Chapter 3, the Special Feature of this report, discusses commonalities and specificities of revenue systems in African countries, identified via the development and analysis of National Revenue Lists. This analysis could inform the development of an African revenue classification, which can accelerate the harmonisation of revenue statistics in Africa and foster regional integration.
Revenue Statistics in Africa 2025
Commonalities and Specificities across African Revenue Classifications
Abstract
Harmonised revenue statistics are a key basis for improving domestic revenue mobilisation. By enhancing countries’ ability to compare the level and structure of their public revenues with those of other countries, internationally harmonised data allows countries to assess empirically the relative merits of different tax policies and measures in other countries and thereby design and implement policies for their own country. In addition, harmonisation can promote regional integration, including through a standardisation of statistical methodologies. To accelerate the harmonisation of revenue statistics in Africa, the African Union Institute for Statistics (AU-STATAFRIC) has highlighted the value of developing an African revenue classification, drawing on data and analysis on African revenue systems produced via the Revenue Statistics in Africa initiative.
Revenue Statistics in Africa data are classified using the OECD classification of taxes and non-tax revenues (see Annex A and B) that is harmonised with other international statistical classifications (e.g. the System of National Accounts [SNA], the Government Finance Statistics Manual [GFSM] and the European System of Accounts [ESA]). As part of the second phase of the Pan-African Statistics programme (PAS II),1 the Revenue Statistics in Africa initiative was able to scale up its contribution to harmonising statistics on public revenues across the continent by producing data and analysis to inform the development of an African revenue classification.
This Special Feature describes how Revenue Statistics in Africa data and analysis identified commonalities and specificities of revenue systems that could be used as the basis for a common African revenue classification. It starts by describing the National Revenues Lists (NRLs) that underpin the analysis in this chapter. It then discusses commonalities and specificities across African revenue classifications in three areas: (i) the degree of granularity in the revenue data that countries report; (ii) the revenue categories countries use and how these categories compare with the OECD classification; and (iii) the measurement of – and data gaps in – revenues from the extractive sector. It ends by introducing objectives and principles that may underpin an African revenue classification based on this analysis.
The structure and complexity of revenue classifications in Africa, and the commonalities and differences between them, can be analysed using NRLs. NRLs are data files that match national tax and non-tax revenue categories with the corresponding categories in the OECD classification and other international classifications (SNA, GFSM, ESA), as well as showing the revenue generated by each tax type.
NRLs show how African countries name, organise and structure their revenue data, including how they disaggregate different types of revenue, which revenue types are most common, and which revenue types generate the largest share of public revenues. An NRL thus serves both (i) as a source of information on individual revenues or categories of revenues and how these correspond to the OECD and other international classifications and (ii) as a basis for revenue analysis both at the level of an individual country and across countries.
The NRL methodology is a natural extension of the Revenue Statistics in Africa data collection and harmonisation process, which uses a bottom-up approach, i.e. the most disaggregated revenue categories in national reporting are assigned an appropriate category in the OECD classification according to its base. Each revenue line (hereafter called revenue items) of an NRL corresponds to a single revenue source, or grouping of revenues, defined either according to the national revenue classification or according to the harmonised Revenue Statistics in Africa classification. The NRL also includes a flag identifying whether, for each revenue item, a substantial fraction comes from extractive industries. Between 2021 and 2023, NRLs were produced for 27 African countries based on data from Revenue Statistics in Africa (OECD/AUC/ATAF, 2023[1]).
The NRLs for different countries indicate that the granularity of revenue data varies significantly within and across African countries, as well as over time. Figure 3.1 shows a count of revenue items by country, as found in the NRLs of the 27 countries that provided them. The number of items shown in Figure 3.1 includes all revenue items that have been reported by a given country over the entire period up to 2021 and shows how they were reported for the year 2021. Due to changes in reporting or in the tax system itself, some tax or non-tax categories reported in the past ceased to be reported in later years. There are also differences between countries in terms of the number of years for which they have reported revenue data, which can impact the number of revenue items contained in the respective NRLs. The number of revenue items for each country may also reflect structural differences in tax systems and differences in statistical or administrative capacity.
