THE INDONESIA-MIDDLE EAST BUSINESS CYCLE SYNCHRONIZATION

This study aims to analyze the business cycle synchronization (BCS) between Indonesia and 14 Middle Eastern countries and its determinants. The BCS was measured using BCS Index. The panel data was analysed using Feasible General Least Square with estimated coefficients. The results show that the Indonesia-Middle East BCS is strong in terms of index and trend. Trade openness positively and significantly affects the BCS. This study provides evidence for the endogeneity of trade relations and the BCS. Price factors, such as inflation and exchange rates, are more difficult to forecast. This information allows stakeholders to promote Indonesia’s open trade policies and liberalization in the Middle East. This study proposes an update for how Indonesia and Middle East have benefited from the openness of trade and investment that exists between them. The BCS indices and trends in the two regions are important sources for expanding their economic partnership.


INTRODUCTION
The primary goal of economic development is to achieve economic integration among countries.This issue has become the main platform for a number of economic blocs, including the Organization of Islamic Cooperation (OIC).Business Cycle Synchronization (BCS) is an economic condition measured by the proximity of economic growth between two countries.A small BCS represents the degree to which two countries' business are synchronized (Azcona, 2022;Campos, Fidrmuc, & Korhonen, 2017).The BCS is often used to explain determining factors in the formation of a single currency.Many empirical researches have previously revealed the close relationship between BCS and Optimum Currency Area (OCA) (Campos, Fidrmuc, & Korhonen, 2019;Stiblarova, 2023).On the other hand, the BCS is also associated with international trade.Trade integration grows as the BCS rises (Antonakakis & Tondl, 2014;Glick & Rose, 2016;Li, 2017).Strong monetary integration has also been shown to improve the BCS (Degiannakis, Duffy, & Filis, 2014).
In the context of globalization, economic indicatorssuch as the BCS, price, and trade opennessare critical.They have a multiplier impact on the trade openness and intensity (Beck, 2019;Campos et al., 2019).Trade expansion will impact the demand for currency across nations.Foreign exchange symmetry among the countries will increase their monetary integration.Inflation may increase further due to the trade expansion in the early stages, but it will stabilize if the market continues to expand (Beck, 2019;Flood & Rose, 2010).The question of whether the openness will always establish symmetries between the economic indicators or create imbalances is a neverending debate.Many studies have demonstrated that the openness creates a balance, or even a new imbalance (Lee, Lee, & Lien, 2020;Pástor & Veronesi, 2021).The balance is a dynamic factor that changes over time.

Source: SESRIC (2022) Figure 1. Macroeconomic Indicators of Indonesia and Middle East in 2021
Indonesia is an important concern in this study for several factors.First, the role of Indonesia in relation to Middle East economies is getting increasingly complex.Indonesia has the largest population in the OIC (300 million) and the largest gross domestic product (GDP) (more than USD 1 trillion).It is a big market for Middle East and North Africa (MENA), particularly in Gulf Cooperation Council (GCC) countries.Ministry of Trade (2020)reported that Indonesia's trade with the GCC countries increased by 40% from USD 8.68 billion in 2016 to USD 12.15 billion in 2018.At the same time, the total investment of the GCC countries in Indonesia increased by 26% from USD 60.3 billion in 2016 to USD 76.1 billion in 2018 (Kemendag, 2020).Although there has been an increase in the economic cooperation between Indonesia and Middle Easte, it has not been sufficiently strong compared to that in the world trade.Second, the above Figure 1 shows the average progress of BCS determinants from 1980 to 2021.The average inflation ranges from 9% to 10%, but Turkey, Lebanon, and Yemen have higher rates than 10% (SESRIC, 2022).High inflation is generally caused by a high demand for a budget financed by foreign debt.This price increase is caused by the increase in consumption spending.The largest open trade is in Saudi Arabia, Turkey, Iran, Iraq, and the UAE.Iran and Indonesia have the highest exchange rates, ranging between USD 5,000.00 and USD 10,000.00.

