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Macroeconomic Fluctuations and Asymmetries in Selected East Mediterranean and Gulf Countries: An Empirical Investigation
Macroeconomic Fluctuations and Asymmetries in Selected East Mediterranean and Gulf Countries: An Empirical Investigation Simon Neaime1 American University of Beirut Abstract. After the latest financial crises in several emerging markets worldwide, it is of paramount importance for the East Mediterranean and Gulf (EMG) region to assess accurately the impact of external shocks on its domestic economies. Currency and financial crises tend to spread along the lines of trade linkages. The extensive openness and trade relationships of the EMG region with the rest of the world made the transmission of shocks to the region a matter of great concern that deserves serious consideration. This paper uses a Vector Autoregression model, Variance Decomposition and Impulse Response Functions to study the impact of major macroeconomic developments on the EMG region. It is argued that external shocks are likely to have high propagation power within the economies of the region, which is a clear signal that these countries’ macroeconomic policies need to be reformed. JEL Classification: F02, F42, E60, C32. Keywords: VAR, Macroeconomic asymmetries, East Mediterranean and Gulf Region Manuscript received: July 16, 2004; Accepted: October 21, 2004 1. Introduction Over the last two decades, the EMG region has experienced disappointing macroeconomic performances. Weak macroeconomic policies characterized by highly volatile exchange and inflation rates, high levels of external debts and fiscal deficits, coupled with a deteriorating external environment with significant declines in global output, high volatility in oil prices and revenues, sharp rise in interest rates in international credit markets, appreciation of real effective exchange rates, have all contributed to put further strains on the region’s macroeconomic performances. Obviously, such shocks to the world economy might undermine and even damage macroeconomic performances in this region and could degenerate into a full-fledged economic and/or financial crisis. On the other hand, after the 1998 financial crises in East Asia and South America, it is of paramount importance for the EMG region to assess accurately the impact of external shocks on its domestic economies. Currency and financial crises tend to spread along the lines of trade linkages. The extensive openness and trade relationships of the EMG region with the rest of the world has made the transmission of shocks to the region a matter of great concern that deserves serious consideration. The 143
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main purpose of this paper is to provide policy makers in the EMG region the opportunity to preempt an impending macroeconomic or financial crisis, hence making it less likely for policy makers to have to resort to painful economic measures after the fact to resolve a crisis. While developed countries with relatively large economies can better absorb and neutralize the effects of external and exogenous shocks, it is somehow more difficult in the case of developing economies which are usually smaller in size and dependent, as is the case in oil-producing EMG countries, on the export of nearly one commodity being oil. This paper, which constitutes the first attempt at studying the macroeconomic linkages and asymmetries in the EMG region, will identify the current macroeconomic asymmetries in the region and will highlight the sensitivity and vulnerability of the EMG economies to various external shocks being an essential step in the formulation of adequate and appropriate future macroeconomic policies. The rest of the paper is divided as follows. Section 2 gives some background information and reviews related literature. After identifying the quantitative macroeconomic factors that determine the performance of the EMG region, Section 3 builds a dynamic macroeconomic model to analyze the effects of selected world variables on EMG economies. Section 4 concludes the paper with some policy implications. 2. Background and Related Literature EMG countries rely heavily on trade and enjoy a relatively high degree of economic and trade openness (see Table 1). This is due to the particular factor endowment of the region (rich in oil, poor in water) resulting in considerable oil exports and food imports rather than regular trade. This fact has made most of the economies of the region vulnerable to external shocks. The more open an economy is the more it is vulnerable to external shocks and the smaller it is the more the impact domestically. Among the EMG countries, oil-producing Gulf Cooperation Council (GCC) countries tend to be the most vulnerable to external shocks because of their high level of openness. These shocks come mainly from fluctuations in the world price of oil, in global output, and in the value of the US dollar. The remaining EMG countries are affected not only by shocks mentioned above, but also by fluctuations in world interest rates, and terms of trade.
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Table 1: Trade Openness of EMG Countries, 1999-2003 Degree of Openness of EMG Countries: (X+M)/GDP Country 1999 2000 2001 2002 2003 1.35 1.43 1.10 0.90 1.46 Bahrain 0.25 0.25 0.23 0.24 0.22 Egypt 0.78 0.87 0.72 0.71 0.68 Jordan 0.78 0.73 0.90 0.69 0.60 Kuwait 0.67 0.68 0.57 0.48 0.39 Lebanon 0.74 0.78 0.80 0.71 0.75 Oman 0.69 0.87 0.92 0.84 0.73 Qatar Saudi 0.62 0.63 0.61 0.54 0.54 Arabia 0.47 0.60 0.50 0.42 0.53 Syria 1.05 1.05 1.10 1.35 1.18 UAE 0.96 0.83 0.76 0.61 0.67 Yemen Source: IMF, Direction of Trade Statistics, Yearbook 2003. X refers to total exports, while M is total imports. The macroeconomic asymmetries in the EMG region are modeled following the vector autoregression (VAR) model. VAR models are particularly useful because they permit measurement of the importance of external shocks on a given economy, in our case, the economies of the EMG region. They also provide a framework for analyzing the adjustment of a given economy (for example the adjustment of per capita GDP growth rates, exports, imports, and fiscal imbalances) to external economic shocks (for instance, shocks in world interest rates, in the value of the US dollar, in the terms of trade, in the price of oil, and world output and inflation rates). The VAR approach to economic fluctuations pioneered by Sims (1980), has been widely used in the empirical literature. It has been used to study the implications of macroeconomic fluctuations and linkages among countries and regions, and to measure the degree to which each shock accounts for their variability through time (see for example Choudhri (1983), Batten and Ott (1985), Gregory and Reynauld (1985), Backus (1986), Bordo, Choudrhi and Schwartz (1987), and Bailey (1989)). More recent studies have used the VAR model to explore the international transmission of economic fluctuations and macroeconomic asymmetries among developed countries. For example, Ambler (1989) constructs a nine variable VAR system that incorporates Canadian and US data. He estimates a standard unrestricted VAR as well as a Vector Error Correction Model (VECM). Naka (1997) notes that the VAR is flexible enough, and when two or more variables have common stochastic trends (that is, are cointegrated), it can be estimated as a VECM. The VAR model was also used to study the impact of external and country-specific shocks on GDP, inflation and the trade balance for seven OECD
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countries. Prasad and Gable (1998), using the same framework, concentrate on the impact of such shocks on exchange rates,2 imports and exports. Hoffmaister, Roldos and Wickham (1998) studied the macroeconomic fluctuations in 23 Sub-Saharan countries also using a VAR model. They show that external shocks, especially those related to terms of trade, have the most significant influence on domestic GDP and real exchange rate. Kireyev (2000) explores the impact of some selected external shocks on the macroeconomic dynamics of 18 Arab countries using a Panel VAR model. In that paper, the Arab countries are divided into oil and non-oil producing countries and into IMF program and non-IMF program countries. 3. Empirical Investigation 3.1. Data and Sample In this paper, the VAR model includes two sets of variables. The first set of variables is country specific summarizing the internal macroeconomic situation in each EMG country. These variables are the fiscal balance, the trade balance and the rate of growth of per capita GDP. The second set includes variables that are common to all EMG countries and convey external shocks to the EMG region. These are foreign interest rates, foreign rate of growth of GDP, world oil prices, and the world rate of inflation (see Figure 1). Figure 1. Dynamics of the World Macroeconomic Variables: 1960-2001 (b) World Inflation (%)
( a) World Nominal Oil Prices (USD) 40
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(d) World Real Rate of Growth of GDP (%) 10 9%)(%)G G (%)(%)(%)
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Source: International Monetary Fund, International Financial Statistics, 2002 Notes: The horizontal axis represents the time period 1960-2001. Among the external factors the price of oil and the world rate of inflation represent the terms of trade related shocks. Even though the model assumes that these external shocks are common to all EMG countries, however, they are assumed to affect each country or group of countries in a different way owing to the current macroeconomic characteristics specific to each country, as well as, the country specific factor endowments. Fortunately, one can easily divide the EMG region into two distinctive groups of countries each with similar macroeconomic characteristics. The first includes the oil-producing EMG countries3 with non-diversified exports and production structures, little or no external foreign debt, high per capita GDP, and surpluses in the external balance and fiscal positions. The second group includes the
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non-oil producing EMG countries4 characterized by significantly higher external debt, fiscal and external deficits and lower per capita GDP (see Figure 2). Figure 2. Macroeconomic Dynamics of EMG Countries: 1960-2001 (b) Per Capita GDP Non-Oil EMG Countries (USD)
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Source: International Monetary Fund, International Financial Statistics, 2002 Notes: The horizontal axis represents the time period 1960-2001. For our purpose two different versions of the dynamic model will be empirically tested. In the first version, we include the EMG oil-producing countries, and in the second the diversified economies of the region, that is, the non-oil producing EMG countries. The seven variables of interest are defined as follows. Poil is the price of a basket of crude oil deflated by the US producer price index (PPI); Pw is the average inflation rate for the world economy; Yw is the average growth rate of GDP for the world economy; iw is the average world real interest rate. The EMG countries’ specific variables are: Ya, the average real per capita GDP growth rate; FSa, a measure of fiscal balance computed as government surplus/deficit as a percent of GDP; and TBa, the trade balance computed as exports minus imports as a percent of GDP. Yearly average data for the external variables and each EMG sub-group are used from 1960 to 2001. The data have been compiled from the International Financial Statistics. All external variables included in the model are in logarithmic form and in levels except for the real rate of interest taken in percent. The world economy is proxied by the economies of the United States (US) and the European Union (EU). This plausible approximation rests on the fact that about 70 percent of EMG countries’ foreign trade and financial transactions are with the US and EU. It is thus assumed that in addition to shocks in oil prices, external shocks emanating from the US and EU economies are more likely to affect the EMG region than shocks in other regions of the world. 3.2. Unit Root Tests In order to establish the order in which the variables should enter the VAR model, we examine the time series properties of the macroeconomic variables to be used in the model. To test for the existence of a long run relationship, the Johansen (1988)
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cointegration test will be used after establishing non−stationarity of the series by applying the Phillips-Perron (PP) unit root test. It is common for time−series data to demonstrate signs of non−stationarity; typically both the mean and variance of macroeconomic variables trend upwards over time. In any case tests for non−stationarity are carried out as a preliminary step to explore the possibility of a significant long−run relationship between the variables concerned, that is, cointegration tests. The following regressions are carried out: k
∆X t = β1 + β 2 t + β 3 X t −1 + ∑ δ i ∆X t −i + ε t ,
(1)
i =1
where ∆ is the first−difference operator, βi, δi, are constant parameters, X is a vector of external, and EMG specific variables ( Poil , Pw , Yw , iw , Ya , FS a , TBa ), t is a time trend, and εt is a stationary stochastic process. The number of lags (k) will be determined based on the Akaike Information Criterion (AIC). To determine the order of integration of the series, model (1) is modified to include second differences on lagged first and k lags of second differences. That is:
k
∆2 X t = λ 0 + λ1t + λ 2 ∆X t −1 + ∑ µ i ∆2 X t − i + ε1t , i =1
(2)
where, ∆2 Xt = ∆Xt − ∆X t-1,; λi, µi, are constant parameters, t is a time trend, and ε1t is a stationary stochastic process. The k lagged difference terms are included so that the error terms εt and ε1t in both equations are serially independent. To test for stationarity, the (PP) test is applied to equations (1) and (2), and the results are summarized in Tables 2 and 3. The null hypotheses are β3 = 0, and λ2 = 0 respectively, that is, a unit root exists in Pt and ∆Pt-1 implying that the series are non−stationary. Equation (1) and (2) are also estimated with a constant and no time trend. The PP test results indicate that the two EMG group series as well as the external series are non-stationary in the levels (equation 1). However, unit roots in the first differences of the series (equation 2) are rejected at the 1 percent significance level, suggesting that the external and internal series are stationary. Thus, the external and EMG specific macroeconomic series are integrated of order one, I (1).
