A PRACTICAL GUIDE TO
TRADE POLICY ANALYSIS
A Case Study on Pakistan
Institute of Business Administration, Karachi
University Road, Karach-75270
Pakistan
Email address: [email protected]
Aadil Nakhoda
November 2016
1
Contents
Introduction ................................................................................................................................................................... 3
Chapter 1 : Analyzing Trade Flows ................................................................................................................................. 4
Trade Openness ......................................................................................................................................................... 4
Trade over GDP measure ...................................................................................................................................... 4
Trade Composition .................................................................................................................................................... 6
Sectoral, Geographical and Product Orientation of Trade ..................................................................................... 6
Intra-industry Trade ............................................................................................................................................ 11
Margins of Export Growth ................................................................................................................................... 12
Revealed Comparative Advantage ....................................................................................................................... 15
Trade Complementarity Index ............................................................................................................................. 16
Regional Intensity of Trade ...................................................................................................................................... 17
Other Important Concepts....................................................................................................................................... 19
Source: ................................................................................................................................................................ 19
Chapter 2 : Quantifying Trade Policy ........................................................................................................................... 21
Background on Pakistan-China Free Trade Agreement: ..................................................................................... 21
Source: ................................................................................................................................................................ 21
Chapter 3 : Analyzing Bilateral Trade Using the Gravity Equation .............................................................................. 26
Gravity Equation: ..................................................................................................................................................... 26
Source: ................................................................................................................................................................ 26
Empirical Equation: ............................................................................................................................................. 26
Chapter 4 : Partial-Equilibrium Trade Policy Simulation ............................................................................................... 30
Empirical Tool ..................................................................................................................................................... 30
Theoretical Model ............................................................................................................................................... 31
Downloading the Data ........................................................................................................................................ 34
Background ......................................................................................................................................................... 35
Results ................................................................................................................................................................. 35
Chapter 5 : Analyzing the Distributional Effects of Trade Policies ............................................................................... 37
Simple Model Linking Trade Policy to Household Welfare ................................................................................. 37
Source ................................................................................................................................................................. 38
Background on Trade Liberalization in Pakistan between 2004 and 2011 ......................................................... 39
Empirics ............................................................................................................................................................... 39
Results ................................................................................................................................................................. 43
Appendix A: HS Classification by Section ..................................................................................................................... 47
Appendix B: Correspondence Between HS Product Codes and HIES .......................................................................... 48
2
Tables:
Table 1-1: Exports of Products at the Margins ........................................................................................... 13
Table 1-2: Exports to Market at Margin ...................................................................................................... 14
Table 2-1: Summary Statistics of Tariffs on Imports into China from Pakistan .......................................... 22
Table 2-2: Frequency Distribution of Import Tariffs on Imports into China from Pakistan ........................ 23
Table 2-3: Tariffs on Imports into China from Pakistan and Imports by HS Section ................................... 24
Table 3-1: Regression Analysis of Exports from Pakistan ........................................................................... 28
Table 4-1: SMART Output for Total Trade Effect for the case of Pakistan between trading partners, trade
liberalization of imports from Turkey (zero tariff) ...................................................................................... 35
Table 4-2: SMART Output for ‘Market View Report’ for the case of Pakistan, trade liberalization of
imports from Turkey (zero tariff) ................................................................................................................ 36
Table 5-1: Income, Consumption and Overall Effects by Deciles ................................................................ 44
Table A-1: Description of HS Classifications by Section .............................................................................. 47
Table B-1: Four Digit HS Product Codes Corresponding to Details of Household Expenditure to Determine
Consumption Effect ..................................................................................................................................... 48
Table B-2: Six Digit HS Product Codes Corresponding to Details of Household Expenditure to Determine
Income Effect .............................................................................................................................................. 51
Figures:
Figure 1-1: Exports, Imports and Trade Openness of Pakistan ..................................................................... 4
Figure 1-2: Percentage Change in Openness and GDP per Capita, 2004-2014 ............................................. 5
Figure 1-3: Exports, Imports and Trade Openness of Pakistan, South Asian Countries and World as a
Percentage of GDP ........................................................................................................................................ 6
Figure 1-4: Percentage of Total Exports from Pakistan to its Top Ten Export Destinations in 2014 ............ 7
Figure 1-5: Percentage of Total Exports from Pakistan in Top Ten HS Sections in 2014 .............................. 8
Figure 1-6: Total Exports from Pakistan and Global Imports (By Destination) ............................................. 9
Figure 1-7: Geographical Orientation of Exports from Pakistan in 2014 ...................................................... 9
Figure 1-8: Product Orientation of Exports from Pakistan in 2014 at HS Six Digit-Level (Textile Products vs
Non-Textile Products) .................................................................................................................................. 10
Figure 1-9: Product Orientation of Exports from Pakistan by HS Section ................................................... 10
Figure 1-10: Overlap Trade and Country Similarity Index, 2014 ................................................................. 12
Figure 1-11: Revealed Comparative Advantage and Global Imports, 2014 ................................................ 16
Figure 1-12: Trade Complementarity Index at HS Six Digit Level ................................................................ 17
Figure 1-13: Share of Regional Exports from Pakistan................................................................................ 18
Figure 1-14: Regional Intensity of Trade for Consumer and Intermediate Goods Exported from Pakistan,
2014 ............................................................................................................................................................ 18
Figure 1-15: Nominal Exchange and Real Effective Exchange Rate of Pakistani Rupee ............................. 20
Figure 4-1: The Impact of a Tariff on Domestic Market and Import Market .............................................. 30
Figure 4-2: Trade Diversion and Trade Creation ......................................................................................... 32
Figure 5-1: Consumption Effect by Per Capita Expenditure ........................................................................ 43
Figure 5-2: Income Effect by Per Capita Expenditures ................................................................................ 43
Figure 5-3: Total Effect by Provinces ........................................................................................................... 45
Figure 5-4: Tariff and Change in Tariff Sorted on Income Distribution ....................................................... 46
3
Introduction The previous decade witnessed global economic uncertainties and serious doubts were raised on whether
globalization had been clear-cut beneficial to the global economy. The total global exports peaked to more
than US $18.9 trillion in 2014 from a low of US $12.3 trillion in 2009. With ever increasing importance of
international trade, it is crucial to analyze the benefits and losses from globalization. Trade policies in
response to globalization cannot be implemented as ‘one-size-fits-all’ across countries. It is imperative to
determine the impact of globalization not only at the macro-level but also at the micro-level. This study aims
to achieve this objective by analyzing the international trading activities of Pakistan as it has observed growth
and decline in international trading activities during the last decade.
Many countries in the developing world have recently adopted measures for trade liberalization.
Policymakers around the world must not only have access to detailed analysis of global trading patterns but
also trading patterns observed by their own countries. As a supplement to “A Practical Guide to Trade Policy
Analysis”, outcome of joint work by the Secretariats of UNCTAD and WTO, the purpose of this teaching
material is to serve as an additional manual for policymakers and academics who analyze trading patterns
and their impact on the economy using data available primarily from UNCTAD, World Bank and from
household surveys. The theories and empirical tools presented in this study are a replication of those
provided in “A Practical Guide to Trade Policy Analysis”.
The export flows from Pakistan is analyzed in Chapter 1. The analysis determines the top trading partners and
products, the diversification of exports using product and sectoral orientations as well as the
complementarity of the exports with the global demand of trading partners. Trade policies are quantified by
summarizing the tariff profile on the exports into China from Pakistan in Chapter 2. The exports from Pakistan
to China have increased significantly over the previous decade. A free trade agreement entered into force in
2007, which allowed preferential treatment to the exports from Pakistan into China. The analysis on tariff
profiles summarizes the final bound rates, MFN applied tariff rates and preferential tariff rates, determines
the frequency distribution of various tariff rates and examines the tariffs at the sectoral-level. The trade flow
from Pakistan is estimated through gravity equation in Chapter 3. This estimates the significance of variables
accounting for the size of economy, income levels, and bilateral variables, such as distance, and
commonalities between Pakistan and its trading partners on the trading patterns. A reduction in tariffs will
decrease tariff revenue and increase consumer surplus. Economic gains can be measured by such changes.
Additionally, the reduction in tariffs between two trading partners can increase trade between the two
countries but divert trade away from a third country that is not party to the reduction in tariffs. The SMART
tool is employed for empirical analysis of tariff reduction in Chapter 4 to calculate the change in total
imports, tariff revenue and consumer surplus. The SMART tool also determines the value of trade creation
and trade diversion. Finally, the impact on tariff reduction is determined at the household-level in Chapter 5.
Survey data from Household Integrated Economic Survey 2010-11 published by the Pakistan Bureau of
Statistics is used to determine the impact on consumption and income from reduction in tariffs across
households in the rural areas.
The author would like to acknowledge the help and assistance of Julia Seiermann (DITC-UNCTAD). Her
insights and comments on the drafts were extremely beneficial. The author would also like to acknowledge
Vlasta Macku (UNCTAD Virtual Institute) for providing an opportunity to write this case study in collaboration
with UNCTAD. The author is also thankful to Muhammad Anwar (Head Librarian and In-charge Research Data
Centre at IBA (Karachi)) for providing access to data sources, in particular the household survey, to make this
case study possible.
4
Chapter 1 : Analyzing Trade Flows
Trade Openness
Trade openness measures the integration of a country into global trade. Openness is calculated as the
sum of exports as a percentage of GDP and imports as a percentage of GDP. A country with a higher
degree of openness is likely to be more integrated into global trade. However, resource-intensive poor
countries that may have a small domestic economy may exhibit higher levels of openness if they export
majority of their output and import the goods consumed due to lack of domestic manufacturing
activities.
Source:
The data on exports of goods and services as a percentage of GDP, imports of goods and services as a
percentage of GDP and GDP per capita is borrowed from the World Development Indicators (WDI).
Trade over GDP measure
Figure 1-1: Exports, Imports and Trade Openness of Pakistan
0
10
20
30
40
50
0
5
10
15
20
25
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Op
en
ne
ss
Exp
ort
s a
s %
of
GD
P,
Imp
ort
s a
s %
of
GD
P
Exports of goods and services (% of GDP) (Left-axis)Imports of goods and services (% of GDP) (Left-axis)Openness (Right-axis)
5
Figure 1-2: Percentage Change in Openness and GDP per Capita, 2004-2014
The pattern for (i) exports as a percentage of GDP, (ii) imports as a percentage of GDP and (iii) trade
openness for Pakistan, is presented in Figure 1-1. Although, imports as a percentage of GDP has
increased from 2004 to 2014, exports as a percentage of GDP has declined. Trade openness, which
increased by about 15 percent between 2004 and 2006 has fallen in 2014 to the levels experienced in
2004. Therefore, Pakistan is less globally integrated in 2014 than it was in 2006. Openness is equally
driven by exports and imports as a percentage of GDP. As changes in both variables have a similar
impact on openness, the declining trend in exports as a percentage of GDP negates the impact of the
increasing trend in imports as a percentage of GDP on trade openness.
The scatterplot in Figure 1-2 shows the relationship between the compound annual growth rate
(henceforth CAGR) in openness and the CAGR in GDP per capita for all countries between 2004 and
20141. The solid line is the best quadratic fit line. It is likely to be influenced by the outliers, countries
that have reported extreme values for both x and y axes. Even though, the countries that have
experienced the largest change in their GDP per capita have done so on the basis of growth of their
domestic economy rather than increasing their international trading activities relative to their GDP,
Pakistan lies below the curve and the regional economies, India and Bangladesh2.
1 The formula for CAGR is ((End Value/Initial Value)^(1/number of years)) -1. We assume a steady rate of growth
throughout the period. 2 The manual “A Practical Guide to Trade Policy Analysis” determines the relationship between openness and GDP
per capita. It suggests that openness rises faster with GDP per capita at low levels of GDP per capita than at higher
levels. Therefore, developed and advanced countries are more likely to have a robust domestic economy that
makes them less dependent upon international trade. Similarly, countries with the highest level of growth rates in
GDP per capita may be more reliant upon the growth in the domestic sector than the external sector. This explains
the negative relationship between percentage change in openness and percentage change in GDP per capita.
BGD
IND
PAK
-10
-50
5C
AG
R in O
pen
ness (
%)
-5 0 5 10 15CAGR in GDP per Capita (%)
Major South Asian Economies Fitted Values
Growth Rate in Openness and GDP per Capita, 2004-2014
6
Figure 1-3: Exports, Imports and Trade Openness of Pakistan, South Asian Countries and World as a Percentage of GDP
The exports of goods and services as a percentage of GDP, the imports of goods and services as a
percentage of GDP and the trade openness of Pakistan, the South Asian countries and the World
respectively is presented in Figure 1-3. Pakistan reports the lowest across all three indicators relative to
the South Asian average and the world average. This indicates the lack of participation in international
trading activities from Pakistan relative to its South Asian neighbors as well as all the countries in the
world.
Openness may not be a perfect measure for cross-country comparisons. For instance, geographical
location, historical linkages with large trading partners, quality of institutions, play an important role to
influence both openness and growth. However, the South Asian economies, with their similarities makes
comparison easier. For instance, they share cultural and historical linkages, while exhibiting similar levels
in quality of governance. Therefore, the lack of both economic growth and openness in Pakistan relative
to India and Bangladesh should be a cause of concern for government policymakers and academics alike.
