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The Impact of Tax expenditures on Revenue Performance in Rwanda

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Adrian Bizumugabe, Rwanda Revenue Authority (RRA)

Published in: Economy & Finance
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The Impact of Tax expenditures on Revenue Performance in Rwanda

  1. 1. October, 2017
  2. 2. 1. INTRODUCTION • Kassim and Mansour (2016) define tax expenditures as the amount of revenue forgone through the application of special tax regimes or tax provisions that are intended to promote and encourage a particular sector, activity or taxpayer. • Include: exemptions, deductions, credits, rate reliefs, deferrals.
  3. 3. 2. MOTIVATION AND OBJECTIVE OF THE RESEARCH • Tax to GDP ratio in Rwanda within the last five fiscal years was 13.41%, 13.88%, 14.83%, 15.26% and 16.07% respectively. • The contribution of tax to Government budget was 42.1%, 45.5%, 48.7%, 54.5% and 55.6% respectively. • The ratio of tax expenditure to GDP was 8.73%, 8.80%, 8.35%, 7.76% and 7.86% respectively. • A report from Tax Justice Network-Africa and ActionAid criticizes the many tax incentives and exemptions that governments in East Africa provide.
  4. 4. 2. MOTIVATION AND OBJECTIVE OF THE RESEARCH CONT’D • The report states that the money lost from tax expenditures can be mind-boggling. • The objective of this research was to assess the impact of tax expenditure on tax revenue performance in Rwanda.
  5. 5. • The study occupied a causal research with the aim of examining the responsiveness of tax revenue performance with respect to changes in tax expenditures. • According to Sekaran and Bougie (2010) a causal study is done when it is necessary to establish a definite cause and effect relationship. • Zikmund et al,.(2010) observed that causal research allows causal inferences to be made and that causal inference is a conclusion that when one thing happens, another specific thing will follow.
  6. 6. • The study involved six variables:  Tax revenues,  VAT exemptions (Customs taxes),  VAT exemptions (Domestic taxes),  Import duty exemptions,  Excise duty exemptions; and  Investment allowances. • The study used secondary data on monthly basis from 2008 to 2015. • The research applied econometric analytical methods.
  7. 7. 3. METHODOLOGY CONT’D • Unit Root Test • If one wishes to use hypothesis tests, either singly or jointly, to examine the statistical significance of the coefficients, then it is essential that all of the components in the VAR are stationary, Chris Brooks (2014). • If a time series is non-stationary, the regression analysis done in a traditional way will produce spurious results, Shrestha et Al (2005). • This paper applied Augmented Dickey-Fuller Unit Root Test.
  8. 8. 3. METHODOLOGY CONT’D • Autoregressive Distributed Lag (ADRL) Model • According to Dritsakis the ARDL approach has the advantage that it does not require all variables to be I(1) as the Johansen framework and it is still applicable if we have I(0) and I(1) variables in our set. • Furthermore, it can distinguish dependent and explanatory variables, and allows to test for the existence of relationship between the variables. • Finally, with the ARDL it is possible that different variables have differing optimal number of lags.
  9. 9. 3. METHODOLOGY CONT’D
  10. 10. • Unit root test • Four variables were found stationary at levels:  VAT exemptions (Customs taxes),  Import duty exemptions,  Excise duty exemptions; and  Investment allowances. • Two variables were found stationary at first difference:  Tax revenues; and  VAT exemptions (Domestic taxes).
  11. 11. 4. EMPIRICAL RESULTS CONT’D • Test for serial correlation and heteroscedasticity Breusch-GodfreySerialCorrelationLMTest: F-statistic 0.104628 Prob.F(2,82) 0.9008 Obs*R-squared 0.236722 Prob.Chi-Square(2) 0.8884 HeteroskedasticityTest:White F-statistic 1.082205 Prob.F(44,48) 0.3933 Obs*R-squared 46.31374 Prob.Chi-Square(44) 0.3770 ScaledexplainedSS 50.96799 Prob.Chi-Square(44) 0.2186
  12. 12. 4. EMPIRICAL RESULTS CONT’D • ARDL Bounds Test for cointegration and stability of the model -30 -20 -10 0 10 20 30 40 45 50 55 60 65 70 75 80 85 90 CUSUM 5% Significance F-Bounds Test Null Hypothesis: No levels relationship Test Statistic Value Signif. I(0) I(1) F-statistic 16.89957 10% 2.08 3 k 5 5% 2.39 3.38 2.5% 2.7 3.73 1% 3.06 4.15
  13. 13. 4. EMPIRICAL RESULTS CONT’D • ARDL Long run coefficients Levels Equation Case 2: Restricted Constant and No Trend Variable Coefficient Std. Error t-Statistic Prob. LOGVATEXEMPTION... 0.025646 0.108793 0.235734 0.8142 LOGVATEXEMPTION... 0.361890 0.030887 11.71658 0.0000 LOGIMPORTDUTYEX... 0.133722 0.117383 1.139197 0.2579 LOGEXCISEDUTYEX... -0.145425 0.037539 -3.873941 0.0002 LOGINVESTMENTAL... 0.122647 0.068268 1.796556 0.0760 C 1.022635 0.070600 14.48500 0.0000 EC = LOGTAXREVENUE - (0.0256*LOGVATEXEMPTIONSCUSTOMS + 0.3619*LOGVATEXEMPTIONSDTD + 0.1337*LOGIMPORTDUTYEXE MPTIONS -0.1454*LOGEXCISEDUTYEXEMPTIONS + 0.1226 *LOGINVESTMENTALLOWANCES + 1.0226 )
  14. 14. 4. EMPIRICAL RESULTS CONT’D • Granger causality Null Hypothesis: Obs F-Statistic Prob. LOGVATEXEMPTIONSDTD does not Granger Cause LOGTAXREVENUE 90 4.96337 0.0032 LOGTAXREVENUE does not Granger Cause LOGVATEXEMPTIONSDTD 4.97453 0.0032 Null Hypothesis: Obs F-Statistic Prob. LOGEXCISEDUTYEXEMPTIONS does not Granger Cause LOGTAXREVENUE 90 0.77525 0.5111 LOGTAXREVENUE does not Granger Cause LOGEXCISEDUTYEXEMPTIONS 0.72067 0.5424
  15. 15.  Conclusion • Three variables were found statistically significant:  VAT exemptions on domestic taxes (1% : -0.36%),  excise duty exemptions; and  investment allowances (1% : -0.12%). • Two variables were found not statistically significant:  VAT exemptions in customs; and  import duty exemptions.
  16. 16. 5. CONCLUSION AND POLICY IMPLICATION CONT’D  Policy implication • In order to raise more revenue for public services such as health, education and infrastructure the Government can reduce VAT exemptions on domestic taxes and investment allowances.

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