MEASURING THE EFFECTS OF NON-TARIFF BARRIERS (NTBs) ON SERVICES
TRADE IN ECOWAS COUNTRIES
This study analyses the determinants of trade in services, measures the effects of non-tariff
barriers on services trade for selected ECOWAS Countries and their main services trading
partners, the focus was on total services trade between years 2001 to 2010. These were achieved
using a gravity model approach, which relates the level of trade between countries to their
physical and economic characteristics. Introducing a variable measuring the level of a country’s
barriers to services trade into the gravity equation allows for a tariff equivalent of the barriers to
be estimated, but the NTBs variable is statistically insignificant.
Key words: Trade in Services, ECOWAS, Gravity model, Non Tariff Barriers (NTBs)
JEL Classification: F1, L7, L8, L9
1
1
Introduction
International trade in services has become more important in recent years as advances in
technology have permitted new means of providing services across borders. While there is little
doubt that services trade is an essential ingredient to economic growth and sustainable
development, it is widely accepted that it can only make such positive contribution if
appropriately liberalised and implemented across countries (Copeland and Mattoo, 2008).
The performance of the services sector is vital for growth and poverty reduction in developing
countries. Directly because services are already a large if not the largest part of their economy.
Indirectly because services like finance, communication, and transport, as well as education and
health, affect other sectors of the economy and the productive potential of the people. Today, in
many countries around the world, inadequate access to services hurts people, not just in their role
as consumers; it also perpetuates poverty by undermining the productivity of firms and farms as
well as their ability to engage in trade.
The growing role of services and their increasing importance to trade flows led to the General
Agreement on Trade in Services (GATS) in 1995. This agreement governs the rights and
obligations of World Trade Organisation (WTO) member countries in the area of services trade.
To facilitate the negotiations to liberalize services trade in the context of the round of multilateral
negotiations agreed at Doha, Qatar, in November 2001, it is important to have measures of the
various possible barriers to international trade in services.
Unlike most developed countries where studies that use different methods of measuring the
Barriers to trade in services are common, see for instance: Hoekman (1995), Brown and Stern
(2001), Findlay and Warren (2000), Ec (2002), Grunfeld and Moxnes (2003), Lejour and de
Paiva Verheijden (2004), Lennon (2006), Walsh (2006), Mcguire (2008) and among others.
However, to our knowledge research into this area in developing countries especially Africa is
not well documented in the literature, which necessitates this study. There are convincing
reasons justifying the need for this study in Africa: (1) Most African countries are part of
negotiations to liberalize services trade and this call for measures of the various possible barriers
to international trade in services. (2) With the share of services in world trade increasing 1, it
becomes important to be able to model services trade and measure the impact of changes in
1
See Escaith, 2008; Francois, Christen, Francois, and Hoekman, 2011;
2
restrictions on trade flow.(3) Our results can be used as a representative of other sub regions in
Africa.
2
Trade in services: An overview
2.1
Defining services
There exists no unique classification list of services. Different studies and organizations classify
services differently. According to the International Monetary Fund (IMF), services include
distributive services (e.g. transportation), producer services (e.g. banking and finance), social
services (e.g. education) and personal services (e.g. catering) (Lehmann, et al, 2003). The WTO,
on the other hand, classifies services on the basis of the UN Central Product Classification:
business services; communication services; construction and related engineering services;
distribution services; educational services; environmental services; financial services; health
related and social services; tourism and travel related services; recreational, cultural and sporting
services; transport services; and other services not included elsewhere (WTO, 2001).
2.2
Classification of Trade in Services
The General Agreement on Trade in Services (GATS), which provides the framework for the
liberalization of international trade in services, classifies trade in services into 155 service types
and four modes of supply:
Mode one: cross-border supply
This mode of delivery is the most similar to trade in goods in that the service is supplied from
abroad. Examples of cross-border trade would include distance education. In this case,
production takes place somewhere other than where the consumer is located. This also means
that the factors of production do not have to move to meet the consumer.
Mode two: consumption abroad
The second mode of service provision is called consumption abroad. In this instance, consumers
move to the service providers. This has important implications for the production of the service
and the location of the factors of production. Examples of this mode of service provision include
the education of students in countries other than their home country, or tourism.
3
Mode three: commercial presence
The most important method of service provision is commercial presence. In this case, Foreign
Service providers establish a local branch and supply to the local market from within the
country’s borders. Once again this means that the factors of production come into contact with
the consumers, which does not have to occur in the market for goods. Examples of commercial
presence include banking, catering and accommodation, transport and insurance, amongst others.
Mode four: movement of natural persons
The last mode of service provision is related to the input of foreign workers, rather than the
production itself. It is referred to as the movement of natural persons. In this mode the service is
provided through workers moving between countries and using their skills in foreign countries.
In this case, countries can usually only determine the level of such service imports through
regulations restricting the entry of foreigners.
2.3 Brief descriptions of Trade flows between 1980 to 2010
Table 1a below shows that, the World Export of Goods and Services in 1980 was about 2373.43
Billion US Dollars, out of which about 395.66 Billion US Dollars was Services Exports, while
1977.77 Billion US Dollars accounted for World Goods Export. In year 2000 the World Export
of Goods and Services increased to 7930.13 Billion US Dollars, Services Export was 1521.68
Billion US Dollars, while Goods export was 6408.45 Billion US Dollars. In year 2010 World
Export of Goods and Services was 18946.76 Billion US Dollars, out of which 3834.99 Billion US
Dollars was Services Export, while, Goods Export accounted for about 15111.77 Billion US
Dollars. This shows there was relative increase in the Export of Goods and Services.
