Mahdi Al-Mouksit Akim

Ben Jelloul

Leo Czajka

Anne-Sophie Robilliard Coordination

Anda David

Mohammed Vi

Anne-Sophie Robilliard

Collect more, spend better? Assessing the incidence of fiscal systems and public spending in three Francophone West African countries

Keywords: Fiscal incidence, Inequality, West Africa JEL Classification H22, I3, O1 Inégalités, mobilité salariale, dynamiques de cycle de vie, panels synthétiques, Afrique du Sud

The objective of this study is to analyse and compare the incidence of fiscal systems of 3 western African countries: Côte d'Ivoire, Mali and Senegal. The analysis relies on different data and tools: (1) individual and household level data from 3 recent household surveys (EMOP 2011, ESPS 2011, ENV 2014), (2) a detailed description of the 3 fiscal systems, (4) the CEQ conceptual framework, and (4) the Openfisca platform an open source tax-benefit calculator parameterized to simulate the fiscal systems of each country. Results indicate that fiscal systems in Mali, Senegal and Côte d'Ivoire have a slightly progressive impact on inequality. This stems from the combination of slightly progressive direct taxes, regressive indirect taxes, and progressive public spending on education. Various features are likely to explain these results: (1) Direct taxes are paid by a very small fraction of the population; (2) Indirect taxes such as VAT and import tariffs affect poorest households more

Collect more, spend better? Assessing the incidence of fiscal systems and public spending in three Francophone West African countries

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since they consume a higher share of their income; (3) Primary schooling rates are high and poorer households tend to have more children. These results point to some recommendations to enhance the redistributive power of existing systems: expand the reach of direct PIT ; reexamine the incidence of exemptions to VAT and import taxes ; increase transparency on public spending to maintain Willingness-to-Paytaxes at high levels; improve household survey data quality and promote access to fiscal data.

Résumé

L'objectif de cette étude est d'analyser et de comparer l'incidence des systèmes fiscaux de 3 pays d'Afrique de l'Ouest: la Côte d'Ivoire, le Mali et le Sénégal. L'analyse s'appuie sur différentes données et outils : (1) des données individuelles et au niveau des ménages provenant de 3 enquêtes récentes auprès des ménages (EMOP 2011, ESPS 2011, ENV 2014), (2) une description détaillée des 3 systèmes fiscaux, (4) le cadre conceptuel du CEQ,et (4) With this background in mind, the objective of this paper is to analyse and compare the incidence of benefit, tax and public spending in three western African countries: Côte d'Ivoire, Mali and Senegal. These three countries share a number of characteristics: they are of similar size in terms of population, they are democracies, they were under French rule during colonial times and have kept relatively similar fiscal and administrative systems since gaining Independence. Despite these similarities, these countries achieve different levels of fiscal performance, whether measured in terms of tax ratio or redistribution. Understanding why is the main objective of this paper. To that end, this analysis aims at answering the following questions:

Introduction

• Who pays direct and indirect taxes and who benefits from transfers and public spending?

• What is the global impact of tax and benefit systems on inequality?

• Which tax instruments will allow raising domestic resources while achieving poverty and inequality reduction?

• How do country characteristics interact with tax and benefit system parameters to determine the incidence of tax benefit systems?

Using the CEQ conceptual framework, an open source fiscal calculator and recent household survey data, tax and benefit incidence analysis is carried out to try and answer these questions. The approach provides a picture of the distribution of taxes and public spending between households to determine whether fiscal instruments and public spending are progressive or regressive. The approach is based on an analysis of the tax code to extract key fiscal parameters, administrative data to estimate the costs of public services provision, and household survey data. The latter is used to extract data on income and consumption as well as variables needed to simulate tax payment, entitlement to benefits, and use of public services.

Using this information, we make use of "static" tax incidence analysis tools that allows (i) measuring the "first order" distributive impact of existing tax and social systems and (ii) simulating the impact of the reforms applied to them (Bourguignon and Spadaro, 2006). Similar static approaches, such as Benefit Incidence Analysis, can also be used to analyse the distributive impact of public spending on social services such as health and education. Recently, these two "branches" of static impact simulation have been brought together by the CEQ project (Lustig and Higgins (2017); Inchauste and Lustig

Conceptual Framework, Methodology and Data

The CEQ Framework

The CEQ project provides a framework to analyse how fiscal systems impact inequality and poverty. This framework relies on the definition of four income concepts 1 that are connected by the different tax and benefit instruments. Figure 1.1 provides a picture of how these income concepts are related. Next, Disposable Income -also known as Post Tax Income or Secondary Income -refers to the income that is available to households after they have paid taxes and benefited from direct public transfers. Third, Consumable Income refers to income available after taking into account indirect taxes and indirect subsidies. Last, Final Income -also known as Tertiary Income -includes the monetized value of the use of assignable public services such as education and health.

By comparing the distribution of these different income concepts, one can assess the distributive impact of the different elements that constitute the fiscal system. The comparison of the distribution of Disposable Income with that of Market Income provides an estimation of the incidence of direct taxation. Similarly, the comparison of the distribution of Consumable Income with that of Disposable Income provides an estimation of the incidence of indirect taxation. Finally, the comparison of the distribution of Final Income with that of Consumable Income provides an estimation of the incidence of public spending.

As mentioned above, this framework brings together techniques and tools that have been developed in different contexts: tax benefit models (TBM) in developed countries, price incidence analysis (PIA) and benefit incidence analysis (BIA) in developing countries.

The analysis of how taxes and benefits affect the distribution of income is a central objective of tax and benefit models (TBM) developed since the late 1980s in many OCDE countries. Another important objective of these models is to simulate the "cost" of the tax-benefit system. Indeed, given the complexity of tax and benefit systems in developed countries, this assessment is 1 Sometimes five. not straightforward. It requires a precise knowledge of the rules and parameters in order to identify tax payers and beneficiaries and to compute the value of the taxes they must pay and the transfers they are entitled to. Since the late 1990s, TBMs have been in use in many developed countries, both by public administrations and by think tanks and research centers. These models contribute to fiscal and social policy management as well as to policy evaluation.

In developing countries, tax and benefit systems are much smaller than in OECD countries.

Accordingly, the share of GDP collected by fiscal systems (or tax ratio) is much smaller. Hence the need for full-fledged TBMs did not appear pressing from a policy management perspective.

Instead, in the 90s, the need to rationalize public spending while enhancing the provision of social services for the more vulnerable gave rise to benefit incidence analysis (Castro-Leal et al., 1999). Benefit incidence analysis (BIA) shows who is benefiting from public services and describes how government spending affects the welfare of different groups of people or individual households (Castro-Leal et al., 1999). It does so by combining information about the unit costs of providing those services with information on their use. In effect, this approach amounts to imputing to those households using a particular service the cost of providing it. While simple in principle, the implementation of the approach requires appropriate data, and, depending on its availability and quality, a number of assumptions will be needed to provide an accurate picture of the benefit incidence. Similarly, price incidence analysis (PIA) tools have also been used to analyze indirect tax incidence questions in the context of the transition from trade to domestic taxes to compensate for the fiscal loss resulting from trade liberalization (Younger et al., 1999).

The CEQ conceptual framework brings together these different approaches to provide a more complete picture of the incidence of fiscal systems, including both the receipt and spending sides. Its very flexible nature allows OpenFisca to simulate any legislation that can be translated into arithmetic operations. Each system will consist of a set of different fiscal objects (variables, parameters, formulas and reforms) which together form the building blocks of any Tax and Benefit systems. Fiscal objects can be parameterized so as to represent the country-specific legal rules, and within countries, these elements can be further refined to account for variations across any dimensions (type of taxpayers, space, time). They are already multiple examples of analysis using the Openfisca calculator. For instance, It is used to investigate the distributional impact of the budget.ipp.euFrench budget law. It has also been used in a prospective way to assess the potential impact of a reform -e.g. reform housing benefits or introduction of basic income. Finally, it can be a very useful tool to analyze counter-factual legislation and its distributional impact.

The

Unlike many microsimulation tools, a key feature of the OpenFisca approach to microsimulation is to disconnect the implementation of the legislation in code from the specification of survey data or any particular data. Adhering to the original legislation concepts and matching them to appropriate entities (individual, fiscal unit, ...), eases and speeds up the coding of the legislation. The testing and maintenance of the legislation content is also considerably simplified.

The injection of the data, either from survey or administrative origin, is done in a separate module by projecting the available data on the legislative concepts, thus clarifying the assumption made in this stage and disentangling it from the implementation of the legislation.

Moreover, modulo a marginal redefinition of the data projection, the same legislation can be used on different data sets. This clear separation between data and legislation also allows the analyst to easily refine a previous coarse-grained legislation implementation or introduce a reform without having to deal with the entanglement between data and legislation. Finally, applying the socio-fiscal system of one country to another one is made easier since it simply requires an adequate projection of the data.

We use the OpenFisca framework to simulate the tax and benefit systems of Côte d'Ivoire, Mali and Senegal for two reasons essentially. First, for transparency: OpenFisca like Python is a free and open-source software which anybody can freely use, and, if appropriately trained, understand. Each country specific package (https://github.com/openfisca/openfisca-cote-divoireopenfisca-cote-d-ivoire, https://github.com/openfisca/openfisca-maliopenfisca-mali, https://github.com/openfisca/openfisca-senegalopenfisca-senegal) is freely accessible online. This enhances replicability. Secondly and most importantly, for reutilization: the availability of the code online, together with OpenFisca documentation allows anybody willing to contribute to build upon our microsimulation models. This reuse may take several forms.

The mutual legislation component can be reused by different state agencies for their specific purpose. Additional modules can be added to simulate elements which were not coded initially, possible mistakes can be corrected, potential reforms can be tested. Additional data sources can be mobilized. A promising approach is to use statistical matching to pair survey and administrative data to produce rich data sets for policy evaluation.

Eventually, the microsimulation models can be used by other software through OpenFisca's application programming interface (API). This implies for instance that a website can be designed to send some specific input data to OpenFisca and receive the results of applying some tax and benefit system to the input data. In other words, using this framework incidentally allows opening the by-product of economic research to non academic members of the society.

