SLIDE 1
Income tax collection and non-compliance in Ghana
Edward Asiedu (University of Ghana), Chuqiao Bi (World Bank), Dan Pavelesku (World Bank), Ryoko Sato (World Bank), Tomomi Tanaka (World Bank)
and non-compliance in Ghana Edward Asiedu (University of Ghana), - - PowerPoint PPT Presentation
and non-compliance in Ghana Edward Asiedu (University of Ghana), - - PowerPoint PPT Presentation
Income tax collection and non-compliance in Ghana Edward Asiedu (University of Ghana), Chuqiao Bi (World Bank), Dan Pavelesku (World Bank), Ryoko Sato (World Bank), Tomomi Tanaka (World Bank) Objective To estimate potential income tax
SLIDE 2
SLIDE 3
Comparison of income distributions between household survey data and administrative income tax data (2014)
50,000 100,000 150,000 200,000
0-1,980 1,981-4,200 4,201-7,800 7,801-12,000 12,001-18,000 18,001-30,000 30,001-42,000 above 42,000
GLSS Income Tax data
Public sector
50,000 100,000 150,000 200,000 250,000 300,000 350,000
0-1,980 1,981-4,200 4,201-7,800 7,801-12,000 12,001-18,000 18,001-30,000 30,001-42,000 above 42,000
GLSS Income Tax data
Private sector
SLIDE 4
Comparison of income distributions between household survey data and simulation results (2014)
10 20 30 20 40 60 80 100 Income (1,000 GHC) GLSS 6 Administrative Data (Simulation)
10 20 30 40 50 100 150 Income (1,000 GHC) GLSS 6 Administrative Data (Simulation)
Another problems with administrative income tax data Discrete -> need to produce continuous version of income distributions by simulations (Blachet, Founier and Piketty 2017)
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GLSS/LFS Simulation GLSS 6 - Public 7.92 14.78*** (0.23) (0.50) GLSS 6 - Private 7.53 13.15*** (0.30) (0.25) GLSS 6 - Total 7.69 13.96*** (0.19) (0.29) LFS - Public 15.25 16.79** (0.44) (0.49) LFS - Private 12.37 18.60*** (1.07) (1.77) LFS - Total 14.19 16.62*** (0.49) (0.61)
- Comparison of mean income between household survey
data and simulation results (2014 and 2016) Comparison of the number of formal private sector workers between administrative income tax data and business census
Admin data (2014) Admin data (2016) Business Census (2014) 1,145,183 309,763 1,417,368
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Simulation results (2014)
100 200 300 400 500
0-1980 1981-4200 4201-7800 7801-12000 12001-18000 18001-30000 30001-42000 above 42000
Millions
Actual tax revenue Simulation
Public sector
200 400 600 800
0-1980 1981-4200 4201-7800 7801-12000 12001-18000 18001-30000 30001-42000 above 42000
Millions
Actual tax revenue Simulation I Simulation II
Private sector
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Changes in tax rate in 2016
Until 2015 Income Rate 0- 1,584 1,585-2,376 5 2,377-3,480 10 3,481-31,680 17.5 31,681 and above 25 From 2016 Income Rate 0- 2,592 2,593-3,888 5 3,889-5,700 10 5,701-38,880 17.5 38,881 and above 25
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Simulation results (2016)
200 400 600 800 1,000
0-2,592 2,593-6,000 6,001-12,000 12,001-24,000 24,001-60,000 60,001-120,000 above 120,000
Millions
Actual tax revenue Simulation I
Public sector
500 1,000 1,500 2,000
0-2,592 2,593-6,000 6,001-12,000 12,001-24,000 24,001-60,000 60,001-120,000 above 120,000
Millions
Actual tax revenue Simulation I Simulation II Simulation III
Private sector
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Conclusion and Recommendations
There is a large scale of non-compliance of income tax.
▪
Main cause in 2014: non or under payments in the public sector.
▪
Main cause in 2016: non-filing of PAYE in the private sector.
If everyone paid income tax in full amount, tax revenue could have been higher by 1.2 billion Cedi (=1.4% of GDP) and 3.6 billion Cedi (=2.2 %
- f GDP) in 2014 ad 2016, respectively.
Data limitation
Simulation results depend on the accuracy of 1) the number of workers in each income bracket, 2) mean income in each bracket, and 3) distribution
- f people who did not pay income tax.
Ideal to use mean income from administrative income tax data. Policy recommendations
Digitalization of tax records, linked to national ID.
Enforcements of audit and penalty (proved to be effective in other countries).