The Export of Goods and Services from ECOWAS Countries in the year 1980 was about 35.76
Billion US Dollars, Services Export was about 17.22 Billion US Dollars, while Goods Export
was about 18.54 Billion US Dollars. In the year 2000, ECOWAS Countries exported about 32.64
Billion US Dollars, worth of Goods and Services, about 15.02 Billion US Dollars services
Export, while, Goods Export was about 17.62 Billion US Dollars. In the year 2008, there was a
shift in the flow of ECOWAS Countries Goods and Services Export, as total Export increased to
about 123.00 Billion US Dollars, ECOWAS Countries services Export was about 76.04 Billion
US Dollars, while, ECOWAS Countries Goods Export was about 46.96 Billion US Dollars.
Finally, in year 2010, ECOWAS Countries Goods and Services Export, increased to about 119.08
Billion US Dollars, out of which services Export was about 71.73 Billion US Dollars, while,
4
Goods Export was about 47.36 Billion US Dollars. The ECOWAS Countries Services Export
later displaced the Goods Export.
Table 1b shows that, 16.67% of the world Export was services Export, while, 83.33% of the
world Export was Goods Export, in 1980. This later rise to 20.24% for world services Export
and 79.76% for World Goods Export. The percentage share of ECOWAS Countries in the World
Export of Goods and Services is about 1.51%, 0.41%, 0.57%, 0.62% and 0.63% in 1980, 2000,
2006, 2008 and 2010 respectively. The percentage share of ECOWAS Countries Goods Exports
is Greater than Services Export in 1980 with 48.16% for Services, and 51.84% for Goods. But,
the reverse was the case since year 2006 with 55.06% for Services Export, and 44.96% for
Goods Export, which increased to 60.23% for Services Export and 39.77% for Goods Export in
Year 2010; this could be as a result of Technological advancement that encourages Trade in
services in recent times.
5
Table 1a: Exports of goods and services, (in Billions US Dollars) 1980-2010
YEAR
1980
1990
2000
2007
2008
2009
ECONOMY
2373.43 4274.14 7930.13 17368.45 19867.66 15889.98
World (Goods and Services Exports)
395.66
830.24 1521.68
3488.21
3914.74
3486.62
World Services Exports
1977.77 3443.90 6408.45 13880.25 15952.92 12403.36
World Goods Exports
Developing economies
671.77
988.58 2438.84
6255.58
7367.57
5969.08
(Goods and Services Exports)
Developing economies
73.39
150.39
351.00
896.71
1036.59
940.48
Services Exports
Developing economies
598.39
838.19 2087.84
5358.87
6330.98
5028.59
goods Exports
Transition economies
63.35
112.27
180.39
642.72
850.01
578.83
(Goods and Services Exports)
Transition economies
9.81
16.69
24.26
90.37
113.28
94.62
Services Exports
Transition economies
53.53
95.58
156.13
552.35
736.74
484.21
Goods Exports
Developed economies
1638.31 3173.29 5310.90 10470.14 11650.08
9342.08
(Goods and Services Exports)
Developed economies
312.46
663.16 1146.42
2501.12
2764.88
2451.52
Services Exports
Developed economies
1325.85 2510.12 4164.48
7969.02
8885.20
6890.56
Goods Exports
Developing economies:
136.35
127.43
183.07
531.21
663.22
481.11
Africa (Goods and Services Exports)
Developing economies:
13.44
21.71
33.12
78.84
90.36
81.58
Africa Services Exports
Developing economies:
122.92
105.72
149.95
452.36
572.86
399.53
Africa Goods Exports
Sub-Saharan Africa
87.40
81.16
112.38
328.93
405.28
304.59
(Goods and Services Exports)
Sub-Saharan Africa
8.55
11.43
16.43
38.87
42.32
38.86
Services Exports
Sub-Saharan Africa
78.85
69.73
95.94
290.05
362.95
265.73
Goods Exports
ECOWAS Countries
35.76
24.58
32.64
96.24
123.00
92.82
(Goods and Services Export)
ECOWAS Countries
17.22
12.70
15.02
59.43
76.04
64.73
Services Export
ECOWAS Countries
18.54
11.89
17.62
36.82
46.96
28.09
Goods Export
Source: UNCTAD, UNCTAD stat, 2011
6
2010
18946.76
3834.99
15111.77
7568.24
1137.77
6430.48
732.79
103.09
629.70
10645.73
2594.13
8051.59
595.73
90.54
505.19
384.53
44.41
340.12
119.08
71.73
47.36
Table 1b: Exports of goods and services, (in Percentage) 1980-2010
Year
1980
1990 2000 2005 2006 2007 2008 2009 2010
_
_
_
_
_
_
_
_
_
Individual economies
