IIMS Journal of Management Science
issue front

Subrata Roy1 and Monika Pal1

First Published 7 Nov 2023. https://doi.org/10.1177/0976030X231202623
Article Information
Corresponding Author:

Subrata Roy, Department of Commerce, Mahatma Gandhi Central University, Motihari, East Champaran, 845401, Bihar, India.
Email: subrata1_roy@yahoo.com

1Department of Commerce, Mahatma Gandhi Central University, Motihari, Bihar, India

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Abstract

This article has examined the impact of selected macroeconomic variables on economic growth in the Bay of Bengal Initiative for Multi-sectoral, Technical and Economic Cooperation (BIMSTEC). Thus, quarterly data have been considered over a period from 2000 to 2021. The study has taken into consideration the Cobb–Douglas production function as a model specification to examine the above issue. It has been found that agricultural production is an important macro-economic determinant to justify economic growth in BIMSTEC and its members. However, foreign direct investment is a significant macroeconomic factor for economic growth in BIMSTEC and also in India. Moreover, GDP in Bhutan, Sri Lanka and Thailand is positively and significantly affected by the balance of trade.

Keywords

AGP, BIMSTEC, EMP, FDI, BoT

Introduction

The evolution of a sub-regional cooperation among South and South East Asian nations was first initiated in June 1997 by establishing BIST-EC (Bangladesh, India, Sri Lanka and Thailand Economic Cooperation). Myanmar joined this organization in December 1997, and it became BIMST-EC. In 2004, Nepal and Bhutan became members and subsequently BIMST-EC became BIMSTEC (Bay of Bengal Initiative for Multi-sectoral, Technical and Economic Cooperation). This sub-regional group came into existence with an aspiration towards India’s look East and Thailand’s look West policies for deeper cooperation in the region. The BIMSTEC region provides a unique similarity like rich historical and cultural linkages that helps to promote deeper cooperation in the region. BIMSTEC brings together 1.68 billion people (22%) of the world population with a combined GDP of US$3.697 trillion (2021). BIMSTEC focuses on 14 priority sectors for cooperation and integration: (i) transport and communication, (ii) tourism, (iii) environment and disaster management, (iv) counter-terrorism and transnational crimes, (v) trade and investment, (vi) cultural cooperation, (vii) energy, (viii) agriculture, (ix) poverty alleviation, (x) technology, (xi) fisheries, (xii) public health, (xiii) people-to-people contract and (xiv) climate change. According to Batra (2010), these priority sectors for cooperation have been clearly identified keeping in view harmonizing and establishing adequate infrastructure facilities such as road, rail, air and shipping in the member countries. In February 2004, BIMSTEC signed an agreement for a Free Trade Area (FTA) in order to strengthen economic, trade and investment cooperation among the member countries. During the global financial turmoil in 2008, BIMSTEC was less affected as compared to other regional treaties (SAARC, ASEAN, SAFTA, NAFTA, EU, APEC and OPEC). In 2018, BIMSTEC reaffirmed in its fourth summit declaration in Kathmandu, Nepal, with a strong commitment to make it a dynamic, effective and result-oriented regional organization that will promote peace, prosperous and sustainable economic growth in the Bay of Bengal Region through meaningful cooperation, deeper integration and collective efforts. The fourth summit recognized the need for poverty alleviation in the region and expressed its firm commitment to work together for the implementation of a sustainable development agenda by 2030. BIMSTEC is a more powerful and active regional cooperation that enhances inter-linkages and interdependence within the region and provides greater opportunities to advance regional cooperation. It has been well established that a significant volume of trade is covered by the regional trading arrangements and its importance is increasing in the present era. However, South Asian countries are not successful in the world to form unbeaten regional trading arrangements next to sub-Saharan African countries (Bhattacharya, 2007). In 2007, Banik opined that BIMSTEC is purely guided by economic interest rather than political and, thus, more successful as compared to SAFTA.

A limited number of studies have focused on the probable impact of BIMSTEC-FTA. However, few studies have applied (Bhattacharya, 2007; Kabir & Selim 2010) quantitative techniques to examine the possible impact of BIMSTEC-FTA. On the other hand, a significant number of studies have examined the impact of macroeconomic variables on economic growth. However, very few studies have explored the above relationship in BIMSTEC. Therefore, a more extensive study is required to explore BIMSTEC properly. Against this backdrop, this study tries to examine the probable impact of macroeconomic indicators on economic growth in BIMSTEC.

