would the implementation of the gold dinar affect trade among oic countries

Would Implementation of the
Gold Dinar Affect Trade Among
OIC Countries?
M. Luthfi Hamidi
Abstract: This study attempts to analyze whether the implementation of the gold
dinar proposal would affect trade among OIC members, since gold is considered a
more stable currency, as it is less risky than fiat money. Empirical results produced by
thisstudyprovethatfiatmoney,particularlyrealexchangerateof theUSdollaragainst
gold, has an absolutely negative impact on developing countries’ exports. Although
its direct effect on trade is very low, the variable itself is statistically significant at 5%
level of significance. In contrast, those involved in the gold dinar trade bloc will likely
increase their respective intra-trade by more than 2 units. In other words, the gold
dinar bloc offers to those members a trade-creating effect. This effect is more robust
than the benefit generated by the outstanding economic bloc within OIC including
AMU and CEAU. Furthermore, gold dinar also generates considerable efficiency in
terms of trade payment. If six leading OIC exporting countries join in the gold dinar
bloc, trade efficiency in terms of payment will be around 76.68%.
JEL Classification: F02, F10, F36, E42, P45.
I. Introduction
The gold dinar has been a controversial issue among OIC member states
ever since the former Prime Minister of Malaysia, Mahathir Mohammad,
M. Luthfi Hamidi, Bank Muamalat Indonesia (BMI), and Lecturer in Islamic Business at
the University of Paramadina, Jakarta, Indonesia.
This paper is a shorter version of the author’s master dissertation submitted to Markfield
Institute of Higher Education, Leicester, UK. The author would like to thank to Dr. Mehmet
Asutay for his supervision and insightful comments during completing this work. The
author wishes to extend his thanks to Dr. Mustafa Edwin Nasution for his suggestion on the
econometric estimation procedure.
© 2009, international association for islamic economics
Review of Islamic Economics, Vol. 13, No. 1, 2009, pp. 31–68.

32 Review of Islamic Economics, Vol. 13, No. 1, 2009
initiated public discussion of the proposal in 2001, arguing that the gold
dinar would boost inter-OIC trade.
That trade is certainly extremely low. One of the striking factors
inhibiting such trade is high exchange rate volatility of the member states’
domestic currencies so that, in order to reduce that risk, most of them use
‘hard’ currency such as the US dollar in international trade. But that does
not altogether remove the risk since the dollar, like other fiat money, also
tends to fluctuate in value: the notion of gold dinar is therefore seen as an
attractive alternative since its value is expected to be more stable than fiat
money.
The important question that needs to be asked and properly answered
is whether the gold dinar really would affect trade among the member states
of the OIC. Unless there is a positive answer, the gold dinar will remain a
wish or fantasy and not become a reality. This paper attempts to evaluate
and examine the gold dinar issue from several standpoints. Firstly, it seeks
to assess whether the gold dinar would be a stable currency and whether
it would (or not) have a significant impact on trade. For this reason, the
performance of gold is contrasted with that of the dollar and other hard
currencies such as the DM and Yen. Secondly, the paper tries to evaluate
whether a gold dinar trade bloc is likely (or not) to trigger a trade-creating
effect1
which can increase trade among its members. Finally, if the first two
questions are answered positively, the further task is to calculate how much
gold is required to support the trade and then to determine whether the
member states own sufficient gold reserves.
II. Literature Review
2.1. Trade in the Islamic world
In the Islamic tradition, trade has not only been the way to obtain reciprocal
benefits for the parties involved, but also a primary means to spread Islamic
teachings to different parts of the world. It has also played an important
role in unifying the Islamic world.2
Thus, the failure of trading among
Islamic countries implies disunity among them and is a signal of the
weakened bargaining power they have at their disposal in relation with other
countries.
The establishment in 1972 of the Organization of Islamic Conference
(OIC) prompted a hope that Muslim countries that share similar political
and economic interests would cooperate. The OIC was also intended to

33Review of Islamic Economics, Vol. 13, No. 1, 2009
serve as a forum where the problems faced by Muslim countries could be
tackled by them together. Among other concerns, the issue of trade gained
priority in OIC as trade volume among Muslim countries was and remains
very low, even though most of the Muslim countries are blessed with
abundant natural resources. In 2003, the trade amongst Muslim countries
was only around 13% of the total trade. This is the highest record during the
last five years (see Table 1).
Table 1: Trade among OIC Member Countries (US million dollar)
Trade 2003 2002 2001 2000 1999
Total intra-trade 151,272 123,972 115,793 109,376 83,402
Total trade 1,153,847 947,629 907,947 919,203 751,098
Percentage 13.11 13.08 12.75 11.89 11.1
Source: SESRTCIC statistics
In general there is an incremental trade growth of OIC members, but
most of it is dominated by oil and relies on primary commodity exports
(Table 2a and Table 2b) while being heavily dependent on industrialized
countries for the import of manufactured products (Table 3). That, in turn,
makes the Muslim countries dependent on and enormously affected by the
world economy, and they are, in consequence, subordinate to the economy
of the industrialized world (Shalaby, 1988:90).
Table 2a: Share of Oil to the Total Export for the OIC Leading Exporters (%)
No. Countries 2002 2001 2000 1999
1 Saudi Arabia 89 90 92 85
2 UAE 92 - - -
3 Malaysia 9 9.7 9.6 7
4 Indonesia 24 25.3 25 23
5 Turkey 1.9 1.4 1 1
6 Pakistan 1.9 2 1 1
7 Nigeria 100 99.6 98.9 99
8 Iran 86 84 91.2 75.7
9 Kuwait - 79 79 90.2
10 Syria 72 76 76.3 68
Source: SESRTCIC Statistics

Table 2b: Share of Primary Commodity to the Total Export of the OIC
Leading Exporters (%)
No. Countries 2002 2001 2000 1999
2 UAE 96 - - -
3 Malaysia 21 20 19.6 20
4 Indonesia 46 44 43 46
5 Turkey 14.5 15.1 15 16
6 Pakistan 53 50 47 53
7 Nigeria 97 97.2 98 98
8 Iran 91 90 93 -
9 Kuwait - 80 80 91.5
10 Syria 93 92 92.2 92.6
Source: SESRTCIC Statistics
Table 3: Share of Manufactures to the Total Imports of the OIC Top Ten
Exporters (%)
No. Countries 2002 2001 2000 1999
2 UAE 86 - - -
3 Malaysia 83 83 83.9 85
4 Indonesia 59 61 61 69
5 Turkey 68 67 70 73
6 Pakistan 53 50 47 53
7 Nigeria 76 76 76 67
8 Iran 82 76 73 -
9 Kuwait - 79 79 79
10 Syria 64 65 65 59
Source: SESRTCIC statistics
2.2. Economic cooperation among OIC countries
Economic cooperation among OIC member countries is believed to be a
way forward to overcome the obstacles inhibiting trade flow. A significant
achievement in 1978 was the establishment of The Islamic Chamber of
Commerce, Industry and Commodity Exchange (ICCICE)3
in Karachi.
Furthermore, the focus on economic cooperation has improved consistently
since the founding of The Islamic Centre for Development of Trade (ICDT)4
in Casablanca in 1981.