Agustiar
The exchange rate for oil-producing countries is approximately USD 5.The currencies of Jordan, Bahrain, Oman, and Kuwait are stronger than that of the USD.Kuwait, Iraq, and Qatar have the highest average economic growth, which is largely influenced by increased demand for oil.The countries most open to the trade are Saudi Arabia, Turkey, Iraq, Iran, and the UAE.
Third, countries with the highest inflation and depreciated exchange rates are Lebanon, Indonesia, Iran, and Iraq.Turkey has the highest inflation rate, but its exchange rate is reasonably stable.Countries with high trade openness and growth are generally oil-producing.This difference is due to the fact that a country with sufficient foreign exchange reserves (derived from oil revenues) can maintain the exchange rate stability and inflation.However, major trading countries with low foreign exchange reserves are affected by the inflation and exchange rate fluctuations (Alshubiri, 2022;Hou & Knaze, 2022).
The purpose of this study is to examine the Indonesia-Middle East BCS and its determinants.This study also contributes to the BCS literature in relation to other economic indicators.First, this study systematically account for the heterogeneity of the country pairs and common global shocks.Second, this study associates the BCS with the trade integration between countries.This association serves to examine the closeness of trade integration with the BCS in many Middle Eastern countries.Thus, this research is expected to encourage the making of policies that add new dimensions to explain the role and trend of BCS between Indonesia and Middle East, both in the short and long term.
Furthermore, this study contributes to the existing literature in the following aspects.First, the Middle East economy is generally stronger than that of Indonesia.This can result in a less unidirectional relationship with the BCS.Second, the exchange rates between Indonesia and GCC countries are different.However, the growth rate of the exchange rate remains relatively constant.From this perspective, the monetary integration can have a favourable impact on the BCS.Third, the trade openness between these countries is increasing and they are growing more interdependent.Indonesia imports oil from the Middle East and exports palm oil, jewellery, pulp, and its derivatives.However, their level of trade openness and BCS remain low.Theoretically, the BCS relationship is negative to the OCA theory, especially in the OCA theory.If the BCS between the two countries grows stronger, then the two countries have the potential to develop the economic integration.

