West African Economic and Monetary Union: Selected Issues

Abstract

West African Economic and Monetary Union: Selected Issues

Fiscal Space in WAEMU1

West African Economic and Monetary Union (WAEMU) countries face an important common challenge of creating sufficient fiscal space to finance ambitious growth, development, and poverty-reduction programs in individual countries. Such additional fiscal space can be created by either enhancing tax revenue or improving the efficiency of spending. While WAEMU countries are broadly in line with comparator countries in total tax collection, WAEMU’s tax revenue relies heavily on trade taxes, which will inevitably be reduced by the impending trade liberalization. Also, high reliance on trade taxes makes the WAEMU’s revenue base vulnerable to fluctuations of international prices. Panel regression and stochastic frontier analysis suggest substantial room to improve domestic tax collection in the WAEMU by up to 0.8 to 2 percent of GDP. The effort should be country-specific with each government focusing on its underperforming tax category. On the expenditure side, WAEMU countries have significant scope to improve efficiency of their spending on education and health. If all WAEMU countries achieve the spending efficiency of the top performer among them, the fiscal savings on average can add 1 to 3 percent of GDP annually to the available fiscal space of the region.

A. The Need for Scaling Up

1. WAEMU countries need to mobilize substantial financial resources to address the infrastructure gap, which has been widely identified as a growth bottleneck. Many studies find that inadequate infrastructure impedes growth (for example, Commission for Africa, 2005 and Foster and Briceño-Garmendia, 2009). Infrastructure development was estimated to have contributed about 1 percentage point to per-capita growth in West Africa in 2001–05 (for example Calderon (2009)). For Benin, Domínguez-Torres and Foster (2011) estimate that infrastructure contributed 1.6 percent points to per capita growth; while in Senegal, Torres, Briceño-Garmendia, and Dominguez (2011) find the contribution was about 1 percent point. Also, raising the two countries’ infrastructure endowment to that of Africa’s middle-income countries could boost annual growth by 3.2 and 2.7 percentage points respectively. Recent reports also confirmed a continued infrastructure bottleneck in other WAEMU countries (for example, IMF 2013a, b, c).

2. To finance the scaling up of public investment while preserving macroeconomic stability, WAEMU countries have to use their fiscal space efficiently. While WAEMU countries’ external debt levels declined owing to the heavily indebted poor countries (HIPC)multilateral debt relief initiative, leaving some scope for external borrowing, the availability of financing at attractive terms is limited. Also, some countries’ total government debt has increased considerably since those countries received the debt relief, which suggests that caution is warranted in additional borrowing. Therefore, it is essential for the sustainable financing of scaling up infrastructure investment that the two major channels for creating fiscal space be used. These channels are increasing tax revenue and increasing the efficiency of spending.

B. Raising Tax Revenue

3. Improving tax collection remains the main channel for enlarging the fiscal space. This has been well recognized in the WAEMU, which has a convergence criterion of 20 percent for the tax-to-GDP ratio, even though several member countries have not been in compliance with this criterion for years.

4. The WAEMU’s relatively high indirect tax rates have not resulted in higher tax collection. Indirect tax rates in the WAEMU are higher than the average rates in sub-Saharan Africa and in low-income countries2, especially for goods and services taxes and trade taxes. However, higher rates have not generated higher revenues. Roughly, the tax-to-GDP ratio has been below the sub-Saharan Africa average throughout the observation period (2000 to 2011), and just broadly in line with the low-income countries average (Figure 1a). Looking at the trend over time, the WAEMU’s tax-to-GDP ratio improved from 11.7 percent of GDP in 2000 to 14.7 percent of GDP in 2011, driven by a broad trend in all member countries except Côte d’Ivoire, where results were affected by internal conflicts. However, the size of improvements varied considerably among the countries. For instance, Benin’s total tax revenue increased by 2.1 percent percentage points, while Togo’s total tax revenue rose by 6.6 percent percentage points.

5. Looking at the performance tax by tax, the improvement in the WAEMU’s tax ratio is driven by higher collection from income tax and goods and services taxes, while trade revenues are broadly flat due to limited trade liberalization.

  • Trade taxes: In contrast with sub-Saharan African and low-income countries, where weighted average tariff rates declined, reflecting trade liberalization over the last decade, the WAEMU’s tariff rates dropped only marginally and the tax-to-GDP ratio has remained broadly stable over time. In the comparator groups, sub-Saharan Africa’s drop in trade tax revenues reflects the rate decline, while it seems that low-income countries were able to offset the rate decline by efficiency measures that allowed these countries to broadly preserve the trade- tax-to-GDP ratio. (Figure 1b)

  • Personal income taxes: The WAEMU increased the tax-to-GDP ratio from about 3 to close to 4 percent of GDP, but it remained below the ratios for low-income countries and sub-Saharan Africa. (Figure 1c)

  • Goods and services taxes: The francophone tradition of relying more on direct than on indirect taxation is reflected in the relatively higher rates. This translates into a higher level of tax revenues than is the case in comparator countries by around 0.6 to 0.8 percent of GDP. Also, the improvement in the WAEMU countries over the observation period was most pronounced in this tax category. (Figure 1d).