Full economic cooperation gained momentum once there has been a
proposal to establish an Islamic Common Market (ICM). The ICM will be
a kind of vehicle based on ideological unity to bridge the gap among Muslim
countries with the main target to facilitate optimal intra-Islamic utilization
of their resources (Sadeq, 1996: 39). The ICM is considerably more complex
than a Free Trade Area (FTA)5
or Customs Union (CU),6
since it encourages
cooperation not only in the trading of goods, but also in regard to mobility
of factors of production. Thus, the ICM is to be characterized by free
movement of capital, labour, service, as well as entrepreneurship (Satiroglu,
1996: 6; Nienhaus, 1996; 91; Sadeq, 1996: 44; Alatas, 1987: 32).
Some scholars hold that the establishment of the ICM will overcome
trade barriers and enhance effective economic cooperation. On the other
hand, few of those scholars have taken care to point out that the low level
of intra-trade among OIC members is a result of the low level of their
development (Bhuyan, 1996:26). According to Bhuyan, the establishment of
economic cooperation among the members should be a catalyst to accelerate
their industrialization rather than directly to improve the level of trade.
Another concern in light of the establishment of the ICM is its
applicability. It is believed that the ICM is a long-term target that needs
to be consistently adhered to. Regrettably, however, the future of the ICM
is unclear since there is barely even a definite timetable agreed, among the
members, to formalize the work of ICM.7
Instead of planning to move
together gradually as a step closer towards ICM goals, some of OIC members
have been reluctant to remove the barriers to free movement of capital and
labour which protect their national interest (Nienhaus, 1996:101).
2.3. The impact of currency volatility on trade
Currency volatility is considered to be one of the barriers to increase
trade. Many of the developed countries adopted a floating rate regime
on their national currency in 1973. Since the inception of that regime, the
industrialized countries have experienced a significant decline in their
economic growth. Between 1960 and 1973, their average yearly growth rate
was 4.4%, while, during 1973–1990, it dropped to 1.3% (Grauwe, 1998:240).
The decline in economic growth can be traced to the change that has
taken place in international trade since the floating exchange regime.Because
the variability of exchange rate increases uncertainty, it leads investors to
reduce risk by shifting a part of their international trade into domestic trade.
This in turn hits the international trade and results in slowdown in growth.

One may argue that the agents are free to utilize hedging facilities to protect
themselves against loss. However, since this facility is not without cost, it
means that any resulting gains or benefits are set off against the losses or
drawbacks (Grauwe, 1998:241).
Recent studies provide more robust evidence with respect to the effect
on trade of the currency volatility. A study on G-38
exchange rate volatility
suggests that one percentage increase in that rate decreases real exports of
developing countries by, on average, about 2 percent (Esquivel and Larrain,
2002). Vergil (2002) examines trade flows between Turkey and its trade
partners and finds that exchange rate volatility has a significant negative
effect on trade. Arize et al. (2004) came up with the same result. They
investigated exchange rate volatility in eight Latin American countries and
report that the exchange rate has a significant negative impact on export
demand both in the short and long run.
III. Methodology
3.1. Measuring exchange rate volatility and modelling its impact on trade
One of the most common measures to capture time-varying volatility on
exchange rates is Generalized Autoregressive Conditional Heteroscedasticity,
known as the GARCH model, developed by Bollerslev (as cited by Verbeek,
2000:266). This model has been employed in previous studies such as
McKenzie (1998) and Vergil (2002).
Another way to measure exchange rate volatility is coefficient of
variation (CV) of exchange rate. Esquivel and Larrain (2002) define
exchange rate volatility as the time-varying twelve-month coefficient of
variation (CV) of the real exchange rate. This method is actually used to
indicate a dispersion of the real exchange rate. The formula to calculate CV
can be written as follows:
�
CVt +m =
1
m
(εt +i−1 −ε)2
i=1
m
∑






1
2
ε

(1)
where m is the order of moving average and ε is the mean of the bilateral
exchange rate between month t and t+m-1. In this study, CV will be used to
measure the level of volatility of exchange rate, which can then be utilized
to predict its impact on trade. Since in this study annual data is used, the
−

moving average method is perhaps no longer relevant. Instead of using
the formula given above, this study proposes to calculate CV based on the
following formula:

(2)
where, x is an individual value, n is the number of observations (test values)
and x is the mean of n values. In other words, CV is calculated by dividing
standard deviation by mean value (Chou, 1965:107; see also Kane, 1969:78).
Esquivel and Larrain (2002) investigate the impact of the volatility of G-3
currencies on developing countries trade. They propose a model to estimate
the exports of developing countries as a function in the following form:
X = f (world demand, bilateral dollar real exchange rate, G-3 currency),
which can be expressed as follows:
(3)
where Xt
denotes exports of country t, GDPw
represents the real World GDP,
RERUS
is bilateral real exchange rate in terms of the dollar and the variables
VOL are the coefficients of variation (CV) of the G-3 real exchange rates. In
this study, the model will be utilized with subtle modification by introducing
variable Ddollar/gold
in the model. Besides, a model based on pure exchange rate
will also be assessed.
Model 1:
(4)
Model 2:
(5)
Model 3:
(6)
where VG represents volatility values based on the GARCH technique, VC
is volatility values based on the coefficient of variance (CV) method, and
RER represents the absolute values of real exchange rate of the respective

countries. Xt
is total export of developing countries, in dollar millions.
GDPw
is World’s Gross Domestic Product in dollar millions. Ddollar/gold
is real
exchange rate of dollar in terms of gold, while DMDM/dollar
and Yyen/dollar
are real
exchange rate of DM and yen with respect to the dollar.
GDPw
represents the scale economy of the world. It is expected to be
positive since the larger the value of GDPw
, the larger the value of exports
will be. Ddollar/gold
, DMDM/dollar
and Yyen/dollar
represent risk which may occur as a
result of exchange rate variability in international trade. These are expected
to be inversely correlated with the exports. Increase in the exchange rate
volatility will likely decrease the volume of exports.
3.2. The gravity model to capture trade-creating effect
One of the famous econometric models to assess trade between countries is
the gravity model, developed by Tinbergen (1962) and Poyhonen (1963). The
simple gravity model introduces trade between two countries as a function
of their gross national product (GNP) or gross domestic product (GDP),
and the distance between them, usually calculated as the distance between
their respective capital cities. The volume of trade should positively correlate
with their respective GDP and income per capita, since a country with
considerably large GDP will tend to trade more than the one with a small
GDP. Greater distance between them should reduce trade volume, since it
incurs transportation and information costs.
Employing the gravity model, many researchers have observed the
effect of common currency on trade. Recent research by Glick and Rose
(2002) suggest that participation in a currency union has doubled trade
and is statistically significant. However, Glick and Rose’s work is challenged
by other researchers. Pakko and Wall (2001), for instance, also use the
gravity model but find that the trade-creating effect of common currency is
definitely lower then the findings of Glick and Rose.
As for OIC member countries, Bendjilali (2000) suggests an empirical
gravity model for these countries by adding a variable of IDB’s import trade
financing operations to country i, while Hassan and Islam (2001) employ
the basic gravity model to assess whether there is (or not) a significant
trade-creating effect from implementing a trade bloc among OIC members.
In this study, that model is modified by introducing a gold dinar trade bloc.
The model can be written as follows:
log(TRADEijt
) = α ± β1
log(GDPit*
GDPjt
) ± β2
log(PCIit*
PCIjt
) ± β3
log(DISTANCEij
) (7)
± β4(BORDERij
) ± β5
(BLOCij
) ± εij