THEORETICAL FRAMEWORK AND EMPIRICAL STUDIES
Frankel & Rose (1998) were the first to examine the effects of BCS on increased trading intensity and openness for Euro.They observed that the BCS was strongly correlated in favour of a single currency and assessed the possible endogeneity between BCS and monetary unions (Miles & Vijverberg, 2018).The study by Frankel & Rose (1998) provided a comprehensive explanation for the Euro and they elaborated the phenomenon of endogeneity between the BCS and currency integration.Several earlier studies have assessed the strength of the relationship between BCS and trade (Campos et al., 2019;Nguyen, Hoang, & Nguyen, 2020;Zouri, 2020).Several studies also pointed to the weak impact of BCS on the trade (Beck, 2019;Crosby, 2003;Rana, Cheng, & Chia, 2012).The strength of BCS and trade is determined by the model specifications (Beck, 2019).According to Li (2017), intra-industry trade serves as a transmission channel for business cycles and as an explanatory power of total trade.Empirical researches examining the impact of structural similarity on BCS have been widely conducted.The findings generally establish the existence of a positive and significant relationship between BCS and the similarity of economic structures (Beck, 2021;Lee & Azali, 2010;Siedschlag, 2010).Monnet & Puy (2016) explored the negative relationship between macroeconomic variables and BCS.The model used by Kalemli-Ozcan, Papaioannou, & Peydró (2013) explained the influence of Foreign Direct Investment (FDI) relations on the BCS.The FDI was found to have a positive effect on the BCS (Jansen & Stokman, 2014;Shi, 2019).Theoretically, similar fiscal and monetary policies can produce a higher BCS in the presence of symmetric shocks (Bunyan, Duffy, Filis, & Tingbani, 2020).
Another explanation for the changes in business cycles is the similarity in production structure.Theoretically, the same pattern of production should positively affect the synchronization, because two economies producing similar type of goods will experience similar shocks.Countries with similar production patterns tend to have synchronous economic cycles (Kose, Otrok, & Prasad, 2012).A variant of the value-at-risk (VAR) methodology proposed by Blanchard & Quah (1989) examines the nature of macroeconomic disruptions in different groups of countries (Huh, Kim, Kim, & Park, 2015;Sun, 2017).The studies showed that supply shocks were symmetrical between two groups of countries: (1) Japan, South Korea, and Taiwan; and (2) Hong Kong, Indonesia, Malaysia, and Singapore.The demand shocks were highly symmetrical in the second group.Based on the OCA symmetry criteria for underlying disruptions, the researchers concluded that these two groups of countries tended to form separate OCAs.The results on the correlation, size, and speed of adjustment for the underlying disruptions in Asia have been updated in a study by Alam, Li, & Baig (2019) and Bayoumi & Eichengreen (1994).
Existing theoretical models examining the BCS are largely based on international standard of real business cycle models.Two countries with open economic models and strong and complete financial integration can strengthen their BCS.Kose et al. (2012) suggested that a higher degree of trade integration can lead to a more or less synchronization, depending on the nature of trade and the type of shocks that affect the economy.Conversely, if higher trade relations increase the intra-industry trade, the consequent stronger trade relationships can lead to a higher BCS.Other models also show that bilateral trade may be associated with correlated business cycles (Duval, Li, Saraf, & Seneviratne, 2016).
For these reasons, this study aims to fill the research gaps in the existing literature.There are socioeconomic differences between Indonesia and Middle East.Although Indonesia is a big country with a large population, its economy lags behind that of the Middle East.Further, Indonesia's rate of economic growth is closely related to the economies of East Asia (Japan, China, and Korea), while the Middle Eastern countries are highly oriented towards the United States and European Union.This difference allows for a pattern of BCS between them.In addition, although most previous studies analyze the influence of BCS on the trade and other macro variables, this study focuses on the determinants of BCS.As a result, this study aims to provide a comprehensive explanation of BCS in selected OIC countries (Olimat, 2023).
The OCA theory supporting the hypothesis states that the formation of a single currency is influenced by several factors, namely the BCS, inflation similarity, size of country, and distance (Frankel & Rose, 1998;Horvath & Komarek, 2002).These two studies served as the basis for subsequent BCS and OCA.The diversity of economic growth between Indonesia and Middle east Agustiar causes research gaps in the Indonesia-Middle East BCS studies.Due to the economic heterogeneity of the two countries, the potential for actual integration will take a long time, but it can be accelerated if the exchange rate changes are relatively stable.This study shows that there is a high level of security in the exchange rate stability between Indonesia and Middle East, and that it continues to follow the developments in the world economy.Strengthening the relationship between Indonesia and Middle East is one strategy to increase the monetary integration of the two countries.

RESEARCH METHODS
The Statistical, Economic and Social Research and Training Center for Islamic Countries (SESRIC) (http://www.sesrtcic.org/)was the primary source of data.The SESRIC collected the data from various sources, including the World Bank, International Monetary Fund, and other institutions that specifically compiled the data from Islamic countries.The following Table 1 presents four variables used in this study and their measurement.This study employed a research period of 1980-2021 (43 years) and divided it into six categories, namely 1980-1986, 1987-1993, 1994-2000, 2001--2007, 2008-2014, 2015-2021.A combination of 14 country pairs with six period categories resulted in 70 observations.There was a total of 15 countries used as the sample, consisted of Indonesia as the benchmark and 14 Middle Eastern countries.
The BCS was calculated using the data on the economic growth rate of each country.The GDP growth (%) in a country was compared with its paired counterpart.Instead of the nominal value of GDP, the dynamic growth of the country was calculated annually together with the standard deviation.The closer the standard deviation between the country pairs, the stronger their synchronization was perceived, and vice versa (Horváth & Komárek, 2002).where the BCS represents the business cycle synchronization and Y represents the real GDP.The BCS was formulated from (1) GDP conversion (constant price) whose data was transformed into logarithmic form.Then, the GDP growth of each country (yiyit-1) was calculated in percentage, and then it was converted into a standard deviation.This study used a multiple panel data regression model to explain the BCS.The model was calculated from the combined data of the two countries in the form of matrix multiplication, which resulted in a combination of many countries.where: Tr refers to the trade openness, as measured by the mean of the total exports and imports of country i and country j; x refers to exports; m refers to imports; i refers to the year; and j refers to the subjective country.
The trade openness was calculated by adding the exports and imports of the country pairs, and the sum was transformed into a logarithmic equation.Then, the average sum of the results was calculated.The trade openness formula is as follows: In is the similarity of inflation as measured by the difference in average consumer prices per year (%) in country i and country j; Pii is the consumer price index of country i; Pij is the consumer price index of country j; and t is the year.The inflation equation was calculated from the average consumer prices in country i and country j, and subtracted from each other.The inflation equation is as follows: where Xr ij is the exchange rate volatility, and eij is the nominal change in the nominal exchange rate of country i and country j from year t to t+1.The exchange rate data for each country was converted into logarithms and changes, including the standard deviation.

