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Economics World, December 2015, Vol. 3, No. 11-12, 270-282 doi: 10.17265/2328-7144/2015.1112.003
Trade Agreements in Arab Countries: What Are the Effects on
Their Trade? (A Gravity Modeling Approach)
Mahmoud Reda Fath-Allah
League of Arab States, Cairo, Egypt
This paper investigates the effects of proliferating Regional Trade Agreement (RTA) in Arab countries on their
trade performance. The main question that the paper is trying to answer is: Why countries in Arab region are racing
toward signing RTAs with different trading partners. Are there any evidences that the trade performance of the
countries that have membership in many RTAs is better than those of countries that have less number of RTAs? By
focusing on the trade creation and trade diversion effects, a gravity model has been applied using panel data on
variables including Arab countries and their most important trading partners during the period from 1980 to 2010.
The results of the gravity model found that the Arab countries with overlapping membership in a number of trade
agreements have more significant positive effects in the form of having bigger volume of trade than those of the
other countries. The result of the paper supports the argument that countries engaged in many and overlapping trade
agreement would benefit by countervailing their negative effects in some separate agreements with positive overall
gains resulting from being “trade hub” that has an access to different markets with preferential terms.
Keywords: Regional Trade Agreements (RTAs), gravity models, preferential market access, overlapping RTAs,
Arab countries, Pan-Arab Free Trade Area (PAFTA)
Introduction
Arab countries are engaged in many preferential trade agreements with both regional and international
parties. The main reasons for this trend in most cases are to benefit from being a member in the WTO specially
in using article (XXIV) of Regional Trade Agreements (RTAs) and to have preferential access to the markets of
their main trade partners. Proliferation of RTAs in Arab countries reflexes multiple memberships in different
RTAs for many countries and overlapping their commitments in different agreements. This situation raises the
question of “what is the benefit that Arab countries would gain from being members in such overlapped trade
agreements”.
The study will examine the hypothesis test that the country which is member of overlapped RTAs has the
opportunity to trade more than the country that is a member of just one trade agreement.
The paper is organized in five sections: Section II presents an overview of the main trade agreements of
the Arab countries; section III discusses brief literature review; section IV is about model specification; section
V talks about estimation technique; and section VI presents concluding remarks.
Mahmoud Reda Fath-Allah, Ph.D., senior economist at Economic Sector of the League of Arab States, and adjunct lecturer at
Faculty of Economics and Political Science, Cairo University, Cairo, Egypt. Correspondence concerning this article should be addressed to Mahmoud Reda Fath-Allah, League of Arab States, Tahrir
Square, Cairo 11642, Egypt. E-mail: [email protected].
DAVID PUBLISHING
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Overview of the Main Trade Agreements in Arab Countries
This section will present an overview of the main trade agreements of the Arab countries. Arab countries
participate in different trade agreements among themselves and with other international parties as shown in
Table 1 and Figure 1. The first is the most important trade arrangement for Arab countries, Pan-Arab Free
Trade Area (PAFTA) which used to be known as the Greater Arab Free Trade Area (GAFTA). This
arrangement has 18 members of Arab countries and has a long history of try and error. The second is the
Euro-Mediterranean Partnership Agreements which seven Arab countries are engaged in. The third is the US
bilateral agreements which four Arab countries have signed.
Table 1
Arab Countries’ Membership in WTO and Selected RTAs Number of RTAs the country has membership
FTA with EFTA
FTA with Singapore
FTA with Turkey
Agadir Agreement
COMESAUS-FTAEU-MEDGCC PAFTAWTO Country
3 X X X X Bahrain
2 X X X Saudi Arabia
2 X X X Qatar
2 X X X UAE
2 X X X Kuwait
3 X X X X Oman
5 X X X X X X Tunisia
1 X X Yemen
3 X X X Lebanon
7 X X X X X X X X Jordan
6 X X X X X X X Egypt
6 X X X X X X X Morocco
4 X X X X Palestine
2 X X Algeria
2 X X Syria
2 X X Sudan
1 X Iraq
1 X X Djibouti
1 X Comoros
0 X Mauritania
2 X X Libya
0 Somalia
6 1 6 4 5 4 7 6 18 12 Arab country membership in each RTA Source: Retrieved from http://www.escwa.un.org/information/pubaction.asp?PubID=1702; http://www.ustr.gov/trade-agreements/ free-trade-agreements; http://www.arableagueonline/org/las/index.jsp; http://rtais.wto.org/UI/PublicMaintainRTAHome.aspx.
