Globalisation and Economic Growth in India: An ARDL Approach

Abstract

This study aims to investigate the relation between globalisation, which includes foreign direct investment (FDI), exports, imports, foreign remittances and economic growth in India. To achieve the said objective, Autoregressive Distributed Lag bounds testing approach has been utilised. The study indicates that imports and FDI positively affect economic growth in India. On the other hand, exports and foreign remittances have negative and significant relationship with economic growth. This suggests that exports and foreign remittances take more time to spillover positive impact on economic performance of India. The findings suggest that FDI should be encouraged to promote exports, export-led growth and joint ventures with foreign investors in the country.

JEL Codes: F30, F10, F10, F24, C22

Keywords

FDI exports imports foreign remittances ARDL

I. Introduction

In the present day world, the primary objective of every state is to ensure continuous improvement in the living standard of the masses. Those policies and strategies are adopted, which help promote economic growth without any business fluctuations. Every state emphasises primarily on the enrichment of human and social capital, innovation and entrepreneurial skills and accumulation of physical capital. With wave of globalisation and liberalisation, the significance of international capital flows in generating economic activities have increased. Nowadays, resource-deficit countries make policies and provide incentives to attract FDI to accelerate their economic growth. Some recent research has highlighted the role of FDI in economic growth of developing countries. Borensztein (1998) finds that the effects of foreign direct investment (FDI) on economic growth of a country is on educational attainment (human capital) of the host country. There is also evidence of crowding-in-effects in host countries as FDI becomes the source of an increase in domestic investment. Findlay (1978) postulates that FDI increases the rate of technical progress in the host country through a contagion effect from more advanced technology and management practices used by foreign firms. Giirsoy and Kalyona (2012) stated that FDI is the means by which technology and physical capital can be transferred from advanced to developing countries which can increase their pace of growth and development as has happened in case of Georgia. However, FDI is most attracted to those countries that have robust social infrastructure in the shape of well-functioning state institutes, stable government, sound economic policies and an adequate level of physical infrastructure. Hall and Jones (1999) find that FDI helps the developing countries to import improved technology and know-how, increase productivity and ensure maximum utilisation of their resources. Alfaro et al. (2004) suggests that the functioning of local financial markets should be appropriately regulated to reap short-term as well as long-term benefits of FDI. Blomsrtom et al. (1992, pp. 1–36) stated that lower the initial per capita income relative to the USA (1960), the faster the subsequent growth. Thus, developing countries can gain from FDI, improved technology and knowledge developed by other countries. However, FDI inflow has more positive effects on countries with a higher income level. Thus, developing countries should achieve a minimum level of development to absorb and get maximum benefits from investment by foreign firms. Like other developing countries, FDI can play an essential role in economic development of India. After three decades of inward policies since independence, Rajiv Gandhi in mid-1980s realised the importance of opening its economy and benefit from wave of globalisation and liberalisation. The process of liberalisation got momentum after India faced economic crisis in 1991–1992 and decided to change its economic policy. There was remarkable change in policy, and many incentives were provided to attract FDI. Besides, trade policy changes were made with the intention to boost exports and clear way for import of necessary machinery and other related material. The country soon became an attractive destination for FDI due to large market and cheap labour force not only for its traditional trade partners but also for many newly industrialised Asian economies. FDI, exports, imports and foreign remittances have increased at an increasing rate and have played an important role in economic development of the country. There is enough literature on role of FDI in economic development of developing countries that were facing resource and technology constraint. The same has happened in case of India as with opening of economy, gross domestic product (GDP) growth has shown an upward trend as shown in Figure 1.

It should be noted that after opening their economy for outside world, imports increased at higher pace as compared to exports. There are many reasons for rapid growth of imports as compared to exports, including need for largescale machinery, advanced technological equipment, technical know-how and low competitiveness of domestic goods in world market. The growth of exports, imports, FDI and foreign remittances is presented in Figure 2.

A look at Figure 2 indicates that imports have increased at much higher rate as compared to exports, foreign remittances and FDI. In recent times, many researchers have attempted to examine relationship between variables. In recent times, time–series analysis with Autoregressive Distributed Lag (ARDL) bounds testing approach has gained popularity among researchers to examine relationship between variables. In the present study, the same approach has been applied to analyse relation between exports, imports, FDI inflow, foreign remittances and economic growth of India during the period 1988–2018. The previous studies have analysed relation between one or two variables and economic growth, and there is hardly any study which has included all the factors of globalisation.

