Potential Revenue Implications of Free Trade Agreements: An Empirical Analysis on Bangladesh

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Bangladesh Trade Policy and Negotiation Capacity Building Support Project Phase I
Potential Revenue Implications of Free Trade
Agreements: An Empirical Analysis on
Bangladesh
Mohammad A. Razzaque, Zaidi Sattar and Jillur Rahman
May 2021
Final Draft submitted to Ministry of Commerce, Bangladesh.
 Mohammad A. Razzaque is Research Director, Policy Research Institute; Zaidi Sattar is Chairman, Policy Research
Institute; and Jillur Rahman is Assistant Professor of Economics at Jagannath University.

2
Table of Contents
Executive summary ................................................................................................................................. 3
I. Introduction .................................................................................................................................... 5
II. Bangladesh’s Imports and Tariff Structure: Stylized Facts .............................................................. 7
III. Literature Review: An Overview of the Relationship between Tariff Cuts and Government
Revenue ................................................................................................................................................ 16
IV. Methodology for Estimation ..................................................................................................... 19
V. Estimated Results .......................................................................................................................... 23
Partial equilibrium results ................................................................................................................. 23
General equilibrium results .............................................................................................................. 27
VI. Policy Implication and Conclusion ............................................................................................ 37
VII. Reference .................................................................................................................................. 40
Annex .................................................................................................................................................... 43

3
Executive summary
Import tariffs are an important source of government revenue in Bangladesh. About one-third of total
government revenue comes from imposing tariffs and other supplementary duties on imports. The
collected import revenue in 2018/19 stood at $7.7 billion, which is about 2.5% of gross domestic
product (GDP). Compared to many other comparator countries, Bangladesh has maintained much
higher import tariffs. Along with customs duty, the tariff regime is characterized by supplementary
duty, regulatory duty, advance income tax, advance value-added tax (VAT), and other duties. These
para-tariffs constitute almost a half of the nominal protection rate of more than 26%. The high
dependency on the import revenue makes it very difficult to use trade policy in improving incentives
for exporters and in exploring new trade opportunities through bilateral trade agreements and
regional trade agreements (RTAs) where Bangladesh may have to undertake tariff cuts because of
reciprocity-based trade negotiations. The cross-country evidence suggests that as a country makes
progress on its economic development, it will not be able to maintain the high protective tariff. As
Bangladesh is set to graduate from the group of least developed countries (LDCs), it will need to
establish bilateral trade agreements to continue with the current preferential market access
conditions after graduation. Any tariff cuts resulting from agreements with important trade partners
would lead to apprehension about the likely loss of tariff revenues.
The current study attempts to estimate the potential impact of tariff liberalization under free trade
agreement (FTA) situations with major trade partners on government revenue in Bangladesh. The
study uses ex-ante analyses in partial and general equilibrium frameworks. The World Integrated
Trade Solution (WITS) SMART partial equilibrium and the Global Trade Analysis Project (GTAP) general
equilibrium models are used for empirical investigations.
The partial and general equilibrium simulations suggest that the largest import flows would occur for
FTAs with China and India – Bangladesh’s two largest import partners. Consequently, the revenue loss
would be the highest. The partial equilibrium model results indicate Bangladesh could lose 27% of
baseline revenue for each FTA. The general equilibrium results show the revenue loss in case of a BGD-
CHN FTA would be around 60% of baseline revenue or 1.2% of GDP; the same result for a BGD-IND
FTA is 22.9% of baseline revenue and 0.5% of GDP.
According to the WITS/SMART simulation, a 5.8% loss in revenue would be incurred with full tariff
liberalization under an FTA with the European Union; and the impact would be 5.7% for an FTA with
Thailand. The corresponding figures as estimated from GTAP model are 5.4% and 4.1%, respectively.
For the Bangladesh-US FTA, the partial equilibrium simulation suggests Bangladesh could lose 1% in
revenue. However, the GTAP-based general equilibrium model indicates a completely different
picture. It suggests that reverse trade diversion (i.e. trade creation) for non-member trade partners
could occur in the case of an FTA with the United States. Consequently, there are positive revenue
implications equivalent to 4.3% of baseline revenue. It also provides maximum welfare gain for
Bangladesh among all the FTAs that have been considered as part of this study.
The Bangladesh-Indonesia FTA and Bangladesh-Malaysia FTA could imply respectively 4.2% and 2.8%
revenue losses for Bangladesh as simulated in the WITS/SMART model. The general equilibrium results

4
indicate the loss to be 5% and 4.1% of the baseline revenue, respectively. The welfare implications are
comparatively lower.
Both the partial and general equilibrium models suggest that Bangladesh’s FTA with Brazil and the
United Kingdom have relatively low revenue implications of less than 1% each. They are the less
important import partners (respectively 11th and 25th largest), which explains the low revenue impact.
However, signing FTAs with these countries, particularly with the UK, could be important for
continuing preferential access and expanding exports after graduation.
Bangladesh’s imports are highly dependent on East Asian countries aside from India and China. Data
from the National Bureau of Revenue (NBR) show that about 17% of imports are sourced from
Association of Southeast Asian Nations (ASEAN) countries. Our simulation exercise suggests that a
significant amount of import tariff revenue could be lost if Bangladesh signs an FTA with three biggest
trade partners from ASEAN nations: Indonesia, Malaysia, and Thailand. On the other hand, the same
model indicates Bangladesh could expand its overall exports by 3.2% under full tariff liberalization
with these three countries. Therefore, a part of import revenue loss can be compensated by expanded
export volume.
For a country like Bangladesh, reducing revenue dependency will be important for trade policy
flexibility. However, concerns about revenue loss have dominated discussion of trade policy options.
It is therefore critical to strengthen domestic tax efforts to enable Bangladesh to make use of its trade
policy options for boosting exports and striking trade agreements with other countries.

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Potential Revenue Implications of Free Trade Agreements: An
Empirical Analysis on Bangladesh
Mohammad A. Razzaque, Zaidi Sattar, and Jillur Rahman
I. Introduction
Bangladesh’s fiscal regime is highly dependent on international trade, specifically imports, for revenue
generation. About one-third of total government revenue comes from imposing tariffs and other
supplementary duties on imports. This makes it very difficult to use trade policy in improving
incentives for exporters and in exploring new trade opportunities through bilateral and regional trade
agreements where the country may have to undertake tariff cuts as part of reciprocity-based trade
negotiations. As the economy has been growing fast, maintaining high tariffs (using custom duties and
various other para-tariffs) makes investments in import-competing sectors more lucrative than in
export sectors, which cannot be supported with an equivalent policy-backed assistance.
Bangladesh has managed to maintain robust economic growth under a highly protected environment.
Its importing partners face an average Most-Favoured Nation (MFN) tariff rate (customs duty) of 14%.
1
This is much higher than in other high-growth countries, including China, Indonesia, Malaysia, the
Philippines and Vietnam.
2
The average applied tariff rates on Bangladesh’s imports is 8.64% – much
higher than China (2.5%), India (6.6%), Indonesia (2%), Malaysia (4.2%), the Philippines (1.7%),
Thailand (3.5%), and Vietnam (1.7%). Bangladesh also imposes other para-tariffs,
3
and recent evidence
suggests that para-tariffs account for almost half of nominal tariff rates. Thus, the effective protection
rate is much higher than can be anticipated from the customs duty alone. Cross-country evidence
suggests that as countries develop, average MFN rates declines (Figure 1a).
4
A similar trend is evident
from the cross-country plot of applied tariff rates and per capita GDP (Figure 1b). Bangladesh is in
transition to graduate from the group of least developed countries (LDCs) in 2026 and to achieve
upper-middle-income country status by FY2031 (and high-income country status by FY2041). As it
develops, it is quite natural to expect that Bangladesh will have to reduce dependence on import
tariffs for revenue generation.
1
The MFN tariff rate comprise custom duties only and does not incorporate other tariffs imposing on imports.
2
Although the simple average of MFN tariff in India (17.6%) is higher than that in Bangladesh, the weighted applied tariff of
the former (6.6%) is lower than the later (8.64%).
3
Para-tariffs mean border charges and fees other than tariffs on foreign trade transactions that are levied solely on
imports – they do not include indirect taxes and charges which are levied in the same manner on domestic products.
4
The Republic of Korea has a high per capita GDP maintained at relatively higher MFN tariff rate at 14%. However, the
applied tariff rate (simple mean) is just 5.2% and the applied weighted mean of tariff rate is 4.8%.

6
Source: Authors’ analysis using data from the World Development Indicators (WDI).
Bangladesh currently enjoys duty-free preferential market access for its exports to major export
destinations, except the United States. The preferences are mostly unilateral in nature – being an LDC,
Bangladesh gets those benefits as per WTO rules and preference providing countries’ generalized
scheme of preferences (GSP), which do not require reciprocity. As Bangladesh is fast approaching its
LDC graduation, it will no longer remain eligible for these unilateral schemes. Consequently, market
access conditions in most important export destinations will become more stringent. Several studies
predict that Bangladesh will see a significant loss in export earnings (UNCTAD, 2016; Razzaque and
Rahman, 2019; Rahman and Bari, 2019). By signing bilateral and multilateral FTAs with major partners,
Bangladesh could preserve market access conditions and thus not disturb export flows and
competitiveness in the post-graduation period.
Any FTA negotiations, however, mean high tariffs will have to be reduced for the partners. Many
stakeholders are of the view that Bangladesh’s approach to bilateral trade negotiations has been less
than proactive due to the concerns of the National Board of Revenue. Given the current heavy
dependence on tariff revenue, potential trade agreements with countries that are important trade
partners would lead to loss of tariff revenues. In fact, post LDC-graduation trading opportunities
BGD
BTN
BRA
CAN
CHN
IND
IDN
JPN
KOR
MAL
MMR
PAK
SRL THA
USA
0
5
10
15
20
25
6 7 8 9 10 11 12
MFN tariff rate, simple mean (%), 2019
Log of per capita GDP (PPP), 2019
Figure 1a: Relationship between MFN tariff rate and economic devemopment
BGD
BTN
BRA
CAN
CHN
IND
IDN
JPN
KOR
MAL
MMR
NPL
PAK
PHL
SGP
SRL
THA USA
VNM
0
5
10
15
20
25
6 7 8 9 10 11 12
Applied tariff rate, simple mean (%) 2019
Log of per capita GDP (PPP), 2019
Figure 1b: Relationship between applied tariff rate and economic devemopment

7
through trade agreements vis-à-vis the prospect of tariff revenue loss represents a fundamental
dilemma in Bangladesh’s contemporary trade and development policy discourse.
Against this backdrop, the objective of this study is to provide an assessment on the likely implications
of tariff liberalization under FTAs with major trade partners for government revenue and overall
welfare. Based on the available literature, presents a summary of empirical evidence on the
relationship between tariff cuts and government revenues is presented. Then, the paper makes use
of quantitative partial and general equilibrium modelling techniques to estimate the impact of tariff
liberalization on government revenue from international trade.
The paper is organized as follows. Section II provides the stylized facts of Bangladesh’s import and
tariff structures. Section III reviews the literature, theory and empirical evidence on the relationship
between tariff cuts and government revenue. Section IV provides the estimation methodology of the
potential impact of tariff liberalization under FTA with Bangladesh’s major partners. Section V explains
the estimated results of the simulation exercise. Finally, section VI provides policy implications and
concludes.
II. Bangladesh’s Import and Tariff Structures: Stylized Facts
Bangladesh’s imports
Over the past two decades or so, Bangladesh has made impressive progress in expanding its trade
volume. Its merchandise trade (exports plus imports) exceeded $100 billion in 2018/19, increasing
from just above $5 billion in early 1990s. This trade is dominated by its imports, which accounts for
more than 60% of merchandise trade. Imports rose from less than $4 billion in the 1990s to $61 billion
in 2018/19 – experiencing an average annual growth of 10.8% (Figure 2). The average annual growth
rate in the most recent past decade (2010-19) was 9.7%. During this time, overall exports grew at
10.5% per annum – reaching $40.5 billion in 2018/19.
Source: Authors’ presentation using NBR data.
-20
-10
0
10
20
30
40
50
60
0
10000
20000
30000
40000
50000
60000
70000
FY90
FY91
FY92
FY93
FY94
FY95
FY96
FY97
FY98
FY99
FY00
FY01
FY02
FY03
FY04
FY05
FY06
FY07
FY08
FY09
FY10
FY11
FY12
FY13
FY14
FY15
FY16
FY17
FY18
FY19
Import growth rate (%)
Imports (billion $)
Figure 2: Bangladesh's total imports and its growth rates
Imports (billion $) Import growth rate (%)

8
A significant portion of Bangladesh’s imports comes through bonded-warehouse facilities, exempt
from customs and associated duties (Figure 3). The government of Bangladesh offers bonded
warehouse facilities to export-oriented industries for duty-free imports of raw materials and
packaging materials to facilitate exports. Using these facilities, export-oriented industries enhance
their export competitiveness. Bangladesh’s readymade garments industry has been the largest
beneficiary. Duty-free imports are also allowed for packaging materials used for deemed exports and
for imports of inputs/raw materials used by industries located in Export Processing Zones (EPZs).
5
In
2018/19, more than a quarter of imports were duty-free – coming through bonded warehouse
facilities (i.e. bond for deemed export and supervised bond, and special bond for export-oriented
industries in readymade garments sector) or EPZ imports. The share of duty-free imports, however,
has slightly declined from 31.3% in 2014-15.
Source: Authors’ presentation using NBR data.
Capital machinery and raw materials are the major importing items in Bangladesh. At the HS 2-digit
product level, cotton (HS 52), which is the major raw material used in garment manufacturing, is the
top import, accounting for 13.5% of all merchandise imports in 2018/19 (Figure 4 and Table 1). This is
followed by machinery and mechanical appliances (11.4%), mineral fuel oil and products (8.3%),
electrical machinery and equipment (6.5%), iron and steel (5.3%), and plastics (4.1%).
The composition of Bangladesh’s imports by source country shows that China is the major importing
partner, accounting for almost a quarter of all imports into Bangladesh in 2018/19, worth close to $15
billion (Figure 5). It is followed by India and Singapore, supplying respectively 13.8% and 7.2% of
Bangladesh’s imports. Among others, the European Union was the source of 6.7% of imports, Hong
Kong 4.9%, Indonesia 3.6%, United Kingdom 3.6%, Malaysia 2.9%, Japan 2.9% and Brazil 2.4%.
5
A deemed export is defined as exporting of goods embedded into other products like buttons, hangers used in the
readymade garments sector, packaging materials/carton, label, polybags used in virtually all other industries.
-5
5
15
25
35
45
55
65
2014-15 2015-16 2016-17 2017-18 2018-19
Bonded and non-bonbed imports (bil $)
Figure 3: Bonded Vs non-bonded imports (billion $)
Non-bond imports Bond Imports

9
Source: Authors’ presentation using NBR data.
Table 1: Imports of major items at HS 2-digit level (2018-19)
HS
code
Product description
Imports (million
$)
Share in total imports
(%)
52
Cotton
8285.4
13.54
84
Machinery, mechanical appliances
7004.8
11.45
27
Mineral fuels, oils and products
5062.1
8.27
85
Electrical machinery and equipment
3975.4
6.50
72
Iron and steel
3250
5.31
39
Plastics and articles thereof
2503.8
4.09
10
Cereals
1894
3.10
55
Man-made staple fibres
1865.1
3.05
87
Vehicles other than railway or tramway rolling
stock
1782.6
2.91
15
Animal or vegetable fats and oils
1691.5
2.76
25
Salt; sulphur; earths and stone
1655.6
2.71
89
Ships, boats and floating structures
1648.7
2.69
54
Man-made filaments; strip and textile materials
1648.7
2.69
31
Fertilizers
1412.9
2.31
60
Knitted or crocheted fabrics
1251.9
2.05
73
Articles of iron or steel
1011.4
1.65
29
Organic chemicals
921.4
1.51
12
Oil seeds and oleaginous fruits
876.5
1.43
32
Tanning or dyeing extracts
795.7
1.30
7
Edible vegetables and certain roots and tubers
760.1
1.24
Total
61183.71
100
Source: Authors’ analysis using NBR data.
Analyses of import data by source country suggests that about 45% of imports from China in 2018/19
were duty-free, while the equivalent share for India was around 25%. The duty-free and dutiable
import shares by major importing partners are depicted in Figure 6. Less than 1% of imports from
Russia are duty-free, while the duty-free shares are 4.2% and 10.7% for the United States and the
European Union, respectively.
HS52-Cotton
13.5%
HS84-
Machinery,
mechanical
appliances
11.4%
HS27-Mineral
fuels, oils and
products
8.3%
HS85-Electrical
machinery and
equipment
6.5%
HS27-Iron and
steel
5.3%
HS39-Plastics
and articles
thereof
4.1%
HS10-
Cereals
3.1%
HS55-Man-
made staple
fibres
3.0%
HS87-Vehicles
other than
railway or
tramway rolling
stock
2.9%
HS15-Animal or
vegetable fats
and oils
2.8%
Others
39.0%
Figure 4: Composition of Bangladesh's
imports (%)
China
23.5%
India
13.8%
Singapore
7.2%
EU
6.7%
Hong
Kong
4.9%
Indonesia
3.6%
United
States
3.6%
Malaysia
2.9%
Japan
2.9%
Korea,
Rep.
2.5%
Brazil
2.4% Others
26.1%
Figure 5: Bangladesh's imports by
source country (%)

