Export, Import and Growth in Bhutan

RIM Export, Import and Growth in Bhutan (1980-2014) Chimi Dema PGDPA 2016 P a g e

Acknowledgement Firstly, I would like to thank my supervisor, Mr. Singhye Wangchuk for supervising my research. I would also like to express my gratitude to few of my classmates: Jigme Dorji for answering my statistical related question, Dorji Peljor and Wangchuk for their help in data collection. i P a g e

Abstract Utilizing annual time series data from 1980-2014, this paper investigates the relationship among export, import and economic growth in Bhutan using the framework of vector-autoregressive (VAR) to examine the Granger causality. Unit root tests using the Augmented-Dickey Fuller test was conducted to check the stationarity of the variables to ensure further analysis of cointegration. Employing Johansen cointegration test, it was found that the GDP rate, real exports and real imports display a long-run dynamic relationship. Nevertheless, the short-run Granger causality results show that there is no causation between export and growth, growth and import. Thus, this paper guided by the framework of export-led growth theory, import-led growth theory and the growth-led export theory does not support any of the aforementioned growth hypotheses in case of Bhutan. ii P a g e

Table of Contents Acknowledgement... i Abstract... ii Introduction... 1 Literature Review... 3 Data and Methodology... 5 Data... 5 Descriptive Statistics... 6 Methodology... 8 Findings, Analysis and Discussion... 9 Conclusions and Recommendations... 13 Bibliography... 14 Appendix 1... 17 iii P a g e

Export, Import and Growth in Bhutan Introduction Bhutan s trade deficit in 2015 stood at Nu 32.8 Billion and it is expected to persist with large growth in imports (Royal Monetary Authority, 2016; Dorji, 2016). The value of imports has increased from 56.88 billion in 2014 to 68.03 billion in 2015 while exports decreased very minimally from 35.58 Billion in 2014 to Nu. 35. 22 Billion in 2015 ( (Dorji, 2016) On the other hand, the GDP growth rate rose to 5.2 % compared to lows of below 4% the preceding two years (Royal Monetary Authority, 2016). Widening trade deficit has dominated the economic policy debate in Bhutan ever since the rupee crunch crisis. At the same time, Bhutan s growth story is one of a success among the developing countries with the Bhutan set for graduation away from the LDC status.there are numerous factors attributed for economic growth Bhutan has experienced since the inception of the Five Year Plans. For a developing country, vast amount of literature on trade emphasizes the important role of trade in increasing the income of a country. Thus, this paper s main objective is to explore the role of trade in determining economic growth in Bhutan. Bhutan s international trade is mainly concentrated with India which is guided by a free-trade regime between the two countries. In 2015, Bhutan s export to India amounted to Nu 31.8 billion and only Nu 3.4 billion were exported to other third countries. Similarly, Bhutan imported 78.9% of the total imports from India (NSB, 2016). While Bhutan is guided by free-trade regime with India, it is also a signatory of regional free trade agreements such as SAFTA and BIMSTEC that allows easy access to regional markets. As Bhutan increasingly becomes more integrated with the regional markets and beyond, the widening of trade deficit has dominated the economic policy debate. According to RMA, the trade deficit and more specifically the current account deficit is the biggest medium-term challenge for the economy (2016). One of key challenges of rising deficit has been experienced by the Bhutanese economy in the form of Indian rupee shortage and other consequences associated with high demand for Indian rupees. In this context, looking into the relationship 1 P a g e

between imports, exports and growth could provide policy making framework in addition to investigating the role of trade in economic growth as proposed by the literature. Introductory economics textbook presents the ideas from Adam Smith to Ricardo highlighting the advantages of trade and the corresponding benefits that nations can reap from engaging in trade. Proponents of free trade present a positive relationship between openness of trade and growth. Thus, this paper will be guided by the theoretical framework of Export-Led growth (ELG) and import-led growth (ILG) hypotheses and growth-led exports (GLE)) Thus, given the conceptual framework and the context of the Bhutanese economy, this paper investigates the relationship between exports, imports and economic growth. In determining the relationship between trade and growth in Bhutan, this paper will infer whether the growth in Bhutan is primarily driven by exports or imports or rather that growth drives the exports and imports sector in Bhutan. Given the literature on growth and trade and the context of Bhutanese economy, I hypothesized that firstly, there will be significant relationship between exports, imports and GDP with positive relationship between export, import and growth. In terms of whether import or export has a causal relationship with GDP, I hypothesize that imports may have a greater impact on GDP and may primarily drive growth as imports have been increasing owing to the hydropower projects which mainly involves imports of capital goods or imports as an intermediate good necessary for capital formation consequently leading to growth in the country. Utilizing the data from 1980 to 2014, this paper applies cointegration framework and the Granger causality test in determining the relationship between trade and growth. This paper finds that while the data displays a long-run relationship among GDP rate, real import and real export, the short-run Granger causality shows no causation. This paper is organized as follows: Section II provides the literature review on trade and growth including the conceptual framework used for this study. Section III presents the data sources, descriptive statistics and methodology. Section IV discusses the results and the findings of the paper. The last section contains the concluding remarks and recommendations. 2 P a g e

