Business cycle synchronization in the asean degree and driving forces

UNIVERSITY OF ECONOMICS ERASMUS UNIVERSITY ROTTERDAM HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS BUSINESS CYCLE SYNCHRONIZATION IN THE ASEAN: DEGREE AND DRIVING FORCES BY NHU DINH HIEP MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY January 2018 UNIVERSITY OF ECONOMICS ERASMUS UNIVERSITY ROTTERDAM HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS BUSINESS CYCLE SYNCHRONIZATION IN THE ASEAN: DEGREE AND DRIVING FORCES A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS BY NHU DINH HIEP Academic Supervisor Dr VO HONG DUC HO CHI MINH CITY January 2018 ABSTRACT Business cycle synchronization can be understood as the symmetric movements of business fluctuations over a timeframe In the event of economic shocks, the degree of business cycle synchronization will indicate if a country can rely on its economic policy to overcome the shocks or it needs the policy coordination from other countries Business cycle synchronization is perceived as one of the irreplaceable criteria to assess the degree of regional economic integration The ASEAN is currently at the stage which can be considered to resemble the early stages of the European Union Most ASEAN countries are also members of the Asia-Pacific Economic Cooperation (APEC) forum, so the concept of business cycle synchronization becomes more and more important Empirically, there are few studies that incorporated the term “business cycle synchronization” in the analysis of a potential currency union in ASEAN However, such a study that can explore the matter directly is hard to find This study is conducted to evaluate the degree of the business cycle synchronization in the 10 ASEAN economies from 2000-2016 Additionally, the study expects to uncover the driving forces that lead to the synchronization of business cycles in the region To overcome the limitation of the old measuring technique and data constraints, this study makes use of the new measure of business cycle synchronization using Abiad, et al (2013)’s instantaneous quasi-correlation measure A growing literature has also shortlisted three candidates for the determinants of business cycle synchronization: trade intensity, similarity of industrial structures, and financial integration These three variables are tested for their explaining powers in a regression model To reduce the possibility of the endogeneity problem, control variables (the product of log GDP, the product of log population and per capita GDP difference) are added to the model to control for omitted-variables bias Also, lagged values of right-hand side variables are used to prevent the reverse causality To purge the autocorrelation and heteroskedasticity existed in the model, the Feasible Generalized Least Squares estimator is the main estimation method in this study (OLS and Random-effects results are used as references) The calculation using new measure signifies that the degree of business cycle synchronization in ASEAN is moderate from 2000-2016 The level of synchronization reaches to the highest points in 2009, which was the time when the effects of global financial crisis took place Correlations of business cycles among ASEAN-5 members are generally higher than among the CLMV group, or among the whole region The regression-based analysis from this study also presents some key findings Bilateral trade intensity and the similarity of industrial sectors are positively correlated with the synchronization of business cycles Their explaining powers are robust and consistent across models The mutual capital control is likely to weaken the degree of synchronization Capital control is not significant in OLS estimate, fairly significant in Random-effects model, but highly significant in FGLS model In summary, empirical evidence from this study underlines the three positive determinants of cycle synchronization: (i) trade integration; (ii) industrial similarity; and (iii) financial integration As such, relevant policy implications are also discussed Keywords: Business cycle synchronization, ASEAN, Trade, Specialization, Financial integration JEL Classification: C21, E32, F15 ACKNOWLEDGEMENTS I would like to seize this chance to express my gratitude to Doctor Vo Hong Duc for his patience and sharp critiques while supervising me Without his valuable comments, I could never complete my thesis Special thanks are also due to Vo The Anh for his assistance in econometric techniques Finally, I am grateful to my colleagues who took over my responsibilities