Trade Composition
Sectoral, Geographical and Product Orientation of Trade
The sectoral and geographical composition of trade provide important information to determine crucial
drivers of technological improvement and economic growth within a country. The geographical
composition shows the global linkages of production by identifying the intensity of trading relationships
with different countries. In order to understand the constraints on growth, sectoral-level decomposition
of trade is necessary. The export bundle of a developing country may be highly concentrated in a few
sectors and analysis at the sectoral level is needed to develop policies that focus on the selected sectors
rather than the whole economy. Further, disaggregated data at the sectoral-level and at the product-
level allows the analysis to calculate indicators, such as those on intra-industry trade, trade
complementarity index and the margins of export growth.
0
10
20
30
40
50
60
70
Pakistan South Asia World
Pe
rce
nta
ge
of
GD
P
Exports of goods and services (% of GDP) Imports of goods and services (% of GDP)
Trade Openness
7
Source:
The data on bilateral trade-flows at the product-level is borrowed from UN COMTRADE3. The exports
from Pakistan to all trading partners are downloaded at six-digit level Harmonized System (HS) 2002
codes. The Harmonized System (HS) codes are divided into 22 sections4.
Figure 1-4: Percentage of Total Exports from Pakistan to its Top Ten Export Destinations in 2014
3 Only direct data is considered. Data based on the reported values by the partner country (mirror data) is not
considered. 4 HS Classifications by Section is listed in Appendix A.
0 5 10 15 20 25
USA
China
Afghanistan
United Kingdom
United Arab Emirates
Germany
Spain
Italy
Bangladesh
Netherlands
Percentage of Total Exports
Percentage of Total Exports from Pakistan to Top Ten
Export Destinations in 2014
2014 2004
8
Figure 1-5: Percentage of Total Exports from Pakistan in Top Ten HS Sections in 2014
The geographical concentration of exports from Pakistan is presented in Figure 1-4, showing the
significance of each trading partner. Although regional trading partners, China, Bangladesh and
Afghanistan have risen in prominence over the previous decade, the United States remains the single
most important export destination for Pakistani products. The sectoral composition of exports from
Pakistan is shown in Figure 1-5. The corresponding description of each section in Figure 1-5 is listed in
Appendix A. The majority of exports from Pakistan are reported in the textile sector, followed by
vegetable products, mineral products and leather products. The exports from Pakistan are highly
concentrated in terms of geographical as well as sectoral composition.
0 10 20 30 40 50 60 70 80
11 ('Textile)
2 (Veg. Products)
5 (Min. Products)
8 (Leather)
4 (Prep. Food)
1 (Live Animals)
15 (Base Metals)
6 (Chem. Products)
20 (Misc. Products)
7 (Plastics, Rubber)
Percentaage of Total Exports
HS
Sect
ion
s
Percentage of Total Exports from Pakistan in
Top Ten HS Sections in 2014
2014 2004
9
Figure 1-6: Total Exports from Pakistan and Global Imports (By Destination)
Figure 1-7: Geographical Orientation of Exports from Pakistan in 2014
The scatterplot in Figure 1-6 presents the relationship between total exports from Pakistan and global
imports into the destination market. There is a positive relationship between the two variables, which
suggests that Pakistan exports to the markets that are likely to receive the largest shares of global
imports. Exceptions include, for example, Afghanistan. Being a neighbor of Pakistan, hence closely
located to Pakistan, it is a major trading partner for Pakistan but less significant as a global export
destination. Therefore, it is represented by the red dot significantly below the linear fit line. The
scatterplot in Figure 1-7 shows a slight negative relationship between the percentage share of
destination in total exports from Pakistan and the annual growth of global imports into the destination
18
20
22
24
26
28
Glo
ba
l Im
port
s into
De
stina
tion
(ln
)
5 10 15 20 25Exports from Pakistan (ln)
Top-Ten Destinations Non-Top Ten Destinations Fitted Values
By Destination
Total Exports from Pakistan and Global Imports
ALB
DZA
AND
ARGARM
ABW
AUS
AUT
AZE
BHS
BHR
BLR
BEL
BOL
BIH
BRA
BRNBGR
BFA
KHM
CAN
CHL
CHN
HKG
MAC
COL
HRV
CYP
CZECIV
DNK
ECU
SLV
EST
ETH
FJI
FIN FRA
PYF
DEU
GRC
GTMHND
HUN
ISL
IND
IRL
ITAJAM
JPN
JOR
KAZ
LVALBN
LTU
LUX
MDG
MWI
MYS
MDV
MLT MUS MEX
MNG
MAR
MOZ
NAM
NLDNCL
NZL
NIC
NER
NOR
OMN
PRYPER
POL
PRT
KOR
MDA
ROM
RUSSAU
SENSGP
SVK
SVN
ZAF
ESP
LKA
SWE
CHEMKD THA
TGO
TUR
USA
UGA
ARE
GBR
URY
VNMZMB
ZWE
05
10
15
20
25
CA
GR
in Im
port
s 2
00
4-2
01
4 (
%)
-15 -10 -5 0 5% Share in Total Exports (ln), 2014
Fitted Values
Geographical Orientation of Exports from Pakistan
10
market between 2004 and 2014. The advanced and developed markets reported lower growth rates
than the developing markets, particularly the markets that either liberalized trade during this period.
Figure 1-8: Product Orientation of Exports from Pakistan in 2014 at HS Six Digit-Level (Textile Products vs Non-Textile Products)
Figure 1-9: Product Orientation of Exports from Pakistan by HS Section
-100
-50
05
0C
AG
R in
Im
port
s 2
004
-20
14
(%
)
-20 -15 -10 -5 0% Share in Total Exports (ln), 2014
Textile Products Non-Textile Products Fitted Values
Textile Products vs Non-Textile Products
Product Orientation of Exports from Pakistan at HS Six Digit-Level
1
23
4
5
67
8
9 1011
12
13
14
1516
17
18
19 20
21
22
05
10
15
CA
GR
in Im
port
s 2
004
-20
14
(%
)
-10 -5 0 5% Share in Total Exports (ln), 2014
Fitted Values
By HS Section
Product Orientation of Exports from Pakistan
11
The product orientation of exports from Pakistan is plotted in Figure 1-8, comparing textile to non-
textile products5. The regression line is flat, which suggests that there is no relationship between import
growth between 2004 and 2014 and the share of products in the total exports from Pakistan in 2014.
The growth rates are calculated for products that report positive exports in 2004 and 2014, while the
percentage share of exports is calculated for products that report positive exports in 2014. As expected,
majority of the products with larger share of total exports are textile products. However, when the
relationship is studied at the sectoral-level, the results in Figure 1-9 suggest a positive relationship
between the share of sectors in total exports from Pakistan and the growth in total imports between
2004 and 2014. Mineral products, vegetable products and precious metals reported the highest growth
rates in their trading values. These growth rates were likely driven by their price-levels as several
commodities reached their historical peak between 2004 and 2014.
Intra-industry Trade
The United States not only exports cars to Germany but also imports car parts from Germany. This is an
example of intra-industry trade. A large proportion of global trade takes place within the same industry,
when two countries may trade in the same product in both directions. Intra-industry trade is more likely
between countries that produce as well as consume a greater number of varieties, such as countries
with higher levels of GDP and GDP per capita. In addition, intra-industry trade is more likely between
trading partners that trade more frequently.
In order to determine the degree of intra-industry trade, the total value of products traded in both
directions (the sum of imports and exports of products that are both exported as well imported by the
two trading partners) is divided by the sum of total exports and imports between the two countries. This
formula indicates the trade overlap between two countries as it determines the ratio of bilateral trade
that flows in both directions to total bilateral trade. For instance, if Pakistan exports $100 million of
cotton yarn and imports $ 100 million of cotton yarn from China as well as imports $ 200 million of
mobile phones from China but does not export any mobile phones to China, the trade overlap will be (�������)����������� = 0.5. The numerator is the sum of products that are exported to China as well as imported
into Pakistan (two-way trade) and the denominator is the sum of total exports and imports between
Pakistan and China. The products are considered at HS six digit-level.
The similarity index, which is calculated as the lack of difference in the GDP between the reporting
country and the partner country, is likely to play an important role in influencing the overlapping trade
between two countries. The similarity index can be written as:
� = 1 − ������������ + �����
�− ���������� + �����
�
5 Section numbers and short title in brackets: 1 (Live Animals), 2 (Vegetable Products), 3 (Animal Fats), 4 (Prepared
Foodstuff), 5 (Mineral Products), 6 (Chemical Products), 7(Plastics, Rubber), 8 (Leather), 9 (Wood Products), 10
(Paper Products), 11 (Textiles), 12 (Footwear), 13 (Stone, Glass), 14 (Pearls and Jewelry), 15 (Base Metals), 16
(Machinery), 17 (Vehicles), 18 (Optical Instruments), 19 (Arms and Ammunition), 20 (Misc. Products), 21 (Works of
Art)
12
Where j is the GDP at purchasing power parity of the partner country. Larger the difference, lower the
similarity index. The similarity index will be bounded between 0 and 0.5, where 0.5 indicates that the
two countries have the same GDP.
Figure 1-10: Overlap Trade and Country Similarity Index, 2014
The GDP at purchasing power parity of Pakistan is at approximately US $ 900 billion. Other economies of
similar size to Pakistan will be placed higher on the similarity index6. Further, as larger economies tend
to have greater demand for varieties of products, the relationship between the similarity index and
trade overlap is also likely to be positive. The relationship between the similarity index of Pakistan and
trade overlap is shown in Figure 1-10. The products that report the highest value for two-way trade are
petroleum oils, cotton yarn, raw cotton and bath linen.
Margins of Export Growth
Promotion of trade requires access to new markets and the expansion of export opportunities. The
latter involves introducing new products, reducing the dropping of old products that become redundant
(extensive margin) and the growth in the value of the existing exports to the same destinations
(intensive margin).
6 The United States of America and China are multiple times larger than Pakistan in terms of the GDP. Therefore,
such countries will be placed lower on the similarity index with Pakistan. Economies with a GDP greater than that
of Pakistan are marked in Figure 1-10.
AUS
BRA
CAN
CHNDEU
EGY
ESPFRA
GBR
IDN
IND
IRN
ITA
JPN
KOR
MEX
NGA
POL
RUSSAU
THA
TUR
USA
AFG
ARE
AUTBEL
BGD
BGR
BHR
BHS
BLR
CHE
CHLCMRCOG COLCRICYP
CZE
DJI
DNK
DOMDZAEST ETH
FIN
GHA
GRC
GRD
HKG
HRV
HUN
IRL
IRQISL
JAM
JOR
KAZKEN
KHM
KWTLBN
LBY
LKA
LTULUXLVA MARMDGMDV MOZMUSMWI
MYS
NAM
NLD
NORNPL
NZLOMNPANPER
PHL
PRTPRY QAT ROMSDNSEN
SGP
SSD
SVK
SVN
SWE
SWZTGO
TJK
TUNTZAUGA UKRURY
VNM
ZAF
ZMBZWE0.2
.4.6
.8O
ve
rla
p T
rad
e
0 .1 .2 .3 .4 .5Similarity Index
Greater GDP (PPP) than Pakistan's GDP (PPP)
Fitted Values
Overlap Trade and Country Similarity Index, 2014
13
In order to determine the importance of the aforementioned margins in Pakistan’s bilateral trade with
its trading partners between 2004 and 2014, we must decompose the variation in total exports between
the two years. The monetary value of exports in 2004 is:
��� =� �������
Where ��� is the set of products exported in 2004.
The monetary value of exports in 2014 is:
��� =� �������
Where ��� is the set of products exported in 2014.
The variation in total exports between the two years can be expressed as:
∆� =� ∆� +� �� −� �����/ �����/ �����∩ ��
Where �04 ∩ �14 designates products exported both in 2004 and 2014, and �14/�04 designates
products which were exported in 2014 but not in 2004 (and vice versa). This translates into the sum of
the change in export value at the intensive margin, the new product margin and the ‘product death’
margin respectively. Therefore, exports can be promoted by exporting more products which existed in
2004, exporting newer products which did not exist in 2004 and by reducing the redundancy of products
that existed in 2004 but not in 2014. A similar study can be conducted to determine the change in
exports of different products across markets by considering the export variation of products at the
intensive margin, the introduction of products into newer markets and the withdrawal of products from
markets.