Descriptive Statistics and BCS Calculation
The BCS was calculated for 14 country pairs using the data on differences in the economic growth.The smallest BCS value indicated the greatest synchronization, and vice versa.The calculation results are presented in Table 3 and Figure 2. Table 2 displays the descriptive statistics of the data analyzed.The results show a striking difference between the average and maximum BCS data, especially for Jordan.A further analysis in Jordanian data showed that there were investment opportunities in 1984-1995 (26% and 17%), 1991-1992 (40% and 42%), and 1996-1997 (25% and 17%).These investment values were considered quite high for the GCC countries.The BCS rates in Turkey were 70% and 68% in 1989 and 1990, respectively, whereas the inflation, trading, and exchange rates were relatively more Agustiar stable.However, the Jordan's economic growth rate was relatively extreme.One possible reason was the liberal economic policies introduced after the accession of King Abdullah II in 1999, which spurred rapid investment and economic growth (Powers, 2020).In general, the BCS had been shown to be progressively stronger than the values in the previous few years.The trend of synchronization reduction occurred in several countries, including Indonesia-Jordan and Indonesia-Saudi Arabia; Indonesia-Lebanon and Indonesia-Turkey.The trend of increasing (weakening) BCS was seen in three country pairs: Indonesia-Iraq, Indonesia-Kuwait, and Syria.
The extreme number of BCS (more than 10) occurred in two pairs of Indonesia-Turkey and Indonesia-Jordan, but eventually declined in recent years.There were several explanations for this extreme number of BCS.First, several countries had experienced an increase in public financing through an increase in government budgets.These countries had recently initiated economic development that required large investments from foreign loans.Second, there was an influence of intra-regional crises, such as the Iran-Iraq war that occurred in the early 1990s (Goldstone, 2011).Third, the occurrence of similar growth patterns, such as those in Indonesia-Bahrain, Indonesia-Turkey, Indonesia-Oman, and Indonesia-Yamen, where the economic conditions in Arab countries were not much different from those in Indonesia.Indonesia was leading in the service and trade sectors, and as such, the changes in growth were slightly more vulnerable compared to those in the oil-producing countries in the Middle East.
This study attempted to classify the BCS of the countries into three groups: strong, moderate, and weak synchronization; and categorized the periods into three groups: the total period , the last 20 years, and the last 10 years.The following formula was used for this calculation: where N is the classification, Yn is the maximum BCS, Y0 is the minimum BCS, and t is the number of classes required (three classes).This study divided each of these sub-indicators into three categories.In the first category, countries with a strong synchronization grew stronger over time.Seven country pairs had a strong status (50%), which increased to eight pairs (57%) and subsequently to ten pairs (72%) in the last ten years.This increase from 50% to 72% over 40 years indicated a significant improvement.Second, an increase in the number of countries in the strong category resulted in a shift in the number of countries in the moderate synchronization category, decreasing from four pairs to one or two pairs of countries.Third, the number of countries with a weak BCS status had increasingly experienced narrow changes due to the increase in the world oil prices (Oilprice, 2022;Szafranek, 2021).The commodity price changed from its lowest point of USD 20.36 per barrel in 1989-1999, soaring to USD 157.25This analysis suggested that the synchronization between Indonesia and Middle Eastern economies was improving and continued to strengthen over time (Figure 3, Figure 4, and Figure 5).The number of strong trends was considered large, with seven countries showing strong synchronization and four in the moderate category.Only three countries were in the weak category.
The Indonesia-Middle East Business Cycle Synchronization 248 This trend indicated promising prospects for the Indonesia-Middle East BCS relationship.As a result, the trade relationship was getting stronger, the foreign investment cooperation continued to grow, and the labor relationship was so forging strong that it should generate a good impact on future synchronization.
The three country pairs showing an increasing BCS trend were the largest oil producers, namely Syria, Iraq, and Kuwait.When the world oil prices increased in early 1989, the economic growth sharply increased.However, countries that were not major oil producers, such as Indonesia, faced a stagnant growth.This disparity produced a relatively sharp difference in the BCS between Indonesia and the three countries.Consequently, their BCS weakened to a moderate level.Table 5 displays the matrix between the synchronization status (strong, moderate, and weak) and BCS trend.The results indicate that there were ten pairs of countries showing a decreasing trend.Uniquely, this decline occurred not only in highly integrated countries, but also in weakly integrated ones.The former countries showed relatively similar patterns of economic growth decline.However, in countries with a weak integration, their economic growth showed an improvement over the past years.It suggested that the Indonesia-Middle East BCS trend was gradually strengthening and had promising and positive prospects for the future.