The rest of the article has been organized as follows: After a brief introduction in Section 1, Section 2 extensively discusses the literature review and identifies the research gap. Section 3 depicts the data and the study period. Similarly, Section 4 has shown the theoretical linkage of economic growth with the macroeconomic variables followed by hypothesis formulation. Section 5 deals with methodological aspects. Section 6 explains the outcomes. Finally, the conclusion and recommendation have been given in Section 7.

Literature Survey

Various studies have established the relationship between economic growth and macroeconomic variables in developed countries (Grossman & Helpman, 1992; Lucas, 1988; Solow, 1956). Similarly, in developing countries, many studies have dealt with the same issue (Das et al., 2009; Sarma et al., 2005). On the other hand, a significant number of studies have developed many economic theories and statistical approaches and many of them have empirically examined the above issue by applying various econometric tools and techniques. A quite number of studies have shown a clear connection between economic growth and the macroeconomic variables and some of them have shown opposite outcomes. Therefore, this economic debate has given immense importance to the academic and professional communities for hunting new knowledge and linkages and, as a result, new evidence is added to the existing literature. Therefore, social scientists are interested in examining the impact of macroeconomic variables (foreign direct investment (FDI), export, import, foreign exchange, inflation, oil price, equity market, etc.) on economic growth from the very beginning.

Theoretical Perspective

Both theoretical and empirical growth researchers have recognized that macroeconomic factors can affect economic growth but with little agreement. The economic growth theory is extensively focused on neoclassical and endogenous growth theories. Solow (1956) has opined that importance is given to factors such as physical capital accumulation and human capital growth (Lucas, 1988). On the other hand, many economic growth contributors (Easterly & Wetzel, 1989; World Bank, 1990) have preferred growth theories. Solow-Swan (1956) has developed the neoclassical growth theory (endogenous growth model), where it has been stated that physical capital accumulation is an input of short-run economic growth while technology is the principal determinant of long-term economic development. Further, neoclassical theory has considered human capital stock as the central driving factor of economic growth (Islam, 1995; Mankiw et al., 1992). Oppositely, the proponents of endogenous growth theory have given attention to efficiency (Aghion & Howitt, 1992b; Grossman & Helpman, 1991; Lucas, 1988). It is well recognized that every country tries to accumulate human capital stock, physical capital and productivity factors for economic development (Aghion et al., 1991a; Frankel, 1962; Grossman & Helpman, 1992; Lucas, 1988; Mankiw et al., 1992; Solow, 1956). According to Easterly and Wetzel. (1989), savings and investment have been considered indispensable economic growth factors (Fischer, 1992). Further, these factors became popular in the 1990s by focusing on macroeconomic stability, the efficacy of an economic institutional system and the regulatory environment for the market. Fischer (1992) opines that economic growth depends on macroeconomic stability that has been affected by economic uncertainty. Similarly, economic uncertainties are caused by policies that reduce the market mechanism’s capacity. Similarly, another type of economic uncertainty is caused by the investors’ holding on assets which is very temporal. However, this kind of economic uncertainty affects the capital market badly because there is a chance of capital flight if it is not controlled (Pindyck & Somalino, 1993; World Bank, 1990). Many studies have considered a variety of macroeconomic variables with little agreement on their effects on economic growth (e.g., Barro & Sala-i-Martin, 2004; Boserup, 1996; Bruno et al., 1998; Burnside & Dollar, 2000; Fischer, 1983; Knight et al., 1993; McKinnon, 1973; Mundell, 1963; Shaw, 1973; Solow, 1956). Many growth economists have considered time-series data to examine the effect of macroeconomic factors on economic growth (Ciccone & Jarocinski, 2010; Sala-i-Martin et al., 2004). Few studies have claimed that consideration of many macroeconomic determinants enhances the model efficiency (Bayraktar & Wang, 2006; Doppelhofer et al., 2004) but it behaves differently when pooled together and, thus, the policymakers become confused sometimes when they develop country-specific economic policies. Antwi et al. (2013) have considered a large number of macroeconomic factors to study economic growth. Thus, from the above theoretical discussion, we can get an idea about the origin and development of many theories and different kinds of macroeconomic variables.