Table 1-1: Exports of Products at the Margins
Decomposition of Export Growth between 2004 and 2014
Exports from Pakistan to Trading Partner
Partner Change in
Exports
Total Intensive
Margin
New Product
Margin
Product Death
Margin
World 11,347,508,224 1.00 0.04 0.04
China 1,952,518,016 0.81 0.20 0.01
Afghanistan 1,414,572,288 0.91 0.15 0.06
United Kingdom 685,865,920 0.96 0.16 0.12
Germany 550,934,656 1.00 0.12 0.12
USA 524,957,952 1.09 0.30 0.39
Bangladesh 489,993,280 0.89 0.16 0.05
Spain 467,760,160 0.89 0.20 0.09
Belgium 385,230,016 0.89 0.25 0.13
Netherlands 317,069,344 0.91 0.25 0.16
Kenya 273,238,208 0.88 0.15 0.03
14
Table 1-2: Exports to Market at Margin
Decomposition of Export Growth between 2004 and 2014
Exports from Pakistan in Products with Largest Change
HS
Code
Commodity Name Change in
Exports
Total
Intensive
Margin
New
Market
Margin
Market
Death
Margin
TOTAL All commodities 11,343,167,488 0.997 0.005 0.002
100630 Semi-milled/wholly milled rice, whether or not
polished/glazed
1,227,585,536 1.00 0.01 0.01
520512 Cotton yarn, single (excl. sewing thread), of
uncombed fibres, cont. 85%/mo
608,663,232 1.02 0.001 0.02
630239 Bed linen (excl. knitted or crocheted), of textile
mats. other than cotton/man-made fibres (excl.
printed)
562,855,360 0.95 0.05 0
630210 Bed linen, knitted or crocheted 505,054,464 0.99 0.01 0.003
252329 Portland cement (excl. white cement, whether or
not artificially coloured), whether or not coloured
473,448,544 0.51 0.50 0.01
620462 Women's/girls', trousers, bib & brace overalls,
breeches & shorts (excl. swimwear; excl. knitted or
crocheted), of cotton
399,821,760 0.96 0.04 0
520942 Woven fabrics of cotton, cont. 85%/more by wt. of
cotton, denim, weighing >200g/m2
392,127,616 0.95 0.05 0.01
620342 Men's/boys' trousers, bib & brace overalls,
breeches & shorts (excl. swimwear; excl. knitted or
crocheted), of cotton
345,981,184 0.96 0.04 0.001
630260 Toilet linen & kitchen linen, of terry towelling/sim.
terry fabrics, of cotton
300,803,552 0.995 0.015 0.01
170199 Cane/beet sugar & chemically pure sucrose, in
solid form, not cont. added flavouring/colouring
matter
274,884,704 0.73 0.27 0
The growth in exports between 2004 and 2014, decomposed into the percentage contribution of the
intensive and the extensive margins is presented in Table 1-1 and Table 1-27. The sum of the total
intensive margin and the new product or market margin less the product or market death margin must
equal one. The results signifies the importance of the export destinations as well as the products
between 2004 and 2014. The product death margin is smaller in Table 1-1 for the exports to China,
Afghanistan, Kenya and Bangladesh than for the exports to the more developed and advanced trading
7 The slight differences in the value between total change in exports to world and total change in exports
of all commodities in Table 1-1 and Table 1-2 is due to the dropping of special commodities which are
not provided with regular HS codes.
15
partners, which illustrates their growing importance as export destinations for Pakistani products over
the time period. The new market margins and the market death margins reported in
Table 1-1 are almost negligible, which suggests that exporters are likely to maintain their trading
relationships with the same markets. The contribution of the extensive margin is usually small relative to
the intensive margin. It is rare that the new products launched in the first year are likely to generate
substantial sales such that the extensive margin outweighs the intensive margin. This may also have a
spillover on the product death margin as new exports with lower sales are more likely to fail than
products that have gained popularity in the export markets.
Revealed Comparative Advantage
Revealed comparative advantage (RCA) determines the prominence of the set of products in the export
bundle of the exporting country relative to its prominence in global trade. In other words, the products
that report RCAs greater than one are likely to constitute a larger proportion in the total exports of the
source country relative to its proportion in global trade. RCA is calculated as:
$%&'( = �'( /�(�'/�
Where �'( is the total value of exports of product ) from country *, �( is the total value of exports from
country *, �' is the total value of exports of product ) in global trade, and � is the total value of global
trade. For instance, if rice constitutes 10 percent of the exports from Pakistan but only 1 percent of total
global exports, the RCA is 10.
16
Figure 1-11: Revealed Comparative Advantage and Global Imports, 2014
The scatterplot in Figure 1-11 reveals the correlation between the prominence of textile products in the
exports from Pakistan and the RCA of the product. The linear trend line is negative, which suggests that
the products with the highest RCAs are less likely to contribute to global trade. This is an important
implication for trade policy in Pakistan as it has a comparative advantage in textile products that do not
contribute significantly to global trade. In order to make the graph simpler, approximately 1 percent of
the total products, which are all non-textile products, have been dropped as the products had negligible
RCA.
Trade Complementarity Index
The trade complementarity index measures the degree to which the global exports of one trading
partner match the global imports of the other trading partner. In other words, the global exports of one
trading partner overlap with the global imports of the other trading partner. For instance, if Pakistan
exports mainly textile products to the world and textile products constitute a large proportion of the
imports into its trading partner from the world, the trade complementarity index will be high. The index
is likely to be higher for countries whose set of products that constitute the majority of the exports of
the exporting country are similar to the set of products that constitute the majority of the imports of the
importing country. The index is calculated as:
+%(� = 100[1 − (� |.'( −/
'0�1'�|/2)]
0.0
5.1
.15
.2S
hare
in G
lob
al Im
port
s (
%)
0 100 200 300 400 500Revealed Comparative Advantage
Textile Non-textile Fitted Values
RCA of Exports from Pakistan
Revealed Comparative Advantage and Global Imports, 2014
17
Where .'( is the share of product k in the imports of country i and 1'� is the share of product k in the
exports of country j. An index of zero suggests that no products imported by country i overlaps with the
exports of country j and index of 100 suggests that all imports of country i overlap with the exports of
country j, implying that countries i and j are natural trading partners.
Figure 1-12: Trade Complementarity Index at HS Six Digit Level
There is a negative correlation between the trade complementarity index and the total exports from
Pakistan in Figure 1-12. This suggests that the set of products prominently imported by the major
trading partners are not prominently exported by Pakistan. On the other hand, the set of products
exported by Pakistan are likely to match the set of products for importers which do not trade frequently
with Pakistan.
Regional Intensity of Trade
The regional intensity of trade measures the extent to which countries may trade relatively more
intensively with certain partners than other trading partners. It can be used to analyze the need for
potential free trade agreements and the welfare effect of such trade agreements. The regional intensity
of trade measures the share of a region or a country in the total exports originating from the exporting
country.
AFG
ALB
DZA
AND ATG
ARGARM
ABW
AUSAUT
AZE
BHS
BHR
BRB
BLR
BEL
BLZBEN
BMU
BOLBIH
BWABRA
BRN
BGR
BFABDI
CPV
KHM
CMR
CAN
CAF
CHL
CHNHKG
MAC
COL
COG
HRVCYP
CZE
CIV
DNKDOMECU
EGY
SLV
EST
ETHFJI
FINFRA
PYF
GMB
GEO
DEUGRCGTM
GINGUY
HND
HUN
ISL
IND
IDN
IRL ITA
JAM
JPN
JOR
KAZ
KWT
LVALBN
LTULUX
MDG
MWI
MYS
MDV
MLT
MRT
MUS
MEX
MNG
MAR
MOZ
NAMNPL
NLD
NCL
NZL
NIC
NER
NGANOROMN
PAN
PRYPER
PHLPOL
PRTQAT
KOR
MDAROM
RUS
RWALCA
STP
SAUSEN
SRB
SLE
SGPSVK
SVNZAF
ESP
LKA
SUR
SWECHE
MKD
THA
TGO
TON
TURUSA
UGA
UKRARE
GBRTZAURY
VNM
YEM
ZMB
ZWE
10
20
30
40
50
Ind
ex
5 10 15 20 25Total Exports from Pakistan (ln)
Fitted Values
Trade Complementarity Index for Exports of Pakistan (HS 6 Digit), 2014
18
Figure 1-13: Share of Regional Exports from Pakistan
The values in Figure 1-13 are calculated as the sum of exports to the region or the country from Pakistan
over the sum of total exports from Pakistan in the given period. Exports are primarily destined to the EU
and to the US. However, exports to the regional trading partners, SAFTA countries and China, have
increased from 2004 to 2014 and the proportion of the total exports from Pakistan to the US has fallen8.
Figure 1-14: Regional Intensity of Trade for Consumer and Intermediate Goods Exported from Pakistan, 2014
8 The SAFTA members are listed in the following url http://saarc-
sec.org/areaofcooperation/detail.php?activity_id=35. The EU member states in 2014 are listed in the following url
https://europa.eu/european-union/about-eu/countries_en.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
China EU SAFTA USA
Sha
re o
f T
ota
l Exp
ort
s fr
om
Pa
kist
an
2004 2014
0
0.5
1
1.5
2
2.5
3
China EU SAFTA USA
RIT
Consumer Goods Intermediate Goods
19
The regional intensity of trade can also be calculated at the product-level as well as by product groups,
i.e. consumer goods and intermediate goods. This analysis will provide us information on the type of
products that are likely to be destined to a particular region. The regional intensity of trade for
consumer goods and intermediate goods can be calculated as:
$+'(� =�'(�/�'(�(�/�(
Where $+'(� is the regional intensity of trade for product k exported from country i to country j.
Similarly, �'(� is the total value of exports of product k from country i to country j, �'( is the total exports
of product k from country i. �(� and �( are exports from country i to country j, and total exports from
country i. If the regional intensity of trade of product k is greater than one, it suggests that a larger
proportion of product k is destined to country j than the total exports of Pakistan to country j. For
example, if Pakistan exports 40 percent of all intermediate goods to China but exports only 10 percent
of the total goods to China, the regional intensity ratio for intermediate goods is 4. The results in Figure
1-14 suggest that Pakistan is likely to export intermediate goods to China and to the SAFTA region and
export consumer goods to the EU and to the USA9.
Other Important Concepts
The real effective exchange rate is an important measure to determine the competitiveness of domestic
prices against its trading partners' prices. The nominal exchange rate depreciation (appreciation) is the
loss (gain) in the value of the domestic currency with respect to foreign currencies. The real exchange
rate is the nominal exchange rate of a currency multiplied by the ratio of the foreign price index relative
to the home price index. If the home price index rises faster than the foreign price index, the home
economy will lose competitiveness. However, the depreciation in its currency may cover some of the
losses from the differential inflation rates between home and foreign.
The ‘’effective’’ part is calculated as the share of the total imports and exports between country i and
country j in the total exports and imports of country i. This is then multiplied by the real exchange rate
between country i and country j. The sum across all trading partners is then used to obtain the real-
effective exchange rate. Therefore, the real effective exchange rate is the trade-weighted average of the
bilateral exchange rates between the trading partners of country i. If the real effective exchange rate is
greater (less) than 100, the domestic currency is overvalued (undervalued) and the domestic price index
is too high (low) relative to the foreign price index. The values of the real effective exchange rate can be
obtained from the World Development Indicators dataset.
Source:
The real effective exchange rate and the nominal official exchange rate have been downloaded from
World Development Indicators.
9 The data on the product classifications is borrowed from http://wits.worldbank.org/referencedata.html
20
Figure 1-15: Nominal Exchange and Real Effective Exchange Rate of Pakistani Rupee
The trend of the nominal exchange rate (Pak Rs to US Dollar) and the real effective exchange rate of the
Pakistani rupee is plotted in Figure 1-15. The Pakistani rupee has depreciated in nominal terms between
2004 and 2014 from Rs 60 to a US Dollar to Rs 100. However, the real effective exchange rate is more
volatile. The increasing trend between 2013 and 2014 in the real effective exchange rate suggests that
the inflation rates in Pakistan are higher than the inflation rates in the trading partners and that the
nominal depreciation in the Pakistani rupee is not enough to compensate for the loss in competitiveness
due to the inflation rate differentials that are not in favor of Pakistan.
85
90
95
100
105
110
115
0
20
40
60
80
100
120
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Re
al
Eff
ect
ive
Exc
ha
ng
e R
ate
(2
01
0 =
10
0)
Pa
kist
an
i Ru
pe
es
pe
r U
S D
olla
r
Pakistani Rs per US Dollar (Left-axis) REER (Right-axis)
21
Chapter 2 : Quantifying Trade Policy An import tariff is imposed on the imports of goods at the border, with a purpose to increase the price
of the imported product above the world price. The tariffs generate revenue in the form of custom
duties and protect domestic industries from foreign competition as they increase the price of imported
goods into the country. We consider two types of tariffs, namely ad valorem and specific duties (non-ad
valorem duties). Ad valorem duties are collected as a percentage of the total value of imported goods
and specific duties are collected as a fixed amount (in monetary terms) per unit of imported goods. Ad
valorem duties are more prevalent than specific duties.
There are several distinctions in the type of tariffs that need to be taken into account in order to
determine the tariff profile between two trading partners. The first distinction is between applied tariffs
and bound tariff rates. The actual tariff rates applied on the imports of a product is called the applied
tariff rate. The bound tariff rate is the maximum tariff rate committed by WTO (World Trade
Organization) members on a product. The second distinction is between the most-favored nation tariff
rates (MFN) and the preferential tariff rates. MFN tariff rates are applied on trade between WTO
members with whom there have been no free trade agreements and preferential tariff rates are applied
on the imports from trading partners with whom free trade agreements have been negotiated and
enforced. Therefore, the applied MFN tariff rates are either equal to or lower than the bound MFN tariff
rates. The MFN tariff rates are higher than or equal to the preferential tariff rates negotiated between
trading partners. Exceptions could arise with trading partners who are not WTO members.