10
Source: Authors’ analysis using NBR data.
Tariff structure and import revenue
Customs duties (tariffs) are considered the main instrument of Bangladesh’s trade policy regime for
protecting domestic industries from imports as well as one of the major sources of government
revenues. The evolution of the MFN tariff shows that Bangladesh’s average tariff rate has been on the
decline from above 21% in 2000 to 14% in 2019 (Figure 7). Apart from India and Bhutan, the average
MFN applied rates in Bangladesh are much higher than comparators including China (7.6%), Indonesia
(8.1%), Malaysia (5.6%), Myanmar (6.5%), the Philippines (6.1%), Sri Lanka (9.3%), Thailand (10.2%),
and Vietnam (9.6%) (Figure 8). The average MFN rates in India is 17.6%.
Bangladesh’s average tariff for agricultural products was 17.5% and for non-agricultural products
13.4%. A summary of average tariffs, duty-free tariff lines and maximum duty by broad product
categories is given in Table 2. It shows that the clothing items, dairy products, fish and fish products,
coffee and tea, and fruit, vegetables and plants attract higher tariff rates, at more than 20%. The MFN
tariffs on cotton and non-electrical machinery are less than 5%.
Source: WTO Tariff Profile, World Trade Organization (WTO)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Figure 6: Bonded and non-bonded imports by major source countries in 2018-19
Bonded imports (duty free) Non-bonded imports (dutiable)
5
7
9
11
13
15
17
19
21
23
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Figure 7: Simple average of MFN applied
tariffs of Bangladesh
0
5
10
15
20
25
Bangladesh
Bhutan
Brazil
China
India
Indonesia
Malaysia
Maldives
Myanmar
Nepal
Pakistan
Philippines
Sri Lanka
Thailand
Viet Nam
Figure 8: Simple average of MFN
applied tariffs of selected countries in
2019 (%)

11
Table 2: Product wise MFN tariff structure in Bangladesh, 2019
Product groups
Average tariffs (%)a
Duty-free tariff
lines (%)b
Maximum duty (MFN
applied) (%)
Animal products
19.3
7.7
25
Dairy products
24
0
25
Fruit, vegetables, plants
21.2
2.4
25
Coffee, tea
22.5
0
25
Cereals & preparations
16
13.2
25
Oilseeds, fats & oils
10.3
23.9
25
Sugars and confectionery
19.6
0
25
Beverages & tobacco
25
0
25
Cotton
3.5
30
5
Other agricultural products
11.4
15.1
25
Fish & fish products
23.7
4
25
Minerals & metals
12.7
6.3
25
Petroleum
15.8
0
25
Chemicals
9.7
6.3
25
Wood, paper, etc.
15.2
7.9
25
Textiles
19.5
0.1
25
Clothing
24.4
0
25
Leather, footwear, etc.
14.3
0.6
25
Non-electrical machinery
4
1.1
25
Electrical machinery
13.6
0.5
25
Transport equipment
11.9
9.8
25
Manufactures, n.e.s.
12.8
3.1
25
Note: a Simple average of the ad valorem or AVE HS 6-digit duty averages. b Share of duty-free HS 6-digit subheadings in the
total number of subheadings in the product group
Source: WTO Tariff Profile
Apart from customs duties, the trade policy regime in Bangladesh is also characterized by certain other
trade taxes known as para-tariffs. Some of these taxes are supposed to be trade-neutral (to be
imposed on both imports and import-competing domestic production), but in practice they often
discriminate against imports. These include VAT, supplementary duty (SD), regulatory duty (RD),
advance trade VAT and advance income tax (AIT). Among these, SD and VAT should be applicable to
domestic production as well as to imports. Advance trade VAT and advance income tax are levied at
the time of import.
Although customs duties applied in Bangladesh are not that high, the nominal protection rate (NPR)
6
is much higher because of the para-tariffs which are imposed to enhance protection for domestic
import-substitute production. The para-tariffs are also considered as important source of government
revenue. Since the 2000s, when applied customs duty declined substantially, the nominal protection
rates decreased marginally from 28.2% in 2001 to 26.7% in 2019/20 (Figure 9 and Table 3). The
nominal protection is preserved by the introduction of a ranges of supplementary and regulatory
duties. During this time, the average para-tariffs almost doubled to 13.2% in 2019/20, rising from 7.1%
in 2000/01 (Figure 9). Currently, roughly a half of NPR comes from these para-tariffs. The nominal
6
NPR for Bangladesh is measured as Nominal Protection Rate (NPR) = Customs Duty (CD) + Regulatory Duty (RD) +Protective
Supplementary Duty (SD) + Protective Value Added Tax (VAT). When calculating NPR, the protective element of VAT and SD
(calculated as effected rate on imports minus effective duty at on domestic production) are considered.

12
protection rate in Bangladesh appears high relative to regional comparators and other developing
countries. The Total Tariff Incidence (TTI),
7
a revenue indicator, is a measure of all trade taxes on
imports (protective or trade neutral). Table 3 provides the nominal as well as weighted and un-
weighted TTIs imposed on imports in Bangladesh. An increasing trend of import-weighted TTI implies
that the import regime is becoming more protective, with higher applied tariff rates.
Source: PRI staff estimates based on NBR data.
Table 3: Bangladesh’s average tariffs, FY2011-FY2018 (%)
Un-weighted average MFN tariffs
Un-weighted
Import-weighted
Fiscal
Year
Avg CD
Avg para-tariff
Avg NPR
TTI
TTI
FY2019
13.5
13.2
26.7
-
-
FY2018
13.40
13.15
26.55
51.49
-
FY2017
13.25
12.39
25.64
49.85
20.53
FY2016
13.01
12.59
25.60
49.87
19.99
FY2015
13.16
13.53
26.69
50.62
18.20
FY2014
13.19
14.9
28.09
52.15
16.84
FY2013
13.88
15.05
28.93
53.15
17.40
FY2012
13.57
13.39
26.96
49.81
17.56
FY2011
13.55
10.19
23.74
46.10
16.81
Source: PRI staff estimates based on NBR data.
Disaggregated product level analysis at the HS 2-digit code seems to suggest that among the major 20
items, vehicles other than railway or tramway rolling stock (HS 87) attract the highest applied tariff
rates (implicit tariff rate).
8
Applied customs duty on these products is 17.8% (Table 4). Along with the
7
TTI for Bangladesh is measured as Total Tariff Incidence (TTI) = Customs Duty (CD) + Regulatory Duty (RD) + Supplementary
Duty (SD) + Value Added Tax (VAT) + Advance Income Tax (AIT) + Advance Trade VAT (ATV).
8
This is the weighted applied rate (implicit tariff rate) calculated as tariff revenue divided by total imports under the
particular HS code.
0
5
10
15
20
25
30
FY01 FY05 FY10 FY15 FY20
Figure 9: Nominal protection rates in Bangladesh, %
Avg. CD Avg. para-tariffs

13
18% VAT and 31.4% other duties, the implicit tariff rate on these items is 66.2%. Cotton (HS 52), the
most imported product, has an applied rate less than 1%. This is because almost all cotton is imported
through bonded warehouse facilities and used as raw material for the export-oriented apparel
industries. The second most imported product – machinery, mechanical appliances (HS84) – attracts
a 5.4% applied tariff, of which 3.5% are different types of para-tariffs. Among others, mineral fuels,
oils and products (HS27) have 7.1% duty rates, and electrical machinery and equipment (HS85) 18.6%.
The applied customs duty and para-tariffs on major 20 imports items are given in Table 4. It is worth
highlighting that tariffs on raw materials and intermediate inputs are much lower compared to those
on final commodities.
Table 4: Weighted tariff rates on major importing items at HS 2-digit level (%)
HS code
Product description
Customs duty
VAT
Other duty
Applied duty
52
Cotton
0.03
0.03
0.04
0.10
84
Machinery, mechanical appliances
2.04
1.38
2.00
5.43
27
Mineral fuels, oils and products
0.80
4.05
2.21
7.07
85
Electrical machinery and equipment
6.87
7.49
4.27
18.62
72
Iron and steel
5.52
6.95
4.90
17.37
39
Plastics and articles thereof
4.35
9.18
4.48
18.01
55
Cereals
0.29
0.30
0.16
0.75
10
Man-made staple fibres
1.08
0.00
0.61
1.70
87
Vehicles other than railway or tramway rolling stock
16.79
17.98
31.38
66.15
54
Animal or vegetable fats and oils
0.71
0.63
1.12
2.46
89
Salt; sulphur; earths and stone;
3.58
0.53
2.12
6.24
25
Ships, boats and floating structures
8.31
15.14
9.39
32.84
15
Man-made filaments; strip and textile materials
0.57
3.40
0.38
4.35
31
Fertilisers
0.00
0.00
0.02
0.02
60
Knitted or crocheted fabrics
0.11
0.10
0.18
0.39
73
Articles of iron or steel
9.91
8.96
6.58
25.45
29
Organic chemicals
3.62
12.48
5.63
21.72
12
Oil seeds and oleaginous fruits
0.29
0.42
0.30
1.01
32
Tanning or dyeing extracts
3.33
6.37
3.31
13.01
7
Edible vegetables and certain roots and tubers
1.05
0.01
0.81
1.86
Source: Authors’ analysis using NBR data.
Figure 10 depicts applied tariff rates faced by different countries for their exports to Bangladesh. China
– the largest source of imports – faces a 12.4% tariff on average for exporting its products to
Bangladesh. The implicit tariff on imports from India is 21.3%. Among the 20 most prominent
importing partners, Japan confronts the largest average tariff, 36%. It is followed by Thailand and
Vietnam with average tariff rates of 27.9% and 23% respectively. On the other hand, products

14
originated from Argentina, Brazil, Canada, and the United States face the lowest tariff rates of around
3%. The variations in applied tariff rates among the partner countries arise according to the types of
products and shares of duty-free (or dutiable) imports.
Source: Authors’ analysis using NBR data.
Bangladesh generates significant revenue by imposing customs duty and other para-tariffs. Analysis
of NBR data shows that total revenue earned by applying tariffs on imports in 2018/19 stood at $7.7
billion (BDT 647 billion). The highest portion of this revenue is generated from imports of heavy
manufacturing products. In terms of the HS 2-digit code, the highest share – $1,252 million or 16.3%
of total revenue in 2018/19 – was generated from imports of vehicles other than railway or tramway
rolling stock (HS 87) (Table 5). This is attributable to the highest applied tariff rates, as discussed above.
It was followed by electrical machinery and equipment (HS 85), which accounts 10% of import
revenue; iron and steel (HS 72), 7.4%; salt, sulphur, earths and stone (HS 25), 7.4%, plastics and articles
thereof (HS 39), 6.1%; and machinery, mechanical appliances (HS 84), 5.1%. Cotton is Bangladesh’s
top import by value but its contribution to revenue earnings is negligible.
Table 5: Import revenue on top 20 items in Bangladesh (at HS 2-digit level), 2018/19
HS
code
Product description
Import revenue (million
$)
Share in total import
revenue (%)
52
Cotton
8.1
0.11
84
Machinery, mechanical appliances
396.3
5.14
27
Mineral fuels, oils and products
359.2
4.66
85
Electrical machinery and equipment
777.9
10.10
72
Iron and steel
571.1
7.41
39
Plastics and articles thereof
473.1
6.14
10
Cereals
32.4
0.42
55
Man-made staple fibres
14.8
0.19
0 5 10 15 20 25 30 35 40
China
India
Singapore
EU
Hong Kong
Indonesia
United States
Malaysia
Japan
Korean Republic of
Brazil
New Taiwan
Thailand
United Arab Emirates
Russia
Qatar
Pakistan
Canada
Vietnam
Argentina
Figure 10: Applied tariff rates on imports from selected countries in Bangladesh, (%)

15
HS
code
Product description
Import revenue (million
$)
Share in total import
revenue (%)
87
Vehicles other than railway or tramway
rolling stock
1251.9
16.25
15
Animal or vegetable fats and oils
74
0.96
25
Salt; sulphur; earths and stone;
569.1
7.39
89
Ships, boats and floating structures
110.2
1.43
54
Man-made filaments; strip and textile
materials
44.2
0.57
31
Fertilizers
0.3
0.00
60
Knitted or crocheted fabrics
5
0.06
73
Articles of iron or steel
272.5
3.54
29
Organic chemicals
212.4
2.76
12
Oil seeds and oleaginous fruits
9
0.12
32
Tanning or dyeing extracts
105.1
1.36
7
Edible vegetables and certain roots and
tubers
15
0.19
Source: Authors’ presentation using NBR data.
Although China is the largest source of imports, Bangladesh earns more revenue from imports from
India. This is attributed to the fact that a large portion of imports from China are coming through
bonded warehouse facilities which attract zero tariff. According to NBR data, in 2018/19, $1,795
million revenue was generated from imposing tariff on imports from India, or 23.3% of total import
revenue (Figure 11). It was closely followed by China, which accounted 23.1% (equivalent to $1,783
million) of import revenue in Bangladesh. Other major sources of import revenue included Japan
(8.3%), Singapore (4.9%), the European Union (4.6%), Thailand (4.3%), and Indonesia (3.4%).
Source: Authors’ presentation using NBR data.
0200 400 600 800 1,000 1,200 1,400 1,600 1,800
Argentina
Canada
Brazil
Russia
Pakistan
United States
Qatar
Taiwan
Malaysia
Vietnam
Korea, Rep.
Hong Kong
UAE
Indonesia
Thailand
EU
Singapore
Japan
China
India
Figure 11: Major source of revenue, by country (million $)

16
Customs duty accounts for as much as 29% of all government revenue; the rest comes from different
para-tariffs including supplementary duty, regulatory duty, VAT, advance income tax, advance VAT,
etc. (Table 6). The VAT on imported products is the largest source of import revenue, accounting for
more than one-third of total revenue. VAT is also imposed on domestic production, and thus might
not be fully considered as protective tariff. Supplementary duty and advance income tax account for
11.8% and 11.7% of total import revenue, respectively.
Table 6: Import revenue by different duties (%)
2016-17
2017-18
2018-19
Customs duty
28.8
30.2
28.7
Regulatory duty
3.2
2.8
2.2
Supplementary duty
14.4
12.8
11.8
Value Added Tax
36.2
36.3
37.1
Advance Income Tax
10.8
11.4
11.7
Advance Trade VAT
6.6
6.4
7.7
Other duties
0.0
0.1
0.7
Total import revenue
100
100
100
Source: Authors’ presentation using NBR data.
III. Literature Review: An Overview of the Relationship between Tariff Cuts and
Government Revenue
Considerable evidence over time suggests that trade liberalization is welfare enhancing. It improves
allocative, scale, technical and x-efficiencies. There have been numerous analytical studies that have
found tariff liberalization to promote economic growth (Escolano, 1995; Pritchett and Sethi, 1994;
Hye, Wizarat and Lau, 2016; etc). Trade liberalization and foreign competition due to reduction of
tariffs can improve allocative efficiency by shifting resources to where they can be most productive
based on comparative advantage (Krugman, 1979; Helpman and Krugman, 1985; Bhagwati, 1990).
Along with this, increased foreign competitive pressure enables domestic firms to gain scale efficiency
through large-scale production to achieve higher export productivity and to exploit different forms of
externalities (Balassa, 1984; Bhagwati, 1990; Krueger, 1998). Liberalization improves the access to
superior inputs and technology, which increases technical efficiency (Ethier, 1982; Grossman and
Helpman, 1991; Rivera-Batiz and Romer, 1991). A liberalized economy can augment x-efficiency
(because of competitive pressure – firms are always under pressure to reduce their cost) and this
makes the overall economy competitive. This in turn contributes to economic growth: freer trade
means more competition, which promotes efficiency of local producers. Trade liberalization helps
improve export incentives by reducing anti-export bias. The efficiency gains in terms of allocative,
scale, technical and x-efficiencies as a result of trade liberalization promote long-term economic

