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Relationship between rule of law and tax revenues: dynamic panel data analysis
Article | Year: 2022 | Pages: 403 - 419 | Volume: 46 | Issue: 3 Received: September 6, 2021 | Accepted: April 28, 2022 | Published online: August 29, 2022
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FULL ARTICLE
FIGURES & DATA
REFERENCES
CROSSMARK POLICY
METRICS
LICENCING
PDF
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Variables
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Period
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Frequency
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Source
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Symbol
|
|
Tax
Revenue (GDP %)
|
2002-2018
|
Annual
|
World Bank
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TR
|
|
Rule
of Law
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2002-2018
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Annual
|
World Bank
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RL
|
|
Import
(GDP %)
|
2002-2018
|
Annual
|
World Bank
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ImGDP
|
|
GDP
Per Capita
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2002-2018
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Annual
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World Bank
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GDPPC
|
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Agriculture
Sector (GDP %)
|
2002-2018
|
Annual
|
World Bank
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Agri
|
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Industry
(GDP %)
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2002-2018
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Annual
|
World Bank
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Ind
|
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Service
(GDP %)
|
2002-2018
|
Annual
|
World Bank
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Ser
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Note: All with annual frequency, in 2002-2018 period, source World Bank. Source: Authors’ preparation.
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Variables
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No. of observations
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Mean
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Std. Deviation
|
Minimum
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Maximum
|
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Tax
Revenue (GDP %)
|
1,003
|
18.67
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5.75
|
7.03
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48.56
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Rule
of Law
|
1,003
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64.50
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26.81
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4
|
100
|
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Import
(GDP %)
|
1,003
|
50.90
|
27.81
|
11.25
|
208.33
|
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GDP
Per Capita
|
1,003
|
22,675.04
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23,677.72
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474.94
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111,968.3
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Agriculture
Sector (GDP %)
|
1,003
|
5.90
|
6.24
|
0.0284
|
34.55
|
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Industry
(GDP %)
|
1,003
|
25.23
|
5.93
|
9.88
|
45.09
|
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Service
(GDP %)
|
1,003
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59.02
|
8.25
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35.02
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79.33
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Variables/Tests
|
Breusch-Pagan
LM
|
Pesaran CD
|
|
TR
|
5711.564(0.000)
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14.786(0.000)
|
|
RL
|
5557.867(0.000)
|
3.59 (0.0003)
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ImGDP
|
9028.183
(0.000)
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44.392(0.000)
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|
GDPPC
|
17723.44(0.000)
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110.6822(0.0000)
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Agri
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9371.135
(0.000)
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63.469
(0.0000)
|
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Service
|
9979.556(0.000)
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51.36084(0.0000)
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Indp
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9439.447(0.0000)
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42.49648(0.0000)
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Variables
|
Test
Statistic Value
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Probability
|
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TR
|
-4.11287
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0.000
|
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RL
|
-5.38299
|
0.000
|
|
ImGDP
|
-8.42850
|
0.000
|
|
GDPPC
|
-5.28732
|
0.000
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Agri
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-10.3820
|
0.000
|
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Service
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-5.27322
|
0.000
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Indp
|
-5.82446
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0.000
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Dependent
Variable: TRit
|
|
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Independent
Variables
|
(1)
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(2)
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(3)
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TRit-1
|
-0.0409***
(0.006580)
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-0.0786***
(0.005999)
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-0.076729***
(0.007228)
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|
RLit
|
0.0672***
(0.002716)
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0.0650***
(0.002504)
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0.0666***
(0.001732)
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ImGDPit
|
-0.296***
(0.017728)
|
-0.3291***
(0.019130)
|
-0.3376***
(0.013551)
|
|
Agriit
|
-0.4495***
(0.013394)
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-0.4807***
(0.02472)
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-0.4505
(0.021178)
|
|
Serviceit
|
-.0.3197***
(0.011396)
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-0.3538***
(0.01706)
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-0.3311***
(0.015736)
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Indpit
|
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0.2133***
(0.014250)
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|
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RLit×KUKit
|
|
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3.84E-06***
(3.97E-07)
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RLit×GDPPCit
|
|
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-0.000374***
(3.44E-05)
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GDPPCit
|
-0.0409***
(0.006580)
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-0.0786***
(0.005999)
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-0.076729***
(0.007228)
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Sargan
Test
|
53.23329
(0.42)
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54.3208
(0.38)
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54.30157
(1.000)
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AR(1)
|
(0.044)
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(0.045)
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(0.0038)
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AR
(2)
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(0.777)
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(0.842)
|
0.2255
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Note: *** shows that the variables are statistically significant at the 1% significance level. The instrumental variables used in the model based on the probability values of the Sargan Test are valid. Furthermore, contrary to the expectations, there is no autocorrelation problem in the model according to the AR (2) probability value. Source: Authors’ calculation.
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Countries
=>20.000 $
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Australia,
Austria, Belgium, Cyprus, Czechia, Denmark, Finland, France, Germany, Greece,
Hungary, Iceland, Ireland, Israel, Italy, Luxemburg, Netherland, Norway,
Portugal, Singapore, Slovenia, Spain, Sweden, Switzerland, United Kingdom
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Countries
<20.000 $
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Argentina,
Belarus, Bhutan, Brazil, Bulgaria, Burkina, Cambodia, Costa Rica, Cote d’Ivoire,
El Salvador, Estonia, Georgia, Guatemala, Jamaica, Jordan, Latvia, Lebanon,
Lithuania, Croatia Mauritius, Moldova, Morocco, Nicaragua, Peru, Philippines,
Poland, Romania, Russia, Slovakia, South Africa, Sri Lanka, Thailand, Ukraine,
Zambia
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September, 2022
III/2022
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