The number of revenue items reported ranged from fewer than 28 in Lesotho to more than 900 in the Democratic Republic of the Congo. Nine countries (Chad, Cabo Verde, Ghana, Lesotho, Madagascar, Malawi, Namibia, Nigeria and Sierra Leone) had fewer revenue categories than there were OECD categories. By contrast, in the data over the entire period up to 2021, Botswana had 216 distinct non-zero disaggregated revenue categories while the Democratic Republic of the Congo had 388.
There is also a high degree of variability in revenue items within African countries over time. In all but one of the 27 countries analysed, there were more disaggregated revenue categories for which no revenue was reported in 2021 than there were categories containing some revenue. This implies a high degree of churn within revenue data, characterised by the addition or removal (or change of classification) of a large number of revenue streams.
Note: Disaggregated revenue items are revenue items that are not themselves sums or aggregations of other revenue items. These correspond to the greatest level of granularity. Aggregated revenue categories refer to reported totals for a given class of revenues.
Source: Author’s calculations based on analysis of National Revenue Lists (NRLs) based on data provided for (OECD/AUC/ATAF, 2023[1]).
Figure 3.2 displays the degree of churn within each country’s NRL by showing the average annual percentage of disaggregated revenue items that change from having a non-zero reading in one year to either being blank or zero in the next (or vice versa). On average across all countries over the timeframe under analysis, 7% of revenue items in one year cannot be matched to a corresponding revenue item in a previous year. There were a few cases where a country’s entire national revenue classification was overhauled, with the result that almost no revenue items in one year could be matched to items reported in previous years.
Percentage of total number of revenue categories
Note: The average share of revenue items added or removed are based on the number of items that have been reported by a given country over the entire period up to 2021.
Source: Author’s calculations based on analysis of National Revenue Lists (NRLs) based on data provided for (OECD/AUC/ATAF, 2023[1]).
NRLs can be used to show how OECD classifications and definitions can be mapped onto African national reporting and to identify the most important revenue categories for African countries after harmonisation with the OECD classification. The importance of different OECD revenue categories in African countries can be analysed with reference to two indicators:
Revenue from a given OECD revenue category as a percentage of total revenue; and
The frequency of NRL items reporting a non-zero or non-blank value for this OECD category as a percentage of total NRL items.
Most revenue in Africa is found in a few OECD categories, which are usually the categories that include most revenue items. On average, 85% of total revenue was generated by items that correspond to just eleven of 50 OECD revenue categories and accounted for about 75% of Africa NRL items (Figure 3.3). Given their prominence, these revenue categories could be strongly considered for inclusion within an eventual African revenue classification; since they are frequently disaggregated in the tax systems of individual African countries, the possibility of including subdivisions of these categories within such a regional classification is explored later in this chapter.
The tax categories that generated the largest share of total revenue on average between 2016 and 2021 were within taxes on goods and services (value-added taxes [VAT], customs duties, excises and taxes on specific services) and within taxes on income, profits and capital gains (personal income tax [PIT] and corporate income tax [CIT]). Meanwhile, the majority of non-tax revenue was derived from three main categories: ‘Grants’, ‘Rents and royalties’, and the residual ‘Miscellaneous and unidentified revenue’ category.
Note: The revenue categories shown in this figure correspond to the OECD classification and are those at the most disaggregated level (in a total of 74 revenue categories, 50 are disaggregated categories and 24 are their subtotals)
Source: Author’s calculations based on analysis of National Revenue Lists (NRLs) based on data provided for (OECD/AUC/ATAF, 2023[1]).
NRLs also facilitate analysis of the hierarchy of revenue classifications within countries’ own national reporting. The approach for this analysis was to identify the aggregated tax categories in each country’s hierarchy before harmonisation and find out which categories were least or most often found in African revenue data. This shows the extent to which revenue categories in African countries match OECD categories.
The main OECD tax categories (income, payroll and workforce, property, and goods and services) are frequently found in African countries’ revenue hierarchies. Several revenue categories under income taxes and taxes on goods and services are common to most African countries. Under income taxes, a majority of African countries distinguish between CIT and PIT. Under taxes on goods and services, most distinguish between VAT, excise taxes, import duties and export duties.
