The Dynamic Relationship Between Financial Development,Economic Growth,Foreign Direct Investment and Trade Openness: Evidence from South Asian Countries

Abstract

This study examines the nature of the association among financial development, economic growth, foreign direct investment and trade openness in four South Asian countries from the period 1990–2019. The study employed Granger Causality test in Vector Error Correction Model (VECM) framework to find out the dynamic relationship among the variables. Further, variance decomposition analysis (VDA) and impulse response function (IRF) is also applied to determine the relationships among the variables beyond the sample period. The empirical result shows support for growth-led financial development, growth-led foreign direct investment and growth-led trade openness hypothesis for India. For Pakistan, the results suggest growth-led financial development and growth-led foreign direct investment. In the case of Sri Lanka, the results suggest foreign direct investment-led growth and trade openness-led growth hypothesis. The results do not support any kind of causal relationship among the variables in the case of Bangladesh in the short run. Furthermore, no bidirectional causality among the variables was found for all the countries. The findings imply that all four countries should adopt policies to promote further trade liberalization, financial sector development and also need to fast-track reforms to improve the investment climate and attract investments to attain high economic growth in the long run.

Keywords

Financial development economic growth foreign direct investment trade openness causality

I. Introduction

In both theoretical and empirical research, the connection between financial development, trade openness, foreign direct investment, and economic growth has received considerable discussion and debate and is seen as one of the key contributors to the economic progress of any nation.

In the long run, financial development is crucial for economic progress. Better-developed financial systems tend to have nations flourish more quickly (Menyah et al., 2014). A developed financial sector allows credit-constrained entrepreneurs to start their businesses and undertake the supply function (Shahbaz & Rahman, 2012). The expansion of financial institutions, financial instruments, and financial markets as a result of the financial sector development promotes investment and economic growth (Rahman et al., 2015). The writings of Goldsmith (1969), Gurley and Shaw (1967), and Shaw (1973) show how crucial the financial sector is to a nation’s economic development.

Trade between nations has increased as a result of the trade liberalization process (Gaurav & Bharti, 2018). The trade and investment liberalization has helped many countries across the world to achieve higher rate of economic growth (Joseph, 2013). Some advantages of international trade include more choices for consumers at reduced prices and efficient resource usage (Maryam & Mittal, 2019). According to Mityakov et al. (2013), economic liberalization encourages global trade, which raises welfare. Trade openness and financial development are crucial policy factors that are strongly connected with economic growth globally (Sachs & Warner, 1995). Foreign direct investment also contributes significantly to global business. FDI offers finance, organization, investments, and management capabilities (Shahbaz & Rahman, 2012).

Following their independence, the emerging countries of South Asia India, Pakistan, Bangladesh, and Sri Lanka have adopted inward-looking economic policies and are subject to heavy government regulation. In the 1980s and 1990s, these nations gradually ushered in a period of liberalization as a result of their unfavourable balance of payments and economic performances. The reforms sequence followed by these countries was macroeconomic, trade and industrial policy reforms (Kathuria, 2019).

India the largest nation in South Asia in the early 1990s announced economic and trade reforms to open up the economy (Batra & Khan, 2005). The free trade policies adopted by India by dismantling quantitative restrictions and reductions in tariff rates have had a large impact on the volume and structure of trade (Shahzeb et al., 2021). The policies related to foreign direct investment were gradual at the beginning by allowing limited foreign ownership in a select number of sectors and industries. Pakistan has also initiated a series of economic reforms since the 1990s (Ahad et al., 2019). The objectives of these reforms were to increase industrial production (Khan & Qayyum, 2007). The foreign direct investment and portfolio investments were liberalized. Pakistan moved to a system of multiple exchange rate systems in 1998 (Haque, 2011). Similarly, trade liberalization policies were also introduced by dismantling the quantitative restrictions and lowering tariff rates. Bangladesh started economic liberalization policies in the trade and financial sector in the 1980s and 1990s. Financial sector reforms were launched in 1990 to decontrol and liberalize the financial markets to strengthen and develop the banking and domestic markets (Chowdhury, 2001). The reforms were incremental in nature and the foreign investments were promoted both from FDI and FII (Bashar & Khan, 2007). It also initiated reforms in the trade sector and public enterprise sectors (Raihan, 2008). Bangladesh moved on the path of a free-market economy by opening various sectors to private players, limiting restrictions, and attracting foreign capital by making suitable policies since the 1990s (Shafiullah & Navaratnam, 2016). Sri Lanka started economic liberalization in 1977 (Shafiullah & Navaratnam, 2016). During the 1970s its economy was inward-oriented and the trade and exchange rates were regulated by the government (Rajapatirana, 1989). The economic and trade reforms were introduced much earlier than in other South Asian countries because the economy performed badly due to strictly regulated economic policies (Panagariya, 2003). The entry of foreign banks into Sri Lanka and the interest rates were made determined by the markets (Athukorala & Rajapatirana, 1993).