Data Data Sources

The main data sources used in this work are household surveys collected in each country including information at both household and individual levels (table 1). These surveys were selected because of their representativeness at the national level on the one hand, and for containing detailed information relating to employment, the income, personal characteristics and expenditure they contain on the other. As administrative data would only cover a very small share of the economy in society where the informal sector is still dominant, such surveys are the only available micro data to study the full distributional incidence of the tax and benefit system. Unfortunately they remain very sporadic in time, can be hard to make comparable across countries, and suffer from a number of shortcomings2 . In addition to the micro surveys, this simulation exercise requires essentially two other type of sources : tax codes, to collect complete and reliable legal information, national accounts and aggregate figures from miscellaneous administrative reports (the tax authority, the customs etc ...), to gauge the extent by which the simulated micro-economic totals (before and after simulation) depart from those assembled by the administration.

Data Preparation and Harmonization

Harmonizing data sets produced by different actors can be difficult. For this study the challenged proved particularly complicated as several types of data sources had to be harmonized both within and across the three countries, and with respect to the CEQ framework.

Table 1.3 presents the different income modules included in each household survey. Most types of incomes are measured at the individual level in each survey except for own-consumption and imputed rent. We estimate imputed rents first by estimating the average predictive power of a set of housing and geographic characteristics on the (log of the) rents paid by tenants, then by applying these coefficients to the same characteristics but on the homeowner sample to obtain predicted values. For each of the micro-surveys, we define implausibly extremes as values whose natural logarithm would exceed the mean by more than 3 standard deviations, as is common in the literature. We assume they are due to measurement errors and set them to the average calculated from the distribution after excluding them. We applied this method for each income component separately. We cannot discard the possibility for such method to decrease high but well-measured income level. However, in the absence of more reliable data, there is no perfectly accurate method to distinguish the later from pure measurement errors.

Several data limitations are important to highlight here, which make the three country- Extensive information about the source and method applied to construct the different variables is available on https://github.com/openfisca/openfisca-ceq/blob/master/documentation/description.mdgithub. We uncovered a few other serious inconsistencies such as the systematic mismatch between agricultural income measured at the household level from a section entirely devoted to agricultural activities, and the sum of revenues streams individually declared by farmers.

Given these data issues, our results should be considered with caution. More generally our inspection demonstrate that much better data would be needed to more precisely answer the questions addressed in this paper.

Comparative Analysis

Although the three West African countries considered here share a number of characteristics -population, political regime, former colonial rule -they differ on a number of features that are explored in more detail in the following sections.

Aggregate revenue and spending

The size and composition of government revenue is presented in Figure 3. The three countries are ranked by GDP per capita. Senegal presents a higher ability to mobilize fiscal resources with respect to country wealth compared to Mali and Cote d'Ivoire. Indeed, the global tax to GDP ratio of Senegal reaches 19.9% exceeding the average tax ratio in Sub-Saharan Africa.

While the Cote d'Ivoire tax ratio (15.3%) is close to the average of Sub-Saharan Africa countries, the ratio for Mali (13.7%) is lower. Figure 3 also shows that indirect taxes constitute the principal source of government revenue in all three countries. Indeed, the volume of indirect taxes represents more than 60% of government revenue in each of the three countries. • First, low-income countries typically have a large informal economy and many smallscale firms and farms. These economic activities are difficult to tax (Tanzi, 1992). In addition, many developing countries tend to be dependent on natural resources and foreign aid, which may crowd out domestic revenue collection efforts.

• Second, political systems, political control and the political economy determine the level, type and incidence of taxation (Besley and Persson, 2014). Weak institutional capacity makes corruption and evasion more likely and lobbying more profitable (Reinikka and Svensson, 2004). For example, strong control by rich elites and low contestability, which characterize many political systems of poor countries, may be associated with less progressive taxation. Similarly, tax exemptions may be used to buy-off opposition or to favour political supporters (Svensson, 2005).

• Third, the argument has been made that a "culture of compliance" -based on intrinsic motivations beyond material costs and benefits -is a key parameter that determines taxation (Putnam et al., 1994). However, this cultural explanation remains contested, as the lack of compliance may also be traced back to other fundamental factors, in particular the failure of the state to provide public goods. The latter assertion is confirmed for example by a study by Ali et al. ( 2014) that shows a positive correlation between tax compliance and the provision of public services in four African countries using Afrobarometer data.

There does not seem to be a simple correlation between tax ratio and income in our three data points. A thorough investigation of the fiscal system will allow us to better understand the origin of tax ratio differences between the three countries.

The Afrobarometer fiscality indicators provide some elements to understand the perception that people have of their countries fiscal system. Four indicators are investigated (Figure 4). We begin by analyzing the willingness to pay taxes approximated by a direct question asking the respondent4 if he or she agrees that citizens must pay taxes. The proportion of the population who agrees that citizens must pay taxes is high in all 3 countries and ranges from 79% in Mali to 83.9% in Cote d'Ivoire. The willingness to pay taxes in the three countries is higher than the average in Sub-Saharan Africa. While the willingness to pay taxes is high and similar across the three countries, the actual tax compliance seems in contrast to be heterogeneous5 .

The proportion of people who comply with taxation in Mali (68.2%) is considerably higher than in Senegal (50.1%) and Cote d'Ivoire (32.4%). Tax compliance in Mali exceeds the average in Sub-Saharan Africa (58.0%) while it is lower in Senegal and Cote d'Ivoire. The higher tax compliance observed in Mali could be explained by two other indicators. On the one hand, the proportion of people thinking it easy to avoid paying taxes is higher in Senegal and Cote d'Ivoire than in Mali. This suggests that people are less tempted to avoid paying taxes in Mali in relation to the other countries. On the other hand, the share of people saying it easy to find out information on the use of fiscal revenues is higher in Mali than in the other two countries.

This result suggests that public resource management is more transparent in Mali than in Senegal and Cote d'Ivoire. Cote d'Ivoire in the World Bank Indicators Dataset so that it is not possible to compare between the three countries.

Population

Demography and household structure

The three countries differ in terms of population structure by residence area (Figure A1). Cote The three countries present a similar pattern regarding the spatial distribution of population (Figure A2). Population is concentrated in the urban centers particularly in the capital cities.

The population density is higher in the region including the capital cities compared to other regions. As evidence, population of region including capital cities represent more than 20% in Senegal and Cote d'Ivoire and more than 10% in Mali.

In terms of age structure, the population of the three countries is relatively young compared to Western European countries (Figure A3). The structure of the consumption across the three countries show two common patterns (Figure 6). First, food represents the biggest share of household consumption in each country. This is specially true in Mali where food represents almost half of total consumption. Second, dwelling and related costs represent a significant spending for households, particularly in Senegal. Globally, the three countries do not have the exact same employment structure but share some patterns. In particular, the contribution of formal employment including both public and formal private employees is very low. Formal employment represents roughly 6.6% of total labor force in Cote d'Ivoire, 5.2% in Senegal and only 3.3% in Mali. Instead, the share of non agricultural self-employment is quite similar between the three countries with a proportion between 22% and 25%.

Consumption structure

The main contributing sectors to total employment also generate an important share of household income (figure 8). Agricultural and self-employment income appear to be the most important households' income sources in Mali and Senegal. Agricultural income represents 28% of total income in Mali and 22% in Senegal. Income generated by self-employment is proportionally comparable to agricultural income in these two countries. In Cote d'Ivoire however, informal wages seem to be the main households' income source (23%) even though agricultural (14%) and self-employment (16%) incomes are also important.

Poverty and Inequality

The overview of the structure of government revenue and social expenditures is completed by a comparative analysis of the income inequality across the three countries (Figure 9). Two of the three countries are different in many respects even though they are similar regarding some concepts inherited from the French colonial system. For example, all three countries have a tax on wages and a value added tax. Nonetheless, taxation rules and taxation basis differ from one country to another. Given the complexity of each fiscal system, only tax categories that contribute significantly to fiscal revenue are presented and which are likely to be simulated in the study given available data.

Direct taxes

Tax on wages

The tax schedule for wages that applies in each country is presented in Figure 10. Tax rates in Côte d'Ivoire are overall lower for wages between 1,5 million and 9.5 millions than the rates in both Senegal and Mali. This comparison presents however some limits as taxation rules are different. Taxable income is specific to each country regarding tax allowances, how the tax reduction for family is considered and the rules used to calculate the amount of the tax. is applied to the total taxable income. Second, progressive taxation rates are applied to taxable income divided by the number of shares (or "quotient familial"). Table 3 presents the definition of the number of shares according to family situation of the taxpayer. Tax reductions for family are somewhat similar in Senegal and Mali. The rates are different though and the amount of the reductions is capped in Senegal. The final amount of tax to be paid is assessed after taking into account these reductions in Senegal and Mali. In Cote d'Ivoire, taxable income per family share is multiplied by the number of shares to calculate the total tax. The number of shares increase by 0.5 for each additional child up to 5.

Payroll tax and social security contribution

Payroll tax and social contributions are paid by both employers and employees to varying degrees (Figure 11). Social contributions include pension contributions and all benefits related to the position covering industrial accidents and family benefits. Overall, employers' contribution is higher in comparison to employees' contribution within each of the three synthetic tax in Cote d'Ivoire varies from 491,400 (for turnover between 5 millions FCFA and 6 millions) to 3.9 millions FCFA when turnover is between 48 millions FCFA and 50 millions FCFA (table A6). In Mali, the synthetic tax range from 10,585 FCFA to 1.2 million FCFA. In contrast, pastry with a turnover between 25 million FCFA and 30 million FCFA or advertising agency or travel agency with similar range of turnover are included among the top synthetic tax payers (see art. 74 for details on categories).

Other direct taxes

Other income categories such as rental income or securities income are also taxed, with different taxation rules across the three countries (table A1, A6). For example, rental income tax is 3% in Cote d'Ivoire while the rate is 15% in Mali and progressive rates are applied in Senegal (table A6). In addition, the taxation basis for rental income tax is not the same for the three countries. Taxable income is obtained after deducting some operating costs in Senegal.