Moreover, this research is unique from the recent and previous studies regarding the use of advanced econometric technique. India, being an emerging economy, has experienced many ups and downs in the path of development. This study may contribute by highlighting role of different elements of globalisation in economic development of India.

II. Literature Review

Chakraborty and Basu (2002) have used structural cointegration model with vector error correction model (VECM) mechanism to examine the link between FDI and GDP growth in India for period the 1974–1996. The empirical results show that there is two-way relationship between FDI and GDP in the long run, whereas in the short run there is a uni-directional link between GDP and FDI flows in the country.

Calvo and Robles (2003) examine the relations between FDI, economic freedom and economic growth by taking a sample of 18 countries from Latin America by applying panel data set covering the period 1970–1999. The study concludes that FDI positively affects economic growth and acts as an effective channel for transfer of technology, know-how and managerial skills in these countries. However, to benefit from FDI, countries should achieve a minimum level of development and give more freedom to economic agents.

According to Frenkel et al. (2004), FDI is identified as a vital requirement factor for sustainable growth in developing economies, which is mostly transfer of capital from developed and emerging economies to these countries benefiting global world as a whole. FDI can accelerate growth in countries by way of generating employment in the host countries, fulfilling the saving gap and huge investment demand and sharing knowledge and management skills through backward and forward linkage in the host countries.

Lall and Narula (2004) found that a country can only benefit from FDI if the government is playing an active role and has a proactive industrial policy to benefit from FDI. Moreover, the country should have attained minimum level of development to benefit from externalities created by FDI and multinational entrepreneurs.

Thangamani et al. (2010) have investigated the determinants and growth impact of FDI by applying gravity and growth model using the time series data from 1995 to 2008 in South Asian countries. The authors suggest that many factors, including GDP growth rate, level of employment, poverty and other such factors, play an important role in determining the FDI inflow in a country. Other factors, including trade openness index, Human Development Index, population and infrastructure also play an important role. On the other hand, FDI flows are inversely related to the distance between the home and the host country, supporting the view that distance shows costs associated with FDI activities. The study concluded that FDI has average effect on the growth rate of these countries.

Maqsood and Azam (2010) stated that every country should improve human capabilities through increasing investment in education and health sector. Improved human capabilities will surely enhance the productivity and economic growth of a country.

Batarseh and Ananzeh (2015) examine the causal relationship between economic growth and FDI during the period 1975–2013 in Jordan. Using cointegration, Granger causality test and error correction model, the study finds uni-directional relation between economic growth and FDI in Jordan and not vice versa.

Using quarterly data for the period 2001–2010 and applying VECM and cointegration method, Szkorupova (2014) examines causal relationship among FDI, exports and GDP in Slovakia. The study confirms the casual uni-directional link from FDI to GDP growth in the said country.

Mawugnon and Qiang examined causal relationship between economic growth and FDI inflows in Togo during the period 1991–2009. Using times methods of unit root, cointegration and ganger casualty test, author found a uni-directional relationship between FDI and GDP. The direction of causation ran from FDI to GDP only and not for vice versa.

Using pooled OLS and applying the panel data, Tiwari and Mutascu (2011) have examined the impact of FDI on the economic growth of Asian countries for the period 1986–2008. The author found that FDI and exports have enhanced the growth of Asian countries and have increased labour productivity and capital accumulation in the process. Thus, those countries that have opened their economies should go ahead with the process of globalisation and should promote their exports. Globalisation is beneficial, particularly for those countries which do not have sufficient resources to bring more advanced technology to private homes. The more advanced technology would not only create an attractive environment for FDI but would also require an extensive investment for large improvements in the country’s infrastructure.

Feridun and Sissoko (2011) used Granger causality and vector autoregression covering the period 1976–2002 to examine the impact of FDI on GDP per capita of Singapore. The study finds that FDI positively affects GDP per capita, and there is a uni-directional link from FDI to GDP per capita in the country.