First: GAFTA or PAFTA
GAFTA is triggered by the changes in the global economic system. GAFTA is a contractual framework
that is based on legal tools and dispute settlement mechanisms. The legal framework is based on an agreement
of the year 1981 for trade facilitation among the Arab countries. In 1997, 14 Arab nations began talks
concerning the formation of a GAFTA. The end of the grace period for the transition to free movement of
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goods within GAFTA was moved up from 2007 to 2005. The general secretariat of the League of Arab States
(LAS), mainly the Economic and Social Council, is responsible for the follow-up of the agreement’s
implementation. GAFTA has notified to the WTO as RTA under article XXIV and became known as PAFTA.
Currently, PAFTA has 18 member countries, of which three are less developing countries (LDCs) namely
Palestine, Sudan, and Yemen. The trade of PAFTA members represents 93% of total trade of all Arab countries.
Intra-trade among members’ countries are 6% in exports and 11% in imports. The importance of PAFTA to the
Arab countries represents 99% from the total Arab exports which is 904.5bUS$ in 2010 and represents 93% of
total Arab imports which represents 655.2bUS$. In terms of market size, PAFTA represents a huge market with
population more than 298.2 million which represents about 85% of total population of all Arab countries (Arab
Monetary Fund, 2012).
Figure 1. Overlapping trade agreements in Arab countries.
Second: Euro-Mediterranean Partnership Agreements
The great majority of Arab countries are involved in negotiations with the EU, through the
Euro-Mediterranean or EU-GCC context. Currently, seven Arab countries have signed the agreements and
implemented their commitments. The seven countries are Egypt, Tunisia, Morocco, Jordan, Palestine, Algeria,
and Lebanon. The EU approach is based on the universal model of trade liberalization, which links it to growth,
economic reform, and attraction of foreign direct investments (FDIs). The EU agreements lack agricultural
sector liberalization and free movement of persons. In addition, the EU has linked liberalization of trade flows
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with “substantial” amount of aid cooperation funds; the EU partnership administers these funds through the
Middle East Development Assistance (MEDA) program. Before the association agreements were signed, most
Mediterranean partner countries accessed the EU market through preferential schemes administered. However,
accession problems are often supply-side and not barriers on the recipient side. Accordingly, these countries
will suffer macro-economic deterioration and their trade balance will be negatively impacted due to the free
trade agreement. This could lead to budgetary and employment vulnerability. EU partnership is a bilateral
agreement. That means, different commitments and aspect coverage, although, in general, a typical EU
partnership agreement has the following aspects (EU Commission, 2012):
market access in goods;
general services;
specific services chapters dealing with financial services and telecommunications;
intellectual property rights;
investment;
government procurement;
competition policy that impacts policy space and country economic structures;
labor and environmental standards and issues.
Third: US Bilateral Trade Agreements
The US is “picking off” individual countries, in pursuit of largely geopolitical and strategic objectives, and
setting its parallel trade agenda in the region. Four Arab countries signed bilateral FTAs with US, namely,
Jordan, Bahrain, Oman, and Morocco. FTAs are a tool for the US to enforce changes in domestic policies and
secure guarantees for US corporations, which are not achieved through the WTO.