Tariffs can be aggregated as simple averages or weighted averages. Simple averages are calculated by
adding all the tariffs and dividing by the number of products traded. On the other hand, the weighted
averages are calculated by multiplying the tariff rates to the proportion of total imports of each product
and then aggregating the resultant value across all products. Simple average tariff rates allocate the
same weight to all products regardless of the value of imports. However, the weighted average tariff
rates may result in lower values if the tariffs imposed on the products limit the imports of goods.
Background on Pakistan-China Free Trade Agreement:
Pakistan and China signed a free trade agreement in 2007. Even though the two countries have been
associated as strong political allies since 1970s, trade between the two countries only gained
importance since the early 2000s. The signing of the free trade agreement witnessed a significant
increase in the trade flow between the two countries. More than US $13 billion worth of goods has been
traded between the two countries in 2015, with the exports from China into Pakistan constituting more
than US $11 billion.
Source:
The data is downloaded from World Integrated Trade Solution (WITS). The data on the tariff rates
imposed by China is downloaded using the ‘Quick Search’ facility followed by the ‘Tariff- View and
Export Raw Data’ option. The different distinctions of tariffs imposed by China, which include bound
tariffs, MFN applied tariffs, and preferential tariff rates for imports into China from Pakistan are
considered in the analysis below. Data on imports into China from Pakistan at eight digit-level tariff line
is downloaded from ITC’s Trade Map. As the tariff data is at ten digit-level, the imports data is at eight-
digit level and the agricultural products are classified at six digit-level, the values are collapsed to six-
digit level before reporting the results.
22
Table 2-1: Summary Statistics of Tariffs on Imports into China from Pakistan
Summary Total Ag Non Ag
Simple average final bound 10.01 15.76 9.14
Simple average MFN Applied 9.37 14.34 8.54
Share of non ad-valorem duty in MFN Imports (%) 0.28 0.46 0.26
Trade weighted average MFN applied 6.90 23.06 5.20
Simple average PRF rate 4.47 6.00 4.29
Share of non ad-valorem duty in PRF Imports (%) 0.12 0.52 0.08
Trade weighted average PRF rate 2.16 1.36 2.19
Imports in billion US$ 2,759,209 262,935 2,496,274
The summary statistics of the tariff profile on the imports into China from Pakistan are reported in Table
2-1. The simple average final bound is the legally binding ceiling on tariff rates. The agricultural products
face higher tariff rates on the imports into China than non-agricultural products. The main reason is that
China protects its domestic farmers from foreign competition by taxing the imports of agricultural
goods. The simple average MFN applied rates are the tariff rates member countries impose on the
imports of other member countries who are not participating in a preferential trade agreement. These
are typically lower than the legally binding ceilings imposed on imports. The share of non ad-valorem
duties is the percentage of imports that face either specific tariff rates, mixed tariff rates or compound
tariff rates. The specific tariff rates are fixed rates based on a physical quantity, for instance a certain
monetary value of tariffs on the imports of a ton of steel. The mixed tariff rates may involve specific
tariffs or ad valorem rates depending on which ever generates a certain level of desired income. On the
other hand, compound tariff rates involve both specific tariff rates and ad valorem rates. For instance,
tariffs on oil may face a certain monetary value based on the physical quantity in addition to ad-valorem
duties. The tariffs on the imports into China are ad-valorem duties, which is that the tariffs imposed are
likely to be a percentage of the value imported.
China imported approximately US $2.8 billion dollars’ worth of goods from Pakistan in 2014.
Approximately 90 percent of the goods imported were non-agricultural products. The agricultural
products face higher tariff rates than the non-agricultural products as the final bound, MFN applied
rates and the preferential rates for the former are higher than the latter. More than 99 percent of the
imports face ad-valorem duties instead of non-ad valorem specific duties.
Although, the simple average rates are an increasing function of the tariff rates, the trade-weighted
average tariff rates can differ depending upon the value imported. Higher tariff rates may restrict
imports, lowering the trade-weighted average rate. If the trade-weighted tariff rates are lower relative
to the simple average tariff rates, it suggests that tariffs may be prohibitive as goods with higher tariff
rates receive lower imports. On the other hand, if the trade-weighted tariff rates are higher than the
simple average tariff rates, it is likely that higher tariffs are imposed on products that are commonly
imported and constitute a greater share of the total imports and tariffs are revenue generating for the
government. Therefore, the trade-weighted average rates may under represent higher tariff rates if such
tariffs restrict trade. When considering the imports into China from Pakistan, the trade-weighted
preferential rates for the agricultural products is less than the corresponding simple averages, which
23
suggests that China offers trade concessions to agricultural products imported from Pakistan as part of
its free-trade agreement.
Table 2-2: Frequency Distribution of Import Tariffs on Imports into China from Pakistan
Agricultural Products Non-Agricultural Products
Range Final bound MFN
Applied
PRF Share of
Imports
Final bound MFN
Applied
PRF Share of
Imports
Duty-free 8.34 9.84 23.64 67.07 6.90 8.24 33.61 21.20
0 <= 5 8.74 11.54 37.96 18.79 18.53 20.96 45.96 73.26
5 <= 10 25.03 25.79 16.70 12.28 45.84 44.78 10.09 4.45
10 <= 15 24.11 22.06 18.22 1.86 15.02 14.79 8.90 1.06
15 <= 25 24.77 22.06 2.39 0.00 12.21 10.05 1.09 0.03
25 <= 50 6.75 6.00 0.65 0.00 1.50 1.05 0.23 0.00
50 <= 100 2.25 2.38 0.00 0.00 0.00 0.00 0.00 0.00
> 100 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
NAV 0.00 0.34 0.43 0.00 0.00 0.12 0.13 0.00
The frequency distribution of the range of tariff rates under final bound duties, MFN applied rates and
the preferential rates is reported in Table 2-2. The share of imports for each tariff bracket is listed under
‘Imports’. More than 60 percent of the tariff lines under the preferential tariff regime for imports into
China from Pakistan are either duty-free or less than or equal to 5 percent level for both agricultural
goods and non-agricultural goods. This number drops to less than 30 percent under the MFN tariff
regime for both agricultural and non-agricultural products. More than 67 percent of the agricultural
goods imported into China from Pakistan receive duty-free access under the preferential tariff regime.
On the other hand, 21 percent of the imports of non-agricultural products are duty-free while more than
73 percent have tariffs between 0 and 5 percent. The main findings from the above observations in
Table 2-1 and Table 2-2 suggest that China protects its agricultural sector but has provided Pakistani
agricultural producers easier access relative to the tariff rates under the MFN trade regime. Second,
Pakistani exporters to China may not have taken advantage of the lower tariff rates offered under the
preferential trade regime as the imports into China from Pakistan are still less than 0.15 percent of total
global imports into China10.
10 The exports from Pakistan constitute approximately 0.15 percent of global trade. However, the negotiating of
the free-trade agreement along with the growing importance of China as a destination market should suggest that
the proportion of goods imported from Pakistan to be higher than 0.15 percent of total imports into China.
24
Table 2-3: Tariffs on Imports into China from Pakistan and Imports by HS Section
Bound Duties Applied MFN Duties PRF Duties Imports
Section Average Max. Duty-
Free
Share
Average Max. Duty-
Free
Share
Average Max. Duty-Free
Share
Share Duty-free
Share
1 (Live
Animals)
12.87 25 11.97 12.09 25 14.90 6.50 14.4 3.26
1.95 18.36
2 (Veg.
Products)
15.06 65 11.58 13.19 65 9.83 4.83 20 38.98
1.90 29.65
3 (Animal
Fats)
13.28 30 0.00 11.96 30 0.00 2.25 4.5 50.00
0.00 0.00
4 (Prep.
Foodstuff)
18.04 65 0.40 16.86 65 0.26 6.71 50 29.07
4.17 94.10
5 (Min.
Products)
4.04 12 17.11 2.77 11 28.74 1.09 5 81.25
1.11 99.92
6 (Chem.
Products)
6.98 50 0.70 6.24 50 4.38 3.31 40 35.00
0.07 67.07
7(Plastics,
Rubber)
9.31 25 0.41 9.18 25 0.30 5.26 17 10.49
1.78 0.00
8 (Leather) 13.58 23 0.00 12.71 23 0.00 5.86 16 40.00 3.69 12.87
9 (Wood
Products)
5.39 20 10.62 4.49 20 39.85 3.11 5 56.03
0.00 0.00
10 (Paper
Products)
5.34 7.5 19.25 5.00 7.5 23.20 5.00 5 0.00
0.00 0.00
11 (Textiles) 11.65 40 0.00 11.18 40 0.03 3.83 18 49.14 84.68 19.40
12
(Footwear)
18.62 25 0.00 18.42 25 0.00 9.36 19 0.00
0.02 0.00
13 (Stone,
Glass)
13.43 28 1.71 12.21 28 1.32 6.31 20 14.20
0.02 96.28
14 (Pearls
and Jewelry)
9.95 35 37.31 9.25 35 40.19 6.13 30 34.04
0.06 98.54
15 (Base
Metals)
7.66 30 0.61 7.16 30 3.85 3.46 16.2 41.56
0.18 71.06
16
(Machinery)
8.71 35 22.37 7.85 35 19.30 4.93 26 23.15
0.01 11.81
17 (Vehicles) 11.23 45 0.35 10.97 45 0.42 4.70 32.8 18.06 0.00 0.00
18 (Optical
Instruments)
10.63 30 14.81 9.56 30 10.50 6.09 14.4 32.70
0.20 98.74
19 (Arms
and
Ammunition)
13.35 15 0.00 13.00 13 0.00 6.50 6.5 0.00
0.00 0.00
20 (Misc.
Products)
12.52 25 32.94 12.53 25 33.78 7.11 22.5 32.86
0.18 99.62
21 (Works of
Art)
9.29 14 22.22 6.52 14 26.67 9.47 11.2 0.00
0.00 0.00
The section-wise breakup of final bound duties, MFN applied rates and preferential rates on imports
into China from Pakistan are reported in Table 2-3. The ‘duty-free share’ is the percentage of tariff lines
25
that are provided duty-free access under the respective trade regime11. The share of imports is
calculated as the percentage share of total imports into China from Pakistan in each section. The duty-
free share of imports indicates the percentage of imports that are imported into China from Pakistan
duty-free under the preferential trade regime12. China provides significant benefits to Pakistani
exporters as the share of duty-free tariffs under the preferential tariff regime is higher than the share of
duty-free tariffs under the MFN regime for majority of the HS sections.
11 The number of products reported under MFN tariffs differ from the number of products reported under PRF.
Therefore, this may give rise to the discrepancy between the percentages of tariff lines that are duty-free under
the respective trade regime. 12 More than 90 percent of the imports into China from Pakistan in mineral products, wood, and base metals is
already duty-free under the MFN applied tariff regime.
26
Chapter 3 : Analyzing Bilateral Trade Using the Gravity Equation
Gravity Equation:
The size of the bilateral flow between two countries can be estimated by the “gravity equation”.
Analogous to the Newtonian law of gravity, countries are likely to trade in proportion to their sizes and
proximity to each other. The simple gravity equation can be formulized in the following terms13:
�(� ��(4�∅(�
Where �(� is the total exports from i to j, �( comprises of exporter characteristics, 4� comprises of
importer characteristics and ∅(� represents the degree of access of exporter i into the market of
importer j.
Source:
The bilateral export flow from Pakistan to the respective trading partners is borrowed from UN
COMTRADE. The total number of export destinations with positive flows ranged from 183 in 2009 to 190
in 2014. The time period considered is from 2004 to 2014. Pakistan signed free trade agreements with
multiple trading partners as well as witnessed a doubling of its total exports during this time period. The
GDP per capita at PPP (current international dollars) and GDP at PPP (current international dollars) for
the importers are borrowed from World Development Indicators. Distance between Pakistan and the
trading partner, the dummy variables on border with Pakistan, common official language, colonizer,
common colonizer and whether the importing country is landlocked is borrowed from CEPII. Pakistan
has signed trade agreements with Sri Lanka (2005), other SAFTA countries (2006), China (2007) and
Malaysia (2008)14.
Empirical Equation:
The gravity equation can be represented by taking the natural logarithms of the variables. The exports
can be estimated using the following logarithmic equation:
67�(� 67� � 67�( � 674� � 67∅(�
The monetary value of exports, GDP of the respective countries and distance can be converted to
natural logarithmic values. However, some of the variables included are taken as dummy variables, such
as border, common official language, colonizer, common colonizer, and regional trade agreements.
These variables are included in the binary form (0 or 1). The aforementioned dummy variables tend to
lower the search costs between businesses across countries as presence in countries sharing borders,
having historical linkages and engaging in trade agreements are likely to lower their costs of business.