For simplicity, the above equation is rewritten as follows:
(8)
Trade is expected to increase with the size of domestic economy G (GDP),per
capita income Y (PCI), common border B (BORDER), joining in the same
trade bloc (BLOC) and to decline with distance D (DISTANCE). Coefficient
of β1
, β2
, β4
, and β5
should be positive, while β3
should be negative.
3.3. Measuring the amount of gold to serve trade and its generated
efficiency
Once the gold dinar is implemented, one may ask: How much gold is
required to support the trade? How much efficiency can be generated?
Yakcop (2002a and 2002b) introduces the Bilateral Payment Agreement
(BPA) model, which can be extended into a Multilateral Payment Agreement
(MPA) to illustrate how trade will be facilitated by gold dinar. Furthermore,
Meera and Larbani (2004) develop an efficient trade matrix to indicate the
detail of trade conducted by gold dinar.
In this study, the MPA scheme is utilized. It is essential to introduce
the BPA scheme first before turning to further steps. Let us say Malaysia and
Indonesia sign a BPA scheme whereby trade balances will be settled up in
quarterly periods. Both countries agree that trade will be denominated in
gold dinars, one unit being defined as one troy ounce of gold. Bank Negara
will pay Malaysian exporters in ringgit, based on the ringgit/gold dinar
exchange rate prevailing at the date of export. Likewise, Malaysian importers
have to pay ringgit to Bank Negara to the same amount of their import
value. Meanwhile, Bank Indonesia will also do the same for Indonesia’s
exporters and importers. At the end of the first quarter, the trade cycle is
ended, the total export from Malaysia to Indonesia, say, is 3 million gold
dinar and the total export from Indonesia to Malaysia is worth 2.5 million
gold dinar (Table 4).
Table 4: Gold Dinar Settlement by BPA Scheme
Gold Dinar (million)
Export to Malaysia Indonesia Total Export Net Payment
Malaysia 2.5 2.5 -0.5
Indonesia 3 3 0.5
Total Import 3 2.5 5.5

Table 4 shows that at the end of the quarter, Malaysia has a trade deficit
of 0.5 million gold dinar, while Indonesia has a surplus of 0.5 million gold
dinar. Accordingly, Bank Negara as central bank will pay Bank Indonesia
0.5 million gold dinar. Initially, it is agreed that both of them have a gold
custodian account in Bank of England, for instance. Then the actual
payment can be transferred into the account of Bank Indonesia in Bank of
England. It is interesting to note that only a small amount of 0.5 million gold
dinar is able to support the total trade value of 5.5 million gold dinar.
This scheme will be more efficient, if other participants join and
therefore the mechanism of payment will use the MPA scheme which in
essentials is the same as the BPA, but involves more members. From the
above sample we can calculate the amount of gold dinar to support trade by
simple equation as follows:
(1) Gold required to last trade:
(9)
where, Xi
and Ii
represent the total export and import of country i to its
trading partners, in millions of gold dinar. n is number of countries involved
in the transaction (the economic bloc).
(2) The efficiency generated from gold can be written as:
(10)
where TVT stands for total value of trade and Np is the net payment.
3.4. Data source and definition
The statistical data in this study is mainly obtained from three sources: (i)
Statistical, Economic and Social Research and Training Centre for Islamic
Countries (SESRTCIC) for all data pertaining to OIC member countries;
(ii) Direction of Trade Statistics, which is prepared quarterly by IMF for
data particularly related to detail intra-trade (export and import) among
members; (iii) United Nations Conference on Trade and Development
(UNCTAD) for data particularly related to export volume of developing
countries.

In addition, some data is retrieved from other sources, such as the distance
between capital cities among OIC member countries,which is easily accessed
in (http://www.wcrl.ars.usda.gov/cec/java/capitals.htm). Data relating to the
exchange rates of DM and Yen against dollar and dollar in terms of gold are
obtained from www.econstats.com and Global Financial Data, respectively.
The definition of each of the selected variables in the models is as
follows:
Exports (X): Data show the f.o.b. (free on board) value of goods provided to
the rest of the world valued in US dollars. Export of Developing Countries (X)
is total export generated from developing countries.
GDP (G): GDP is defined as the sum of the gross value-added by all resident
and non-resident producers in the economy plus indirect taxes and minus
any subsidies not included in the value of the products, in US dollars. GDP
World (GDPw
) is the sum of total GDP generated from all countries in the
world. GDP Per Capita Income (Y) is calculated by dividing GDP by the
mid-year population. The following charts represent GDP of OIC Countries
(GDP OIC), exports of developing countries (X), and GDPw
.
Real Exchange Rate (RER): It is the exchange rate of one particular country
compared with another at a particular point in time, taking into account
their respective inflation. One of the common measures to calculate RER
is by adjusting nominal exchange rate by the ratio of the foreign price level
to the domestic. Due to availability of data, in this study RER is computed
by indexing, that is, creating an index. The first year of observation, namely
1972, is taken as the base value, with the first exchange rate value for that
year, say x, equated to 100. Mathematically this can be simply written as
follows:
(11)
where, xt
is real exchange rate at time t, and x0
is real exchange rate at the
base year which is 100. Yyen/dollar
and DMDM/dollar
represent the bilateral real
exchange rate of yen and DM with respect to the dollar, while Ddollar/gold
illustrates the real exchange rate of dollar in terms of gold price. These are
displayed in Figures 1, 2, and 3.

Figure 1: Real Exchange Rate Yen/USD
Figure 2: Real Exchange Rate DM/USD
Figure 3: Real Exchange Rate USD/Gold
Source: www.econstats.com and Global Financial Data
TRADEijt
(T): the bilateral trade comprising total exports and imports
between countries i and j at time t in dollar.

DISTANCEij
(D): The distance in kilometres between two countries (i.e.
between the capital cities i and j).
BORDERij
(B): A dummy variable, which takes a value of 1 if two countries
have a common border and zero otherwise.
BLOCKij
: A dummy variable, which takes a value of 1 if one country joins in
the trade bloc, and zero otherwise. The trade blocs to be evaluated are those
within OIC countries. A new trade bloc, namely the gold dinar trade bloc,
will be introduced.
IV. Empirical Result
4.1. Exchange rate volatility and its impact on trade
As discussed in the previous section, volatility of exchange rate is evaluated
by computing coefficient of variance (CV). The results are depicted in
Figures 4, 5, and 6.
Figure 4: Volatility Dollar/Gold (Coefficient of Variance Method)
Figure 5: Volatility DM/Dollar (Coefficient of Variance Method)

Figure 6: Volatility Yen/Dollar (Coefficient of Variance Method)
Recalling the model described in the previous section, there are three
models presented which refer to equation 4, 5, and 6. By conducting unit
root test, we found the variable(s) in Model 1 (see Table 5) and Model 3 (see
Table 7) to indicate a non-stationary feature.
Table 5: Unit Root Test for Model 1
Variables Test Statistics & Critical Values Adjusted
Levels 1st differences Regression
ADF CV(5%) ADF CV(5%) Variable
LX -4.0662(0) -3.5796 - - -
L(GDPw) -4.0121(1) -3.5943 - - -
VG(D) -3.7158(0) -2.9665 - - -
VG(DM) -3.3137(1) -2.9706 - - -
VG(Y) -1.9079(3) -2.9798 -6.365 -2.9706 DVG(Y)
Table 6: Unit Root Test for Model 2
Variables
Test Statistics &
Critical Values
Levels
ADF CV(5%)
LX -4.0662(0) -3.5796
L(GDPw) -4.0121(1) -3.5943
VC(D) -4.1887(1) -2.9706
VC(DM) -4.1713(0) -2.9665
VC(Y) -3.2690(3) -2.9798