16.67 19.42 19.19 19.81 19.51 20.08 19.70 21.94 20.24
World Services Exports (in %)
83.33 80.58 80.81 80.19 80.49 79.92 80.30 78.06 79.76
World Goods Exports (in %)
Developing economies
28.30 23.13 30.75 34.58 35.71 36.02 37.08 37.57 39.94
(% of World Exports)
Developing economies
10.92 15.21 14.39 14.07 13.79 14.33 14.07 15.76 15.03
Services Exports (in %)
Developing economies
89.08 84.79 85.61 85.93 86.21 85.67 85.93 84.24 84.97
Goods Exports (in %)
Transition economies
2.67
2.63
2.27
3.25
3.46
3.70
4.28
3.64
3.87
(% of World Exports)
Transition economies
15.49 14.86 13.45 13.69 13.49 14.06 13.33 16.35 14.07
Services Exports (in %)
Transition economies
84.51 85.14 86.55 86.31 86.51 85.94 86.67 83.65 85.93
Goods Exports (in %)
Developed economies
69.03 74.24 66.97 62.18 60.84 60.28 58.64 58.79 56.19
(% of World Exports)
Developed economies
19.07 20.90 21.59 23.33 23.21 23.89 23.73 26.24 24.37
Services Exports (in %)
Developed economies
80.93 79.10 78.41 76.67 76.79 76.11 76.27 73.76 75.63
Goods Exports (in %)
Developing economies:
5.74
2.98
2.31
2.95
2.99
3.06
3.34
3.03
3.14
Africa (% of World Exports)
Developing economies:
9.86 17.04 18.09 15.61 15.00 14.84 13.62 16.96 15.20
Africa Services Exports (in %)
Developing economies:
90.14 82.96 81.91 84.39 85.00 85.16 86.38 83.04 84.80
Africa Goods Exports (in %)
Sub-Saharan Africa
3.68
1.90
1.42
1.85
1.85
1.89
2.04
1.92
2.03
(% of World Exports)
Sub-Saharan Africa
9.79 14.08 14.63 12.45 12.17 11.82 10.44 12.76 11.55
Services Exports (in %)
Sub-Saharan Africa
90.21 85.92 85.37 87.55 87.83 88.18 89.56 87.24 88.45
Goods Exports (in %)
ECOWAS Countries
1.51
0.58
0.41
0.61
0.57
0.55
0.62
0.58
0.63
(% of world Export)
ECOWAS Countries
48.16 51.65 46.02 38.17 55.04 61.75 61.82 69.74 60.23
services Exports(in %)
ECOWAS Countries
51.84 48.35 53.98 61.83 44.96 38.25 38.18 30.26 39.77
Goods Exports(in %)
Source: UNCTAD, UNCTAD stat, 2011
Table 2a below shows that, the World Import of Goods and Services in 1980 was about 2388.59
Billion US Dollars, out of which about 447.77 Billion US Dollars was Services Import, while
1940.82 Billion US Dollars accounted for World Goods Import. In year 2005 the World Import
7
of Goods and Services increased to 12780.62 Billion US Dollars, Services Import was 2467.44
Billion US Dollars, while Goods export was 10313.18 Billion US Dollars. In year 2010 World
Import of Goods and Services was 18531.18 Billion US Dollars, out of which 3685.11 Billion US
Dollars was Services Import, while, Goods Import accounted for about 14846.07 Billion US
Dollars. This shows there was relative increase in the Import of Goods and Services.
The Import of Goods and Services from ECOWAS Countries in the year 1980 was about 31.46
Billion US Dollars, Services Import was about 8.61 Billion US Dollars, while Goods Import was
about 22.85 Billion US Dollars. In the year 2007, ECOWAS Countries Imported about 87.29
Billion US Dollars, worth of Goods and Services, about 29.71 Billion US Dollars services
Export, while, Goods Import was about 57.58 Billion US Dollars. Finally, in year 2010,
ECOWAS Countries Goods and Services Import, increased to about 119.42 Billion US Dollars,
out of which services Import was about 35.86 Billion US Dollars, while, Goods Import was
about 83.56 Billion US Dollars.
Table 2b shows that, 18.75% of the world Import was services Import, while, 81.25% of the
world Import was Goods Import, in 1980. This later rise to 19.89% for world services Import
and 80.11%
for World Goods Import, in the year 2010. The percentage share of ECOWAS
Countries in the World Import of Goods and Services is about 1.32%, 0.33%, 0.47%, 0.59% and
0.64% in 1980, 2000, 2006, 2008 and 2010 respectively. The percentage share of ECOWAS
Countries Services Import in 1980 was 27.37% for Services, and 72.63% for Goods. But,
increased to 33.74% for services Import, and 66.26% for goods Import in the year 2007. Finally,
in the year 2010, the percentage share of Services Import by ECOWAS Countries was 30.03%,
while, Goods Export was 69.97%.