The regression analysis below estimates the exports from Pakistan to its trading partners based on
independent variables such as GDP of the importing country, GDP per capita of the importing country,
whether the importing county is land locked (dummy variable) and bilateral variables, such as distance,
13 Several additions to the simple gravity model have been introduced. One such addition adjusts the trade costs
between two countries using land and border characteristics such as whether countries are either landlocked or an
island or whether the two countries share a contiguous border, a common language or colonial linkages. 14 SAFTA countries include Afghanistan (acceded in 2011), Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan
and Sri Lanka. The information on RTAs is available at World Trade Organization’s http://rtais.wto.org/
27
common border, common official language, trading partner as colonizer, common colonizer and the
enforcing of a regional trade agreement. Except for distance, all bilateral variables are binary dummy
variables with a value of either 1 if true or 0 if false. The analysis is conducted using simple ordinary least
squares estimation as well as Tobit and Poisson estimations.
There are several countries that do not trade with each other in a given year. The bilateral exports from
Pakistan to the trading partner will not be reported for such instances. The natural logarithmic value for
such either missing values or zeros is undefined. The values can be dropped if zeros are randomly
distributed and that the presence of zero trade flow is not informative. However, trade theory suggests
that there is substantial costs involved to participate in international trading activities and that zero
trade can indicate either or all of the following: prohibitive transportation costs, low returns to
investments in costs related to trade or relatively small size of the trading partner. Therefore, the
observation of zero trade flow is informative and the value should be included in the estimations. The
Tobit and Poisson estimations are introduced in order to account for the zero values.
The Tobit estimation is a standard method to estimate equations in which the dependent variable is
likely to consist of a larger number of zeros. As Tobit model requires some observations to be censored,
it is a plausible assumption for measuring trade flow for countries in which data may not be accurate. If
the data is accurate, the Tobit estimation may however eliminate crucial information necessary to
determine the trading patterns. The Poisson estimation avoids dropping zeros and is used to model
count data. The Poisson distribution involves a skewed, discrete distribution that includes non-negative
numbers only. The Poisson estimator determines the probability that trade occurs between the trading
partners.
The OLS is estimated using export values where zero trade values are treated as missing values after log
transformation15. However, all trade values (including zeros) are scaled by adding one before the log
transformation for the Tobit estimations. The log of trade values between countries that do not report
any trade are treated as zero in the Tobit estimations and the estimation is left-censored. As the Poisson
estimation is a count model for the number of times an event occurs, the zero trade values are included
in the estimations.
15 Approximately 6.5 percent of the observations reported for the export value are zero trade values.
28
Table 3-1: Regression Analysis of Exports from Pakistan
(1) (2) (3)
OLS Tobit Poisson
Dependent Variable: Exports (ln)
GDP per Capita, PPP (ln) -0.39*** -0.41*** 0.35***
(0.04) (0.08) (0.06)
GDP, PPP (ln) 1.09*** 1.45*** 0.66***
(0.03) (0.05) (0.06)
Distance between Pakistan and Trading Partner (ln) -0.65*** -0.64*** -0.57***
(0.07) (0.09) (0.11)
Border with Pakistan -0.06 -0.44 -0.24
(0.47) (0.60) (0.32)
Common Official Language -0.29*** -0.07 0.37**
(0.11) (0.25) (0.18)
Colonizer (United Kingdom) 1.60*** 0.61** 0.50***
(0.14) (0.26) (0.17)
Common Colonizer 0.81*** 0.69** -0.02
(0.11) (0.32) (0.32)
Land Locked -2.03*** -1.81*** 0.09
(0.12) (0.19) (0.18)
Regional Trade Agreement with Pakistan -0.42 -1.01* 0.28
(0.28) (0.55) (0.38)
Constant -1.79* -10.86*** 2.29
(1.04) (1.55) (2.31)
Observations 1,898 1,985 1,985
R-squared 0.68
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Includes year fixed effects
The Tobit estimation is left-censored after replacing missing values with zero
The Poisson estimation includes zero trade values
The results of the regressions in Table 3-1 determine the trading pattern of the exports from Pakistan.
Although, Pakistan is likely to trade more with larger economies in terms of their GDP, it may trade more
with countries that have a lower level of GDP per capita16. Pakistan is also likely to trade more with
countries that were colonized by the British post 1945. There is no significant relationship between
sharing a border with Pakistan and trading relationship with Pakistan. Interestingly, the regional trade
agreements do not influence the value of exports between Pakistan and its trading partners, except for
a negative coefficient significant at 10 percent level in Column 2. Excluding the variables on common
official language and regional trade agreements, the results reported for the independent variables in
Column 2 are similar to the results reported for the independent variables in Column 1. However, the
16 Although, major trading partners of Pakistan are developed countries, they also have large economies in terms
of GDP. Therefore, their GDP rather than their GDP per capita may positively influence exports from Pakistan in
Columns 1 and 2.
29
Poisson estimations in Column 3 reveal certain differences. As the Poisson estimation is a count model it
estimates the impact on the count of the dependent variable due to changes in the independent
variable. The expected increase in log count of trading relationships between Pakistan and its trading
partner with an increase in one-unit of the independent variable is determined by the coefficient. As
reported in Column 3, Pakistan is more likely to report trading relationships with larger economies and
countries with higher GDP per capita as well countries with common official language and with United
Kingdom, its colonizer. It is less likely to report trading relationships with countries located at a further
distance. The Poisson distribution may seem more realistic as the effect of GDP per capita and the
common official language are positive. This is more consistent to the theoretical aspects of the gravity
model.
30
Chapter 4 : Partial-Equilibrium Trade Policy Simulation
The main objective of this chapter is to determine the impact of a change in trade policy on trade flows,
tariff revenues and welfare. Consider Figure 4-1. Suppose a small country faces fixed world price, �∗ and
charges a tariff, t, for every unit of good, x, imported into the country. The demand curve is denoted by
D and the supply curve is denoted by S in graph (a). The import price is �∗ � 9. The domestic demand at
import price is %�, domestic supply is :�, and imports is 4�, calculated as %� � :�. With the elimination
of tariffs, domestic demand increases to %�, domestic supply decreases to :�, and imports is 4�,
calculated as %� � :�. Tariff elimination results in producer surplus loss, denoted by a, and loss in tariff
revenue, denoted by c. However, tariff elimination recovers the deadweight loss, denoted by b and d.
When a tariff is imposed, the economy faces an efficiency loss due to higher prices paid by consumers
and higher marginal costs of production incurred by domestic producers as the goods in the domestic
market are produced and consumed at a higher price than the world price. The total gain to the
economy due to tariff elimination is the area of the two triangles (b and d) in graph (a) and the triangle
under the import demand curve labeled M in graph (b). The base and the height of the triangle
representing the deadweight loss is dependent on the tariff. Higher the tariff, greater the deadweight
loss when the tariff is imposed. Therefore, greater the gain to the economy when the tariff is removed.
On the other hand, the consumer surplus increases as the price falls and the quantity consumed
increases.
Figure 4-1: The Impact of a Tariff on Domestic Market and Import Market
Empirical Tool
The SMART tool available at World Integrated Trade Solutions (WITS) is used to analyze the impact of
trade liberalization. There is strong evidence that trade between countries takes place at a highly
disaggregated level in terms of the number of products traded between countries. The ‘Armington
assumption’ suggests that the varieties of goods are nationally differentiated. Each country produces
differentiated varieties that can be identified by the country of origin. Therefore, the imports from
different trading partners are considered as imperfect substitutes. The model also assumes that
importing country takes the world prices as given and the change in imports has no impact on the
export supplies. Therefore, the supply curve is perfectly elastic.
31
Theoretical Model
Assume that consumers have a quasi-linear utility function of the form:
;<=0, =1,……,=7@ =0�� �=)* �7*1
Where =� stands for consumption of a composite good and ='( is the consumption of good k imported
from county i. The utility function is increasing, concave and identical across all countries. Due to the
additive nature of the utility function, there is no substitution effect between goods k.
Consider Pakistan and its two trading partners, denoted by i and I. Both countries export good k. Assume
P to be the domestic price in Pakistan of good k, �∗ to be the world price of good k and M as the
quantity imported.
The ratio of imports into Pakistan of good k from exporter i to the imports into Pakistan from exporter I
is expressed as 4'ABC =4'( /4'B . The ratio of the landed price (after the payment of duty) in Pakistan of
good k from exporter i to the landed price in Pakistan of good k from exporter I is �'ABC=�'(/�'B. The
elasticity of substitutionD, in Pakistan between the two varieties imported from importer i and I, is:
D �'ABC4'ABCE4'ABCE�F'(B
The elasticity of import demand in Pakistan is:
G �4E4E�
where domestic price in Pakistan is equal to:
� �∗�1 � 9� and where tariff imposed on the imports from a trading partner is the tariff to most favored nations
reduced by the preferential margin. For instance, if Pakistan imposes 25 percent tariffs on the imports
from the most favored nations and provides a preferential margin of 80 percent to the trading partners
with whom it has negotiated a trading agreement, will impose a tariff of 5 percent on the imports from
it. Therefore, the tariffs can be expressed as:
9 9HIJ�1 � K� where t is the tariff imposed on the imports from trading partner, 9HIJis the tariff imposed on the
imports from most favored nations and K is the preferential margin on the imports from the trading
partner with whom it has a preferential trading agreement. It is assumed that the world price is taken as
given, which implies that P is fixed and that the change in the domestic price is directly proportional to
the changes in the tariff rates.
The distribution of consumption due to trade diversion and trade creation is presented in Figure 4-2.
The imports from Country i are depicted on the y-axis and the imports from Country I are depicted on
the x-axis. Lower tariffs awarded to Country i compared to Country I lowers the relative price of good k
imported from Country i. The consumption of good k increases from country i from *�to *� and lowers
32
the imports from Country I from �to � in panel (a). This transfer in consumption from Country I to
Country i is referred to as trade diversion.
On the other hand, the lowering of the price of good k from country i, allows consumers in Pakistan to
purchase a larger volume of good k and reach a higher utility level. This transition is referred to as trade
creation. It is depicted as the movement from *�to *� in panel (b).
Lowering of tariffs on import of good k from Country i will lead to positive trade creation and positive
trade diversion, increasing the total imports in favor of Country i. However, there will be negative trade
diversion impact on the imports of good k from Country I, with zero trade creation. This will decrease
the total imports from country I.
Figure 4-2: Trade Diversion and Trade Creation
Trade Creation
Trade creation is calculated as an increase in the total imports from a trading partner due to a tariff
reduction. The domestic price in Pakistan will fall consequent to a trade reduction and lead to an
increase in the quantity of imports. This is expressed as:
+% ≡ �E4 G4E�
As E� �∗E9, and � �∗�1 � 9� �E4 G4�∗E9
�∗�1 � 9�E4 G4�∗E9 E4 G4E91 � 9
33
Therefore, trade creation is defined as the increase in imports due to the change in tariff levels adjusted
by the level of imports into Pakistan and the level of tariffs originally imposed.
If tariffs are reduced across several importers, the trade creation is expressed as:
+%' � +%'(/(0� � G'4'(/
(0� �'∗( ∆9'(1 + 9'(
Where ∆9'( denotes the set of tariff reductions applied on good k, and the elasticity of import demand
for good k is assumed to be identical across all trading partners.
Trade Diversion
Tariff reduction on imports into Pakistan from country i will impact imports from other trading partners.
The increase in imports into Pakistan from country i due to substitution of imports away from other
trading partners can be defined as ‘trade diversion’. The change in the ratio of the domestic prices of
imports can be expressed as:
E�'ABC�'ABC E9'(1 + 9'(
Now, consider, 4'ABC , the ratio of imports into Pakistan of good k from exporters i and I,HMNHMO . The
differential can be expressed as:
E4P'(B 4'BE4'( �4'( E4'B�4'B ��
14'B (E4'
( −4'( E4'B4'B )
= 14'B (E4'
( −4'ABC E4'B ) Assume that E4'( =-E4'B , the increase in the ratio of imports from country i to the imports from country
I can be expressed as:
E4P'(B 14'B (E4'
( +4'ABC E4'( )
E4P'(B = 1 +4'ABC
4'B E4'(
The elasticity of substitution is = �MQOCHMQOC
RHMQOCR�FMNO , which can be sorted to obtain E4P'(B = DHMQOC�MQOC E�'ABC
DHMQOC�MQOC E�'ABC =��HMQOCHMO E4'(
34
D4P'(B E9'(
1 + 9'( =1 +4'(4'B4'B E4'(
D4'(4'BE9'(1 + 9'( =
1 +4'(4'B4'B E4'(
D4'( E9'(1 + 9'( =
1 +4'(4'B4'B 4'B E4'(
D E9'(1 + 9'( =
4'B +4'(4'B4'( E4'(
D 4'B4'(4'B +4'(
E9'(1 + 9'( = E4'
(
As trade diversion cannot be larger than the initial value of imports from country I, the amount of trade
diverted will be bounded by 4'B . The trade diversion effect is expressed as:
TD=SD HMOHMNHMO�HMNRTMN��TMN *U � E4'B V 4'B4'B W9XYZ[*\Y
There trade diversion increases with the elasticity of substitution, the total of value imports from i and I,
the change in tariffs, while decreases with the size of initial tariffs imposed on the imports from exporter
i into Pakistan.