17
growth. Higher long-term growth in turn expands the tax base and improves the potential of tax
revenue.
Some developing countries express the concern that trade liberalization through tariff cuts could have
significant negative impacts on government revenue. However, the empirical literature on this is
ambiguous. Several studies (Agbeyegbe et al., 2004; Ebrill et al., 1999; and Adam et al., 2001) have
found that trade liberalization is linked with higher trade tax revenue. On the other hand, others
(Nashashibi and Bazoni, 1994; Khattry and Rao, 2002; and Cagé and Gadenne, 2018) show that trade
liberalization reduces tax revenues, while the findings of Agbeyegbe, Stotsky and WoldeMariam
(2006) on the relationship are inconclusive.
Lowering tariff rates implies less import revenue, but on the other hand, when tariffs reduce as a result
of liberalization, import volumes tend to increase due to a trade creation effect, and import tariff
revenues may start to recover. The reason, as explained by Epaphra (2014), is that rationalizing the
tariff system reduces opportunities and incentives for evasion, so compliance increases, and thus tariff
revenue goes up too. The overall impact on tariff revenue will depend on the price elasticity for the
import demand and the elasticity of substitution for imported goods. If either demand elasticity of
imports or price elasticity of supply of import substitutes is sufficiently high, the revenue should
increase at the later stage of liberalization (Ebrill et al., 1999; Agbeyegbe et al., 2006)
The impact of tariff liberalization on revenue thus can be positive, negative or neutral depending on
country characteristics (i.e. level of development, tax infrastructure, and elasticities of import demand
and supply) and nature of trade restrictions (Ebrill et al., 1999). When a trade regime is highly
restrictive, liberalization can expand trade revenue by lowering restrictions and tariff rates. Increased
import demand can off-set the revenue loss of liberalization by generating larger trading volumes to
tax (OECD, 2005). Again, when tariff rates are rationalized, reduced incentives to smuggle can expand
the tax base (Greenaway and Milner, 1991; Rodrik, 1990). On the other hand, when the tariff rates
are already low or rationalized, further liberalization cannot increase import volume sufficiently to
offset the revenue loss. This relationship between tax rates and revenue is well illustrated by the Laffer
Curve, which implies that if initial tariff rate is higher that the revenue-maximizing rate, trade
liberalization will increase tariff revenue; conversely, if the tariff is lower than the revenue-maximizing
level, liberalization will result in revenue loss.
Using a first-order dynamic panel model, Agbeyegbe et al. (2004) postulates that reduction in trade
tax increases revenue. Adam et al. (2001), using the dynamic generalized method of moments (GMM)
examines the empirical relationship between trade liberalization trade tax revenue for sub-Saharan
African countries and concluded that trade tax revenue increased in the CFA countries as a result of
liberalization. A later study by Agbeyegbe et al. (2006), using data for 22 countries for the period 1980-
1996, also concludes that that trade liberalization is positively linked with trade revenue. A more
recent study by Kassim (2016) estimates the revenue impact of trade liberalization for 28 countries in
sub‐Saharan Africa using data for the period 1981–2010 and suggests that trade reforms significantly
improve total tax revenue. Fukasaku (2003), on the other hand, finds that impact of trade liberalization
in sub-Saharan Africa is ambiguous.

18
On the other hand, Khattry and Rao (2002) applying regression analysis to a sample of 80 countries,
both developed and developing countries, for the period 1978-1999 finds a significant negative
relationship between tariff liberalization and government revenue in low-income countries. The study
concludes that structural factors have critical role in determining the trade-tax-revenue-to-GDP ratio
across countries. A study by OECD (2005) warns about the risks associated with lost revenue and
considers it as a serious threat in low-income countries highly dependent on trade taxation.
Cirera et al. (2011) investigated the impact of tariff cuts on fiscal revenue in developing countries using
both ex-ante and ex-post analysis. They reported that tariff reductions are likely to reduce trade tax
revenue in the short run, other things remaining constant.
Literature on the fiscal implications of trade liberalization stresses the utilization of other sources of
revenue as compensating measure (Kowalski, 2005). Many countries shifted from trade taxes to other
form of taxes, including income, sales and VAT, to mobilize revenue flows. Developed countries can
quickly adjust and compensate for trade tax revenue loss by other form of domestic taxation because
they have high-quality institutions and efficient administration. However, trade liberalization can bring
new fiscal challenges for developing countries as they have low tax-to-GDP ratio (Aizenman and
Yothin, 2006) and low institutional capacity to compensate for the loss of tariff revenue through
domestic indirect tax collection. Low-income countries are relatively highly dependent on trade tax
for their fiscal space, so trade tax revenue foregone due to liberalization is a serious issue (OECD,
2005).
Baunsgaard and Keen (2010) analyse whether countries were able to recoup the loss in trade revenue
from other sources. Their findings suggest that higher-income and middle-income countries
experienced a significantly robust tax recovery while low-income countries experienced a drop in total
tax revenue compared to the initial value. Thus, no statistically significant tax recovery is found for
low-income countries. Waglé (2011), however, finds that the tax recovery for these countries was
more robust than shown by Baunsgaard and Keen (2010). Moller (2016) finds that the recovery of lost
revenues by alternative sources of taxation was highly uneven among these countries. He concludes
that tax recovery is significantly stronger in democratic countries.
Trade policy reform and liberalization can have a significant impact on socio-economic variables in
low- and middle-income countries. Bhagwati (2004) and Taylor (1994) find an inverse relationship
between trade openness and macroeconomic variables and economic stability (Bevan, 1995).
Empirical investigations of the impact of tariff liberalization, whether FTA or preferential trade
arrangement (PTA) or unilateral liberalization, are based on two broad approaches: i) ex-ante analysis
and ii) ex-post analysis. Ex ante analyses are undertaken to assess or anticipate the possible economic
consequences of liberalization. The use of this sort of quasi-experimental tool is widespread due to its
simplicity and ease of simulation. This is also important from a policy perspective as policymakers want
to understand the potential economic effects of any policy move they are considering making. In the
empirical literature, both partial equilibrium (the WITS/SMART or Tariff Reform Impact Simulation
Tool – TRIST) and general equilibrium (computable general equilibrium (CGE) and GTAP framework)
models are used for such assessment.

19
Another area of empirical literature is the use of ex-post analytical tool to assess the impact of tariff
liberalization on aggregate output, import revenue, overall tax revenue, employment, etc. after the
policy reforms have taken place. These analyses are highly sophisticated and use micro and macro
data in econometric modelling.
IV. Methodology for Assessing Revenue Implications
This study employs two different methods to assess the potential implications of tariff liberalization
under an FTA situation with major trade partners. First, a partial equilibrium model is conducted using
WITS to assess the impact on overall impact on import flows, revenue, and consumer welfare. Second,
a CGE model using GTAP framework is used to capture the general equilibrium effect of bilateral or
unilateral tariff liberalization on the import and revenue.
Partial equilibrium models are most widely used to simulate and measure the effects of changes in
trade policy arising from FTAs/RTAs. These models assess the effects of specific changes in tariffs or
other taxes on trade flows, revenue, prices and some measures of welfare (consumer surplus) at a
given point in time. The starting point in these models is the elimination or reduction of existing tariffs
between members, which then causes import prices from the partner to change relative to the
domestic substitutes. Consumer responsiveness to the resultant price changes will influence the
volume of goods traded. As tariff preferences are provided to certain import partners, the changes in
trade affect government import revenue earnings. Partial equilibrium models capture only the sector-
specific impact without considering the economic interactions with other sectors.
The most popular partial equilibrium model for studying the impact of PTAs is the WITS-linked SMART
model. Jointly developed by UNCTAD and the World Bank, this applied tool has been extensively used
by negotiators of both bilateral and multilateral trade agreements. The web-based WITS-SMART
model uses UNCOMTRADE and the UNCTAD Trade and Analysis and Information System (TRAINS)
databases, which provide access to data on trade flows and MFN tariff rates at the HS 6-digit level of
disaggregation. Along with the web-based model, WITS has recently released new software which
makes use of user-provided data to undertake trade and tariff simulations. Bangladesh’s imports and
applied tariff data in the UNCOMTRADE and the TRAINS being relatively backdated, the simulation
exercise in this paper uses the ‘SMART with user data’ application in the WITS software. Data on
Bangladesh’s imports from partner countries and respective applied tariff rates at HS 6-digit level have
been collected from the NBR. Information on import demand elasticity has been taken from WITS-
simulated smart model.
9
Despite its limitations, one key feature of partial equilibrium analysis is that it can work with highly
disaggregated trade data that are often of direct interest to trade negotiators. Models like SMART
have an in-built property of considering products imported from different regions as imperfect
substitutes. As mentioned in the terms of reference, the simulations are conducted under a full
liberalization scenario for major trading partners, namely Brazil, China, the European Union, India,
Indonesia, Thailand, Malaysia, the United Kingdom and the United States. Another simulation is
conducted to assess the implication of partial tariff liberalization for all trading partners. All the
9
For supply and substitution elasticity, the default values are used.

20
estimations are undertaken for Bangladesh’s complete tariff liberalization for FTA partners where
Bangladesh offers duty-free access to each FTA partner across all product lines. The impact of partners’
tariff liberalization on Bangladesh’s exports was not part of this study, and no simulations were
undertaken on this.
In contrast to the partial equilibrium analysis, a general equilibrium framework allows inter-sectoral
interactions in an economy. It captures the linkages between markets in which goods from one sector
can be used as inputs for production as forward and backward linkages. The impact of reallocation of
resources between sectors as a result of tariff changes in one sector can also be studied under this
framework. In exploring the possible effects of bilateral and multilateral tariff liberalization, one of the
most useful CGE frameworks is the Global Trade Analysis Project (GTAP) model. The GTAP analytical
structure is a comparative, static, global general equilibrium model that brings together global
economies (known as regions) and all production and trading activities, including policy instruments
affecting production and trade.
The GTAP model used in this paper is a widely used CGE comparative static framework (Hertel, 1997)
for undertaking wide-ranging analysis of the likely impact (ex-ante) of various policy changes including
the economic and trade consequences of multilateral or bilateral trade agreements. The GTAP model
is based on neoclassical theories,
10
is linearized, and uses a common global database for the CGE
analysis. The model assumes perfect competition in all markets, constant returns to scale in all
production and trade activities, and profit- and utility-maximizing behaviour among firms and
households respectively. The GTAP framework used in this exercise incorporates all standard features
of the model including competitive markets and homogeneous technology. The Armington
assumption is employed for traded commodities (i.e. goods are imperfect substitutes). Consumers
maximize their utility following a CES function and a linear budget constraint. Factors of productions
include land, labour, capital and natural resources, with labour disaggregated as skilled and unskilled.
In the model, a full FTA scenario is considered where each partner undertakes complete bilateral tariff
liberalization for the other. The simulations are run separately for each FTA partner. Finally, a
completely different sets of simulations are carried out to provide an empirical assessment of the
overall relationship between tariff rationalization and import revenue for a unilateral tariff
liberalization in Bangladesh. For this, two different simulations are made, where Bangladesh
undertakes a 10% and 25% unilateral tariff liberalization for all trading partners.
The full-employment closure of the standard GTAP model is relaxed to allow unemployment in
Bangladesh but is kept unchanged for partner countries. The GTAP model comes with an integrated
database with the current version (version 10). There are 65 sectors (45 goods and 20 services sectors),
141 regions/countries and 7 factors. The 65-commodity classification is kept unchanged but the GTAP
regions are aggregated into 27 and factors are classified into 5 (land, skilled labour, unskilled labour,
capital and natural resource). The regional and sectoral classifications can be found in Tables 7 and 8
respectively. The GTAP-10 database has 2014 as the base year, updated to 2019 for undertaking the
simulation exercises.
10
Full documentation of the GTAP model and the database can be found in Hertel (1997) and also in Dimaranan and
McDougall (2002).

21
Table 7: GTAP regional aggregation
Model aggregation
GTAP region
Australia
Australia (AUS)
Bangladesh
Bangladesh (BGD)
Brazil
Brazil (BRA)
Cambodia
Cambodia (CAM)
Canada
Canada (CAN)
China
China (CHN)
Hong Kong
Hong Kong (HKG)
India
India (IND)
Indonesia
Indonesia (IDN)
Japan
Japan (JPN)
Malaysia
Malaysia (MAL)
Pakistan
Pakistan (PAK)
Philippines
Philippines (PHL)
Russia
Russia (RUS)
Singapore
Singapore (SGP)
South Korea
South Korea (KOR)
Thailand
Thailand (THA)
Turkey
Turkey (TUR)
United Kingdom
United Kingdom (UK)
United States of America
United States of America (USA)
Vietnam
Vietnam (VNM)
European Union 27
Austria (AUT), Belgium (BEL), Bulgaria (BGR), Croatia (HRV), Cyprus (CYP),
Czech Republic (CZE), Denmark (DNK), Estonia (EST), Finland (FIN), France
(FRA), Germany (DEU), Greece (GRC), Hungary (HUN), Ireland (IRL), Italy (ITA),
Latvia (LVA), Lithuania (LTU), Luxembourg LUX), Malta (MLT), Netherlands
(NLD), Poland (POL), Portugal (PRT), Romania (ROU), Slovakia (SVK), Slovenia
(SVN), Spain (ESP), Sweden (SWE)
Latin America and Caribbean
Argentina (ARG), Bolivia (BOL), Chile (CHL), Colombia (COL), Ecuador (ECU),
Paraguay (PRY), Peru (PER), Uruguay (URY), Venezuela (VEN), Rest of South
America (XSM), Costa Rica (CRI), Guatemala (GTM), Honduras (HND),
Nicaragua (NIC), Panama (PAN), El Salvador (SLV), Rest of Central America
(XCA), Dominican Republic (DOM), Jamaica (JAM), Puerto Rico (PRI), Trinidad
and Tobago (TTO), Caribbean (XCB)
Middle East and North
Africa
Bahrain (BHR), Islamic Republic of Iran (IRN), Israel (ISR), Jordan (JOR), Kuwait
(KWT), Oman (OMN), Qatar (QAT), Saudi Arabia (SAU), United Arab Emirates
(ARE), Rest of Western Asia (XWS), Egypt (EGY), Morocco (MAR), Tunisia
(TUN), Rest of North Africa (XNF)
Sub-Saharan Africa
Benin (BEN), Burkina Faso, (BFA), Cameroon (CMR), Côte d’Ivoire (CIV), Ghana
(GHA), Guinea (GIN), Nigeria (NGA), Senegal (SEN), Togo (TGO), Rest of
Western Africa (XWF), Central Africa (XCF), South Central Africa (SAC),
Ethiopia (ETH), Kenya (KEN), Madagascar (MDG), Malawi (MWI), Mauritius
(MUS), Mozambique (MOZ), Rwanda (RWA), Tanzania (TZA), Uganda (UGA),
Zambia (ZMB), Zimbabwe (ZWE), Rest of Eastern Africa (XEC), Botswana
(BWA), Namibia (NAM), South Africa (ZAF), Rest of South African Customs
(XSC)