Table 7: Unit Root test for Model 3
Test Statistics & Critical Values Adjusted
Variables Levels 1st differences Regression
ADF CV(5%) ADF CV(5%) Variable
LX -4.0662(0) -3.5796 - - -
L(GDPw) -4.0121(1) -3.5943 - - -
RER(D) -8.3549(0) -2.9665 - - -
RER(DM) -2.6850(0) -2.9665 -4.0074 -2.9706 DRER(DM)
RER(Y) -2.0152(0) -2.9798 -3.5134 -2.9800 DRER(Y)
Given this condition, the regression results may be misleading and
incorrect (Koop, 2000:152). Thus, while the regression for Model 2 remains
the same, Model 1 and 3 need to be adjusted as follows:
Model 1:
(11)
Model 3
(12)
The OLS results for all models are set out in Tables 8, 9, 10 and 11.
Table 8: Regression Result for Model 1
No. Variables
Expected
Results
Actual
Results
Coefficient t-stat
Significant
level
1 Intercept - - -25.5887 -10.6263 0.000 *
2 LogGDPw + + 2.3057 16.6202 0.000 *
3 VG(D) - - -.6627E-4 -3.3867 0.002 *
4 VG(DM) - - -.3940E-4 -1.7939 0.085 **
5
∆ VG(Y)
- + .4733E-4 1.9730 0.060 **
Notes: ‘*’ indicates significance at α=5% (two-tailed); ‘**’ indicates significance at α=10% (2 tailed)
No. Variables
Expected
Results
Actual
Results
Coefficient t-stat
Significant
level
1 Intercept - - -27.2689 -9.4127 0.000 *
2 LogGDPw + + 2.4022 14.1914 0.000 *
3 VC(D) - - -0.018090 -3.4178 0.002 *
4 VC(DM) - - -0.007324 -0.39427 0.697
5 VC(Y) - - -0.008019 -0.5504 0.584
Note: ‘*’ indicates significance at α=5% (two-tailed)

No Variables
Expected
Results
Actual
Results
Coefficient t-stat
Significant
level
1 Intercept - - -26.9085 -12.2342 0.000 *
2 LogGDPw + + 2.3827 18.6695 0.000 *
3 RER(D) - - -0.009723 -3.2855 0.003 *
4
∆ RER(DM)
- + -0.010074 2.5180 0.019 **
5
∆ RER (Y)
- + 0.001525 .36465 0.719
Notes: ‘*’ indicates significance at α=5% (2 tailed); ‘**’ indicates significance at α=10%
(2 tailed)
Table 11: Overall results Models 1, 2, and 3
No. Variables Model 1 Model 2 Model 3
1 2
R 0.96230 0.95033 0.96163
2 2
R 0.95602 0.94238 0.95523
3 F statistics 153.1557 119.5711 150.3674
4 DW statistics 0.80718 0.91669 1.1093
As can be seen from Tables 11, 12 and 13, the individual variable performs
well. The coefficient of determination of each model ( 2
R ) also indicates
that the explanatory variables in each model are able to explain more than
95 percent of variation taking place in the dependent variable. Besides,
the models also demonstrate overall significance as indicated by high
F-statistics. The overall coefficient of variables is statistically significant even
with one percent level of significance. However, the models may encounter
severe serial autocorrelation since their DW statistics are terribly low (Table
11). Some remedies are taken to completely remove the problem from the
equation by transforming the variables into first differences form. Thus, the
above model is revised as follows:
Model 1
(13)
Model 2
(14)
Model 3
(15)
The regression results of the above models are presented in Table 12.

Table 12: Regression Results of the Revised Models
No.
Model 1
GARCH
Estimated
Values
Model 2
Coef. of
Variance
Estimated
Values
Model 3
RER
Estimated
Values
1
Dependent
Variable
∆log Xt
Dependent
Variable
∆log Xt
Dependent
Variable
∆log Xt
2 Intercept
-.004693
(-.06248)
Intercept
0.071721
(1.0246)
Intercept
0.093767
(1.6045)
3 ∆log GDPW
3.7468
(1.6984)
∆log GDPW
0.66625
(0.31141)
∆log GDPW
0.11494
(0.06396)
4 ∆VG (D)
-0.3111E-4
(-1.5566)
∆VC (D)
-0.011259
(-2.5102)
∆RER (D)
-0.0099251
(-3.1461)
5 ∆VG (DM)
-0.9410E-5
(-0.33142)
∆VC(DM)
-0.009850
(-0.66785)
∆RER
(DM)
-0.0028690
(-0.71586)
6 ∆VG (Y)
0.1647E-4
(0.54677)
∆VC (Y)
0.018811
(1.3252)
∆RER (Y)
0.0072179
(1.9074)
7 2
R 0.19624 0.29622 0.48737
8 2
R 0.062277 0.17893 0.40193
9 F-statistics 1.4649 2.5254 5.7044
10
DW-
statistics
1.2342 1.6194 2.0846
Note: Figures in parentheses are t-statistics
The revised models are able impressively to increase DW statistics,
which is a signal of improvement in dealing with problem of serial
autocorrelation. However, 2
R and F-statistics decrease sharply. The low of
2
R indicates that the explanatory variables no longer have strong power
to explain the independent variable, which was not the case in the original
model. Meanwhile, the low F-statistics (except for Model 3) illustrate
that the coefficient of variables as a whole are likely having no statistical
significance. Accordingly, Model 3 is chosen as the final model and will be
analysed subsequently.
The equation for the revised model (Model 3) can be written as follows:
∆logXt
= 0.093767 + 0.11494∆logGDPw
– 0.0099251∆RERDollar/Gold
(16)
– 0.002869∆RERDM/Dollar
+ 0.0072179∆RERYen/Dollar
Two of the most noteworthy outcomes from equation 13 are that,
if world GDP increases by one unit, holding other variables constant,