6. WAEMU countries show considerable variation in the drivers for revenue collection by tax categories. For example, in Togo, income tax revenue declined from 2.9 percent to 2.5 percent of GDP, but goods and service tax revenue rose sharply from 2 percent to 9.2 percent of GDP. In Benin, the revenue gain was driven by higher trade tax revenue, while goods and services tax revenue declined. In Cote d’Ivoire, however, the decline in tax revenue was mainly driven by falling trade tax revenues.

Figure 1.
Figure 1.

WAEMU: Tax Revenue Developments

Citation: IMF Staff Country Reports 2016, 098; 10.5089/9781475550009.002.A002

7. Panel regressions were used to analyze the tax potential of WAEMU countries based on determinants identified in the literature. Drawing on the existing literature on determining tax potential (for example, Gupta, 2007; Davoodi and Grigorian, 2007; and Pessino and Fenochietto, 2010), the following variables were considered as the determinants to estimate the tax potential, which was defined as the maximum level of tax revenue that a country can achieve given its macroeconomic fundamentals: GDP per capita, consumption, gross fixed capital formation, inflation, import and export as a share of GDP, share of agriculture in GDP, share of the urban population, natural resource rents, and broad money as a share of GDP (Annex Table 1.1). Obtaining the tax potential allows for calculating the tax gap, which is the percentage deviation of actual revenue from potential revenue.3 The regression analysis is not only conducted for total tax revenue, it is also conducted for revenue in the subcategories of goods and services, trade, and income (see Annex Table 1.2 through Annex Table 1.4).

8. Our analysis suggests that WAEMU countries are ahead of comparator countries in their total tax collection, but have room to improve income tax collection. In 2011, total tax collection in the WAEMU exceeded the potential revenue by around 6 percent and 12 percent when compared with low-income countries and sub-Saharan African countries respectively (Figure 2a). This shows an improvement compared to 2000 when the WAEMU’s total tax collection was below potential by around 4 percent compared with both low-income and sub-Saharan African countries. The following factors explain this trend:

  • Goods and services taxes: The relative improvement between 2000 and 2011 was mainly driven by a more positive goods and services tax gap (Figure 2b). However, higher tax rates in the WAEMU explain at least part of this positive tax potential.

  • Trade taxes: Despite the higher average tariff, our tax potential analysis indicates only a moderately positive tax gap in 2011. However, improvements of the trade tax revenue compared 2000 range from below to slightly above potential (Figure 2c) for both benchmark groups.

  • Income taxes: Revenue performance as measured by the tax gap deteriorated from around −1½ percent to around -5½ percent compared with the gap in sub-Saharan African countries (Figure 2d), and closed only slightly from around -3½ percent to around -2 percent with respect to the low-income country benchmark.

Figure 2.
Figure 2.

WAEMU: Tax Revenue Gap Analysis

Citation: IMF Staff Country Reports 2016, 098; 10.5089/9781475550009.002.A002

Sources: IMF staff estimates.

This signals a need for the WAEMU to improve income tax collection compared with the peer groups.

9. The analysis on the country level shows deviations from the overall WAEMU trend for the tax gap. During the observation period, six of the eight WAEMU members increased their tax collection efforts compared with what was done in sub-Saharan African and low income countries (Figure 2a). As for income tax revenue collection efforts, only Mali, Niger and Senegal experienced an increase in comparison with the benchmarks (Figure 2c). The goods and services tax gap widened in the case of Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali and Senegal compared with at least one benchmark group (Figure 2b). Among these countries, the trade tax revenue gap closed significantly or turned positive for Benin, Burkina Faso, Niger and Senegal, meaning that the overall improvement for these countries was mostly driven by an improved trade tax collection relative to the benchmarks.4 In sum, the varied developments within the WAEMU group suggest the need for country-specific policies to improve revenue collection in the identified tax categories.