The empirical examination between financial development, economic growth, trade openness and foreign direct investment has mixed results in emerging and developing countries. There is no agreement on whether financial development causes economic growth or trade openness causes economic growth or foreign direct investment causes economic growth. This issue is of great importance for governments and policymakers to frame suitable policies for the economic development of countries. From the economic literature, it is observed that there are some testable causal relationships like the financial development-led growth hypothesis, trade-led growth hypothesis, and foreign direct investment-led growth hypothesis. In view of the foregoing, this study tries to address the following questions; To what extent is growth led by financial development, trade openness and foreign direct investment in the case of four South Asian countries namely India, Pakistan, Bangladesh and Sri Lanka. Is trade-led growth, foreign direct investment-led growth and financial development-led growth a long or short-term phenomenon, and to know whether these four economies are able to exploit the economic reforms that were introduced to accelerate economic growth?

The rest of this article is organized as follows. Section Ⅱ presents literature review, Section Ⅲ explains the methodology and data sources. Empirical results of the study are discussed in Section Ⅳ and finally, the conclusion of the study has been presented in Section Ⅴ.

II. Review of Literature

The empirical association among financial development, trade openness, foreign direct investment, and economic growth has been examined widely in the empirical literature and the evidence from the literature is varied and differing across methodologies.

Din et al. (2003) studied the relationship between openness and economic growth for Pakistan’s economy from 1960–2001. The results showed long-run relationship between openness and economic growth however, no such causality was seen in the short run. Chakraborty (2008) examined the relationship between financial development and economic growth in India since 1996. The results indicate a long run relationship between bank credit, stock market capitalization, and real GDP growth rate. Ravinthirakumaran (2014) using ARDL bounds test for cointegration established that trade openness has significantly promoted economic growth from the period 1965–2012 in Sri Lanka. Pradhan et al. (2015) studied relationships among growth, trade openness, and financial sector depth in India from 1994–2011, and found causality exists amongst them. Lenka (2015) used the ARDL bound testing approach to cointegration and error correction model to investigate the long-term impact of financial development and economic growth in India during 1980–2011, and found that there is a cointegration relationship between financial sector development and growth. Dutta et al. (2017) using the VECM revealed unidirectional causality in Bangladesh between economic growth and trade openness. Ahad (2017) using the ARDL method investigated the relationship between Pakistan’s financial development, trade balance, exchange rate, and inflation from 1972 to 2014. The study revealed that exchange rates and inflation have a short- and long-term positive and negative association with the trade balance. Granger causality findings also show that inflation, currency rates, and financial development cause trade balance in the long run. Sehrawat and Giri (2017) confirmed the unidirectional relation from financial development to economic growth and further, a bidirectional relation between economic growth and trade openness in the case of India. Boachie et al. (2020) using ARDL estimation technique from 1960–2013 found that financial development and trade openness has encouraged private sector investment in India. Tripathy and Mishra (2021) looked into the connection between financial development and economic growth for India from 2003 to 2018. The study’s findings point to a long-term connection between financial development and economic growth.