Mali and Cote d'Ivoire consider however the gross rental income including operating costs as taxable income. Likewise, taxation rates on securities income differ between the countries (table A1). Tax on interests from Bank saving account varies from 1% to 13.5% in Cote d'Ivoire depending on interest payment date. The rate is 9% in Mali and only 8% in Senegal.

Indirect taxes

Similarly to direct taxes, main indirect taxation rules are different in each country even though the taxation basis seems similar. The taxation basis of tax on import includes cost of the product, insurance and freight of import duties. The taxation basis of special taxes on products includes the same elements plus taxes on import. VAT applies to value added of production augmented of import taxes and any other taxes or fees.

Tax on imports

As member of the Economic Community of West African States (ECOWAS), the three countries belong to a customs union and, as result, share the same tax rules on imports. This tax is divided into 5 categories (Table 7). Social goods such as cereal products or health products are exempted. Rates range from 5% for inputs such as fertilizer or agricultural equipment to 35% for sensitive products regarding their strategic dimension for economic development of the region. This latter category is meant to protect local production and include products such as poultry products, onions, wheat flour, etc.

35%

Value Added Tax (VAT)

The normal VAT rate is the same within the three countries (18%). However, the three countries introduced a reduced rate on some products specific to each (Table 8). The reduced VAT rate is 5% in Mali and applies to computers, agricultural materials and solar materials production (Table 7). The reduced rate is 9% in Cote d'Ivoire for a larger range of products such as milk, petroleum products, solar energy production, etc. In Senegal, accommodation and restaurant services benefit from a reduced VAT rate of 10%. Many products are also exempted of VAT within each country. These products are generally first necessity products such as cereal and bread or pharmaceutical products (Table 9).

Exempted products are not systematically the same for the three countries. Transportation services are for instance exempted in Cote d'Ivoire while they are not in the two other countries.

Because exempted products are not the same might introduce difference of incidence of VAT across countries.

Excise taxes

On top of VAT and tax on imports, the three countries raise excise taxes on some products.

Special taxes on fuel and petroleum products

Fuel and petroleum products are also subject to special taxes (

Tax allocation

The principle of a static model consists in allocating taxes, benefits and social contributions to each household in the sample. The CEQ framework considers three alternative allocation methods depending on the information available in the survey data Lustig and Higgins (2017).

The first method is direct identification which is used when the survey allows identifying both the actual taxpayers (or beneficiaries) and the actual amount of tax paid (or benefit received).

The second method is imputation which is used when the survey identifies taxpayers (or beneficiaries) but not the amount of tax paid (or benefits received). The third allocation method is simulation which is used when no information related to who actual pays or receive the benefit is reported in the survey. Given the data available in the surveys, simulation is the main method used to allocate taxes to households while imputation is used to allocate public spending in education.

Table 12 presents which category of tax is included in the model. On the direct taxes side, the model includes payroll taxes, social contributions, and personal income taxes. On the indirect taxes side, VAT and import taxes are simulated in the model.

Direct tax allocation

When the survey data does not report the actual direct taxes paid by households, these taxes must be simulated at the personal or household level. The simulation rests on two steps:

• Identifying taxpayers,

• Computing the amount paid by each taxpayer given its characteristics.

Personal Income Tax, Payroll Tax, and Social Contributions

Concerning the identification of taxpayers, we assume the Personal Income Tax (PIT), the Payroll Tax (PT) and Social Contributions (SC) are paid by all employees of the formal sector.

Given that these taxes are withheld by the employer, the assumption is reasonable. The formal sector includes employees of public administration plus employees of private firms covered by social security or receiving a payslip.

Next, making use of the fiscal system rules and parameters for direct taxes in each country (see section 4.1.1), one can compute the amount of tax paid by each taxpayer given its characteristics. As presented in section 4.1.1, beyond income, the important characteristics to be taken into account in the case of the personal income taxes are the marital status and the number of children. The model then combines information on characteristics and parameters to compute the amount of PIT, PT and SC paid by each individual.

Tax on self-employment income

In the case of self-employment income, we assume taxpayers are all those who declare to have paid taxes or who have a fiscal number. Beyond income, the variables to be taken into account are the sector of activity or products. The combination of this information with the tax rules (see section 4.1.2) allows to simulate the amount of taxes paid by each individual or household.

Indirect tax allocation

The allocation of the three indirect taxes (import duties, VAT, excise taxes) rests on the combination of (country specific) fiscal rules and parameters (see section 4.2) with information on each household expenditures. For instance, if a household h spends a given amount Y(h,i) on good i and if we know this good is subject to an indirect tax at a rate r(i), then we assume that the amount of indirect tax paid by the household will be equal to r(i)*Y(h,i).

While simple in principle, the actual allocation of indirect taxes raises a number of difficulties:

• The identification of goods that are subject to each tax (and tax rate) is not straightforward as the categories used in Tax Code differ significantly from the categories used in the surveys. This can be seen when comparing the list of goods mentioned in the Tax

Codes with the classification of goods used in the consumption module of the surveys (see Appendix B).

• In addition, the categories of goods vary between surveys, making it difficult to allow for "cross" simulations (see Appendix B)

• As mentioned in the "Data" section, expenditures are higher than income for a significant number of households. In some cases, this entails that the amount of indirect tax paid will be higher than household income.

• The location of purchase is unknown which is problematic because goods bought on informal markets are likely to evade some taxes.

• A significant portion of indirect taxes, for instance import duties and excise taxes, are raised on intermediate goods -e.g. wheat and petroleum. However, the information at the household level is on final consumption only.

In this study we tackle the first two issues. Concerning the classification of goods, a significant harmonization effort was undertaken to match all 3 survey-specific good classifications to a common harmonized classification (COICOP). Country-specific exemptions and rates for each tax are then applied.

Concerning the third issue, one option is to follow the CEQ strategy which is to compute the effective taxation rate of consumption at the household level given the structure of its expenditure and then apply that rate not to consumption but to each household's income (Lustig, 2018). This approach has two disadvantages however. First, it "neutralizes" the incidence of indirect taxes with respect to income. A more satisfactory method should account for the fact that richer households save a bigger share of their income and are thus taxed indirectly on a smaller share of their income, making indirect taxes regressive.

The second disadvantage is related to the use of an incorrect tax base -income instead of consumption. This is problematic because it will bias the simulated aggregate fiscal income.

Concerning the last 2 issues (location of purchase and indirect taxation of intermediate goods), we leave their resolution to future work.

Benefit allocation

For a complete picture of fiscal incidence both the receipt side (the tax system) and the expenditure side (cash transfers and public spending) need to be taken into account.

Concerning the latter, the distributional impact of the public sector's budget has been a topic of enduring interest for economists and policymakers (Castro-Leal et al., 1999). The main question to be addressed is how to measure the benefit that household derive from public spending. Measurement is not problematic for direct transfers since the monetary value of the benefit received is clear. But the problem arises when governments subsidize the provision of goods and services or when governments provide them free of charge. This makes it more difficult to account for the benefit obtained by the users of the service.

Two types of benefits are included in the model: social transfers and public education spending.

Social assistance transfers are incorporated in the model for Senegal and Cote d'Ivoire but not for Mali given the available data. The direct identification method is used to allocate the benefit since the surveys in these two countries identifies the beneficiaries and report the amount of benefits received.

Concerning education, the structure of public expenditure across levels is presented in table 13.

Since the schooling status of individuals is collected in the survey, the actual beneficiaries of education spending can easily be identified except for Mali. However, the monetary equivalent of the benefit -i.e. the amount "received" by each household corresponding to its use of education services -has to be imputed.

Different approaches exist to value public services (Bastagli, 2015). The first is the opportunity cost method which consists in valuing public services by what an individual would spend if similar services were private, i.e. brought by the market. The second approach is based on estimating individuals willingness to pay which requires carrying out surveys to elicit WTP.

These two approaches are very data intensive which motivates our choice to use the unitary cost of production approach. This approach consists in dividing total government spending on a particular service by the number of users of that service. We present the different steps and data used in the remainder of this section.

Government spending

Table 14 presents government spending on education and health collected from official documents. The source of the main documents are presented in table A2. The figures show that health expenditures represent a much smaller amount of public spending than education9 .

Estimates of number of users

Two sources of information are used to estimate the number of users (Table 15). The first source is official documents such as statistical yearbook or national report on education (table A2), the second is household survey data. The two sources of information yield similar figures except for Mali where public schools' students can not be identified due to the lack of information related to the type of school in the survey. As a result, the unit cost is likely to be underestimated in Mali if household survey data are used as estimates since they include both public and private school users. The percentage of students enrolled in private schools is estimated at roughly 22% for fundamental primary and 62.7%10 for general secondary in 2010 providing hence a minimum magnitude of the potential upper bias of the household survey data estimates.

Concerning the number of users of health public service, the available data does not provide enough information to compute credible estimates. First, official documents are insufficiently detailed. Second, while household surveys include information about the use of health service, the recall period is less than on month and does not allow to estimate the annual users of health service. Given the lack of the data on the users of health public survey, we are not able to estimate the unit cost of this service.

Estimation of unit cost of education

Levels of education are harmonized for comparative analysis purpose between Senegal and Cote d'Ivoire (Table 16). Four levels are considered: preschool, primary, secondary and tertiary. Based on this harmonization, we compute unit cost by dividing the expenditure and the number of users associated to each level of education11 (Table A5). In overall, total education This unit cost is considered as the monetary equivalent of the benefit for Cote d'Ivoire and Senegal. In Mali, this unit cost will be adjusted by the probability for the household to use public education service (Table A5). This probability is estimated by the ratio of the number of users provided by the household survey and the administrative data for a given education level 12 . The comparison across education level in the three countries shows in furthermore a positive correlation between unit cost and the level of education (Figure 12). The higher is the education level, the higher is the unit cost. 3.4. Calibration

From survey income to market income

Since direct taxes on (formal) wages are withheld by the employer in all three countries, the wage income collected by the survey corresponds to the concept of "disposable" wage income. As a result, it is necessary to compute for each individual his or her "market" wage income. This is done by simulating the direct tax rules "backwards".

