Back Matter

Back Matter

Author(s):
Karim Barhoumi, Larry Cui, Christine Dieterich, Nicolas End, Matteo Ghilardi, Alexander Raabe, and Sergio Sola
Published Date:
January 2016
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    References

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      Davoodi, Hamid R., and David A.Grigorian. 2007. “Tax Potential vs. Tax Effort: A Cross-Country Analysis of Armenia’s Stubbornly Low Tax Collection.Working Paper 07/106, International Monetary Fund, Washington, DC.

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      Ghilardi, Matteo F., and SergioSola. 2015. “Investment Scaling-up and the Role of Government: The Case of Benin.Working Paper 15/69, International Monetary Fund, Washington, DC.

      Grigoli, Francesco, and JavierKapsoli. 2013. “Waste Not Want Not: The Efficiency of Health Expenditure in Emerging and Developing Economies.Working Paper 13/187, International Monetary Fund, Washington, DC.

      Gupta, Abhijit Sen. 2007. “Determinants of Tax Revenue Efforts in Developing Countries.Working Paper 07/184, International Monetary Fund, Washington, DC.

      Hausmann, Ricardo, DaniRodrik, and AndresVelasco. 2005. “Growth Diagnostics,Harvard Kennedy School Working Paper. Available at http://ksghome.harvard.edu/~rhausma/new/growthdiag.pdf

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      International Monetary Fund (IMF). 2012. Benin: 2012 Article IV Consultation and Fourth Review Under the Extended Credit Facility Arrangement—Staff Report. Washington, DC.

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      Moretti, D., B.Taiclet, N.End, and S.Ramangalahy. 2014. “Renforcer la Chaine de la Dépense (volet Investissement).FAD TA Report, May.

      Pessino, Carola, and RicardoFenochietto. 2010. “Determining Countries’ Tax Effort.Hacienda Pública Española / Revista de Economía Pública195 (4): 6587.

      Pessino, Carola, and RicardoFenochietto. 2013. “Understanding Countries’ Tax Effort.Working Paper 13/244, International Monetary Fund, Washington, DC.

      Rota-Graziosi, Gregoire, MichelBua, Anne-MarieGeourjon, and MarcelSteenlandt. 2013. “Simplifier et Améliorer le Systélme Fiscal et Son Administration.Technical Assistance Report for the Republic of Benin, International Monetary Fund, Washington, DC, January.

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    Annex 1. Stochastic Frontier Analysis

    The stochastic frontier model of Aigner, Lovell, and Schmidt (1977) and Pessino and Fenochietto (2010 and 2013) can be represented as follows:

    Where α represents a set of country-specific intercepts, and X is the vector that represents variables affecting tax revenue. The error term ɛ is a composite error term made of the standard component v, and of a component u that is distributed following a probability density function, which is positively definite. The element u is the time-varying element that represents the degree of inefficiency: higher values correspond to higher inefficiencies; therefore, the efficiency is calculated as 1 − u. Similar to the panel regression, four different models are used to analyze each type of tax revenue. Starting from a general model for the estimation of the determinants of tax revenue to GDP, the model is then modified to exclude statistically insignificant explanatory variables.

    Annex Figure 1.Tax Efficiency in Benin by Category

    Source: IMF staff estimations.

