Telecommunications sector is one of the most dynamic economic sector in Sub-Saharan African countries. However, the rapid adoption of mobile services triggered debate regarding the adequate level of taxation that should apply to the sector.
Conducing tax policies, requires some informations regarding the current tax systems. In line with this context, our analysis estimates the tax burden borne by mobile phone companies in some Sub-Saharan African countries. Our methodology follows the standard approach of forward-looking AETR through a representative firm.
Given the lack of availability of granular financial data, we build TELCO, a representative mobile phone company, using the GSMA Intelligence database. The financial data and economic activities of TELCO are expressed in terms of percentage of final consumption or subscribers for each country. We consider the tax regime applying in fiscal year 2018 over the length of a typical license period, which we assume to be fixed at 15 years. Such assumptions allow us to determine the AETR. The AETR results from the ratio of the Net Present Value (NPV) of paid taxes to the NPV of cash flows generated by the license.
This application presents the steps taken in our AETR computation with our baseline assumptions. It has the status of being interactive in the sens that it gives the possibility to modify parameters, the consider MNO data, and exemptions.
Our study also include analysis sections including a breakdown of tax revenues between general and mobile specific taxation, beneficiary institutions, and a cross sectoral comparisons section. The cross-sectoral comparison considers three sectors: telecommunications', gold mining's, and a standard sector without any specific taxation.
The preliminary data page permits to modify all parameters by double clicking on the targeted cell. In the tax computation page, we have two parts including exemptions and losses carry forward specification and the AETR results, and a graphical analysis subsection. In the first subsection, the user has two options. The first one is the automatic tax data computation which considers our calibrated exemptions and losses carry forward parameters taken from tax and investments codes. The second is to manually specify all details in the AETR computation. The user could then decide to make simulations by directly applying some specific exemptions.
Tax revenues breakdowns and the cross-sectoral comparison are presented in the graphical analysis subsection.
The figure below presents the market penetration distribution in term of unique subscribers for 2018 of the 25 countries we study in our analysis.
This group constitutes a significant part of Africa as it represents 60 percent of total Africa's GDP, 79 percent of the total population, and 81 percent of the continent unique subscribers.
The tax burden on mobile network operators in Africa
In this page, we present an illustration of the approach considering the case of Cameroon. As explained in the paper, we need data on final consumption and the number of subscribers to derive TELCO financial and market data. The tables below present these data and our computations (in million Euro).
The figure below presents a breakdown of TELCO net cash flows.
This page presents the used data for the AETR computation. A value added of the application is that each user could modify all assumptions he wants by double clicking into the cell and press enter to save.
WARNING : This page may take a few moments to load. In addition, after each data modification, the user will have to wait for the application to finish reloading before continuing.
 The multiplicative factor is 10e-4 * Final consumption.  The multiplicative factor is the total number of subscribers.  It is expressed in Euro per minute
 Fill only if there a differentiated rate for foreigner employees.  Lump-sum contribution in Euro.
 Expressed in Euro per minute except for Benin where it expressed in percentage of the turnover related to international incoming calls.  Specific tax in Euro per subscriber number.  Spectrum fees are expressed in percentage of the turnover
In this page, are presented all exemptions and losses carry forward details, the AETR results, and some graphical analysis. We present an automatic selection of all these parameters by default using tax and investments codes. However, each user could modify and apply his own scenarios by selecting the manual procedure in the exemption selection mode. AETR, AESTR, and AEGTR respectively represent the total AETR, special taxes AETR, and general taxes AETR of TELCO.
This figure presents the breakdown of tax revenues by type of taxation.
This figure presents the breakdown of tax revenues by beneficiary institution.
The treemap presents the same results than the two figures above but in obsolute term. It compares tax revenues accross countries and presents inside each country box, how tax revenues are shared depending on the selected axe.
This figure presents the breakdown of the AETR by type of taxation.
This figure presents the breakdown of the AETR by beneficiary institution.
This figure presents the cross-sectoral comparison of the AETR.
The radar chart permits to compare two countries at the same time. Futhermore, the AETR are compared from a relative and not in an absolute point of view.
This figure presents the evolution of pre-tax and post-tax Internal Rate of Return (IRR). Furthermore, it gives the possibility to focus on one or more of the represented elements. For example, it is possible to analyze the evolution of the pre-tax IRR during the exploitation of the license by double-clicking on pre-tax IRR in the legend or by clicking on post-tax IRR to deselect it.
This figure presents the breakdown of TELCO net cash flows. In addition, it gives the possibility to focus on one or more of the represented elements. For example, it is possible to analyze the evolution of taxes and fees during the exploitation of the license by double-clicking on taxes and fees in the legend or by clicking on the other elements to deselect them.
This figure presents the choosen variable for the studied countries in 2018.
This figure presents the correlation between the choosen AETR in x axis and the choosen variable in y axis.