8
Table 2a: Imports of goods and services, (in Billions US Dollars) 1980-2010
YEAR
1980
1990
2000
2005
2006
2007
2008
2009
2010
ECONOMY
World (Goods and Services
Imports)
2388.59
4275.92
7933.48
12780.62
14618.47
16952.78
19539.96
15529.08
18531.18
447.77
877.74
1515.96
2467.44
2766.11
3287.40
3759.27
3366.54
3685.11
1940.82
3398.18
6417.52
10313.18
11852.37
13665.38
15780.69
12162.54
14846.07
617.70
951.42
2283.33
3978.79
4654.37
5530.17
6673.59
5566.33
7116.05
139.58
193.76
415.18
700.43
822.45
998.09
1186.86
1087.22
1298.74
478.12
757.66
1868.15
3278.36
3831.91
4532.08
5486.73
4479.12
5817.30
65.03
117.77
131.70
326.24
392.11
544.97
701.75
493.47
593.08
12.41
30.58
28.39
68.47
79.96
105.14
131.31
107.27
123.71
52.62
87.19
103.31
257.77
312.15
439.83
570.44
386.19
469.37
1705.86
3206.73
5518.45
8475.59
9571.99
10877.64
12164.62
9469.28
10822.05
295.78
653.40
1072.39
1698.55
1863.69
2184.17
2441.10
2172.05
2262.66
1410.08
2553.33
4446.06
6777.05
7708.30
8693.47
9723.52
7297.23
8559.40
124.75
122.51
164.66
330.23
380.50
480.83
610.09
524.71
606.06
29.41
30.31
41.27
77.12
94.00
118.82
152.43
135.57
151.27
95.34
92.19
123.40
253.11
286.49
362.01
457.66
389.14
454.79
81.64
75.53
103.18
220.21
255.99
318.02
394.63
336.32
400.29
20.03
21.53
27.39
53.44
68.15
86.82
109.32
95.02
107.74
61.61
54.00
75.79
166.77
187.84
231.20
285.31
241.30
292.55
31.46
19.44
26.24
62.43
69.40
87.29
114.75
94.28
119.42
8.61
6.35
7.51
15.03
23.41
29.71
38.02
32.37
35.86
22.85
13.09
18.73
47.40
45.99
57.58
76.73
61.91
83.56
World Services Imports
World Goods Imports
Developing economies
(Goods and Services
Imports)
Developing economies
Services Imports
Developing economies
Goods Imports
Transition economies
(Goods and Services
Imports)
Transition economies
Services Imports
Transition economies
Goods Imports
Developed economies
(Goods and Services
Imports)
Developed economies
Services Imports
Developed economies
Goods Imports
Developing economies:
Africa (Goods and Services
Imports)
Developing economies:
Africa Services Imports
Developing economies:
Africa Goods Imports
Sub-Saharan Africa
(Goods and Services
Imports)
Sub-Saharan Africa
Services imports
Sub-Saharan Africa
Goods imports
ECOWAS Countries
(Goods and Services Import)
ECOWAS Countries Services
Import
ECOWAS Countries Goods
Import
Source: UNCTAD, UNCTAD stat, 2011
9
Table 2b: Imports of goods and services, (in Percentage) 1980-2010
MEASURE
YEAR
ECONOMY
World Services Imports ( in %)
World Goods Imports (in %)
Developing economies
(% of World Imports)
Developing economies
Services Imports (in %)
Developing economies
Goods Imports (in %)
Transition economies
(% of World Imports)
Transition economies
Services Imports (in %)
Transition economies
Goods imports (in %)
Developed economies
(% of World Imports)
Developed economies Services
Imports (in %)
Developed economies Goods Imports
(in %)
Developing economies: Africa (% of
World imports)
Developing economies: Africa
Services Imports (in %)
Developing economies: Africa Goods
Imports (in %)
Sub-Saharan Africa (% of World
Imports)
Sub-Saharan Africa Services imports
(in %)
Sub-Saharan Africa Goods imports (in
%)
ECOWAS Countries (% of world
Import)
ECOWAS Countries services
Imports(in %)
ECOWAS Countries Goods Imports(in
%)
Source: UNCTAD, UNCTAD stat, 2011
US Dollars at current prices and current exchange rates in millions
1980 1990 2000 2005 2006 2007 2008 2009 2010
18.75 20.53 19.11 19.31 18.92 19.39 19.24 21.68 19.89
81.25 79.47 80.89 80.69 81.08 80.61 80.76 78.32 80.11
25.86 22.25 28.78 31.13 31.84 32.62 34.15 35.84 38.40
22.60 20.37 18.18 17.60 17.67 18.05 17.78 19.53 18.25
77.40 79.63 81.82 82.40 82.33 81.95 82.22 80.47 81.75
2.72
2.75
1.66
2.55
2.68
3.21
3.59
3.18
3.20
19.09 25.97 21.55 20.99 20.39 19.29 18.71 21.74 20.86
80.91 74.03 78.45 79.01 79.61 80.71 81.29 78.26 79.14
71.42 75.00 69.56 66.32 65.48 64.16 62.26 60.98 58.40
17.34 20.38 19.43 20.04 19.47 20.08 20.07 22.94 20.91
82.66 79.62 80.57 79.96 80.53 79.92 79.93 77.06 79.09
5.22
2.87
2.08
2.58
2.60
2.84
3.12
3.38
3.27
23.57 24.75 25.06 23.35 24.71 24.71 24.98 25.84 24.96
76.43 75.25 74.94 76.65 75.29 75.29 75.02 74.16 75.04
3.42
1.77
1.30
1.72
1.75
1.88
2.02
2.17
2.16
24.53 28.51 26.54 24.27 26.62 27.30 27.70 28.25 26.92
75.47 71.49 73.46 75.73 73.38 72.70 72.30 71.75 73.08
1.32
0.45
0.33
0.49
0.47
0.51
0.59
0.61
0.64
27.37 32.66 28.62 24.07 33.74 34.04 33.13 34.33 30.03
72.63 67.34 71.38 75.93 66.26 65.96 66.87 65.67 69.97
10
3
Review of relevant literature
Prior to the formal recognition of services as an item of international exchange, several
trade-growth theories had existed in the literature for goods trade, of which some have
been tested for its applicability to services trade. The first relates to the theory of
comparative advantage whereby, Ricardo (1817) developed the idea that both parties can
benefit from trade if each party specializes in the good in which it relatively produces
cheaply, thus creating static gains from trade which contributes to overall growth and
welfare of the nations. Studies which have shown the applicability of the theory of comparative
advantage to services trade include Hindley and Smith (1984) and Deardorff (1985).
Secondly, the factor endowment theory by Ohlin (1933) stipulates that the main reason why
countries trade is due to the differences in factor endowments or the supply of land, labour and
capital in the various countries. This trade pattern encourages specialisation which in turn
leads to welfare gains and economic growth analogous to that of the Ricardian model. Studies
by Bhagwati (1984) and Melvin (1989) have shown that the factor endowment theory is
compatible with services trade.
