|1,577.2|5,087.6|
|LBY|154|224|92|10|480|4524|5004|66539|
|LSO|.<br>17.8|.<br>3.2|.<br>0.5|.<br>0.3|.<br>21.8|.<br>313.4|.<br>335.2|,.<br>2,254.1|
|MAR|108.1|95.3|29.0|5.9|238.4|3,219.1|3,457.5|36,688.8|
|MDG|599.8|31.3|4.9|1.3|637.2|6,570.9|7,208.1|28,225.2|
|MLI|16.3|18.8|7.4|0.9|43.4|3,934.5|3,977.9|21,224.0|
|MOZ|183.2|118.8|16.1|3.5|321.5|8,368.3|8,689.8|31,178.2|
|MRT|23.3|10.1|2.3|0.3|35.9|436.0|472.0|4,498.6|
|MUS|7.9|6.3|2.4|0.6|17.3|80.9|98.2|1,266.0|
|MWI|73.2|18.3|5.1|1.6|98.3|3,855.0|3,953.2|19,377.1|
|NAM|32.6|11.1|1.7|0.5|46.0|201.6|247.6|2,489.1|
|NER|51.0|13.3|2.0|0.4|66.7|5,649.7|5,716.3|24,333.6|
|NGA|267.3|781.5|186.2|9.7|1,244.7|42,340.1|43,584.8|208,327.4|
|RWA|17.9|17.4|5.5|1.0|41.8|1,617.4|1,659.2|13,146.4|
|SDN|278.8|167.2|35.1|5.0|486.1|3,869.2|4,355.3|44,440.5|
|SEN|61.3|18.8|4.0|1.8|85.9|2,411.1|2,497.1|16,436.1|
|SLE|64.0|7.8|0.5|0.1|72.3|2,091.8|2,164.1|8,234.0|
|SOM|19.1|5.5|0.8|0.2|25.6|1,451.6|1,477.2|16,537.0|
|SSD|30.0|14.9|2.3|0.3|47.4|1,635.3|1,682.7|10,606.2|
|STP|0.6|0.5|0.1|0.0|1.3|28.1|29.3|218.6|
|SWZ|1.8|2.5|1.2|0.2|5.7|87.4|93.1|1,180.7|
|TCD|40.7|19.6|2.0|0.3|62.5|3,617.1|3,679.6|16,644.7|
|TGO|29.9|10.6|2.9|0.8|44.2|1,950.7|1,994.8|8,442.6|
|TUN|107.2|55.6|15.2|3.4|181.3|484.8|666.1|12,161.7|
|TZA|727.4|82.9|10.7|1.3|822.3|13,603.0|14,425.4|61,704.5|
|UGA|619.7|40.4|3.5|0.4|664.0|9,691.7|10,355.7|44,404.6|
|ZAF|718.6|262.7|53.8|12.4|1,047.4|1,732.7|2,780.1|58,801.9|
|ZMB<br>|3.0<br>|6.2<br>|7.3<br>|0.6<br>|17.0<br>|2,897.4<br>|2,914.4<br>|18,927.7<br>|
|ZWE|2.0|7.3|7.6|0.4|17.3|3,237.1|3,254.4|15,669.7|
|Total|7,682.9|3,997.3|933.1|127.2|12,740.6|231,688.8|244,429.4|1358715.0|

Source: Own calculations. Note: Micro firms exclude own-account businesses, where the proprietor constitutes the sole employee.

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Table 3: Estimated number of formal and informal businesses (thousands)

||(1)<br>Formal|(2)<br>Informal|(3)<br>Informal Share|
|---|---|---|---|
|Micro (less than 5)|1,361|6,322|82|
|Small (5-19)|1,377|2,620|66|
|Medium (20-100)|577|356|38|
|Large (100+)|101|26|20|
|Total Firms|3,416|9,324|73|
|Own-account|13,355|218,334|94|
|Total Firms + Own-account|16,771|227,658|93|

Source: Own calculations.

Note: Micro firms exclude own-account businesses, where the proprietor constitutes the sole employee.

Excluding own account, we estimate that firms absorbs 38% of the labor force on the African continent. Labor force, in this case, involves anyone engaged in labor that is not an employer. This definition, thus, is the sum of individuals working as employees and individuals engaged in own-account activities. We estimate that 62% of the labor force is employed in own-account activities, of which the large majority works informally. In addition, 21% is employed by formal firms. The remaining 17% is employed by informal firms. Notably, informal micro firms employ 11.2% of the total labor force alone, which means small, medium and large informal firms together only account for a combined 5.9% of the total labor force.

The labor share across firm size in the formal market is less concentrated; however, it exhibits the well-known regularity of the missing middle found with other data sources as in Abreha et al. (2022). While formal micro-firms absorb 5.1% of the labor force, small formal firms employ 3.5%. Medium-sized formal firms, on the other hand, account for 5.6% of workers. The group of large firms absorbs the largest share of labor within the formal sector, with 6.9% of the total labor force employed by these firms.

Table 4: Labor absortion

|(1)<br>Formal|(2)<br>Informal|
|---|---|
|Micro (less than 5)<br>5.1|11.2|
|Small (5-19)<br>3.5|3.7|
|Medium (20-100)<br>5.6|1.6|
|Large (100+)<br>6.9|0.6|
|Total Firms<br>21.2|17.1|
|Own-account<br>3.6|58.1|
|Total Firms + Own-account<br>24.7|75.3|

Source: Own calculations Note: Micro firms exclude own-account businesses, where the proprietor constitutes the sole employee.

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It is also worth noting that our methodology is not confined to the context of Africa. It can be adapted for various regions, offering policymakers and researchers a valuable tool to understand the dynamics of the informal sector in different settings. Such insights are essential for devising strategies that encourage formalization and foster economic growth.

This paper describes an initial attempt to estimate measures of firm demographics in Africa. The methodology combines several data sources under strong assumptions to overcome the challenges of limited information on business demographics in the region. This is an important avenue for future research and filling the data gap worldwide, particularly in developing countries. Increasing the availability of harmonized establishment and firm-level censuses, along with robust representative surveys for Africa, will further validate and improve these estimates. It would also allow further disaggregation of the indicators and better understanding of the business dynamics in the continent. Three key steps are needed to advance this agenda: 1. Improve the assessment and availability of existing data; 2. Invest in _ex-ante_ and _ex-post_ harmonization of firm-level data globally; 3. Enhance the capacity of National Statistical Agencies to generate and provide business statistics. We plan to continue this effort to improve the availability and quality of harmonized firm-level data across the African continent and globally.

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