Literature Review The literature on international trade is guided by three frameworks: ELG hypothesis, ILG hypothesis and GLE which also guides this paper. ELG hypothesis postulates that exports are the main drivers of economic growth owing to efficiency of resource allocation, economies of scale and the advantages of competition. In an ELG framework, increase in the exports of a country can create more employment and increase income in the export sector that consequently lead to overall output growth (Awokuse, Trade openness and economic growth, 2008). Additionally, exports can cause growth because it reallocates resources from less efficient non-export sector to high-productivity export sector (Feder, 1982). One way of more efficient export sector results from the competition faced by the local firms from foreign markets and thus, leading to higher labor productivity (Thangavelu & Rajaguru, 2004). Exports also play the role of providing a country with foreign exchange needed for the import of intermediate and capital goods which in turn stimulate growth (Esfahani, 1991). Alternatively, GLE argue for a reverse causal from growth to exports, the growth in the country drives the export sector (Awokuse, 2008) ILG hypothesis argue that the causality flows from imports to economic growth. ILG hypothesis suggests that economic growth could be primarily driven by growth in import through the transfer of intermediate goods, technology and R&D. Endogenous growth models have emphasized the role of imports in long-run economic growth because of transfer of intermediate goods and foreign technology into a domestic economy (Coe & Helpman, 1995; Lee J.-W., 1995; Mazumdar, 2001). Thus, import provides access to improved technology and foreign knowledge that will in turn enhance the productivity growth of the domestic economy especially from a developed to a developing country. In addition to the growth enhancing transfer of R&D, it is suggested that imports of competing products spur innovation as domestic producers learn from foreign rivals and competition (Lawrence & Weinstein, 1999). International trade as a driver of economic growth has been emphasized in numerous empirical studies and has demonstrated a positive relationship between trade and economic growth in both 3 P a g e

developing and industrialized countries. However, there is a lack of consensus on the significance, magnitude and the causal direction of exports and imports on the growth. Despite extensive literature on trade and growth, there isn t a definitive result in the empirical literature on the causal relationship between international trade and economic growth. There are vast amount of existing empirical literature that emphasize the outward-oriented trade policies and support the ELG hypothesis. Feder (1982) finds a statistically significant relationship between the growth rate and growth rate of exports in a panel data of 31 semi-industrialized countries from 1964-1973. Trade openness is found to play a significant role in determining the growth among East Asian Countries (Frankel, Romer, & Cyrus, 1994). In a sample of 45 developing countries, there is a positive short-run relationship between export and non-export GDP and vice versa while there was a negative effect of exports on non-export GDP in the long-run (Dreger & Herzer Dierk, 2013). A bidirectional causal relationship exists between exports and income in Singapore while imports have a positive effect on economic growth (Lee C. G., 2012). On the other hand, Thangavelu and Rajaguru (2004) found export-led growth in Singapore. In a study of four African countries, the size of the economy and the importance of trade relative to the GDP determined the effects of openness on economic growth (Razafimahefa & Shigeyuki, 2003). Mauritius, a larger economy with a higher trade share showed a considerable effects of trade on economic growth while Comoros displayed that trade is insignificant given the country being the smallest economy with the lowest trade share (Razafimahefa & Shigeyuki, 2003). The Japanese economy meanwhile displayed bidirectional causality between export and GDP using a vector autoregressive model (Awukose, 2006). Recent studies have emphasized the importance of import in growth models and found significant relationship between import and growth. Ratio of imported to domestically produced capital goods had a significant effect on per capital income growths while share of total imports in GDP has no significant effect on growth (Lee J.-W., 1995). Thus, Lee highlights the importance of investment and total accumulation to be an important determinant of growth in developing countries. Similarly, a panel data of 42 countries, finds that investment in imported machineries increases the growth rate while domestically produced equipment reduces the rate (Mazumdar, 2001). 4 P a g e

