## _wp13188 ## Source details **Canonical URL:** [_wp13188](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13188.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13188.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13188.pdf.json) --- ### I. Introduction — research question and background - Research question: whether the availability of higher resource revenue leads to a lower taxation effort of other revenue categories, analyzed both as: - the relationship between non-resource tax revenue and resource revenue, and - the relationship between non-resource tax revenue and statutory tax rates. - Context: High resource prices have produced sizeable increases in fiscal revenue for resource-collecting countries, but resource revenue is volatile and countries must rely on other forms of taxation to fund public expenditure needs (health, education). - Established determinants of tax revenue ratios cited: per capita income, import and agriculture shares, and sectoral composition (Tanzi 1987, 1992; Leuthold 1991); later work confirms per capita income, share of agriculture GDP, and trade openness (Gupta 2007; Davoodi and Grigorian 2007). Foreign aid and institutional factors (especially corruption) have been incorporated as explanatory variables. - Prior empirical findings on resource rents and nonresource tax revenue: - Morrison (2005): elasticity of income tax revenue with respect to nontax revenue (royalties and foreign aid) is negative (-0.05) and significant for dictatorships but insignificant in democracies. - Bornhorst, Gupta, and Thornton (2009): negative coefficients ranging from -0.15 to -0.43. - Drummond, Srivastava, and Oliveira (2012): negative effect of resource rents on the tax ratio ranging from -0.1 for oil to -0.18 for other natural resources. - Key novel finding of this paper: - Non-resource revenue ratios are negatively influenced by higher ratios of resource revenue to GDP. - The lower take up of nonresource taxes in resource-rich countries is correlated with higher levels of corruption in those countries rather than any differences in statutory tax rates. - Implication: lower nonresource revenue in resource-rich countries may reflect incentives for tax evasion, large tax exemptions, and/or weaker enforcement. ### II. Data, sample, and coverage - Data source: database assembled in the African Department of the IMF covering all SSA economies except South Sudan; the database separates resource revenue and nonresource revenue by country and is available for 2000–11. - Resource-intensive country definition: countries where natural resource exports exceeded 25 percent of total merchandise exports in 2005–10 (20 countries identified). - Fiscal dependence definition: “fiscally dependent” countries are those where revenue from natural resources exceeded 20 percent of budgetary revenue in 2005–10 (10 countries identified among the resource-intensive group). - Caveat: Resource revenue may not cover all revenue accruing to the state from extraction because corporate tax payments of resource companies are not always separated out. - Sample for panel regression estimations: a group of 42 countries in sub-Saharan Africa between 2000 and 2011. ### III. Country-specific patterns and key statistics - Resource revenue and exports (averages for 2005–10): - Resource-intensive subgroup: 20 countries with natural resource exports > 25 percent of merchandise exports (2005–10). - Among fiscally dependent resource-intensive countries, oil exporters show very high budgetary reliance on oil receipts: - Equatorial Guinea: 90 percent of budgetary revenue from oil receipts. - Gabon: slightly below 60 percent of budgetary revenue from oil receipts. - Countries with the lowest share of budgetary receipts from natural resources in 2005–10: Ghana, Sierra Leone, South Africa, and Tanzania at less than 5 percent of total revenue. ### Box 1: Non-Renewable Natural Resources in Sub-Saharan Africa - Overview: - Nearly 10 percent of the annual output of sub-Saharan African (SSA) countries and 50 percent of their exports come from non-renewable natural resources. - Natural resources are a major export in about 20 of the 45 countries in the region. - Seven of these countries are oil exporters, accounting for more than half of the region’s natural resource exports. - The other 13 resource-rich economies receive at least a quarter of their export proceeds from mining. - Major commodity exports for non-oil resource-rich economies: gold, diamonds, and other precious stones; some countries depend heavily on base metals and uranium (Niger, Zambia) or a broad mixture of products (Democratic Republic of the Congo, Guinea, Namibia, Sierra Leone). - Several