Literature Review
The debate of the effects of trade agreements on the trade of a specific country manly lays on one of the
two arguments (Urata & Okabe, 2007). The first one is that the countries which have membership in multiple
trade agreements are likely to face positive effects on the trade. This argument, supported by the
hub-and-spokes (HAS) concept, which is prevalently used in the transportation literature and first introduced to
international trade as a “two-sided triangle” by Wonnacott (1975), is a useful framework for unraveling this
noodle bowl of FTAs. The HAS is unique to FTAs, because there is no restriction on the number of FTAs that a
region can sign. As a result, the region acts like a “hub”, linking up several free trade areas and trading on
preferential terms with every “spoke” partner. Chong and Hur (2007) have highlighted the importance of
hub-and-spokes as a trading system in a world of overlapping free trade agreements. Their study indicates that
small and open economies like Singapore prefer hub status to a free trade zone involving the same country
group. They are not likely to stop at one agreement, once they embark on the FTA path. Although FTAs
especially those with deep integration can be attractive, significant changes in industrial composition (due to
specialization in production) can lead to temporary spells of frictional unemployment.
The second argument is that the countries which have membership in multiple overlapping RTAs are
likely to have negative effects on the trade flow. This argument is supported by the idea of “transaction cost of
trade”, as it will be higher if the number of trade agreement for respective country gets higher. The explanation
is that the membership of a country in different RTAs where each of RTA has its own rules of origins,
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certificate of origins, customs procedures, trade provisions, list of eligible goods, and standards requirements
will complicate trade system and needs more managerial effort. All of those will lead to increasing transaction
cost of trade in terms of time of release for shipment and number of documents required. So, this paper needs to
imperially investigate which one of those arguments works in the case of Arab countries.
To do so, this paper estimates whether or not an FTA has had a statistically significant effect on trade
flows. If the respective coefficient of FTA is statistically significant with a positive sign, that would lead to
concluding that the FTA has a positive effect on trade flows with a magnitude relation to the size of the
coefficients. This, however, is an inference about the FTA’s effect on total trade flows and not due to trade
creation, trade diversion, or both. To estimate these effects separately, another binary variable would need to be
included. With this extended specification, the binary variable for observations where both the importing and
exporting countries are members of the FTA would capture trade creation, while the second binary variable for
observations where one of the trading partners is not a party to the FTA would capture trade diversion.
The gravity model is an econometric method of estimating trade flows. This model has been used to
analyze the impact of not only FTAs, but also the effects of General Agreement on Tariffs and Trade-WTO
membership, currency unions, migration flows, foreign direct investment, and even disasters (Freund &
McLaren, 1999). The main benefit of the gravity model in evaluating an FTA is that it can control the effects of
as many other trade determinants besides the FTA as necessary and can therefore isolate the effects of the FTA
on trade. The basic gravity model of trade, which is analogous to Newton’s law of universal gravitation in
physics, relates the imports of country i from country j (Mij) positively to the gross domestic product (GDP) of
the importing country (Yi) and the GDP of the exporting country (Yj), but negatively to the geographical
distance between the importing and exporting countries (Dij) (Anderson, 1979):
ij i j ijM GYY D (1)
where G is a constant.
Expressed in logarithmic form and attaching a random error term (uij), the basic gravity equation becomes:
1 2 3ln ln ln ln
jij i ij ijM G b Y b Y b D u (2)
where b is coefficient. Given the hypothesized relationships contained in the gravity model, b1 and b
2 are
expected to be positive, while b3 is expected to be negative. In the gravity equation, geographical distance
between the importing and exporting countries is actually a proxy for trade costs, which impede bilateral trade.
Other variables that capture trade costs (e.g., adjacency, common language, colonial links, common currency,
or whether the importing or exporting countries are islands or landlocked) may be added to the equation
(Plummer, Cheong, & Hamanaka, 2010).