Downloading the Data
The SMART data tool is available at http://wits.worldbank.org/. It requires the user to Login and select
the ‘SMART’ option under the ‘Tariff and Trade Simulations’ tab. After filling in the details for the query
and choosing TRAINS as the data source, the user should proceed to the next page. Here, the user will
need to fill in the details on the importing country, the year, and the level of disintegration of the
products. For the purpose of the exercise below, the importing country is Pakistan, the year chosen is
2014 and the products are disaggregated at HS six digit –level. The ‘HS-Combined’ nomenclature is
selected in the modify tab for products.
The user must select the scenario to determine trading patterns. As the purpose of this exercise is to
determine the impact on imports of trade liberalization with zero tariffs on the imports from Turkey, the
user needs to set the name and the description of the scenario using the ‘Modify’ tab. Turkey is selected
as the partner and the products are listed at six digit HS codes. Finally, the new rate is set at 0 in order to
determine the effects at zero tariff rates. Pakistan and Turkey are negotiating a free-trade agreement
which is likely to be concluded by the end of 2016. This will lower tariff rates on traded goods between
the two countries.
The substitution elasticity is set at the default value of 1.5 and the supply elasticity is set at the default
level of 99. Lowering the substitution elasticity will reduce trade diversion. A high value of supply
35
elasticity, set at 99, suggests that Pakistan takes the world price as given. The import demand elasticity
is set as ‘system defined’. We apply the tariff changes on applied rates.
Once the file is downloaded, the ‘Market View Report’ and ‘Detailed Data’ files are extracted. The
‘Market View Report’ contains data on the total value of imports, change in import value, information
on tariff revenues and consumer surplus at the six digit HS product-level. On the other hand, the
‘Detailed Data’ file contains information on the level of trade creation, trade diversion and the total
trade effect by country and product.
Background
Pakistan and Turkey are members of the Economic Cooperation Organization (ECO) and the Developing
8 Countries (D-8). The two countries are negotiating a free-trade agreement, expected to be signed
towards the end of 2016. Although, Pakistan received only US $ 192 million in imports from Turkey in
2014, Turkey exported more than US $ 150 billion to the world in 2014. This suggests that there is a
strong potential for strengthening the trade links between Pakistan and Turkey. The benefits from trade
liberalization that would drive down import duty to zero is analyzed in the section below.
Results
Table 4-1: SMART Output for Total Trade Effect for the case of Pakistan between trading partners, trade liberalization of imports
from Turkey (zero tariff)
Partner Import Value in
US $ 1000
Total Trade
Creation in US
$ 1000
Total Trade
Diversion in US $
1000
Total Effect in US $
1000
World 47,374,544 35,905.92 0 35,905.92
Turkey 191,598.59 35,905.92 28,711.2 64,617.12
China 9,586,381 0 -9,072.68 -9,072.68
USA 1,796,780 0 -1,972.43 -1,972.43
United Arab Emirates 7,071,749 0 -1,921.73 -1,921.73
The global value of imports into Pakistan, total trade creation, total trade diversion and total effect due
to trade liberalization on imports into Pakistan from Turkey is listed in Table 4-1. The results report that
trade creation is positive only between Pakistan and Turkey, while trade diversion is likely to reduce
imports from other trading partners, hence the negative sign. As the gain from trade diversion in
imports from Turkey cancel out the negative impact of trade diversion away from other countries, the
global impact of trade diversion is zero. Other trading partners do not gain from trade creation. The
trade liberalization of imports into Pakistan from Turkey would have increased the total imports into
Pakistan by US$ 65 million in 2014, which is more than 1/3rd of the total amount imported from Turkey
in 2014. The amount of imports diverted away from the trading partners is approximately 0.1 percent
36
for China and the United States and less than 0.03 percent for United Arab Emirates17. This suggests that
some of the largest origin countries for imports into Pakistan will face minimal trade diversion due to
trade liberalization in imports into Pakistan from Turkey.
Table 4-2: SMART Output for ‘Market View Report’ for the case of Pakistan, trade liberalization of imports from
Turkey (zero tariff)
The global value of imports into Pakistan, the change in total imports due to tariff liberalization with
Turkey, the amount of tariff revenue generated, the new tariff revenue generated after trade
liberalization and the consumer surplus due to trade liberalization have been reported in
Table 4-2. The products report the largest change in import value as a result of tariff reduction. The
consumer surplus is the gain in consumer welfare due to the reduction in price and increase in
consumption given the utility levels of the consumers. The tariff revenues generated will decrease but
the consumer surplus will increase after an episode of trade liberalization.
17 China and the United Arab Emirates ranked as the 1st and the 2nd largest source for imports into Pakistan in
2014. United States of America ranked as the 7th largest source of imports into Pakistan. However, the trade
diversion due to trade liberalization with Turkey is the highest for the aforementioned countries.
HS
Code
Product Label Imports
Before in
US$ 1,000
Import
Change
Tariff Revenue
in US$ 1,000
Tariff New
Revenue in
US$ 1,000
Tariff Change
in Revenue in
US$ 1,000
Consumer
Surplus in
US$ 1,000 Total Products 47,374,544 35,905.92 4,033,040 4,001,194 -31,846.45 5,700.73
Machinery 7,273,295 7,886.34 622,374.8 615,757.3 -6,617.69 1,407.80
Textile Products 2,896,605 6,617.44 243,922.2 240,508.7 -3,413.55 835.20
961900 Sanitary towels
(pads) and
tampons,
napkins and
napkin liners for
babies, and
similar articles,
109,682.80 2,973.92 23,704.93 21,554.67 -2,150.26 605.87
841940 Distilling or
rectifying plant
9,290.64 1,683.79 1,847.94 1,807.66 -40.29 306.13
520942 Denim (Yarn of
Different Color;
Cotton 85% or
More; More
than 200g/m2)
6,057.09 1,605.15 1,211.98 925.86 -286.12 257.57
848310 Transmission
Shafts
(Including
Camshafts and
Crankshafts)
and Cranks
50,168.78 1,587.94 15,530.90 14,116.71 -1,414.20 462.35
37
Chapter 5 : Analyzing the Distributional Effects of Trade Policies
Trade policies impact households through two main factors. The first factor is the influence of world
prices on domestic prices. The second factor is the redistributive effect of trade policies on the
households depending upon the production activities of the households and the amount of
expenditures incurred on tradable goods. The redistributive effects may benefit certain households and
penalize other households based on the composition of the goods produced and consumed. For
instance, certain households may consume or produce goods that receive better trade concessions than
other households.
This study determines the differences in the consumption effect, the income effect and the total effect
across households due to the reduction in import tariffs. The assumption is that the country adopts
trade policies which influences the border prices. Internal factors, such as taxes, regulatory measures,
transportation costs affect the retail prices of goods received by the households, but this study abstracts
from those. It is assumed that the country is small, the world prices are given and that the import tariffs
are ad-valorem. Therefore, the border price is the sum of world price and import tariffs. Any changes in
the border prices will have a direct proportional impact on the domestic retail prices. However, as
households differ on the basis of the commodities produced and consumed, the consumption effect, the
income effect and the total effect will differ across households. Considering per capita expenditure as a
proxy for the purchasing power across each household, richer households that incur higher per capita
expenditure are likely to report different values for the income and consumption effects than the poorer
households.
Simple Model Linking Trade Policy to Household Welfare
Consider a small open economy that produces and consumes traded goods at price, ]^, and non-traded
goods at price, ]J. Let w be the wage earned by the household, t is government transfer, _ is exogenous
income, ` be the tariff on the goods imported into the country and ]∗ be the world price.
Four basic equations on income-expenditure identity, wage equation, a non-traded good pricing
equation and a pass-through equation for traded goods are introduced below:
Expenditure-income identity, with the expenditure on the LHS and income on the RHS:
Y�]^ , ]J , a� _ � 9 � [
Wage-equation with the Stolper-Samuelson effect on the RHS, suggesting that the owners of the factor
benefit (lose) with an increase (decrease) in the output price of the factor:
[ [�]^� Non-traded good pricing equation based on the zero profit condition:
]J ]J�[� A pass-through equation, which bases the domestic price on world price and tariffs imposed on the
imports of tradable goods.
]^ ]^�]∗, `�
38
The change in real consumption can be stated as:
∆_Y �\^ � \JbJ,^ � cd,^�eΔ`
where \^ is the share of tradable goods in household expenditure, \J is the share of non-tradable goods
in household expenditure, bJ,^ is the elasticity of non-traded good’s price relative to trade good’s price
and cd,^ is the elasticity of wage income relative to traded good’s price. The pass-through parameter,e,
is assumed to be 1, indicating perfect pass-through of border prices to domestic retail prices. Any
reduction in the tariff rates will be transmitted perfectly to the domestic retail prices. For simplicity,
government transfers, t, are excluded from the calculations. As e is the minimum expenditure needed to
attain a level of utility at given prices, ∆gh measures the change in quantity of goods purchased with
respect to changes in exogenous income level.
A rural household, belonging to the farm sector, is likely to be a consumer as well as a producer of
agricultural products. An increase in price of the agricultural products is likely to have a positive effect
on the income levels of the household and a negative effect on the consumption of agricultural
products. A decrease in price of the agricultural products will have the opposite effect.
A rural household, ai, derives its utility from the income generated by providing labor, the consumption
and production of tradable goods and the consumption and production of non-tradable goods. For
simplicity, the tradable goods are food items consumed and crops produced by rural households in the
farming sector. All other goods are listed as non-tradable goods.
The change in utility, ∆aij, for a household belonging to a region can be expressed as:
∆aij �ki∆[j ��<\i�̂ � \i^l @∆]^j ��<\iJ� � \iJl @∆]JjJ^
Where ki is the labor income generated by the household, \i�̂ is the share of income generated by
selling tradable goods, \iJ� is the share of income generated by selling non-tradable goods, \i^l is the
share of expenditure in consumption of tradable goods and \iJl is the share of expenditure in
consumption of non-tradable goods. <\i�̂ � \i^l @ and <\iJ� � \iJl @ are the exposure of households to
changes in prices of tradable goods and non-tradable goods respectively. Larger the difference, greater
the impact of changes in prices of the respective goods and consequently, the total effect. An increase in
the tariff rates will result in ∆]^j > 0 and a decrease in the tariff rates will result in ∆]^j < 0. For
simplicity, it is assumed that the change in tariff rates has no impact on ∆]Jj and ∆[j.
Source
The data is borrowed from the Household Integrated Economic Survey (HIES) 2010-11. The HIES is a
component of the Pakistan Social and Living Standard Measurement (PSLM) Project by the Pakistan
Bureau of Statistics. The HIES collects information on several social and economic indicators of
households across provinces in Pakistan. The primary purpose of the HIES is to collect information on
39
the income and consumption patterns of households. Household characteristics such as size of family
and the employment status of household member amongst others are also collected.
Although, the data collected from the surveys is extensive and across both urban and rural areas, this
study is limited to households in the rural areas as the purpose of this study focuses on the income and
consumption effects of a change in tariff on food products. Rural households are more likely than urban
households to be consumers as well as producers of food products. The households are surveyed on
their bi-weekly expenditure on food items and the income generated from the cultivation of crops. The
per capita expenditure is calculated to measure the purchasing power of the households.
The tariff data is borrowed from World Integrated Trade Solution (WITS) available at
http://wits.worldbank.org. World average tariff rates on the imports of goods into Pakistan for 2004 and
2011 is downloaded from WITS18. The data is downloaded at six digit HS code. The HS codes are
matched to the list of products specified in HIES, provided in Appendix B, in order to determine the tariff
rates necessary to calculate the consumption effect and the income effect. The products listed in
household expenditures sheet is matched with the tariff data available from WITS at four digit HS codes
and the products listed in the household income sheet is matched with the tariff data available from
WITS at six digit HS codes.
Background on Trade Liberalization in Pakistan between 2004 and 2011
Pakistan adopted trade liberalization policies in early 2000s. The policies to lower import tariffs increase
the import of food items such as sugar, livestock and vegetables along with other commodities.
According to COMTRADE, the imports increased from US $18 billion in 2004 to US $25 billion in 2005, an
increase of almost 50 percent in one year. The imports reached US $43.5 billion in 2011. The imports of
food items included in the household expenditure survey increased from US $453 million in 2004 to US
$1.47 billion in 2011. The simple average tariff rate for all imports in 2004 was 16.64 percent and the
weighted average tariff rate was 13.01 percent. The simple average tariff rate for all imports in 2011 was
12.34 percent and the weighted average tariff rate was 9.02 percent. This suggests a drop in the tariff
rates between 2004 and 2011. Pakistan undertook policies to lower import tariffs on several goods
including food items. The income effect and the consumption effect of rural households will reflect the
results of such policies. During this period, Pakistan also negotiated and signed free trade agreements
with China, Sri Lanka and Malaysia signifying the adoption of trade liberalization in Pakistan.
Empirics
Downloading the Tariff Data and Matching with the HIES Survey Data
First, the tariff data is downloaded from WITS to calculate the changes in the import tariffs between
2004 and 2011. The tariff data is downloaded at six digit HS codes, converted to four digits HS code to
match the product codes of food items in the expenditure survey. As the import tariffs are listed at six
18 Although, it is possible that the majority of the change in the price levels between 2004 and 2011 may have
occurred in the earlier years rather than the latter years. This exercise does not discount for such a scenario. The
purpose of the exercise is to determine the simple percentage price change between 2004 and 2011. Please refer
to the section on the background for a summary analysis of import flow into Pakistan between 2004 and 2011 and
the change in tariff rates.