The empirical studies were also carried out in the context of economic blocs, geographical regions, and between a group of countries. Among others, Din (2004) revealed a long-term connection between Bangladesh’s and Pakistan’s growth, imports, and exports in the South Asian region. There was no evidence of such a connection in the cases of Sri Lanka, India, and Nepal. Christopoulos and Tsionas (2004) looked into the relationship between economic growth and financial development for ten emerging nations between 1970 and 2000. The results of panel cointegration analysis show that growth and financial development have long-term causal relationships. Sehrawat and Giri (2015) investigated the role of financial development for SAARC countries from 1994–2013. The financial development index was constructed using the principal component method. The results indicate that the index of financial development affects per capita GDP. The study also found that trade openness in the region has a positive and significant effect. Rani and Kumar (2018) using panel data from 1993 to 2015 examined the connections between FDI, trade openness, and economic growth in the BRICS nations. The study discovered that FDI negatively affects BRICS nations while trade openness positively affects economic growth. Khatun (2019) revealed the long-term association between openness in financial services trade and financial development in BRICS nations using FMOLS and the dynamic least square approach for the years 1990–2012.

This study examines the impact of financial development, foreign direct investment, and trade openness on economic growth in four South Asian nations while taking into account the inadequacies in the literature. The study makes use of annual data for four South Asian nations from 1990 to 2019. To accomplish the research objectives, several econometric techniques are used. The study provides the following important contributions to the literature. First, this study analyses the experiences of economic liberalization of the four South Asian economies and also ascertains whether these countries are able to exploit the extensive liberalization policies that began in the 1980s and 1990s. Second, the present research specifically classifies the role of financial development, the role of foreign direct investment, and the role of trade openness on economic growth individually in each of these countries separately. Finally, the study uses the most suitable econometric methods such as Johansen’s cointegration test, vector error correction estimation, Granger causality test, VDA, and IRF analysis in the investigation in order to arrive at robust estimates. These findings are essential for the governments and policymakers to construct policy actions to further promote the economic growth in these emerging and developing countries of South Asia.

III. Methodology and Data Sources

The long run, short run, and causal relationships between financial development, economic growth, foreign direct investment, and trade openness have been stud-ied for the four South Asian countries namely India, Pakistan, Bangladesh, and Sri Lanka from 1990–2019. To capture the economic growth GDP per capita at constant 2015 US$ prices are used for all countries. Foreign direct investment also plays a vital role in the economic growth of a country and, thus, the foreign direct investment net inflows (% of GDP) have been included in the model. Financial development is measured through Domestic credit to private sector (% of GDP) for all countries. Trade openness (% of GDP) is taken to measure the openness of the economy. The data provided by the World Development Indicators of the World Bank has been used for the analysis. The study uses the logarithmic transformation of the variables under consideration.

The empirical model for estimation is as follows

L G D P P C = a_{0} + a_{1} L F D + a_{2} L F D I + a_{3} L T O P + ε_{t}

(1)

Where,

LGDPPC is Log of GDP per capita, LFD is Log of Domestic credit to private sector as a percentage of GDP, LFDI is Log of Foreign Direct Investment, net inflows percentage of GDP, LTOP = Log of trade as a percentage of GDP. a₀, a₁, a₂, a₃ are the parameters, Ɛ_t is the disturbance term and the subscript t is the time period.

The error correction form of equation (1) is written as

[\begin{matrix} Δ L G D P P C_{t} \\ Δ L F D_{t} \\ Δ L F D I_{t} \\ Δ L T O P_{t} \end{matrix}] = [\begin{matrix} Δ α_{0} \\ Δ β_{0} \\ Δ γ_{0} \\ Δ δ_{0} \end{matrix}] + \sum_{i = 1}^{m} \begin{array}{l} [\begin{matrix} α_{1 i} + α_{2 i} + α_{3 i} + α_{4 i} + α_{5 i} + α_{6 i} \\ β_{1 i} + β_{2 i} + β_{3 i} + β_{4 i} + β_{5 i} + β_{6 i} \\ γ_{1 i} + γ_{2 i} + γ_{3 i} + γ_{4 i} + γ_{5 i} + γ_{6 i} \\ δ_{1 i} + δ_{2 i} + δ_{3 i} + δ_{4 i} + δ_{5 i} + δ_{6 i} \end{matrix}] \\ [\begin{matrix} \begin{matrix} {\begin{matrix} Δ L G D P P C \end{matrix}}_{t - i} \\ Δ L F D_{t - i} \\ Δ L F D I_{t - i} \\ Δ L T O P_{t - i} \end{matrix} \end{matrix}] + [\begin{matrix} Δ α_{1} \\ Δ β_{2} \\ Δ γ_{3} \\ Δ δ_{4} \end{matrix}] [E C T_{t - 1}] + [\begin{matrix} Δ μ_{t} \\ Δ ε_{t} \\ Δ V_{t} \\ Δ W_{t} \end{matrix}] \end{array}