Empirical Model
Extended gravity model in double-log form as in the work of Lee, Innwon, and Kawanho (2005) will be
used:
0 1 2 3 4
5 6 1 2 3
GDP GDPln(Trade ) ln(GDP GDP ) ln( ) ln Dist ln(Area Area )
PDP PDP
Border Language RTA Insiders RTA Outsiders RTA Overlap
i j
ijt i j t t ij i j
i j
ij ij ijt ijt ijt ijt
(3)
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where Tradeijt is real bilateral trade between i and j; GDPiGDPj are real GDP for countries i and j respectively;
PDPiPDPj are population of countries i and j; Distij is the distance between countries i and j; AreaiAreaj are land
mass of the country i and j respectively; Borderij is binary variable which is in unity if i and j share a land
border; Languageij is binary variable which is in unity if i and j have a common language; RTA Insidersijt is
dummy variable which is in unity if i and j belong to the same RTA and take zero otherwise; RTA Outsidersijt
is the dummy variable which is in unity if country i has existing trade agreement with a country other than j;
RTA Overlapijt is the dummy variable which is in unity if country i has existing trade agreement with at least
one country other than j in addition to the case which RTA Insidersijt and/or RTA Outsidersijt dummies are in
unity; εijt is the independent and identically distributed (IID) random variable with zero mean and constant
standard deviation.
The data came from Rose (2004) which covered 175 countries from 1948 to 1999 and the dataset has been
expanded by adding more observations using UN-COMTRADE (United Nations Statistics Department, 2012),
WITS (World Integrated Trade Solution, 2012), WTO (WTO, 2011; 2012), World Bank’s world development
indicators databases. Table 2 shows list of countries included in data sample.
Table 2
List of Countries Included in Sample
Arab countries EU-27 EFTA Other countries
1 Algeria 1 Austria 1 Iceland 1 China
2 Bahrain 2 Belgium 2 Liechtenstein 2 India
3 Comoros 3 Bulgaria 3 Norway 3 Japan
4 Djibouti 4 Cyprus 4 Switzerland 4 Australia
5 Egypt 5 Check Republic 5 USA
6 Iraq 6 Denmark 6 Canada
7 Jordan 7 Estonia 7 Brazil
8 Kuwait 8 Finland 8 Mexico
9 Lebanon 9 France 9 Turkey
10 Libya 10 Germany 10 Singapore
11 Mauritania 11 Greece 11 Iran
12 Morocco 12 Hungary 12 South Korea
13 Oman 13 Ireland 13 Russia
14 Qatar 14 Italy
15 Saudi Arabia 15 Latvia
16 Somalia 16 Lithuania
17 Sudan 17 Luxembourg
18 Syria 18 Malta
19 Tunisia 19 Netherlands
20 UAE 20 Poland
21 Yemen 21 Portugal
22 Romania
23 Slovakia
24 Slovenia
25 Span
26 Sweden
27 UK
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Three groups of explanatory variables have been used as follows:
(1) Variables affect demand and supply of importing and exporting partners: Those variables include
economic mass of paired countries, such as GDP and population. The expected signs of respective variables are
positive (β1 and β2 > 0) (Shepherd, 2012);
(2) Variables represent natural factors affecting trade between respective partners: Those variables include
distance, land area, border, and language. The expected signs of respective variables are: β3 < 0 and β4, β5, and
β6 > 0;
(3) Variables capture trade policy effects: Those variables include RTA Insiders, RTA Outsiders, and RTA
Overlap. The dummy RTA Insiders measures the degree of trade-creation effects of the RTA among members,
while the dummy RTA Outsiders captures the degree of trade-diverting effects between members and
nonmembers, compared to the “normal” bilateral trade flows. RTA Overlap captures just the additional trade
creation taking place between an overlapped country and a member country not overlapped together. Table 3
shows possible combinations of dummy variables and respective implications on trade policy.
Table 3
Possible Combinations of Dummy Variables and Respective Implication on Trade Policy RTAIn RTAOut RTAOverlp
Respective meaning of the dummy variable combination Trade agreement exists between i and j
Country i has existing trade agreement with a country different than j
Country i has at least one trade agreement with a country other than j
0 0 0 Arab country has no existing trade agreement with its respective trade partner or any other country. Though, no overlap exists in this case.