A time-series data of nine developing Asian countries using a multivariate VAR framework suggested an import-led growth which showed that imports tend to have a significant effect on labor productivity growth (Thangavelu & Rajaguru, 2004). Awokuse (2008) finds a bidirectional causal relationship between imports and GDP growth in case of Latin American countries. While trade can have a positive impact on growth, it can also constrain growth through its negative impacts on balance of payment due to the deterioration in trade balance (Parikh, 2004) There are several studies that have looked into the relationship between trade and economic growth in South Asian Economies. In a time series data from 1973 to 2005 of Pakistan, it is found that marginal factor productivities to be unequal in export and non-export sectors with the marginal factor productivities significantly higher in the export sector. The results support export-oriented, outward-looking approach to trade relations (Aurangzeb, 2006). In Nepal, the causality ran from trade to GDP and not vice versa and supported the ELG hypothesis with the display of positive relationship between exports and GDP while imports and GDP showed a negative effect (Bastola & Sapkota, 2015). In Bhutanese context, while research only for Bhutan has not been conducted, a cross-country analysis of SAARC countries found that there is no causation between export and real GDP in Bhutan (Sampathkumar & Rajeshkumar, 2016). However, the study of SAARC countries does not include import as a function of growth and thus, does not look at the role of import in growth story. Therefore, this paper adds import to the analysis and adds to the literature on trade and growth which is very minimal and almost non-existent for Bhutan. Data and Methodology Data Annual Time series data from 1980 to 2014 for Bhutan was taken to estimate the causal relationships among Economic growth, Exports and Imports in Bhutan. The data for the value of exports and imports were collected from the Statistical Year Book published by National Statistics Bureau (NSB) and the value for imports and exports for the year 1980 were obtained from World s World Development Indicators ( (NSB, 2016; Bank, 2016) The data points for real 5 P a g e

GDP and GDP rate are from NSB s National Accounts Report (NSB, 2016) All the variables are in constant 2000 prices. The export and import values were adjusted to the 2000 prices using the export value index and import value index of the World Bank. The data will be analyzed in the statistical software package, Stata and the real export and real import are transformed into logarithmic function. Descriptive Statistics The annual data from 1980 to 2014 for Bhutan is summarized in table 1. The real GDP of Bhutan has averaged at Nu 21864 million for the past 3 decades. On average, the Bhutan s GDP grew at 7.27%. The real export averaged at Nu 3282.6 million and the import at Nu 6032.79 million. It can be surmised from the table that Bhutan s export on average is 15% of the GDP. On the contrary, imports form 27.5% of the real GDP which is almost twice the value of exported goods and services. Table1: Summary statistics of the variables (Nu. Million and in constant 2000 prices) Variable Mean Std. Deviation Min Max Real GDP 21864.6 15212.5 5284 55606.5 Real Export 3282.6 1657.327 889.45 6604.06 Real Import 6032.79 2821.784 1623.313 12303.9 GDP rate 7.27 4.98 1.7 25.4 Looking at Graph 1 which depicts the GDP growth rate of Bhutan from 1980 to 2014, the highest growth rate experienced was 25.4% in 1987 with a record low of 1.7% in 1984 followed by 2.1% in 2013. The highest growth rate in 1987 corresponds with the commissioning of Chukha Hydropower Project in 1986. So, this may explain one of the highest growth rate for Bhutan. 6 P a g e

0 5 Trend of GDP Growth in Bhutan (1980-2014) 10 15 20 25 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Year Graph1: GDP growth rate trend in Bhutan from 1980 to 2014 From Graph 2 illustrates the trend of export and import in Bhutan and shows a widening gap between export and import in Bhutan. The continuous increase in the gap between export and import has resulted in Bhutan increasingly facing trade deficit problem. 7 P a g e

0 Trends in Export and Import(1980-2014) 2000 4000 6000 8000 1000012000 1980 1982 1984 1986 1988 1990 1992 1994 1996 Export Year 1998 2000 2002 2004 2006 Import Graph2: Trends in real export and real import of Bhutan 2008 2010 2012 2014 Methodology This paper applies co-integration test in order to estimate the causal relationship between export, import and growth in Bhutan. The Granger Causality test will be applied to examine the relationship between export and growth and import and growth. This study takes Y (GDP) as a function of exports and imports. In order to apply the causality test, the variables under the study must be stationary and cointegrated (Sampathkumar & Rajeshkumar, 2016; Stock & Watson, 2012). Thus, this paper follows the following methodology: Unit Root Test: All the variables are tested for stationarity before testing the co-integration relationships and granger causality. The Augmented Dickey Fuller test will be used to test whether the variables are stationary in which the null hypothesis is that the variable is nonstationary (Stock & Watson, 2012). Augmented-Dickey Fuller test will be used to test for the presence of a unit root. In this case, if the variable is non-stationary at the level the first order difference will be employed to make the variable stationary. The Augmented-Dickey 8 P a g e