From survey income aggregate to national accounts aggregates

The three household samples used in this study are representative at the national level. This in principle implies that properly weighted income and consumption aggregates computed using survey data should be equal to corresponding national accounts aggregates for each country in the year of the survey. There are however at least three sets of reasons why this might not be the case. The first reason is related to sampling and non-response bias, in particular at the top of the income distribution. Indeed, rich households are more difficult to reach and are more likely to decline to participate to the survey. As a result, they are under represented in the sample. Second, incomes at the individual or household level are likely to be underestimated, either because individuals do not remember all their sources of income, or because they are inclined to omit or hide some income sources, or because the design of questionnaires does not allow to properly report and measure all income sources. A third reason is related to the fact that aggregate income concepts include some income (e.g.

undistributed profits) that are not reported in household surveys. A number of techniques have been developed to try and correct for all these sources of underestimation and bias (see for instance Alvaredo et al. (2016)). Here, for lack of more informed assumptions, we simply rescale all income components in order to match official national accounts aggregates of household income. This rescaling assumption leaves income distribution between households unchanged.

Simulation results

Targeting fiscal aggregates

As presented in section 2.2, the OpenFisca platform is a tax-benefit calculator that allows simulating fiscal incidence either for individuals (or households) with given characteristics or for a full sample of households. When the household sample is representative at the national level, weighted fiscal aggregates simulated by the calculator should correspond to fiscal accounts data. For instance, the weighted sum of income taxes paid by each individual (or household) should be equal to the fiscal receipt of the income tax. However, depending on the assumptions of the model (see above), some discrepancies are to be expected. This is for instance the case for indirect taxation since the current models simulate the taxation of final consumption goods only and not that of intermediate goods 13 . Some of these discrepancies can be seen in Table 17 where fiscal receipts from national accounts are reported ("actual")

as well as simulated receipts to asses how they perform in terms of targeting. Two versions of simulated aggregates are reported: one takes the household data as given ("direct"), while the other provides aggregates obtained once the data has been rescaled ("inflated") as explained in section 4. As a result, one expects to obtain much smaller simulated aggregates than actual aggregates. This is indeed the case in all three countries for custom duties. However, when using the inflated version of the data, the microsimulation model "overshoots" VAT receipts in Mali and Senegal by more than 20%. This can be explained by the fact that the model does not account for the fact that some goods are sold on informal markets and evade the payment of VAT.

Fiscal incidence

As explained above, the microsimulation model simulates different sets of income in each country. Following the CEQ framework, we focus on 4 main income concepts: market income, disposable income, consumable income and final income.

We first report inequality indicators of each income concept for each country. The comparison of these indicators provides both a global view of the progressivity or regressivity of each fiscal system and a more specific diagnostic of the distributional impact of the three main components of the fiscal system: direct taxes, indirect taxes, and public spending.

Figure 13 presents the Gini coefficient and the income shares of the bottom 40% and of the top 10% for each income concept. Results are relatively similar across countries and can be summed up as follows:

• the distributional impact of fiscal systems is overall slightly progressive but very small,

• direct taxation is slightly progressive,

• indirect taxation is regressive,

• public spending is progressive.

More precisely, the data indicates that the fiscal system reduces the Gini coefficient by 1 to Figure 14 reports the incidence of the main fiscal instruments (PIT, custom duties, VAT, education spending) for each decile of per capita market income 14 . Results are consistent with those of the CEQ analysis and can be summed up as follows:

• the incidence of the PIT is progressive in the three countries but some difference are worth highlighting: in Senegal, the top 4 deciles have a positive incidence in Senegal, while only the top 2 deciles in Cote d'Ivoire and the top 10% in Mali.

• Both the VAT and custom duties are regressive with higher rates of incidence for poorer deciles. This seems to be specially the case for Mali where the incidence of VAT is 27%

for the second poorest decile while it is only 5% for the top decile 15 .

• Public spending in education is progressive in all 3 countries, more so in Mali and Cote d'Ivoire than in Senegal. The incidence of education spending can be explained by different factors. The reason why it appears more progressive in Mali might be related to the fact that primary education represents a higher share of public education spending than in the other two countries (see table 13). 14 Incidence is equal to the ratio of the amount of tax paid (or benefit received) to market income. 15 We do not comment on the values for the bottom decile where the issue of zero incomes mentioned in the data section may lead to overestimate the rates of incidence.

Figure 15 reports the concentration shares of the main fiscal instruments (PIT, custom duties, VAT, education spending) for each decile 16 . Results can be summed up as follows:

• In all 3 countries, the top 10% contribute 86% of the fiscal income of PIT.

• Concerning indirect taxes, the contribution of the top 10% varies from 14% in Mali to 23% in Cote d'Ivoire for custom duties, and from 16% in Mali to 28% in Cote d'Ivoire for VAT.

• The concentration shares of education spending are relatively uniform across deciles in Cote d'Ivoire and Mali and slightly skewed towards the top decile in Senegal which gets 15% of total education spending. This could be related to the fact that Senegal spends a higher share of public education spending on tertiary education than the other two countries (see Table 13).

Discussion

The main results described above are consistent with those obtained in other CEQ case studies (Lustig, 2018): fiscal systems are redistributive overall, direct taxes are progressive, indirect taxes are regressive, and public spending is progressive.

Two results stand out however. First, the degree of regressivity of indirect taxes is very high.

While indirect taxes are expected to be regressive with respect to income (since they are raised on a higher share of income for poorer households), at least 4 features of indirect taxation are expected to be progressive and thus have a mitigating impact on the regressive "character" of indirect taxes. These features are:

• VAT is not raised on goods sold in informal markets.

• VAT is not raised on own consumption.

• Food products and "basic necessities" are exempted from VAT (See Table 9).

• Social goods are exempted from taxes on imports (See Table 9).

Since the location of purchase is unknown, we cannot account for the first feature but the last 3 features are accounted for in the model. However, they do not seem to weight enough to make indirect taxes progressive.

The second striking result is the low proportion of PIT taxpayers in the top decile which can explain the small redistributive impact of direct taxation which is revealed both in the CEQ analysis and in the incidence analysis. The low proportion is related to the share of formal wage workers in each country. It is possible that the model results underestimate the proportion of PIT taxpayers since individuals outside the formal sector also have the possibility of declaring their incomes to the administration. They are unlikely to be many, however.

These results point to a few general policy recommendations:

• PIT should be made more progressive by increasing the number of its taxpayers.

• VAT exemptions should be reexamined to insure that their impact is progressive.

• Public spending in education should be increased.

Conclusion

In the context of the "Collect More, Spend Better" Agenda, it is important to understand how existing tax and benefit fiscal systems impact poverty and income distribution in aid recipient countries. Using country specific tax benefit calculators and recent household survey data, this paper attempts to address this question by analysing and comparing the incidence of benefit, tax and public spending in three western African countries: Cote d'Ivoire, Mali and Senegal.

The analysis allows providing some answers to the questions raised in the introduction.

• Who pays direct and indirect taxes and who benefits from transfers and public spending?

-Formal wage workers pay the bulk of direct taxation; they belong mainly to the top decile of the income distribution.

-Indirect taxes are paid by all households, including the poorest deciles.

-Public spending in education benefits households from all the deciles of the income distribution.

-Social transfers beneficiaries represent a very small number of households -Results are similar across countries

• What is the global impact of tax and benefit systems on inequality?

-The distributional impact of existing fiscal systems is slightly progressive but small.

• Which tax instruments will allow raising domestic resources while achieving poverty and inequality reduction?

-The PIT is progressive. In order to increase receipts while achieving poverty and inequality reduction, it should be extended to a much higher proportion of top decile households.

-Indirect taxation is regressive, VAT exemptions should be examined at the country level.

-Public spending is progressive. Raising public expenditure in the primary sector should allow increasing quality while maintaining that progressivity. However, higher education should not be sacrificed.

• How do country characteristics interact with tax and benefit system parameters to determine the incidence of tax benefit systems?

-The low proportion of PIT taxpayers in Mali -and its related low redistributive powercan be explained by the small number of formal wage workers in Mali.

-The higher redistribution achieved through education spending in Mali can be explained by the high share of spending going to the primary level compared to Senegal and Cote d'Ivoire.

Two last important points concern the data used in the microsimulation models. As mentioned, the data comes from three representative household surveys collected in 2011 in Mali and Senegal and 2014 in Cote d'Ivoire. In order to compare results across countries and allow for the possibility of cross-simulations 17 , it is important to make the data as comparable as possible: ideally, income and consumption constructs should be strictly identical across countries. This is unfortunately not the case since National Statistical Offices use widely different questionnaires designs both on the income and the expenditure side (see section 2.3) 18 . This is a source of concern. First, carrying out cross country analysis entails a huge effort of harmonization between surveys. Second, even after this huge effort, the comparison remains problematic because the process of harmonization is imperfect. This calls for significant changes in the process of data collection in aid recipient countries. While not all household surveys can be made identical, the objective of comparison across countries should be given more importance when designing questionnaires.

A second point concerning to data relates to the quality of the data collected. Household survey collection is a difficult task. Yet, household survey data is key to analyse different types of policy questions in particular related to poverty and income distribution. More efforts and resources should be invested in the collection of good quality household survey data at the national level.

  Advocacy for Domestic Resources Mobilization (DRM) is promoted both by the 2030 Agenda for Sustainable Development, the Addis Ababa Action Agenda (AAAA), and the "Collect more, Spend Better" Agenda. The promotion of DRM is based on two arguments. First, it provides fiscal space, allowing the well-functioning of the state and the provision of public goods. Second DRM is part of a social contract that underpins participation, social cohesion and contributes to shaping good governance. While the sustained economic growth of African economies over the last decades has provided some room to raise more fiscal resources domestically, the objectives of DRM must be pursued in agreement with the other SDGs set by the 2030 Agenda for Sustainable Development, in particular the need to eliminate extreme poverty (SDG1) and to reduce inequality (SGD10).