NRL analysis also enables the identification of revenue categories that are frequently reported in African countries but are not found in the OECD classification, and conversely, which distinctions within the OECD classification are not generally found in African countries.
As shown in Table 3.1, unlike in the OECD classification, African countries often distinguish between taxes on domestic and international trade. Moreover, VAT and excises are commonly broken down between domestically produced versus imported goods and services, since in many countries a different authority handles customs and import duties from the institution responsible for domestic revenue collection. These distinctions are particularly relevant given the continued importance of revenues from trade taxes in many African countries (OECD/AUC/ATAF, 2024[2]). In addition, withholding taxes and stamp duties are not included as a separate category in the OECD classification but are often found in the revenue classifications of African countries, due to the advantages they bring in terms of administrative simplicity.
Source: Author’s calculations based on analysis of National Revenue Lists (NRLs) based on data provided for provided for (OECD/AUC/ATAF, 2023[1]).
Under the main tax categories there are large numbers of revenue items that share similar themes. For example, PIT includes revenue items that can be grouped together by income type to form new PIT sub-categories (e.g. employment, business, professional). Excises on some key products, such as fuel and cigarettes, can be reported separately while taxes on specific services can be reported by sector (insurance, gambling, financial services, etc.)
Conversely, certain African revenue categories are not disaggregated in the same way as in the OECD classification, such as a distinction between individuals or households versus companies (for example, within capital gains taxes or motor vehicle registrations); recurrent versus non-recurrent taxes (for example, within property taxes, taxes on use of goods, or on permission to use goods or perform activities); and payroll basis and income basis for social security contributions.
NRLs can be used to identify gaps in the reporting of revenues related to specific policies or sectors, in particular resource revenues. Some African countries collect substantial revenues from mineral and petroleum resources but these revenues tend to be more volatile and less sustainable than non-extractive revenues, due to fluctuating commodity prices and the finite nature of these resources. Distinguishing between extractive revenues and other public revenues can enhance fiscal policy in a number of ways, for example, helping governments to track revenue fluctuations and plan for long-term economic sustainability and ensuring that these revenues are properly accounted for and support development goals.
In collaboration with countries and partners participating in Revenue Statistics in Africa, the OECD developed an approach using NRLs to map extractive-related revenues. First, the following definition of extractive revenues was agreed upon: revenues collected from companies involved in exploration and extraction of oil, gas, and mineral resources. Countries were subsequently asked to flag tax and non-tax categories in their NRL where a substantial share of revenue comes from extractive activities. Focal points answered “yes” or “no” to the question “is there a substantial amount of revenues in this category coming from extractive industries (oil, gas or mining)?” for each main revenue category in their data.
Two variables were created for analysis:
1. Explicitly identified extractive revenues. These are revenue categories clearly labeled as related to extractive industries and include only extractive-related income (e.g., corporate income tax from petroleum companies).
2. Other substantially extractive revenues. These categories contain a significant share of extractive-related income, although the exact proportion is unknown. This group excludes the explicitly identified extractive revenues mentioned above.
There was no quantitative threshold to measure what constitutes a “substantial” amount of revenues, since for some countries, the extractive-related component of revenues was not quantified. For these countries, the extractive-related flag serves to focus attention on the components of the fiscal system that are impacted by extractive industries, even where accurate estimates of the extractive-related revenues are not available.
As a result of this exercise, some African countries introduced changes to their data submission in subsequent years, for example by introducing an extractive-non-extractive split in their revenue categories through which extractive-related revenues were reported (e.g. The Democratic Republic of the Congo, Sierra Leone). The exercise also informed new analysis of extractive revenues in this report (see Chapter 2).
As shown in Figure 3.4, the share of explicitly identified extractive revenues in total revenues (tax and non-tax) varies greatly across countries. In 2021, the share exceeded 10% of total revenues in most of the resource-rich countries included in Revenue Statistics in Africa (Chad, Republic of the Congo, Equatorial Guinea and Mauritania). However, in many countries, a large portion of revenue is flagged as substantially extractive but the exact share attributable to extractive industries is unknown.