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Table 2: Unit Roots Test for the External Variables Mackinnon’s Critical Values PP Unit Roots Poil Tests Constant and Time Trend PP Levels (3) -1.94 PP FD (3) -5.88**
Pw
Yw
-1.25 -4.26**
-2.22 -7.39**
iw
-0.63 -5.22**
5%
1%
-3.52 -3.53
-4.20 -4.20
Constant PP Levels (3) -1.56 -1.07 -2.63 -1.09 -2.93 -3.60 PP FD (3) -5.96** -3.94** -7.29** -4.82** -2.94 -3.60 Notes: 1- PP is the Phillips-Perron test and FD is the first difference. 2-The numbers in parenthesis are the proper lag lengths based on the Akaike Information Criterion (AIC). 3- A * indicates rejection of the null hypothesis of non-stationarity at the 5% level of significance, while ** indicates a stronger rejection at the 1% level. 4-For most variables the time trend variable is statistically insignificant. Table 3: Unit Roots Test for the EMG Countries Specific Variables Mackinnon’s Critical Values PP Unit Roots Ya FS a TBa 5% 1% Tests Oil-Producing EMG Countries Constant and Time Trend PP Levels (2) -3.53 -4.21 -2.07 -2.44 -3.29 PP FD (2) -3.53 -4.22 -5.20** -6.59** -5.72** Constant PP Levels (2) -3.53 -4.21 -1.82 -2.51 -3.37 PP FD (2) -3.53 -4.22 -5.26** -6.68** -5.98**
Non-Oil Producing EMG Countries Constant and Time Trend PP Levels (4) -3.53 -4.21 -1.14 -2.04 PP FD (4) -3.54 -4.22 -4.44** -8.50** Constant PP Levels (4) -2.94 -3.61 -1.276 -2.20 PP FD (4) -2.94 -3.61 -5.40** -8.31** See Notes of Table 2.
-2.33 -5.76** -1.72 -5.84**
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3.3. Cointegration Tests Next we establish whether the series are cointegrated. Specifically, having established the presence of a unit root in the levels of each variable, we need to test whether the series have different unit roots (not-cointegrated), or shares the same unit root (cointegrated). Cointegrated variables, if disturbed, will not drift apart from each other and thus possess a long–run equilibrium relationship. The Johansen efficient maximum likelihood test is used to examine the existence of a long−term relationship between first the external variables, and then between the external variables and the individual EMG sub-groups, at the 5 and 1 percent levels of significance respectively. It is applied using alternative lag lengths in the vector autoregression (VAR). Engel and Granger (1987) argued that a linear combination of two or more non-stationary series may be stationary. If such a stationary combination exists, the non-stationary time series are said to be cointegrated. The stationary linear combination is called the cointegrating equation and may be interpreted as a long-run equilibrium relationship between the variables. The cointegration tests are conducted for the period 1960−2001, and the existence of a long−term relationship between external variables on one hand, and between the EMG and external variables on the other could not be rejected. The Likelihood Ratio Test indicates two−cointegrating vector at the 5 percent significance level among the external variables (Table 4). It also indicates three−cointegrating vectors at the 5 percent significance level among the external variables and the oil and non-oil EMG variables (Table 5). The PP unit roots tests conducted earlier have shown that all external and internal variables are non-stationary in levels and they need to be differenced once to become stationary, that is, they are integrated of order one I(1). In addition, the Johansen tests indicated that there exist two cointegrating relationships between the external variables, thus the existence of at least two variables being stationary can easily be assumed. The same is true for the internal variables; the existence of at least three cointegrating relationships may convey the existence of at least three stationary variables. Therefore, even though each of the seven variables turned out to be non-stationary, the two sub groups can be viewed as stationary. The usual approach in this case is to estimate an unrestricted VAR including the seven variables in levels since trying to impose stationarity by including the first differenced variables may remove important information concerning the dynamic co-movements from the time series.
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Table 4: Johansen Cointegration Test for the External Variables Hypothesis Critical Values Null Alternative Test Statistics (5%) (1%) 63.18** 47.21 54.46 r=0 r≥1 34.77* 29.68 35.65 r≤1 r ≥2 11.18 15.41 20.04 r≤2 r ≥3 r=4 2.53 3.76 6.65 r≤3 Notes: 1-The Johansen cointegration likelihood ratio test is based on the trace of the stochastic matrix. 2-The test allows for a linear deterministic trend in the data. 3-The symbol r represents the number of cointegrating vectors. Maximum lag 1 year in VAR. 4-A * and ** indicate rejection of the null hypothesis at the 5and 1 percent level of significance respectively. 5-The asymptotic critical values are from Osterwald-Lenum (1992).
Table 5: Johansen Cointegration Test for the EMG Specific Variables External and Oil-EMG Countries Hypothesis Critical Values Null Alternative Test Statistics (5%) (1%) r=0 213.24** 124.24 133.57 r≥1 124.35** 94.15 103.18 r≤1 r ≥2 72.54* 68.52 76.07 r≤2 r≥3 43.55 47.21 54.46 r≤3 r ≥4 25.67 29.68 35.65 r≤4 r ≥5 11.64 15.41 20.04 r≤5 r ≥6 r≤6 r=7 2.92 3.76 6.65
External and Non-Oil EMG Countries Hypothesis Critical Values Null Alternative Test Statistics (5%) (1%) r=0 190.11** 124.24 133.57 r≥1 121.28** 94.15 103.18 r≤1 r ≥2 69.05* 68.52 76.07 r≤2 r≥3 42.53 47.21 54.46 r≤3 r ≥4 24.57 29.68 35.65 r≤4 r ≥5 10.35 15.41 20.04 r≤5 r ≥6 2.90 3.76 6.65 r≤6 r=7 Notes: 1-The Johansen cointegration likelihood ratio test is based on the trace of the stochastic matrix. 2-The test allows for a linear deterministic trend in the data. 3-The symbol r represents the number of cointegrating vectors. Maximum lag 1 year in VAR. 4- A * and ** indicate rejection of the null hypothesis at the 5 and 1 percent level of significance respectively. 5-The asymptotic critical values are from Osterwald-Lenum (1992).
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3.4. The Dynamic VAR Model Our earlier finding that all seven series contain a unit root but some linear combinations of two or more of the non-stationary series are stationary leads us next to estimate a VAR model. The VAR model is commonly used for analyzing the dynamic impact of random disturbances on the system of variables. This approach is very useful because it models every variable in the system as a function of the lagged values of all the endogenous variables in the system. Based on the Akaike Information Criterion (AIC) and Schwarz Criterion (SC), we run the following VAR model of order 1:
Z t = λ1Z t −1 + ..... +β x Z t − x + ε t ,
(3)
where Zt is our 7-vector of the non-stationary I(1) internal and external macroeconomic time series, λ1 , and β x are the coefficient matrices to be estimated, Zt − x contain the lags of the 7 series to insure that the vector of innovations ε t is not serially correlated. Table 6 shows the empirical estimates of the VAR model containing the seven variables discussed earlier and a constant term. While variations in the world macroeconomic variables have a significant impact on the EMG region, the fundamentals of the EMG economies enter insignificantly the equations of the world economy. Specifically, the terms of trade related shocks and world demand seem to have the most significant impact on EMG countries’ rate of growth of per capita GDP. In addition, the fiscal position seems to be significantly affected by all the external variables. The trade balance is mostly affected by world demand. For the estimated VAR including the non-oil EMG countries, again the EMG variables have an insignificant impact on the world variables. The external variables with the most significant impact are (1) world demand, interests rates and the price of oil on the fiscal balance; (2) growth in world output and world inflation on the trade balance; and (3) the price of oil and world demand on EMG’s per capita GDP (see Table 7).
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Table 6: VAR Model, Oil-EMG Countries External Variables Internal Variables Right Hand No. Side of Poil Pw Yw iw Ya FS a TBa Variable Lags Poil 1 -0.03 -0.16 -0.06 -0.40 -0.45 -0.65 0.05 t-Stat -0.11 -3.47 -3.3 -0.66 -4.51 -2.85 1.28 Pw 1 -0.14 -1.05 0.78 0.01 0.62 0.32 -0.45 t-Stat 0.56 -24.1 4.22 0.17 2.11 3.77 -1.93 Yw 1 0.00 -0.00 0.54 -0.00 0.02 0.007 0.00 t-Stat 1.19 -0.26 4.64 -0.17 2.73 3.37 2.85 iw 1 4.08 0.79 0.02 1.01 0.13 0.02 0.08 t-Stat 4.75 5.35 1.18 3.62 3.34 3.67 1.66 Ya 1 0.00 0.00 0.00 -0.00 0.81 0.12 -0.18 t-Stat 2.36 0.38 0.61 -0.19 5.82 2.44 -0.53 FS a 1 0.00 0.000 0.00 0.00 -0.05 0.41 0.38 t-Stat 1.23 1.11 1.32 0.46 -0.11 2.22 0.28 TBa 1 -000 0.00 0.00 0.00 1.75 -0.10 -0.33 t-Stat -2.6 0.30 1.20 0.36 1.92 -2.94 -1.27 Constant 1 -18.6 -3.91 0.00 0.43 0.22 -0.10 0.32 t-Stat -3.18 -3.83 2.98 0.22 1.65 -1.04 0.18 Notes: The number of lags in the VAR specification (1) has been determined based on the Akaike Information Criterion (AIC) and Schwarz Criterion (AIC). Source: Author’s estimates.
The ordering of the equations in the above VAR model is not ad hoc; it is meant to allow for meaningful interpretation of the impulse response functions and variance decomposition to be used in the following analysis. The model uses economic theory and intuition, as well as, the concept of orthogonality of structural innovations. First, it is assumed that EMG countries can be categorized as small open economies; therefore, shocks originating in this region are assumed to have no impact on world variables. However, external shocks are assumed to have important implications on per capita GDP, the fiscal situation and the external sector of the EMG region.
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Table 7: VAR Model, Non-Oil EMG Countries External Variables Internal Variables Right Hand No. Side of Poil Pw Yw iw Ya FS a TBa Variable Lags Poil 1 0.42 -0.14 -0.045 -0.05 0.13 -0.45 0.09 t-Stat 2.30 -4.17 -2.82 -1.08 2.51 -1.85 0.06 Pw 1 -0.80 0.89 0.52 -0.17 0.45 0.32 0.89 t-Stat -2.76 16.3 2.63 -2.09 0.32 0.77 3.45 Yw 1 0.00 0.00 0.75 0.00 0.86 0.75 0.96 t-Stat 3.27 2.47 6.82 2.12 3.01 2.39 3.85 iw 1 1.89 0.99 0.11 1.01 0.84 0.02 -0.09 t-Stat 3.07 8.63 2.73 5.95 1.65 3.45 0.05 Ya 1 0.00 0.00 -0.26 0.00 0.37 0.03 0.01 t-Stat 1.52 1.43 -0.92 0.90 1.88 0.04 0.08 FS a 1 -0.00 -0.00 -0.19 -0.00 -0.15 -0.36 0.07 t-Stat -0.55 -0.19 -1.91 -1.61 -2.20 -0.85 0.12 TBa 1 0.00 0.0 0.47 0.00 1.75 0.12 0.74 t-Stat 1.27 0.13 1.24 0.43 4.56 0.95 4.12 Constant 0.88 -2.77 -0.11 2.55 0.42 93.2 0.69 t-Stat 0.16 -2.71 -3.01 169 -1.66 0.15 0.70 Notes: The number of lags in the VAR specification (1) has been determined based on the Akaike Information Criterion (AIC) and Schwarz Criterion (AIC). Source: Author’s estimates.