The third trade-growth theory challenged the classical models through authors such as
Myint 1958, 1969; Balough 1973; Smith and Toye 1979; Myrdal 1970; Riedel 1983; and Toye
1987. This led to the North-South (i.e. developed and developing countries) trade proposition
that associate trade with uneven development. The North-South trade represents the trade
relations between rich (North) and poor (South) countries and argues that there is unequal
exchange between the North and the South due to historical forces, differences in factor
endowments and income elasticities of demand of the North’s capital goods and the South’s
consumption goods.
Generally speaking, measures of barriers to trade in services parallel those that were previously
developed to measure NTBs to merchandise trade, and thus can be classified into: frequency
measures, quantity-based measures and price-based measures.
The most widely used frequency measures are those developed by Hoekman (1995) using the
GATS commitment schedules of member countries. Hoekman classifies these commitments into
three categories, and assigns a numerical score to each category:
11
If no restrictions are applied for a given mode of supply in a given sector, a value of 1 is
assigned. If no policies are bound for a given mode of supply in a given sector, a value of 0 is
assigned. If restrictions are listed for a given mode of supply in a given sector, a value of 0.5 is
assigned.
Hoekman calls these scores the openness/binding factors. Since, there are 155 non-overlapping
service sectors in the GATS classification list, and for each sector there are four possible modes
of supply, a total of 620 such openness/binding factors exist for each member country.
Using these factors, Hoekman calculates three sectoral coverage indicators (hereafter the
"Hoekman indices"). The first is calculated as the number of commitments made by a country in
its GATS schedules divided by the 620 maximum possible.
The second, which Hoekman calls "average coverage," is equal to the sectors/modes listed as a
share of maximum possible, weighted by the openness/binding factors.
The third is also a frequency ratio. It is the share of "no restriction" commitments in either (a) a
member's total commitments, or (b) relative to the 155 possible sectors.
While the original purpose of these coverage indicators was to quantify GATS commitments,
Hoekman argues that they provide information on the relative restrictiveness of policy regimes
pertaining to service industries because the coverage in each country's schedule is an indicator of
its policy stance--the higher the coverage, the more open the regime.
There are two ways in which these coverage ratios can be used for this purpose. First, the
restrictiveness of a country's policy in a sector can be measured by the ratio equalling (1- the
Hoekman indices). For example, if a country has made commitments in 10 percent of the 620
possible sector/modes, then using the first Hoekman index it would get a restrictiveness score of
.9, meaning that 90 percent of its sector/modes are closed. Alternatively, "tariff equivalents" can
be constructed using a country's coverage ratio, as Hoekman (1995) has done. He does so by first
constructing a list of benchmark guess estimates of what tariff equivalents of the most
protectionist nation might be. Then the "tariff equivalent" of a given country is obtained by
multiplying this guesstimate by (1 minus the Hoekman indices). Thus, if the most restrictive
12
country worldwide had restrictions equivalent to a 50 percent tariff, then a country with a 0.9
restrictiveness index as in the preceding example, would have a tariff equivalent of 45 percent
(i.e. 0.9 times 50).
A more elaborate set of frequency measures, called the trade restrictiveness indices, has been
constructed by a team of researchers from Australia's Productivity Commission, the University
of
Adelaide,
and
the
Australian
National
University
for
six
service
industries:
telecommunications (Warren 2001), banking (McGuire and Schuele 2001), maritime transport
(McGuire et al 2001), education (Kemp 2001), distribution (Kalirajan 2000) and professional
services (Nguyen-Hong 2000). In addition, Hardin and Holmes (1997) have developed frequency
indices to measure the size of barriers to FDI across service industries.
Quantity-based measures of trade restrictiveness are typically derived using econometric models
based on the standard models of trade determination: the Heckscher-Ohlin model where trade is
motivated by comparative advantage; the Helpman-Krugman model where trade is motivated by
product differentiation, and the gravity model, where an important part of trade is determined by
the relative size and proximity of trading partners (in terms of both distance but also other factors
such as language, culture, etc.). The sizes of NTBs are measured either by the residuals from the
estimated regression (i.e. the difference between the level of actual trade and the level predicted
by the model), or by using various dummy variables (Deardorff and Stern 1998, p. 24).
Kimura, and Lee (2004) selected the data on trade in services and goods of 10 OECD member
countries and 47 trading nations in the 1999-2000 to test the effect of standard gravity model.
The conclusion was that it is complementary between goods export and services imports, and
trade in services can be better forecasted by gravity equation. Walsh (2006) estimated gravity
model for the total services trade of 1999-2001 in a sample of 27 OECD member countries and
55 non-OECD countries. The paper found that the Hausman-Taylor gravity model was most
suitable and the gravity variables of economic size, a common language were significant factors
in services trade, while the distance was not significant. Janer Ceglowski (2006) estimated a
standard gravity equation for bilateral services trade in 28 countries; the results indicated that
economic scale and geographic proximity were significant in services trade. The research
13
implied that efforts to enhance goods trade –bilateral or multilateral – should lead to more
services trade as well.
Price-based measures derive estimates of barriers to trade from differences in domestic and
foreign prices ("price wedges"). If there are sufficient data on prices, then, following Deardorff
and Stern (1998), such measures can be constructed directly by comparing the domestic price of
the imported good (P) with a reference foreign price (P*).9 In this approach, the percentage
difference between the domestic and foreign price is comparable to a tariff (T), provided the
price differences are not due to factors such as sunk costs and entry deterrence strategies by
incumbent firms, rather than government-imposed barriers (see Ross (1999) for a detailed
discussion on various types of barriers to entry). Price wedges can also be quantified using
econometric methods or derived from quantity-based measures with the help of elasticities of
demand and supply.