currently nonresource-rich countries have significant resource export potential: examples include Mozambique, Sāo Tomé and Príncipe, Uganda, Liberia (offshore prospects), and Malawi (potentially large uranium deposits). - Some resource exporters are broadening commodity exports (Ghana: oil; Sierra Leone: iron ore; Tanzania: gas). - Collier (2011) point: identified level of exploitable natural resources in the region is currently far below that of other areas, implying many resources remain to be revealed. - Fiscal Revenue and Expenditure Patterns (2000–11): - Revenue in relation to nonresource output for fiscally dependent countries has averaged about 50 percent in the past decade with large variations associated with movements in natural resource prices. - Revenue ratios for the other resource and nonresource countries have been stable, averaging about 20 percent of nonresource output. - Nonresource revenues are more stable than total revenues for fiscally dependent countries; fiscally dependent countries receive less nonresource revenue than other countries, but the difference is much smaller than for total revenue. - Higher revenue translates into higher expenditure for the fiscally dependent group; resource-rich countries outside SSA show non-resource revenue ratios considerably higher than SSA countries and revenue ratios that fluctuate with resource prices, suggesting stronger linkages between resource and nonresource sectors outside SSA. - Institutional quality example: Kaufmann and Kraay index shows Cape Verde and Equatorial Guinea have the lowest and highest levels of corruption among SSA economies respectively; difference in nonresource revenue ratios between these two countries is about 6 percentage points in 2000–11. ### IV. Empirical approach and main estimation results - Estimation framework: - Panel regression for SSA (2000–11, excluding South Sudan) estimating determinants of nonresource revenue follows Bornhorst, Gupta, and Thornton (2009). - Dependent variable: ratio of nonresource revenue to GDP (with RR = 0 for nonresource exporters). - Controls: grants-to-GDP ratio, log real per capita GDP, trade openness (exports plus imports to GDP), share of agriculture, urban population share, and ICRG corruption index. Country-specific fixed effects allowed. - Main regression findings: - The coefficient on resource revenue (β) is negative and significant in baseline specifications. - Contemporaneous estimate: a 1 percent increase in resource revenue reduces nonresource revenue by 0.12 percentage point. - With inclusion of lagged dependent variable: effect decreases to 0.07; the lagged dependent variable is positive and significant (revenue ratios are persistent). - Long-run cumulative impact implied with dynamics: -0.15 (comparable to initial estimate without dynamics). - Other coefficient patterns: - Grants (foreign aid): coefficient -0.02. Interpretation given: an additional 10 percent of GDP in grant funding reduces nonresource revenue by 0.2 percent. - Real per-capita GDP: positive and significant (becomes insignificant when lags of revenue ratios included). - Trade openness and share of urban population: positive and significant. - Share of agriculture: significant only in baseline regression. - Corruption (ICRG, higher value = less corruption): positive and robust across specifications — lower corruption associated with higher nonresource revenue. - Robustness and alternative specifications: - Controlling for endogeneity using lagged variables as instruments: β remains negative but not significant in that specification. - Using nonresource revenue to nonresource GDP as dependent variable and controlling for endogeneity: effect of resource revenue not significant. - Interaction term for resource countries that are not fiscally dependent (resource revenue < 20 percent of total revenue): β negative and significant; interaction coefficient also negative and significant and larger than for the rest — suggests countries that rely less on resource revenue reduce fiscal effort more when receiving additional resource revenue, though absolute magnitude weaker because resource revenue ratio is small. - Inclusion of log real effective exchange rate: negative and significant (real appreciation associated with lower tax effort); effect remains for fiscally dependent countries but cancels out for less fiscally dependent countries when interaction included. ### V. Tax Policy versus Tax Effort (statutory rates, effective rates, and institutions) - Statutory tax rates (2011) comparison: - Statutory VAT, corporate tax, income tax rates are very similar across fiscally dependent resource countries, all resource-rich countries, and nonresource