1. INDIAN PUBLIC FINANCE TERMPAPER: CUSTOMS DUTIES
Ashish Bharadwaj, M.Sc. Economics II, MSE (Nov 2007)
The central government has four major taxes: personal income tax, corporation tax,
central excise duties and customs duties. In our analysis, we will confine ourselves to
customs duties only.
The customs duty in India consists of ‘basic’ tariff, the additional duty of customs (AD),
and special excise duty (SAD). The additional duty is the counterpart of excise duties
paid by domestic manufacturers i.e. countervailing duty (CVD). The special additional
duty has been introduced to serve as the counterpart of state sales taxes on domestically
produced goods. Since the customs duty is collected under the three sub-heads (basic, AD
and SAD), its custom duty collection rate should be obtained by dividing total collections
under the three heads by the total value of imports during the year. Some commodities
like alcohol have a ‘basic’ rate higher than the ‘peak’ rate. During the reform years, the
“peak” rate has come down from 150 % in 1991-92 to 40 % in 1997-98. The “peak” rate
was reduced to 35% in 2001-02 and 30% in 2002-03. The reduction in the peak duty rates
has continued. The 2005-06 Budget reduced the peak duty rate to 15% for nonagricultural products. The average tariff rate has therefore declined over the 1990s. As a
result the customs duty collection rate has declined from about 47% in 1998-99, and to
21% in 2000-01. The average customs tariffs rates still remain among the highest in the
world 1 .
1. Revenue Performance of Customs and Excise Duty relative to GDP
Excise and Customs revenue as % of GDP
6.00
Revenue (as % GDP)
5.00
4.00
3.00
2.00
1.00
2002-03
2000-01
1998-99
1996-97
1994-95
1992-93
1990-91
1988-89
1986-87
1984-85
1982-83
1980-81
1978-79
1976-77
1974-75
1972-73
1970-71
1968-69
1966-67
1964-65
1962-63
1960-61
1958-59
1956-57
1954-55
1952-53
1950-51
0.00
Year
Excise revenue as % of GDP
1
Customs revenue as % of GDP
Srivastava, D.K., “Issues in Indian Public Finance”(2005), New Delhi
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2. The profile of the share of the customs duties shows more of an inverted U-shape, similar
to Union excise duties, although these started at a level much higher than excise duties. In
1950-51, the customs duties accounted for nearly 1.6% of GDP, which was the highest
among all major central taxes. (Table 1, Appendix A) These were overtaken by excise
duties in 1957-58. Following a slightly volatile pattern, these reached a peak of 3.9% in
1986-87, after which they started declining. By 2001-02, these had reached a level,
relative to GDP, of 1.8%, which is compared to its level in the mid sixties. Thus, customs
duty, which was the highest contributor, is fast becoming the lowest contributor now.
2. Relative Shares of Major Central Taxes in Centre's Gross Tax Revenues
60.0
50.0
Share in %
40.0
30.0
20.0
10.0
2002-03
2000-01
1998-99
1996-97
1994-95
1992-93
1990-91
1988-89
1986-87
1984-85
1982-83
1980-81
1978-79
1976-77
1974-75
1972-73
1970-71
1968-69
1966-67
1964-65
1962-63
1960-61
1958-59
1956-57
1954-55
1952-53
1950-51
0.0
Year
Income Tax
Corporation Tax
Excise Duty
Customs Duty
The chart highlights the inverted U-shape of the curve representing the share of Union
excise duties, which can be contrasted with the more U-shape type of curve of the
customs duties until the beginning of the nineties. What is also clearly observable is that
the four taxes are tending to come close to each other in terms of their shares in the
centre’s gross revenue receipts. (Table 2, Appendix A)
3. Customs Duties: Annual Buoyancy
Tax buoyancy is measured as % change in tax revenue over a given period by % change
in tax base, which is generally taken to be GDP, over the same period.
While the contribution that a tax makes to overall collection of tax revenues is an
important consideration, another important feature of the tax sources is their volatility. To
examine this, we look at the yearly growth rates. Customs duties are more noticeable for
their negative growth in more recent years. (Table 3, Appendix A)
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3. Annual Customs Duty Buoyancy
15.00
10.00
Buoyancy
5.00
2001-02
1999-00
1997-98
1995-96
1993-94
1991-92
1989-90
1987-88
1985-86
1983-84
1981-82
1979-80
1977-78
1975-76
1973-74
1971-72
1969-70
1967-68
1965-66
1963-64
1961-62
1959-60
1957-58
1955-56
1953-54
1951-52
0.00
-5.00
-10.00
Year
Comparison of custom rate among countries
16.8
China
22.2
Bangladesh
20
Sri Lanka
7.1
Malaysia
S.Korea
8.7
Taiwan
8.8
10.9
Country
Indonesia
17.1
Thailand
20.5
Egypt
13.9
Russia
11
Argentina
10.1
Mexico
10
Chile
8.5
S.Africa
8.2
Turkey
32.2
India
0
5
10
15
20
25
30
35
Custom Tariff Rate
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4. EMPIRICAL STUDY I
Testing for Cointegration between the following
a) Customs Revenues and Other Central Tax Revenues such as Personal
Income Tax, Corporation Tax and Total Direct Tax Revenues (1970-2006)
b) Customs Revenues and Imports of Principal Commodities 2
Dependent Variable:
CR = customs revenues {Yt}
Possible Independent Variables {Xt} to test for Cointegration with CR
ER = excise revenues
ITR = income tax revenues
CTR = corporation tax revenues
DTR = total direct tax revenues
IMP = imports
Yt = β0 + β1Xt + ut
Yt, Xt ~ Cointegration (d,b) if both series are integrated of same order i.e. d=b
Testing for Stationary using Augmented Dickey-Fuller Test (ADF Test)
Variable
First Difference
ADF Test Statistics*
Second Difference
Δ CRt
-3.05
(stationary)
-
ERt
ADF Test
Statistics*
0.837
(non
stationary)
3.818
Δ ERt
Δ ERt-1
ITRt
3.710
Δ ITRt
CTRt
3.801
Δ CTRt
-0.958
(non stationary)
0.038
(non stationary)
0.907
(non stationary)
DTRt
4.319
Δ DTRt
IMPt
2.344
Δ IMPt
CRt
2.734
(non stationary)
3.514
(non stationary)
Δ ITRt-1
Δ CTRt-1
Δ DTRt-1
Δ IMPt-1
ADF Test
Statistics*
-
Order of
Integration
I(1)
-6.367
(stationary)
-4.256
(stationary)
-1.787
(non
stationary)
-3.104
(stationary)
-0.560
(non
stationary)
I(2)
*Compared with (-2.975) Critical Value at 5% level
(Table 4, Appendix B and Table 5, Appendix C)
Conclusion:
We can conclude that any regression model containing CR with any other variable
mentioned above would be meaningless and cannot be tested for Cointegration since
they are integrated of different orders.