The first external variable in the ordering is the price of oil and this variable is assumed to have effects on all external and EMG specific variables, but none of the remaining variables is assumed to have effects on the first. The world inflation is assumed to impact world GDP growth rates and interest rates, and all the regional variables. The ordering of the remaining two variables pertaining to the world has been determined in a similar fashion. The internal variables pertaining to the two EMG subgroups are determined as follows. Fluctuations in the world price of oil will impact directly on per capita GDP in EMG countries. World inflation will also impact on the region’s growth rates through its effect on the terms of trade and the real bilateral exchange rate. World growth rates are assumed to impact regional growth through the demand for exports from the EMG region. The assumption of a small open economy means that interest rates are determined abroad. Therefore, an increase in world interest rates implies an increase in domestic rates and would, consequently, lower GDP growth rates through the crowding out effect.
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3.5. Variance Decomposition Variance decomposition provides a different method of depicting the system’s dynamics. While impulse response functions trace the effects of a shock to an endogenous variable on variables in the VAR system, variance decomposition decomposes variation in an endogenous variable into the component shocks to the endogenous variables in the VAR. Hence, variance decomposition gives information about the relative importance of each random innovation to the variables in the VAR. Each entry in Table 8 represents the percentage of the variance due to each innovation for oil-EMG countries. The top quadrant of the table shows the percentage of per-capita GDP volatility explained by shocks to volatility in the 7 external and internal variables. Specifically, the variability in the rate of growth of per-capita GDP of oil producing EMG countries is mostly captured by world interest rates in the medium and long-run, followed by terms of trade related variables and world demand. All three impacts explain a significant portion of the volatility in per-capita GDP with a lag of three years for world output and prices. The variability in the fiscal balance is mostly captured by the price of oil followed by world interest rates. In other words, the fiscal balance is mainly affected by world oil prices and interest rates. The effect of oil on the fiscal balance has a relatively high magnitude, explaining about 37 percent of the variation in the first year but dying out a little to about 20 percent after 10 years. This is a reflection of the high share of oil revenues in the budgetary structure of oil-producing EMG countries. The same is true for the trade balance where again the price of oil and the world interest rates are capturing most of its variability, that is, only the price of oil and interest rates have a significant impact on the volatility of the trade balance. It should be noted that the proceeds from international oil sales enter the current account, which means that fluctuations in oil prices lead directly to fluctuations in oil revenues and, therefore, to variations in the external balance.
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Table 8: Variance Decomposition by Structural Shocks, Oil-EMG Countries (%) External Variables Internal Variables Total Effect Ext# of IntPoil Pw Yw iw Ya FS a TBa ernal Years ernal Per Capita GDP 1 8.27 3.40 0.03 1.13 87.1 0.00 0.00 12.82 87.10 2 14.0 15.10 3.97 9.83 53.6 0.37 2.99 42.97 56.96 3 10.3 16.55 7.40 23.73 39.6 0.30 1.98 58.01 41.88 4 8.85 15.55 10.0 30.40 32.2 0.36 2.56 64.82 35.12 5 8.53 14.92 11.3 32.91 28.8 0.45 2.90 67.68 32.15 6 8.45 14.58 11.7 34.08 27.5 0.56 3.04 68.82 31.10 7 8.38 14.38 11.9 34.37 27.2 0.65 3.12 68.96 30.97 8 8.34 14.27 11.8 34.34 27.2 0.69 3.16 68.84 31.05 9 8.32 14.22 11.8 34.27 27.4 0.72 3.18 68.66 31.30 10 8.31 14.20 11.8 34.21 27.5 0.73 3.18 68.55 31.41 Fiscal Balance 1 37.29 0.02 1.31 0.00 6.40 54.9 0.00 38.62 61.36 2 29.26 17.69 3.56 4.18 4.39 25.5 15.3 54.69 45.27 3 21.59 16.63 7.27 17.92 5.64 19.2 11.6 63.41 36.55 4 20.45 14.78 10.0 19.52 5.06 17.2 12.8 64.81 35.16 5 20.46 14.63 10.7 19.06 5.26 16.9 12.8 64.92 35.04 6 20.70 14.68 10.6 19.21 5.48 16.6 12.6 65.23 34.74 7 21.10 14.73 10.3 19.77 5.47 16.1 12.3 65.98 33.99 8 21.42 14.78 10.1 20.61 5.33 15.7 11.9 66.95 33.02 9 21.58 14.78 9.95 21.54 5.18 15.2 11.6 67.85 32.12 10 21.66 14.74 9.82 22.32 5.07 14.9 11.4 68.54 31.43 Trade Balance 1 0.30 4.45 7.61 25.95 0.13 5.03 56.4 38.31 61.65 2 23.82 6.37 6.97 17.83 0.69 3.35 40.9 54.99 44.97 3 22.26 10.23 6.59 23.62 0.67 3.24 33.3 62.70 37.26 4 19.98 10.19 7.15 28.50 0.60 3.21 30.3 65.82 34.15 5 19.38 9.88 7.53 29.67 0.81 3.17 29.5 66.46 33.54 6 19.30 9.77 7.56 29.78 1.22 3.17 29.1 66.41 33.55 7 19.25 9.72 7.53 29.74 1.51 3.18 29.0 66.24 33.72 8 19.22 9.71 7.52 29.68 1.66 3.19 28.9 66.13 33.83 9 19.19 9.73 7.54 29.66 1.73 3.18 28.9 66.12 33.84 10 19.16 9.76 7.56 29.69 1.75 3.18 28.8 66.17 33.80 Source: Author’s estimates. Ordering: Poil , Pw , Yw , iw , Ya , FS a , TBa .
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When contrasting the above results with those of non-oil producing EMG countries, it is obvious that the price of oil has less direct influence on the dynamics of the macroeconomic fundamentals in these countries (see Table 9). The variability in percapita GDP is mostly captured by world inflation and demand. The largest impact occurs after 6 years explaining about 31 percent of the volatility variation in the rate of growth of per-capita GDP. Moreover, no one variable seems to capture the variability in the fiscal balance in the short-run. However, in the long-run the price of oil and world interest rates capture some of the variability in the fiscal balance. The variability in the trade balance is mostly captured by world demand, which impact directly on those countries demand for exports. The low and almost insignificant impact of current account developments on both per capita GDP and fiscal balance attests to better insulation of non-oil producing EMG countries from external shocks than oil-producing EMG countries. The last two columns in Tables 8 and 9 sum up the importance of external versus internal factors in explaining the variability in EMG specific variables. For oilproducing EMG countries, external factors account more for the variability in internal variables in both the short and long-run. In particular, it is seen in the fact that the variability in the internal variables is mainly captured by the price of oil, and the world interest and inflation rates. Overall, such an easy transmission of external shocks into oil-producing EMG countries can be explained by higher overall openness of oilproducing EMG countries, and might signal deficiencies in adequate macroeconomic policies in mitigating the impact of exogenous shocks. For non-oil producing EMG countries the internal factors seem to play a more important role. In the short-run, the internal factors capture most of the variability in percapita GDP and the fiscal balance. This trend is, however, reversed after five years where the external factors become dominant. Thus, except for the trade balance in nonoil producing member countries, the external factors are dominant in the long-run whether we are looking at oil or non-oil producing countries.
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Table 9: Variance Decomposition by Structural Shocks, Non-Oil EMG Countries (%) External Variables Internal Variables Total Effect # of ExtIntPoil Pw Yw iw Ya FS a TBa Years ernal ernal Per Capita GDP 1 10.97 8.51 0.06 1.51 78.90 0.00 0.00 21.05 78.90 2 19.74 14.55 7.49 1.44 51.39 5.36 0.00 43.22 56.75 3 16.67 21.67 15.2 2.17 37.29 6.32 0.61 55.74 44.22 4 11.88 26.21 21.1 3.25 27.96 8.22 1.32 62.46 37.50 5 8.43 28.84 24.6 3.67 22.74 9.66 2.04 65.54 34.44 6 6.41 30.28 26.7 3.60 19.70 10.6 2.59 67.00 32.97 7 5.23 31.11 28.1 3.33 17.91 11.2 2.96 67.82 32.15 8 4.50 31.58 29.2 3.03 16.83 11.6 3.17 68.36 31.61 9 3.99 31.84 30.1 2.77 16.17 11.7 3.27 68.75 31.22 10 3.63 31.95 30.8 2.56 15.76 11.8 3.31 69.03 30.94 Fiscal Balance 1 9.24 0.24 2.00 0.16 9.84 78.4 0.00 11.64 88.32 2 7.06 0.24 9.21 1.73 21.37 59.0 1.29 18.24 81.72 3 13.26 0.45 10.6 1.63 20.97 51.8 1.21 25.96 74.01 4 18.98 2.23 9.18 3.64 20.38 44.4 1.13 34.03 65.94 5 19.83 5.70 8.13 7.79 19.27 38.0 1.20 41.45 58.51 6 17.51 9.48 7.84 11.80 18.17 33.5 1.62 46.63 53.32 7 14.91 12.44 7.60 14.63 17.23 30.8 2.28 49.58 50.37 8 13.05 14.41 7.20 16.44 16.55 29.3 2.98 51.10 48.87 9 11.87 15.62 6.76 17.62 16.11 28.4 3.55 51.87 48.09 10 11.12 16.34 6.39 18.45 15.85 27.8 3.97 52.35 47.67 Trade Balance 1 7.53 0.23 13.5 2.43 2.07 3.14 71.0 23.72 76.25 2 7.94 0.16 11.6 3.55 7.58 2.34 66.7 23.29 76.67 3 8.76 0.28 10.8 4.51 10.9 2.25 62.3 24.41 75.49 4 9.38 0.85 10.6 5.08 13.2 2.62 58.1 25.96 73.93 5 9.56 2.04 10.9 5.44 14.51 3.30 54.2 27.96 72.01 6 9.27 3.81 11.6 5.62 15.00 4.11 50.5 30.34 69.63 7 8.71 5.84 12.6 5.64 15.02 4.93 47.1 32.87 67.09 8 8.11 7.82 13.8 5.53 14.81 5.67 44.1 35.33 64.63 9 7.58 9.54 15.0 5.34 14.54 6.28 41.6 37.51 62.45 10 7.14 10.96 16.1 5.13 14.30 6.75 39.5 39.37 60.60 Source: Author’s estimates. Ordering: Poil , Pw , Yw , iw , Ya , FS a , TBa .