countries. - Largest difference in corporate tax rate: fiscally dependent group ~35 percent, nonresource countries ~30 percent. - Cross-section tests (2011) for statutory tax rates: - If no evasion/exemptions, coefficient on statutory tax rate should be similar across groups; if evasion/exemptions present in resource-rich countries, coefficient would be lower for resource-rich group. - Coefficients on statutory tax rates for fiscally dependent countries are lower than for all countries together; income tax rate coefficient lower at 10 percent significance for fiscally dependent group. - Adding corruption index: corruption is significantly positive and inclusion lowers effect of statutory tax rates on revenue categories for fiscally dependent subsample. - Example interpretation: holding other elements constant, difference in corruption between Equatorial Guinea and Cape Verde would lead to a difference of more than 12 percentage points in the non-resource revenue ratio. - Effective VAT analysis: - Effective VAT rate = VAT revenue / total consumption. - Replacing nonresource revenue ratio with effective VAT rate yields a significantly weaker coefficient on statutory VAT rate for fiscally dependent countries compared to others; significance vanishes when corruption index added. - Interpretation: - Statutory tax rates are fairly comparable between resource and nonresource countries. - Lower nonresource revenue in resource countries appears to reflect lower effective tax collection (weaker effective non-resource tax rate), possibly due to higher tax exemptions/tax holidays (policy choice) and/or weaker administration/corruption. ### VI. Conclusions and further research - Evidence: resource exporters in SSA have significantly lower nonresource revenue than other countries in the region, controlling for a number of factors; strength of relationship depends on assumptions about exogeneity of resource revenue. - Quantified effect: for every 1 percentage point increase in resource revenue as a proportion of GDP, nonresource revenue is lower by about 0.07 to 0.12 percent of GDP. - Country-specific factors and incidence of corruption are associated with lower nonresource revenue to GDP, supporting view that fragile institutions adversely affect nonresource tax collection. - Because statutory tax rates are comparable across groups, lower tax take among resource countries likely reflects either higher tax exemptions/tax holidays (policy choices) or inability to collect revenue effectively (as proxied by the corruption index). - Recommended further research: gather data on tax exemptions to distinguish between administrative/institutional weaknesses and policy-driven tax concessions as drivers of the nonresource revenue shortfall among resource-rich countries. *Source: IMF African Department database and paper text (excerpts provided).* ### References ............................................................................................................. ### _wp13188 - References ### I. Introduction — research question and background - Research question: whether the availability of higher resource revenue leads to a lower taxation effort of other revenue categories, analyzed both as: - the relationship between non-resource tax revenue and resource revenue, and - the relationship between non-resource tax revenue and statutory tax rates. - Context: High resource prices have produced sizeable increases in fiscal revenue for resource-collecting countries, but resource revenue is volatile and countries must rely on other forms of taxation to fund public expenditure needs (health, education). - Established determinants of tax revenue ratios cited: - per capita income, import and agriculture shares, and sectoral composition (Tanzi 1987, 1992; Leuthold 1991). - later work confirms per capita income, share of agriculture GDP, and trade openness as main determinants (Gupta 2007; Davoodi and Grigorian 2007). - foreign aid and institutional factors (especially corruption) have been incorporated as explanatory variables; foreign aid effects are not consistently negative, while the adverse effect of corruption on revenue is more widely supported. - Prior empirical findings on resource rents and nonresource tax revenue: - Morrison (2005): elasticity of income tax revenue with respect to nontax revenue (royalties and foreign aid) is negative (-0.05) and significant for dictatorships but insignificant in democracies. - Bornhorst, Gupta, and Thornton (2009): negative coefficients ranging from -0.15 to -0.43. - Drummond, Srivastava, and Oliveira (2012): negative effect of resource rents on the tax ratio ranging from -0.1 for oil to -0.18 for other natural