the exports of developing countries also increase slightly by 0.114 units.
Inversely, if the dollar real exchange rate in terms of gold rises by one point,
the exports of developing countries decrease by 0.009 units. The variable
of RERdollar/gold
is statistically significant at 5% level of significance. However,
its impact on trade as indicated by the coefficient (0.009) is considered
low. Similarly, a one point increase in the real exchange rate of DM against
dollar (RERDM/dollar
) has an inverse effect on developing countries’ exports by
a mere 0.002 units (ceteris paribus). In addition, the variable is statistically
insignificant.
These low effects on trade are definitely interesting since other previous
researchers – Esquivel and Larrain (2002) for instance – reported a significant
impact. They reported that each one-unit increase in the volatility of DM
against dollar reduces developing countries’ exports by around 2 percent.
The difference may be caused by the data for developing countries. In this
study, aggregate data of developing countries’ exports, which is time series
data, is used whereas Esquivel and Larrain used panel data (individual data)
of developing countries’ exports. Moreover, in this study the regression
model chosen is based on the differenced form, while their study was based
on logarithmic form. The latter, as presented in the previous section, did not
work well, since it contains severe serial correlation problems.
Surprisingly, the variable of real exchange rate of yen against dollar
(RERyen/dollar
) in this study has positive effect on trade. A one point increase
in RERyen/dollar
tends to increase developing countries’ exports by 0.007 units.
Even though the impact is considerably low, the variable itself is statistically
significant at 10% level of significance. The positive sign on this variable
is the same as in Esquivel and Larrain’s results. However, they did not give
any special attention to why the sign of this variable is not as expected. This
anomaly might emerge because the yen actually is a less influential currency
for developing countries. The share of yen in the global currency in 2002
and 2003 is only 5.2 and 4.8 percent respectively (Table 13), while the share
of DM, which has merged into the euro, is 19.3 and 19.7 percent respectively
in the same year. Perhaps, most of the developing countries in Africa tend to
use European currency including DM (euro) rather than yen. It is reasonable
to argue that they tend to maintain their trade partnership with European
countries rather than with Japan for reasons of geographical proximity.

Table 13: Official Foreign Exchange Reserve: Currency Share (%)
Currency 2000 2001 2002 2003
US dollar 66.6 66.9 63.5 63.8
Euro 16.3 16.7 19.3 19.7
Japanese yen 6.2 5.5 5.2 4.8
Pound sterling 3.8 4 4.4 4.4
Swiss franc 0.5 0.5 0.6 0.4
Unspecified currency 6.6 6.4 7.1 6.8
Source: European Central Bank (2005)
In general, the model has no strong explanatory power as indicated
by its low 2
R . That is a mere 48%, implying that the selected explanatory
variables within the model can only explain 48% of the variation taking
place in the dependent variable. However, this is still acceptable since, in the
financial field, 2
R is rarely impressive. Furthermore, the model as a whole
is statistically significant at 5% level of significance, as illustrated by its high
F-statistics. In addition the model is also free of the serial autocorrelation
problem. However, the model contains heteroscedasticity problem. This
does not destroy the unbiasedness and consistency of the estimations, but
these are no longer efficient (Gujarati, 1995:381).
4.2. Gold dinar’s trade creating effect: empirical result from gravity model
The gravity model utilized in this study contains eight explanatory variables,
namely GDP (the product of GDP’s country i and country j), Y (the product
of their per capita income), D (DISTANCE between their capital cities), and
dummy variables B (BORDER), GOLD9
(Gold Dinar Trade Bloc), CAEU10
,
AMU11
, and D-812
.
log(T) = a ± b1
log GDP ± b2logY ± b3
logD ± b4
B ± b5
GOLD ± b6
CARU (17)
± b7
D8 ± b8
AMU
The complete set of regression results is presented in Table 14.

Table 14: Regression Results of Gravity Model
No.
Original model
Estimated
Values
Observation 31
1 Dependent Variable Log(T)
2 Intercept
-4.0686
(-.44709)
3 LogGDP
.4001
(1.2348)
4 LogY
.12409
(.33743)
5 LogD
-.5176
(-.9495)
6 B
2.3309
(2.1959)
7 GOLD
1.6537
(1.9665)
8 CAEU
.4534
(.6658)
9 D-8
1.9810
(1.9943)
10 AMU
.16978
(.1684)
11 2
R .82133
12 F-statistics 12.6415
13 DW-statistics 1.8634
Note: Figure in parentheses are t-statistics
The model can be expressed in the equation as follows:
log(T) = –4.0686 + 0.4001logGDP + 0.12409logY – 0.51762logD + 2.3309B
+1.6537GOLD + 1.45349CAEU + 1.9810D8 + 0.16978AMU
The estimation of equation 17 suggests that an increase in one unit of
GDP and per capita income will tend to increase trade by 2.5 and 1.33 units
respectively.13
On the other hand, a one point additional distance between
countries will likely decrease trade by 3.23 units.14
However these variables are
statistically insignificant. Thus within a 5% level of significance, only variable
B (BORDER) is statistically significant. If we extend the level of significance
to 10%, variable GOLD and D8 are also statistically significant (Table 15).

Table 15: The Property of t-test for the Equation 17
Variables tcal
ttab
(α=5%) Decision ttab
α=10% Decision
LogGDP 1.2348 2.079 Insignificant 1.7207 Insignificant
LogY 0.33743 2.079 Insignificant 1.7207 Insignificant
LogD -0.9495 2.079 Insignificant 1.7207 Insignificant
B 2.1959 2.079 Significant 1.7207 Significant
GOLD 1.9665 2.079 Insignificant 1.7207 Significant
CAEU 0.6658 2.079 Insignificant 1.7207 Insignificant
D-8 1.9943 2.079 Insignificant 1.7207 Significant
AMU 0.1684 2.079 Insignificant 1.7207 Insignificant
Note: n=30, k=9, n-k=21, for α=5% or 0.025(two tails) ttab
value = 2.079; α=10% or 0.05(two
tails) ttab
value =1.7207
Another point from the model is that having the same border and
participation in a trade bloc supports greater volume of trade. In the case
of sharing the same border, trade is increased by 2.3 units, whereas those
involved in gold dinar trade bloc will likely enjoy an increase in their trade
by 1.65 units.A similar result prevails for those in CAEU, D8, and AMU trade
blocs with an increase in trade of 1.45, 1.9, and 0.16 units, respectively. These
positive impacts on trade as a result of joining trade blocs is similar with
Hassan and Islam’s (2001) study. It also proves that joining in a trade bloc
will open a trade-creating effect. One interesting result here is conformity
of each sign. The actual sign for all of the variables does confirm with those
expected. It is a bit different with Hassan and Islam’s (2001) study, which
finds per capita income is negatively correlated with bilateral trade.
The 2
R value is 82% which means that the model has a high
explanatory power, and indicates that it has high goodness of fit. The
explanatory variables selected in the model can simultaneously explain 82%
of the variation taking place in the model. However, the model contains
heteroscedasticity problem. Multicollinearity may also arise since some
of its variables are statistically insignificant with low t-statistics including
variable per capita income (Y) and two trade blocs namely CAEU and AMU.
Perhaps, this is not surprising as variable Y is gained from the GDP value
divided by the respective population of each country. Similarly, few of the
members of CAEU are involved in the D8 group. In this respect, it is sensible
to omit these three variables from the equation presented in the revised
model, and the new results can be seen in Table 16.

Table 16: Regression results of the revised model
No.
Revised model
Estimated
values
Observation 31
1 Dependent variable log(T)
2 Intercept
-9.5117
(-1.3793)
3 logGDP
.80253
(4.0997)
4 logD
-.60904
(-1.2415)
5 B
1.9587
(2.1096)
6 GOLD
2.1136
(2.7355)
7 2
R .78287
8 F-statistics 23.4353
9 DW-statistics 2.1948
Note: figure in parentheses are t-statistics
The equation of the model can be written as follows:
(18)
It can be seen that the reduced model performs better, as illustrated
by improving statistically significant variables (see Table 16). Another
interesting finding is the effect of gold if it were implemented as the
international currency for trade. Recalling equation 18 and its statistical
summary, it can be seen that only the distance variable (D) is statistically
insignificant. However, the sign of the variable is as predicted before. The
insignificant variable is a bit surprising since other researchers (Bendjilali,
2000, and Hassan and Islam, 2001) reported that distance is statistically
significant. Other variables such as GDP and border (B) are statistically
significant too. These results strengthen the previous studies.
The distinction of this study stems from the introduction of the gold
dinar trade bloc variable, something not witnessed in other studies. Those
involved in this bloc would enjoy an increase of their respective intra-trade
of more than 2 units. Moreover, this variable is statistically significant at 5%
level of significance, which is further convincing evidence.