Percentage of total revenues
Note: The figures above exclude Seychelles, Madagascar, Malawi and Mauritius as these countries indicated that they did not receive a substantial proportion of revenues from extractive industries. For some countries in this analysis, information on other revenues containing a substantial fraction of extractive revenues were not identified by countries or were not readily available through public sources at the time of this analysis, meaning that the share of revenues explicitly identified as extractive in the NRL is a lower bound and does not represent the totality of extractive revenues.
Source: Author’s calculations based on analysis of National Revenue Lists (NRLs) based on data provided for (OECD/AUC/ATAF, 2023[1]).
Nigeria, for example, reported explicitly identified extractive revenues amounting to 24% of total revenues in 2021 while other substantially extractive revenues summed to 42% of total revenues. For each category of explicitly extractive revenues, 100% of revenue can be assumed to come from extractive companies, while for each category of other substantially extractive revenue, the percentage of revenues coming from the extractive sector will be somewhere between 0% and 100%. This means that the share of Nigeria’s revenues coming from extractive companies is between 24% and 66% of total revenues.
This wide range of estimates illustrates the need for more precise data to support tax policy analysis related to the extractive sector. African tax administrations could consider facilitating the disaggregation of revenues by sector and ensuring that revenues from extractive industries are disclosed and verified. As the extractive sector is often managed by different agencies, specific collaboration may be required between tax authorities, ministries of finance, natural resource ministries, and customs agencies to share revenue data or to create a repository that consolidate revenue data from multiple sources.
Note: Some revenue categories clearly identified as containing 100% extractive revenues may include revenues from downstream activities, such as the conversion of oil and gas into finished products, as it was not possible to separate these revenues.
Source: Author’s calculations based on analysis of National Revenue Lists (NRLs) based on data provided for (OECD/AUC/ATAF, 2023[1]).
Explicitly identified extractive revenues cut across several revenue categories as illustrated in Figure 3.5. They are mainly in the category Rents and royalties and (to a lesser extent) the categories CIT and Interest and dividends. Rents and royalties on extractive industries were explicitly reported by 19 countries and represented on average 8% of total revenues. Nine countries reported explicitly extractive revenues in the category CIT (4% of total revenues) and six in the category Interest and dividends (2% of total revenues). In contrast, other substantially extractive revenues are distributed across a broader range of revenue categories, although most are concentrated in PIT, CIT and VAT, as shown in Figure 3.6.
In Nigeria, for instance, 10% of total revenues in 2021 were generated entirely from the extractive sector through rents and royalties and 11% through CIT (Figure 3.5). These two categories also include additional extractive revenues that are not distinguishable from non-extractive revenues, which could amount to up to 16% and 10% of total revenues respectively (Figure 3.6). However, in other categories such as PIT and customs duties, Nigeria is unable to separate extractive from non-extractive revenues.
Source: National Revenue Lists for 22 countries; questionnaires on extractive revenues circulated to government officials; and data from the Extractive Industries Transparency Initiative (EITI, 2023[3]) for countries whose responses to the questionnaires were not available.
Source: Author’s calculations based on analysis of National Revenue Lists (NRLs) based on provided for (OECD/AUC/ATAF, 2023[1]).
The advantages of an African revenue classification
For Revenue Statistics in Africa, harmonisation is carried out according to the OECD classification, which is designed to be applied at the global level. A new classification designed specifically for African countries could yield advantages to African policy makers and data users. An African revenue classification would benefit from African ownership and buy-in; with greater African input into its design, it may be more responsive to African priorities and could more readily address Africa-specific issues. Pan-African objectives and strategies such as the second Strategy for the Harmonization of Statistics in Africa (SHaSA II) could be incorporated directly into an African revenue classification (see Box 3.1). These benefits can create a virtuous cycle of increased harmonisation leading to more up-take in use, in turn promoting continental integration and facilitating data sharing, co-operation and statistical capacity building as well as improved analysis.