40
digit HS code, the tariffs are weighted at the six-digit level using import data and then summed up to the
respective product-level category in the HIES.
The HS codes are maintained at six digit-level to match the products listed in the income survey and
subsequently calculate the income effect. The trade-weighted import tariffs for each product code
reported in HIES are calculated using the collapse function in STATA by assigning import values as
weights for the respective tariffs.
Calculating the Change in Weighted Tariff Rates
Next, the weighted tariffs for 2004 and 2011 are used to calculate the change in price levels19. The
following formula is used to calculate the change in tariff rates, ∆`(,����p����, ∆ �̀,����p����:20
∆`(,����p���� = 100 + `(,����100 + `(,���� − 1
∆ �̀,����p���� = 100 + �̀,����100 + �̀,���� − 1
where each product group in the household bi-weekly expenditure survey is denoted by i, and each
product group in the household income survey is denoted by j21. `(,���� is the weighted average tariff
rates for the products listed in the household surveys in 2004 and `(,���� is the weighted average tariff
rates for the products listed in the household surveys in 2011.
The level of expenditure incurred and the level of income earned in 2004 for the consumption and the
production respectively of the same bundle of goods as in 2011 will be higher if the prices of the
tradable goods decrease between 2004 and 2011. The consumption effect is the impact of trade policies
on household expenditures through the prices of goods consumed by the household. The income effect
is the impact of trade policies on household income through the prices of goods produced by the
household. Theoretically, a reduction in the prices of goods will lead to a positive consumption effect
and a negative income effect.
The change in weighted average tariff is multiplied by the share in expenditure of each product group in
total expenditure on food products22. The resultant value is the change in the share in expenditure of
each product group. Therefore, an increase in the total share implies that the households are incurring
greater expenditure on food products in order to remain at the same utility level in the base year, 2011.
This is the case if the prices of a product are higher in 2004 than in 2011, our base year for the survey
19 The household surveys were conducted in 2011. Therefore, all percentage changes have been calculated taking
2011 as the base year. A lower tariff rate in 2011 than 2004 will imply a fall in prices between 2004 and 2011.
Therefore, the consumption effect will be positive and the income effect will be negative for a tariff rate that is
lower in 2011 than in 2004. 20 As the product groups are classified differently in the household expenditure sheet and the household income
sheet, different notations have been adopted to denote the product groups across the two sheets. 21 Bi-weekly expenditures can be scaled up to annual expenditures. The shares in bi-weekly expenditures will be
the same as in annual expenditures. 22 For simplicity, only the food products are classified as tradable goods. All the remaining products are classified as
non-tradable goods.
41
data. The summation of this across all product groups will provide the value of the consumption effect.
Tariff liberalization will have a positive impact on the consumption effect. A similar inference can be
made for the income effect.
Calculating the Consumption Effect
The change in share of expenditure on food products at the household-level between 2004 and 2011, \i(,����lq/r , is calculated by multiplying the change in weighted tariff between 2004 and 2011, as
formulated above, with the share of expenditure on food items incurred for each of the nine product
groups of tradable goods listed in the bi-weekly household expenditure sheet in 2011:
\i(,����lq/r = ∆`(,����p���� ∗ (\i(,����l ) The consumption effect at the household level is then calculated as the summation of the change in share
of all food products purchased between 2004 and 2011, where i=1,2,……,T:
\i^,����lq/r = ∑ \i(,����lq/r(̂0�
The total consumption effect in the economy across all households in the rural areas is the summation
of all the values calculated at the household level.
Calculating the Income Effect
After calculating the consumption effect, the next step is to determine the income effect. The change in
share of income generated by cultivating and selling tradable products between 2004 and 2011, \i�,�����jqR
, is the income share of that product sold in 2011 multiplied by the change in weighted tariff for the
respective product between 2004 and 2011.
\i�,�����jqR = ∆ �̀,����p���� ∗ (\i�,����
� )
The income effect at the household level is calculated as the summation of the change in share of all
tradable products sold between 2004 and 2011, where where j=1,2,……,T: :
\i^,�����jqR = ∑ \i�,�����jqR�̂0�
The total effect for a household from a reduction in price of tradable goods between 2004 and 2011 is:
\i^,����^qT \i^,����lq/r � \i^,�����jqR
where the consumption effect and the income effect have opposite signs.
The per capita annual expenditure is calculated as the total annual expenditure incurred by a household
divided by the total number of individuals in the households. The consumption effect and the income
effect are plotted against per capita annual expenditure incurred by a household in order to capture the
42
purchasing power of a household with respect to the number of individuals belonging to a household. A
richer household will have a greater purchasing power. If government policies favorably redistribute
income, the consumption effect of a decrease in price should be higher for the poorer households and
the income effect of a decrease in price should be smaller for poorer households.
As the number of households surveyed is substantially lower than the number of households actually
present within each primary sampling unit, a sampling weight is assigned to each primary sampling unit
in order to determine the actual size of the primary sampling unit with respect to the number of
households surveyed. The weight determines the probability that the household will be selected. If the
household has a high probability of being selected, then the household will be over-represented in the
survey, and a lower weight will be assigned to a household. The weights used in the study are provided
in the HIES data.
43
Results
Figure 5-1: Consumption Effect by Per Capita Expenditure
Figure 5-2: Income Effect by Per Capita Expenditures
The consumption effect at household-level is plotted in Figure 5-1. The consumption effect falls as the
per capita expenditure increases across households. This was expected, as this study focuses on food
tariffs, and the share of food in household expenditures is generally higher for poorer households. The
income effect at household-level is plotted in Figure 5-223. The majority of the households experience
negative income effect24. The income effect oscillates in a wave pattern for the richest households.
23 The products for which the change in tariff is calculated are listed in Appendix B. I have focused on food products
only. 24 The import tariffs on fruits were higher in 2011 than they were in 2004. Therefore, households that generated
majority of their income from the production of fruits experienced a positive income effect. On the other hand,
-.1
-.05
0.0
5.1
Con
sum
ptio
n E
ffect
6 8 10 12 14Per Capita Expenditure (ln)
kernel = epanechnikov, degree = 0, bandwidth = .04
Local Polynomial Smooth
-.15
-.1
-.05
0.0
5In
co
me E
ffect
6 8 10 12 14Per Capita Expenditure (ln)
kernel = epanechnikov, degree = 0, bandwidth = .05
Local Polynomial Smooth
44
The weighted average tariff rates on the product group defined as fruits is greater in 2011 than in 2004.
Therefore, the prices of fruits has increased during the time period. This suggests a positive income
effect for households that earn a larger proportion of their income by cultivating fruits.
Table 5-1: Income, Consumption and Overall Effects by Deciles
Decile Income Effect Consumption
Effect
Total Effect
1 -0.044 0.045 0.001
2 -0.043 0.043 0.000
3 -0.033 0.042 0.009
4 -0.036 0.041 0.005
5 -0.034 0.041 0.007
6 -0.031 0.040 0.009
7 -0.035 0.039 0.004
8 -0.032 0.038 0.006
9 -0.028 0.036 0.007
10 -0.026 0.032 0.006
The total effect by decile of per capita expenditure is listed in Table 5-1. The poorest households in the
rural areas experience the smallest total effect as the larger positive consumption effect is canceled by
the negative income effect, which is similar in magnitude. The consumption effect falls as the level of
per capita expenditure by households increase. However, the income effect is oscillatory across the per
capita expenditure incurred by households. Although, the poorest households face lower total effect
than the richest households in the rural areas, the households belonging to the middle deciles of per
capita expenditure levels benefit the most in terms of total effect than the households in the two
extremes of per capita expenditure levels.
wheat reported the highest change in tariff rates between 2004 and 2011. Households concentrated towards the
bottom of the graph, below -0.15, are likely to generate income predominantly from wheat.
45
Figure 5-3: Total Effect by Provinces
The box plot for the total effects by provinces is shown in Figure 5-3. The lower and the upper edges of
the boxes display the 25th and the 75th percentile of the rural households respectively, the horizontal line
within the box displays the 50th percentile and the upper horizontal line above the boxes display the 95th
and below the boxes display the 5th percentile of the rural households in the respective provinces. The
outliers are represented by dots. The box plot shows that more than 50 percent of the rural households
will gain from trade liberalization across all provinces. Only the outliers will lose in Balochistan due to
trade liberalization, suggesting that only an insignificant proportion of households in the province lose
from trade liberalization. It is also interesting to note that the gap between the respective percentile
identifiers is spread out for the lower percentiles than for the upper percentiles. This suggests that there
is lower dispersion in the total effect amongst households that report higher levels of gain in total
effect25.
In order to determine whether the removal of a tariff has a progressive or a ‘pro-poor’ bias, that is it
benefits the poor households more than the richer households, it is important to quantify the impact of
tariffs and tariff changes on different income levels based on either the quantiles, deciles or centiles of
income. This can be achieved by performing the Locally Weighted Scatterplot Smoothing (LOWESS
estimation in STATA).
25 A deeper analysis, for instance using population weights and GDP weights for provinces, is necessary to obtain a
better understanding of the distribution of the total effect across provinces. Such an exercise is beyond the scope
of this project.
-.15
-.1
-.05
0.0
5.1
Tota
l E
ffe
ct
Balochistan KP (NWFP) Punjab Sindh
46
Figure 5-4: Tariff and Change in Tariff Sorted on Income Distribution
The consumption-weighted average tariffs and average change in tariffs are calculated on the basis of
the weight of the products (food items) consumed by the households. The total income earned by the
households is distributed based on centiles. Sampling weights provided in the survey data are used. The
lowess smoother curves are plotted in Figure 5-4. The richer households faced higher tariff rates than
the poorer households. Further, the reduction in the tariff rates between 2004 and 2011 was ‘pro-poor’
as the change had a greater impact on the consumption bundle of the poorer households than the
richer households.