Empirical Evidence

Lots of studies have empirically examined the association between economic growth and macroeconomic variables. In 1992, Fischer examined the relationship between macroeconomic stability and economic development in SSA and LAC countries over a period from 1970 to 1985 and depicted that economic development is positively affected by human capital, investment and budget surplus while initial real GDP, inflation and dummy variables have a negative impact. Here, the study has opined that economic growth largely depends on the stability of the macroeconomic determinants. Similarly, Knight et al. (1993) examined the same issue with a slight difference in respect of the selection of macroeconomic variables and the evidence is almost the same as observed by Fischer (1992). Similarly, Chen and Feng (2000) examined a similar issue in China and reported positive and negative associations between economic growth and macroeconomic indicators. Here, the study has identified (Knight, 1993; Roy, 2020; Seyfried, 2011) a few macroeconomic indicators (private business, foreign trade and education) that can promote long-term economic growth (Fischer, 1992; Knight et al., 1993) in China. Sharma and Panagiotidis (2005) examined the causal relationship between export and economic growth in India under the VAR environment. The study showed the presence of a co-integrating association between the variables where a causal relationship was absent. However, the impulse response function has shown a positive response that runs from export to economic growth (Bakari, 2017; Kumar, 2016; Mathiyazhagan, 2005). Similarly, Dash (2009) applied the same technique to examine the causal relationship between Indian export and economic growth in post-liberalization period (1992–2007) where a short-run causal relationship was absent (Dritsaki & Stiakakis, 2014; Mukerji et al., 2014;). However, Elbeydi et al. (2010) have shown both short-run and long-run equilibrium relationships between export and economic development in Libiya and claimed that Libiya is an export-led country (Ali et al., 2018; Kaur et al., 2017). Das et al. (2009) have examined the impact of agriculture credit on agricultural production (AGP) in India (Golait, 2007) by applying the dynamic panel data approach proposed by Arellano and Bond (1991). Here, the study has reported that agricultural credit has a significant positive impact on agricultural output. However, there are many gaps in agricultural delivery and, thus, agriculture is not properly developed. Moreover, the study has recommended framing appropriate policy for the development of agriculture in the country (Pattanayak & Mallick, 2017). In 2010, Mawugnon et al. examined the association between FDI and economic growth in Togo over a period from 1999 to 2009 by applying the Granger causality test. The study has reported about presence of a unidirectional causal relationship runs from FDI to GDP, and it has also been observed that FDI has a significant positive impact on economic growth (Alagidede et al., 2011; Mehrara & Firouzjaee, 2011; Raghuram et al., 2020). Along with this, the study has focused on developing appropriate infrastructural facilities in the country with a view to attracting FDI into the country. In contrast, Chang and Mendy (2012) examine the relationship between trade openness and economic growth in African countries over a period from 1980 to 2009. Here, the study applied a panel data approach and reported a significantly positive association between trade openness and economic growth. However, the study has also depicted that (Acaravci & Ozturk, 2012; Hemzawi and Umutoni 2021; Sahni & Atri, 2012) domestic investment and gross national savings have negatively associated with economic growth. Moreover, Acaravci et al. have opined that FDI may be improved if the country promotes free trade zones, trade regime, tax incentives, human capital base, financial market regulations, financial system and infrastructure quality. Following the same notion, Manh et al. (2014) examined the dynamics between employment and economic growth in Vietnam. Here, the study has applied Cobb–Douglas production function to observe the above issue and reported that employment has a significantly positive impact on economic growth (Ajakaiye et al., 2015). Finally, the study has recommended for introducing vocational courses in the country for the improvement of labour productivity. Likewise, Ahmed et al. (2015) considered stock market and economic growth to check the econometrical association between them over a period from 1984 to 2013 in Nigeria under the VAR environment. The study has reported the presence of both long-run and short-run associations between the variables (Chaudhary et al., 2016; Farahmand & Ethem, 2020; Gokmenoglu et al., 2015; Ibrahiem, 2015; Khan & Khan, 2018; Nwaolisa & Chijindu, 2016; Yenipazarli, A., & Yilmaz, 2016). On the other hand, Ali et al. (2016) have tried to forecast the bilateral trade between India and Bangladesh over a period from 1991 to 2014 by considering quarterly time-series data. Thus, the study developed ARIMA and ARMA approaches and reported that the ARIMA model is superior to forecast bilateral trade as compared to ARMA and the study further opined that both the countries may benefit from bilateral trade that enables economic prosperity and cooperation. Bhattacharya and Gupta (2015) examined the association between the macroeconomic variables (food inflation, international prices, fuel inflation) and agricultural wages. Here, the study applied the SVAR and FEVD approaches and reported that fuel inflation affects both industrial growth and global food inflation. It has also been reported that a 14% variation in wage inflation occurred due to food inflation followed by the industrial sector. The study has argued that food and aggregate inflation are affected by increase in food inflation. Here, the study has identified agricultural wage growth, which is an important factor of food inflation, should be adjusted with productivity growth. In contrast, Kang and Dagli (2018) examined the dynamics between international trade and exchange rates in the context of the global financial crisis over a period from 2001 to 2015 in 72 countries. Thus, the study has applied the Fisher-type unit-root test and the Gravity model and reported that export is positively affected by real exchange rates (Jana et al., 2019). In 2019, Gokmenuglu et al. tried to establish the impact of carbon emissions on financial development and industrialization in Turkey by considering a long time period (1960–2010) under the VAR framework. The study reported the presence of a long-run equilibrium association between the variables and also confirmed the presence of a unidirectional relationship which runs from financial development to carbon emission (Jijian et al., 2021). The study has recommended to the policymakers regarding the framing of financial policy that can protect the environment from carbon emissions and promote environmental sustainability. In 2019, Sener et al. examined the causal association between competitiveness, innovation and foreign trade over a period range between 2007 and 2017. The study applied the Dumitrescu and Hurlin (2012) panel causality test and observed the presence of unidirectional causality that passes from GII to IDI and GCI. Moreover, the study has recommended developing R&D in collaboration with university, industry and government for the improvement of foreign trade in the country.