22
Model aggregation
GTAP region
Rest of Asia
Mongolia (MNG), Taiwan (TWN), Rest of East Asia (XEA), Brunei Darussalam
(BRN), Lao People's Democratic Republic (LAO), Rest of Southeast Asia (XSE),
Nepal (NPL), Sri Lanka (LKA), Rest of South Asia (XSA)
Rest of World
New Zealand (NZL), Rest of Oceania (XOC), Mexico (MEX), Rest of North
America (XNA), Switzerland (CHE), Norway (NOR), Rest of EFTA (XEF), Albania
(ALB), Belarus (BLR), Ukraine (UKR), Rest of Eastern Europe (XEE), Rest of
Europe (XER), Kazakhstan (KAZ), Kyrgyzstan (KGZ), Tajikistan (TJK), Rest of
Former Soviet Union (XSU), Armenia (ARM), Azerbaijan (AZE), Georgia (GEO),
Rest of the World (XTW)
Source: Author’s aggregation using GTAP Database Version 10.
Table 8: GTAP commodity classification in the present study
#
Sector Name
#
Sector Name
1
Paddy rice
34
Basic pharmaceutical products
2
Wheat
35
Rubber and plastic products
3
Cereal grains nec
36
Mineral products nec
4
Vegetables, fruit, nuts
37
Ferrous metals
5
Oil seeds
38
Metals nec
6
Sugar cane, sugar beet
39
Metal products
7
Plant-based fibres
40
Computer, electronic and optic
8
Crops nec
41
Electrical equipment
9
Bovine cattle, sheep and goats
42
Machinery and equipment nec
10
Animal products nec
43
Motor vehicles and parts
11
Raw milk
44
Transport equipment nec
12
Wool, silk-worm cocoons
45
Manufactures nec
13
Forestry
46
Electricity
14
Fishing
47
Gas manufacture, distribution
15
Coal
48
Water
16
Oil
49
Construction
17
Gas
50
Trade
18
Minerals nec
51
Accommodation, Food and service
19
Bovine meat products
52
Transport nec
20
Meat products nec
53
Water transport
21
Vegetable oils and fats
54
Air transport
22
Dairy products
55
Warehousing and support activities
23
Processed rice
56
Communication
24
Sugar
57
Financial services nec
25
Food products nec
58
Insurance
26
Beverages and tobacco products
59
Real estate activities
27
Textiles
60
Business services nec
28
Wearing apparel
61
Recreational and other service
29
Leather products
62
Public Administration and defence
30
Wood products
63
Education
31
Paper products, publishing
64
Human health and social work
32
Petroleum, coal products
65
Dwellings
33
Chemical products
Source: GTAP Database Version 10

23
V. Estimated Results
Partial equilibrium results
Simulations using the WITS-SMART modelling framework and user’s data on Bangladesh’s imports
from partner countries, the partial equilibrium results suggest that the largest imports flow into
Bangladesh would occur through an FTA with its largest import partner, China.
11
In terms of absolute
value, Bangladesh’s total imports could increase by $2,567 million which is about 4.2% of baseline
imports in 2018/19. The second largest import flows, equivalent to almost $2,000 million or 3.2% of
baseline imports, would take place because of an FTA with India. The impact of FTA on Bangladesh’s
overall imports would be quite small for all other important partners. This could be $660 million (1.1%
of total imports) for an FTA with Thailand, $575 million (0.94%) with the European Union, $283 million
(0.46%) with Malaysia, $241 million (0.39%) with Indonesia, $88 million (0.14%) with the United
Kingdom, $70 million (0.11%) with the United States and $65 million (0.1%) with Brazil. The overall
impact of an FTA between Bangladesh and its major import partners on Bangladesh’s imports are
shown in Figure 12.
Source: Authors’ simulation based on WITS-SMART model
The impact of an FTA on the import flows from the members of the FTA is transmitted through trade
creation and trade diversion effects. Trade creations occurs when tariff preference alters the price of
imported commodities from the FTA members, such that less efficient domestic production is replaced
by more efficiently produced imported commodities from the FTA partner. Trade diversion occurs
when the tariff preferences lead to substitution of goods imports from a relatively more efficient non-
member to less efficient member of FTA. From the definition, the total impact on overall imports of
the host country can be inferred as trade creation from FTA partners. The trade diversion can be
considered as the cost of non-members.
11
The impact of duty-free access in the FTA partner on Bangladesh’s exports is not measured in this study.
-1
0
1
2
3
4
5
0
500
1000
1500
2000
2500
3000
BGD-BRA BGD-CHN BGD-IND BGD-IDN BDG-MAL BGD-THA BGD-UK BGD-USA BGD-EU27
Increment as% of total imports (%)
Increment in overall imports (million $)
Figure 12: Impact on FTA on Bangladesh's overall imports
Increment in overall imports (million $) Change (%)

24
The total trade effect is the sum of trade creation and trade diversion effects and is the increment in
exports from the FTA members.
12
The simulation suggests that an FTA between Bangladesh and China
would create a total trade effect of $3,428 million, equivalent to almost a quarter increment in the
baseline imports from China to Bangladesh – meaning that imports from China will increase by this
amount (Table 9). In this case, trade creation is almost three-quarters of the total trade effect. An FTA
with India would generate $2,707 million (or almost one-third of baseline imports from India) as the
total trade effect, of which 73% is due to trade creation – inefficient domestic producers being
replaced by imports from FTA partner. The total trade effect of Bangladesh’s duty-free access for the
FTA partners would be $91.8 million (6.1% of baseline imports from the FTA partner) for an FTA with
Brazil; $383.7 million (17.3%) for Indonesia; 423.1 million (23.7%) for Malaysia; $895.6 million (74.7%)
for Thailand; $133.4 million (29.7%) for the United Kingdom; $137.0 million (6.3%) for the United
States; and $876.6 million (21.3%) for the European Union (Table 9 and Figure 13). If measured as a
percentage increase in the baseline imports from the partner, the Bangladesh-Thailand FTA would
generate the highest total trade effect. The applied import tariff on imports from Thailand being
relatively high compared to other partners, duty-free access would generate large import flows. For
all partners, the trade creation effect is expected to outweigh the trade diversion effect. This implies
that inefficient domestic producers would be replaced rapidly by more efficient foreign suppliers,
while the same replacement from efficient non-member to relatively inefficient production in the FTA
member would be relatively low. This is an indication of welfare enhancement for Bangladesh.
Table 9: Trade creation, trade diversion and total trade effect of FTA
Baseline import
from partners
(million $)
Trade creation
(million $)
Trade diversion
(million $)
Total trade effect/total increment
in imports from partners (million $)
BGD-BRA
1484.6
65.3
26.5
91.8
BGD-CHN
14352.4
2566.6
862.1
3428.7
BGD-IND
8415.4
1970.4
736.5
2706.8
BGD-IDN
2214.2
241.1
142.6
383.7
BDG-MAL
1784.0
283.3
139.8
423.1
BGD-THA
1199.6
658.2
237.3
895.6
BGD-UK
448.9
88.1
45.3
133.4
BGD-USA
2182.8
70.0
67.1
137.0
BGD-EU27
4084.0
574.6
302.0
876.6
Source: Authors’ simulation based on WITS-SMART model.
12
For example, in case of Bangladesh-China FTA, the total trade effect of duty-free access in Bangladesh can be measured
as increase in Chinese total exports to the former.

25
Source: Authors’ simulation based on WITS-SMART model.
The tariff liberalization as part of FTA/PTA or unilateral tariff cut can put downward pressure on
government import revenues. As import revenue is a substantial part of Bangladesh’s total tax
revenue, there is concern that an FTA would considerably limit government’s fiscal space. Figures 14
and 15 show the revenue impact of FTAs in Bangladesh. The estimated results from the SMART model
simulation suggest that under a full FTA scenario, Bangladesh could lose respectively $2,117 million
and $2,086 million in revenue through an FTA with China and India. The revenue loss is above 27% of
total import revenue and 0.7% of GDP if Bangladesh signs an FTA either with China or India. The loss
of revenue for a full-fledged FTA with other major import partners is relatively low due to lower import
flows from these countries in the baseline and post-FTA periods. An FTA with Brazil, the United
Kingdom or the United States would mean about a 1% decline in Bangladesh’s overall import revenue.
An estimated revenue loss of around $450 million (equivalently 5.8% reduction in baseline revenue)
could be incurred due to full tariff liberalization under an FTA with either the European Union or
Thailand. Import revenue could fall respectively by 4.2% and 2.8% if Bangladesh signed an FTA with
Indonesia and Malaysia. The revenue implications of FTAs as a percentage of baseline revenue in
2018/19 at the HS 2-digit product code is shown in annex Table A3.
0
10
20
30
40
50
60
70
80
BGD-BRA BGD-CHN BGD-IND BGD-IDN BDG-MAL BGD-THA BGD-UK BGD-USA BGD-EU27
Per cent in basekine imports from FTA partners
Figure 13: Total trade effect of FTA on imports from partner country (% of baseline
imports)

26
Source: Authors’ simulation based on WITS-SMART model.
Source: Authors’ simulation based on WITS-SMART model.
As production from less efficient home producers is replaced by more efficient producers in the FTA
partners, the increase in overall imports in Bangladesh will be welfare enhancing. The WITS-SMART
-2200.0 -1700.0 -1200.0 -700.0 -200.0
BGD-BRA
BGD-CHN
BGD-IND
BGD-IDN
BDG-MAL
BGD-THA
BGD-UK
BGD-USA
BGD-EU27
Figure 14: Revenue implication of FTA (million $)
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
-30
-25
-20
-15
-10
-5
0
BGD-BRA BGD-CHN BGD-IND BGD-IDN BDG-MAL BGD-THA BGD-UK BGD-USA BGD-EU27
Revenue loss (% of GDP)
Revenue loss (% of total revenue)
Figure 15: Revenue impact of FTA with partners
Revenue loss (% of total revenue) Revenue loss (% of total GDP)

27
model allows us to estimate the welfare impact computed by comparing the consumer surplus in the
pre- and post-FTA situation. The welfare impact is positive if the trade creation effect is higher than
the trade diversion effect. In our case, as the trade creation is higher than trade diversion, Bangladeshi
consumers would benefit from higher consumer surplus. Estimated results show that although there
are negative consequences on import revenue, the welfare impact is positive for all potential FTAs
studied in this paper. The Bangladesh-China FTA could generate a welfare gain for the Bangladeshi
consumer which is equivalent to $709 million or 0.23% of baseline GDP (Figure 16). An FTA with either
India or Thailand would increase welfare for Bangladesh by around $550 million as the consumer
benefits from lower relative prices. The welfare enhancement for potential FTAs with each of
Indonesia, Malaysia, and the United Kingdom is less than $100 million. The corresponding gains for
Bangladesh-Brazil and Bangladesh-USA FTAs are negligible. It is important to note that the welfare
impact analysis in the SMART model does not account the impact on producer surplus generated from
higher export earnings from tariff-free access under an FTA.
Source: Authors’ simulation based on WITS-SMART model.
General equilibrium results
One limitation of the partial equilibrium model is that it does not take into account the important
interactions between various sectors (i.e. inter-sectoral input/output linkages) and markets. It also
does not consider the resource constraints in the economy. On the other hand, general equilibrium
models analyse the simultaneous equilibrium in different sectors and across all economies. In order
to avoid the limitations of the partial equilibrium and to account for the interaction between sectors
and economies, the GTAP-based CGE model is used to analyse the impact of FTAs on exports, imports,
government revenue and welfare.
0
0.05
0.1
0.15
0.2
0.25
0
100
200
300
400
500
600
700
800
BGD-BRA BGD-CHN BGD-IND BGD-IDN BDG-MAL BGD-THA BGD-UK BGD-USA BGD-EU27
Welfare (% of GDP)
Welfare (million $)
Figure 16: Welfare impact of FTA with partners
Welfare (million $) Welfare (% of GDP)

28
Figure 17 presents the impact of FTAs between Bangladesh and its major partners on Bangladesh’s
overall exports. It is important to note that as an LDC, Bangladesh enjoys duty-free access in its major
destinations apart from the United States. An FTA with a country that is already offering unilateral
preference could have negligible or no impact on the exports of the beneficiary country. As more than
70% of Bangladesh’s exports currently get preferential market access, FTAs with the partners offering
preferential access would have very little impact on exports. However, as the focus of the current
study is not on imports and their revenue implications, we do not dwell on export-related findings.
The simulation results indicate that the largest gain in terms of overall export earnings can be
generated from an FTA between Bangladesh and China. The BGD-CHN FTA could increase
Bangladesh’s total export earnings by 17.4%.
13
The result is almost close to Raihan and Razzaque’s
(2020), who undertook a similar exercise to study the impact of an FTA between the two countries.
The second largest impact on exports, equivalent to almost 6% of overall exports, would be generated
from an FTA with India.
14
Tariff liberalization for Bangladesh in the United States could lead to a 2.6%
increment in overall exports earnings.
15
Although the current applied tariff on Bangladesh’s exports
to the United States is high, the impact on overall exports seems to be very low. The corresponding
figures for Bangladesh’s bilateral FTA with Indonesia, Thailand and Malaysia would be 1.3%, 1% and
0.9% respectively. The impacts are relatively small because the base exports to these countries are
already very small.
Bangladesh currently enjoys duty-free quota-free access in the European Union and the United
Kingdom under the EU’s Everything But Arms (EBA) initiative, having a preference margin of around
12% for most exporting apparel items (Razzaque and Rahman, 2019). Thus, the impact of an FTA with
these countries is expected to be zero. But the simulation results suggest a relatively low positive
impact on export earnings. This is because there are some products for which Bangladesh could not
satisfy the rules of origin criteria and thus ended up paying tariffs. According to recent data, the
preference utilization rate of Bangladesh in the EU is almost 97% (Razzaque and Rahman, 2019). This
implies that the country pays tariffs on the remaining 3% of its exports to the EU (including the UK).
The GTAP model cannot account the impact of such preference non-utilization or rules of origin
criteria, so the removal of tariffs on these items has a positive impact on exports. It is important to
note that although the current impact of an FTA with the European Union and the United Kingdom is
negligible, this could generate a high export response in the post-graduation period after 2027 when
Bangladesh is expected to be subject to Standard Generalized Scheme of Preferences in the EU.
13
Before July 2020, Bangladesh had been enjoying duty-free quota-free access in the Chinese market for 61% tariff lines
(Razzaque, Rahman and Akib, 2020). Recently China has provided duty-free access for 8,256 Bangladeshi products (about
97% of tariff lines). The simulation is based on the exports and tariff when Bangladesh used to enjoy duty free access for
61% tariff lines.
14
India allows duty-free access to all Bangladeshi products except for some alcoholic beverages. The high impact on export
arises because the preferential access in this market remains unused. Besides, India imposes antidumping duty and other
measures which raises the applied tariff rates for Bangladeshi products. A complete removal of this tariff rates will boost
exports to this market.
15
The United States has excluded Bangladesh from the duty-free access to its markets in 2013. Currently above half of all
Bangladeshi exports to the US face tariff rates ranging 5.0% to 9.9% while another one-third is subject to tariff rates
between 10% and 15% (Razzaque, Abbasi and Rahman, 2020).