during my absence CONTENTS Abstract Acknowledgements Contents Tables & Figures CHAPTER 1: INTRODUCTION Problem statements Research objectives and questions 10 Scope of the study 10 Structure of the thesis 11 CHAPTER 2: LITERATURE REVIEW 12 The literature review 12 Review of empirical studies 15 CHAPTER 3: RESEARCH METHODOLOGY 25 Measurement methods 25 Estimation technique 26 Variable measurement and data 29 3.1 Business cycle synchronization 29 3.2 Trade integration 30 3.3 Industrial similarity 31 3.4 Financial integration 32 3.5 Other variables 32 CHAPTER 4: RESEARCH RESULTS 34 Business cycle synchronization in ASEAN 34 The driving forces of business cycle synchronization in ASEAN 37 2.1 Descriptive statistics 37 2.2 Regression results 40 2.3 Discussions 42 CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATIONS 44 Conclusions 44 Policy implications 46 Limits of the study 47 REFERENCES 48 TABLES Table 1: Summarized specification link test result Table 2: Ramsey RESET test using powers of the fitted values of QCORR Table 3: Test of overidentifying restrictions: fixed vs random effects Table 4: Tests of heteroskedasticity and autocorrelation Table 5: Descriptive statistics of variables Table 6: Correlation matrix among variables Table 7: OLS, Random Effect and FGLS regression results FIGURES Figure 1: The mechanisms of business cycle synchronization Figure 2: The dynamics of business cycle synchronization in ASEAN (2000-2016) CHAPTER 1: INTRODUCTION Problem statements A large labor pool and a growing consumer class have catapulted the Association of Southeast Asian Nations (ASEAN) to be the world’s third engine of growth just behind China and India Economic relations among these ASEAN economies are reasonably expected to boost business expansions in the region through free trade, cross-border investment and labor mobility Country-specific business fluctuations from one country may get transmitted to others and influence the co-movement of outputs indirectly Therefore, the characteristics and causes of business cycle behavior among ASEAN members should be scientifically comprehended Lucas (1977) defined business cycles as the recurrent fluctuations of macroeconomic aggregates around trend Business cycle synchronization means that similar movements in countries' business cycles exist over time Within the context of this thesis, these terms “business cycle synchronization”, “correlation of business fluctuations”, “cycle co-movement” or “symmetric business cycles” are used synonymously Furthermore, the term “business cycle synchronization” should not necessarily be confused with “economic convergence” as the latter refers to the catch-up effect when the poor or less developed countries have faster growth rates than the rich ones, narrowing down the gaps between their economies The degree of cycle synchronization is of great importance because it shows the necessity of policy coordination among integrated economies If economic shocks are purely countryspecific, then national fiscal and monetary policies can help the country get back to the original equilibrium However, if the shocks are common, or one national shock spreads beyond borders, then uniform or cooperative policy interventions should be more effective This is not only essential for the ASEAN region but also for any economic bloc such as the MERCOSUR, the South Asia region, the ANZCERTA, or the East Asia region Business cycle synchronization becomes a key measure of economic integration (Dorrucci, et al 2002) ASEAN is one of the most vibrant economic regions in the world However, the ASEAN regional integration process is very different from the European countries the 20th century While EU established its single market twenty years ago, it took ASEAN almost twenty three years (from 1992 to 2015) to finalize the ASEAN free trade area (with the formation of the AEC) The profound differences in culture, history or political systems as well as the current level of economic development between the ASEAN old members and new ones could be one of the reasons for the ASEAN economies are difficult to retrace the footsteps from the EU Thus, some skeptical arguments arise in response to the true economic integration among members within the ASEAN Against all odds, the ASEAN members are now making efforts to strengthen economic and financial cooperation regionally and globally Out of 10 ASEAN members, of them (Brunei Darussalam, Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam) also belong to the Asia-Pacific Economic Cooperation (APEC) forum Finding new driving forces for regional economic integration is one of the key commitments of the APEC leaders in the latest Da Nang Declaration 2017 in Vietnam A question is raised, ‘What are the driving forces?’ Clear insights of business cycle synchronization now become integral to answer the question convincingly Extensive research, such as empirical papers by Frankel and Rose (1997, 1998), Baxter and Kouparitsas (2005), a series of papers by Imbs (particularly Imbs and Wacziarg, 2003; Imbs 2004, 2006), or Calderon, Chong and Stein (2007) has indicated some impacts of trade intensity, specialization and financial integration on business cycle synchronization Böwer and Guillemineau (2006) which studied European countries solely also shortlisted some key determinants of business cycle synchronization Similar papers which include an Asian context (Kumakura, 2006; Park and Shin, 2009; Duval, 2014) also pointed out possible factors that drive a business cycle synchronization The concept of business cycle synchronization in ASEAN was mentioned in several papers that examined the suitability of a currency union in ASEAN, such as Bayoumi, Eichengreen and Mauro (2000) or Bacha (2008) However, studies on the determinants of business cycle synchronization in the ASEAN region exclusively are limited or virtually nonexistent One of the biggest obstacles is that the traditional estimation of business cycle synchronization does not allow econometric regressions to be properly conducted due to the lack of data for interested countries Normally, the calculation of business cycle synchronization is based on the Pearson correlation of actual or de-trended time series for each country pair over a window period This requires either a large amount of country pairs or many rolling windows to maintain the required number of observations Nevertheless, the statistics data for ASEAN countries are limited, especially for the less developed members Research objectives and questions Analysis of the business cycle synchronization is important and hotly debated for the ASEAN region In response to this debate this study is conducted to achieve the following two key objectives - First, an assessment of the degree and dynamics of business cycle synchronization can be used as a measurement of the regional economic integration With a high degree of business cycle synchronization, regional policymakers are more likely to respond with a common policy and they cooperate with each other to stabilize economic shocks This implies ASEAN is a real emerging powerhouse The goal is to unfold the current status of business cycle synchronization in ASEAN’s economies - Second, understanding the driving forces of business cycle synchronization will play a crucial role in forming sustainable governance for the ASEAN region Each country member will adjust the economy system respectively to enhance the synchronization The second objective of this study is to discover the determinants that spur the level of synchronization In order to achieve those stated objectives, this thesis attempts to provide the answers to the following questions: - At what level is the synchronization of business cycles in the ASEAN? - Which factors drive business cycle synchronization, to be named the driving forces in the ASEAN? Scope of the study This thesis will observe the synchronization of national business cycles among 10 ASEAN countries They are: ASEAN-5 (Singapore, Thailand, the Philippines, Malaysia, and Indonesia), CLMV (Cambodia, Lao PDR, Myanmar, and Vietnam), and Brunei Darussalam Each country will pair up with the other countries to make a total of 45 country pairs Due the limited availability of data, the investigation of the determinants of business cycle synchronization is confined to the period 2000-2016 10 and joined the WTO in 2013 Both Cambodia and Vietnam are still classified by World Bank as lower middle income countries, which means that the development gaps with Singapore or Brunei are still wide It is generally perceived that integrating countries economically and/or financially with the same or similar level of development is easier The diverse structures of the ASEAN economies along with the dissimilar levels of economic development make the moderate level of synchronization intelligible Third, the peak level of business cycle synchronization in 2009 is the striking example of how sensitive the ASEAN economies are to the global financial crisis The global crisis was originated from the subprime mortgage sector in the US, when banks lent mortgages to individuals