Now, it is observed from the extensive literature survey that a large number of studies have developed various theoretical and mathematical frameworks to examine the various economic issues and many of them have empirically examined the association between the macroeconomic variables and economic growth by applying various statistical and econometrical equations and shown diverse evidences which are quite natural due to countries’ ideology, sentiment, internal and external geopolitical tension, economic and political environment. No doubt, those studies have contributed new insights to the existing literature. However, a very limited number of studies have focused on BIMSTEC.

Thus, with this economic intuition, this study examines the impact of macroeconomic determinants on economic growth in BIMSTEC. Many studies in the past have explored macroeconomic relationships but those are not adequate to explain the exact nature of the macroeconomic variables because the nature of the variables is changing over time. Thus, it is important to explore the economic association between them by considering a representative data set. Therefore, the study has considered a few selected macroeconomic variables to study the impact on economic growth in BIMSTEC, and it is expected that this study will surely provide new insights.

Data and Study Period

The study has considered quarterly real GDP at factor price that represents economic growth, balance of trade (BoT), AGP, employment (EMP) and inflows of FDI of the BIMSTEC and its members (India, Bangladesh, Nepal, Bhutan and Sri Lanka, Myanmar and Thailand). Here, the selection of control variables has been guided by economic intuition and previous studies. The data have been obtained from the official website of the World Bank (www.worldbank.org) and cross-checked with various reports published by the central banks of the respective countries with a study period ranging between 2000 and 2021.

Theoretical Interpretation and Hypothesis Formulation: Foreign Trade (BoT) and GDP

Foreign trade refers to the exchange of goods and services from the domestic country to others and vice-versa. It helps to boost economic growth in diverse ways. A country is said to be export-led when it exports a large amount of goods and services to other nations and earns foreign currencies that promote economic growth. Similarly, imports of goods represent an outflow of funds from the domestic country that sometimes adversely affects economic growth but high imports of productive assets signify huge domestic demand for industrialization which is a good sign for economic growth in the long run. Hence, every country tries to maintain a healthy balance between export and import for economic development. The association between foreign trade and economic growth is recognized in the 18th century when David Ricardo and Adam Smith opined about the significance of foreign trade for economic growth (Awokuse, 2007; Baines, 2003; Chia, 2015;Frieden & Rogowski, 1996) and this opinion has been well accepted by the economists (Carbaugh, 2011; Lee 1995; Hachicha, 2003). With this notion, the following hypothesis has been formulated:

H1: BoT has no effect on GDP.

GDP and FDI

It has already been established that the inflow of FDI into the core sectors plays an important role as a source of capital, trade technology and management in transition economies that promotes economic development in the domestic economy (Caves, 1974, 1996; Kokko, 1994; Sahoo et al., 2002) and, thus, FDI is an important determinant for economic growth that affects the economy positively. Many studies have opined that FDI has a long-run association with economic growth (Sahoo & Mathiyazhagan, 2003). With this economic insight, the following hypothesis has been developed:

H2: FDI has no effect on GDP.