An African revenue classification could thus result in a closer interplay between the design of the revenue classification and fiscal policy. The choice of whether to include or exclude specific categories within the African revenue classification would focus attention on revenue gaps and options for introducing new revenue types reflecting the main priorities of African policy makers. For example, as shown above, resource revenues are not explicitly identified in the OECD classification but accurately measuring such revenues is a major concern to many African countries and the continent as a whole.
An African revenue classification could build on considerable work already done on revenue data harmonisation in Africa. Some Africa-specific issues with revenue data are already well-studied, such as the aforementioned importance of extractive revenues (Harshali, 2021[4]; Siakwah, 2024[5]).
Some African regional organisations, such as the Economic and Monetary Community of Central Africa (CEMAC) and Economic and the Monetary Community of Western Africa (WAEMU), have already developed their own specific classifications that their members use for data harmonisation and reporting. These initiatives have created a pool of experts and officials that could contribute to the development of an African revenue classification.
The second Strategy for the Harmonisation of Statistics in Africa (SHaSA 2) provides a continental framework to standardise definitions, classifications, and data collection methods, thereby fostering regional economic integration. Linking Revenue Statistics in Africa with SHaSA 2 is essential for improving the quality, comparability, and use of fiscal data across the continent.
Revenue Statistics in Africa – which provides tax and non-tax revenue data harmonised according to the well-established OECD classification – is essential for tracking domestic resource mobilisation, designing effective tax policies, and meeting development goals such as the AU’s Agenda 2063. The advantages of harmonising revenue statistics under SHaSA 2 include improved comparability, better monitoring of fiscal performance, enhanced transparency, and stronger coordination among African Union Member States and African institutions. Harmonised revenue statistics are also critical for effective governance.
Harmonising revenue statistics requires sustained investment in national statistical systems, strong political will, and close collaboration between statistical offices, finance ministries and regional bodies. At present, many countries struggle with outdated or fragmented data systems, making consistent reporting difficult; there are also disparities in statistical capacity, limited funding, weak institutional coordination. Harmonisation is also constrained by differing definitions of revenue in the absence of an African revenue classification
SHaSA 2 provides the strategic vision and tools to address some of these challenges, notably the development of an African revenue classification, which is an ongoing activity of STATAFRIC in collaboration with OECD and other partners. Its success depends on practical implementation, capacity-building, and ongoing commitment from all stakeholders.
Objectives and principles for an African revenue classification
Before developing an African classification, consultation with experts, officials and institutions would be required to identify (i) the institutional and statistical objectives of such a classification, and (ii) the data concerns that are both specific to African countries and common across the continent. The following section includes a preliminary set of propositions that emerged from research and discussions with African organisations and countries in the Revenue Statistics in Africa initiative.
Potential alignment between principles for an African revenue classification and the African Charter on Statistics
An African revenue classification could be based on principles that also underpin international classifications (as called for by SHaSA 2, Strategic Objective 1.3) and that are aligned with the African Charter on Statistics (AUC, 2009[6]). The principles outlined below are applicable to all types of economies, regardless of institutional or legal structures and statistical capacity. The following principles proposed for an African revenue classification draw on principles that underpin international classifications in general (as called for by SHaSA 2, Strategic Objective 1.3), principles of professional independence as well as quality, accuracy and reliability in the African Charter on Statistics (AUC, 2009[6]), and the knowledge acquired through the Revenue Statistics in Africa initiative.
These principles align with two key principles of the African Charter on Statistics:
Principle 1: Professional independence – Impartiality: “Statistics authorities shall produce, analyse, disseminate, and comment on African statistics in line with the principle of scientific independence, and in an objective, professional and transparent manner.”
Principle 2: Quality:
Relevance: “African statistics shall meet the needs of users.”
Specificities: “Statistical data production and analytical methods shall take into account African peculiarities.”
Topicality: “African statistics shall reflect current and topical events and trends.”
Coherence and comparability: ‘’African statistics shall be internally coherent over time and allow for comparison between regions and countries. To this end, these statistics shall make combined use of related data derived from different sources. It shall employ internationally recognized and accepted concepts, classifications, terminologies and methods.”