with low credit ratings Fannie Mae, Freddie Marc, and the lesser known Ginnie Mae, are government-sponsored enterprises which purchase loans from mortgage lenders They either hold them or repackage them to create mortgage-backed securities Also, during the years 2006-2007, subprime mortgages became the key collaterals in the collateralized debt obligations, which are the asset-backed securities According to some analysts, rating agencies during the time from 2007-2008 failed to address adequately the risks of those derivatives when they were pumped excessively into the financial system The housing bubble since 2007 turned those toxic loans into a crisis when hedge funds and big investment banks in the secondary market incurred huge losses The ASEAN financial markets were less vulnerable to the mortgage-related products because the development of these markets was still lagging behind those in the US or the Europe Even in Singapore, the exposure was limited because the market were well-regulated However, the global financial crisis clearly had some effects on the ASEAN economies through the capital flow channels and the trade channels Many US financial institutions needed to secure their cash and capital by monetizing all their domestic and foreign assets That could lead to the capital outflows from the ASEAN markets, especially the ASEAN-5 economies The aftermath of the global financial crisis could have negatively affected the ASEAN countries through the trade channels because major advanced economies like the US, the Europe, or Japan are the key destinations of the ASEAN’s export products The slowdown in these developed economies resulted in the weaker demand for products from manufacturing hubs like the ASEAN Slow exports of non-oil products and the reduction of FDI inflows subsequently caused the economic growth in the ASEAN economies to fall The year 2009 witnessed the slumps in all ASEAN countries’ GDP growth rates Some countries even fell in recession, for example, Singapore (-0.6%), Malaysia (-1.5%), or Thailand (-0.7%) (Source: IMF's 2017 World Economic Outlook database) As all economies suffered the ripple 36 effects of the global financial crisis simultaneously, the synchronization of business cycles in this year is, therefore, extremely high The driving forces of business cycle synchronization in ASEAN 2.1 Descriptive statistics Table shows the descriptive statistics of variables used in the regressions Table 5: Descriptive statistics of variables Variables Obs Mean Std Dev Min Max QCORR 765 0.274 1.067 -3.587 7.712 TRADE 720 0.668 1.028 6.581 SIMILARITY 720 -0.631 0.305 -1.577 -0.162 FINANCE 448 1.247 0.423 0.180 1.925 POPLOG 720 284.6 41.02 193.0 356.9 GDPLOG 720 625.6 53.55 492.2 737.4 CAPITA 720 1.448 1.002 0.00661 4.088 QCORR: Quasi-correlation of business cycles; TRADE: bilateral trade intensity; SIMILARITY: similarity in industrial structures; FINANCE: sum of capital control indexes; POPLOG: product of log GDP; GDPLOG: product of log GDP; CAPITA: Absolute difference in log PPP GDP per capita Source: Author’s calculations  The dependent variable, QCORR, has the mean value of 0.274 with a standard deviation of 1.067 The standard deviation is high, compared to the mean that there is volatility, possibly because of the high values of QCORR in the year 2009 The value is -3.587 and the max value is 7.712  The values of independent variables including TRADE, SIMILARITY, FINANCE, POPLOG, GDPLOG and CAPITA are the values in the previous years (lagged values)  The TRADE variable has the mean value of 0.068 with a fairly high deviation of 1.028 The minimum value is zero, assuming that there is virtually no trade between the two countries For example, it may happen to data of Lao DPR when the landlocked country has no transactions with its partners in that year The maximum value is 6.581  The SIMILARITY variable has the mean value of -0.632 with standard deviation of 0.305 The value is -1.577 and the max value is -0.162  The FINANCE variable has the mean value of 1.247 with a standard deviation of 0.423 The and max values are 0.180 and 1.925, respectively 37  The POPLOG variable has the mean value of 284.6 with a standard deviation of 41.02, the value of 193.0 and the max value of 356.9  The GDPLOG variable has the mean value of 625.6 with a standard deviation of 53.55, the value of 492.2 and the max value of 737.4  The CAPITA variable has the mean value of 1.448 with a high standard deviation of 1.002 This implies the per capita GDP may vary across countries For example, Brunei is a rich