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Back Matter

Back Matter

Karim Barhoumi, Larry Cui, Christine Dieterich, Nicolas End, Matteo Ghilardi, Alexander Raabe, and Sergio Sola
Published Date:
January 2016
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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
    Inflation, consumer prices (annual percentage−0.040***0.002
    Imports (percent of GDP)−0.235*−0.090
    Exports (percent of GDP)0.223*0.128
    Agriculture (percent of GDP)−0.027−0.166**
    Consumption (percent of GDP)0.246*0.105
    Gross fixed capital formation (percent of GDP)0.390***0.077
    Urban population (percent of total)0.140**0.109
    Total natural resources rents (percentof GDP)−0.049−0.015
    M2 (percent of GDP)0.080***−0.001
    Number of Countries838
    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
    Inflation, Consumer Prices (annual %)−0.001−0.004
    Imports (percent of GDP)−0.002−0.034
    Exports (percent of GDP)0.0200.021
    Urban Population (percent of total)−0.118−0.012
    Total Natural Resources Rents (percent of GDP)0.028−0.008
    Number of Countries838
    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***
    Agriculture (percent of GDP)−0.049−0.035*
    Consumption (percent of GDP)−0.0100.025*
    Gross Fixed Capital Formation (in percent)0.0210.010
    Urban Population (in percent of total)0.0360.091**
    Total Natural Resources Rents (in percent0.043*0.006
    M2 (percent of GDP)0.0070.023**
    Public Wage Bill (percent of GDP)0.132*−0.000***
    Number of Countries835
    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***
    Inflation, Consumer Prices (annual percentage)−0.0300.006
    Agriculture (percent of GDP)0.022−0.026
    Government Consumption (percent of GDP)0.0790.023
    Household Consumption (percent of GDP)−0.1300.042
    Gross Fixed Capital Formation (percent of GDP)−0.0500.003
    Urban Population (in percentof total)0.196*0.129*
    M2 (percent of GDP)0.0560.023
    Imports (percent of GDP)0.1870.021
    Exports (percent of GDP)−0.1920.021
    Number of Countries838
    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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