GDP and Agricultural Production

It is well recognized that agriculture plays an important role in economic growth, particularly in labour-intensive countries where adequate agricultural land is available. The BIMSTEC region is well known for its agricultural richness and provides raw materials to the agricultural industry that promotes economic activities and development (Madi et al., 2020). Many authors have argued that economic growth generates agriculture when countries invest with large-scale farmers (Collier & Dercon, 2009; Maxwell, 2004; Reardon et al., 2006). In addition, productivity can transform agriculture into a growth-driven economy that leads to the formulation of the following hypothesis:

H3: AGP has no impact on GDP.

GDP and Employment (EMP)

Economic growth and development are closely associated that generate employment (Mandloi & Bansal, 2014). On the other hand, the labour market can either promote or restrict economic growth (Boltho & Glyn, 1995; Herman, 2011; Phan, 2006). Similarly, employment is an important macroeconomic factor that reduces poverty and promotes economic growth simultaneously (Dopke, 2001; Kapos, 2005). Schmid (2008) talks about both extensive and intensive growth theories that help to create employment in the country. Thus, with this economic insight, the following hypothesis is formulated:

H4: EMP has no effect on GDP.

From the above discussion, the relationship between GDP and the above-mentioned macroeconomic variables can be presented in a diagram (Figure 1).

 

 Figure  1. Relationship Between GDP and Macroeconomic Variables.

 

Methodology

The study has been started by transforming the data of the macroeconomic variables into natural logarithm forms as follows:

                                                            (1)

The pattern of time-series distribution has been examined by applying the Jarque–Bera (1980) test statistic as follows:

                                                  (2)

where n denotes the number of observations. S and K are the skewness and kurtosis, respectively.

The distribution follows normality if the expected values of skewness and kurtosis are 0 and 3, respectively.

The outcome of descriptive statistics of the selected macroeconomic variables has been presented in Table 1. The mean FDI of Bangladesh, Nepal, Bhutan, Sri Lanka and Myanmar is negative, which means the above-mentioned countries were not in a position to attract FDI during the study period, which adversely affected the economy. It has also been found that Bhutan has a negative average BoT, which means the country was not in a position to export goods and services to its members and it may be for its heavy internal demand. The table also shows that the computed J-B statistic of EMP in Bangladesh, Nepal, Sri Lanka and Myanmar is statistically insignificant, which means the time-series observation follows a normal distribution. Moreover, the normality assumption is true for FDI in Thailand.

 

 

Thereafter, the study applied augmented Dickey–Fuller (ADF) and Philips–Pherron (P-P) tests to examine the stationarity of the time-series observation and, thus, the following equation has been considered:

                                                       (3)

Similarly, the P-P test (1988) is the modification of the ADF test that considers AR(1) process as follows:

                                                             (4)

Table 2 shows the outcome of the unit-root test based on two test statistics. It has been found that the time-series observations are non-stationary at their levels but become stationary when the first difference operator has been used.

 


 

The classical, neoclassical and modern growth theories have identified those determinants which are expected to be effective for economic growth (Antwi et al., 2013). Here, the study has considered Cobb–Douglas production function as the model specification with a view to establishing economic association between economic growth and the selected macroeconomic variables as follows:

                                                  (5)

where output (Q) is a function of labour (L) and capital (C), respectively. The above production function can be written as:

                                 (6)

Equation (6) is a double log functional form that cannot be estimated through OLS due to economic uncertainty and, thus, the above equation can be rewritten as follows:

                                                  (7)

Thus, the movement of Q depends on L and C that means if L and C change 1%, then Q will react β1 and β2 percent assuming other things remain constant. It means that the impact of labour variation (L) on production (Q) decreases when L gets larger or vice-versa, and it holds true for C. Therefore, change in production depends on labour and capital. The present study has developed the functional form based on the assumption of Cobb–Douglas production function as follows:

                          (8)

The above functional form can be written as follows:

                      (9)

In the same way, equation (9) has encountered the same problem as explained in equation (6). Therefore, to remove this problem, equation (9) can be rewritten as follows:

                            (10)

where α is the intercept term. β values s are the slope coefficients to be estimated. D is the difference operator and ε is the error term with 0 mean and constant standard deviation. Equation (10) has been estimated through the OLS technique.

The study applied the Brock–Dechert–Scheinkman (BDS) independence test to examine the non-linear pattern of the residual distribution that has been derived from equation (10). According to Brock et al. (1996), a sample of independently and identically distribution (i.i.d.) {xt: t = 1, 2, 3, …, n} can be written as follows:

                                              (11)

where bm,n(d) = Cm,n(d) – C1,n-m+1(d)m, Cm,n(d) and C1,n-m+1(d)m are the correlation integrals. σm,n(d) is the standard error of bm,n(d). d is the distance and m is the dimension. Here, it is assumed that d = 0.7 and m = 2 to 6, which means for a given d and m>1 then Cm,n(d) – C1,n-m+1(d)m = 0.