10. Similar results were obtained with stochastic frontier analysis. Following Pessino and Fenochietto (2010), the stochastic frontier analysis estimation applies a time-varying parameter of technical inefficiency in tax collection to the different tax categories already discussed in the panel regression analysis. The results confirm the findings using panel regressions, but with some modifications (Figure 3). Namely, regardless of the reference group of countries (sub-Saharan African and low-income countries) used in the estimation, the WAEMU performed relatively well in terms of tax collection. This result, however, seems to be mostly driven by trade taxes. When looking at goods and services and income taxes, WAEMU countries seem to be less efficient compared with sub-Saharan African countries and all low-income countries. The main difference across the two methods is the assessment of the Value Added Tax performance, which was positive in the panel regression approach and negative in the stochastic frontier analysis. In contrast, results were broadly consistent for income tax and trade taxes, even though the stochastic frontier analysis gave a more positive assessment of the trade tax performance than did the panel regression model.

Figure 3.
Figure 3.

WAEMU: Tax Collection Efficiency The WAEMU By Category

Citation: IMF Staff Country Reports 2016, 098; 10.5089/9781475550009.002.A002

Sources: IMF staff estimates.

11. WAEMU countries have substantial room to improve domestic tax collection. WAEMU countries have recently initiated reforms towards trade liberalization, most notably, the introduction of a common external tariff for all Economic Community of West African States countries in January 2015. While the implementation will be gradual, it is expected that trade revenues will decline. Therefore, it is important for the WAEMU to enhance its domestic tax revenue base, in particular, income tax revenues, where both analytical approaches indicate room for improvement by around 0.8 to 2 percent of GDP.

C. Improving spending efficiency

12. Is there scope for creating fiscal space by improving the efficiency of public spending? Our analysis focused on the technical efficiency of translating public spending into the corresponding results by comparing WAEMU’s input-output performance in public spending to those of other sub-Saharan African countries with similar levels of development. In addition, to reflect WAEMU countries’ aspirations to accelerate growth, specific comparisons with the fast-growing non-resource rich sub-Saharan African countries were provided.5 Quantitative assessments were conducted through a nonparametric data envelopment analysis (DEA).6 While public spending covers many sectors, only the education and health sectors were analyzed because these are sectors in which public spending plays a major role, and consistent cross-country data are readily available. Furthermore, based on data for 2008-12, we estimated the potential budgetary savings from higher efficiency in education and health to better inform policy discussions.

13. The DEA methodology provides a parsimonious model at the aggregate level to assess the efficiency of public spending based on cross-country comparison of the input-output relationships. It uses a nonparametric approach to identify an “efficiency frontier” from the input-output relationships across the countries that share the same technology (see Herrera and Pang, 2005 and Grigoli and Kapsoli, 2013 for details). Each country’s efficiency is then compared with this frontier in the corresponding range of spending to obtain an efficiency score of between 0 and 100 percent, and variable returns to scales are taken into account in the estimation given the observed patterns of the data. In this analysis, data for 46 low-income and lower-middle income sub-Saharan countries in 2003–12 were used in defining the efficiency frontier. However, data used in estimating potential savings were limited to those from WAEMU countries7 in 2008–12 to further strengthen the cross-country comparability to arrive at the most realistic estimates.

14. Education sector indicators in WAEMU countries have improved significantly in recent years, largely supported by higher spending. Between 2003-07 and 2008-12, average education spending increased from 4 percent to 4.4 percent of GDP in WAEMU countries. The increased spending also supported better result indicators, such as an increase of about 14 percentage points in primary school enrollment rates and an increase of about 11 percentage points in adult literacy. But the relative magnitude of the increase in result indicators was less than that of the spending increase. For example, education spending in peer countries remains stable, but achieved a similar increase (11 percentage points) in enrollments and a much higher increase (18 percentage points) in the adult literacy rate—which better reflects the quality of the education. This suggests lower efficiency in WAEMU countries in achieving quality education results, as compared with the fast-growing sub-Saharan African countries.

Education Spending and Result Indicators

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15. The quantitative DEA analysis confirms that WAEMU countries rank low in the efficiency of education spending compared with the peers. Despite progress in improving education indicators, WAEMU countries lag behind the peer countries in the technical efficiency of their education spending. This is illustrated by the fact that all WAEMU countries are below the efficiency frontier achieved by the most efficient countries in translating education spending into the result indicators in two periods: 2003–07 and 2008–12 (Figure 4a). For example, at an average public education spending close to 5 percent of GDP in 2008–12, Benin stays well within the efficiency frontier (Figure 4b) and lags behind peer countries (for example, Liberia, Rwanda, and Togo) that are located to the northwest of Benin in the scatter plot chart. This means that these countries achieved a higher adult literacy rate at a lower per-capita spending8 than did Benin. Another example is Togo, which is closer to the frontier than is Benin, but achieved much lower adult literacy when compared with Uganda, which had the same level of spending. Based on the distance to the efficiency frontier, potential fiscal saving in achieving the same results can be estimated for WAEMU countries. To achieve realistic estimates, the calculation is limited to WAEMU countries that possess similar institutions and development status. Using this method, on average, WAEMU countries could save between 1 and 2 percent of GDP by improving their spending efficiency to the highest level in the Union.Sources: World Development Indicators, FAD database, and IMF staff calculations
Figure 4.
Figure 4.