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3.6. Impulse Response Functions Impulse response functions trace the effect of a one standard deviation shock to one of the innovations on current and future values of the endogenous variable. In other words, a shock to the j-th variable directly affects the j-variable, and is also transmitted to all of the endogenous variables through the dynamic structure of the VAR. The impulse response functions shed light on the dynamics of the variables included in the VAR system as a result of shocks to either one of these variables. The impulse response functions 5 permit us to explore how the internal and external variables might respond to various shocks to the system. In oil producing EMG countries, a one standard deviation positive shock to the price of oil has a significantly positive impact on the growth of per capita GDP, and on the fiscal and trade balances in the short run (see Figure 3 (a-c)). Specifically, the percapita GDP growth rate increases by about 0.35 percent during the first 4 years and returns to its steady state thereafter. The response of the fiscal balance is also important over the same period where we see an improvement of about 2.5 percent of average GDP. Similar dynamics are reported for the external sector where the trade balance improves by about 0.1 percent of average GDP. These dynamics are in line with economic intuition and are reflecting the high share of oil revenues in the budgetary structures of these countries and the high proceeds of oil exports in the trade balance. The positive impact on the fiscal and trade balances is manifested mostly during the first 3-4 years owing to the nature of the fiscal and trade account balances as being short term phenomena. In the long-run the impact seems to be either dying out or becoming negative due mainly to the fact that after the initial positive impact of an increase in oil prices on oil revenues and the proceeds from oil exports, the world economy might respond by lowering the demand for oil which then might impact negatively on oil revenues and the proceeds from oil exports. Figure 3. Response of EMG Countries to an Oil Price Shock Oil-EMG Countries Non-Oil EMG Countries (a) Response of Per-Capita GDP Growth Rate
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Figure 4 (a-c) highlights the response of EMG countries to an improvement in their terms of trade. Specifically, a one standard deviation positive shock to world prices, which means a deterioration in the EMG terms of trade, seems to have a more persistent impact on the non-oil-producing EMG countries’ per capita GDP growth rate. The increase in per-capita GDP growth rate is by about 0.25 percent during the first 4 years while it increases only by 0.15 percent over the same period in oil producing countries. The trade balance of non-oil producing EMG countries is subject to a more pronounced impact by a deterioration in the terms of trade than is the case for oilproducing countries, which are more affected by changes in world oil prices. This clearly suggests the excessive vulnerability of non-oil-producing EMG countries to changes in their terms of trade with a direct impact on their exports and the growth in per-capita GDP. The improvement in the trade balance is about 4.5 percent of average GDP in non-oil-producing countries, while we see a deterioration of 0.28 percent for oilproducing EMG countries. The fiscal balance seems to be improving for all EMG countries as a result of an improvement in their terms of trade. In the opposite scenario, unfavorable terms of trade for an EMG country translates into lower exports and consequently into a rise in the stock of foreign debt. Figure 4. Response of EMG Countries to Terms of Trade Shock Oil-EMG Countries Non-Oil EMG Countries (a) Response of Per-Capita GDP Growth Rate 0.15
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ambiguous for both sub-groups, while it is improving for the non-oil-producing countries, due mainly to higher proceeds from exports, it deteriorates for the oilproducing EMG countries. The improvement in the trade balance of both EMG subgroups is noticeable but it is much more pronounced for non-oil-producing countries, reaching 0.5 percent of average GDP after only 4 years, while it does exceed the 0.06 percent level in oil-producing EMG countries.
Figure 5. Response of EMG Countries to a Demand Shock Oil-EMG Countries
Non-Oil EMG Countries (a) Response of Per-Capita GDP Growth Rate
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Figure 6. Response of EMG Countries to an Interest Rate Shock Oil-EMG Countries Non-Oil EMG Countries (a) Response of Per-Capita GDP Growth Rate
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Notes: The horizontal axis represents the time period from 1-10 years. 4. Conclusions and Policy Recommendations This paper has shown that although the impact of external shocks on the macroeconomic dynamics of the oil and non-oil producing EMG countries is significant, it differs across countries and depends on the macroeconomic asymmetries existent in these countries, economic policies adopted in each individual country, country specific factor endowments, and the time horizon under consideration. The VAR results have shown that for both oil and non-oil EMG countries, fluctuations in the world macroeconomic variables have a significant impact on the EMG region. However, the fundamentals of the EMG economies enter insignificantly the equations of the world economy, attesting to the fact that these economies are relatively small and vulnerable to external shocks. In the case of the oil EMG economies, the terms of trade related shocks and world demand are shown to have the most significant impact on those countries’ rate of growth of per capita GDP. In addition, the fiscal position seems to be significantly affected by all the external variables. The trade balance is mostly affected by world demand. In the case of the nonoil EMG economies, the external variables with the most significant impact on those economies are (1) world demand, interests rates and the price of oil on the fiscal balance; (2) growth in world output and world inflation on the trade balance; and (3) the price of oil and world demand on EMG’s per capita GDP Variance decomposition and impulse responses have shown that external shocks are likely to have high propagation power within the economies of the EMG region, which is a clear signal that these countries’ macroeconomic policies need to be reformed. Specifically, the variability in the rate of growth of per-capita GDP of oil producing EMG countries is mostly captured by world interest rates in the medium and long-run followed by terms of trade related variables and world demand. Also, the variability in the fiscal balance is mostly captured by the price of oil followed by world interest rates. The effect of oil on the fiscal balance has a relatively high magnitude, reflecting the high share of oil revenues in the budgetary structure of oil-producing EMG
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countries. The same is true for the trade balance where the price of oil and the world interest rates are capturing most of its variability. When contrasting the above results with those of non-oil producing EMG countries, the price of oil is shown to have less direct influence on the dynamics of the macroeconomic fundamentals in these countries. Moreover, no one variable seems to capture the variability in the fiscal balance in the short-run. However, in the long-run the price of oil and world interest rates capture some of the variability in the fiscal balance. The variability in the trade balance is mostly captured by world demand, which impact directly on those countries demand for exports. The low and almost insignificant impact of current account developments on both per capita GDP and fiscal balance attests to better insulation of non-oil producing EMG countries from external shocks than oilproducing EMG countries. Impulse response functions have shown that for oil producing EMG countries, a positive shock to the price of oil has a significantly positive impact on the growth of per capita GDP, and on the fiscal and trade balances in the short run. The same response is observed for the fiscal balance. These dynamics are in line with economic intuition and are reflecting the high share of oil revenues in the budgetary structures of these countries, and the high proceeds of oil exports in the trade balance. It was also shown that while an oil price shock constitutes a positive shock for oil-producing EMG countries, it constitutes a negative supply side shock for non oil-producing EMG countries in the short-run. On the other hand a deterioration in the EMG terms of trade, is shown to have a more persistent impact on the non-oil-producing EMG countries’ per capita GDP growth rate. The trade balance of non-oil producing EMG countries is subject to a more pronounced impact by a deterioration in the terms of trade than is the case for oilproducing countries, which are more affected by changes in world oil prices. This clearly suggests the excessive vulnerability of non-oil-producing EMG countries to changes in their terms of trade with a direct impact on their exports and the growth in per-capita GDP. The greater vulnerability of non-oil producing EMG countries to demand shocks is due to the fact that their growth in per-capita GDP depends primarily on their exports. The impact on the fiscal balance seems to be ambiguous for both sub-groups, while it is improving for the non-oil-producing countries, due mainly to higher proceeds from exports, it deteriorates for the oil-producing EMG countries. Finally, a positive shock to the world real interest rate has a clear negative impact on the fiscal balance of non-oil-producing countries owing to the fact that the highly indebted non-oil-producing EMG countries will have to service a higher level of external debt leading to more budget deficits. The impact on the fiscal balance of oilproducing EMG countries was shown to be exactly the opposite and constitutes an improvement in their fiscal balance. This is due to the fact that most oil-producing EMG countries invest the proceeds of their oil revenues in US treasury bills and bonds, and any increase in world rates of interest translates into more fiscal revenues and to an improvement in their fiscal position.
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In short, when formulating future macroeconomic policies, policy makers and academics in the EMG region should take into consideration the excess vulnerability of the economies of the region to external shocks. Notes 1 Simon Neaime, Associate Professor, Department of Economics, American University of Beirut, Beirut, Lebanon. Mailing address: 3 Dag Hammarskjold Plaza, New York, NY 10017-2303, US. Tel (9613) 829944-Fax (9611) 744484. Email: [email protected] Professor Neaime is also research fellow of the Athenian Policy Forum, and research fellow of the Institute of Financial Economics, American University of Beirut. Financial support from the University Research Board of the American University of Beirut is gratefully acknowledged. The author is also grateful to the Editor of the Journal and two anonymous referees for very valuable comments and suggestions on an earlier draft. 2 For a detailed discussion of terms of trade and exchange rate policies see Neaime (2000), Neaime and Paschakis (2002), and Mansoorian and Neaime (2000, 2002, and 2003). 3 The oil-producing EMG countries include the Gulf Cooperation Countries (GCC) of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. 4 The non-oil producing EMG countries include: Egypt, Jordan, Lebanon, Syria and Yemen. 5 Impulse response functions with Asymptotic and Monte Carlo response standard errors have been computed and are available from the author. These tests are all pointing to statistically significant responses. 6 One important observation is that the positive impact from remittances, mainly on percapita GDP growth rates in non-oil-producing EMG countries, manifests itself in the long-run. Hence, these countries would benefit in the long-run after each hike in oil prices. Therefore, workers remittances from oil-EMG countries seem to have important dampening effects on the magnitude and persistence of external shocks on the macroeconomic situation of the non-oil-producing countries. 7 An important fiscal development in oil-producing EMG countries is that some of these countries have moved during the late 1990s into the category of debtors countries due to the increase in their external debts and deficits. For example, in Saudi Arabia the budget deficit in the last five years has averaged 4.5 percent of GDP, and government total debt is now at about 95 percent of GDP. With a budget deficit that is due mainly to public salaries and debt service payments, the Saudi government will feel the pressure of any decrease in oil-prices. Despite this, our empirical results have shown that the positive impact of an increase in world interest rates still outweighs the negative impact of increased debt service payments in some oil-producing EMG countries.
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References Ambler, S., (1989), “Does Money Matter in Canada? Evidence from a Vector Error Correction Model”, The Review of Economics and Statistics, 71, 651-658. Bailey, W., (1989), “The Effects of US Money Supply Announcements on Canadian Stock, Bond, and Currency Prices”, Canadian Journal of Economics, 3, 607618. Backus, D., (1986), “The Canadian-US Exchange Rate: Evidence from a Vector Autoregression”, The Review of Economics and Statistics, 68, 628-637. Batten, D., and Ott, M., (1985), “The Interrelationship of Monetary Policies Under Floating Exchange Rate Regimes”, Journal of Money Credit and Banking, 17, 103-110. Bordo, M., Choudhri. E.U., and Schwartz, A.J., (1987), “The Behavior of Money Stock Under Interest Rate Control: Some Evidence for Canada”, Journal of Money Credit and Banking, 19, 181-197. Choudhri, E., (1983), “The Transmission of Inflation in A Small Economy: An Empirical Analysis of the Influence of U.S. Monetary Disturbances on Canadian Inflation”, Journal of International Money and Finance, 2, 167-78. Engle, R., and Granger, W., (1987), “Co-integration and Error Correction Representation, Estimation and Testing”, 55(2), 251-276. Gregory, A and Raynauld, J., (1985), “An Econometric Model of Canadian Monetary Policy over the 1970’s”, Journal of Money Credit and Banking, 17, 43-58. Hoffmaister, A., Roldos, J., and Wickham, P., (1998), “Macroeconomic Fluctuations in Sub-Saharan Countries”, International Monetary Fund (IMF) Staff Papers, 45(1). Johansen, S., (1991), “Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models”, Econometrica, 59, 1551-1580. Kireyev, A., (2000), “Comparative Macroeconomics Dynamics in the EMG World: A Panel VAR Approach”, IMF Working paper Series, (WP/00/54). Naka, A., (1997), “Examining Impulse Response Functions in Cointegrated Systems”, Applied Economics, 29, 1593-1603. Mansooriann A., and Neaime, S., (2002), “Habits and Durability in Consumption and the Effects of Exchange Rate Policies”, International Economic Journal, 16(2), 97-114. Mansoorian A., and Neaime, S., (2003), “Durable Goods, Habits, Time Preference, and Exchange Rates”, North American Journal of Economics and Finance, 14(1), 115-130. Mansoorian A., and Neaime, S., (2000), “Habits and Durability in Consumption and the Effects of Tariff Protection”, Open Economies Review, 11(3), 195-204. Neaime S., and Paschakis, J., (2002), “The Future of the Dollar-Euro Exchange Rate”, North American Journal of Economics and Finance, 13, (1), 57-72. Neaime S., (2000), The Macroeconomics of Exchange Rate Policies, Tariff Protection and the Current Account: A Dynamic Framework, APF Press, Toronto, Canada. Osterwald-Lenum, M., (1992), “A Note with Quantiles of the Asymptotic Distribution
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of the Maximum Likelihood Cointegration Rank Test Statistics”, Oxford Bulletin of Economics and Statistics, 54, 461-472. Phillips, P.C., and Perron, P., (1988), “Testing for a Unit Root in Time Series Regression”, Biometrika, 75, 335-346. Prasad, E., and Gable, J., (1998), “International Evidence of Trade Dynamics”, International Monetary Fund Staff Papers, 3, 401-439. Sims, C., (1980), “Comparison on Interwar and Post War Business Cycles: Monetarism Reconsidered”, American Economic Review, 70, 250-257.