  (2017)) to analyze the "full" incidence of fiscal systems and public spending and compare it across countries. In the context of this study, we use the CEQ conceptual framework to analyse the incidence of taxation and public spending in Côte d'Ivoire, Mali and Senegal. The use of the CEQ methodology allows comparisons between the three countries of the study, as well as with other case studies of the CEQ project. In each country, we use a relatively recent national household survey: • Enquête Niveau de Vie des Ménages (ENV 2014) for Côte d'Ivoire; • Enquête Modulaire et Permanente auprès des Ménages (EMOP 2011) for Mali; • Enquête de Suivi de la Pauvreté au Sénégal (ESPS 2011) for Senegal. For the imputation of taxes and benefits at the household level, we use the OpenFisca platform that allows a rigorous modeling of tax systems and provides flexibility to simulate fiscal reforms.

Figure 1 :

 1 Figure 1: CEQ Income concepts Source : Authors

  OpenFisca platform https://fr.openfisca.org/OpenFisca is an open source software written in Python that allows translating any tax and benefit legal systems into a computer code. Initially created in 2011, the first objective was to replicate the microsimulation models used by the French administrations to calculate taxes and transfers at the national level in an open-source environment. Since then the platform has been incrementally enriched by several contributors (developers and economists) and now allows to model elements of the French system since 1914. This work has resulted into the consolidation of a specific branch -https://github.com/openfisca/openfisca-franceOpenFisca-France -which is now used by the Institut des Politiques Publiques (www.ipp.euIPP) to simulate the impact of past and future public reforms of the French system. Other teams of researchers or developers from New Zealand, Spain and Tunisia, have started designing other country-specific extensions.

Figure 2 :

 2 Figure 2: Number of items by expenditure module Source : Authors

Figure 3 :

 3 Figure 3: Structure of government revenue Source : Authors

Figure 4 :

 4 Figure 4: Tax to GDP ratio and Afrobarometer indicators Source : Authors

  d'Ivoire's population is larger compared to the other countries which have similar population size. In the same way, there are differences in terms of residence area of population between the 3 countries. Whereas the majority of the population in Cote d'Ivoire (50.1%) lives in urban area, the reverse is observed in Senegal and Mali: urban population represents 43.3% in Senegal and only 22.2% in Mali.

Figure 5 :

 5 Figure 5: Structure of government spending Source : Authors

Figure 6 :

 6 Figure 6: Consumption structure by country Source: EMOP-2011, ESPS-2011, ENV-2014, authors' elaboration

Figure 7 :

 7 Figure 7: Workers population by activity sector Source: EMOP-2011, ESPS-2011, ENV-2014, authors' calculations

  indicators based on the most recent available data from World Income Inequality Database are considered: the Gini Index and the income share of the top 10%. Income inequality appears higher in Cote d'Ivoire in relation to the other countries w.r.t the Gini index (43.2) and the share of income held by the top 10% (32.6%). With a Gini index evaluated at 40.3, income inequality is lower in Senegal than in Cote d'Ivoire. Income distribution in Mali seems the most equal among the three countries with a Gini index estimated at 33.0. Furthermore, the top 10% holds only 25.7% of total income vs more than 30% for the two other countries.

Figure 8 :

 8 Figure 8: Income structure by country Source: EMOP-2011, ESPS-2011, ENV-2014, authors' calculations

  Tax allowances in Senegal and Cote d'Ivoire are considerably higher in comparison to Mali: taxable income represents gross wages after deducting a tax allowance of 30% in Senegal, 20% in Cote d'Ivoire and only 4% in Mali.

Figure 10 :

 10 Figure 10: Tax schedule for a single taxpayer with no kids Source : Authors

countries 8 .

 8 It is worth highlighting that employers contribute less in social security contribution and payroll tax in Cote d'Ivoire than the two other countries.

Figure 11 :

 11 Figure 11: Payroll and social security contribution by country Source: https://www.cleiss.fr/docs/regimes/regimemali.htmlhttps://www.cleiss.fr/docs/regimes/regimemali.html, authors' calculations

Figure 12 :

 12 Figure 12: Unit cost of education public service Source: Authors' elaboration based on table A5

3

  points overall. Direct taxation contributes to that reduction in Senegal and Cote d'Ivoire but not in Mali. Instead, indirect taxation appears highly regressive as it increases the Gini coefficient by up to 5 points in Mali. However, public spending compensates for that effect as it reduces the Gini coefficient by 4 points in Senegal, 3 points in Cote d'Ivoire and 6 points of Gini in Mali. Results using income shares are similar.

Figure 13 :

 13 Figure 13: Fiscal and public spending incidence Source: EMOP-2011, ESPS-2011, ENV-2014, authors' calculations based on table A7, A8 and A9

Figure 14 :

 14 Figure 14: Fiscal and public spending incidence by decile Source: EMOP-2011, ESPS-2011, ENV-2014, authors' calculations based on table A10, A11, A12 and A13

Figure 15 :

 15 Figure 15: Fiscal and public spending concentration shares Source: EMOP-2011, ESPS-2011, ENV-2014, authors' calculations based on table A14, A15, A16 and A17

Figure 16 :

 16 Figure 16: Taxpayers and beneficiaries of in-kind transfers Source: EMOP-2011, ESPS-2011, ENV-2014, authors' calculations based on table A18, A19, A20 and A21

Figure A1 :

 A1 Figure A1: Population structure by gender and area of residence

Table 1 : Sample size by household survey

 1

	Source : Authors
	Country	Households Individuals	Sources
	Mali	6,914	78,140	Modular and permanent household survey-2011 (EMOP-2011)
	Senegal	5,953	55,016	Senegal Poverty Monitoring Survey-2011 (ESPS-2011)
	Cote d'Ivoire	11,797	47,607	Survey on households living standards in Cote d'Ivoire-2014 (ENV-2014)

Table 2 :

 2 Income modules : presence, scale and time unit To harmonize the several items, each of them were assigned to the closest COICOP category using the U.N (2018) nomenclature. Then the different COICOP categories were matched to the tax legislatures to assign VAT, excise and customs rates to each COICOP category 3 .

		Mali		Senegal		Côte d'Ivoire
	Pensions	NO		Yes ind.	year	Yes	ind.	diverse
	Rental income	NO		Yes ind.	year	Yes	ind.	diverse
	Agricultural mixed income	Yes ind.	month	Yes ind. month Yes ind. and hh.	diverse
	Informal wages	Yes ind.	month	Yes ind. month Yes	ind.	diverse
	Public wages	Yes ind.	month	Yes ind. month Yes	ind.	diverse
	Formal private wages	Yes ind.	month	Yes ind. month Yes	ind.	diverse
	Non-agricultural mixed income Yes ind.	month	Yes ind. month Yes	ind.	diverse
	Public transfers	NO		Yes ind.	year	Yes	ind.	diverse
	Dom. Private transfers	Yes hh. trim/year Yes ind.	year	NO
	Int. Private transfers	Yes hh. trim/year Yes ind.	year	Yes	ind.	diverse
	Own consumption	Yes hh. trim/year Yes hh.	year	Yes	hh.	days per months
	Imputed rent	Yes hh. estimates Yes hh.	year	Yes	hh.	estimates
	Other capital income	NO		Yes ind.	year	Yes	ind.	diverse
	Notes: Authors' elaboration
	Overall, ENV-2014 survey of Cote d'Ivoire and ESPS-2011 of Senegal include more income
	modules than EMOP-2011 of Mali. The latter does not measure pension, rental income, public
	transfers, nor other capital income (dividends, interest). Importantly also, transfers received
	from other households in Côte d'Ivoire are absent from the data set. The question does
	appear in the questionnaire but none of the version of the data we obtained actually features
	a corresponding variable. This component is a relatively important one in Senegal and Mali,
	where it can be measured. Moreover, Côte d'Ivoire's micro survey has significantly more
	household with no income (not even imputed rent or auto-consumption). We assume that
	households having no income in Côte d'Ivoire live from those unreported transfers received
	from other households. We then used consumption as a proxy for income for these households
	only.
	On the expenditure side, ENV-2015 of Cote d'Ivoire includes many more items compared to
	Senegal and Mali (figure 2). In total, ENV-2015 includes roughly 250 items whereas ESPS-2011
	has 150 items and EMOP-2011 only has 100 items. Most of these items are food or equipment

Table 3

 3 Overall, the size of our aggregates is comparable to that of official sources, although there are differences. Estimates of total consumption in Côte d'Ivoire are similar to the aggregate provided by the World Bank(12 636 billion FCFA in 2015). The gap between our estimates and official sources is however greater in Senegal and Mali. We find a total of 4 228 billion FCFA in

	Senegal while the National Institute of Statistics provides a total of 3 880 billion FCFA. On the
	other hand, our aggregate seems underestimated compared to the World Bank estimates
	(4 901 billion FCFA in 2006). In Mali, our aggregate is lower in relation to the most of official
	sources except for economic accounts of 2012 (3 097 billions FCFA).

presents our own estimates of consumption aggregates by country obtained from household surveys in comparison to official sources. As expected, Cote d'Ivoire has higher consumption in total and per capita compared to Senegal and Mali. Estimated at 12,440 billion FCFA, Cote d'Ivoire consumption aggregate is more than triples that of Senegal or Mali. Cote d'Ivoire consumption per capita is 535,830 FCFA against 203,081 FCFA in Mali and 310,171 FCFA in Senegal.