    Annex 2. Regression Results by Tax Category
    Annex Table 1.Determinants of Total Tax Potential
    Total Tax RevenueWAEMUSSA
    GDP per capita5.969**2.637
    [1.882][1.879]
    Inflation, consumer prices (annual percentage−0.040***0.002
    [0.007][0.013]
    Imports (percent of GDP)−0.235*−0.090
    [0.108][0.089]
    Exports (percent of GDP)0.223*0.128
    [0.115][0.102]
    Agriculture (percent of GDP)−0.027−0.166**
    [0.051][0.062]
    Consumption (percent of GDP)0.246*0.105
    [0.121][0.093]
    Gross fixed capital formation (percent of GDP)0.390***0.077
    [0.100][0.098]
    Urban population (percent of total)0.140**0.109
    [0.054][0.138]
    Total natural resources rents (percentof GDP)−0.049−0.015
    [0.035][0.043]
    M2 (percent of GDP)0.080***−0.001
    [0.018][0.027]
    Observations201707
    Number of Countries838
    R-squared0.7010.113
    R20.6860.101
    Robust standard errors in brackets
    *** p<0.01, ** p<0.05, * p<0.1
    Source: IMF staff estimations
    Annex Table 2.Determinants of Trade Tax Potential
    Trade Tax revenueWAEMUSSA
    GDP per Capita−1.009−1.545
    [2.091][1.375]
    Inflation, Consumer Prices (annual %)−0.001−0.004
    [0.010][0.003]
    Imports (percent of GDP)−0.002−0.034
    [0.036][0.045]
    Exports (percent of GDP)0.0200.021
    [0.066][0.040]
    Urban Population (percent of total)−0.118−0.012
    [0.264][0.204]
    Total Natural Resources Rents (percent of GDP)0.028−0.008
    [0.038][0.034]
    Trend0.029−0.009
    [0.144][0.115]
    Observations200716
    Number of Countries838
    R-squared0.0460.036
    R20.01090.0261
    Robust standard errors in brackets
    *** p<0.01, ** p<0.05, * p<0.1
    Source: IMF staff estimations
    Annex Table 3.Determinants of Income Tax
    Income Tax RevenueWAEMUSSA
    GDP per capita1.636*2.370***
    [0.698][0.626]
    Agriculture (percent of GDP)−0.049−0.035*
    [0.028][0.020]
    Consumption (percent of GDP)−0.0100.025*
    [0.010][0.013]
    Gross Fixed Capital Formation (in percent)0.0210.010
    [0.020][0.013]
    Urban Population (in percent of total)0.0360.091**
    [0.034][0.035]
    Total Natural Resources Rents (in percent0.043*0.006
    [0.021][0.014]
    M2 (percent of GDP)0.0070.023**
    [0.008][0.009]
    Public Wage Bill (percent of GDP)0.132*−0.000***
    [0.057][0.000]
    Observations170629
    Number of Countries835
    R-squared0.4300.201
    R20.4010.191
    Robust standard errors in brackets
    *** p<0.01, ** p<0.05, * p<0.1
    Source: IMF staff estimations
    Annex Table 4.Determinants of Goods and Services Tax
    Good and Services Tax RevenueWAEMUSSA
    GDP per Capita8.984**4.415***
    [2.710][1.270]
    Inflation, Consumer Prices (annual percentage)−0.0300.006
    [0.019][0.006]
    Agriculture (percent of GDP)0.022−0.026
    [0.074][0.036]
    Government Consumption (percent of GDP)0.0790.023
    [0.341][0.046]
    Household Consumption (percent of GDP)−0.1300.042
    [0.285][0.037]
    Gross Fixed Capital Formation (percent of GDP)−0.0500.003
    [0.263][0.034]
    Urban Population (in percentof total)0.196*0.129*
    [0.100][0.064]
    M2 (percent of GDP)0.0560.023
    [0.037][0.019]
    Imports (percent of GDP)0.1870.021
    [0.286][0.036]
    Exports (percent of GDP)−0.1920.021
    [0.280][0.035]
    Observations199698
    Number of Countries838
    R-squared0.5890.397
    R20.5670.388
    Robust standard errors in brackets
    *** p<0.01, ** p<0.05, * p<0.1
    Source: IMF staff estimations

    Following IMF (2013), the human-capital-augmented Solow growth accounting exercise uses data from the Penn World Table version 8.0 and decomposes growth into inputs of capital as well as education and labor, while the residue is labeled TFP.

    We extend the analysis of Buffie and others (2012) along four dimensions: (1) we enrich the set of fiscal tools by introducing differentiated tax rates on domestic consumption, labor, and capital; (2) we endogenize labor supply; (3) we introduce government inefficiency in tax collection, which we calibrate using results from estimation; and (4) we introduce windfall revenues.

    For a complete description of the model, see Ghilardi and Sola (2015).

    The VAT on domestic consumption increases by 0.8 percentage points, the tax on capital goods increases by slightly more than 2 percentage points, and the tax on labor increases by slightly less than 1 percentage point.

    Consistent with findings on informal trade in World Bank (2015), the estimate of total revenue gain is based on the assumption that 80 percent of the goods cleared at “adjusted value” are destined for final consumption to Nigeria. As such, a liberalization of the Nigerian trade regime would cause these goods to be imported as goods in international transit, on which the average tariff of 2.9 percent would apply. The data do not allow a breakdown of the total revenues collected on informal reexports in VAT and customs revenue. Based on a comparison of the average effective customs rate and the VAT rate, approximately two-thirds of the revenue represents VAT, and one-third customs revenues. This estimate is also consistent with other estimates in recent Technical Assistance reports. See additional details in Geourjon, Chambas, and Laporte (2008) and Rota-Graziosi and others (2013).

    Even though generous transition arrangements allow for a gradual change in trade patterns over time.

    For example, Gupta (2007), Davoodi and Grigorian (2007), and Pessino and Fenochietto (2010, 2013).

    This is distributed as one-third for customs revenue and two-thirds for goods and services tax revenue as an empirical estimate.

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

    While basic education is generally considered a public good that is fully supported by public spending, the health sector requires significant private spending beyond public spending to achieve results, and thus the DEA analysis included both sources of spending in most specifications.

    Given the decreasing return to scale pattern exhibited in the cross-country data, this is estimated as (XiXmin)*(1 – Ei), where X refers to education spending as well as health spending in percent of GDP and Ei refers to the relative efficiency score for country i. This refers to potential savings while achieving the same level of result indicators.

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