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main purpose of this paper is to provide policy makers in the EMG region the opportunity to preempt an impending macroeconomic or financial crisis, hence making it less likely for policy makers to have to resort to painful economic measures after the fact to resolve a crisis. While developed countries with relatively large economies can better absorb and neutralize the effects of external and exogenous shocks, it is somehow more difficult in the case of developing economies which are usually smaller in size and dependent, as is the case in oil-producing EMG countries, on the export of nearly one commodity being oil. This paper, which constitutes the first attempt at studying the macroeconomic linkages and asymmetries in the EMG region, will identify the current macroeconomic asymmetries in the region and will highlight the sensitivity and vulnerability of the EMG economies to various external shocks being an essential step in the formulation of adequate and appropriate future macroeconomic policies. The rest of the paper is divided as follows. Section 2 gives some background information and reviews related literature. After identifying the quantitative macroeconomic factors that determine the performance of the EMG region, Section 3 builds a dynamic macroeconomic model to analyze the effects of selected world variables on EMG economies. Section 4 concludes the paper with some policy implications. 2. Background and Related Literature EMG countries rely heavily on trade and enjoy a relatively high degree of economic and trade openness (see Table 1). This is due to the particular factor endowment of the region (rich in oil, poor in water) resulting in considerable oil exports and food imports rather than regular trade. This fact has made most of the economies of the region vulnerable to external shocks. The more open an economy is the more it is vulnerable to external shocks and the smaller it is the more the impact domestically. Among the EMG countries, oil-producing Gulf Cooperation Council (GCC) countries tend to be the most vulnerable to external shocks because of their high level of openness. These shocks come mainly from fluctuations in the world price of oil, in global output, and in the value of the US dollar. The remaining EMG countries are affected not only by shocks mentioned above, but also by fluctuations in world interest rates, and terms of trade.
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Table 1: Trade Openness of EMG Countries, 1999-2003 Degree of Openness of EMG Countries: (X+M)/GDP Country 1999 2000 2001 2002 2003 1.35 1.43 1.10 0.90 1.46 Bahrain 0.25 0.25 0.23 0.24 0.22 Egypt 0.78 0.87 0.72 0.71 0.68 Jordan 0.78 0.73 0.90 0.69 0.60 Kuwait 0.67 0.68 0.57 0.48 0.39 Lebanon 0.74 0.78 0.80 0.71 0.75 Oman 0.69 0.87 0.92 0.84 0.73 Qatar Saudi 0.62 0.63 0.61 0.54 0.54 Arabia 0.47 0.60 0.50 0.42 0.53 Syria 1.05 1.05 1.10 1.35 1.18 UAE 0.96 0.83 0.76 0.61 0.67 Yemen Source: IMF, Direction of Trade Statistics, Yearbook 2003. X refers to total exports, while M is total imports. The macroeconomic asymmetries in the EMG region are modeled following the vector autoregression (VAR) model. VAR models are particularly useful because they permit measurement of the importance of external shocks on a given economy, in our case, the economies of the EMG region. They also provide a framework for analyzing the adjustment of a given economy (for example the adjustment of per capita GDP growth rates, exports, imports, and fiscal imbalances) to external economic shocks (for instance, shocks in world interest rates, in the value of the US dollar, in the terms of trade, in the price of oil, and world output and inflation rates). The VAR approach to economic fluctuations pioneered by Sims (1980), has been widely used in the empirical literature. It has been used to study the implications of macroeconomic fluctuations and linkages among countries and regions, and to measure the degree to which each shock accounts for their variability through time (see for example Choudhri (1983), Batten and Ott (1985), Gregory and Reynauld (1985), Backus (1986), Bordo, Choudrhi and Schwartz (1987), and Bailey (1989)). More recent studies have used the VAR model to explore the international transmission of economic fluctuations and macroeconomic asymmetries among developed countries. For example, Ambler (1989) constructs a nine variable VAR system that incorporates Canadian and US data. He estimates a standard unrestricted VAR as well as a Vector Error Correction Model (VECM). Naka (1997) notes that the VAR is flexible enough, and when two or more variables have common stochastic trends (that is, are cointegrated), it can be estimated as a VECM. The VAR model was also used to study the impact of external and country-specific shocks on GDP, inflation and the trade balance for seven OECD
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countries. Prasad and Gable (1998), using the same framework, concentrate on the impact of such shocks on exchange rates,2 imports and exports. Hoffmaister, Roldos and Wickham (1998) studied the macroeconomic fluctuations in 23 Sub-Saharan countries also using a VAR model. They show that external shocks, especially those related to terms of trade, have the most significant influence on domestic GDP and real exchange rate. Kireyev (2000) explores the impact of some selected external shocks on the macroeconomic dynamics of 18 Arab countries using a Panel VAR model. In that paper, the Arab countries are divided into oil and non-oil producing countries and into IMF program and non-IMF program countries. 3. Empirical Investigation 3.1. Data and Sample In this paper, the VAR model includes two sets of variables. The first set of variables is country specific summarizing the internal macroeconomic situation in each EMG country. These variables are the fiscal balance, the trade balance and the rate of growth of per capita GDP. The second set includes variables that are common to all EMG countries and convey external shocks to the EMG region. These are foreign interest rates, foreign rate of growth of GDP, world oil prices, and the world rate of inflation (see Figure 1). Figure 1. Dynamics of the World Macroeconomic Variables: 1960-2001 (b) World Inflation (%)
( a) World Nominal Oil Prices (USD) 40
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Source: International Monetary Fund, International Financial Statistics, 2002 Notes: The horizontal axis represents the time period 1960-2001. Among the external factors the price of oil and the world rate of inflation represent the terms of trade related shocks. Even though the model assumes that these external shocks are common to all EMG countries, however, they are assumed to affect each country or group of countries in a different way owing to the current macroeconomic characteristics specific to each country, as well as, the country specific factor endowments. Fortunately, one can easily divide the EMG region into two distinctive groups of countries each with similar macroeconomic characteristics. The first includes the oil-producing EMG countries3 with non-diversified exports and production structures, little or no external foreign debt, high per capita GDP, and surpluses in the external balance and fiscal positions. The second group includes the
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non-oil producing EMG countries4 characterized by significantly higher external debt, fiscal and external deficits and lower per capita GDP (see Figure 2). Figure 2. Macroeconomic Dynamics of EMG Countries: 1960-2001 (b) Per Capita GDP Non-Oil EMG Countries (USD)
(a) Per Capita GDP Oil EMG Countries (USD) 16000
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Source: International Monetary Fund, International Financial Statistics, 2002 Notes: The horizontal axis represents the time period 1960-2001. For our purpose two different versions of the dynamic model will be empirically tested. In the first version, we include the EMG oil-producing countries, and in the second the diversified economies of the region, that is, the non-oil producing EMG countries. The seven variables of interest are defined as follows. Poil is the price of a basket of crude oil deflated by the US producer price index (PPI); Pw is the average inflation rate for the world economy; Yw is the average growth rate of GDP for the world economy; iw is the average world real interest rate. The EMG countries’ specific variables are: Ya, the average real per capita GDP growth rate; FSa, a measure of fiscal balance computed as government surplus/deficit as a percent of GDP; and TBa, the trade balance computed as exports minus imports as a percent of GDP. Yearly average data for the external variables and each EMG sub-group are used from 1960 to 2001. The data have been compiled from the International Financial Statistics. All external variables included in the model are in logarithmic form and in levels except for the real rate of interest taken in percent. The world economy is proxied by the economies of the United States (US) and the European Union (EU). This plausible approximation rests on the fact that about 70 percent of EMG countries’ foreign trade and financial transactions are with the US and EU. It is thus assumed that in addition to shocks in oil prices, external shocks emanating from the US and EU economies are more likely to affect the EMG region than shocks in other regions of the world. 3.2. Unit Root Tests In order to establish the order in which the variables should enter the VAR model, we examine the time series properties of the macroeconomic variables to be used in the model. To test for the existence of a long run relationship, the Johansen (1988)
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cointegration test will be used after establishing non−stationarity of the series by applying the Phillips-Perron (PP) unit root test. It is common for time−series data to demonstrate signs of non−stationarity; typically both the mean and variance of macroeconomic variables trend upwards over time. In any case tests for non−stationarity are carried out as a preliminary step to explore the possibility of a significant long−run relationship between the variables concerned, that is, cointegration tests. The following regressions are carried out: k
∆X t = β1 + β 2 t + β 3 X t −1 + ∑ δ i ∆X t −i + ε t ,
(1)
i =1
where ∆ is the first−difference operator, βi, δi, are constant parameters, X is a vector of external, and EMG specific variables ( Poil , Pw , Yw , iw , Ya , FS a , TBa ), t is a time trend, and εt is a stationary stochastic process. The number of lags (k) will be determined based on the Akaike Information Criterion (AIC). To determine the order of integration of the series, model (1) is modified to include second differences on lagged first and k lags of second differences. That is:
k
∆2 X t = λ 0 + λ1t + λ 2 ∆X t −1 + ∑ µ i ∆2 X t − i + ε1t , i =1
(2)
where, ∆2 Xt = ∆Xt − ∆X t-1,; λi, µi, are constant parameters, t is a time trend, and ε1t is a stationary stochastic process. The k lagged difference terms are included so that the error terms εt and ε1t in both equations are serially independent. To test for stationarity, the (PP) test is applied to equations (1) and (2), and the results are summarized in Tables 2 and 3. The null hypotheses are β3 = 0, and λ2 = 0 respectively, that is, a unit root exists in Pt and ∆Pt-1 implying that the series are non−stationary. Equation (1) and (2) are also estimated with a constant and no time trend. The PP test results indicate that the two EMG group series as well as the external series are non-stationary in the levels (equation 1). However, unit roots in the first differences of the series (equation 2) are rejected at the 1 percent significance level, suggesting that the external and internal series are stationary. Thus, the external and EMG specific macroeconomic series are integrated of order one, I (1).