In general, the model also demonstrates a highly desirable explanatory
efficiency, as the selected explanatory variables are indeed able to explain
more than 78% of variation taking place in the dependent variable. The high
F-statistics illustrate that the overall model is statistically significant even at
1% level of significance. In addition, the model is also free from serious error
derived from the serial correlation error and multicollinearity problem. It
may be deduced that the model used here is quite impressive.
4.3. The amount of gold dinars needed to support trade
As mentioned earlier, it is possible to calculate the quantity of gold dinars
needed to support trade simply by calculating the volume of trade, whether
it produces surplus or deficit. In presenting the gravity model, we said that
the gold dinar trade bloc is assumed to consist of six leading OIC exporting
countries. They are Saudi Arabia, UAE, Malaysia, Indonesia, Turkey, and
Pakistan. Table 17 shows their respective trade (export and import).
Table 17: The Trade among Six Leading OIC Exporting Countries in 2002
(Million Dollar)
Export to
Saudi
Arabia
UAE Malaysia Indonesia Turkey Pakistan
Total
Export
Saudi A 1695 343 1003 716 1196 4953
UAE 490 3827 105 92 1198 5712
Malaysia 370 835 1586 221 486 3498
Indonesia 475 720 2030 238 265 3728
Turkey 535 440 113 28 52 1168
Pakistan 390 837 64 81 110 1482
Total
Import
2260 4527 6377 2803 1377 3197 20541
Source: IMF, Direction of Trade Statistics Yearbook, 2003
In 2002, total trade among these six countries was more than 20.5
billion dollars. UAE is at the top rank of exporting countries with total
exports of around 5.7 billion dollars, while Malaysia is in the top rank of
importing countries with total imports of more than 6.3 billion dollars. It is
noteworthy that Turkey holds the lowest position both in terms of import
and export, perhaps because it tends to trade with its European partners
rather than Muslim countries.

From Table 17, the amount of gold dinars required to facilitate the
respective trade of these countries can be estimated. The first thing needing
to be taken into account is the gold price in the prevailing year which is in
2002. According to Global Financial Data, the price of one troy ounce of
gold in 2002 was 346.7 dollar. Hence, for instance, the total imports and
exports of Saudi Arabia are equivalent to 14.28 and 6.51 million gold dinars,
respectively. (This is worked out by dividing column (1) and (2) by 346.7
(see Table 18). The net payment for Saudi Arabia is thus equal to 7.76 million
gold dinars which is obtained by subtracting the figure in column (4) from
that in column (3). The amount of gold needed to support the trade is then
equal to 59.2 million gold dinars. This amount is equal to 13.4 million troy
ounces of gold or around 4.292 tons of gold (see Table 19).
4.4. The efficiency of the gold dinar
The last stage of the calculation is to calculate the efficiency that would be
generated if the gold dinar were to be implemented. Recall equation 10:
Efficiency = (10)
From Table 18 the amount of net payment (Np) is the same with the
amount of gold dinar payable (or receivable),which is 13.8 million gold dinar.
Total value of trade (TVT) is the total amount of trade in the conventional
sense (if gold dinar would not be implemented), which is 20,541 million
dollars or equal to 59.2 million gold dinars. Hence the efficiency generated
from implementing the gold dinar is:
Efficiency = x 100 = 76.68 %
This figure 76.68% implies that the six leading exporting countries
enjoy the trade by only paying the remaining (around 23.31%) at the end
of the year. In other words, if the gold dinar were implemented, they could
save almost 4/5 of their budget for other economic allocations. It is indeed
a giant step forward that should encourage prosperity among the member
countries in the gold dinar trade bloc.

Table 18: Total Trade of Six Leading Exporting Countries, 2002
(million gold dinar)
Countries
Million Dollar Million Gold Dinar
ApproximationExport Import Export Import
Net
Payment
(1) (2) (3) (4) (3)-(4)
Saudi A 4953 2260 14.286 6.5186 7.767522 7.7
UAE 5712 4527 16.475 13.0574 3.417941 3.4
Malaysia 3498 6377 10.089 18.3934 -8.30401 -8.3
Indonesia 3728 2803 10.752 8.0848 2.6680125 2.7
Turkey 1168 1377 3.3689 3.97173 -0.6028267 -0.6
Pakistan 1482 3197 4.2745 9.22123 -4.9466397 -4.9
Total 59.247 59.247 0 0
Table 19: Gold Required to Support Trade (million gold dinar, troy ounce,
and ton units)
Deficit by
Countries
Gold
Dinar
Surplus
Gold
Dinar Gold
Dinar
Total
Trade
Gold Required
Payable Countries Receivable Troy ounce Ton
Malaysia -8.3 Saudi A 7.7
59.215
13,800,000 4,29216
UAE 3.4
Turkey -0.6 Indonesia 2.7
Pakistan -4.9
Total -13.8 13.8

V. Discussion
The first question, whether the variability of exchange rate – particularly
dollars in terms of gold – has significant impact on trade, has already
been answered. As can be seen from equation 13, the model shows that
an additional one point on the exchange rate variability of the dollar in
respect of gold (RERdollar/gold
) tends to decrease the value of developing
countries’ exports by 0.009 units (holding other explanatory variables as
constant). The variable of RERdollar/gold
is statistically significant at 5% level
of significance. This is further proof that exchange rate variability tends to
decrease trade. However, its impact on trade as indicated by the coefficient
(0.009) is considered low and inconsistent with what the relevant literature
leads one to expect.
The establishment of a Gold Dinar Trade Bloc (GDTB), proposed for
the first time in this study, indicates a new hope for OIC members since
members of this bloc would enjoy an increase of their respective intra-trade
by more than 2 units. It is an initial indicator that a GDTB would be more
fruitful than other existing trade blocs among OIC countries. Moreover, this
variable is statistically significant at 5% level of significance, which is further
convincing evidence.
The model also promises the hope for OIC member countries of
making enormous budget savings. Assuming that the six leading countries
of OIC are to be GDTB members, the value of their intra-trade in 2002
stands at 20.5 billion dollars or almost 60 million gold dinars. However,
those taking part would not need to supply gold dinars in the same amount
as the total value of their trade, but only the amount of the net difference,
namely 13.8 million gold dinars. In other words, they would only pay for
around 23.31% of the total amount payable if paid in the conventional
way using dollars. Thus, the efficiency generated by using the gold dinar is
around 76.68%.
That figure would be even higher if the trade between the mentioned
member countries were to become larger. If, for instance, ten leading
exporting countries within the OIC agreed to settle their trade in gold
dinars (i.e. if four additional countries join to the bloc17
), the efficiency
will increase. In 2002, for the total trade among the 10 leading exporting
countries of 32.2 billion dollars (equivalent to 92.98 million gold dinar, see
Table 20), the net payment would be only 18.51 million gold dinars. In other
words, the efficiency generated would be 80.09%.18