resources. - Key novel finding of this paper: - Non-resource revenue ratios are negatively influenced by higher ratios of resource revenue to GDP. - The lower take up of nonresource taxes in resource-rich countries is correlated with higher levels of corruption in those countries rather than any differences in statutory tax rates. - Implication: lower nonresource revenue in resource-rich countries may reflect incentives for tax evasion, large tax exemptions, and/or weaker enforcement. ### II. Data, sample, and coverage - Data source: database assembled in the African Department of the IMF covering all SSA economies except South Sudan; the database separates resource revenue and nonresource revenue by country and is available for 2000–11. - Resource-intensive country definition: countries where natural resource exports exceeded 25 percent of total merchandise exports in 2005–10 (20 countries identified). - Fiscal dependence definition: “fiscally dependent” countries are those where revenue from natural resources exceeded 20 percent of budgetary revenue in 2005–10 (10 countries identified among the resource-intensive group). - Caveat: Resource revenue may not cover all revenue accruing to the state from extraction because corporate tax payments of resource companies are not always separated out. - Sample for panel regression estimations: a group of 42 countries in sub-Saharan Africa between 2000 and 2011 (Section III). ### III. Country-specific patterns and key statistics - Resource revenue and exports (averages for 2005–10): - Resource-intensive subgroup: 20 countries with natural resource exports > 25 percent of merchandise exports (2005–10). - Among fiscally dependent resource-intensive countries, oil exporters show very high budgetary reliance on oil receipts: - Equatorial Guinea: 90 percent of budgetary revenue from oil receipts. - Gabon: slightly below 60 percent of budgetary revenue from oil receipts. - Countries with the lowest share of budgetary receipts from natural resources in 2005–10: Ghana, Sierra Leone, South Africa, and Tanzania at less than 5 percent of total revenue. ### IV. Empirical approach and structure of the paper - Methods: - Panel regression estimations of determinants of nonresource revenue for SSA countries (Section III). - Investigation of whether panel regression results reflect lower tax effort (tax evasion) or optimal tax policy actions (Section IV). - Organization: - Section II: data and country characteristics. - Section III: panel regression estimations for 42 SSA countries (2000–11). - Section IV: interpretation—tax effort vs. policy choice. - Section V: conclusions and possible extensions. *Source: IMF African Department database and paper text (excerpts provided).* ### Box 1: Non-Renewable Natural Resources in Sub-Saharan Africa ### Box 1: Non-Renewable Natural Resources in Sub-Saharan Africa ### Overview - Nearly 10 percent of the annual output of sub-Saharan African (SSA) countries and 50 percent of their exports come from non-renewable natural resources. - Natural resources are a major export in about 20 of the 45 countries in the region. - Seven of these countries are oil exporters, accounting for more than half of the region’s natural resource exports. - The other 13 resource-rich economies receive at least a quarter of their export proceeds from mining. - Major commodity exports for non-oil resource-rich economies: gold, diamonds, and other precious stones; some countries depend heavily on base metals and uranium (Niger, Zambia) or a broad mixture of products (Democratic Republic of the Congo, Guinea, Namibia, Sierra Leone). - Several currently nonresource-rich countries have significant resource export potential: examples include Mozambique, Sāo Tomé and Príncipe, Uganda, Liberia (offshore prospects), and Malawi (potentially large uranium deposits). - Some resource exporters are broadening commodity exports (Ghana: oil; Sierra Leone: iron ore; Tanzania: gas). - Collier (2011) point: identified level of exploitable natural resources in the region is currently far below that of other areas, implying many resources remain to be revealed. ### Fiscal Revenue and Expenditure Patterns (2000–11) - Revenue in relation to nonresource output for fiscally dependent countries has averaged about 50 percent in the past decade with large variations associated with movements in natural resource prices. - Revenue ratios for the other resource and nonresource countries have been stable, averaging about 20 percent of nonresource output. - Nonresource revenues are more stable than total revenues for fiscally dependent countries; fiscally dependent countries receive less nonresource revenue than other countries, but