Table 3 : Consumption aggregates by country

 3

	Source: EMOP-2011, ESPS-2011, ENV-2014, WDI, Statistic National Institutes, authors' elaborations
	Country	Own estimates		National Institute of Statistics		World Bank
		Total	Per	Total	Per		Total
		(billions	capita	(billions	capita	Sources	(billions	Sources
		FCFA)	(FCFA)	FCFA)	(FCFA)		FCFA)
						INSTAT
						(2013),
						rapport
	Mali	3 119	195 230	3 912	251 004	de l'EMOP d'analyse	3 840	WDI 2010
						2010-2011,
						passage 1-4.
						INSTAT,
						comptes
		-	-	3 097	-	économiques	4 283	WDI 2011
						du Mali
						2012
						ANSD
						(2013),
						rapport
						du deuxième
	Senegal	4 313	316 388	3 880	284 615	de suivi enquête	4 901	WDI 2006
						de la pauvreté
						au Sénégal
						de 2011.
		-	-	-	-	-	6 381	2011 -Imputation*
						INS (2015),
						rapport de
						l'enquête sur
						le niveau de
	Cote d'Ivoire	11 836	512 900	_	386 215	ménages vie des	11 005	WDI 2014
						en côte
						d'Ivoire
						de 2014-2015.
		-	-	-	-	-	12 636	WDI 2015
	* Note: Imputation based on the growth rate of WDI's final consumption dataset between 2006 and 2011.

Table 4 : Family deductions

 4

	Source : Authors

Table 5 : Taxes on industrial and commercial benefits

 5

			Source: General tax codes, authors
	Regime	Mali		Senegal	Cote d'Ivoire
		Physical persons or corporations		Corporations with a		Physical persons or corporations	i. 25% on benefit*
	Simplified	with a turnover between 30 and 100 millions FCFA	35 % on benefit*	turnover between 50 and 100 millions FCFA	30% on benefit	with a turnover between 50 and 150 millions FCFA	ii. 30% on benefit* for ICT companies.
							i.Minimum
							lump-sum
							tax : 0.5%
							on turnover
							ii.25%
		Corporations		Corporations		Corporations	on benefit*
	Normal	with a turnover exceeding 100 millions FCFA	35 % on benefit*	with a turnover exceeding 100 millions FCFA	30% on benefit*	with a turnover exceeding 150 millions FCFA	* The difference with the simplified regime
							is the
							calculation
							of benefit
							as it
							includes
							(see art 48).

Table 6 : Synthetic tax by country

 6

		Source: General tax codes, authors
	Mali
	Theoretical fiscal population	Taxation rules
			Lump-sum tax based on
	Individual operator whose turnover is	a list of different
	less than 30 million FCFA		categories of operators
			(see art 74 for more details).
	Senegal
	Nature of the product	Theoretical fiscal population	Taxation rules (amount expressed in FCFA)
			i. 0 -0.5 millions: 4%
			ii. 0.5 -3 millions: 5%
	Service provision		iii. 3 -10 millions: 6%
			iv. 10 -37 millions: 7%
		Firms whose annual	v. 37 -50 millions: 8%
		turnover does not	i. 0 -10 millions: 1%
	Cement and food retail	exceed 50 million FCFA	ii. 10 -37 millions: 2%
			iii. 37 -50 millions:2.8%
			i. 0 -10 millions: 2%
	Other products		ii. 10 -37 millions: 3%
			iii. 37 -50 millions:3.8%
	Cote d'Ivoire
	Theoretical fiscal population	Taxation rules
	Individual operator whose turnover is between 5 and 50 millions FCFA	Lump-sum tax based on different range of annual turnover (see table A6)

Table 7 : Tax on Imports

 7

		Source: General tax codes, authors
	Categories Products	Description	Rates
	0	Social goods (pharmaceutical, medical, surgical devices, books, etc.)	Health products, cultural goods and cereal products	0%
		Commodities, basic	Inputs to produce
	1	materials, capital	other goods (fertilizer,	5%
		equipment, etc.	agricultural equipment)
			More elaborate products
			than the previous ones
	2	Intermediate consumption	but not being manufactured in enough quantity in	10%
			the region or not likely to be
			manufactured there.
		Final consumption products	Finished products having
	3	and any products not included	reached the final	20%
		in the previous 3 categories	stage of processing.
			Sensitive products
			because of their strategic
		Specific goods for	dimension for the economic
	4	economic and	development of the region.
		social development	This rate is meant to
			promote a sector or
			to protect vulnerable sectors.

Table 8 : Value Added Tax rates

 8

	Source: General tax codes, authors
	VAT	Mali Senegal Cote d'Ivoire
	Normal rate (%)	18	18	18
	Reduced rate (%)	5	10	9

Table 9 : Products benefiting from reduced and exempted VAT

 9

		Source: General tax codes, authors
	VAT	Mali	Senegal	Cote d'Ivoire
				Milk, hard wheat
	Reduced	Computer and agricultural materials and solar energy production	Accommodation and restaurant services	semolina pasta, petroleum products, solar energy production materials, etc.
				(see art 359)
			i. Sales, imports and
			printing of
			books, newspapers
			ii. Bank and insurance
		i. Export related to	services	Transportation
	Exempted	the sale and transformation of aircraft ii. Sale of unprocessed agricultural products iii. Bank and insurance	iii. Delivery of water and electricity within the limits of the social bracket	(road, rail, river / sea, air), sales of unprocessed food for consumption, bread, cereal meal, medical member fees,
		services iv. Some first necessity products and pharmaceutical	iv. Delivery and sale of pharmaceutical products	educational activity, fertilizer, seeds, social bracket of water and electricity
		products (see art. 195)	v. Butane gas for	delivery.
			domestic use
			vi. Unprocessed or
			first necessity products
			(see art. 361)

Table 10

 10 rate varies between 10 and 40% in Mali. Unlike Senegal and Mali, Cote d'Ivoire has a single rate of 37% and does not differentiate according to the type of cigarettes. In Senegal, the excise tax applies to more products than in the two other countries. Beyond tobacco and alcohol, additional products such as perfumes, cosmetic products, sodas, butters/creams, coffee and tea are also taxed.

		Table 10: Excise Tax
		Source: General tax codes, authors
			Taxation rules
		Mali	Senegal	Cote d'Ivoire
	Champagnes, sparkling wines Ordinary wines Beers Other alcohol of degrees over 35	10 to 45%	40% for any alcohol plus: i. 800 FCFA for alcohol of degree between 6 and 15 degrees. ii. 3 000 FCFA for alcohol of degree over 15 degrees.	40% 35% 17% 45%
	Perfumes	_	10%	_
	Soda and other beverages	0 to 20%	3%	_
	coffee and tea	_	5%	_
	Cheap cigarette Premium cigarettes	10 to 40%	40% 45%	37%
	Butters, creams and
	substitutes,
	or mixtures	_	12%	_
	containing
	butter or cream
	Other fatty substances	_	5%	_
			10% (15% for
	Cosmetic products	_	depigmenting	_
			cosmetics)
	Cola nuts	10 to 30%	_	_

presents the most relevant products regarding our study. Two similar categories of products are taxed across countries. First, alcohol is taxed at 40% at least in Senegal, 10 to 40% in Mali and 17 to 45% in Cote d'Ivoire. Second cigarettes are taxed in the three countries. The taxation rate is higher in Senegal with a range of 40 to 45% according to the type of cigarettes while the

  For instance, tax on ordinary gasoline is 18 847 FCFA per hectoliter in Senegal while it is 7 528 FCFA in Mali and only 2 500 FCFA in Cote d'Ivoire. Another example is the Super fuel taxed at 20 665 FCFA per hectoliter in Senegal and only 8 500 FCFA in Cote d'Ivoire.

Table

11

). Contrary to other indirect taxes, these taxes are not proportional and apply to the volume consumed. The list of products concerned by this tax differs from a country to another except for ordinary gasoline which is taxed in the three countries. Levels in Senegal taxes are higher than in Mali and Cote d'Ivoire.

Table 11 : Special taxes on oil products

 11

		Source: General tax codes, authors
	Products		Taxation rules
		Mali	Senegal	Cote d'Ivoire
	Super fuel	_	20 665 FCFA per hectoliter	8 500 FCFA per hectoliter
	Ordinary gasoline	7 528 FCFA per hectoliter	18 847 FCFA per hectoliter	7 500 FCFA per hectoliter
	Gasoline for canoe	_	3 856 FCFA per hectoliter	_
	Diesel fuel	4 007 FCFA per hectoliter	9 395 FCFA per hectoliter	2 500 FCFA per hectoliter
	Petroleum	355 FCFA per hectoliter	_	_
	Mineral oils	_	_	2 500 FCFA per hectoliter
	Distillate Diesel Oil (DDO)	_	_	45 FCFA per kilo
	Fuel oil (light, domestic, heavy)	_	_	10 FCFA per Kilo
	Consistent grease	_	_	20 FCFA per kilo

Table 12 : Category of tax revenue included in the microsimulation model

 12

		Source: IMF, Ministry of finance and Economy, authors' elaboration
			Mali (2011)	Senegal (Senegal 2012)	Cote d'Ivoire (2015)
	Taxes	Millions FCFA	%	Included	Millions FCFA	%	Included	Millions FCFA	%	Included
	Total tax revenue	873.9	100	-	1,430.0	100	-	3,117.9	100	-
	Direct taxes	307.5	35	-	494.5	35	-	1,186.8	38	-
	Personal Income Taxes	57.6	7	YES	238.1	17	YES	395.6	13	YES
	Social
	Security	86.7	10	YES	95.4	7	YES	383.5	12	YES
	contributions
	Corporate Income Tax	113.3	13	NO	113.9	8	NO	318.9	10	NO
	Other Direct Taxes	49.9	6	NO	47.1	3	NO	88.8	3	NO
	Indirect taxes	566.4	65	-	935.5	65	-	1,931.1	62	-
	VAT	282.2	32	YES	519.1	36	YES	732.5	23	YES
	Import Taxes	112.1	13	YES	195.2	14	YES	490.1	16	YES
	Excise taxes of which	16.1	2	-	79.8	6	-	33.1	0	-
	-Oil	4.7	1	YES	53.8	4	YES	0.1	0	YES
	-Non-oil	11.4	1	YES	26.0	2	YES	33.0	0	YES
	Other Indirect Taxes	156.0	18	NO	141.4	10	NO	675.4	22	NO