(2)

Where, for period ‘t’ μ_t, ε_t, V_t, and W_t respectively, are residuals with zero mean and constant variance. Δ is the first difference operator, α, β, γ, and δ are short-run parameters, and m is the lag length selected based on the lag length criteria. The stationary residuals produced by the long-run cointegrating of the Johansen multivariate process, which reflect a position of disequilibrium in period t, are called ECTs, or error correction terms. The coefficients α₁, β₂, γ₃, and δ₄ are the short-run adjustment coefficients.

IV. Empirical Results

The next subsections analyse the empirical findings.

Unit Root Test Results

The Augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test are used to determine whether the variables LGDPPC, LFD, LFDI, and LTOP are stationary or not. The ADF and PP tests’ respective unit root test results are presented in Tables 1 and 2, respectively.

Table 1.

Augmented Dickey-Fuller Test Results at Level and First-Order Difference (for India, Pakistan, Bangladesh and Sri Lanka).

Variable	India	Pakistan	Bangladesh	Sri Lanka
LGDPPC	2.163 (.997)	–0.142 (.932)	3.154 (1.000)	–0.312 (.912)
DLGDPPC	–5.014 (.000)	–3.353 (.023)	–1.325 (.000)	–3.874 (.000)
LFD	–0.495 (.876)	–1.163 (.672)	–0.776 (.815)	–1.333 (.604)
DLFD	–1.854 (.347)	–4.093 (.000)	–5.767 (.000)	–6.282 (.000)
LFDI	–2.192 (.211)	3.153 (1.000)	–1.936 (.312)	–7.762 (.200)
DLFDI	–6.105 (.000)	–1.327 (.606)	–5.041 (.000)	–6.891 (.000)
LTOP	–2.161 (.225)	–1.945 (.304)	–1.946 (.307)	–0.673 (.831)
DLTOP	–4.451 (.000)	–5.173 (.000)	–5.173 (.000)	–4.634 (.000)

Source: The author.

Note: Values in parentheses are p-values.

Table 2.

Phillips-Perron Test Results at Level and First-Order Difference (for India, Pakistan, Bangladesh and Sri Lanka).

Variable	India	Pakistan	Bangladesh	Sri Lanka
LGDPPC	3.212 (1.000)	0.213 (.962)	10.772 (1.000)	–0.312 (.912)
DLGDPPC	–5.204 (.000)	–3.357 (.022)	–1.312 (.000)	–3.876 (.000)
LFD	–0.653 (.844)	–1.468 (.534)	–0.824 (.791)	–1.385 (.575)
DLFD	–4.791 (.000)	–4.091 (.000)	–6.173 (.000)	–6.434 (.000)
LFDI	–2.801 (.075)	–2.373 (.154)	–2.255 (.192)	–5.512 (.204)
DLFDI	–5.945 (.000)	–4.732 (.000)	–5.486 (.000)	–6.617 (.000)
LTOP	–2.086 (.254)	–1.935 (.312)	–2.005 (.281)	–0.758 (.812)
DLTOP	–4.517 (.000)	–5.317 (.000)	–5.173 (.000)	–4.634 (.000)

Source: The author.

Note: Values in parentheses are p-values.

From the test results, it can be observed that in the case of India and Pakistan the null hypothesis of having a unit root in the variables at the level is accepted because the p-value is greater than the 5% level of significance. This confirms the non-stationary of these variables at the level. However, we failed to accept the null hypothesis of unit root upon first differencing. The variables have become stationary at first difference. In the case of Bangladesh and Sri Lanka, the order of integration of all variables is I(1).

The variables LFD of India and LFDI of Pakistan in the ADF test result at the first difference is not significant at the 5% level of significance as the p-value is above 5%. However, the result of the PP test has shown that it is significant at 5%. The result of the Phillips-Perron (PP) test shows that variables became stationary at first difference. Thus, the variables are integrated of order one.