1 0 0 Arab country has existing trade agreement with only its respective trade partner. There are no agreements with any other countries. Though, no overlap exists in this case.
0 1 0 Arab country has only one existing trade agreement with a country otherthan its respective trade partner. Though, no overlap exists in this case.
0 1 1 Arab country has trade agreements with more than one country other than respective partner. Though, overlap is existing in this case.
1 1 1 Arab country has trade agreement with its respective trade partner and with other countries. Though, overlap is existing in this case.
Estimation Technique
The data set has a feature of panel structure consisting of 32,886 annual observations clustered by 1,134
country pair groups from 1980 to 2010. The number of observations varies per year as the data set is
Un-Balanced Panel Data. Summary statistics for the whole data used in the estimation are presented in Table 4
and diagnostic scatterplots appear in Figure 2.
The following steps in estimation process have been applied (Baltagi, 2005):
(1) estimate pooled data using OLS;
(2) estimate fixed effects model using least square dummy variable (LSDV);
(3) testing pooled model vs. fixed effects model;
(4) estimate random effects model;
(5) testing fixed effect vs. random effects models.
The followings are illustration of those steps.
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Table 4
Summary Statistics
Variable Mean Std. Dev. Min Max Observations
ltrade overall 11.73751 2.781319 0.2307641 18.80282 N = 16,109
between 3.054619 1.705306 17.69675 n = 1,040
within 0.9082277 1.068073 17.27022 T-bar = 15.4894
lrgdp overall 49.20263 2.290296 40.99001 56.01982 N = 16,109
between 2.431357 41.08295 54.9786 n = 1,040
within 0.4314612 47.07972 51.13304 T-bar = 15.4894
lrgdppc overall 17.08778 1.400499 11.71127 20.80505 N = 16,109
between 1.430628 12.25521 20.16171 n = 1,040
within 0.3437182 14.75803 19.08219 T-bar = 15.4894
ldist overall 7.779295 0.742171 4.016798 9.226302 N = 16,109
between 0.687129 4.016798 9.226302 n = 1,040
within 0 7.779295 7.779295 T-bar = 15.4894
lareap overall 24.47382 3.238323 12.18546 31.33648 N = 16,109
between 3.284881 12.18546 31.33648 n = 1,040
within 0 24.47382 24.47382 T-bar = 15.4894
border overall 0.0277485 0.1642564 0 1 N = 16,109
between 0.1590971 0 1 n = 1,040
within 0 0.0277485 0.0277485 T-bar = 15.4894
comlan overall 0.1623316 0.3687662 0 1 N = 16,109
between 0.3664252 0 1 n = 1,040
within 0 0.1623316 0.1623316 T-bar = 15.4894
rtain overall 0.066857 0.249782 0 1 N = 16,109
between 0.1595534 0 1 n = 1,040
within 0.1609761 -0.883143 1.031143 T-bar = 15.4894
rtaout overall 0.4225588 0.4939818 0 1 N = 16,109
between 0.4200256 0 1 n = 1,040
within 0.2673273 -0.5404041 1.386845 T-bar = 15.4894
rtaove~p overall 0.0733131 0.2606578 0 1 N = 16,109
between 0.0743976 0 0.3103448 n = 1,040
within 0.2452565 -0.2370318 1.034852 T-bar = 15.4894
Source: Stata output.
Estimating Pooled Data Using OLS
Pooled model is a restricted form of the panel data assuming no difference across all units or time. That
means equation (1) is a single equation for all country pairs (i), in this case (Baltagi, 2005):
1 2
0 = 0 = ... = 0 = 0i
(4)
Table 5 shows estimation results of the four forms of the gravity model of equation (1).