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Table 2: Unit Roots Test for the External Variables Mackinnon’s Critical Values PP Unit Roots Poil Tests Constant and Time Trend PP Levels (3) -1.94 PP FD (3) -5.88**
Pw
Yw
-1.25 -4.26**
-2.22 -7.39**
iw
-0.63 -5.22**
5%
1%
-3.52 -3.53
-4.20 -4.20
Constant PP Levels (3) -1.56 -1.07 -2.63 -1.09 -2.93 -3.60 PP FD (3) -5.96** -3.94** -7.29** -4.82** -2.94 -3.60 Notes: 1- PP is the Phillips-Perron test and FD is the first difference. 2-The numbers in parenthesis are the proper lag lengths based on the Akaike Information Criterion (AIC). 3- A * indicates rejection of the null hypothesis of non-stationarity at the 5% level of significance, while ** indicates a stronger rejection at the 1% level. 4-For most variables the time trend variable is statistically insignificant. Table 3: Unit Roots Test for the EMG Countries Specific Variables Mackinnon’s Critical Values PP Unit Roots Ya FS a TBa 5% 1% Tests Oil-Producing EMG Countries Constant and Time Trend PP Levels (2) -3.53 -4.21 -2.07 -2.44 -3.29 PP FD (2) -3.53 -4.22 -5.20** -6.59** -5.72** Constant PP Levels (2) -3.53 -4.21 -1.82 -2.51 -3.37 PP FD (2) -3.53 -4.22 -5.26** -6.68** -5.98**
Non-Oil Producing EMG Countries Constant and Time Trend PP Levels (4) -3.53 -4.21 -1.14 -2.04 PP FD (4) -3.54 -4.22 -4.44** -8.50** Constant PP Levels (4) -2.94 -3.61 -1.276 -2.20 PP FD (4) -2.94 -3.61 -5.40** -8.31** See Notes of Table 2.
-2.33 -5.76** -1.72 -5.84**
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3.3. Cointegration Tests Next we establish whether the series are cointegrated. Specifically, having established the presence of a unit root in the levels of each variable, we need to test whether the series have different unit roots (not-cointegrated), or shares the same unit root (cointegrated). Cointegrated variables, if disturbed, will not drift apart from each other and thus possess a long–run equilibrium relationship. The Johansen efficient maximum likelihood test is used to examine the existence of a long−term relationship between first the external variables, and then between the external variables and the individual EMG sub-groups, at the 5 and 1 percent levels of significance respectively. It is applied using alternative lag lengths in the vector autoregression (VAR). Engel and Granger (1987) argued that a linear combination of two or more non-stationary series may be stationary. If such a stationary combination exists, the non-stationary time series are said to be cointegrated. The stationary linear combination is called the cointegrating equation and may be interpreted as a long-run equilibrium relationship between the variables. The cointegration tests are conducted for the period 1960−2001, and the existence of a long−term relationship between external variables on one hand, and between the EMG and external variables on the other could not be rejected. The Likelihood Ratio Test indicates two−cointegrating vector at the 5 percent significance level among the external variables (Table 4). It also indicates three−cointegrating vectors at the 5 percent significance level among the external variables and the oil and non-oil EMG variables (Table 5). The PP unit roots tests conducted earlier have shown that all external and internal variables are non-stationary in levels and they need to be differenced once to become stationary, that is, they are integrated of order one I(1). In addition, the Johansen tests indicated that there exist two cointegrating relationships between the external variables, thus the existence of at least two variables being stationary can easily be assumed. The same is true for the internal variables; the existence of at least three cointegrating relationships may convey the existence of at least three stationary variables. Therefore, even though each of the seven variables turned out to be non-stationary, the two sub groups can be viewed as stationary. The usual approach in this case is to estimate an unrestricted VAR including the seven variables in levels since trying to impose stationarity by including the first differenced variables may remove important information concerning the dynamic co-movements from the time series.
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Table 4: Johansen Cointegration Test for the External Variables Hypothesis Critical Values Null Alternative Test Statistics (5%) (1%) 63.18** 47.21 54.46 r=0 r≥1 34.77* 29.68 35.65 r≤1 r ≥2 11.18 15.41 20.04 r≤2 r ≥3 r=4 2.53 3.76 6.65 r≤3 Notes: 1-The Johansen cointegration likelihood ratio test is based on the trace of the stochastic matrix. 2-The test allows for a linear deterministic trend in the data. 3-The symbol r represents the number of cointegrating vectors. Maximum lag 1 year in VAR. 4-A * and ** indicate rejection of the null hypothesis at the 5and 1 percent level of significance respectively. 5-The asymptotic critical values are from Osterwald-Lenum (1992).
Table 5: Johansen Cointegration Test for the EMG Specific Variables External and Oil-EMG Countries Hypothesis Critical Values Null Alternative Test Statistics (5%) (1%) r=0 213.24** 124.24 133.57 r≥1 124.35** 94.15 103.18 r≤1 r ≥2 72.54* 68.52 76.07 r≤2 r≥3 43.55 47.21 54.46 r≤3 r ≥4 25.67 29.68 35.65 r≤4 r ≥5 11.64 15.41 20.04 r≤5 r ≥6 r≤6 r=7 2.92 3.76 6.65
External and Non-Oil EMG Countries Hypothesis Critical Values Null Alternative Test Statistics (5%) (1%) r=0 190.11** 124.24 133.57 r≥1 121.28** 94.15 103.18 r≤1 r ≥2 69.05* 68.52 76.07 r≤2 r≥3 42.53 47.21 54.46 r≤3 r ≥4 24.57 29.68 35.65 r≤4 r ≥5 10.35 15.41 20.04 r≤5 r ≥6 2.90 3.76 6.65 r≤6 r=7 Notes: 1-The Johansen cointegration likelihood ratio test is based on the trace of the stochastic matrix. 2-The test allows for a linear deterministic trend in the data. 3-The symbol r represents the number of cointegrating vectors. Maximum lag 1 year in VAR. 4- A * and ** indicate rejection of the null hypothesis at the 5 and 1 percent level of significance respectively. 5-The asymptotic critical values are from Osterwald-Lenum (1992).
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3.4. The Dynamic VAR Model Our earlier finding that all seven series contain a unit root but some linear combinations of two or more of the non-stationary series are stationary leads us next to estimate a VAR model. The VAR model is commonly used for analyzing the dynamic impact of random disturbances on the system of variables. This approach is very useful because it models every variable in the system as a function of the lagged values of all the endogenous variables in the system. Based on the Akaike Information Criterion (AIC) and Schwarz Criterion (SC), we run the following VAR model of order 1:
Z t = λ1Z t −1 + ..... +β x Z t − x + ε t ,
(3)
where Zt is our 7-vector of the non-stationary I(1) internal and external macroeconomic time series, λ1 , and β x are the coefficient matrices to be estimated, Zt − x contain the lags of the 7 series to insure that the vector of innovations ε t is not serially correlated. Table 6 shows the empirical estimates of the VAR model containing the seven variables discussed earlier and a constant term. While variations in the world macroeconomic variables have a significant impact on the EMG region, the fundamentals of the EMG economies enter insignificantly the equations of the world economy. Specifically, the terms of trade related shocks and world demand seem to have the most significant impact on EMG countries’ rate of growth of per capita GDP. In addition, the fiscal position seems to be significantly affected by all the external variables. The trade balance is mostly affected by world demand. For the estimated VAR including the non-oil EMG countries, again the EMG variables have an insignificant impact on the world variables. The external variables with the most significant impact are (1) world demand, interests rates and the price of oil on the fiscal balance; (2) growth in world output and world inflation on the trade balance; and (3) the price of oil and world demand on EMG’s per capita GDP (see Table 7).
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Table 6: VAR Model, Oil-EMG Countries External Variables Internal Variables Right Hand No. Side of Poil Pw Yw iw Ya FS a TBa Variable Lags Poil 1 -0.03 -0.16 -0.06 -0.40 -0.45 -0.65 0.05 t-Stat -0.11 -3.47 -3.3 -0.66 -4.51 -2.85 1.28 Pw 1 -0.14 -1.05 0.78 0.01 0.62 0.32 -0.45 t-Stat 0.56 -24.1 4.22 0.17 2.11 3.77 -1.93 Yw 1 0.00 -0.00 0.54 -0.00 0.02 0.007 0.00 t-Stat 1.19 -0.26 4.64 -0.17 2.73 3.37 2.85 iw 1 4.08 0.79 0.02 1.01 0.13 0.02 0.08 t-Stat 4.75 5.35 1.18 3.62 3.34 3.67 1.66 Ya 1 0.00 0.00 0.00 -0.00 0.81 0.12 -0.18 t-Stat 2.36 0.38 0.61 -0.19 5.82 2.44 -0.53 FS a 1 0.00 0.000 0.00 0.00 -0.05 0.41 0.38 t-Stat 1.23 1.11 1.32 0.46 -0.11 2.22 0.28 TBa 1 -000 0.00 0.00 0.00 1.75 -0.10 -0.33 t-Stat -2.6 0.30 1.20 0.36 1.92 -2.94 -1.27 Constant 1 -18.6 -3.91 0.00 0.43 0.22 -0.10 0.32 t-Stat -3.18 -3.83 2.98 0.22 1.65 -1.04 0.18 Notes: The number of lags in the VAR specification (1) has been determined based on the Akaike Information Criterion (AIC) and Schwarz Criterion (AIC). Source: Author’s estimates.
The ordering of the equations in the above VAR model is not ad hoc; it is meant to allow for meaningful interpretation of the impulse response functions and variance decomposition to be used in the following analysis. The model uses economic theory and intuition, as well as, the concept of orthogonality of structural innovations. First, it is assumed that EMG countries can be categorized as small open economies; therefore, shocks originating in this region are assumed to have no impact on world variables. However, external shocks are assumed to have important implications on per capita GDP, the fiscal situation and the external sector of the EMG region.
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Table 7: VAR Model, Non-Oil EMG Countries External Variables Internal Variables Right Hand No. Side of Poil Pw Yw iw Ya FS a TBa Variable Lags Poil 1 0.42 -0.14 -0.045 -0.05 0.13 -0.45 0.09 t-Stat 2.30 -4.17 -2.82 -1.08 2.51 -1.85 0.06 Pw 1 -0.80 0.89 0.52 -0.17 0.45 0.32 0.89 t-Stat -2.76 16.3 2.63 -2.09 0.32 0.77 3.45 Yw 1 0.00 0.00 0.75 0.00 0.86 0.75 0.96 t-Stat 3.27 2.47 6.82 2.12 3.01 2.39 3.85 iw 1 1.89 0.99 0.11 1.01 0.84 0.02 -0.09 t-Stat 3.07 8.63 2.73 5.95 1.65 3.45 0.05 Ya 1 0.00 0.00 -0.26 0.00 0.37 0.03 0.01 t-Stat 1.52 1.43 -0.92 0.90 1.88 0.04 0.08 FS a 1 -0.00 -0.00 -0.19 -0.00 -0.15 -0.36 0.07 t-Stat -0.55 -0.19 -1.91 -1.61 -2.20 -0.85 0.12 TBa 1 0.00 0.0 0.47 0.00 1.75 0.12 0.74 t-Stat 1.27 0.13 1.24 0.43 4.56 0.95 4.12 Constant 0.88 -2.77 -0.11 2.55 0.42 93.2 0.69 t-Stat 0.16 -2.71 -3.01 169 -1.66 0.15 0.70 Notes: The number of lags in the VAR specification (1) has been determined based on the Akaike Information Criterion (AIC) and Schwarz Criterion (AIC). Source: Author’s estimates.
The first external variable in the ordering is the price of oil and this variable is assumed to have effects on all external and EMG specific variables, but none of the remaining variables is assumed to have effects on the first. The world inflation is assumed to impact world GDP growth rates and interest rates, and all the regional variables. The ordering of the remaining two variables pertaining to the world has been determined in a similar fashion. The internal variables pertaining to the two EMG subgroups are determined as follows. Fluctuations in the world price of oil will impact directly on per capita GDP in EMG countries. World inflation will also impact on the region’s growth rates through its effect on the terms of trade and the real bilateral exchange rate. World growth rates are assumed to impact regional growth through the demand for exports from the EMG region. The assumption of a small open economy means that interest rates are determined abroad. Therefore, an increase in world interest rates implies an increase in domestic rates and would, consequently, lower GDP growth rates through the crowding out effect.