WAEMU: Education Spending Efficiency

Citation: IMF Staff Country Reports 2016, 098; 10.5089/9781475550009.002.A002

16. Health spending in WAEMU countries has increased significantly and the result indicators have improved. Between 2003–07 and 2008–10,9 average health spending in the WAEMU increased from about 2.3 to 2.7 percent of GDP. The results indicators also improved, including an increase in life expectancy of 2 years and a reduction of the child mortality rate by about 3 percentage points. Figure 11b shows that while WAEMU countries are also below the efficiency frontier in general, a few countries moved closer to the frontier in 2008–12 (for example, Burkina Faso, Mali, and Senegal), where result indicators improved faster, relative to the change in public health spending.

Health Spending and Result Indicators

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Sources: World Development Indicators, FAD database, and IMF staff calculations

17. The DEA analysis confirmed that the efficiency of health spending was higher than in education, but there is still a scope for potential savings. In applying the DEA to the health sector, both public and private spending needs to be included. While basic education is generally considered a public good that should be fully supported by public spending, the health sector requires significant private spending beyond public spending to achieve the corresponding results, and thus, the DEA analysis included both sources of spending. Following the same method applied to education spending, WAEMU countries’ efficiency score ranges from 43 percent (Guinea Bissau) to 100 percent (Senegal) in the baseline estimate. As a result, the estimated potential savings of increased efficiency of spending on health are about 0.4 to 0.8 percent of GDP on average.

Figure 5.
Figure 5.

WAEMU: Health Spending Efficiency

Citation: IMF Staff Country Reports 2016, 098; 10.5089/9781475550009.002.A002

18. Improving the efficiency of public education and health spending can contribute not only to increase fiscal space, but also to more inclusive growth in WAEMU countries. Our analysis found that WAEMU countries have significant scope to improve the efficiency of their spending in education and health. If all WAEMU countries could achieve the highest efficiency already reached by the top country in the union, the fiscal savings are estimated to be about 1 to 3 percent of GDP. Therefore, complementary to improved tax efforts, enhancing the efficiency of spending could provide a significant contribution to WAEMU countries’ fiscal space to support the scaling up of infrastructure investments. Furthermore, education and health services are essential to enhance the wellbeing of WAEMU citizens and to enhance human capital and build a more productive labor force, and thus improving spending efficiency could also support more inclusive growth.

References

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Annex I. Regression Results by Tax Category

Annex Table 1.

WAEMU: Determinants of total tax potential

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Robust standard errors in brackets, *** p<0.01, ** p<0.05, * p<0.1
Annex Table 2.

WAEMU: Determinants Of Trade Tax Potential

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Robust standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1
Annex Table 3.

WAEMU: Determinants Of Income Tax

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Robust standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1
Annex Table 4.

WAEMU: Determinants Of Goods And Services Tax

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Robust standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1
1

Prepared by Karim Barhoumi (AFR), Christine Dieterich (AFR), Larry Cui (AFR), Sergio Sola (MCD), and Alexander Raabe (The Graduate Institute of International and Development Studies, Geneva)

2

Excludes upper middle-income countries of South Africa, Botswana, Nigeria, Angola and Namibia. The low-income countries refer to the World Bank definition of countries with a per-capita GNI of $1,025 or less in 2012.

3

A positive gap signals that tax revenue collection is above potential. A negative gap implies that tax revenue collection falls short of the potential.

4

Regarding the determinants of trade tax potential, we used different models, such incorporating a proxy for the openness or using import/GDP as well as export/GDP separately.

5

These are countries with top growth performance in sub-Saharan Africa, as discussed in IMF (2013d): Ethiopia, Mozambique, Rwanda, Tanzania and Uganda.

6

The DEA method has been used in a recent analysis on the efficiency of public spending in Iceland and in cross-country studies by FAD, such as Belhocine (2013) and Grigoli and Kapsoli (2013).

7

Data may not be available for all WAEMU countries, and this limits the coverage of the efficiency and saving estimates.

8

Similar analysis using education spending as a share of GDP and the corresponding result indicators yields consistent results.

9

Due to data constraints, the latest period is limited to 2008−10

West African Economic and Monetary Union (WAEMU): Selected Issues
Author: International Monetary Fund. African Dept.