Model P1 fits the data well, explaining a major part of the variation in bilateral trade flows. The signs of
the coefficients meet the economic expectations except those (lareap and comlang) of which both have negative
signs. Lee et al. (2005) encountered the same results and found that the variable of the land area could lead to
the same results of the distance variable (Mayer & Zignago, 2011) in a country with large land area. Model P2
fits data slightly better than P1 but with a negative signs for RTAin and RTAout which lead to the result that
trade agreements of the Arab countries have trade diversion effect. In model P3, the introduction of the variable
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(RTA Overlap) has significant positive effect which means that Arab countries with overlapping trade
agreement encountered better trade flows than other normal Arab countries. Finally, model P4 slightly less fits
data with significant parameters except those of RTA Overlap.
Figure 2. Diagnostic scatterplots.
Table 5
OLS Estimates for Pooled Data
(P1)
Standard gravity model
(P2) (P1) + trade policy variables without overlap variable
(P3) (P2) with overlap variable
(P4) (P3) after omitted non-significant variables
ltrade Estimated parameter
t-statistics Estimated parameter
t-statistics Estimated parameter
t-statistics Estimated parameter
t-statistics
lrgdp 1.04994 117.76 1.071241 117.75 1.0671 116.06 0.955379 146.23
lrgdppc 0.22367 18.33 0.257506 20.53 0.264093 20.76
ldist -0.86244 -41.5 -0.86135 -40.21 -0.86399 -40.31 -0.97142 -46.28
lareap -0.16469 -25.89 -0.17941 -27.72 -0.17819 -27.49
border 1.091818 12.42 1.091883 12.45 1.096459 12.5
comlang -0.11477 -2.83 0.010194 0.24 0.01324 0.32
rtain -0.30262 -5.28 -0.37143 -6.04 0.200394 3.13
rtaout -0.30944 -10.28 -0.34344 -10.71 0.295374 9.68
rtaoverlp 0.178938 3.07 -0.10918 -1.77
_cons -33.0161 -100.9 -34.1601 -99.62 -34.0728 -99.05 -27.8429 -87.98
Adjusted R2 0.6417 0.6444 0.6446 0.588
RSS 44630.54 44,288.32 44,262.35 51,318.93
F test 4809.05 3649.76 3,246.98 4,599.32
Notes. P1 estimates the core gravity variables regardless any trade policy variables; P2 estimates the same variables in P1 plus trade policy variables except that of overlap; P3 estimates P2 model plus overlap variable; and P4 estimates P3 model after omitting variables with non-significant parameters.
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Estimating Fixed Effects Model Using LSDV
The problem in estimating pooled data model is that this is a restricted form of specification and if this
assumption does not meet the real situation, the OLS estimates yield biased and inconsistent estimates of the
regression parameters. Because this is an omission variable bias, due to the fact that OLS deletes the individual
dummies when in fact they are relevant. To overcome this problem, starting by estimating fixed effects model
and then testing wither pooled or fixed effect are the best specification for the data. Using LSDV which has two
sided estimations, one is called within units’ estimation which assumes the focus on the units (country pair)
though, variables, such as area, language, and border do not change over time. The second side is called among
units, in this case, the focus is on the cross-sections and its variations across time (Retrieved from
http://www.wto.org/english/res_e/publications_e/practical_guide12_e.htm). Table 6 shows fixed effect
estimates of the gravity model.
Table 6
Fixed Effects Estimates Using Within and Between Units Variations
(F1)
FE—Within units (F2)
FE—Between units ltrade Estimated parameter t-statistics Estimated parameter t-statistics
lrgdp 0.307045 11.44 1.102518 31.32
lrgdppc -0.10957 -3.51 0.210389 3.99
ldist -0.98845 -11.36
lareap -0.18665 -7.71
border 1.133116 3.26
comlang 0.127101 0.8
rtain 0.022933 0.44 -0.99911 -2.77
rtaout -0.23432 -7.14 -0.72786 -4.83
rtaoverlp 0.202156 5.35 4.329017 5.42
_cons -1.41487 -1.42 -33.9836 -25.9
R2 0.0193 0.7259
F test 59.22 303.02
Model F1 has low fits data, even though the parameters are significant and lead to concluding that
overlapping trade agreements have small positive effects on a trade flow of a country. Model F2 fits data better
than model F1 and also F2 has significant parameters except for the variable common language which has
negative sign anticipated as the majority of trade for Arab countries existing with non-Arab speaking countries.