Finally, the CUSUM test has been applied to check the parameters’ stability of the regression equation (10).

Result and Analysis

The outcome of equation (10) has been presented in Table 3 and the estimated coefficients of AGP are found to be positively significant in BIMSTEC and its member countries, which means a 1% change in AGP, GDP changes accordingly. Thus, AGP is recognized as a significant determinant that has the power to influence GDP in this case. Additionally, the BIMSTEC countries are efficient in agricultural activities due to their geographical location, good weather conditions, fertile agricultural land, advanced technology, credit facilities and government support towards farmers. Similarly, the coefficient of FDI in India has been found to be positively significant, which means economic growth in India is positively affected by FDI, but Bangladesh has been found to be negatively significant, which means if FDI is increased by 1%, economic growth will decrease by 10.96%. Thus, policymakers should take necessary measures to correct this situation. But, it is surprising that the coefficient of FDI in BIMSTEC is positively significant, which signifies that economic growth in the BIMSTEC region is favourably affected by FDI. Likewise, the coefficient of EMP in Bangladesh, Nepal, Bhutan, Sri Lanka and Myanmar is positive and significant, which implies that GDP is significantly and favourably influenced by EMP, and this evidence is also true in BIMSTEC. However, GDP in Thailand has been found to be statistically significant and negative. But in the case of India, the effect of EMP on GDP has been found to be insignificant. Similarly, the BoT has a significant positive impact on GDP in Bhutan, Sri Lanka and Thailand, which is a good sign. However, BoT has no significant impact on GDP in BIMSTEC, India, Bangladesh and Nepal. Moreover, the estimated F-statistic has been found to be significant and positive in BIMSTEC and its members, which means the macroeconomic variables can jointly and significantly influence GDP. In addition, it has been found that economic growth in BIMSTEC has been significantly and positively affected by FDI, AGP and EMP. Therefore, it may be opined that these macroeconomic factors have the power to justify economic growth in BIMSTEC except BoT. Thus, there is ample opportunity to enhance trade and cooperation in BIMSTEC and its member countries that can promote sustainable economic growth.

 

 

Table 4 has reported the outcome of the Pearson correlation matrix for checking multicollinearity. According to Gujrati (2004) and Hair et al. (2011), the problem of multicollinearity takes place when the Pearson correlation coefficient exceeds 0.90. The table shows that the correlation coefficient between the independent variables lies between 0.3191 and 0.8945, which means the absence of multicollinearity.

 

 

The outcome of the BDS test has been presented in Table 5. The BDS test statistics of BIMSTEC and its member countries are statistically significant at the distance chosen distance (d = 0.7) and dimensions (m = 2–6), which means the standardized residuals series are not independently and identically distributed (i.i.d.).

Finally, the study has applied the CUSUM test to examine the parameters’ stability and the outcome has been presented in Figure 2. The figure shows that the position of cumulative sums of scaled recursive residuals (blue line) lies in between two red lines, which means the parameters of equation (10) are stable and, thus, the estimated regression model is adequate.

 

 

 Figure  2. CUSUM Test.

Conclusion and Recommendation

AGP has played a significant role in the economic growth of BIMSTEC and its members. It has been observed that BIMSTEC is rich in its agricultural activities and added significant contributions to the region. Similarly, FDI has been considered as an important macroeconomic determinant for economic development in BIMSTEC and also in India, whereas other members are not. Likewise, employment is an essential macroeconomic factor for economic growth in BIMSTEC and its members such as Bangladesh, Nepal, Bhutan, Sri Lanka and Myanmar. Similarly, BoT has also been recognized an important macroeconomic determinant for economic growth in BIMSTEC and its members such as Bhutan, Sri Lanka and Thailand.

Therefore, the outcome of this study is significant and helpful for policymaking. Policymakers may urge the BIMSTEC leaders for the improvement of trade cooperation among the members that will help to establish strong cooperation and peace among the members, and as a result, all member countries can gain competitive economic benefit. The study has also been suggested to develop a common platform for FDI in the region where the developed members can extend their FDI support to the less developed members and can grow in the future.

Thus, there is ample opportunity for future research in BIMSTEC in various dimensions the researchers can explore.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Subrata Roy   https://orcid.org/0000-0001-8392-5452

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