Behname (2012) while using the panel data for the period 1977–2009 has examined impact of FDI on economic growth in Southern Asia. The author concludes that FDI has positively and significantly affected economic growth in the Southern Asian region.

Covering the period 1997–2010 and using Engle–Granger cointegration and Granger causality test, Giirsoy and Kalyona (2012) have studied the relationship between FDI and growth in Georgia. The study confirms the uni-directional link from FDI to GDP growth of the country.

Ahmad et al. (2012) have found a positive relationship between FDI and economic growth of Pakistan. FDI can contribute to its economic growth both in short-run and long-run. Government of Pakistan should formulate policies to attract FDI, particularly in education and training to improve human resource development which will increase the productivity of labour and other factors of production. They should control political instability and formulate policies according to the needs of the country.

Furthermore, Cambazoglu and Karaalp (2013) studied the impact of FDI and international trade on the economic growth of Turkey in the period 1980–2010 by applying the VAR model. The results show that FDI has positively contributed to the economic development of Turkey; however, given its geostrategic position, population and economic size, it has not received its due share of foreign investment flows.

Azam (2013) has empirically studied the impact of FDI on the economic growth of Kazakhstan by using the period 1995–2011 and applying Error Correction Model. The author found that there is a uni-directional link between FDI and GDP growth in Kazakhstan and between financial deepening and economic growth in Azerbaijan.

Azam and Ahmad (2015) have used linear regression model and applied panel data set for the period 1993–2011 to find the impact of FDI on economic growth of Commonwealth of Independent States. The study finds that there is weak link between FDI and GDP growth in these countries, perhaps due to restricted policies of government in initial years after independence.

Azam (2016) has applied probabilistic econometric model by using panel data set covering the period 1986–2012 to find the impact of foreign capital inflows and good governance on the economic growth of 20 Organisation of Islamic Cooperation (OIC) countries. The study found that FDI and remittances are positively contributing to the economic growth of OIC countries.

Iqbal et al. (2010) have examined the relationship between trade, FDI and economic growth in Pakistan by using quarterly time series data for the period 1998–2009 and applied VAR model. The study finds that there is bi-directional causality between FDI and economic growth in the country in the long run.

Reza et al. (2018) have studied causal link between FDI and GDP growth in Bangladesh by using cointegration and VECM test covering the period 1990–2015. The empirical results show uni-directional link from FDI flows to GDP both in the short run and long run.

The above literature suggests that the relation between FDI and economic growth has been studied extensively, but there is hardly any specific study which takes all factors of globalisation into account. Thus, this study aims to investigate impact of globalisation which includes exports, imports, foreign remittances and FDI inflow on economic growth of India. The recent advanced technique ARDL has been used to fulfil the said objective.

III. Data Source

The article has incorporated some important variables to check the relationship between globalisation and economic growth of India. For this purpose, annual secondary data for the period 1988–2018 as provided by World Development Indicators, World Bank has been used. Economic growth has been proxied by GDP growth rate which is denoted by GDP; foreign remittances have been proxied by personal remittances and denoted by FR, exports by Exp and imports by Imp in this study.

IV. Definition of Variables

Exports: ‘Exports of goods and services comprise all transactions between residents of a country and the rest of the world involving a change of ownership from residents to non-residents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. Data are in current U.S. dollars’ (World Bank [WDI], 2019).

FDI inflow: ‘Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 per cent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. Data are in current U.S. dollars’ (World Bank [WDI], 2019).

Foreign remittances: ‘Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in-kind made or received by resident households to or from non-resident households. Personal transfers thus include all current transfers between resident and non-resident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers. They are employed in an economy where they are not resident and of residents employed by non-resident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees. Data are in current U.S. dollars’ (World Bank [WDI], 2019).

GDP growth rate: ‘Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources’ (World Bank [WDI], 2019).