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3.5. Variance Decomposition Variance decomposition provides a different method of depicting the system’s dynamics. While impulse response functions trace the effects of a shock to an endogenous variable on variables in the VAR system, variance decomposition decomposes variation in an endogenous variable into the component shocks to the endogenous variables in the VAR. Hence, variance decomposition gives information about the relative importance of each random innovation to the variables in the VAR. Each entry in Table 8 represents the percentage of the variance due to each innovation for oil-EMG countries. The top quadrant of the table shows the percentage of per-capita GDP volatility explained by shocks to volatility in the 7 external and internal variables. Specifically, the variability in the rate of growth of per-capita GDP of oil producing EMG countries is mostly captured by world interest rates in the medium and long-run, followed by terms of trade related variables and world demand. All three impacts explain a significant portion of the volatility in per-capita GDP with a lag of three years for world output and prices. The variability in the fiscal balance is mostly captured by the price of oil followed by world interest rates. In other words, the fiscal balance is mainly affected by world oil prices and interest rates. The effect of oil on the fiscal balance has a relatively high magnitude, explaining about 37 percent of the variation in the first year but dying out a little to about 20 percent after 10 years. This is a reflection of the high share of oil revenues in the budgetary structure of oil-producing EMG countries. The same is true for the trade balance where again the price of oil and the world interest rates are capturing most of its variability, that is, only the price of oil and interest rates have a significant impact on the volatility of the trade balance. It should be noted that the proceeds from international oil sales enter the current account, which means that fluctuations in oil prices lead directly to fluctuations in oil revenues and, therefore, to variations in the external balance.
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Table 8: Variance Decomposition by Structural Shocks, Oil-EMG Countries (%) External Variables Internal Variables Total Effect Ext# of IntPoil Pw Yw iw Ya FS a TBa ernal Years ernal Per Capita GDP 1 8.27 3.40 0.03 1.13 87.1 0.00 0.00 12.82 87.10 2 14.0 15.10 3.97 9.83 53.6 0.37 2.99 42.97 56.96 3 10.3 16.55 7.40 23.73 39.6 0.30 1.98 58.01 41.88 4 8.85 15.55 10.0 30.40 32.2 0.36 2.56 64.82 35.12 5 8.53 14.92 11.3 32.91 28.8 0.45 2.90 67.68 32.15 6 8.45 14.58 11.7 34.08 27.5 0.56 3.04 68.82 31.10 7 8.38 14.38 11.9 34.37 27.2 0.65 3.12 68.96 30.97 8 8.34 14.27 11.8 34.34 27.2 0.69 3.16 68.84 31.05 9 8.32 14.22 11.8 34.27 27.4 0.72 3.18 68.66 31.30 10 8.31 14.20 11.8 34.21 27.5 0.73 3.18 68.55 31.41 Fiscal Balance 1 37.29 0.02 1.31 0.00 6.40 54.9 0.00 38.62 61.36 2 29.26 17.69 3.56 4.18 4.39 25.5 15.3 54.69 45.27 3 21.59 16.63 7.27 17.92 5.64 19.2 11.6 63.41 36.55 4 20.45 14.78 10.0 19.52 5.06 17.2 12.8 64.81 35.16 5 20.46 14.63 10.7 19.06 5.26 16.9 12.8 64.92 35.04 6 20.70 14.68 10.6 19.21 5.48 16.6 12.6 65.23 34.74 7 21.10 14.73 10.3 19.77 5.47 16.1 12.3 65.98 33.99 8 21.42 14.78 10.1 20.61 5.33 15.7 11.9 66.95 33.02 9 21.58 14.78 9.95 21.54 5.18 15.2 11.6 67.85 32.12 10 21.66 14.74 9.82 22.32 5.07 14.9 11.4 68.54 31.43 Trade Balance 1 0.30 4.45 7.61 25.95 0.13 5.03 56.4 38.31 61.65 2 23.82 6.37 6.97 17.83 0.69 3.35 40.9 54.99 44.97 3 22.26 10.23 6.59 23.62 0.67 3.24 33.3 62.70 37.26 4 19.98 10.19 7.15 28.50 0.60 3.21 30.3 65.82 34.15 5 19.38 9.88 7.53 29.67 0.81 3.17 29.5 66.46 33.54 6 19.30 9.77 7.56 29.78 1.22 3.17 29.1 66.41 33.55 7 19.25 9.72 7.53 29.74 1.51 3.18 29.0 66.24 33.72 8 19.22 9.71 7.52 29.68 1.66 3.19 28.9 66.13 33.83 9 19.19 9.73 7.54 29.66 1.73 3.18 28.9 66.12 33.84 10 19.16 9.76 7.56 29.69 1.75 3.18 28.8 66.17 33.80 Source: Author’s estimates. Ordering: Poil , Pw , Yw , iw , Ya , FS a , TBa .
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When contrasting the above results with those of non-oil producing EMG countries, it is obvious that the price of oil has less direct influence on the dynamics of the macroeconomic fundamentals in these countries (see Table 9). The variability in percapita GDP is mostly captured by world inflation and demand. The largest impact occurs after 6 years explaining about 31 percent of the volatility variation in the rate of growth of per-capita GDP. Moreover, no one variable seems to capture the variability in the fiscal balance in the short-run. However, in the long-run the price of oil and world interest rates capture some of the variability in the fiscal balance. The variability in the trade balance is mostly captured by world demand, which impact directly on those countries demand for exports. The low and almost insignificant impact of current account developments on both per capita GDP and fiscal balance attests to better insulation of non-oil producing EMG countries from external shocks than oil-producing EMG countries. The last two columns in Tables 8 and 9 sum up the importance of external versus internal factors in explaining the variability in EMG specific variables. For oilproducing EMG countries, external factors account more for the variability in internal variables in both the short and long-run. In particular, it is seen in the fact that the variability in the internal variables is mainly captured by the price of oil, and the world interest and inflation rates. Overall, such an easy transmission of external shocks into oil-producing EMG countries can be explained by higher overall openness of oilproducing EMG countries, and might signal deficiencies in adequate macroeconomic policies in mitigating the impact of exogenous shocks. For non-oil producing EMG countries the internal factors seem to play a more important role. In the short-run, the internal factors capture most of the variability in percapita GDP and the fiscal balance. This trend is, however, reversed after five years where the external factors become dominant. Thus, except for the trade balance in nonoil producing member countries, the external factors are dominant in the long-run whether we are looking at oil or non-oil producing countries.
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Table 9: Variance Decomposition by Structural Shocks, Non-Oil EMG Countries (%) External Variables Internal Variables Total Effect # of ExtIntPoil Pw Yw iw Ya FS a TBa Years ernal ernal Per Capita GDP 1 10.97 8.51 0.06 1.51 78.90 0.00 0.00 21.05 78.90 2 19.74 14.55 7.49 1.44 51.39 5.36 0.00 43.22 56.75 3 16.67 21.67 15.2 2.17 37.29 6.32 0.61 55.74 44.22 4 11.88 26.21 21.1 3.25 27.96 8.22 1.32 62.46 37.50 5 8.43 28.84 24.6 3.67 22.74 9.66 2.04 65.54 34.44 6 6.41 30.28 26.7 3.60 19.70 10.6 2.59 67.00 32.97 7 5.23 31.11 28.1 3.33 17.91 11.2 2.96 67.82 32.15 8 4.50 31.58 29.2 3.03 16.83 11.6 3.17 68.36 31.61 9 3.99 31.84 30.1 2.77 16.17 11.7 3.27 68.75 31.22 10 3.63 31.95 30.8 2.56 15.76 11.8 3.31 69.03 30.94 Fiscal Balance 1 9.24 0.24 2.00 0.16 9.84 78.4 0.00 11.64 88.32 2 7.06 0.24 9.21 1.73 21.37 59.0 1.29 18.24 81.72 3 13.26 0.45 10.6 1.63 20.97 51.8 1.21 25.96 74.01 4 18.98 2.23 9.18 3.64 20.38 44.4 1.13 34.03 65.94 5 19.83 5.70 8.13 7.79 19.27 38.0 1.20 41.45 58.51 6 17.51 9.48 7.84 11.80 18.17 33.5 1.62 46.63 53.32 7 14.91 12.44 7.60 14.63 17.23 30.8 2.28 49.58 50.37 8 13.05 14.41 7.20 16.44 16.55 29.3 2.98 51.10 48.87 9 11.87 15.62 6.76 17.62 16.11 28.4 3.55 51.87 48.09 10 11.12 16.34 6.39 18.45 15.85 27.8 3.97 52.35 47.67 Trade Balance 1 7.53 0.23 13.5 2.43 2.07 3.14 71.0 23.72 76.25 2 7.94 0.16 11.6 3.55 7.58 2.34 66.7 23.29 76.67 3 8.76 0.28 10.8 4.51 10.9 2.25 62.3 24.41 75.49 4 9.38 0.85 10.6 5.08 13.2 2.62 58.1 25.96 73.93 5 9.56 2.04 10.9 5.44 14.51 3.30 54.2 27.96 72.01 6 9.27 3.81 11.6 5.62 15.00 4.11 50.5 30.34 69.63 7 8.71 5.84 12.6 5.64 15.02 4.93 47.1 32.87 67.09 8 8.11 7.82 13.8 5.53 14.81 5.67 44.1 35.33 64.63 9 7.58 9.54 15.0 5.34 14.54 6.28 41.6 37.51 62.45 10 7.14 10.96 16.1 5.13 14.30 6.75 39.5 39.37 60.60 Source: Author’s estimates. Ordering: Poil , Pw , Yw , iw , Ya , FS a , TBa .
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3.6. Impulse Response Functions Impulse response functions trace the effect of a one standard deviation shock to one of the innovations on current and future values of the endogenous variable. In other words, a shock to the j-th variable directly affects the j-variable, and is also transmitted to all of the endogenous variables through the dynamic structure of the VAR. The impulse response functions shed light on the dynamics of the variables included in the VAR system as a result of shocks to either one of these variables. The impulse response functions 5 permit us to explore how the internal and external variables might respond to various shocks to the system. In oil producing EMG countries, a one standard deviation positive shock to the price of oil has a significantly positive impact on the growth of per capita GDP, and on the fiscal and trade balances in the short run (see Figure 3 (a-c)). Specifically, the percapita GDP growth rate increases by about 0.35 percent during the first 4 years and returns to its steady state thereafter. The response of the fiscal balance is also important over the same period where we see an improvement of about 2.5 percent of average GDP. Similar dynamics are reported for the external sector where the trade balance improves by about 0.1 percent of average GDP. These dynamics are in line with economic intuition and are reflecting the high share of oil revenues in the budgetary structures of these countries and the high proceeds of oil exports in the trade balance. The positive impact on the fiscal and trade balances is manifested mostly during the first 3-4 years owing to the nature of the fiscal and trade account balances as being short term phenomena. In the long-run the impact seems to be either dying out or becoming negative due mainly to the fact that after the initial positive impact of an increase in oil prices on oil revenues and the proceeds from oil exports, the world economy might respond by lowering the demand for oil which then might impact negatively on oil revenues and the proceeds from oil exports. Figure 3. Response of EMG Countries to an Oil Price Shock Oil-EMG Countries Non-Oil EMG Countries (a) Response of Per-Capita GDP Growth Rate
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Figure 4 (a-c) highlights the response of EMG countries to an improvement in their terms of trade. Specifically, a one standard deviation positive shock to world prices, which means a deterioration in the EMG terms of trade, seems to have a more persistent impact on the non-oil-producing EMG countries’ per capita GDP growth rate. The increase in per-capita GDP growth rate is by about 0.25 percent during the first 4 years while it increases only by 0.15 percent over the same period in oil producing countries. The trade balance of non-oil producing EMG countries is subject to a more pronounced impact by a deterioration in the terms of trade than is the case for oilproducing countries, which are more affected by changes in world oil prices. This clearly suggests the excessive vulnerability of non-oil-producing EMG countries to changes in their terms of trade with a direct impact on their exports and the growth in per-capita GDP. The improvement in the trade balance is about 4.5 percent of average GDP in non-oil-producing countries, while we see a deterioration of 0.28 percent for oilproducing EMG countries. The fiscal balance seems to be improving for all EMG countries as a result of an improvement in their terms of trade. In the opposite scenario, unfavorable terms of trade for an EMG country translates into lower exports and consequently into a rise in the stock of foreign debt. Figure 4. Response of EMG Countries to Terms of Trade Shock Oil-EMG Countries Non-Oil EMG Countries (a) Response of Per-Capita GDP Growth Rate 0.15
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ambiguous for both sub-groups, while it is improving for the non-oil-producing countries, due mainly to higher proceeds from exports, it deteriorates for the oilproducing EMG countries. The improvement in the trade balance of both EMG subgroups is noticeable but it is much more pronounced for non-oil-producing countries, reaching 0.5 percent of average GDP after only 4 years, while it does exceed the 0.06 percent level in oil-producing EMG countries.