Results of Lag Order Selection Tests

The present study has used unrestricted Vector Autoregressive (VAR) to select the optimal lag length for all the countries under study. The lags for the model of the four emerging countries were chosen by using the Akaike information criterion (AIC). AIC results in Table 3 show a lag of 3 for India, Pakistan, and Bangladesh as optimal lags. However, for Sri Lanka, it has chosen a lag of 1.

Table 3.

VAR Lag Selection Criterion Based on AIC Criterion (for India, Pakistan, Bangladesh and Sri Lanka).

India
Lag	LogL	LR	FPE	AIC	SC	HQ
0	28.909	NA	1.860	–1.845	–1.653	–1.788
1	160.120	213.825*	3.710	–10.379	–9.419*	–10.093
2	179.590	25.960	3.140	–10.636	–8.908	–10.122
3	202.688	23.952	2.360*	–11.162*	–8.666	–10.419*
Pakistan
Lag	LogL	LR	FPE	AIC	SC	HQ
0	43.588	NA	6.260	–2.932	–2.740	–2.875
1	134.638	148.377*	2.450	–8.491	–7.531*	–8.206
2	154.351	26.284	2.040*	–8.766	–7.038	–8.253*
3	172.737	19.067	2.170	–8.943*	–6.447	–8.201
Bangladesh
Lag	LogL	LR	FPE	AIC	SC	HQ
0	4.937	NA	1.100	–0.069	0.122	–0.012
1	161.484	255.113	3.360	–10.480	–9.520	–10.194
2	192.068	40.778*	1.250*	–11.560	–9.832*	–11.046
3	208.971	17.529	1.480	–11.627*	–9.131	–10.885
Sri Lanka
Lag	LogL	LR	FPE	AIC	SC	HQ
0	14.508	NA	5.440	–0.778	–0.586	–0.721
1	116.739	166.599*	9.240*	–7.165*	–6.206*	–6.880*
2	131.181	19.255	1.130	–7.050	–5.322	–6.536
3	146.187	15.561	1.550	–6.976	–4.481	–6.234

Source: The author.

Notes: (1) LR: sequential modified LR test statistic (each test at 5% level); Akaike information criterion (AIC); Schwarz information criterion (SC); Hannan-Quinn information criterion (HQ); Final prediction error (FPE);

(2) * indicates lag order selected by the criterion.

Results of Cointegration Tests

The long-run relationship of the non-stationary variables for all the countries is confirmed by using the Johansen cointegration technique. The most widely used and improved cointegration tests was given by Johansen (1988). This method has several advantages over the method of Engle and Granger (1987). It uses the maximum likelihood procedure to determine the presence of cointegration vectors. In recent times, several studies have used the Johansen cointegration test for the analysis (Burange et al., 2019; Siddikee & Rahman, 2021). As the order of the integration in this study is I(1) for all the variables accordingly, the Johansen cointegration test was employed. Thus, the Johansen cointegration test based on Trace and Maximum Eigenvalue statistics were employed to infer whether long-run relationships among the variables exist. The null hypothesis ‘no cointegration’, is verified against the alternative hypothesis ‘at most one’ cointegrating relationship. Table 4 and Table 5 depict the trace statistics and maximum eigenvalue statistics respectively.

Table 4.

Johansen Cointegration Test Results of Trace Statistic (for India, Pakistan, Bangladesh and Sri Lanka).

India
Unrestricted Cointegration Rank Test (Trace)
Hypothesized No. of CE(s)	Eigenvalue	Trace Statistic	0.05 Critical Value	Prob.^**
None *	0.945	128.001	47.856	0.000
At most 1 *	0.728	52.566	29.797	0.000
At most 2 *	0.483	18.622	15.494	0.016
At most 3	0.053	1.428	3.841	0.232
Pakistan
Unrestricted Cointegration Rank Test (Trace)
Hypothesized No. of CE(s)	Eigenvalue	Trace Statistic	0.05 Critical Value	Prob.^**
None *	0.934	114.282	47.856	0.000
At most 1 *	0.692	43.279	29.797	0.001
At most 2	0.288	12.615	15.494	0.129
At most 3	0.134	3.749	3.841	0.052
Bangladesh
Unrestricted Cointegration Rank Test (Trace)
Hypothesized No. of CE(s)	Eigenvalue	Trace Statistic	0.05 Critical Value	Prob.^**
None *	0.925	99.126	47.856	0.000
At most 1 *	0.574	31.491	29.797	0.031
At most 2	0.292	9.247	15.494	0.343
At most 3	0.009	0.252	3.841	0.615
Sri Lanka
Unrestricted Cointegration Rank Test (Trace)
Hypothesized No. of CE(s)	Eigenvalue	Trace Statistic	0.05 Critical Value	Prob.^**
None *	0.807	69.221	47.856	0.000
At most 1	0.368	23.052	29.797	0.243
At most 2	0.297	10.201	15.494	0.265
At most 3	0.011	0.299	3.841	0.584

Source: The author.