Model Selection
The following Table 6 presents the results of redundant fixed effect test to determine whether the Common Effect (CE) model was better than Fixed Effect (FE) model.The results showed that the CE model was significantly better than the FE model (p > 0.05).The Lagrangian Multiplier (LM) test was then conducted using the Breusch-Pagan test to produce a cross-sectional chi-square value of 17.090039 with a p-value of 0.1952 > 0.05.The results indicate that the CE model was better than Random Effect (RE) model.Thus, the CE model was confirmed to be the best model used for further analysis.The results in Table 7 show that there is no variable which has a strong relationship with the BCS.All t-statistics are > 0.05.The F-statistic is also not statistically significant.The results indicate that the CE model was not valid.Consequently, this study must perform the classical assumption test to assess whether BLUE requirements actually existed.There is dependence between cross-sections, or between individuals (regions) The Jarque-Bera model was used to test the normality of the residuals.The residuals referred to the difference in the estimated error or difference between the predicted Y and Y values.They were obtained from a regression equation developed previously.The test shows that the pvalue is significant at < 0.05, indicating that the residuals were not normally distributed.Thus, H1 is accepted, denoting the absence of normality.This study also used the Central Limit Theorem (CLT) (Das, 2019;Wooldridge, 2010) which stated that data with more than 30 observations were normally distributed.If n ≥ 30 (this study has 70 observations, the sampling distribution was considered close to normal.The heteroscedasticity test result is negative, indicating that the model met the requirements or assumptions of homoscedasticity.The serial correlation test (autocorrelation) shows that the Breusch-Pagan LM test is significant at p < 0.05, thus establishing the cross-sectional or inter-individual (regional) dependence.
Based on the above analysis, this study concludes that (1) there is a problem, as evidenced by the violation of the classical assumptions on autocorrelation, normality, and inter-crosssectional dependence.This outcome was considered serious and required an adequate solution; (2) the solution offered was the use of CE with Feasible General Least Square (Cross Section Weight); (3) the cross-sectional weight (PCSE) in the literature panel data estimation coefficient provided the model with resistance against violating the assumptions (Wooldridge, 2010).