2
Food & live animals, beverages & tobacco, Crude materials, inedible, except fuels, Mineral fuels, lubricants and
related materials, Animal and vegetable oils and fats, Chemicals, Manufacture goods classified chiefly by material,
Machinery and transport equipment, Miscellaneous manufactured articles
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I(2)
> I(2)
I(2)
> I(2)
5. EMPIRICAL STUDY II
Testing for Causality between Customs Revenue and Effective Customs Duty
CR = Customs Revenue
ETR = Total Customs Revenue/GDPmp
Where ETR is the effective tax rate (customs duty) and GDP has been taken as a proxy
for a tax base for customs duty.
Ln CRt = β0 + β1 (ln ETRt) + β2 (lnETRt)2 + ut
Variable
ADF Test
First Difference
Statistics*
Ln CRt
-1.712
Δ ln CRt
(non stationary)
Ln ETRt
2.001
Δ ln ETRt
(non stationary)
(LnETRt)2
0.075
Δ ln ETR2t
(non stationary)
*Compared with (-2.975) Critical Value at 5% level
ADF Test
Statistics*
-3.611
(stationary)
-3.359
(stationary
-3.222
(stationary
Order of
Integration
I(1)
I(1)
I(1)
Ln CRt = 18.53 + 2.81(lnETRt) + 0.14(lnETRt)2
(4.91)
(2.28)
(14.49)
R2 = 0.9673
DW statistic = 0.13
Figures in parentheses are t-values (5% level of significance)
(Table 4, Appendix B and Table 5, Appendix C)
Testing for stationarity of residuals in the model using ADF test
ADF test statistic = -1.342
Interpolated DF 5% critical value = -2.972
MacKinnon approximate p-value = 0.6098
Observations:
1. Errors are non stationary
2. Durbin-Watson statistic value considerably less than 2.0 implies that the errors
serially correlated
Conclusion:
It can be concluded that the above regression is spurious since both X and Y variables are
stationary of same order but the error term is non-stationary. However, we can apply OLS
to the appropriately differenced series (taking into account the appropriate lag length
using either AIC or SC).
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6. ΔlnCRt = 0.06 - 0.23ΔlnCRt-1 + 0.29ΔlnETRt-1 + 1.36ΔlnETRt + 0.04Δ (lnETRt) 2 + 0.007Δ (lnETRt-1)2
(5.9)
(-1.24)
(0.97)
(8.8)
(2.6)
(0.38)
ΔlnCRt = 0.057 - 0.11ΔlnCRt-1 + 0.09ΔlnETRt-1 + 0.95ΔlnETRt
(5.4) (-0.63)
(0.59)
(25.29)
ΔlnCRt = 0.03 + 0.26ΔlnCRt-1 – 0.1Δ (lnETRt) 2 + 0.03Δ (lnETRt-1)2
(2.93) (1.69)
(-13.5)
(1.92)
lnETR and (lnETR)2 Granger cause lnCR
EMPIRICAL STUDY III
Estimating a Laffer curve for customs duty revenues and effective rate (customs
duty) for India (1970-2006) using a log-lin model.
lnCRt = 228.8ETRt – 3012.2 (ETRt)2
(14.5)
(-7.6)
∂lnCR t
= 228.8 − 6024.4 ETRt
∂ETRt
∂lnCR t
= 0 => ETRt = 0.0379 or 3.79%
FOC:
∂ETRt
SOC:
R2 = 0.928
∂ 2 lnCR t
= −(6024.4) < 0
∂ETRt 2
Since the quadratic term is negative and significant, the Laffer curve has a bell shape. Taking the first
derivative of lnCR, with respect to ETR, and setting the first derivative to zero, we find that the
revenue-maximizing tax rate is 3.79%. This can also be seen from the graph below. We may draw a
conclusion that the Federal government is likely to raise more tax revenues by raising the tax rate up
to this level.
(Table 4, Appendix B and Table 5, Appendix C)
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