29
Source: Authors’ simulation based on GTAP.
On the import side, Bangladesh is regarded as one of the most protected economies in the world,
imposing high applied tariff rates. As such, tariff liberalization or elimination for its partners will yield
high competitiveness gains for FTA partners. The removal of duties will attract high import flows from
the partner country through trade creation and trade diversion effects, as discussed in the previous
section. Figure 18 presents the simulation results related to the impact on Bangladesh’s overall
imports. The GTAP simulation indicates that an FTA with China would increase Bangladesh’s overall
imports by 10% from the baseline import values. This would entail a 66% rise of imports of Chinese
goods and services while imports from India – the second largest source of Bangladesh’s imports –
would decline by 20% due to the trade diversion effect (as shown in Table 10). Under this scenario,
textile products would see the largest absolute increment equivalent to $3,033 million or almost one-
third of total imports of textiles in the baseline (Table 11). Among others, imports of leather and
leather goods would expand by 61%, mineral products 19%, metal products 32%, manufactures nec
21% and transport equipment nec 20%.
Tariff liberalization under an FTA with the United States would give the second largest boost in
Bangladesh’s imports payment. Under a full-fledged FTA with the USA, overall goods and services
imports could increase by almost 5%, whereas imports from the USA could expand by 20% (Figure 18
and Table 10). It is important to note that under BGD-US FTA, where Bangladesh allows tariff-free
access to the USA with no change in import tariffs for other partners, imports from other partners
including China, India, Japan and Malaysia increases moderately – implying a reverse trade diversion
for the non-members. This is due to the fact that FTA will increase Bangladesh’s exports to the United
States, which will surge the demand for raw materials and machineries from other countries.
An FTA with India would yield a rise in overall imports by almost $2 billion or 3.25% of baseline imports
(Figure 18). In this case, imports from India would surge by 54% (Table 10). They would decline by
9.8% from China, 9.4% from Japan, 5.3% from Malaysia, 8.3% from Thailand, and 4.3% from Vietnam,
0
2
4
6
8
10
12
14
16
18
20
BGD-BRA BGD-CHN BGD-IND BGD-IDN BGD-MAL BGD-THA BGD-UK BGD-USA BGD-EU27
Figure 17: Impact of FTA on Bangladesh's exports (% deviation from the baseline
exports)

30
among others, due to the trade diversion effect. Under BGD-IND FTA, textiles products would
experience the largest absolute increment, followed by transport equipment, dairy products, motor
vehicles and parts, and chemical products.
Bangladesh’s bilateral FTA with each of Brazil, Indonesia, Malaysia, Thailand and the European Union
would yield less than 1% increase in overall imports. However, Bangladesh’s bilateral imports from
these countries could increase by 17.1%, 66.5%, 90.2%, 99.6%, and 36.4% respectively compared to
the baseline, with a low to moderate trade diversion effect for the non-members (Table 10). Sectoral
changes in overall imports of these FTAs as percentage of baseline imports are shown in Table 11.
Source: Authors’ simulation based on GTAP.
Table 10: Impact on imports by country/region (% deviation from baseline imports)
Partner country
BGD-
BRA
BGD-
CHN
BGD-
IND
BGD-
IDN
BGD-
MAL
BGD-
THA
BGD-
UK
BGD-
USA
BGD-
EU27
Australia
0.4
-9.1
-6.1
-1.9
-1.3
-0.9
-0.3
5.1
-3.1
Bangladesh
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Brazil
17.1
-2.4
-2.5
-3.6
-1.5
-0.1
-0.1
5.1
-1.3
Cambodia
0.7
-12.6
-4.1
-0.5
-0.5
-0.7
0.0
6.7
-0.6
Canada
0.3
-6.0
-6.1
-2.2
-0.8
-0.7
-0.1
4.4
-1.4
China
0.5
66.9
-9.8
-1.1
-1.5
-2.1
-0.2
4.7
-2.6
Hong Kong
0.7
-5.5
-1.7
-0.3
-0.3
-0.3
0.0
6.2
-0.7
India
0.4
-20.0
54.1
-1.7
-1.3
-1.4
-0.2
4.2
-1.8
Indonesia
0.6
-9.6
-3.6
66.5
-5.7
-0.8
-0.1
5.2
-1.6
Japan
0.5
-14.0
-9.4
-0.6
-1.2
-1.3
-0.5
4.7
-2.9
South Korea
0.6
-22.4
-6.1
-1.3
-1.8
-1.8
-0.3
5.5
-3.5
Malaysia
0.5
-13.3
-5.3
-7.1
90.2
-1.1
-0.2
4.6
-2.2
Pakistan
0.5
-48.0
-12.0
-1.0
-0.9
-2.7
-0.1
5.3
-0.6
Philippines
0.3
-9.8
-2.8
-1.3
-1.3
-0.6
-0.3
5.0
-1.9
Russia
0.5
-14.9
-4.7
-1.3
-1.3
-0.8
0.0
3.1
-2.3
Singapore
0.2
-19.1
-6.6
-0.6
-2.3
-1.2
-0.2
1.8
-1.8
0 2 4 6 8 10
BGD-BRA
BGD-CHN
BGD-IND
BGD-IDN
BGD-MAL
BGD-THA
BGD-UK
BGD-USA
BGD-EU27
Figure 18: Impact of FTA on Bangladesh's imports (% deviation from the baseline
imports)

31
Partner country
BGD-
BRA
BGD-
CHN
BGD-
IND
BGD-
IDN
BGD-
MAL
BGD-
THA
BGD-
UK
BGD-
USA
BGD-
EU27
Thailand
0.6
-23.2
-8.2
-1.2
-1.6
99.6
-0.2
4.9
-2.5
Turkey
0.7
-19.8
-6.6
-0.7
-1.1
-1.5
0.0
5.1
-2.9
United Kingdom
0.6
-11.7
-4.9
-0.6
-0.9
-0.8
31.4
4.9
-2.4
United States of
America
0.6
-4.9
-2.3
-1.6
-0.8
-0.3
-0.1
20.0
-1.1
Vietnam
0.7
-27.3
-4.3
-0.9
-1.4
-0.9
-0.2
6.1
-2.0
European Union 27
0.6
-15.1
-5.5
-0.7
-1.0
-1.0
-0.2
4.9
36.4
Rest of Asia
0.4
-20.4
-6.7
-1.0
-1.6
-1.5
-0.2
4.0
-2.4
Latin America and
Caribbean
0.6
-0.9
-1.4
-16.0
-5.4
0.0
0.0
4.9
-0.6
Middle East and North
Africa
0.4
-7.7
-4.8
-0.8
-1.5
-1.3
-0.2
3.1
-2.1
Sub-Saharan Africa
-0.7
-7.6
-2.5
-0.4
-0.7
-0.4
0.0
0.4
-0.2
Rest of World
0.6
-5.2
-7.0
-0.5
-1.9
-0.5
-0.4
4.8
-3.7
Total
0.8
10.2
3.3
0.7
0.5
0.7
0.1
4.8
0.7
Source: Authors’ simulation based on GTAP.
Table 11: Impact on imports by products (% deviation from baseline imports)
Products
BGD-
BRA
BGD-
CHN
BGD-
IND
BGD-
IDN
BGD-
MAL
BGD-
THA
BGD-
UK
BGD-
USA
BGD-
EU27
Paddy rice
1.9
0.9
-4.3
-1.1
0.0
0.2
0.0
18.1
-0.9
Wheat
0.4
-0.3
0.3
-0.3
-0.2
0.0
-0.1
3.6
-0.4
Cereal grains nec
0.0
-0.7
-0.7
-0.7
-0.7
0.0
0.0
0.7
-0.7
Vegetables, fruit, nuts
1.0
8.3
5.3
2.8
0.3
0.9
-0.1
5.9
-0.2
Oil seeds
0.2
-0.4
0.0
-5.8
-1.9
0.2
0.0
1.9
-0.2
Sugar cane, sugar beet
-10.5
-5.3
-5.3
-5.3
0.0
0.0
0.0
0.0
-5.3
Plant-based fibres
-0.1
-7.8
0.0
0.4
0.2
-0.2
0.1
-1.3
0.6
Crops nec
1.2
3.0
29.1
4.2
1.2
0.0
0.0
10.3
0.0
Bovine cattle, sheep and
goats
0.7
0.3
-1.4
-0.5
0.2
0.2
0.0
6.8
-0.3
Animal products nec
0.5
6.7
-0.5
-0.3
-0.1
0.0
1.1
9.1
0.9
Raw milk
1.0
0.0
-3.1
-1.0
0.0
0.0
0.0
12.5
-1.0
Wool, silk-worm cocoons
-0.3
-5.9
0.0
0.3
0.1
-0.3
0.0
-1.4
0.6
Forestry
1.0
1.3
-0.3
0.3
-0.3
0.0
-0.3
9.0
-0.5
Fishing
0.7
0.9
27.8
-0.2
-0.2
0.1
-0.1
6.4
-0.3
Coal
0.0
-6.7
0.0
0.8
0.0
0.0
0.0
-0.8
0.0
Oil
-0.1
-7.1
-2.7
0.1
-0.9
-0.4
0.0
-1.0
-0.1
Gas
0.0
-40.0
-20.0
0.0
0.0
0.0
0.0
40.0
0.0
Minerals nec
0.0
1.7
1.7
0.0
0.0
0.6
0.0
2.3
0.0
Bovine meat products
1.7
1.7
-1.7
-0.6
16.8
0.0
0.0
14.0
-1.1
Meat products nec
1.7
1.7
-2.3
-0.6
22.9
0.0
-0.6
22.3
56.6
Vegetable oils and fats
0.7
0.9
-0.6
9.6
3.1
0.1
0.0
5.7
-0.2
Dairy products
1.3
3.1
16.7
-0.5
3.6
0.3
0.8
12.4
8.1
Processed rice
1.1
1.1
25.0
-0.5
0.0
0.3
0.0
9.5
-0.5
Sugar
9.7
1.4
0.3
-0.2
0.0
0.1
0.0
5.7
0.1
Food products nec
0.8
4.1
8.9
0.2
1.6
1.6
0.3
7.2
3.0
Beverages and tobacco
products
9.1
0.6
1.2
0.3
0.0
0.3
0.6
6.8
4.4
Textiles
1.2
31.2
9.2
1.4
0.8
1.9
0.1
6.3
0.9
Wearing apparel
1.0
32.4
9.8
0.2
0.0
0.8
0.2
6.3
1.7
Leather products
1.3
61.2
-0.4
-0.4
-0.2
0.4
0.0
10.6
3.6
Wood products
1.5
20.4
-3.0
1.5
18.9
3.0
0.3
11.1
3.3
Paper products,
publishing
0.7
14.2
1.2
3.0
0.4
1.5
0.2
5.9
4.3
Petroleum, coal products
0.0
2.5
1.4
0.0
0.5
0.1
0.0
0.3
0.0
Chemical products
0.6
3.5
1.4
0.2
0.4
0.6
0.0
4.2
0.6
Basic pharmaceutical
products
0.7
2.2
-0.2
-0.5
-0.2
0.0
0.0
7.1
1.2

32
Products
BGD-
BRA
BGD-
CHN
BGD-
IND
BGD-
IDN
BGD-
MAL
BGD-
THA
BGD-
UK
BGD-
USA
BGD-
EU27
Rubber and plastic
products
0.4
12.6
6.2
1.3
1.0
1.8
0.2
3.4
1.1
Mineral products nec
0.9
19.4
-0.1
-0.3
0.1
0.5
0.0
8.0
0.1
Ferrous metals
0.8
7.6
0.5
0.0
0.5
0.3
0.2
6.0
1.4
Metals nec
1.5
13.2
1.7
0.6
4.7
2.1
0.2
13.9
-0.2
Metal products
1.2
32.9
3.0
-0.1
0.6
2.8
0.1
8.4
2.8
Computer, electronic and
optic
0.5
8.0
0.5
0.2
0.5
0.2
0.0
3.8
0.5
Electrical equipment
0.4
8.3
1.4
0.0
0.0
0.5
0.1
3.3
1.4
Machinery and
equipment nec
0.6
3.2
0.0
-0.1
0.1
0.3
0.0
4.7
0.6
Motor vehicles and parts
0.4
2.8
6.2
0.0
0.0
0.8
0.3
3.5
0.1
Transport equipment nec
1.0
20.2
23.3
0.1
0.0
0.4
0.1
8.8
1.0
Manufactures nec
0.7
21.1
2.3
0.3
0.9
0.6
0.1
5.5
0.5
Electricity
1.0
0.0
-1.0
0.0
0.0
0.0
0.0
9.2
0.0
Gas manufacture,
distribution
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5.9
0.0
Water
1.8
9.3
1.6
0.2
0.2
0.7
0.0
12.7
0.0
Construction
0.4
2.4
0.0
0.0
0.0
0.0
0.0
4.9
0.0
Trade
0.9
1.4
-0.5
0.0
0.0
0.0
0.0
6.8
-0.5
Accommodation, Food
and servic
0.7
0.9
-0.4
-0.1
-0.1
0.1
0.0
5.9
-0.3
Transport nec
0.6
0.6
-0.6
0.0
0.0
0.6
0.0
4.5
0.0
Water transport
0.4
0.0
-0.8
-0.4
-0.4
0.0
0.0
5.7
-0.4
Air transport
0.8
1.4
-0.4
-0.1
-0.2
0.1
-0.1
6.4
-0.4
Warehousing and support
activi
0.9
1.4
-0.7
-0.2
0.0
0.2
0.0
7.2
-0.2
Communication
0.9
3.0
0.2
0.0
-0.2
0.2
0.0
6.6
-0.2
Financial services nec
0.9
0.9
0.0
0.0
0.0
0.0
0.0
6.5
0.0
Insurance
0.9
2.6
0.0
0.0
0.0
0.0
0.0
7.5
-0.3
Real estate activities
1.0
2.3
0.0
0.0
0.0
0.3
0.0
7.4
-0.3
Business services nec
0.3
0.5
0.0
0.1
0.0
0.1
0.0
2.2
0.0
Recreational and other
service
0.8
1.7
0.0
0.0
0.0
0.0
0.0
7.4
-0.8
Public Administration and
defe
1.1
1.1
-0.4
0.0
0.0
0.4
0.0
7.6
-0.4
Education
0.9
1.4
-0.4
-0.1
-0.1
0.1
0.0
7.7
-0.4
Human health and social
work a
0.8
1.4
-0.2
-0.1
-0.1
0.1
0.0
6.2
-0.2
Dwellings
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Total
0.8
10.2
3.3
0.7
0.5
0.7
0.1
4.8
0.7
Source: Authors’ simulation based on GTAP.
The GTAP simulation results suggest that, except for the case of the United States, an FTA with other
major partners studied in this paper has negative revenue implications for Bangladesh (Figure 19). The
revenue implication of BGD-CHN FTA could be 1.2% of baseline GDP or equivalent to almost 60% of
baseline import revenue. The high baseline imports from China and massive trade diversion effect
from other countries account for the large revenue loss. Table 12 indicates that an import revenue
loss of about 28% would occur due trade diversion effect from India. The decline in revenue would be
10.6% from Indonesia, 15.5% from Japan, 16.3% from Malaysia, 55% from Pakistan, 31.8% from
Thailand, 20.3% from the Unites States, 31.3% from Vietnam, and 23.5% from the European Union.
The largest absolute fall in revenue will be due to textile products equivalent to 86.5% of baseline
revenue from textiles (Table 13). This is attributed to the high import flows from China in the pre- and
post FTA situation. Among others, government import revenue from petroleum and coal products,

33
electrical equipment, computer, electronic and optic, manufactures nec, rubber and plastic products,
chemical products, and mineral products would decline considerably (Table 13).
The BGD-IND FTA could cause Bangladesh’s import revenue to shrink by 23%, which is about 0.5% of
baseline GDP (Figure 19). In this case, revenue loss on imports from China would be 11.4% of baseline
revenue earnings from this country. Tariff revenue loss on imports from Japan, Malaysia, Thailand, the
United Kingdom, the United States and the European Union would contribute significantly to the total
loss. In the case of BGD-IND FTA, revenue on imports of textiles, petroleum, coal products, motor
vehicles and parts, transport equipment, chemical products, rubber and plastic products, vegetables,
fruit, nuts, and electrical equipment would see the highest absolute reduction.
An FTA between Bangladesh and Indonesia would cause total import revenue to decline by 5% from
the base revenue (Figure 19). The same reduction would be 5.4% of baseline revenue for an FTA
between Bangladesh and the European Union (excluding the United Kingdom). The revenue loss for
the Bangladesh-Malaysia FTA and Bangladesh-Thailand FTA would be 4.1% of base revenue in each
case. Tariff-free access for Brazil and the United Kingdom would have negligible revenue impact as
import flows under these cases are very small, as discussed above. Bangladesh’s revenue loss by
source country/region and by products are shown in Tables 12 and 13.
Source: Authors’ simulation based on GTAP.
Table 12: Revenue implications by country/region (% deviation from baseline import revenue)
Partner country
BGD-
BRA
BGD-
CHN
BGD-
IND
BGD-
IDN
BGD-
MAL
BGD-
THA
BGD-
UK
BGD-
USA
BGD-
EU27
Australia
0.4
-10.2
-11.0
-2.0
-3.1
-0.8
-0.8
7.8
-7.8
Bangladesh
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Brazil
-100.0
-4.7
-3.4
-0.4
-0.4
-0.1
-0.1
5.3
-1.4
Cambodia
0.4
-55.2
-14.2
-1.5
-1.3
-3.5
-0.2
6.1
-0.9
Canada
0.6
-9.4
-5.0
-1.1
-1.1
-0.6
-0.6
5.0
-2.8
China
0.5
-100.0
-11.4
-1.3
-1.4
-2.5
-0.2
5.1
-2.2
Hong Kong
0.5
-43.3
-9.6
-3.4
-1.7
-2.9
-0.4
5.1
-5.1
-0.8
-22.9
-5.0 -4.1 -4.1
-0.6
4.3
-5.4
-60.0
-55.0
-50.0
-45.0
-40.0
-35.0
-30.0
-25.0
-20.0
-15.0
-10.0
-5.0
0.0
5.0
BGD-BRA BGD-CHN BGD-IND BGD-IDN BGD-MAL BGD-THA BGD-UK BGD-USA BGD-EU27
Figure 19: Revenue implications of FTAs (% of baseline import revenue)