Fuller(ADF) test in STATA will check whether the variables under consideration GDP rate, real exports and real imports in logarithmic form are stationary at level i.e. integrated of order I(0) or integrated of order I(1). ADF test is applied with the inclusion of constant and drift. Following the unit root-test, co-integration analysis will be conducted using the Johansen- Juselius method to test for the dynamic relationships between the variables and the long-run relationship (Thangavelu & Rajaguru, 2004). In a Johansen-Juselius method, the trace test statistics and Eigen value will determine the null hypothesis that the variables are co-integrated. Co-integration among the variables indicates there is a long-run relationship and also that at the very least there will be a unidirectional causality. If the variables are co-integrated, then the Granger causality can be run. Granger causality as the name does not imply causality but it represents predictability-whether current and lagged values of one variable, for instance X, help in predicting the current values of variable Y given that the data is a time series one (Stock & Watson, 2012). So, if variable X Granger-causes variable Y, then X is a useful predictor of Y. While running the granger causality test, the following form will take place: gdprate t = β 0 + β 1 gdprate t 1 + β 2 lnexp t 1 + β 3 lnimp t 1 + ε lnexp t = β 0 + β 1 lnexp t 1 + β 2 gdprate t 1 + β 3 lnimp t 1 + ε lnimp t = β 0 + β 1 imp t 1 + β 2 lnexp t 1 + β 3 gdprate t 1 + In the above equations, lnimp is log of import, lnexp is the log of export and indicates the lag difference with t-1 denoting the first difference. Findings, Analysis and Discussion Following the methodology described in the previous section, unit root test, co-integration and granger causality test for GDP rate, log of real exports and log of real imports is tested using STATA and the results reported in table 2, 3 and 4 respectively. 9 P a g e

Table 2 shows the results of Augmented-Dickey Fuller test which shows that the variables are integrated of order I(1). In the ADF test, the null hypothesis is that the variable has a unit root which shows that the variable is non-stationary. So, in the ADF test result, null hypothesis cannot be rejected for both real export and real import in the logarithmic form since the absolute value of t-test is lower than the critical values at all 1%, 5% and 10% level. However, at first differencing, at 10% significance level, null hypothesis of the presence of unit root can be rejected. On the other hand, GDP rate is stationary at both level and the first difference. Thus, it can be concluded form the ADF test as presented in table 2 that all the variables are integrated of order I(1). Table2: Unit-root test (ADF Test) Variable Level 1st difference Log Export -1.25-1.438* Log Import -1.511-1.611* GDP rate -5.472** -3.848** *Indicates significance at 10% level, ** indicates significance at 1% and 5%. Critical values for 1%, 5% and 10% are 2.4, 1.69 and 1.31 respectively. The co-integration test requires a selection of an optimal lag length which is found to be 1 using Akaike Information Criterion and BIC. Appendix 1 presents the optimal length lag test results. Table 3 presents the results for Johansen cointegration test. In the table r denotes the rank of cointegration whereby r=0 has a null hypothesis that there is no cointegration among the variables. So, at r=0, the null hypothesis is rejected since the both the trace statistics and maximum Eigen statistics value of 42.15 and 27.908 are greater than their 5% critical value of 29.68 and 20.97 respectively. The value of r=1 has a null hypothesis that there is at least 1 cointegration and r=2 denotes the null hypothesis of at least 2 cointegreation. Given that the trace statistics and maximum statistics value is less than the 5% critical value, the null hypothesis for rank 1 and 2 of cointegreation cannot be rejected. Thus, it can be concluded from the test there is a long-run dynamic relationship between export, import and GDP rate in Bhutan. 10 P a g e