Most price-based measures for services have been obtained by the Australian team using
econometric methods. These studies are Trewin (2001) for telecommunications, Kalirajan et al.
(2001) for banking, Kang (2001) for maritime transport, Kalirajan (2000) for food distribution,
and Nguyen-Hong (2000) for engineering services. All five studies use the following procedure:
one, a proxy of the domestic price is identified for the industry in question; two, a model is
constructed to identify a list of variables that affect the price, one of which is the barriers to trade
measured by the trade restrictiveness indices developed by the Australian team; three, a
regression model is specified and estimated; and four, the estimated coefficients and trade
restrictiveness index are used to calculate the sizes of price wedges for individual economies.
This last step is done only in three of the five studies, but it could have been done without
difficulty in all studies.
14
4
Methodology
4.1
Model Specification and Estimation Techniques
The concept of the gravity model is based on Newton’s Law of Universal Gravitation relating the
force of attraction between two objects to their combined mass and the distance between them.
The application of gravity to the social sciences was first proposed by James Stewart in the
1940s (Fitzsimons et al., 1999). Originally applied to international trade by Tinbergen (1962),
the gravity model predicts bilateral trade flows between any two countries as a function of their
size and the distance between them.
As the empirical applications of the gravity model have grown, the theoretical foundations of the
model have also been developed. Beginning with Anderson (1979), who showed that the gravity
framework is consistent with a model of world trade in which products are differentiated by the
country of origin (the Armington assumption), a series of papers have shown the gravity model
framework to be consistent with a number of standard trade theories such as Heckscher-Ohlin
and monopolistic competition2.
The initial gravity model estimated in this paper is (1), in which all continuous variables are
expressed in logarithms, as applied by Walsh, 2006.
The dependent variable
is imports of services from country
into country
at time . The
data used covers imports between Five ECOWAS Countries3 and their major Services trading
partners4 over a ten year period (2001-2010). The gravity model is estimated with total services
as dependent variables.
As explanatory variables, in the literature on gravity models, three variables are used as measures
of the size of a country: Gross Domestic Product (GDP), GDP per capita and population. In order
to avoid multicollinearity, the latter two are included in this paper. As countries tend to consume
2
See Anderson (1979), Bergstand (1985, 1989), Helpman and Krugman (1985) and Deardorff (1995).
3
Côte d'Ivoire, Ghana, Liberia, Nigeria, and Senegal
4
Belgium, Czeck Rep., Denmark, Italy, Luxemburg, Netherland, Poland, Sweden, Ukraine, and united Kindom.
15
more service commodities as they become richer, GDP per capita is of more relevant than GDP
itself.
The first two continuous variables are real GDP per capita of the exporting and importing
countries at time t (
and
respectively). The coefficients
and
are
expected to be positive. A higher level of income in the importing country should indicate a
higher level of demand for services (produced domestically or imported), whilst a higher income
level in the exporting country should be positively related to that country’s ability to produce
more services for export. Mirza and Nicoletti (2006) show that the supply of services to foreign
markets is strongly linked to the availability of inputs in both domestic and foreign markets.
The coefficients on
and
, the population in the importing and exporting
countries at time t, may be expected to take either a negative or positive sign. As MartinezZarzosa and Nowak-Lehmann (2002) show, population size may have a negative effect on
exports if countries export less as they become larger (as they rely more on internal trade) or a
positive effect if they export more as they become larger as they are able to achieve economies
of scale. Population size will have a similar effect on imports.
Although distance between the importer and exporter (
) is typically expected to
have a negative impact on trade in goods, it is not clear from the review of the existing literature
that this is necessarily the case for services. Service products do not have to be physically
transported from location to location. Depending on the nature of the service, in some cases it
will require movement of physical persons, but in others it may be communicated electronically.
Consequently, the importance of distance in services trade may be low or even insignificant.
The standard measure of distance, also employed in this paper, is to measure the distance
between the countries’ capitals.
The final regressor is dummy variable indicating whether the importing country and exporting
share a common language (
level of trade. Finally,
). This is expected to be positively related to the
are year dummies to control for any time trends in the data.
Language has been found to be significant in gravity model assessments of goods trade flows
and this effect could be expected to be particularly strong in services, as common language
should greatly facilitate many transactions. There is evidence to suggest that a common language
16
variable may also capture other effects such as cultural or institutional similarities between
countries. This should be borne in mind when interpreting the results of the model.
A key objective of this paper is to estimate tariff equivalents of the barriers to services trade
using the gravity framework. This is accomplished by adding a new variable (
) that
measures the total level of non-tariff barriers (NTBs) of the importing country j to the gravity
equation. A higher level of protection in the importer should reduce the level of services trade
between countries. The model estimated for total services imports becomes (2).
The variable used in equation (2) is primarily based on a Service trade restrictiveness index
produced by the World Bank (iresearch.worldbank.org).
4.2 Data Issues and Data Sources
Data used for the empirical analyses were obtained from the following sources:
(1) Services trade by partner, 2001-2010. Find data at: htpp://www.trademap.org.
(2) GDP per capita, and Population: WDI (2011): World Bank.
(3) Bilateral
distances,
Common
(official)
language:
CEPII
distance
database
(http://www.cepii.fr/anglaisgraph/bdd/distances.htm).