the difference is much smaller than for total revenue. - Higher revenue translates into higher expenditure for the fiscally dependent group; resource-rich countries outside SSA show non-resource revenue ratios considerably higher than SSA countries and revenue ratios that fluctuate with resource prices, suggesting stronger linkages between resource and nonresource sectors outside SSA. - Institutional quality example: Kaufmann and Kraay index shows Cape Verde and Equatorial Guinea have the lowest and highest levels of corruption among SSA economies respectively; difference in nonresource revenue ratios between these two countries is about 6 percentage points in 2000–11. ### Estimating the Tax Effort - Panel regression for SSA (2000–11, excluding South Sudan) estimating determinants of nonresource revenue follows Bornhorst, Gupta, and Thornton (2009). Dependent variable: ratio of nonresource revenue to GDP (with RR = 0 for nonresource exporters). Controls: grants-to-GDP ratio, log real per capita GDP, trade openness (exports plus imports to GDP), share of agriculture, urban population share, and ICRG corruption index. Country-specific fixed effects allowed. - Main regression finding: the coefficient on resource revenue (β) is negative and significant in baseline specifications. - Contemporaneous estimate: a 1 percent increase in resource revenue reduces nonresource revenue by 0.12 percentage point. - With inclusion of lagged dependent variable: effect decreases to 0.07; the lagged dependent variable is positive and significant (revenue ratios are persistent). - Long-run cumulative impact implied with dynamics: -0.15 (comparable to initial estimate without dynamics). - Other coefficient patterns (consistent with literature): - Grants (foreign aid): coefficient -0.02. Interpretation given: an additional 10 percent of GDP in grant funding reduces nonresource revenue by 0.2 percent. - Real per-capita GDP: positive and significant (becomes insignificant when lags of revenue ratios included). - Trade openness and share of urban population: positive and significant. - Share of agriculture: significant only in baseline regression. - Corruption (ICRG, higher value = less corruption): positive and robust across specifications — lower corruption associated with higher nonresource revenue. - Robustness/alternative specifications: - Controlling for endogeneity using lagged variables as instruments: β remains negative but not significant in that specification. - Using nonresource revenue to nonresource GDP as dependent variable (closer tax-base measure) and controlling for endogeneity: effect of resource revenue not significant. - Interaction term for resource countries that are not fiscally dependent (resource revenue < 20 percent of total revenue): β negative and significant; interaction coefficient also negative and significant and larger than for the rest — suggests countries that rely less on resource revenue reduce fiscal effort more when receiving additional resource revenue, though absolute magnitude weaker because resource revenue ratio is small. - Inclusion of log real effective exchange rate: negative and significant (real appreciation associated with lower tax effort); effect remains for fiscally dependent countries but cancels out for less fiscally dependent countries when interaction included. ### Tax Policy or Tax Effort? - Statutory tax rates in 2011 (VAT, corporate tax, income tax) are very similar across fiscally dependent resource countries, all resource-rich countries, and nonresource countries; largest difference in corporate tax rate: fiscally dependent group ~35 percent, nonresource countries ~30 percent. - Empirical test: regress nonresource revenue ratio on statutory VAT, corporate, and income tax rates (cross-section for 2011). Hypothesis: if no evasion/exemptions, coefficient on statutory tax rate should be similar across groups; if evasion/exemptions present in resource-rich countries, coefficient would be lower for resource-rich group. - Institutional quality (corruption, rule of law, government effectiveness) is lower in fiscally dependent group on average; indices highly correlated across countries. - Regression findings concerning statutory rates: - Coefficients on statutory tax rates for fiscally dependent countries are lower than for all countries together; income tax rate coefficient lower at 10 percent significance for fiscally dependent group. - Adding corruption index: corruption is significantly positive and inclusion lowers effect of statutory tax rates on revenue categories for fiscally dependent subsample. - Example interpretation: holding other elements constant, difference in corruption between Equatorial