Table 20: Total Intra-Trade of Ten Leading Exporting Countries, 2002
(Million Dollar and Gold Dinar)
Countries
Million Dollar Million Gold Dinar
Approxi-
mation
Export Import Export Import Netpayment
(1) (2) (3) (4) (3)-(4)
Saudi A. 5593 2560.11 16.1321 7.384223 8.74788001 8.74
UAE 8202 6606.31 23.65734 19.05483 4.60250937 4.6
Malaysia 3895 6547.77 11.2345 18.88598 -7.6514854 -7.65
Indonesia 4284 4521.08 12.3565 13.04032 -0.6838189 -0.68
Turkey 1922 2835.27 5.543698 8.177877 -2.6341794 -2.63
Pakistan 1637 4087.42 4.721661 11.7895 -7.0678396 -7.06
Nigeria 1200 541 3.461206 1.560427 1.90077877 1.9
Iran 3056 2356.08 8.814537 6.795731 2.01880588 2.02
Kuwait 1608 1173.67 4.638016 3.385261 1.25275454 1.25
Syria 840.71 1009 2.424892 2.910297 -0.4854052 -0.48
Total 32237.71 32237.71 92.98445 92.98445 0
This figure implies that the ten leading exporting countries only need
one fifth of their current budget to support their trade. They can allocate
another 80% of the budget to other economic purposes. Hence, using the
gold dinar would be a breakthrough that could enhance prosperity among
the countries involved in the gold dinar trade bloc.
5.1. Transmission how gold dinar would increase trade
The following strategies are available to OIC members which trade among
themselves (see Figure 7):
Firstly, exporters/importers can use their domestic currency. This
is possible for those who enjoy relatively low volatility of their respective
national currency, which is the case only for a few countries, perhaps only
some of the oil-exporting ones. Hence, it is assumed that these few countries
may enjoy more trade than other OIC member countries.
The second strategy is that a hard currency such as the dollar takes
the place of the national currencies. Although hard currencies mostly have
a stable exchange rate, it does not necessarily follow that they are free from
risk. To remove risk completely from the business, there is no choice but
to hedge, for which additional costs are entailed. The trade would still be a

normal trade in the sense that although they have to pay an additional cost
for hedging, still they can predict their costs and benefits.
Finally, some exporters/importers have no opportunity to use hedging
facilities since these are not available in many developing countries (Meera
and Larbani, 2004). Thus, there is no choice in these countries except to use
the dollar, without hedging. This can worsen the uncertainty of business,
which in turn restrains exporters/importers from doing business, thus
leading to less trade.
Figure 7: Domestic Currency vs Hard Currency and Its Impact on Trade

Once the gold dinar is established as the common currency for payment
for trade, some advantages could arise for participants. Joining in a common
currency, according to Grauwe (2000:58), will remove the uncertainty risk
due to volatility of the exchange rate. In the case of gold dinar, Meera (2004)
contends that gold dinar payment will totally eliminate exchange rate risk.
There is no need for a trader to hedge currency to minimize risk, because
hedging cost is fixed against the gold dinar itself. Furthermore, transaction
cost will be minimized since it can be affected electronically (see Figure 8).
Figure 8: The Mechanism of How Gold is Estimated to Increase Trade
Along with the benefits just mentioned, the gold dinar would improve
trade in three ways:
Firstly, the relative stability of gold may influence in two fronts (Izhar
and Asutay, 2005): the fluctuation of general prices is relatively controllable,
thus inflation should be relatively low; there is no opportunity to create

unlimited money, since this will be subject to availability of gold. Hence,
there will be harmonization between the real sector and the monetary
sector. As a result, the phenomenon of appreciation or depreciation will be
relatively manageable.
Secondly, as discussed earlier, implementing the gold dinar based
on MPA will provide massive efficiency in term of the amount of gold to
sustain the trade. In addition, since the members of the gold dinar bloc will
support each other, the trade-creating effect is expected to take place. Given
this situation, the gold dinar may serve as an optimum currency if more
countries are expected to join.On his famous optimum currency area (OCA)
theory, Mundell (1977a; 1977b) states that implementation of OCA would
also reduce transaction costs in trade. He affirms that “the more countries
join a currency area, the more efficient it will be”. The more OIC members
are involved, the more efficient it will be. From the empirical results, we
have already demonstrated that the efficiency of the GDTB increases from
76.68% to 80.09% after four other countries joining in the bloc.
Finally, aside from economic factors, the success of international trade
is basically also determined by political bargaining. The success of the gold
dinar will constitute an immense capital with which to negotiate with
developed countries, especially in respect of unfair trade practices. It is no
secret that the industrialized countries use their position in the World Trade
Organization (WTO) to protect their interests. Stiglitz (2002:244) points
out that some of them demonstrate a hypocritical attitude by advocating
the opening up of markets in developing countries while keeping their
own markets closed to the produce and products of developing countries,
such as textiles and agriculture. Hence, successful implementation of the
gold dinar will be the source of a new energy and confidence among OIC
countries so that they have a better bargaining position to deal with their
trade partners, particularly developed counties. This will produce incentives
which encourage not just intra-trade but also inter-trade.
5.2. Why does the idea not get off the ground?
The trade-creating effect and efficiency generated by GDTB so far are
convincing. Why then does the idea not move towards becoming a reality?
It may be argued that implementation is difficult due to inadequate gold
reserves in the countries concerned. As we just calculated for the year 2002,
the net payment was around 13.8 million gold dinars or the equivalent of
4,292 tons of gold. This entails vast reserves, beyond the capacity of the OIC

member countries (see Table 24). In 2002, total gold reserves held by the
deficit countries in the trade bloc was a mere 314.5 tons of gold.
Table 21: Gold Dinar Required for Intra-Trade and Gold Reserve
Kept by Central Bank in 2002
Countries Gold Required (Ton)20
Gold Reserve (Ton)21
Malaysia 2,581 36.4
Turkey 186.6 116.5
Pakistan 1,525 65.1
Total 4,293 218.0
At first glance, looking at Table 21, those countries could not effect their
trade based on the gold dinar on account of a lack of gold reserves. However,
this is not the case in fact since all of those countries do hold adequate
reserves in dollars. Malaysia, for example, in 2002 held over 34 billion dollars
in its reserve account. That amount is equal to 3,070 tons of gold. Other
countries also recorded large amounts of total reserves in dollars (see Table
22). In other words, basically, these countries are able to implement the gold
dinar by converting a part of their dollar reserves into gold, the potential of
which depicted in Table 23.
Table 22: Countries’ Total Reserve in 2002 and Its Value in Gold
Countries
Reserve Minus Gold22
(Million Dollar)
Reserve Value
(Million Gold Dinar)
Reserve Value
(Ton Gold)
Malaysia 34,221 98.7 30,699
Turkey 27,068 78.07 24,282
Pakistan 8,078 23.29 7,244
Total 69,367 200.06 62,225
Table 23: Potential Gold Reserve (Ton) and Gold Needed
to Settle Transaction (%)
Countries
Gold Required
(Ton)
Actual Gold
Reserve (Ton)
Potential
Gold Reserve
Gold Needed
to Serve %
Malaysia 2,581 98.7 30,699 8.4
Turkey 186.6 78.07 24,282 0.76
Pakistan 1,525 23.29 7,244 21.0
Total 4,293 200.06 62,225