V. Methodology

In many studies previously conducted, the relation between FDI, exports, imports, foreign remittances on economic growth have shown mixed results. In this study, ARDL and error correction metric (ECM) have been applied to investigate the relation and causality (if any) between economic growth and other variables in India. Many studies such as Pesaran et al. (2001), Pacheco-Lopez (2005), Chaudhary (2006), Zachariadis (2006) and Sultanuzzaman et al. (2018) have used ARDL which proved effective to examine dynamic relation between GDP and other related variables. This study attempts to estimate relationship between economic growth, FDI, exports, imports and foreign remittances through ARDL bound estimation both in the short run and long run. However, it should be noted that regressors should be mixture of I (0), I (1), level I (0) or first difference I (1), but not I (2). The ARDL model with I (2) leads to spurious results. In view of Narayan (2005), ARDL model runs well with smalls samples (30–80 observations). In this study, to check robustness of the model, VECM has been employed. The model used in this study is given as

{LnGDP}_{t} = \propto_{0} + \propto_{1} {LnFDI}_{t} + \propto_{2} {LnExp}_{t} + \propto_{3} {LnFr}_{t} + \propto_{4} {LnImp}_{t} + ϵ_{t}

where

LnGDP = log of GDP growth rate (annual)

LnFDI = log of foreign direct investment

LnExp = log of exports

LnFr = log of foreign remittances

LnImp = log of imports

T = time from 1988 to 2018

∈ = error term

∝ = 1 to 4 represents coefficients.

To get an overview of data set and check multicollinearity, we the descriptive statistics. To check stationary, unit root has been examined through Augmented Dickey–Fuller (ADF), DF-GLS and Phillips–Perron (PP) tests. Then to examine long-run cointegration between variables, ARDL model is being run, and both short-run and long-run relationships are examined. Further, we test serial correlation-LM, heteroscadacity and stability tests to check goodness of model. Finally, to check robustness, Granger causality, which is formulated by VECM, has been tested (Eagle & Granger, 1987).

VI. Empirical Analysis

Table 1 presents descriptive statistics which provides an overview of the data used in the study. In addition to mean, median and standard deviation, the results of kurtosis and skewness are also presented to get better idea about the variables used in the study. Mean and standard deviation are presented for determining range and coverage of the data set. Besides, kurtosis and skewness measure the peakedness or flatness and degree of asymmetry of the series, respectively.

Table 1.

Descriptive Statistics.

Variables	LnGDP	LnEXP	LnFDI	LnFR	LnIMP
Mean	1.755804	24.95740	21.42586	23.18182	25.18552
Median	1.854125	24.66264	21.99788	23.13240	24.86366
Maximum	2.264653	27.00934	24.51782	25.09005	27.18935
Minimum	0.055275	23.14227	15.54539	21.52970	23.55223
Std. Dev.	0.405289	1.372675	2.581249	1.331928	1.311890
Skewness	−2.015122	0.212794	-0.406123	0.097687	0.285663
Kurtosis	8.947377	1.544248	1.989586	1.456216	1.542273
Jarque–Bera	83.87302	3.738049	2.731105	3.934840	3.983498
Probability	0.000000	0.154274	0.255240	0.139817	0.136457
Sum	68.47637	973.3388	835.6086	904.0911	982.2352
Sum Sq. Dev.	6.241847	71.60104	253.1882	67.41322	65.40010
Observations	39	39	39	39	39

Source: Authors’ calculation.

According to Hill et al. (2001), regression should not be applied to variables which are non-stationary as it would lead to spurious regression. Thus, it is necessary to check stationary nature of the data set. In this study, ADF and PP have been used to check stationary nature of data set.

ARDL model suggests that all variables to be used should be stationary in I (0) and I (1) for running the model. The results shown in Table 2 show that LnGDP in I (0) and I (1), LnExp in I (1), LnFDI in I (1), LnFR in I (1) and LnImp in I (1) are stationary at 1, 5 and 10% significance levels, respectively. Thus, all variables to be used in the model are stationary for ARDL parameters (i.e., of I (0) and I (1) order). Next, to check for presence of cointegration for long-run relationship between variables over the period 1988–2018 in case of India, ARDL bound testing approach has been employed.

Table 2.

Unit Root Test, ADF and PP Test.