Table 3: Johansen Co-integration test Eigen trace Maximum 5% critical rank value statistics 5% critical Value statistics value r=0-42.1515* 29.68 27.908* 20.97 r=1 0.57074 14.2435 15.41 11.5592 14.07 r=2 0.29551 2.6843 3.76 2.68 3.76 r=3 0.07812 - - - *denotes the rejection of null hypothesis at 5% level. Table 4 presents the results of Granger causality test. The null hypothesis in the Granger causality test is that there is no causation between the variables and the significant F and pvalue determines the rejection of null hypothesis. For the Granger causality test, if the null hypothesis is rejected and the alternative accepted, then there is a unidirectional relationship while a bidirectional relationship will be displayed if both the equations are at significant level. From the table, it can be inferred that the F value is insignificant in all the equations except for the relationship between exports and imports. This indicates that there is no causation occurs from export to GDP, GDP to export, import to GDP or GDP to import. For instance, the null hypothesis that log of export does not Granger cause GDP rate cannot be rejected given that the value of probability i.e. 0.246 is more than the p-value of 0.05. Similarly, none of the null hypothesis except for log import does not Granger cause log Export, cannot be rejected at 5% significance level. Thus, it can be concluded that growth was not significant enough to cause export growth nor the export growth could drive the economic growth. Similarly, the import growth does not drive economic growth and vice versa. 11 P a g e

Table 4: Granger Causality Test Null Hypothesis F-statistics Probability Log Export does not Granger cause GDP rate 1.482 0.246 Log Import does not granger cause GDP rate 0.417 0.664 GDP rate does note granger cause Log Export 0.761 0.81 Log Import does not granger cause Log Export 3.416* 0.049 GDP rate does not granger cause Log Import 0.886 0.425 Log Export does not Granger cause Log Import 0.979 0.437 *indicates rejection of the null hypothesis at 5% So, from the above methodology and results, it can be concluded that while the cointegration test displays the presence of long-run relationship among GDP rate, imports and exports tested through Johansen cointegration test but the short-run Granger causality shows an insignificant result whereby neither exports nor import Granger causes growth and vice-versa. Thus, the nonexistence of relationship between the variables as suggested by results does not support the ILG hypothesis put forward at the start of this research paper. The literature presented mostly either supported an ELG theory or the ILG theory, and few other papers supporting a bidirectional causal relationship. Nevertheless, my result is consistent with Sampathkumar & Rajeshkumar(2016) s study of growth and export that found varying results across SAARC country and a result of no causation for Bhutan along with Maldives,Nepal and Pakistan. One significant relationship in the Granger causality test is the rejection of null hypothesis and the acceptance of the alternative that log import granger causes log export. This result may suggest that imports of intermediate goods are necessary for the production of goods and services in the export sector. This paper did not find causation among the variables in the short-run but a long-run coefficient estimates may shed light on the relationship of export, import and growth and thus, provide concrete evidence on whether the data from Bhutan support the ELG or ILG or GLE hypothesis. The absence of short-run relationship between export, import and growth in Bhutan may suggest that there are other factors not considered in the model. Discipline such as institutional 12 P a g e

economics stresses the importance of institutions in economic activity that tries to go beyond the neoclassical framework of economics as often adopted in trade theories. Therefore, given the institutions of monarchy and its role in Bhutan s growth which is often provided as the main attribute for Bhutan s growth story may have more significant role to play which may not necessarily be captured by the monetary values of exports and imports. The main limitation for this study is the sourcing of the data form various sources i.e. the NSB and the World Bank. The World Bank and the NSB data sets are known to display discrepancies between them, thus the usage of import value index and export value index of the World Bank to adjust the current value of imports and exports extracted from NSB is one of the main limitations. Thus, the real GDP, real export and real import may have few discrepancies. Nevertheless, given the unavailability of price indices to adjust the export and import, the World Bank s price indices were the best option. Moreover, the data extracted from Annual Reports of NSB also shows few inconsistencies. Conclusions and Recommendations This research paper attempts to study the causal relationship between exports, import and growth in Bhutan. Employing the unit root test, Johansen cointegration test and the Granger causality test to study the relationship, it can be concluded that there is a long-run cointegration relationship among the variables but the short-run Granger causality does not show any causation. Thus, the results do not support any of the ELG, ILG or GLE theories. Given that the study finds an insignificant relationship, further studies should involve estimating the long-run coefficients to determine whether a significant positive or negative relationship exists among the variables. Moreover, since the Bhutan s export comprises disproportionately of from the hydropower sector, the studies of import and export by sector may provide better direction on these relationships. 13 P a g e

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Appendix 1 Optimal Lag Selection for Cointegration Test shows lag 1 to be significant. lag LL LR df p FPE AIC HQIC SBIC 0-92.7297.11865 6.38198 6.42681 6.5221 1-39.7606 105.94* 9 0.000.006357* 3.45071* 3.63001* 4.01119* 2-34.1303 11.261 9 0.258.008132 3.67535 3.98913 4.65619 3-29.1014 10.058 9 0.346.011175 3.94009 4.38835 5.34129 4-24.8554 8.4921 9 0.485.017032 4.25702 4.83976 6.07858 17 P a g e