Panel Model Cross Section Weight
A suitable GLS estimator was identified as the weighted least-squares estimator.This study developed a model that was more resistant to violations of classical assumptions by including FGLS with estimated PCSE coefficients.The violation was in the form of autocorrelation, heteroscedasticity, and dependence between cross-sections.Thus, a test for violations was no longer necessary.Only a multicollinearity test between the independent variables was needed.The results of the correlation tests between the independent variables are presented in Table 9.There is no strong correlation between the independent variables, with a correlation coefficient of > 0.9 or < -0.9.The standard error of the initial model results (CE-LM test) varied with the results of the refined model PCSE coefficients.Figure 6 shows the standard error difference before and after the improvement; where the CE-LM testing model is 6,662,590, while the PCSE model is 2,975,986.This discrepancy proves the first test result insignificant.However, after modifying the model, the results were improved when the trade variable passed the test.This result exceeded those of numerous past BCS and trade studies in identifying the trade as an influential variable.
The results show that the trade has a significant positive effect on the BCS, whereas the remaining two variables are considered less influential (Table 10).This finding is consistent with those of previous studies (Jansen & Stokman, 2014).Table 10 presents the results of the PCSE test for the improved model.The BCS increases by 2.24% through open trading.However, the relationship should be negative, since the trade would result in a decrease in the BCS.The results confirm that trading had the potential to weaken the BCS.This contradiction could be explained for the Middle Eastern countries, where their economic growth was driven by a rapid and sudden rise in the oil prices.This oil-driven source of economic growth was in contrast to what Indonesia experienced, where the trade and manufacturing industry were the main driving factors (Habibi & Zabardast, 2020).
Further, the financial sector (including aspects of inflation and exchange rates) was not the only significant factor in determining the BCS.The inflation was often affected by domestic demand, whereas the exchange rate largely depended on external factors (Delgado, Araya, & Pino, 2020;Flood & Rose, 2010).The regression of these factors was not sufficiently strong, with an R 2 of less than 4%.Other possible explanatory factors should also be considered in future studies.
The BCS factors that might explain the relationship between Indonesia and the Middle Eastern countries were mostly aspects in the trade relations.Theoretically, the relationship between trade and BCS was endogenous.There was no theoretical scenario where the BCS affected the trading, or vice versa.If the relationship was positive, the trade openness would increase (weaken) the BCS; conversely, if the relationship was negative, the trade openness would decrease (strengthen) the BCS.De Grauwe & Mongelli (2004) proposed a curve explaining the influence of openness (trade) on the BCS, which showed a negative effect.This suggested that if the trade was more open, the BCS value would also be more synchronized, as what was generally found in developed countries, such as the European Union.However, the pattern was different for the OIC countries, which were mostly categorized as developing or emerging economies.This present study investigates why the trade has a positive effect on the BCS, contrary to the hypothesis proposed by De Grauwe (year).This study illustrates the estimation curve as shown in Figure 7 below.1.If the trade openness moved between 20% and 50%, the trade effect on BCS would be positive.
2. If the trade openness was between 50% and 90% and above, the trade effect on BCS would be negative.
Considering that the trade openness in the Middle East and the OIC as a whole had only reached 20%, the effect of trade on BCS was positive.Another reason for this was the source of growth.The economic growth in Indonesia was based on the strength of the trade and manufacturing sectors, whereas in the Middle Eastern countries, it was driven by the oil products.In the oil-producing countries, the BCS growth was often rapid and abrupt.This was the main cause of the rapidly widening rift in the BCS between Indonesia and Middle East and OIC countries.
In addition, there was very little influence between the inflation and exchange rate on the BCS.Such price factors were more difficult to forecast, because they could change rapidly and were influenced by many factors.The inflation was mostly caused by domestic factors, whereas the exchange rate was influenced by the foreign exchange reserves between the countries.

CONCLUSION, SUGGESTION, AND LIMITATION
This study aims to investigate the Indonesia-Middle East BCS and its determinants.This study used a total sample of 14 Middle Eastern countries, with Indonesia as the benchmark.Based on the results of this study, there are two important conclusions which will be elaborated as follow: First, this study finds that the Indonesia-Middle East BCS is quite strong.There were seven countries with a strong synchronization and four countries with a moderate synchronization.Only three states were found to be weakly synchronized.In general, the trend in BCS was a continual sharp drop in the synchronization shown in ten countries.This development should guarantee that the Indonesia-Middle East BCS would continue to strengthen over time.

Figure 7 .
Figure 7. Curve Estimation of BCS-Trade Relationship Figure 7 shows an inverted U-shaped curve of the influence of trade on the BCS.This pattern indicated two effects: trade openness and BCS.

Table 6 .
The panel linear regression was then tested with the CE model using the LM test.