ADF Test			PP Test
Variables	t-statistics	p value	t-statistics	p value
LnGDP	−5.836417	0.000*	−5.830405	0.000*
DLnGDP	−3.630335	0.0115**	−26.19143	0.0001*
LnEXP	0.303173	0.5794	0.136981	0.9645
DLnEXP	−4.651578	0.0006*	−4.686269	0.0006*
LnFDI	1.495976	0.5228	−0.622483	0.8537
DLnFDI	−4.444801	0.0066*	−7.489189	0.0000*
LnFR	0.262242	0.9731	0.201060	0.9691
DLnFR	−6.806089	0.0000*	−6.733200	0.0000*
LnIMP	0.008995	0.9534	0.182698	0.9679
DLnIMP	−4.209182	0.0021*	−4.192278	0.0022*

Source: Author’s calculation.

Notes: D denotes first difference; ADF and PP are determined by Mackinnon’s (1996) formula.

Significant at *1%, **5%.

Table 3.

ARDL Bound Test Estimates.

K	F-Statistic	Significant (%)	I (0)	I (1)
4	6.298074	10	2.2	3.09
		5	2.56	3.49
		2.5	2.88	3.87
		1	3.29	4.37

Source: World Development Indicators.

Following Pesaran et al. (2001), Narayan, (2005) and Sultanuzzaman et al. (2018), ARDL Bound test has been applied to check for existence of long-run relationship between the variables. In line with Pesaran et al. (2001), the calculated F-statistics (6.298074) as reported in Table 3 is significant and higher than lower bound I (0) and upper bound I (1) at 1, 2.5, 5 and 10% significance level. Narayan (2005) has observed that if calculated F-statistic is lower or median than lower and upper bound critical values, the results will be inconclusive. The results suggest long-run cointegration and relation between the variables used in the model. Next step is to present long-run relationship between the variables.

Table 4.

Long-run Estimates.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
DLnExp	−6.63	2.48	−2.67	0.014**
DLnFDI	0.32	0.21	1.56	0.133
DLnFR	−1.99	0.76	−2.61	0.016**
DLnImp	6.52	2.04	3.18	0.004*
Constant	2.01	0.13	14.42	0.000*

Source: World Development Indicators.

Note: Significant at *1%, **5%.

The long-run estimates are presented in Table 4. The coefficient of export flows is negative and significant at 5% level. If there is 1% increase in exports, it leads to 6.63% decline in GDP growth rate. The results are in contrast to theory of this variable that exports play an important role in economic growth of a country. Similar is the case with foreign remittances which are negative and significant at 5% level.

On the other hand, our results suggest that imports play an important role in economic growth of India. The results show that 1% increase in imports leads to 6.52% increase in GDP of the country. However, FDI is positive, which is in line with theoretical background but insignificant.

Table 5.

ECM of ARDL Model (Short-run Estimates).

Variable	Coefficient	Std. Error	t-Statistic	Prob.
DLnExp	−3.65	1.13	−3.21	0.004*
DLnFDI	0.16	0.08	1.93	0.067***
DLnFR	−1.32	0.26	−5.05	0.000*
DLnImp	4.31	1.05	4.08	0.000*
ECM (−1)	−1.10467	0.16	−6.83	0.000*
R-Square		Adjusted R-Square
0.74		0.69

Source: World Development Indicators.

Note: Significant at *1% and *** 10% level.

Table 5 shows results for short-run analysis. The results show that exports like long run have negative impact on GDP growth rate along with foreign remittances. In case of exports, 1% increase leads to 3.65% decline to GDP growth. On the other hand, imports and FDI play positive and important role in economic growth of the country. In case of FDI, which is significant at 10% level, 1% increase in FDI flow leads to 0.16% increase in GDP growth rate. Like long-run imports play an important role in economic development of the country, it can be concluded that imports cause positive spillover effects on economic growth of India.

The results also show that sign of lagged error correction representation ( $E C M_{t - 1}$ ) is statistically significant at 1% level with negative sign. It shows the existence of relationship between variables. The coefficient indicates speed of adjustment towards equilibrium. The value of coefficient is −1.10467, which indicates that from short run to long run, adjustments are corrected by 110.46% over every year. It should be noted that larger the error term, faster is the rate at which economies correct to equilibrium rate of growth. The value of R-Square is 0.69, which shows that dependent variable is explained by 0.69% differences by independent variables. Thus, our results show that our model is robust and best fitted.