As an illustration, Malaysia in 2002 had a reserve 34,221 million dollar. If
Malaysia would convert this reserve totally to gold, then they would have
potential gold reserve equal to 30,699 ton gold. Theoretically, although
Malaysia has had to pay 2,581 ton gold because of its suffer trade deficit with
its trade partners, still this deficit can be easily settled since it was only 8.4%
of its giant potential gold reserve (see Table 22 and 23).
5.3. Conclusion
This study has attempted to find whether and how the implementation of the
gold dinar proposal would affect trade among OIC members. The findings
from the empirical analysis and available evidence from the literature help
to answer this challenging question. The following remarks can be made in
light of the available evidence and the findings:
(i) The gold dinar is considered to be more stable than fiat money, i.e.,
the US dollar. The term ‘stable’ here has the practical sense of ‘low
volatility’, whereas the dollar is considerably more volatile. Empirical
results produced by this study prove that in terms of gold, dollar
tends to depreciate and its real exchange rate (RER) volatility has
an absolutely negative impact on developing countries’ exports.
The variable is statistically significant at 5% level of significance, as
explained previously. However, its direct impact on trade is very low as
illustrated by its coefficient (0.009), as shown earlier. Thus, the findings
are inconsistent with the hypothesis explored in this study.
(ii) Establishing a gold dinar trade bloc will likely encourage intra-trade
among OIC members. Empirical results in this study suggests that
those involved in the bloc will enjoy an increase of more than 2 units
of their respective intra-trade. This is indeed a very convincing result,
since the variable is also consistently statistically significant in various
models tested at 5% level of significance. In other words, the gold dinar
bloc is an economic bloc which offers to its members a trade-creating
effect. This effect is more robust than those generated by currently
existing economic blocs within the OIC, including AMU and CEAU.
(iii) Another interesting finding for those joining the gold dinar bloc is the
efficiency generated by it. Empirical results in this study demonstrate
that if six countries selected would join in the gold dinar bloc, trade
efficiency in terms of payments will be around 76.68%. which implies
that they only need to pay the remaining 23.32% at the end of the trade

period. This figure becomes larger if the number of member countries
is increased. A simulation by adding another four countries (making
the total number of countries involved 10) showed that the efficiency
increases to almost 80%. Such an improvement is perhaps because the
selected countries are the top ten leading exporting countries within
the OIC. Perhaps, this efficiency will tend to diminish slightly if the
additional joining countries do not have any significant intra-trade.
(iv) From these three basic explanations, it may be deduced that the
implementation of the gold dinar proposal will be a move forward
for Muslim countries, in the sense that it will bring many economic
advantages. Their respective trade tends to increase. Unfortunately, the
project is likely not feasible since there are insufficient gold reserves held
by the members. If this can be tackled, perhaps the gold dinar would
become the main tool, not just to unite Muslim countries but also to
disseminate economic cooperation and foster prosperity among them.
5.4. Recommendation
Insufficient gold reserves held by most of Islamic countries will be the most
serious critical problem to be handled. To confront this shortage, former
Malaysian Prime Minister Mahathir suggested that those taking part in gold
dinar trade should help each other by purchasing some materials from the
poorest gold reserve countries thus enabling them to participate in the trade
(Mahathir, 2002). This probably would render a little help.
Alternatively, this study recommends two points:
(i) At the starting stage, use of the gold dinar can be restricted to facilitate
trade between member governments excluding private trade. There
are two advantages in this strategy: firstly, the amount of trade will be
reduced, that is, each member may hold sufficient gold stocks to sustain
their respective government’s trade; secondly, the administration
is likely to be less complex since the trade is fully backed by the
government.
(ii) The level of gold reserves can be improved by converting a significant
part of the country’s dollar reserves into gold. Almost all of the
countries assumed to be in the gold dinar have massive reserves in
dollars. Thus, by converting up to half of the dollar reserve into gold,
the insufficiency of gold stock to sustain trade can be met.

Notes
1. That trade-creating effect (as opposed to trade-diverting effect) occurs when some
domestic production in a member state of the trade bloc is replaced by lower cost
imports from other members. See Salvatore (2001:328-329).
2. Trade was the main vehicle for the spread of Islamic teaching from Andalusia (Spain)
to the Cape of Good Hope in Africa and to the Malay Peninsula. See, for example,
Yakcop (2002a).
3. ICCICE is intended to promote trade, commerce and industrial cooperation.
4. At the official level this idea was first launched by Bangladesh in the Second Summit
Meeting of OIC Heads of States, held in Taif in 1981. A year later, the issue has gained
more attention once Morocco again sounded the need to shape ICM in the Third
General Assembly of the Islamic Chamber of Commerce (ICC) held in Casablanca.
Finally, the Fourth General Assembly of the ICC held in Jakarta in 1983, repeated the
importance of establishing an ICM (Harjono as cited in Sadeq, 1996:49). ICDT is
intended to encourage regular commercial contacts and promote the harmonization
of commercial and investment policies among member countries.
5. The most striking feature of an FTA is the removal of existing restrictions on trade
among the regional members (Satiroglu, 1986:5).
6. It is the next step after FTA has been established. Custom Union entails, as well as
removal of trade barriers, a common external trade policy to both members and non-
members (Satiroglu, 2000).
7. One of the successful aspects of the implementation of the euro was the establishment
of a definite time table, which had to be adhered to by EU members. Ratification of
the Maastricht Treaty was meant to signal the beginning of the EU’s integration. See,
for example, Jovanovic (1997:18) and Keis (1997:42).
8. G-3 refers to the three major economic powers, namely USA, Japan, and Germany.
9. The six leading exporting countries are assumed to join this bloc. They are Saudi
Arabia, UAE, Malaysia, Indonesia, Turkey, and Pakistan.
10. The Council of Arab Economic Unity was established in 1957. It consists of Egypt, Iraq,
Libya, Jordan, Kuwait, Mauritania, Somalia, Sudan, Syria, UAE, and Yemen. In this
study four countries, namely Iraq, Mauritania, Somalia, and Sudan, were excluded due
to unavailability of data.
11. The Arab Maghrib Union (AMU) was established in 1987, comprising five countries:
Algeria, Libya, Mauritania, Morocco, and Tunisia. Again, due to incomplete data,
Mauritania and Morocco were excluded.
12. The D-8 was founded in 1997. It unites the eight countries of Bangladesh, Egypt,
Indonesia, Iran, Malaysia, Nigeria, Pakistan, and Turkey. All these countries are
included in the study.
13. Since it is a double log model, the antilog value is employed. The antilogs of 0.4 and
0.12 are 2.51 and 1.33, respectively.
14. Antilog of 0.5176 is 3.293
15. Total trade = 20541 million dollar

16. 1 troy ounce = 0.311035 kilogram
13,800,000 troy ounce = 4292283 kilogram
= 4,292 ton
17. It is assumed that Nigeria, Iran, Kuwait, and Syria are involved in the bloc.

18. The complete calculation is as follows:
Net payment = Np = (8.74+4.6+1.9+2.02+1.25) million gold dinars
= 18.51 million gold dinars
Total value of Trade =TVT = 92.98 million gold dinars
(TVT-Np)x100 (92.98-18.51) x 100
Efficiency = =
TVT 92.98
= 80.09%
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