(4) Service trade restrictiveness index: World bank (htpp://www.iresearch,worlbank.org)
5
Result Interpretation
Using panel data techniques captures the relationships between variables over the period of the
sample and can control for the possibility that the unobserved effects may be correlated with the
regressors5. The two most commonly employed panel models are the fixed effects model (FEM)
and the random effects model (REM). In the FEM, the intercept terms are allowed to vary over
the individual units (in this case the importing and exporting country pairs) but are held constant
over time. REM assumes that the intercepts of individual units are randomly distributed and
independent of the explanatory variables. A priori, the FEM would be expected to be a better fit
in the gravity model context as the panel tracks pairs of countries over time and it is not realistic
5
See Baier and Bergstrand (2005) for discussion of the potential sources of bias in gravity model estimation and the
techniques that may be used to overcome this problem
17
to consider them to be randomly drawn. If this is the case and the unobserved effects are
correlated with regressors, the REM estimates will be biased6.
A shortcoming of the FEM is that variables that do not vary over time (distance or common
language for example) cannot be estimated as they are dropped in the fixed effects
transformation.
Both FEM and REM are estimated and their efficiency compared. First, the Breusch-Pagan test
is applied to the REM and compared to the pooled OLS estimator. The null hypothesis is
rejected, indicating that REM is a better estimator than OLS- A test statistic of 195.58, 196.53,
86.63, 46.71 and 40.06 are greater than the critical chi-squared value at one degree of freedom at
1% significance level (6.63). The Hausman test is applied to REM and FEM. The test statistic of
13.88, 16.63, 24.85, 16.67, and 34.51 is greater than the chi-squared critical value at six degrees
of freedom at the 5 percent significance level (12.59), therefore the null hypothesis that the REM
is consistent is rejected, the REM is shown to suffer from correlation and generate biased
estimates.
As an alternative to both the fixed effects and random effects models, Egger (2002, 2005)
proposes using the Hausman and Taylor model (HTM) (See Hausman and Taylor, 1981). The
HTM employs an instrumental variable approach that uses information solely from within the
dataset to eliminate the correlation between the explanatory variables and the unobserved
individual effects that undermines the appropriateness of the REM in the gravity model context.
The HTM is increasingly applied in gravity models of trade in goods; likewise Egger and
Pfaffermayr (2004) apply a HTM to FDI flows.
To test the appropriateness of the HTM, the Hausman-Taylor over-identification test is applied to
the FEM and HTM specifications. The test statistic of 1.53, 4.88, 6.73, 5.18, and 2.67 are less
than the critical chi-squared value with six degrees of freedom at 1 percent significance, so the
null hypothesis that the unobserved effects are correlated with other regressors is not rejected:
HTM is more efficient. Testing of the different specifications appears to confirm the findings of
Egger (2002, 2005), that the HTM is the most appropriate estimator for the gravity equation of
trade in goods, also holds for trade in services.
6
In the gravity model relationship it is more likely than not that the REM will be biased. Egger (2005, p883) cites
the example of an observed variable such as GDP being correlated with unobservable determinants of trade such as
human capital stock or trade barriers in the importing and exporting countries. The explanatory variables are
considered to be endogenous as they are correlated with the error term. See Cheng and Wall (2005) for a more
detailed discussion.
18
As shown in table 3, In the HTM estimation there is mixed results in the selected ECOWAS
Countries. First, GDP per capita determines the supply of services in the selected countries
except Liberia, where GDP per capita is statistically insignificant. A 1 percent increase in GDP
per head increases supply of services by 4.2%, 10.5%, 8.1%, and 10,7% in Côte d'Ivoire, Ghana,
Nigeria, and Senegal respectively. Intuitively, in the exporting country: a richer country (rather
than a larger country) will be able to produce more service commodities and will export more
services.
GDP per capita also determines the importer’s demand for service commodities of three out of
the selected ECOWAS Countries. Intuitively, this would be expected as individuals and
countries tend to consume more services as they become richer. But, GDP per capita of the
importers of services trade from Ghana and Liberia is statistically insignificant.
Population of the Exporting countries has negative significant impact on the supply of services in
three out of the selected countries, but in Ghana and Liberia, Population of the Exporting
countries is statistically insignificant. This could account for the fact that population-size of a
country is not a major determinant of services trade export; this is in line with Walsh, 2006
findings.
Population of the importers of services trade from Senegal, Ghana, and Nigeria determines the
importer’s demand for service commodities. Intuitively, this would be expected as individuals
and countries tend to consume more services as they become larger in size. But, the importers’
population is statistically insignificant for Côte d'Ivoire, and Senegal.
A common language has no significant influence on trade flows using the HTM. This may be
because; the selected ECOWAS Countries did not share common language with most of their
service trade partners.
Distance has no significant influence on trade flows using the HTM. This may reflect the fact
that physical distances have little or no relevance for the movement of service commodities. The
lack of a statistically significant coefficient on the distance variable is particularly interesting
because it confirms an earlier finding of Egger (2002) in relation to the application of the HTM
gravity model to goods trade, likewise, McPherson and Trumbell (2003) also find evidence in
support of this argument.
The variable that accounts for non-tariff barriers in the importing countries is statistically
insignificant but the coefficient has the expected sign. This shows the non-tariff barriers did not
19
has significant impact on the services trade flows between the selected ECOWAS Countries and
their services trade partners.
This seems contradicting the established view those barriers to services trade are important. The
outcome of this study may be as a result of the following among others: The problem of
collecting accurate data on barriers to services trade. Data on services flows are recent but
limited. Barriers to services trade are wide ranging and reflect the heterogeneous nature of
services products. Compiling and categorising restrictions is difficult but progress needs to be
made in this area for the effects of restrictions to be understood properly.
6.