Figure 5. Response of EMG Countries to a Demand Shock Oil-EMG Countries
Non-Oil EMG Countries (a) Response of Per-Capita GDP Growth Rate
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Figure 6. Response of EMG Countries to an Interest Rate Shock Oil-EMG Countries Non-Oil EMG Countries (a) Response of Per-Capita GDP Growth Rate
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Notes: The horizontal axis represents the time period from 1-10 years. 4. Conclusions and Policy Recommendations This paper has shown that although the impact of external shocks on the macroeconomic dynamics of the oil and non-oil producing EMG countries is significant, it differs across countries and depends on the macroeconomic asymmetries existent in these countries, economic policies adopted in each individual country, country specific factor endowments, and the time horizon under consideration. The VAR results have shown that for both oil and non-oil EMG countries, fluctuations in the world macroeconomic variables have a significant impact on the EMG region. However, the fundamentals of the EMG economies enter insignificantly the equations of the world economy, attesting to the fact that these economies are relatively small and vulnerable to external shocks. In the case of the oil EMG economies, the terms of trade related shocks and world demand are shown to have the most significant impact on those countries’ rate of growth of per capita GDP. In addition, the fiscal position seems to be significantly affected by all the external variables. The trade balance is mostly affected by world demand. In the case of the nonoil EMG economies, the external variables with the most significant impact on those economies are (1) world demand, interests rates and the price of oil on the fiscal balance; (2) growth in world output and world inflation on the trade balance; and (3) the price of oil and world demand on EMG’s per capita GDP Variance decomposition and impulse responses have shown that external shocks are likely to have high propagation power within the economies of the EMG region, which is a clear signal that these countries’ macroeconomic policies need to be reformed. Specifically, the variability in the rate of growth of per-capita GDP of oil producing EMG countries is mostly captured by world interest rates in the medium and long-run followed by terms of trade related variables and world demand. Also, the variability in the fiscal balance is mostly captured by the price of oil followed by world interest rates. The effect of oil on the fiscal balance has a relatively high magnitude, reflecting the high share of oil revenues in the budgetary structure of oil-producing EMG
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countries. The same is true for the trade balance where the price of oil and the world interest rates are capturing most of its variability. When contrasting the above results with those of non-oil producing EMG countries, the price of oil is shown to have less direct influence on the dynamics of the macroeconomic fundamentals in these countries. Moreover, no one variable seems to capture the variability in the fiscal balance in the short-run. However, in the long-run the price of oil and world interest rates capture some of the variability in the fiscal balance. The variability in the trade balance is mostly captured by world demand, which impact directly on those countries demand for exports. The low and almost insignificant impact of current account developments on both per capita GDP and fiscal balance attests to better insulation of non-oil producing EMG countries from external shocks than oilproducing EMG countries. Impulse response functions have shown that for oil producing EMG countries, a positive shock to the price of oil has a significantly positive impact on the growth of per capita GDP, and on the fiscal and trade balances in the short run. The same response is observed for the fiscal balance. These dynamics are in line with economic intuition and are reflecting the high share of oil revenues in the budgetary structures of these countries, and the high proceeds of oil exports in the trade balance. It was also shown that while an oil price shock constitutes a positive shock for oil-producing EMG countries, it constitutes a negative supply side shock for non oil-producing EMG countries in the short-run. On the other hand a deterioration in the EMG terms of trade, is shown to have a more persistent impact on the non-oil-producing EMG countries’ per capita GDP growth rate. The trade balance of non-oil producing EMG countries is subject to a more pronounced impact by a deterioration in the terms of trade than is the case for oilproducing countries, which are more affected by changes in world oil prices. This clearly suggests the excessive vulnerability of non-oil-producing EMG countries to changes in their terms of trade with a direct impact on their exports and the growth in per-capita GDP. The greater vulnerability of non-oil producing EMG countries to demand shocks is due to the fact that their growth in per-capita GDP depends primarily on their exports. The impact on the fiscal balance seems to be ambiguous for both sub-groups, while it is improving for the non-oil-producing countries, due mainly to higher proceeds from exports, it deteriorates for the oil-producing EMG countries. Finally, a positive shock to the world real interest rate has a clear negative impact on the fiscal balance of non-oil-producing countries owing to the fact that the highly indebted non-oil-producing EMG countries will have to service a higher level of external debt leading to more budget deficits. The impact on the fiscal balance of oilproducing EMG countries was shown to be exactly the opposite and constitutes an improvement in their fiscal balance. This is due to the fact that most oil-producing EMG countries invest the proceeds of their oil revenues in US treasury bills and bonds, and any increase in world rates of interest translates into more fiscal revenues and to an improvement in their fiscal position.
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In short, when formulating future macroeconomic policies, policy makers and academics in the EMG region should take into consideration the excess vulnerability of the economies of the region to external shocks. Notes 1 Simon Neaime, Associate Professor, Department of Economics, American University of Beirut, Beirut, Lebanon. Mailing address: 3 Dag Hammarskjold Plaza, New York, NY 10017-2303, US. Tel (9613) 829944-Fax (9611) 744484. Email: [email protected] Professor Neaime is also research fellow of the Athenian Policy Forum, and research fellow of the Institute of Financial Economics, American University of Beirut. Financial support from the University Research Board of the American University of Beirut is gratefully acknowledged. The author is also grateful to the Editor of the Journal and two anonymous referees for very valuable comments and suggestions on an earlier draft. 2 For a detailed discussion of terms of trade and exchange rate policies see Neaime (2000), Neaime and Paschakis (2002), and Mansoorian and Neaime (2000, 2002, and 2003). 3 The oil-producing EMG countries include the Gulf Cooperation Countries (GCC) of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. 4 The non-oil producing EMG countries include: Egypt, Jordan, Lebanon, Syria and Yemen. 5 Impulse response functions with Asymptotic and Monte Carlo response standard errors have been computed and are available from the author. These tests are all pointing to statistically significant responses. 6 One important observation is that the positive impact from remittances, mainly on percapita GDP growth rates in non-oil-producing EMG countries, manifests itself in the long-run. Hence, these countries would benefit in the long-run after each hike in oil prices. Therefore, workers remittances from oil-EMG countries seem to have important dampening effects on the magnitude and persistence of external shocks on the macroeconomic situation of the non-oil-producing countries. 7 An important fiscal development in oil-producing EMG countries is that some of these countries have moved during the late 1990s into the category of debtors countries due to the increase in their external debts and deficits. For example, in Saudi Arabia the budget deficit in the last five years has averaged 4.5 percent of GDP, and government total debt is now at about 95 percent of GDP. With a budget deficit that is due mainly to public salaries and debt service payments, the Saudi government will feel the pressure of any decrease in oil-prices. Despite this, our empirical results have shown that the positive impact of an increase in world interest rates still outweighs the negative impact of increased debt service payments in some oil-producing EMG countries.
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References Ambler, S., (1989), “Does Money Matter in Canada? Evidence from a Vector Error Correction Model”, The Review of Economics and Statistics, 71, 651-658. Bailey, W., (1989), “The Effects of US Money Supply Announcements on Canadian Stock, Bond, and Currency Prices”, Canadian Journal of Economics, 3, 607618. Backus, D., (1986), “The Canadian-US Exchange Rate: Evidence from a Vector Autoregression”, The Review of Economics and Statistics, 68, 628-637. Batten, D., and Ott, M., (1985), “The Interrelationship of Monetary Policies Under Floating Exchange Rate Regimes”, Journal of Money Credit and Banking, 17, 103-110. Bordo, M., Choudhri. E.U., and Schwartz, A.J., (1987), “The Behavior of Money Stock Under Interest Rate Control: Some Evidence for Canada”, Journal of Money Credit and Banking, 19, 181-197. Choudhri, E., (1983), “The Transmission of Inflation in A Small Economy: An Empirical Analysis of the Influence of U.S. Monetary Disturbances on Canadian Inflation”, Journal of International Money and Finance, 2, 167-78. Engle, R., and Granger, W., (1987), “Co-integration and Error Correction Representation, Estimation and Testing”, 55(2), 251-276. Gregory, A and Raynauld, J., (1985), “An Econometric Model of Canadian Monetary Policy over the 1970’s”, Journal of Money Credit and Banking, 17, 43-58. Hoffmaister, A., Roldos, J., and Wickham, P., (1998), “Macroeconomic Fluctuations in Sub-Saharan Countries”, International Monetary Fund (IMF) Staff Papers, 45(1). Johansen, S., (1991), “Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models”, Econometrica, 59, 1551-1580. Kireyev, A., (2000), “Comparative Macroeconomics Dynamics in the EMG World: A Panel VAR Approach”, IMF Working paper Series, (WP/00/54). Naka, A., (1997), “Examining Impulse Response Functions in Cointegrated Systems”, Applied Economics, 29, 1593-1603. Mansooriann A., and Neaime, S., (2002), “Habits and Durability in Consumption and the Effects of Exchange Rate Policies”, International Economic Journal, 16(2), 97-114. Mansoorian A., and Neaime, S., (2003), “Durable Goods, Habits, Time Preference, and Exchange Rates”, North American Journal of Economics and Finance, 14(1), 115-130. Mansoorian A., and Neaime, S., (2000), “Habits and Durability in Consumption and the Effects of Tariff Protection”, Open Economies Review, 11(3), 195-204. Neaime S., and Paschakis, J., (2002), “The Future of the Dollar-Euro Exchange Rate”, North American Journal of Economics and Finance, 13, (1), 57-72. Neaime S., (2000), The Macroeconomics of Exchange Rate Policies, Tariff Protection and the Current Account: A Dynamic Framework, APF Press, Toronto, Canada. Osterwald-Lenum, M., (1992), “A Note with Quantiles of the Asymptotic Distribution
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DECEMBER 2004
of the Maximum Likelihood Cointegration Rank Test Statistics”, Oxford Bulletin of Economics and Statistics, 54, 461-472. Phillips, P.C., and Perron, P., (1988), “Testing for a Unit Root in Time Series Regression”, Biometrika, 75, 335-346. Prasad, E., and Gable, J., (1998), “International Evidence of Trade Dynamics”, International Monetary Fund Staff Papers, 3, 401-439. Sims, C., (1980), “Comparison on Interwar and Post War Business Cycles: Monetarism Reconsidered”, American Economic Review, 70, 250-257.