Guinea and Cape Verde would lead to a difference of more than 12 percentage points in the non-resource revenue ratio. - Effective VAT analysis: - Construct effective VAT rate = VAT revenue / total consumption. - Replacing nonresource revenue ratio with effective VAT rate yields a significantly weaker coefficient on statutory VAT rate for fiscally dependent countries compared to others; significance vanishes when corruption index added. - Interpretation: statutory tax rates are fairly comparable between resource and nonresource countries; lower nonresource revenue in resource countries appears to reflect lower effective tax collection (weaker effective non-resource tax rate), possibly due to higher tax exemptions/tax holidays (policy choice) and/or weaker administration/corruption. ### Conclusions and Further Research - Evidence suggests resource exporters in SSA have significantly lower nonresource revenue than other countries in the region, controlling for a number of factors; strength of relationship depends on assumptions about exogeneity of resource revenue. - Quantified effect: for every 1 percentage point increase in resource revenue as a proportion of GDP, nonresource revenue is lower by about 0.07 to 0.12 percent of GDP. - Country-specific factors and incidence of corruption are associated with lower nonresource revenue to GDP, supporting view that fragile institutions adversely affect nonresource tax collection. - Because statutory tax rates are comparable across groups, lower tax take among resource countries likely reflects either higher tax exemptions/tax holidays (policy choices) or inability to collect revenue effectively (as proxied by the corruption index). - Recommended further research: gather data on tax exemptions to distinguish between administrative/institutional weaknesses and policy-driven tax concessions as drivers of the nonresource revenue shortfall among resource-rich countries. *Source: IMF (2012)* ### REFERENCES ### _wp13188 - REFERENCES ### References - Adam, Christopher S., David L. 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An Analysis of Whether Africa Can Absorb More Aid,” Centre for the Study of African Economies, (United Kingdom: Oxford University). - Collier, Paul, 2011, “Savings and Investment Decisions in Low-Income Resource Exporters,” Centre for the Study of African Economies, (United Kingdom: Oxford University). - Davoodi, Hamid R., and David A. Grigorian, 2007, “Tax Potential vs. Tax Effort: A Cross-Country Analysis of Armenia’s Stubbornly Low Tax Collection,” IMF Working Paper 07/106 (Washington: International Monetary Fund). - Drummond, Paulo, Wendell Daal, Nandini Srivastava, and Luiz E. Oliveira, 2012, “Mobilizing Revenue in Sub-Saharan Africa: Empirical Norms and Key Determinants,” IMF Working Paper 12/108 (Washington: International Monetary Fund). - Ghura, Daneshwar, 1998, “Tax Revenue in Sub-Saharan Africa: Effects of Economic Policies and Corruption,” IMF Working Paper 98/135 (Washington: International Monetary Fund). - Gupta, Abhijit Sen, 2007, “Determinants of Tax Revenue Efforts in Developing Countries,” IMF Working Paper 07/184” (Washington: International Monetary Fund). - Gupta, Sanjeev, Benedict Clements, Alexander Pivovarsky, and Erwin R. Tiongson, 2003, “Foreign Aid and Revenue Response: Does the Composition of Aid Matter?” IMF Working Paper 03/176 (Washington: International Monetary Fund). - International Monetary Fund, 2012, Regional Economic Outlook: Sub-Saharan Africa Chapter 3, (Washington, April). - Leuthold, Jane H., 1991, “Tax Shares in Developing Countries: A Panel Study,” Journal of Development Economics, Vol. 35, pp. 173–185. - Morrison, Kevin M., 2005, “Oil, Non-Tax Revenue, and Regime Stability: The Political Resource Curse Reexamines,” (Duke University, Department of Political Science). - Stotsky, Janet, G., and Asegedech WoldeMariam, 1997, “Tax Effort in Sub-Saharan Africa,” IMF Working Paper 97/107, (Washington: International Monetary Fund). - Tanzi, Vito, 1987, “Quantitative Characteristics of the Tax Systems of Developing Countries,” in The Theory of Taxation for Developing Countries, ed. by D. Newbery and N. Stern, (United Kingdom: Oxford University Press). - Tanzi, Vito, 1992, “Structural Factors and Tax Revenue in Developing Countries: A Decade of Evidence,” in Open Economies: Structural Adjustment and Agriculture, ed. by Ian Goldin and L. Alan Winters (United Kingdom: Cambridge University Press). - Tanzi, Vito, and Hamid Davoodi, 1997, “Corruption, Public Investment and Growth,” IMF Working Paper 97/139 (Washington: International Monetary Fund). *Source: _wp13188 - REFERENCES* --- _Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13188.pdf_