Conclusions
This paper employs a gravity model approach to analyse the determinants of services trade and
to measure the importance of non-tariff barriers on trade in services for selected ECOWAS
Countries. Using data from Trade Map (www.trademap.org) providing total services imports a
variety of panel data estimators are applied and tested.
The Hausman-Taylor model is used to estimate the gravity equation for services. It is found to be
superior to the random effects model, which typically suffers from heterogeneity bias in the
gravity model, and avoids the problems associated with dealing with time-invariant variables
using a fixed-effects model.
A variable measuring the non-tariff barriers (NTB) to trade in each importing country, primarily
based on trade restrictiveness indexes of the World Bank (htpp://www.iresearch,worlbank.org),
is added to the gravity equation to calculate a set of tariff equivalents of the restrictions to
services trade. The NTB variable is only found to be insignificant.
20
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23
APPENDIX
Table: 3a Regression Results
Pooled OLS
FEM
Dependent
Variable
Total Services Import
Côte d'Ivoire
LNGDPPC_Exporter
LNGDPPC_Importer
LNPOP_ Exporter
LNPOP_ Importer
Liberia
Nigeria
Senegal
Ghana
Liberia
Nigeria
Senegal
4.45
10.79
.98
5.74
1.66
4.15***
9.72***
.56
7.98**
10.55**
(4.28)
(12.54)
(1.49)
(5.24)
(6.14)
(2.35)
(6.24)
(.99)
(3.89)
(4.39)
4.08
3.55*
3.12*
3.80*
4.08*
1.79**
.05
.52
1.87**
-1.37
(.28)
(.40)
-26.65***
-30.44
.39
(.21)
(.30)
(.80)
(.96)
(1.07)
(.79)
(.96)
-3.92**
-19.93
-9.80
-18.25***
-22.88
-3.67***
-24.36**
-13.46**
(17.39)
(32.20)
1.86
(14.32)
(9.17)
(10.08)
(16.08)
(2.29)
(10.50)
(6.29)
.90*
.78*
.99*
1.24*
1.09*
2.50
16.76*
30.64*
15.38*
8.69
(.12)
(.21)
.19
(.12)
(.12)
(4.44)
(6.13)
(7.31)
(4.96)
(5.75)
.52***
-.72**
.84*
-
-
-
-
-
-
(.33)
.31
(.18)
-
-
-
-
-
-
2.49***
.41
.92
5.41*
1.46
-
-
-
-
-
(1.54)
(2.10)
(2.13)
1.04
(1.46)
-
-
-
-
-
.42
1.08***
2.74*
2.63*
2.31*
-
-
-
-
-
(.46)
(.66)
(.62)
(.36)
(.42)
-
-
-
-
-
0.73
0.57
.48
0.86
0.75
3.88
16.63
24.85
16.67
34.51
Common Language
Distance
LNNTBsTotal
R2
Côte d'Ivoire
Ghana
Hausman Test
Source: Authors’ Computation.
Note: The Standard errors for the coefficients are in italics and bracket below them. *, **, *** represent 1%, 5% and 10% levels of
statistical significance.
24
Table: 3b Regression Results
REM
HTM
Dependent
Variable
Total Services Import
Côte d'Ivoire
LNGDPPC_Exporter
LNGDPPC_Importer
LNPOP_ Exporter
LNPOP_ Importer
Common Language
Distance
LNNTBsTotal_Partner
Côte d'Ivoire
Ghana
Liberia
Nigeria
Senegal
Ghana
Liberia
Nigeria
Senegal
4.26***
11.04***
.458
6.69***
5.29
4.18***
10.49****
.39
8.06**
10.72*
(2.37)
(6.59)
(1.07)
(4.13)
(4.80)
(2.30)
(6.18)
(.99)
(3.86)
(4.28)
2.65*
.46
1.03
(.63)
(.86)
(.92)
2.82*
1.79*
2.03*
-.30
.21
1.59**
-1.62***
(.53)
(.68)
(.72)
.94
(1.07)
(.77)
(.90)
-21.11**
-24.24
-.77
-20.40***
-10.60
-18.73**
-22.46
-1.91
-22.70**
-12.15**
(9.91)
(17.01)
(1.87)
(11.20)
(7.01)
(9.69)
(15.95)
(2.18)
(10.94)
(6.07)
.58
.02
.55
.97*
.61***
.49
7.40***
19.88*
7.75**
1.69
(.41)
(.75)
(.64)
(.34)
(.33)
(.79)
(4.12)
(5.90)
(3.45)
(2.57)
-
1.27
-.40
1.14**
-
-
-5.17
-18.27
-4.64
-
-
(1.32)
(1.13)
(.54)
-
-
(9.75)
(17.21)
(8.32)
-
-1.54
-6.77
-5.22
3.67
-5.78
-3.28
-12.93
-42.46
3.61
-17.65
(5.12)
(8.26)
(7.24)
(3.26)
(3.90)
(9.96)
(63.46)
(113.61)
(50.90)
(31.75)
-.34
-.28
1.80
2.24**
1.41
-.74
-16.10
-38.51
-12.48
-3.05
(1.60)
(2.73)
(2.26)
(1.15)
(1.22)
(3.19)
(20.16)
(34.25)
(17.45)
(11.38)
0.50
0.91
0.75
86.16
46.71
40.06
1.53
4.88
6.73
5.18
2.67
R2
Breusch-Pagan test:
195.58
196.63
Over-identification Test
Source: Authors’ Computation.
Note: The Standard errors for the coefficients are in italics and bracket below them. *, **, *** represent 1%, 5% and 10% levels of
statistical significance.
25
26