Testing Pooled Model vs. Fixed Effects Model
This is a simple chow test with the restricted residual sums of squares (RRSS) being that of OLS on the
pooled model and the unrestricted residual sums of squares (URSS) being that of the LSDV regression. If N is
large, one can perform the “within” transformation and use that residual sum of squares as the URSS. In this
case (Baltagi, 2005), it is as follows:
0 1, 1
RRSS URSS / ( 1)F ~ F
URSS / ( ) N N T K
NNT N K
(5)
Applying this test on model F1 leads to the result: F (1,039; 15,064) = 45.09. According to this test, the
specification of fixed effect model better fits the data.
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Estimating Random Effects Model
Random effects models assume that the individual-specific effect is a random variable that is uncorrelated
with the explanatory variable. This setup allows to estimate coefficients for time-invariant variables, such as
language, distance, border, and land area which fixed effect models do not allow to do so.
Table 7 shows RE estimate of the gravity model (Baltagi, 2005). The results indicate low value of overall
coefficient of determination with higher respective value for between estimates. That would imply the
importance of variations across units (cross sections). The explanation of that results meets the reality of the
agreements of Arab countries which are not having the same effects across all Arab countries, because each of
them has different aspects and scope in terms of inclusive sectors and goods.
Table 7
Random Effects Estimates of the Gravity Model1
ltrade Estimated parameters z-test
lrgdp 0.636634 29.48
lrgdppc -0.12116 -4.61
ldist -0.93785 -10.86
lareap -0.01063 -0.53
border 0.896302 2.53
comlang -0.51451 -3.33
rtain -0.01447 -0.28
rtaout -0.2629 -8.26
rtaoverlp 0.038094 1
_cons -10.3674 -11.19
R2
within 0.0168
between 0.6241
overall 0.5192
sigma_u 1.557954
sigma_e 0.930076
rho 0.73725
Wald chi2 1,884.28
Testing Fixed Effect vs. Random Effects Models
Now, after having fixed effect and random effects results, both results are consistent estimators, while only
random effects is efficient estimator, because random effects model has more degrees of freedom than those of
fixed effect model. So, this is a decision point to select the true specification model between random effect and
fixed effect. If the true model specification is random effects, it means that the estimator is a constant and
efficient. If this is not the case, it has to take fixed effect estimator, as it is consistent estimator even it is not
efficient. In order to take a decision in this regard, Hausman specification test has been used which takes the
form as the following (Baltagi, 2005):
12
'ˆ ˆ ˆ ˆ ˆ ˆVar( ) Var( ) ~FB RB FB RB FB RB kx
(6)
1 Coefficients estimated in random effects models have normal distribution (Gaussian); that is why using z-test instead of t-test in this case.
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where ˆFB is the estimated parameters of fixed effect model; ˆ
RB is the estimated parameters of RE model;
ˆVar( )FB is the variance matrix of fixed effect model; ˆVar( )RB is the variance matrix of RE model; 2kx is
the critical value of qui-square statistics with k degrees of freedoms; and k is the number of explanatory variables.
The resulting value of the test is 1,099.74 which will lead to rejecting the null hypothesis. Consequently,
fixed effects model is the right model to fit the data.
Conclusions
This paper applied gravity model approach on trade flows of Arab countries in order to have empirical
evidence on the effects of proliferating RTA in Arab region on their trade flows and to contribute to the debate
of the two mentioned arguments of whither membership in multiple trade agreements which would gain
positive or negative results. The results of this study indicate that the effects of the overlapping agreements are
positive on the trade flows of the respective countries. The respective parameter of trade agreement without
overlap resulted in negative effects as the agreements itself may have trade diversion effects. Countries engaged
in many overlapping trade agreement would benefit by countervailing their negative effects in some separate
agreements with positive overall gains resulting from being “trade hub” that has an access to different markets
with preferential terms.
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