34
Partner country
BGD-
BRA
BGD-
CHN
BGD-
IND
BGD-
IDN
BGD-
MAL
BGD-
THA
BGD-
UK
BGD-
USA
BGD-
EU27
India
0.3
-28.0
-100.0
-1.3
-1.4
-2.1
-0.3
5.0
-2.1
Indonesia
0.9
-10.6
-4.0
-100.0
-5.7
-0.9
0.0
5.7
-1.3
Japan
0.6
-15.5
-14.9
-0.6
-0.6
-1.7
-0.6
4.6
-1.7
South Korea
0.6
-30.9
-8.0
-1.9
-1.9
-2.5
0.0
5.6
-3.7
Malaysia
0.0
-16.3
-6.5
-7.2
-100.0
-1.3
-0.7
3.9
-2.6
Pakistan
0.7
-55.0
-14.2
-0.7
-0.7
-3.0
0.0
6.0
-0.7
Philippines
-0.6
-14.4
-6.6
-3.6
-2.4
-1.2
-0.6
4.8
-3.6
Russia
0.5
-24.2
-6.3
-1.6
-2.1
-1.1
0.0
3.7
-3.2
Singapore
0.2
-20.2
-7.0
-0.2
-2.3
-0.9
0.0
1.4
-1.2
Thailand
0.8
-31.8
-11.1
-1.6
-1.6
-100.0
0.0
4.8
-2.4
Turkey
0.7
-34.3
-11.7
-1.3
-1.7
-2.3
-0.3
5.3
-3.3
United Kingdom
0.5
-19.1
-10.3
-1.5
-2.1
-2.1
-100.0
4.1
-4.1
United States of
America
0.4
-20.3
-7.4
-1.5
-1.5
-1.5
-0.4
-100.0
-3.3
Vietnam
0.6
-31.5
-6.8
-1.5
-2.0
-1.5
-0.3
4.9
-2.8
European Union 27
0.5
-23.5
-8.0
-1.5
-1.5
-1.5
-0.5
5.0
-100.0
Rest of Asia
0.5
-29.5
-9.0
-1.4
-1.9
-1.9
-0.5
3.8
-2.4
Latin America and
Caribbean
0.7
-9.6
-14.9
-3.2
-0.9
-0.7
-0.2
7.7
-2.5
Middle East and North
Africa
1.0
-11.9
-6.8
-1.0
-1.9
-1.9
0.0
3.8
-2.9
Sub-Saharan Africa
-2.2
-13.5
-7.6
-3.8
-0.5
-1.1
0.0
4.9
-1.1
Rest of World
0.7
-5.4
-12.0
-0.5
-3.2
-0.4
-0.7
6.9
-6.9
Total
-0.8
-60.4
-22.9
0.0
-4.1
-4.1
-0.6
4.3
-5.4
Source: Authors’ simulation based on GTAP.
An exceptional but not implausible result is found for the BGD-US FTA, where Bangladesh’s revenue
increases by 4.3%. This is because the duty-free access for the United States in Bangladesh indirectly
increases the demand for goods from other countries, having an inverse trade diversion effect for non-
members. As the tariffs for non-members are kept unchanged, this trade effect from the non-member
raises the overall import revenue in Bangladesh. This is possible if imports from the U.S. are highly
complementary with goods imported into Bangladesh from other countries. Table 12 shows that trade
creation for non-FTA members increases Bangladesh’s revenue on imports. China has an 5.1%
increment in revenue, while the increase for India is 5%, Indonesia 5.7%, Thailand 4.8%, the United
Kingdom 4.1%, and the European Union 5%. The highest absolute increment in revenue would be due
to imports of textile products, followed by chemical products, ferrous metals, transport equipment,
mineral products, manufactures nec, etc. Government revenue on imports of some products,
including animal products nec, beverages and tobacco products, petroleum, and coal products, may
see an absolute decline (Table 13).
Table 13: Revenue implications of FTA by products (% deviation from baseline import revenue)
Products
BGD-
BRA
BGD-
CHN
BGD-
IND
BGD-
IDN
BGD-
MAL
BGD-
THA
BGD-
UK
BGD-
USA
BGD-
EU27
Paddy rice
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wheat
0.5
-0.5
-59.0
-0.5
-0.5
0.0
0.0
1.5
-2.5
Cereal grains nec
0.0
0.0
0.0
0.0
0.0
-66.7
0.0
0.0
-33.3
Vegetables, fruit, nuts
-2.1
-41.4
-33.6
-17.9
-2.1
-5.0
-0.7
5.0
-1.4
Oil seeds
0.0
-0.4
-41.5
-48.5
-1.8
0.0
0.0
1.5
-0.4

35
Products
BGD-
BRA
BGD-
CHN
BGD-
IND
BGD-
IDN
BGD-
MAL
BGD-
THA
BGD-
UK
BGD-
USA
BGD-
EU27
Sugar cane, sugar beet
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Plant-based fibres
-3.2
-74.4
-1.3
0.6
0.0
0.0
0.0
-1.3
-15.8
Crops nec
0.8
-7.4
-70.8
-14.0
0.8
-0.4
0.0
10.3
-2.1
Bovine cattle, sheep and
goats
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Animal products nec
0.5
-50.5
-1.8
-0.4
-0.1
0.0
-9.0
-28.0
-9.3
Raw milk
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wool, silk-worm cocoons
-0.2
-96.7
-5.9
0.4
0.2
-0.4
0.0
-1.3
-3.3
Forestry
0.6
0.6
-16.9
-6.7
-1.7
-0.6
-0.6
5.6
-2.2
Fishing
0.9
0.9
-94.4
-0.4
-0.4
-0.4
0.0
6.5
-0.4
Coal
0.0
-7.5
0.0
-51.2
0.0
-0.3
0.0
-1.2
-0.3
Oil
0.0
-7.1
-2.8
0.0
-1.0
0.0
0.0
-1.1
0.0
Gas
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Minerals nec
0.0
-15.2
-27.8
-0.8
-5.3
-8.3
-1.5
2.3
-6.1
Bovine meat products
1.3
1.8
-1.8
-0.4
-28.4
0.0
0.0
14.2
-0.9
Meat products nec
1.6
1.9
-1.9
-0.8
-30.4
0.0
-0.4
5.4
-72.0
Vegetable oils and fats
0.6
0.6
-0.6
-83.4
-27.9
0.0
0.0
5.0
-1.1
Dairy products
1.4
1.8
-44.6
-0.5
-10.6
-0.1
-2.4
9.2
-23.3
Processed rice
1.1
1.1
-97.8
-0.5
-0.3
0.0
0.0
9.5
-0.5
Sugar
-85.7
-6.1
-8.8
-0.2
-1.0
0.0
-0.1
4.8
-3.4
Food products nec
0.7
-12.9
-42.2
-1.5
-7.3
-6.5
-1.2
3.0
-15.1
Beverages and tobacco
products
-39.9
0.6
-7.5
-2.8
-1.2
-2.2
-3.4
-5.8
-23.2
Textiles
0.4
-86.5
-25.2
-2.4
-1.7
-5.4
-0.2
5.7
-0.8
Wearing apparel
0.8
-92.9
-30.0
0.0
0.0
-1.7
0.0
5.8
-5.0
Leather products
0.9
-92.5
-5.0
-0.5
-0.5
-1.0
-0.1
10.5
-9.4
Wood products
1.2
-47.7
-8.4
-4.9
-42.2
-6.2
-0.5
7.9
-10.6
Paper products,
publishing
0.6
-53.9
-8.9
-14.8
-2.4
-5.9
-1.2
4.7
-20.7
Petroleum, coal products
0.0
-40.6
-18.6
-0.1
-6.7
-2.0
-0.1
-0.1
-0.8
Chemical products
0.2
-28.8
-21.2
-3.7
-6.7
-7.6
-1.0
3.0
-10.8
Basic pharmaceutical
products
1.0
-34.1
-11.3
0.0
0.0
0.0
-4.1
5.8
-30.9
Rubber and plastic
products
0.6
-73.9
-35.6
-7.8
-6.1
-10.0
-1.1
2.8
-7.2
Mineral products nec
0.8
-86.6
-4.6
-1.5
-2.3
-2.3
0.0
7.6
-3.1
Ferrous metals
0.4
-34.5
-5.4
-1.2
-3.9
-0.8
-1.9
4.7
-10.9
Metals nec
1.6
-33.2
-10.6
-3.4
-16.8
-6.0
-0.8
10.9
-2.6
Metal products
0.7
-81.8
-10.9
0.0
-2.2
-7.2
0.0
6.5
-9.4
Computer, electronic and
optic
0.4
-78.4
-7.7
-3.0
-6.0
-1.3
-0.9
3.0
-7.3
Electrical equipment
0.3
-88.5
-15.6
-1.0
-1.0
-4.5
-1.0
2.1
-14.5
Machinery and
equipment nec
0.8
-49.7
-6.0
-0.8
-5.3
-3.8
-1.5
2.3
-22.6
Motor vehicles and parts
0.5
-14.4
-58.4
-0.5
0.0
-5.3
-1.9
3.4
-1.4
Transport equipment nec
1.4
-61.3
-73.0
-1.4
-0.7
-0.7
0.0
7.7
-7.0
Manufactures nec
0.5
-84.7
-11.8
-2.5
-4.4
-2.0
-0.5
4.9
-3.4
Source: Authors’ simulation based on GTAP.
Tariff liberalization and its associated impact on exports and imports positively affects domestic
economic activities in member countries. The impact on Bangladesh’s real GDP of an FTA between

36
Bangladesh and its major partners is presented in Figure 20.
16
In the case of an BGD-CHN FTA, the full
tariff elimination across all products and associated impact on exports, imports, terms of trade and
efficiency gain, could increase Bangladesh’s real GDP by 0.87%. The BGD-US FTA has the second largest
GDP impact, indicating that full tariff elimination from both sides could increase Bangladesh’s real GDP
by 0.41%. An FTA between Bangladesh and India would raise Bangladesh’s GDP by 0.2% from the
baseline. The BGD-BRA and BGD-IDN FTAs each have a real GDP effect of 0.07% on Bangladesh. The
corresponding figure for the case of such agreement with Malaysia and Thailand is 0.03% each. The
GDP impact of an FTA with the United Kingdom and the European Union is relatively low. This is
because Bangladesh already gets duty-free access in these countries as an LDC, so an FTA would have
little or no effect in expanding Bangladesh’s exports to these countries. However, in the post-
graduation periods, an FTA with these economies could contribute substantially to expanding
Bangladesh’s GDP.
Source: Authors’ simulation based on GTAP.
The impact of tariff liberalization on welfare is presented in Figure 21. It indicates that the highest
welfare gain for Bangladesh would be generated from an FTA with the United States – the single
largest export partner of the country. The welfare gains measured in term of equivalent variation
could be $2,366 million or 0.84% of base GDP. A complete bilateral duty-free agreement between
Bangladesh and China could increase the overall welfare of Bangladeshi consumers by $1,368 million
or almost 0.5% of GDP. Bangladesh could generate additional welfare equal to $350 million (0.12% of
GDP) from an FTA with Brazil. The corresponding figures for a bilateral FTA with India and Indonesia
are $215 million (0.08% of GDP) and $125 million (0.04% of GDP) respectively.
16
It is to note that as Bangladesh is enjoying duty-free access to major export destinations, the impact on exports cannot
be measured fully, thus the impact on real GDP and overall welfare would be subdued.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
BGD-BRA BGD-CHN BGD-IND BGD-IDN BGD-MAL BGD-THA BGD-UK BGD-USA BGD-EU27
Figure 20: Impact of FTA on Bangladesh's GDP (% deviation from the baseline GDP)

37
Source: Authors’ simulation based on GTAP.
VI. Policy Implications
Import tariffs are an important source of revenue in Bangladesh. They account for around one-third
of total government revenue. This substantial dependence is the result of high protective tariffs along
with other duties and charges and large import flows. Bangladesh imposes high applied MFN tariffs
on imports equivalent to 14% in 2019. Including the para-tariffs, the nominal protection rates stood
at more than 26%. There are strong reasons to argue that that Bangladesh will not be able to continue
the high tariff indefinitely, and tariff cuts are amongst the most rational trade policy options. Cross-
country evidence also suggests that as countries develop, import tariffs fall and countries increasingly
rely on domestic sources for government revenue. Furthermore, as part of the LDC graduation
strategy, Bangladesh prioritizes signing FTAs with major export destinations to hold on to the current
market access conditions it enjoys as an LDC. Implementation of this strategy is also associated with
significant revenue concerns.
FTAs and RTAs alter the prices of imports from members of the FTA/RTA as tariffs are reduced or
removed. This results in changes in imports from FTA/RTA partners relative to the rest of the world.
Whether these changes will be beneficial to participants in an FTA/RTA depend on various factors. In
principle, gains will occur if high-cost (domestic) production is replaced by less expensive imports from
a partner country (within the RTA/FTA) – this is known as trade creation. On the other hand, if an FTA
partner country’s supplies replace lower-cost imports from the rest of the world, there will be losses
– the costs of trade diversion. Therefore, membership in a PTA (including an RTA and FTA) could have
both positive and negative effects on an economy and it will be the net impact that will determine
whether there are going to be overall welfare gains or losses. Empirical evidence suggests that the
impact of tariff cut as part of an FTA or unilateral tariff liberalization on government revenue is
uncertain – it can be either negative, positive or zero.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
500
1000
1500
2000
2500
BGD-BRA BGD-CHN BGD-IND BGD-IDN BGD-MAL BGD-THA BGD-UK BGD-USA BGD-EU27
Welfare (% of base GDP)
Welfare (million $)
Figure 21: Impact of FTA on Bangladesh's welfare
Welfare (million $) Welfare (% of baseline GDP)

38
The current study attempts to estimate the potential impact of tariff liberalization under FTA
situations with major trade partners on government revenue in Bangladesh. Empirical literature on
this relationship is based on ex-ante and ex-post analyses. The study uses the ex-ante analysis in partial
and general equilibrium frameworks. The WITS/SMART partial equilibrium and the GTAP general
equilibrium models are used for empirical investigations.
The simulation results using both partial and general equilibrium models show that any FTA with a
potential important import source would have significant revenue implications. FTAs with China and
India would cause the largest import flows. As they are the major import sources, any FTA with these
countries would erode import revenue substantially. The modelling exercise using a partial equilibrium
model suggests that about 27% of current import revenue would be lost in each case. The general
equilibrium simulation implies relatively higher revenue implications in case of China; the same impact
for an FTA with India would be slightly lower compared to the partial equilibrium results. The revenue
loss for FTA with all other countries (except for the United States) may vary in the range of 0.6–6% of
total import revenue at the baseline. The most striking result is found in the case of Bangladesh-US
FTA. The GTAP simulation found that it would generate inverse trade diversion for non-member states
and thus the revenue implication for Bangladesh would be positive.
It could be argued that the revenue implications would be much lower than what the model predicts
because a significant portion of total imports are coming through bonded warehouses, which are
exempt from any tariffs because the goods are inputs for export-oriented industries. However, our
modelling exercises have taken this issue into careful consideration. The SMART simulations used the
import data of Bangladesh at HS 6-digit level by source country, which considers zero tariff for items
coming through bonded warehouse. In the general equilibrium, the GTAP database considers applied
tariff rates (or implicit tariff rates); in this way, the bonded imports are taken into account. Although
these issues are considered, the tariff implications of FTAs remain quite significant.
MFN tariff rates in Bangladesh are relatively high and their impact is further exacerbated by imposing
different para-tariffs (supplementary duty, AIT, advance VAT etc.). Thus, if Bangladesh signs an FTA
with any country, leaving the MFN rates unchanged for non-members, trade diversion effects will be
substantial. In order to minimize trade diversion, the best option would be to lower the MFN tariff to
help consumers benefit from import tariff rationalization and reduce the scope of trade diversion.
Simulation results using the GTAP framework suggest that Bangladesh could lose about 7.6% in
revenue for a 10% reduction in applied tariff rates. A 25% reduction in import tariffs across all products
and for all countries could contract government import revenue by 19.8%. This implies that import
revenue is inelastic to tariff rates – reduction in revenue is less than proportional to tariff change.
Apart from China and India, Bangladesh’s imports depend highly on East Asian countries including
Indonesia, Malaysia and Thailand. Data from NBR show that about 17% of imports are sourced from
ASEAN countries. Our simulation exercise seems to suggest that a significant amount of import tariff
revenue would be lost if Bangladesh signs an FTA with the three biggest trading partners from ASEAN
nations, namely Indonesia, Malaysia, and Thailand.
17
On the other hand, the same model indicate
17
Although Singapore is the largest trading partner from the ASEAN nations, it is not incorporated for this analysis. The
applied tariff rate in Singapore being close to zero, Bangladesh does not have much to gain from exports by signing FTA
with this country.