country with high per capita GDP while Lao DPR and Myanmar are poor The value is 0.00661 and the max value is 4.088 Table presents the Pearson correlation matrix of variables including the explained variable and excluding observations with missing data of FINANCE variable The values of correlation range from -1 and The correlation of FINANCE and POPLOG is high (at 0.8468), that of 38 39 -.0404 0.0028 -.0017 POPLOG GDPLOG CAPITA -0.3905 0.8683 0.6941 0.5894 -0.4467 0.4097 0.8468 -0.4844 0.6775 -0.3298 1 1.36 12.91 11.11 7.47 7.49 2.37 VIF Source: Author’s calculations QCORR: Quasi-correlation of business cycles; TRADE: bilateral trade intensity; SIMILARITY: similarity in industrial structures; FINANCE: sum of capital control indexes; POPLOG: product of log population; GDPLOG: product of log GDP; CAPITA: Absolute difference in log PPP GDP per capita -0.0867 0.6205 0.1582 -.0015 -.0282 FINANCE 0.5496 0.1147 TRADE SIMLARITY 0.0421 QCORR QCORR TRADE SIMILARITY FINANCE POPLOG GDPLOG CAPITA Table 6: Correlation matrix among variables the pair GDPLOG and SIMILARITY is also high (at 0.8683) A column of variance inflation factors (VIF) of explanatory variables is appended to the last column Year dummies are included in the calculation of VIF values, but are not shown due to space limit The VIF values are high for the two variables POPLOG (11.11) and GDPLOG (12.91) However, the near multi-collinearity issue can be safely ignored here because these are the control variables, not the variables of interest 2.2 Regression results Table exhibits the results of the OLS, Random-Effects and FGLS regressions respectively Column I shows the OLS estimates with robust standard errors for Equation The coefficient of TRADE is strongly significant at 1% level The relationship between TRADE and QCORR is positive (one unit increase TRADE is associated with 0.155 unit increase in QCORR, ceteris paribus) Similarly, the coefficient of SIMILARITY is highly significant at 1% level SIMILARITY is positively correlated with QCORR (one unit increase in SIMILARITY is associated with 1.032 unit increase in QCORR, c.p.) The coefficient of FINANCE is negative, however it is not significant at any level of significant This means the coefficient is statistically not different from zero The coefficients for POPLOG and CAPITA are also not statistically significant Those betas’ values are not statistically different from zero The coefficient of GDPLOG is negative and statistically significant at 1% level GDPLOG is negatively correlated with QCORR (one unit increase GDPLOG is associated with 0.155 unit increase in QCORR, c.p.) The reported R2, which represents goodness of fit, is 0.366 Column II demonstrates the result of Random-Effects regressions with standard errors clustered at country pairs The coefficient of TRADE remains positive and statistically significant at 1% significance level, however the magnitude is adjusted slightly lower (one unit increase in TRADE is associated with 0.151 unit increase in QCORR, c.p.) The coefficient of SIMILARITY also remains positive and statistically significant at 1% significance level, and the magnitude is adjusted larger (one unit increase in SIMILARITY is associated with 1.106 unit increase in QCORR, c.p.) The coefficient of FINANCE is now statistically significant though at the modest 10% level As the sign of the coefficient is negative, FINANCE is negatively correlated with QCORR (one unit increase in TRADE is associated with 0.522 unit decrease in QCORR, c.p.) The coefficients for POPLOG and CAPITA continue to be insignificant, their values are statistically not different from zero The coefficient for GDPLOG is negative and statistically significant at 1% level, the same as the OLS results (one unit increase in GDPLOG is associated with 0.0119 unit decrease in QCORR, c.p.) The overall R2, which represents goodness of fit, is 0.352 40 Table 7: OLS, Random Effect and FGLS regression results Dependent variable (I) (II) (III) QCORR OLS Random FGLS Effects 0.155*** 0.151*** 0.180*** (0.0590) (0.0540) (0.0402) 1.032*** 1.106*** 1.126*** (0.320) (0.323) (0.171) -0.377 -0.522* -0.607*** (0.277) (0.282) (0.157) 0.00275 0.00418 0.00364* (0.00326) (0.00413) (0.00202) -0.0108*** -0.0119*** -0.0118*** (0.00363) (0.00408) (0.00164) -0.00173 -0.00266 -0.0402 (0.0497) (0.0359) (0.0301) Year dummies YES YES YES Constant 7.373*** 7.853*** 8.138*** (1.964) (2.082) (1.017) Observations 448 448 448 R-squared 0.366 0.352 TRADE SIMILARITY FINANCE POPLOG GDPLOG CAPITA Number of id 28 28 Robust standard errors in parentheses *** p