Continuity: “Statistics authorities shall ensure continuity and comparability of statistical information over time.”
Sustainability: “African statistics shall be conserved in as detailed as possible a form to ensure their use by future generations, while preserving the principles of confidentiality and protection of respondents.”
Accuracy and reliability: “African statistics shall be an accurate and reliable reflection of the reality.”
Reflecting African realities in an African revenue classification
An African classification needs to reflect African realities, as called for by SHaSA 2 and by the African Charter on Statistics (Principle 2: Quality – Specificities: “Statistical data production and analytical methods shall take into account African peculiarities”). Information collected from Revenue Statistics in Africa and NRLs, as well as through consultation with officials and partners, suggests the following characteristics be considered when interpreting revenue data and developing an African revenue classification:
1. African countries rely heavily on natural resource revenues, which are generated through several instruments (e.g. rents and royalties, CIT, dividends). (AFDB, 2023[8]) projects that Africa’s extractive resources would contribute more than USD 30 billion annually to government revenue by 2024.
2. Trade taxes are an important source of tax revenues in Africa: Trade taxes have been affected by trade liberalisation across the globe but their share of total tax revenues remains relatively high in Africa, especially among countries at lower income levels. In 2021, about 13.3% of total tax revenues was from trade taxes across 28 African countries included in Revenue Statistics in Africa (OECD/AUC/ATAF, 2023[1]).
3. Most African countries have a large informal sector. An estimated one-third of African economic output comes from informal activity, while over 80% of workers in the average African country are in informal employment.2 Many countries are developing and implementing measures to tax the informal sector, such as presumptive or withholding taxes. Local taxes and fees also play an important role in the informal sector.
4. Africa is highly reliant on VAT. VAT is applied in all African countries on the domestic markets and on imports. However, the VAT system does not always function efficiently and several countries do not provide timely VAT refunds.
5. Africa’s public wage bill can be sizeable. In Sub-Saharan Africa, public sector employment makes up around half of formal wage employment against 37% globally (World Bank, 2023[9]) which impacts PIT.
6. Foreign companies may contribute relatively low revenues due to profit-shifting strategies as well as tax incentives provided by African governments to attract foreign investment. This creates unfair conditions for domestic companies. For example, (Albertin, Devlin and Yontcheva, 2021[10]) found that governments in sub-Saharan Africa lose between USD 450 million and USD 730 million per year in CIT revenues as a result of profit shifting by multinational companies in the mining sector.
7. Many African countries resort to taxes that are easier to administer: due to limited administrative capacity, many countries use of simpler taxes such as stamp duties and withholding taxes which are easier to collect.
Towards a proposal for an African revenue classification
Some options for an African revenue classification can be proposed based on existing empirical analyses, the principles and African realities listed above as well as on the key findings emerging from analysis of Revenue Statistics in Africa data and the NRLs. For instance, an African revenue classification could be developed starting with the OECD classification and adding, removing, aggregating, or sub-dividing OECD revenue categories, according to the following key principles:
The definitions of tax and non-tax revenues for an African revenue classification follow those in the OECD Interpretative Guide, so as to ensure international comparability.
The highest-level revenue categories are primarily defined according to the revenue base, while African specificities are reflected through the addition of new sub-categories to aggregated categories. This means retaining the same six main tax categories as the OECD classification (income taxes, social security contributions, payroll, property, taxes on goods and services and other taxes).
The revenue categories may apply to a subset of countries but not necessarily to all.
Options for including (or not) categories and sub-categories of revenues are primarily based on NRL analysis, regional classifications and African realities previously identified, and they serve as a starting point for discussions. The option to merge revenue categories should be carefully considered in light of possible adverse impacts on the granularity of the classification and analysis of key sectors or taxpayer groups (i.e. multinationals and high net worth individuals).
An example of this proposed approach is shown in Table 3.3.
As part of PAS II, the Revenue Statistics in Africa initiative undertook extended work in harmonising public revenue data and identifying the scope for a more refined revenue harmonisation across the continent. Analysis of the NRLs produced in collaboration with African countries uncovered commonalities and specificities across revenue classifications in Africa and in comparison, with the OECD classification.