39
Bangladesh can expand its overall exports by 3.2% under full tariff liberalization in these three
countries.
For a country like Bangladesh, reducing revenue dependency would be highly important for trade
policy flexibility. However, this has implications for government revenue. From this perspective, the
most important policy recommendation for Bangladesh is to strengthen domestic revenue
mobilisation effort and substantially reduce the dependence on tariff revenue.

40
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43
Annex
Table A1: Banded and non-bonded imports at HS 2-digit level (top 50 items)
HS
code
Product description
Bonded
imports
(million $)
Non-bonded
imports
(million $)
Total imports
(million $)
Bonded
imports (% of
total imports)
52
Cotton
5175.6
3109.8
8285.4
62.5
84
Machinery, mechanical appliances
255.2
6749.6
7004.8
3.6
27
Mineral fuels, oils and products
3105.7
1956.5
5062.1
61.4
85
Electrical machinery and equipment
139.9
3835.5
3975.4
3.5
72
Iron and steel
196.1
3053.9
3250.0
6.0
39
Plastics and articles thereof
852.4
1651.4
2503.8
34.0
10
Cereals
0.0
1894.0
1894.0
0.0
55
Man-made staple fibres
1571.4
293.7
1865.1
84.3
87
Vehicles other than railway or
tramway rolling stock
53.5
1729.1
1782.6
3.0
15
Animal or vegetable fats and oils
1314.9
376.6
1691.5
77.7
25
Salt; sulphur; earths and stone
62.2
1593.4
1655.6
3.8
89
Ships, boats and floating structures
13.8
1634.9
1648.7
0.8
54
Man-made filaments; strip and textile
materials
1578.3
70.3
1648.7
95.7
31
Fertilizers
0.3
1412.6
1412.9
0.0
60
Knitted or crocheted fabrics
1248.4
3.5
1251.9
99.7
73
Articles of iron or steel
56.7
954.7
1011.4
5.6
29
Organic chemicals
39.0
882.4
921.4
4.2
12
Oil seeds and oleaginous fruits
0.0
876.5
876.5
0.0
32
Tanning or dyeing extracts
478.3
317.5
795.7
60.1
7
Edible vegetables and certain roots
and tubers
0.7
759.4
760.1
0.1
48
Paper and paperboard; articles of
paper pulp, of paper or of paperboard
577.3
178.4
755.7
76.4
62
Articles of apparel and clothing
accessories, not knitted or crocheted
725.3
17.9
743.2
97.6
17
Sugars and sugar confectionery
689.6
42.9
732.6
94.1
38
Miscellaneous chemical products
169.6
540.5
710.1
23.9
90
Optical, photographic,
cinematographic, measuring,
checking, precision, medical or surgical
35.7
616.0
651.7
5.5
23
Residues and waste from the food
industries; prepared animal fodder
0.0
573.9
573.9
0.0
28
Inorganic chemicals; organic or
inorganic compounds of precious
metals, of rare-earth metals
110.6
345.3
455.9
24.3
76
Aluminium and articles thereof
13.1
437.7
450.8
2.9
8
Edible fruit and nuts; peel of citrus
fruit or melons
0.0
403.5
403.6
0.0
88
Aircraft, spacecraft, and parts thereof
7.6
392.2
399.8
1.9
4
Dairy produce; birds' eggs; natural
honey; edible products of animal
origin, not elsewhere
35.0
358.0
392.9
8.9
9
Coffee, tea, maté and spices
0.3
350.0
350.4
0.1
40
Rubber and articles thereof
83.2
263.2
346.4
24.0
96
Miscellaneous manufactured articles
277.4
59.8
337.2
82.3
58
Special woven fabrics; tufted textile
fabrics; lace; tapestries; trimmings;
embroidery
292.8
9.2
302.0
96.9
59
Impregnated, coated, covered or
laminated textile fabrics; textile
articles of a kind suitable…
241.4
34.9
276.3
87.4

44
HS
code
Product description
Bonded
imports
(million $)
Non-bonded
imports
(million $)
Total imports
(million $)
Bonded
imports (% of
total imports)
47
Pulp of wood or of other fibrous
cellulosic material; recovered (waste
and scrap) paper or…
0.0
268.7
268.8
0.0
74
Copper and articles thereof
62.7
200.9
263.6
23.8
30
Pharmaceutical products
0.0
251.1
251.2
0.0
64
Footwear, gaiters and the like; parts of
such articles
161.7
38.1
199.8
80.9
34
Soap, organic surface-active agents,
washing preparations, lubricating
preparations, artificial…
92.6
105.4
198.0
46.8
41
Raw hides and skins (other than fur
skins) and leather
193.8
2.6
196.4
98.7
26
Ores, slag and ash
1.6
182.6
184.2
0.9
79
Zinc and articles thereof
10.3
145.0
155.2
6.6
49
Printed books, newspapers, pictures
and other products of the printing
industry; manuscripts…
6.3
140.2
146.4
4.3
53
Other vegetable textile fibres; paper
yarn and woven fabrics of paper yarn
129.6
1.4
131.0
98.9
44
Wood and articles of wood; wood
charcoal
4.2
124.9
129.1
3.3
83
Miscellaneous articles of base metal
73.3
51.5
124.9
58.7
94
Furniture; bedding, mattresses,
mattress supports, cushions and
similar stuffed furnishings…
20.8
94.7
115.4
18.0
21
Miscellaneous edible preparations
0.2
113.3
113.5
0.2

45
Table A2: Applied/implicit tariff rates on Bangladesh’s imports at HS 2-digit level (%)
HS
code
Product description
Customs
duty
Regulatory
duty
Supplementary
duty
VAT
Advance
Income Tax
Advance
Trade VAT
Other
duties
Applied
rate
1
Live animals
0.22
0.00
0.00
0.00
0.20
0.00
0.15
0.58
2
Meat and edible meat offal
3.65
1.94
0.00
0.06
3.23
0.03
0.00
8.91
3
Fish and crustaceans, molluscs and other aquatic
invertebrates
24.46
3.44
28.47
0.17
5.73
0.07
0.04
62.38
4
Dairy produce; birds' eggs; natural honey; edible products of
animal origin, not elsewhere…
5.58
0.08
0.46
14.67
4.62
0.30
0.02
25.73
5
Products of animal origin, not elsewhere specified or
included
0.26
0.00
0.00
0.32
0.12
0.03
0.00
0.74
6
Live trees and other plants; bulbs, roots and the like; cut
flowers and ornamental foliage
13.26
0.95
0.00
6.62
6.25
2.76
0.08
29.92
7
Edible vegetables and certain roots and tubers
1.11
0.08
0.50
0.01
0.26
0.00
0.02
1.97
8
Edible fruit and nuts; peel of citrus fruit or melons
22.83
2.74
23.31
20.76
5.10
9.80
0.15
84.67
9
Coffee, tea, maté and spices
15.19
1.33
10.23
1.51
2.22
1.92
0.01
32.39
10
Cereals
1.09
0.14
0.00
0.00
0.47
0.00
0.00
1.71
11
Products of the milling industry; malt; starches; inulin; wheat
gluten
8.57
6.45
0.00
13.90
3.92
1.04
0.09
33.97
12
Oil seeds and oleaginous fruits; miscellaneous grains, seeds
and fruit; industrial or medicinal…
0.29
0.00
0.00
0.43
0.29
0.01
0.00
1.02
13
Lac; gums, resins and other vegetable saps and extracts
4.02
0.14
0.00
0.00
5.64
0.00
0.02
9.81
14
Vegetable plaiting materials; vegetable products not
elsewhere specified or included
20.28
0.00
0.00
31.51
10.59
13.67
0.25
76.31
15
Animal or vegetable fats and oils and their cleavage products;
prepared edible fats; animal…
0.57
0.01
0.04
3.42
0.23
0.10
0.01
4.38
16
Preparations of meat, of fish or of crustaceans, molluscs or
other aquatic invertebrates
27.98
3.38
0.00
21.60
5.63
6.46
0.06
65.11
17
Sugars and sugar confectionery
0.63
0.64
0.13
1.17
0.13
0.03
0.00
2.74
18
Cocoa and cocoa preparations
30.08
3.51
48.83
30.94
6.19
9.76
0.25
129.55
19
Preparations of cereals, flour, starch or milk; pastrycooks'
products
20.75
2.15
14.13
21.14
5.19
5.02
0.16
68.54
20
Preparations of vegetables, fruit, nuts or other parts of plants
31.01
3.73
19.63
26.85
6.23
8.65
0.21
96.32
21
Miscellaneous edible preparations
14.76
1.00
5.59
18.02
5.25
3.02
0.27
47.90
22
Beverages, spirits and vinegar
4.44
0.42
16.27
6.47
1.10
2.02
0.05
30.78
23
Residues and waste from the food industries; prepared
animal fodder
0.50
1.74
0.00
0.02
0.00
0.58
0.09
2.92
24
Tobacco and manufactured tobacco substitutes
4.49
0.54
16.54
5.93
0.90
0.50
0.16
29.06

46
HS
code
Product description
Customs
duty
Regulatory
duty
Supplementary
duty
VAT
Advance
Income Tax
Advance
Trade VAT
Other
duties
Applied
rate
25
Salt; sulphur; earths and stone; plastering materials, lime and
cement
8.70
0.03
3.78
15.84
4.63
1.22
0.16
34.37
26
Ores, slag and ash
4.44
0.48
0.00
14.87
4.80
0.11
0.22
24.93
27
Mineral fuels, mineral oils and products of their distillation;
bituminous substances; mineral…
0.81
0.13
0.00
4.07
1.13
0.93
0.03
7.10
28
Inorganic chemicals; organic or inorganic compounds of
precious metals, of rare-earth metals…
3.87
0.16
0.00
9.46
3.55
1.99
0.18
19.22
29
Organic chemicals
3.84
0.03
0.01
13.24
4.91
0.80
0.23
23.06
30
Pharmaceutical products
0.93
0.00
0.00
1.27
3.03
3.49
0.34
9.07
31
Fertilizers
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.02
32
Tanning or dyeing extracts; tannins and their derivatives;
dyes, pigments and other colouring…
3.38
0.12
0.37
6.47
1.98
0.82
0.07
13.21
33
Essential oils and resinoids; perfumery, cosmetic or toilet
preparations
24.37
2.67
22.62
24.11
5.53
5.74
0.30
85.35
34
Soap, organic surface-active agents, washing preparations,
lubricating preparations, artificial…
8.29
0.43
2.38
9.78
2.71
1.88
0.11
25.58
35
Albuminoidal substances; modified starches; glues; enzymes
3.36
1.40
0.00
4.82
1.97
1.49
0.06
13.10
36
Explosives; pyrotechnic products; matches; pyrophoric alloys;
certain combustible preparations
12.51
1.44
12.66
11.94
4.88
0.33
0.02
43.78
37
Photographic or cinematographic goods
5.40
0.48
0.00
14.04
4.39
5.42
0.38
30.10
38
Miscellaneous chemical products
5.55
0.22
0.27
6.56
3.76
1.12
0.11
17.60
39
Plastics and articles thereof
4.57
0.15
0.95
9.63
2.67
0.81
0.12
18.90
40
Rubber and articles thereof
12.11
0.85
1.66
14.30
4.16
5.11
0.16
38.36
41
Raw hides and skins (other than fur skins) and leather
0.07
0.00
0.00
0.20
0.07
0.04
0.00
0.39
42
Articles of leather; saddlery and harness; travel goods,
handbags and similar containers; articles…
7.78
0.92
7.80
7.25
1.59
3.06
0.06
28.45
43
Fur skins and artificial fur; manufactures thereof
0.05
0.01
0.00
0.05
0.01
0.02
0.00
0.14
44
Wood and articles of wood; wood charcoal
7.24
0.16
0.53
4.01
5.10
0.84
0.04
17.90
45
Cork and articles of cork
10.39
0.00
0.00
14.65
5.40
3.30
0.14
33.87
46
Manufactures of straw, of esparto or of other plaiting
materials; basket ware and wickerwork
10.21
0.00
0.00
16.51
5.10
7.18
0.08
39.09
47
Pulp of wood or of other fibrous cellulosic material;
recovered (waste and scrap) paper or…
0.00
0.00
0.00
0.51
0.00
0.07
0.03
0.61
48
Paper and paperboard; articles of paper pulp, of paper or of
paperboard
4.26
0.37
0.47
4.35
1.19
0.45
0.05
11.14
49
Printed books, newspapers, pictures and other products of
the printing industry; manuscripts…
5.21
0.23
1.08
11.05
0.53
0.40
0.02
18.53

47
HS
code
Product description
Customs
duty
Regulatory
duty
Supplementary
duty
VAT
Advance
Income Tax
Advance
Trade VAT
Other
duties
Applied
rate
50
Silk
1.77
0.29
0.01
1.75
0.48
0.80
0.02
5.11
51
Wool, fine or coarse animal hair; horsehair yarn and woven
fabric
0.05
0.00
0.00
0.08
0.04
0.02
0.00
0.19
52
Cotton
0.03
0.00
0.02
0.03
0.01
0.01
0.00
0.10
53
Other vegetable textile fibres; paper yarn and woven fabrics
of paper yarn
0.04
0.00
0.00
0.03
0.05
0.01
0.00
0.13
54
Man-made filaments; strip and the like of man-made textile
materials
0.77
0.07
0.62
0.69
0.24
0.27
0.01
2.68
55
Man-made staple fibres
0.31
0.02
0.04
0.31
0.05
0.05
0.01
0.79
56
Wadding, felt and nonwovens; special yarns; twine, cordage,
ropes and cables and articles thereof
5.07
0.49
0.00
4.75
1.30
1.05
0.06
12.73
57
Carpets and other textile floor coverings
28.73
3.28
29.27
26.34
5.79
10.86
0.25
104.53
58
Special woven fabrics; tufted textile fabrics; lace; tapestries;
trimmings; embroidery
1.15
0.14
0.79
1.00
0.23
0.41
0.01
3.73
59
Impregnated, coated, covered or laminated textile fabrics;
textile articles of a kind suitable…
1.23
0.11
0.75
1.91
0.53
0.53
0.02
5.07
60
Knitted or crocheted fabrics
0.11
0.01
0.11
0.10
0.02
0.04
0.00
0.40
61
Articles of apparel and clothing accessories, knitted or
crocheted
9.61
1.15
21.72
10.66
1.93
4.63
0.06
49.76
62
Articles of apparel and clothing accessories, not knitted or
crocheted
0.97
0.12
2.23
1.08
0.19
0.39
0.01
4.99
63
Other made-up textile articles; sets; worn clothing and worn
textile articles; rags
19.12
1.95
11.80
16.40
3.60
5.47
0.10
58.44
64
Footwear, gaiters and the like; parts of such articles
5.73
0.40
6.62
5.35
1.15
1.49
0.03
20.77
65
Headgear and parts thereof
14.76
1.77
0.00
11.33
2.94
4.30
0.22
35.32
66
Umbrellas, sun umbrellas, walking sticks, seat-sticks, whips,
riding-crops and parts thereof
32.16
3.86
0.00
24.70
6.43
10.73
0.10
77.98
67
Prepared feathers and down and articles made of feathers or
of down; artificial flowers; articles…
0.91
0.11
0.81
0.83
0.18
0.34
0.02
3.20
68
Articles of stone, plaster, cement, asbestos, mica or similar
materials
11.92
1.19
2.44
12.00
2.70
3.07
0.10
33.42
69
Ceramic products
16.76
1.89
48.12
20.87
3.66
8.53
0.08
99.91
70
Glass and glassware
19.65
2.04
19.20
18.13
4.35
5.92
0.33
69.63
71
Natural or cultured pearls, precious or semi-precious stones,
precious metals, metals clad…
56.63
6.78
58.10
52.22
11.33
22.48
0.45
208.00
72
Iron and steel
5.58
0.32
0.10
7.04
3.46
0.92
0.16
17.57
73
Articles of iron or steel
10.49
0.89
1.10
9.48
2.86
2.00
0.12
26.95