VII. Sensitivity Estimates

Table 6 shows that there is no serial correlation in the model confirmed by Breusch–Godfrey serial correlation LM test. Besides, to check for autoregressive conditional heteroscedasticity, Breusch–Pagan–Godfrey, Harvey, Glejser and ARCH were run. The results presented that there is no sign of heteroscedasticity. Table 6 shows values of F-statistics along with probability value which is greater than 5% significance level. Thus, these findings represent that our model is best fit and perfect.

Table 6.

Diagnostic Tests.

	Obs*R-square	F-Statistics	Probability
Breusch–Godfrey serial correlation LM test	2.412512	0.801710	0.4632
Heteroskedasticity test Breusch–Pagan–Godfrey	14.57835	2.071422	0.0814
Heteroskedasticity test Harvey	9.980346	1.107890	0.3991
Heteroskedasticity test Glejser	13.63978	1.833280	01212
Heteroskedasticity test ARCH	0.159918	0.150057	0.7014

Source: Authors’ calculation.

VIII. Stability Estimates

To check for stability of the model, CUSUM and CUSUMQ test have been applied, which shows whether our model is stable or not. The CUSUM and CUSUMQ in Figures 3 and 4 show that our model is stable. The blue line in both Figures 3 and 4 did not cross red line, which indicates stability of our model.

IX. VECM Granger Causality Robustness Checking

In this study, VECM Granger causality has been run for both short run and long run to check robustness of the model. The results show that there is both short-run and long-run causality between FDI, exports, foreign remittances, imports and economic growth in India as reported in Table 7. The Wald test shows that exports, imports and foreign remittances are significant at 5% and 10% levels. However, the long-run results of ECM show that all variables are significant at 1% level.

Table 7.

VECM Granger Causality Robustness Checking.

Wald Test (short-run estimates) F-Statistics and P Value					VECM Long-Run Estimates	Decision of Long-Run and Short-Run Casualty
LnGDP	DLnFDI	DLnEXP	DLnFR	DLnImp	ECM t-statisticsP value	DLnExp → LnGDP
	1.218036	3.791897	7.078229	7.032882	−3.241003	DLnFR→ LnGDP
	0.2822	0.0650***	0.0146**	0.0149**	0.0015*	DLnImp→ LnGDP
						DLnFDI→ LnGDP

Source: World Development Indicators.

Note: Long-run and short-run probability values significant at *1%, **5% and ***10% levels.

Figure 5 shows uni-directional causality from FDI, exports, imports, foreign remittances and economic growth. Results presented in Tables 4 and 5 are similar to results of VECM Granger causality. So, VECM results are in accordance with our ARDL model. Researchers can check bi-directional causality between FDI, exports, imports, foreign remittances and economic growth for future studies regarding India.

X. Conclusion

The main aim of this study was to provide systematic analysis of impact of FDI, exports, imports and foreign remittances and economic growth of India using ARDL bound testing approach to cointegration of long-run relationship. The results indicate positive and significant relation between FDI, imports and economic growth in the long run. In short run, imports play more significant role in economic growth of country as indicated by results presented above. In case of exports and foreign remittances, there is negative and significant relation between these two variables and economic growth. These results are an indication that exports and foreign remittances take more time for positive spillover effects on economic growth of India. The study of Pereira and Xu (2000) and Sultanuzzaman et al. (2018) also found negative impact of exports on economic growth. Given the trade policy of countries and their composition of exports, it indicates that exports and foreign remittances take more time as compared to imports and FDI for positive spillover effects on economic growth of India.

From the above study, it is clear that India should focus more on FDI and imports which can lead to comparative advantage for export-oriented industries, export-led growth strategy and path of development for other sectors of the economy. Moreover, composition of exports needs to be upgraded to higher value-added products which can help in enhancement of long-run development in the country. The policymakers of the country should promote FDI with reduction of barriers, encourage joint ventures between domestic and foreign firms and enhance exports for stronger spillover effects to accelerate growth of Indian economy.

This article is a step forward to analyse impact of globalisation on economic growth in India. However, our research is limited to few variables and other variables such as exchange rate, inflation and human capital can be included in future work. Moreover, bi-directional causal analysis can be examined in future as compared to uni-directional causality in this study.

Footnotes

Declaration of Conflicting Interests

Funding

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

ORCID iD

Suadat Hussain Wani https://orcid.org/0000-0002-7403-8971

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