Table 13 : Category of expenditure included in the microsimulation model

 13

	Source: Statistical yearbook, Ministry of education, Ministry of budget, authors' elaboration
	Country	Category	Amount (billions FCFA)	% total Included	Allocation method
		Total expenditure	1216	100	-	-
	Mali (2011)	Social assistance & subsidies Education	164 256	13 21	NO	-
		Basic education	183	15	YES	Simulation
		Secondary and
		tertiary	73	6	YES	Simulation
		education
		Health	82	7	NO	-
		Total expenditure	1980	100	-	-
		Social assistance	37	3	YES	Direct identification
		Subsidies	155	13	NO	-
		Education	445	22
	Senegal (2011)	Preschool Elementary	1 190	0 10	YES YES	Imputation Imputation
		Middle and
		general	113	6	YES	Imputation
		secondary
		Tertiary	104	5	YES	Imputation
		Technical and
		professional	28	1	NO	-
		training
		Non formal training	1	0	NO	-
		Administrative management	8	0	NO	-
		Health	99	5	NO	-
		Total expenditure	4970	100	-	-
		Social assistance	180	4	YES	Direct identification
		Subsidies	140	3	NO	-
		Education	909	18	-
	Cote d'Ivoire (2015)	Preschool Elementary	22 373	0 8	YES YES	Imputation Imputation
		General secondary 1	167	3	YES	Imputation
		General secondary 2	102	2	YES	Imputation
		Professional and
		technical formation	70	1	NO	-
		(sec. + post. sec.)
		Alpha. And
		non-formal	5	0	NO	-
		education
		Tertiary	171	3	YES	Imputation
		Health	280	6	NO	-

Table 14 : Education and health expenditure

 14

		Source: General tax codes, authors
			Amount	Total (%
	Country	Category	(billions	total
			FCFA)	expenditure)
		Total expenditure	1215.7	100.0
		Education	256.4	21.1
	Mali (2011)	|Basic education	183.0	15.1
		|Secondary and tertiary education	73.0	6.0
		Health	81.6	6.7
		Total expenditure	1980.2	100.0
		Education	444.5	22.4
		|Preschool	1.4	0.1
		|Elementary	189.8	9.6
		|Middle and general secondary	113.1	5.7
	Senegal (2011)	|Tertiary	103.8	5.2
		|Technical and professional training	28.1	1.4
		|Non formal training	0.9	0.0
		|Administrative management	7.5	0.4
		Health	98.90	5.0
		|National hospitals and intermediary level	67.6	3.4
		|Peripheral level	31.3	1.6
		Total expenditure	4970.1	100.0
		Education	909.3	18.3
		|Preschool	22.2	0.4
		|Elementary	373.2	7.5
	Cote d'Ivoire (2015)	|General secondary 1 |General secondary 2	166.6 101.7	3.4 2.0
		| Professional and technical formation (sec. + post. sec.)	69.7	1.4
		|Alpha. And non-formal education	4.8	0.1
		|Tertiary	171.1	3.4
		Health	279.5	5.6

Table 15 : Number of users of education public service

 15

		Source: General tax codes, authors
	Country	Category	Estimated from
			Official	Household
			sources	survey data
			(millions)	(millions)
		Fundamental Education	1.8	2.8
	Mali (2011)	Secondary and Higher Education	0.1	0.6
		Total	1.9	3.2
		Preschool	0.0	0.0
		Elementary	1.5	1.4
		Middle and general secondary	0.7	0.8
	Senegal (2011)	Tertiary Total	0.1 2.3	0.1 2.4
		Preschool	0.1	0.1
		Primary	2.8	2.9
		General secondary 1	0.6	0.7
	Cote d'Ivoire (2015)	General secondary 2 Tertiary	0.2 0.1	0.2 0.1
		Total	3.8	4.0

Table 16 : Correspondence of education levels

 16

		Source: General tax codes, authors
	Harmonized category	Category collected from official documents
		Senegal	Cote d'Ivoire
	Preschool	Preschool	Preschool
	Primary	Elementary	Primary
		Middle and general secondary	General secondary 1
	Secondary	-	General secondary 2
			Professional and
		Technical and professional training	technical formation
			(sec. + post. sec.)
	Tertiary	Tertiary	Tertiary
	unit cost is significantly higher in Cote d'Ivoire and Senegal compared to Mali. The difference
	goes from 30 to 80 thousands FCFA representing between 22% and 65% of Mali's unit cost.

  2.2.Numbers in table 17 indicate that the ability of the model to simulate fiscal aggregates for direct taxation (personal income taxes and social security contributions) is acceptable when household incomes and consumption have been inflated. For instance, in Cote d'Ivoire, actual income taxes in 2014 were 395.6 billion FCFA and the "inflated" simulated aggregate is 326.9 billion FCFA. In the case of Senegal, the inflated simulation "overshoot" the actual numbers: for instance the "inflated" simulated aggregate is 270.4 billion FCFA while the actual number of 238.1 billion FCFA. Results for Mali are much less satisfactory which is consistent with the fact that the EMOP survey omits many income sources and probably underestimates formal wages (see section on Data).As explained above, the case of indirect taxation is different. The current version of the microsimulation model does not account for indirect taxes paid on intermediate consumption.

Table 17 : Targeting fiscal aggregates

 17

	Source: IMF, OECD, Ministry of finance and Economy, authors' elaboration
			Mali			Senegal		Cote d'Ivoire
		Actual Direct Inflated Actual Direct Inflated Actual Direct Inflated
	Total tax revenue	711	256	449	1128	680	1181	3118	1196	1392
	Direct taxes & SSC	144	27	68	334	265	378	1187	446	638
	|Personal Income	58	7	38	238	162	270	396	179	327
	|Social Security (SSC)	87	20	30	95	103	107	384	267	311
	|Corporate Income	113	NI	NI	114	NI	NI	319	NI	NI
	|Other Direct	50	NI	NI	47	NI	NI	89	NI	NI
	Indirect taxes	566	229	381	794	415	803	1931	750	753
	|VAT	282	208	346	519	342	662	733	559	562
	|Import Taxes	112	21	35	195	73	141	490	191	191
	|Excise taxes	16	NI	NI	80	NI	NI	33	NI	NI
	|Other Indirect	156	NI	NI	141	NI	NI	675	NI	NI

Table A3 : Amount of synthetic tax in Cote d'Ivoire (art. 77)

 A3

		Source: Cote d'Ivoire general tax codes, authors
		Range of turnover (FCFA)	Amount of tax (FCFA)
	Lower bound Upper bound
	-	5,000,000	-

Table A4 : Number of consumption items by category

 A4

	Source: EMOP-2011, ESPS-2011, ENV-2014, authors' elaboration
	Category	Mali Senegal Cote d'Ivoire
	Food	21	54	128
	Other	14	6	12
	Combustible	3	5	7
	Education	3	5	11
	Equipment	32	56	48
	Ceremonies	2	2	2
	Info & communication	4	6	8
	Dwelling and related charges	9	8	3
	Health	5	9	9
	Textile/footwear/jewelry	4	5	8
	Transport	3	4	5
	Water/electricity	2	5	7
	Total	102	165	248

Table A5 : Unit cost of education public service by country-Mali, Senegal and Cote d'Ivoire

 A5

											i. 1.5 %
									Owner		ii. Regarding
									of unbuilt		agricultural
									proprieties		properties,
									such as		a flat tax:
	Tax on unbuilt properties	Owner of built proprieties	Market value		5%	_	_	_	urban buildings, At least 100 hectares of	Market value	-Hevea 7 500 FCFA/ha -Cacao, Coffee,
									exploitation of		Banana,
									hevea, cocoa,		Pineapple,
									coffee,		coconut,
									bananas, etc.		5 000 -etc.
											(see art 165)
		Source: authors' elaboration based on administrative data
	Country	Harmonized category	Spending (billions FCFA)	Number of users (millions)	Unit cost (Thousands FCFA)	Adjusted Unit cost (Thousands FCFA)
		Basic Education	183.0	1.8	101.6		65.4
	Mali	Secondary and Higher Education	73.0	0.1	730.0		121.7
		Total		256.0	1.9	134.7		75.3
		Preschool		1.4	0.0	34.9		_
		Primary		189.8	1.4	133.2		_
	Senegal	Secondary		141.2	0.8	168.0		_
		Higher		103.8	0.1	1956.3		_
		Total		436.2	2.4	184.9		_
		Preschool		22.2	0.1	276.8		_
		Primary		373.2	2.9	128.0		_
	Cote d'Ivoire	Secondary		338.0	1.0	348.1		_
		Higher		171.1	0.1	1922.5		_
		Total		904.5	4.1	223.1		_

Table A7 :

 A7 Bottom 40% share by income concept Market income Disposable income Consumable income Final income

	Cote d'Ivoire	0.09	0.09	0.08	0.10
	Mali	0.13	0.13	0.10	0.14
	Senegal	0.10	0.10	0.09	0.11

Table A8 :

 A8 Gini by income concept Market income Disposable income Consumable income Final income

	Cote d'Ivoire	0.58	0.57	0.59	0.56
	Mali	0.46	0.46	0.51	0.45
	Senegal	0.56	0.54	0.57	0.53

Table A9 : Top 10% share by income concept

 A9 Market income Disposable income Consumable income Final income

	Cote d'Ivoire	0.46	0.45	0.46	0.44
	Mali	0.35	0.35	0.37	0.34
	Senegal	0.44	0.42	0.44	0.42

Table A10 : Incidence of personal income tax by decile

 A10

	Decile Cote d'Ivoire Mali Senegal
	1	0.0%	0.0%	0.0%
	2	0.0%	0.0%	0.0%
	3	0.0%	0.0%	0.0%
	4	0.0%	0.0%	0.0%
	5	0.0%	0.0%	0.0%
	6	0.0%	0.0%	0.0%
	7	0.0%	0.0%	1.0%
	8	0.0%	0.0%	1.0%
	9	1.0%	0.0%	2.0%
	10	3.0%	1.0%	6.0%