48
HS
code
Product description
Customs
duty
Regulatory
duty
Supplementary
duty
VAT
Advance
Income Tax
Advance
Trade VAT
Other
duties
Applied
rate
74
Copper and articles thereof
5.19
0.13
0.00
11.19
3.49
0.12
0.13
20.24
75
Nickel and articles thereof
10.69
0.02
0.00
13.57
3.99
2.87
0.02
31.15
76
Aluminium and articles thereof
7.04
1.47
0.02
15.73
4.85
0.30
0.11
29.52
78
Lead and articles thereof
2.85
0.00
0.00
12.06
3.88
0.04
0.15
18.98
79
Zinc and articles thereof
4.55
0.00
0.00
14.67
4.67
0.18
0.01
24.09
80
Tin and articles thereof
4.61
0.00
0.00
14.27
4.53
0.03
0.00
23.44
81
Other base metals; cermets; articles thereof
7.11
0.00
0.00
15.52
4.82
0.70
0.16
28.31
82
Tools, implements, cutlery, spoons and forks, of base metal;
parts thereof of base metal
11.74
0.99
4.42
14.69
4.08
6.11
0.15
42.17
83
Miscellaneous articles of base metal
11.42
1.28
2.92
9.67
2.42
3.50
0.10
31.32
84
Machinery, mechanical appliances, nuclear reactors, boilers;
parts thereof
2.13
0.07
0.47
1.44
0.40
1.04
0.10
5.66
85
Electrical machinery and equipment and parts thereof; sound
recorders and reproducers, television…
7.22
0.41
0.45
7.87
1.77
1.44
0.41
19.57
86
Railway or tramway locomotives, rolling stock and parts
thereof; railway or tramway track fixtures…
7.76
0.00
0.00
16.17
0.52
0.25
0.03
24.73
87
Vehicles other than railway or tramway rolling stock, and
parts and accessories thereof
17.82
1.96
20.48
19.09
4.39
6.24
0.23
70.23
88
Aircraft, spacecraft, and parts thereof
0.00
0.00
0.00
0.00
0.23
0.26
0.25
0.74
89
Ships, boats and floating structures
3.84
0.01
0.00
0.57
2.21
0.04
0.02
6.68
90
Optical, photographic, cinematographic, measuring, checking,
precision, medical or surgical…
2.77
0.03
0.03
2.91
1.17
2.23
0.11
9.25
91
Clocks and watches and parts thereof
15.77
0.47
0.00
22.59
6.72
9.67
0.26
55.48
92
Musical instruments; parts and accessories of such articles
29.40
3.52
0.00
22.58
5.88
9.59
0.10
71.08
93
Arms and ammunition; parts and accessories thereof
2.94
0.28
12.14
5.48
1.07
0.13
0.01
22.04
94
Furniture; bedding, mattresses, mattress supports, cushions
and similar stuffed furnishings…
10.48
1.05
8.16
9.84
2.65
3.24
0.11
35.52
95
Toys, games and sports requisites; parts and accessories
thereof
22.60
1.72
10.02
26.39
7.17
11.20
0.18
79.29
96
Miscellaneous manufactured articles
4.11
0.29
1.82
3.88
1.03
1.25
0.04
12.43
97
Works of art, collectors’ pieces and antiques
11.75
1.39
0.00
9.20
2.90
3.97
0.06
29.27
98
Commodities not elsewhere specified
115.66
0.00
0.00
0.00
0.00
0.00
1.14
116.79

49
Table A3: Revenue implication of FTA by product at HS 2-digit code (% deviation from the baseline), based
on WITS/SMART model
HS code
BGD-BRA
BGD-CHN
BGD-IND
BGD-IDN
BGD-MAL
BGD-THA
BGD-UK
BGD-USA
BGD-EU
1
0.00
-0.09
-13.10
-0.22
-3.89
-0.15
-12.41
-5.13
-26.95
2
-1.60
0.00
-93.35
0.00
-1.69
-0.54
0.00
0.00
-0.67
3
0.00
-3.94
-43.68
-0.37
-0.11
-1.37
0.00
0.00
-0.19
4
0.00
-0.03
-10.46
-0.05
-1.60
-0.06
-0.51
-1.00
-18.71
5
0.00
-3.12
-17.49
3.41
-0.33
-0.28
-77.60
-0.10
-0.07
6
0.00
-50.13
-30.29
0.00
-8.57
-10.73
0.00
-0.40
0.00
7
0.00
-22.93
-62.03
0.00
-0.01
-1.40
0.00
-0.85
-0.22
8
-4.16
-43.18
-45.94
-8.11
-0.99
-5.10
0.00
-0.39
-0.33
9
-0.36
-7.93
-64.78
-0.72
-0.07
-0.35
-0.04
-0.04
-0.17
10
-17.70
-0.76
-76.84
0.00
0.00
-0.28
-0.08
-0.70
-0.01
11
0.00
-3.93
-27.75
-0.08
-0.97
-27.10
-0.40
-0.17
-33.86
12
0.06
-1.56
-24.56
-53.19
-0.59
-2.69
0.00
0.00
-0.06
13
-1.41
-26.47
-43.34
-15.25
-0.22
0.00
-0.08
-7.32
-7.95
14
0.00
-0.13
-98.82
-0.10
0.00
0.00
0.00
0.00
-0.05
15
-0.04
-1.81
-0.90
-48.36
-18.92
-0.03
-0.10
-0.16
-3.36
16
-7.34
-3.17
-1.19
-0.09
-41.90
-9.19
-0.44
-6.07
-40.82
17
-42.15
-17.35
-16.44
-2.31
-4.22
-3.34
-1.39
-0.45
-13.26
18
-0.39
-7.98
-30.37
-8.90
-25.57
-0.63
-2.72
-0.19
-41.69
19
0.00
-0.50
-60.71
-0.38
-8.47
-4.26
-0.64
-0.18
-29.23
20
-1.09
-12.16
-31.17
-0.28
-20.38
-10.53
-2.24
-12.79
-26.12
21
0.00
-17.90
-24.28
-2.04
-12.29
-9.76
-0.32
-1.64
-9.32
22
0.00
-3.85
-0.37
-0.16
-12.11
-0.55
-9.68
-3.34
-20.22
23
-5.86
-1.47
-49.56
-0.20
-3.99
-1.69
-0.21
-17.07
-2.31
24
0.00
0.00
-49.45
-13.55
-0.03
0.00
-0.01
-0.01
0.00
25
-0.08
-2.03
-18.48
-7.47
-7.53
-23.06
-0.22
-0.03
-0.97
26
0.00
-2.55
-52.23
-2.68
-11.92
-0.14
0.00
0.00
-0.03
27
0.00
-1.46
-6.33
-28.23
-3.00
-0.89
-0.02
-0.05
-1.03
28
-0.17
-48.65
-16.05
-0.20
-3.68
-1.42
-0.17
-3.70
-15.19
29
-0.03
-30.86
-34.74
-3.70
-5.53
-1.78
-0.69
-0.59
-11.87
30
-0.03
-7.48
-13.44
-0.04
-0.12
-0.42
-1.73
-5.49
-49.17
31
0.00
-0.87
-0.05
0.00
0.00
0.00
0.00
-0.02
-0.33
32
0.00
-24.99
-45.49
-1.69
-3.91
-3.19
-0.40
-0.40
-11.87
33
-0.01
-10.90
-67.21
-2.46
-2.63
-14.19
-2.13
-1.41
-6.75
34
-0.03
-14.74
-48.77
-3.38
-1.97
-6.75
-0.83
-1.54
-15.86
35
-0.57
-37.08
-28.41
-2.76
-3.11
-10.69
-0.49
-1.28
-8.78
36
0.00
-2.99
-67.47
0.00
-32.13
0.00
0.00
-2.99
-6.38
37
0.00
-15.36
-7.08
0.00
-1.54
-0.20
-0.15
-2.28
-5.69
38
-0.03
-24.81
-28.47
-7.10
-8.62
-2.79
-0.98
-2.46
-11.53
39
-0.01
-27.00
-17.14
-1.08
-3.38
-8.15
-0.94
-1.26
-3.20
40
-0.01
-39.54
-47.71
-7.96
-3.56
-12.46
-0.16
-0.34
-2.05
41
-0.02
-31.54
-1.39
-0.44
0.00
-2.93
-0.02
-1.92
-7.06
42
0.00
-94.36
-2.68
-0.56
-0.31
-0.36
-0.21
-2.66
-1.16
43
0.00
-4.41
-30.86
0.00
0.00
0.00
0.00
-0.88
-0.88
44
0.18
-27.46
-13.49
-1.29
-11.05
-8.23
-0.01
-5.07
-2.72
45
0.00
-3.08
-94.14
0.00
0.00
-0.08
0.00
-2.01
-2.07
46
0.00
-96.32
-1.95
0.00
0.00
-0.14
-0.08
-0.04
-0.01
47
0.00
-2.50
-1.72
-16.61
0.00
0.00
-1.10
-22.52
-11.19
48
-1.27
-39.49
-13.87
-7.45
-1.54
-6.11
-0.55
-0.78
-11.97
49
0.00
-3.53
-8.70
-0.67
-0.14
-0.14
-16.20
-0.74
-1.37

50
HS code
BGD-BRA
BGD-CHN
BGD-IND
BGD-IDN
BGD-MAL
BGD-THA
BGD-UK
BGD-USA
BGD-EU
50
0.00
-78.86
-21.25
0.00
0.00
0.00
0.00
-0.05
0.00
51
-0.01
-4.23
-66.98
0.01
-0.01
-1.70
-0.17
-0.26
-1.13
52
-0.02
-23.17
-60.40
-0.05
-0.06
-1.38
-0.46
-0.86
-1.86
53
-0.01
-23.59
-19.61
-0.03
-0.01
-0.04
-0.24
-0.48
-30.92
54
0.00
-32.57
-61.37
-0.29
-2.03
-1.14
0.00
-0.01
-0.15
55
-14.47
-31.71
-3.28
-1.66
-0.47
-0.68
-0.01
-0.02
-27.21
56
0.00
-73.96
-9.30
0.00
-14.77
-1.51
-0.04
-0.75
-2.42
57
0.00
-34.54
-7.83
-31.99
-0.24
-2.12
-0.84
-0.10
-1.51
58
0.00
-85.31
-25.00
-2.85
0.00
-1.10
-0.05
-0.06
-0.16
59
-0.04
-60.82
-16.80
-1.50
-0.63
-1.64
0.00
-1.48
-2.14
60
0.00
-93.51
-3.77
-2.02
0.00
-0.01
-0.01
-0.01
-0.56
61
-0.06
-49.62
-35.05
-0.09
0.37
-1.80
-1.37
-1.24
-9.46
62
0.18
-45.68
-52.45
0.17
-0.03
-0.53
-0.62
-1.07
-2.88
63
0.00
-71.52
-2.75
-0.01
-0.60
-0.87
-1.84
-0.17
-0.56
64
-0.13
-89.15
-14.58
-0.01
-1.70
-1.19
-0.06
-0.05
-0.89
65
0.00
-68.36
-41.93
-0.09
-0.06
-0.63
0.00
-0.49
-0.41
66
0.00
-99.07
-0.46
3.22
-0.04
-0.22
-0.03
-0.06
0.00
67
0.00
-97.39
-9.04
0.00
0.00
-1.06
-0.22
-0.35
-0.25
68
0.00
-66.75
-18.88
-17.97
-1.66
-4.91
-0.13
-0.06
-2.03
69
0.00
-96.36
-10.96
-0.29
-3.88
-1.15
-0.02
-0.02
-7.61
70
0.00
-91.62
-7.60
-2.69
-4.93
-3.18
-0.12
-0.52
-2.00
71
-0.01
-17.96
-95.52
0.00
-0.01
-0.01
-0.61
-0.19
-0.04
72
-0.72
-31.84
-10.63
-0.91
-0.83
-0.42
-3.54
-5.40
-9.43
73
-0.01
-60.44
-19.16
-0.12
-0.94
-5.75
-0.37
-0.28
-4.66
74
0.00
-8.37
-7.88
-1.05
-2.42
-2.00
-0.48
-0.01
-7.62
75
0.00
-87.44
-2.99
-0.46
0.00
-0.34
-2.84
0.00
-5.24
76
0.00
-22.93
-50.41
-0.97
-9.59
-1.26
-0.37
-0.10
-1.20
78
0.00
-0.59
-50.95
0.00
-3.15
-22.43
-0.02
0.00
-0.18
79
-13.46
-5.63
-12.07
0.00
-0.54
0.00
-0.67
0.00
-0.67
80
0.00
-1.28
-1.76
0.00
-34.14
0.00
0.00
0.00
-0.12
81
0.00
-29.18
-21.21
0.00
0.00
-22.41
-0.01
-0.03
-0.16
82
0.00
-52.68
-39.62
-0.16
-0.34
-0.38
-0.64
-1.34
-6.14
83
0.00
-81.40
-20.66
-0.07
-0.56
-2.58
-0.15
-1.34
-2.96
84
-0.02
-48.81
-16.77
-0.13
-1.85
-7.98
-1.03
-1.33
-10.56
85
0.00
-46.13
-11.75
-1.10
-1.17
-0.97
-0.65
-0.69
-10.08
86
0.00
-5.60
-8.16
-37.29
-0.05
-0.02
-0.07
-5.77
-44.26
87
0.00
-7.01
-49.96
-2.06
-0.08
-8.74
-1.77
-0.23
-1.11
88
0.00
-0.23
0.00
0.00
0.00
0.00
-0.02
-22.60
-54.87
89
0.00
-8.01
-0.32
-0.18
-1.37
0.00
0.00
-1.06
-3.51
90
-0.72
-46.69
-14.73
-0.10
-1.41
-0.27
-1.57
-3.66
-16.05
91
-0.01
-52.80
-28.32
-0.01
-0.10
-0.01
-0.10
-0.69
-0.46
92
0.00
-37.56
-55.80
0.00
-7.58
-0.11
-0.02
-0.65
-0.06
93
-16.92
-28.99
0.00
0.00
0.00
0.00
-0.02
-2.57
-19.82
94
0.19
-76.31
-8.81
-0.40
-7.05
-4.05
-0.29
-0.49
-7.08
95
0.25
-93.04
-6.35
0.00
-0.33
-2.52
-0.28
-0.84
-0.45
96
0.00
-52.97
-42.41
-4.29
-10.59
-1.31
-0.15
-0.54
-3.38
97
0.00
-11.58
-49.05
-0.02
-2.15
-26.22
-0.42
-2.11
-18.25
98
-0.30
-5.12
-3.07
-1.09
-3.61
-10.13
-8.95
-2.33
-0.92