The following key findings emerged from the analysis:
The degree of granularity of revenue data varies significantly between African countries and over time.
Several revenue categories of income taxes and taxes on goods and services are common to most African countries. Most countries distinguish between income taxes on individuals and income taxes on corporations, for example. Most also distinguish between VAT, excises, import duties and export duties, and taxes on specific services under taxes on goods and services.
Some distinctions within revenue categories are common in Africa but do not exist in the OECD classification: VAT and excises in African countries are commonly broken down between domestic and imported goods.
Certain African revenue categories tend not to include some divisions found in the OECD classification.
There are large numbers of revenue items in African countries within main tax categories that share similar themes. For example, PIT can be differentiated according to income type (e.g. employment, business, professional, etc) and excises can be differentiated by products.
Extractive-related revenues are not systematically identified in national reporting, despite the importance of the extractive sector to many African countries. This is particularly the case for PIT, CIT and VAT, despite the fact that extractive industries have a significant impact on these taxes.
These findings provide an initial framework for the development of an African revenue classification, which is an objective aligned with STATAFRIC’s broader strategy to harmonise statistics across the continent. Further analytical work and consultations with African tax and statistical experts and regional organisations (e.g., ATAF, AUC, Regional Economic Communities, UNECA and AFRISTAT) could improve and extend the analysis described in this Special Feature. This will ensure that a potential African revenue classification is fit-for-purpose, reflects African realities and responds to the continent’s needs and priorities.
References
[8] AFDB (2023), Natural capital for climate finance and green growth in africa, https://www.afdb.org/sites/default/files/aeo_2023-chap3-en.pdf.
[10] Albertin, G., D. Devlin and B. Yontcheva (2021), Countering Tax Avoidance in Sub-Saharan Africa’s Mining Sector, https://www.imf.org/en/Blogs/Articles/2021/11/05/blog-countering-tax-avoidance-sub-saharan-africa-mining-sector.
[6] AUC (2009), African Charter on Statistics, https://au.int/en/treaties/african-charter-statistics.
[3] EITI (2023), Extractive Industry Transparency Initative (EITI) online data, https://eiti.org/open-data (accessed on 5 November 2023).
[4] Harshali, R. (2021), Analysing the extractive industry fiscal policies in sub-Saharan Africa, MPRA Paper No. 118520, https://mpra.ub.uni-muenchen.de/118520/.
[11] ILO (2025), International Labour Organization datasets, International Labour Organization, http://www.ilo.org/data-and-statistics.
[7] OECD/ATAF/AUC (2022), Revenue Statistics in Africa, https://doi.org/10.1787/ea66fbde-en-fr.
[2] OECD/AUC/ATAF (2024), Revenue Statistics in Africa 2024: Facilitation and Trust as Drivers of Voluntary Tax Compliance in Selected African Tax Administrations, OECD Publishing, Paris, https://doi.org/10.1787/78e9af3a-en.
[1] OECD/AUC/ATAF (2023), Revenue Statistics in Africa 2023, OECD Publishing, https://doi.org/10.1787/15bc5bc6-en-fr.
[5] Siakwah, P. (2024), Extractive industries in Africa, https://doi.org/10.4337/9781800885806.00023.
[12] UN (2024), The 2024 Revision of World Population Prospects, UN Department of Economic and Social Affairs Population Division (UN-DESA), https://population.un.org/wpp/.
[9] World Bank (2023), Worldwide Bureaucracy Indicators : Regional Outlook: Sub-Saharan Africa, https://documents1.worldbank.org/curated/en/099062623201156045/pdf/P168703069222708b0807706568cb40c916.pdf.
← 1. Between 2021 and 2024, Revenue Statistics in Africa activities received financial support from the European Union as part of PAS II. The Pan-African Statistics programme is a joint African Union-European Union initiative to improve measurement of progress in the process of African Integration by promoting the use of statistical data of quality in the Africa Integration decision making process and policy monitoring.
← 2. Authors’ calculations based on (ILO, 2025[11]) and (UN, 2024[12]).