Table A11 : Incidence of custom duties by decile

 A11

	Decile Cote d'Ivoire Mali Senegal
	1	17.0%	8.0%	8.0%
	2	6.0%	3.0%	4.0%
	3	4.0%	2.0%	4.0%
	4	3.0%	2.0%	3.0%
	5	3.0%	1.0%	2.0%
	6	3.0%	1.0%	2.0%
	7	2.0%	1.0%	2.0%
	8	2.0%	1.0%	1.0%
	9	2.0%	1.0%	1.0%
	10	1.0%	0.0%	1.0%

Table A12 : Incidence of value-added taxe by decile

 A12

	Decile Cote d'Ivoire	Mali	Senegal
	1	43.0%	80.0%	26.0%
	2	15.0%	25.0%	14.0%
	3	10.0%	19.0%	13.0%
	4	9.0%	16.0%	11.0%
	5	8.0%	14.0%	10.0%
	6	7.0%	11.0%	9.0%
	7	6.0%	10.0%	8.0%
	8	5.0%	9.0%	7.0%
	9	5.0%	7.0%	6.0%
	10	3.0%	4.0%	3.0%

Table A13 : Incidence of education spending by decile

 A13

	Decile Cote d'Ivoire	Mali	Senegal
	1	112.0%	123.0%	69.0%
	2	40.0%	46.0%	32.0%
	3	30.0%	34.0%	22.0%
	4	21.0%	22.0%	21.0%
	5	16.0%	21.0%	13.0%
	6	16.0%	16.0%	10.0%
	7	9.0%	14.0%	9.0%
	8	8.0%	11.0%	8.0%
	9	6.0%	8.0%	6.0%
	10	2.0%	4.0%	3.0%

Table A14 : Concentration shares of personal income tax by decile

 A14

	Decile Cote d'Ivoire	Mali	Senegal
	1	0.0%	0.0%	0.0%
	2	0.0%	0.0%	0.0%
	3	0.0%	0.0%	0.0%
	4	0.0%	0.0%	0.0%
	5	0.0%	1.0%	0.0%
	6	0.0%	1.0%	1.0%
	7	1.0%	3.0%	1.0%
	8	3.0%	3.0%	4.0%
	9	9.0%	6.0%	8.0%
	10	86.0%	86.0%	86.0%

Table A15 : Concentration shares of custom duties tax by decile

 A15

	Decile Cote d'Ivoire Mali Senegal
	1	6.0%	11.0%	6.0%
	2	6.0%	8.0%	6.0%
	3	6.0%	8.0%	8.0%
	4	7.0%	8.0%	8.0%
	5	8.0%	9.0%	9.0%
	6	8.0%	9.0%	10.0%
	7	10.0%	10.0%	10.0%
	8	11.0%	10.0%	12.0%
	9	14.0%	11.0%	13.0%
	10	23.0%	14.0%	18.0%

Table A16 : Concentration shares of value-added tax by decile

 A16

	Decile Cote d'Ivoire Mali Senegal
	1	5.0%	11.0%	4.0%
	2	5.0%	7.0%	5.0%
	3	5.0%	8.0%	6.0%
	4	6.0%	8.0%	6.0%
	5	7.0%	9.0%	8.0%
	6	8.0%	9.0%	9.0%
	7	9.0%	10.0%	10.0%
	8	11.0%	11.0%	12.0%
	9	15.0%	12.0%	16.0%
	10	28.0%	16.0%	24.0%

Table A17 : Concentration shares of education spending tax by decile

 A17

	Decile Cote d'Ivoire Mali Senegal
	1	9.0%	12.0%	9.0%
	2	9.0%	10.0%	8.0%
	3	10.0%	10.0%	8.0%
	4	9.0%	8.0%	10.0%
	5	9.0%	10.0%	8.0%
	6	12.0%	9.0%	8.0%
	7	8.0%	10.0%	9.0%
	8	11.0%	10.0%	12.0%
	9	12.0%	9.0%	13.0%
	10	10.0%	10.0%	15.0%

Table A18 : Personal income taxpayers by decile

 A18

	Decile Cote d'Ivoire Mali Senegal
	1	2.0%	1.0%	1.0%
	2	4.0%	0.0%	2.0%
	3	4.0%	3.0%	2.0%
	4	5.0%	3.0%	2.0%
	5	5.0%	11.0%	3.0%
	6	6.0%	4.0%	8.0%
	7	8.0%	5.0%	12.0%
	8	13.0%	5.0%	25.0%
	9	16.0%	7.0%	32.0%
	10	27.0%	11.0%	45.0%

Table A19 : Custom duties payers by decile

 A19

	Table A20: Value-added taxpayers by decile
	Decile Cote d'Ivoire	Mali	Senegal
	1	100.0%	97.0%	100.0%
	2	100.0%	100.0%	100.0%
	3	100.0%	99.0%	100.0%
	4	100.0%	100.0%	100.0%
	5	99.0%	100.0%	100.0%
	6	100.0%	99.0%	100.0%
	7	100.0%	100.0%	100.0%
	8	100.0%	100.0%	100.0%
	9	100.0%	100.0%	100.0%
	10	100.0%	99.0%	100.0%
	Decile Cote d'Ivoire	Mali	Senegal
	1	99.0%	97.0%	98.0%
	2	100.0%	100.0%	98.0%
	3	99.0%	99.0%	99.0%
	4	100.0%	100.0%	99.0%
	5	99.0%	100.0%	98.0%
	6	100.0%	99.0%	99.0%
	7	100.0%	100.0%	99.0%
	8	100.0%	100.0%	99.0%
	9	100.0%	100.0%	100.0%
	10	100.0%	99.0%	100.0%

Table A21 : Education spending beneficiaries by decile

 A21

	Decile Cote d'Ivoire	Mali	Senegal
	1	37.0%	70.0%	63.0%
	2	43.0%	73.0%	61.0%
	3	46.0%	67.0%	59.0%
	4	39.0%	63.0%	61.0%
	5	37.0%	70.0%	64.0%
	6	35.0%	63.0%	61.0%
	7	31.0%	66.0%	60.0%
	8	32.0%	63.0%	62.0%
	9	25.0%	63.0%	57.0%
	10	15.0%	53.0%	38.0%

For instance, EMOP 2014 does not include

regions which were not surveyed due to security issues.

Excise taxes were not coded in this version of the study. This is left for future work.

Respondents are individuals aged over 18 years old.

Tax compliance is measured through the response to a question asking respondent how often he or she did not pay his/her taxes. It is a dummy which equals to 1 if the respondent never or rarely avoids paying taxes and 0 if he or she often or always avoids paying taxes.

Subsidies are current unrequited payments that governments make to enterprises, resident producers and importers. Subsidies may be designed to influence enterprises level or type of production, or the prices at which the products are sold. Subsidies consists of 'subsidies on products', subsidies payable per unit of a good or a service, and 'other subsidies on production', which cover all other subsidies enterprises receives as a consequence of engaging in production. Data are in current local currency.

http://www.dpee.sn/-TOFE-.html?lang=frDirection de la prévision et des études économiques, tofe 2001-2025

In Senegal, the ceiling amount of social contribution paid by employers is set to 5.076 millions FCFA per year.

In the remainder of this report, we will focus on education expenditure, also because the allocation of public health expenditures at the household level requires estimating models of health risk. This is left for future work.

UNESCO (2015), Diagnostique de la question enseignante en République du Mali, Bureau de Bamako.

We use estimates from household surveys for Cote d'Ivoire and Senegal. Regarding Mali's number of users, we use administrative data figures given the lack of information in Mali's household survey.

The probability is estimated at 64.3% for fundamental education level and 16.7% for secondary and higher education level

This will be included in a future version of the model.

Concentration shares are equal to the ratio of the amount of tax paid (or benefit received) by each decile to the total fiscal income (or cost). The sum of concentration shares is equal to 100%.

This is not undertaken here and is left for future work.

This problem is also found within countries when surveys collected in different years use different questionnaire designs, which is unfortunately often the case

Source:EMOP-2011, ESPS-2011, ENV-2014, authors' calculations

Acknowledgements

The authors acknowledge funding for this research from the Agence Française de Développement through the EU-AFD Research Facility in Inequalities, a program funded by the European Union.

Mahdi Ben Jelloul thanks the support of the EUR grant ANR-17-EURE-0001.

5,000,001 6,000,000 491,400 6,000,001 7,000,000 573,300 7,000,001 8,000,000 655,200 8,000,001 9,000,000 737,100 9,000,001 10,000,000 819,000 10,000,001 11,000,000 900,000 11,000,001 12,000,000 982,000 12,000,001 13,000,000 1,064,000 13,000,001 14,000,000 1,146,600 14,000,001 15,000,000 1,228,500 15,000,001 16,000,000 1,310,000 16,000,001 17,000,000 1,392,000 17,000,001 18,000,000 1,474,200 18,000,001 19,000,000 1,556,100 19,000,001 20,000,000 1,638,000 20,000,001 21,000,000 1,719,900 21,000,001 22,000,000 1,801,800 22,000,001 23,000,000 1,883,700 23,000,001 24,000,000 1,965,600 24,000,001 25,000,000 2,047,500 25,000,001 26,000,000 2,129,400 26,000,001 27,000,000 2,211,300 27,000,001 28,000,000 2,293,200 28,000,001 29,000,000 2,375,100 29,000,001 30,000,000 2,457,000 30,000,001 32,000,000 2,480,000 32,000,001 34,000,000 2,640,000 34,000,001 36,000,000 2,800,000 36,000,001 38,000,000 2,960,000 38,000,001 40,000,000 3,120,000 40,000,001 42,000,000 3,280,000 42,000,001 44,000,000 3,400,000 44,000,001 46,000,000 3,600,000 46,000,001 48,000,000 3,760,000 48,000,001 50,000,000 3,920,000

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What is AFD ?

The Agence Française de Développement (AFD) Group is a public entity which finances, supports and expedites transitions toward a more just and sustainable world. As a French overseas aid platform for sustainable development and investment, we and our partners create shared solutions, with and for the people of the global South. Active in more than 4,000 projects in the French overseas departments and some 115 countries, our teams strive to promote health, education and gender equality, and are working to protect our common resources -peace, education, health, biodiversity and a stable climate.