78
910
11
12
(Mean)
Tariff, 2011
0 20 40 60 80 100100 quantiles of income
bandwidth = .8
Lowess smoother
3.5
44.5
55.5
(Mean)
Perc
enta
ge C
hange in T
ari
ff, 2004-2
011
0 20 40 60 80 100100 quantiles of income
bandwidth = .8
Lowess smoother
47
Appendix A: HS Classification by Section
Table A-1: Description of HS Classifications by Section
Section Description
1 LIVE ANIMALS; ANIMAL PRODUCTS
2 VEGETABLE PRODUCTS
3 ANIMAL OR VEGETABLE FATS AND OILS AND THEIR CLEAVAGE PRODUCTS; PREPARED EDIBLE FATS;ANIMAL OR VEGETABLE WAXES
4 PREPARED FOODSTUFFS; BEVERAGES, SPIRITS AND VINEGAR; TOBACCO AND MANUFACTURED TOBACCO SUBSTITUTES
5 MINERAL PRODUCTS
6 PRODUCTS OF THE CHEMICAL OR ALLIED INDUSTRIES
7 PLASTICS AND ARTICLES THEREOF; RUBBER AND ARTICLES THEREOF
8 RAW HIDES AND SKINS, LEATHER, FURSKINS AND ARTICLES THEREOF; SADDLERY AND HARNESS; TRAVEL GOODS, HANDBAGS AND SIMILAR CONTAINERS; ARTICLES OF ANIMAL GUT (OTHER THAN SILK-WORM GUT)
9 WOOD AND ARTICLES OF WOOD; WOOD CHARCOAL; CORK AND ARTICLES OF CORK; MANUFACTURES OF STRAW, OF ESPARTO OR OF OTHER PLAITING MATERIALS; BASKETWARE AND WICKERWORK
10 PULP OF WOOD OR OF OTHER FIBROUS CELLULOSIC MATERIAL; RECOVERED (WASTE AND SCRAP) PAPER OR PAPERBOARD; PAPER AND PAPERBOARD AND ARTICLES THEREOF
11 TEXTILES AND TEXTILE ARTICLES
12 FOOTWEAR, HEADGEAR, UMBRELLAS, SUN UMBRELLAS, WALKING-STICKS, SEAT-STICKS, WHIPS, RIDING-CROPS AND PARTS THEREOF; PREPARED FEATHERS AND ARTICLES MADE THEREWITH; ARTIFICIAL FLOWERS; ARTICLES OF HUMAN HAIR
13 ARTICLES OF STONE, PLASTER, CEMENT, ASBESTOS, MICA OR SIMILAR MATERIALS; CERAMIC PRODUCTS; GLASS AND GLASSWARE
14 NATURAL OR CULTURED PEARLS, PRECIOUS OR SEMI-PRECIOUS STONES, PRECIOUS METALS, METALS CLAD WITH PRECIOUS METAL AND ARTICLES THEREOF; IMITATION JEWELLERY; COIN
15 BASE METALS AND ARTICLES OF BASE METAL
16 MACHINERY AND MECHANICAL APPLIANCES; ELECTRICAL EQUIPMENT; PARTS THEREOF; SOUND RECORDERS AND REPRODUCERS, TELEVISION IMAGE AND SOUND RECORDERS AND REPRODUCERS, AND PARTS AND ACCESSORIES OF SUCH ARTICLES
17 VEHICLES, AIRCRAFT, VESSELS AND ASSOCIATED TRANSPORT EQUIPMENT
18 OPTICAL, PHOTOGRAPHIC, CINEMATOGRAPHIC, MEASURING, CHECKING, PRECISION, MEDICAL OR SURGICAL INSTRUMENTS AND APPARATUS; CLOCKS AND WATCHES; MUSICAL INSTRUMENTS; PARTS AND ACCESSORIES THEREOF
19 ARMS AND AMMUNITION; PARTS AND ACCESSORIES THEREOF
20 MISCELLANEOUS MANUFACTURED ARTICLES
21 WORKS OF ART, COLLECTORS' PIECES AND ANTIQUES
48
Appendix B: Correspondence Between HS Product Codes and HIES
Table B-1: Four Digit HS Product Codes Corresponding to Details of Household Expenditure to Determine Consumption Effect
Product from HIES Four
Digit HS
Code
Description of Four Digit HS Code
a: Milk and Milk
Products
0401 Milk and cream, not concentrated nor containing added sugar or
other sweetening matter
0402 Milk and cream, concentrated or containing added sugar or other
sweetening matter
0403 Buttermilk, curdled milk and cream, yogurt, kephir and other
fermented or acidified milk and cream
0404 Whey and products consisting of natural milk constituents, nes
0405 Butter and other fats and oils derived from milk; dairy spreads
0406 Cheese and curd
1517 Margarine; edible mixtures or preparations of animal or vegetable
fats or oils or of fractions of different fats or oils of this chapter,
excluding those of heading 1516
b: Meat Poultry and
Fish
0201 Meat of bovine animals, fresh or chilled
0202 Meat of bovine animals, frozen
0204 Meat of sheep or goats, fresh, chilled or frozen
0206 Edible offal of bovine animals, swine, sheep, goats, horses, asses,
mules or hinnies, fresh, chilled or frozen
0207 Meat and edible offal, of the poultry of heading 0105, fresh, chilled or
frozen
0301 Live fish
0302 Fish, fresh or chilled, excluding fish fillets and other fish meat of
heading 0304
0303 Fish, frozen, excluding fish fillets and other fish meat of heading 0304
0304 Fish fillets and other fish meat, fresh, chilled or frozen
0305 Fish, dried, salted or in brine; smoked fish; flours, meals and pellets of
fish fit for human consumption
0306 Crustaceans, live, fresh, chilled, frozen, dried, salted or in brine;
smoked crustaceans; flours, meals and pellets of crustaceans fit for
human consumption
0307 Molluscs, live, fresh, chilled, frozen, dried, salted or in brine; smoked
molluscs; flours, meals and pellets of molluscs fit for human
consumption
0407 Birds' eggs, in shell, fresh, preserved or cooked
49
0408 Birds' eggs, not in shell, and egg yolks, fresh, dried, cooked by
steaming or by boiling in water, moulded, frozen or otherwise
preserved
c: Fresh Fruits:
0803 Bananas, including plantains, fresh or dried
0804 Dates, figs, pineapples, avocados, guavas, mangoes and
mangosteens, fresh or dried
0805 Citrus fruit, fresh or dried
0806 Grapes, fresh or dried
0807 Melons (including watermelons) and papaws (papayas), fresh
0808 Apples, pears and quinces, fresh
0809 Apricots, cherries, peaches (including nectarines), plums and sloes,
fresh
0810 Other fruit, fresh, nes
d: Dry Fruits & Nuts
0801 Coconuts, brazil nuts and cashew nuts, fresh or dried
0802 Other nuts, fresh or dried, nes
0813 Fruit, dried, other than that of headings 0801 to 0806; mixtures of
nuts or dried fruits of this chapter
0814 Peel of citrus fruit or melons, fresh,frozen, dried...etc.
E: Vegetables
0701 Potatoes, fresh or chilled
0702 Tomatoes, fresh or chilled
0703 Onions, shallots, garlic, leeks and other alliaceous vegetables, fresh or
chilled
0704 Cabbages, cauliflowers, kohlrabi, kale and similar edible brassicas,
fresh or chilled
0705 Lettuce and chicory, fresh or chilled
0706 Carrots, turnips, salad beetroot, salsify, celeriac, radishes and similar
edible roots, fresh or chilled
0707 Cucumbers and gherkins, fresh or chilled
0708 Leguminous vegetables, shelled or unshelled, fresh or chilled
0709 Other vegetables, fresh or chilled
0710 Vegetables (uncooked or cooked by steaming or boiling in water),
frozen
0711 Vegetables provisionally preserved, not for immediate consumption
0712 Dried vegetables, whole, cut, sliced, broken or in powder
0713 Dried leguminous vegetables, shelled, whether or not skinned or split
0714 Manioc, arrowroot, salep, Jerusalem artichokes, sweet potatoes and
similar roots and tubers with high starch or inulin content, fresh,
chilled, frozed or dried; sago pith
f: Condiments & Spices
(Whole & Powder )
0904 Pepper of the genus Piper; dried or crushed or ground fruit of the
genus Capsicum or of the genus Pimenta
0905 Vanilla
50
0906 Cinnamon and cinnamon-tree flowers
0907 Cloves (whole fruit, cloves and stems)
0908 Nutmeg, mace and cardamoms
0909 Seeds of anise, badian, fennel, coriander, cumin or caraway; juniper
berries
0910 Ginger, saffron, turmeric (curcuma), thyme, bay leaves, curry and
other spices
2501 Salt (including table salt and denatured salt) and pure sodium
chloride; sea water
g: Sugar, Honey and
Sugar Preparations
1701 Cane or beet sugar and chemically pure sucrose, in solid form
1702 Other sugars in solid form; sugar syrups not containing added
flavouring or colouring matter; artificial honey; caramel
1703 Molasses resulting from the extraction or refining of sugar
1704 Sugar confectionery (incl. white chocolate), not containing cocoa
1806 Chocolate and other food preparations containing cocoa
h: Non Alcoholic
Beverages
2009 Fruit juices (including grape must) and vegetable juices, unfermented
and not containing added spirit
2201 Waters, including mineral waters and aerated waters, not sweetened
nor flavoured; ice and snow
2202 Waters, including mineral waters and aerated waters, sweetened or
flavoured, and other non-alcoholic beverages excluding juices of
heading 2009
i: Readymade Food,
Drinks etc.
0901 Coffee; coffee husks and skins; coffee substitutes containing coffee in
any proportion
0902 Tea
2105 Ice cream and other edible ice, whether or not containing cocoa
2106 Food preparations not elsewhere specified or included
Note: 1. The above is an approximate matching of the HS codes at the four digit level to the product
codes provided in the HIES using http://asycuda.org/onlinehs.asp.
2. Canned fruits and vegetables have been excluded as the HS codes at the four digit level do not
distinguish between canned fruit and canned vegetable
51
Table B-2: Six Digit HS Product Codes Corresponding to Details of Household Expenditure to Determine Income Effect
Product from
HIES
Six Digit HS Code Description of Four Digit HS Code
Wheat
100110 Durum wheat
100190 Spelt, common wheat and meslin
110100 Wheat or meslin flour
110311 Groats and meal of wheat
110321 Wheat pellets
110811 Wheat starch
110900 Wheat gluten
Cotton
520100 Cotton, not carded or combed
520210 Yarn waste of cotton
520291 Garnetted stock of cotton
520299 Cotton waste, nes
520300 Cotton, carded or combed
Sugarcane
121291 Sugar beet, fresh, dried, chilled or frozen
121292 Sugar cane, fresh, dried, chilled or frozen
170111 Raw cane sugar, in solid form
170112 Raw beet sugar, in solid form
170191 Cane or beet sugar, containing added flavouring or
colouring
170199 Cane or beet sugar, in solid form, nes
170310 Cane molasses resulting from the extraction or
refining of sugar
170390 Molasses resulting from the extraction or refining of
sugar (excl. cane)
Rice
100610 Rice in the husk (paddy or rough)
100620 Husked (brown) rice
100630 Semi-milled or wholly milled rice
100640 Broken rice
110230 Rice flour
110314 Groats and meal of rice
Maize
100510 Maize seed
100590 Maize (excl. seed)
110220 Maize (corn) flour
110313 Groats and meal of maize (corn)
110423 Other worked grains of maize (corn), nes
110812 Maize (corn) starch
Pulses
071310 Dried peas, shelled
071320 Dried chickpeas, shelled
52
071331 Dried beans, shelled
071332 Dried adzuki beans, shelled
071333 Dried kidney beans, incl. white pea beans, shelled
071339 Dried beans, shelled, nes
071340 Dried lentils, shelled
071350 Dried broad beans and horse beans, shelled
071390 Dried leguminous vegetables, shelled, nes
Fruits
080300 Bananas, including plantains, fresh or dried
080410 Dates, fresh or dried
080420 Figs, fresh or dried
080430 Pineapples, fresh or dried
080440 Avocados, fresh or dried
080450 Guavas, mangoes and mangosteens, fresh or dried
080510 Oranges, fresh or dried
080520 Mandarins, clementines, wilkings...etc, fresh or
dried
080530 Lemons and limes, fresh or dried
080540 Grapefruit, fresh or dried
080590 Citrus fruit, fresh or dried, nes
080610 Fresh grapes
080620 Dried grapes
080711 Watermelons, fresh
080719 Melons, fresh, (excl.watermelons)
080720 Papaws (papayas), fresh
080810 Apples, fresh
080820 Pears and quinces, fresh
080910 Apricots, fresh
080920 Cherries, fresh
080930 Peaches, including nectarines, fresh
080940 Plums and sloes, fresh
081010 Strawberries, fresh
081020 Raspberries, blackberries, mulberries and
loganberries, fresh
081030 Black, white or red currants and gooseberries, fresh
081040 Cranberries, milberries...etc, fresh
081050 Kiwifruit, fresh
081090 Other fruit, fresh, nes
081110 Strawberries, frozen
081120 Raspberries, blackberries...etc, frozen
081190 Other fruit and nuts, frozen, nes
081210 Cherries, provisionally preserved, not for immediate
consumption
081220 Strawberries, provisionally preserved, not for
immediate consumption
53
081290 Fruit and nuts, provisionally preserved, not for
immediate consumption
081310 Dried apricots
081320 Dried prunes
081330 Dried apples
081340 Other dried fruit, nes
081350 Mixtures of dried fruit and nuts, nes
Vegetable
070110 Seed potatoes
070190 Other potatoes, fresh or chilled
070200 Tomatoes fresh or chilled
070310 Onions and shallots, fresh or chilled
070320 Garlic, fresh or chilled
070390 Leeks and other alliaceous vegetables, nes
070410 Cauliflowers and headed broccoli, fresh or chilled
070420 Brussels sprouts, fresh or chilled
070490 White and red cabbages, kohlrabi, kale...etc, fresh or
chilled
070511 Cabbage lettuce, fresh or chilled
070519 Lettuce, fresh or chilled, (excl. cabbage lettuce)
070521 Witloof chicory, fresh or chilled
070529 Chicory, fresh or chilled, (excl. witloof)
070610 Carrots and turnips, fresh or chilled
070690 Beetroot...radishes and other similar edible roots,
fresh or chilled
070700 Cucumbers and gherkins, fresh or chilled
070810 Peas, fresh or chilled
070820 Beans, fresh or chilled
070890 Leguminous vegetables, fresh or chilled, nes
070910 Globe artichokes, fresh or chilled
070920 Asparagus, fresh or chilled
070930 Aubergines, fresh or chilled
070940 Celery, fresh or chilled
070951 Mushrooms, fresh or chilled
070952 Truffles, fresh or chilled
070960 Fruits of genus capiscum or pimenta, fresh or chilled
070970 Spinach, fresh or chilled
070990 Other vegetables, fresh or chilled, nes
071010 Potatoes, frozen
071021 Shelled or unshelled peas, frozen
071022 Shelled or unshelled beans, frozen
071029 Leguminous vegetables, shelled or unshelled, frozen,
nes
071030 Spinach, frozen
071040 Sweet corn, frozen
071080 Vegetables, frozen, nes
54
071090 Mixtures of vegetables, frozen
071110 Onions provisionally preserved, not for immediate
consumption
071120 Olives provisionally preserved, not for immediate
consumption
071130 Capers provisionally preserved, not for immediate
consumption
071140 Cucumbers and gherkins provisionally preserved
071190 Other vegetables and mixture of vegetables
provisionally preserved
071220 Dried onions
071230 Dried mushrooms and truffles
071290 Dried vegetables, nes
071410 Manioc, fresh or dried, chilled or frozen
071420 Sweet potatoes, fresh or dried, chilled or frozen
071490 Roots and tubers with high starch content, fresh or
dried,chilled or frozen nes
Feed
230810 Acorns and horse-chestnuts, for animal feeding
230890 Other vegetable materials, waste, residues, etc,
used in animal feeding
230990 Other preparations of a kind used in animal feeding,
nes
Note: 1. The above is an approximate matching of the HS codes at the six digit level to the product codes
provided in the HIES using http://asycuda.org/onlinehs.asp.
2. For simplicity, the tradable products do not include livestock as they are treated as assets rather than
as commodities in HIES.
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