Note: * denotes rejection of the hypothesis at the 0.05 level.

Table 5.

Johansen Cointegration Test Results of Maximum Eigenvalue Statistic (for India, Pakistan, Bangladesh and Sri Lanka).

India
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized No. of CE(s)	Eigenvalue	Max-Eigen Statistic	0.05 Critical Value	Prob.^**
None *	0.945	75.435	27.584	0.000
At most 1 *	0.728	33.944	21.131	0.001
At most 2 *	0.483	17.193	14.264	0.016
At most 3	0.053	1.428	3.841	0.232
Pakistan
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized No. of CE(s)	Eigenvalue	Max-Eigen Statistic	0.05 Critical Value	Prob.^**
None *	0.934	71.003	27.584	0.000
At most 1 *	0.692	30.663	21.131	0.002
At most 2	0.288	8.866	14.264	0.297
At most 3	0.134	3.749	3.841	0.052
Bangladesh
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized No. of CE(s)	Eigenvalue	Max-Eigen Statistic	0.05 Critical Value	Prob.^**
None *	0.925	67.634	27.584	0.000
At most 1 *	0.574	22.244	21.131	0.034
At most 2	0.292	8.995	14.264	0.286
At most 3	0.009	0.252	3.841	0.615
Sri Lanka
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized No. of CE(s)	Eigenvalue	Max-Eigen Statistic	0.05 Critical Value	Prob.^**
None *	0.807	46.169	27.584	0.000
At most 1	0.368	12.850	21.131	0.466
At most 2	0.297	9.901	14.264	0.218
At most 3	0.010	0.299	3.841	0.584

Source: The author.

Note: * denotes rejection of the hypothesis at the 0.05 level.

The test statistics (Trace and Maximum eigenvalue) confirm the long-run relationships among the variables under study, thereby rejecting the null hypothesis of no cointegration at 5% level of significance. Similarly, the study has found support for the hypothesis of ‘at most one’ and ‘more than two’ cointegration relations. Thus, the cointegration tests based on Trace statistics and Maximum eigenvalue statistics confirm the existence of three cointegrating relationships in the case of India. Two cointegrating relationships are found for Pakistan and Bangladesh. Whereas, for Sri Lanka, only one cointegrating relationship is found. The test results of the Maximum Eigenvalue statistic are consistent with the Trace statistic as both the test statistics show the existence of the same co-integrating equation.

Results of Vector Error Correction Model

Table 6 presents the VECM to show the short run and long run relationships that exist among variables for all four countries. The error correction terms and lags are presented for each country as per the cointegrating vectors. The error correction shows the short run dynamics. It estimates the speed at which the dependent variable returns to equilibrium after a change in independent variables. The values of error correction terms for India, Pakistan, Bangladesh and Sri Lanka are negative and significant, that is, –0.037, –0.119, –0.055 and –0.043 respectively. It means that there is a long run relationship that exists among economic growth, financial development, foreign direct investment and trade openness in the case of these four South Asian countries. It demonstrated that for India 3.7% deviation of the economic growth (LGDPPC) is corrected every year to achieve the long run equilibrium. Similarly, 11.9%, 5.5% and 4.3% deviation of the economic growth (LGDPPC) for Pakistan, Bangladesh and Sri Lanka is corrected every year to achieve the long run equilibrium. The results, therefore, do suggest the existence of long run causality among these variables in all the four countries under consideration. Furthermore, in the short run, the first lag of LGDPPC has a significant positive effect on the current LGDPPC for Bangladesh. The first lag of LFD for Bangladesh has a significant positive effect on the LGDPPC. Similarly, the first lag of LTOP for Sri Lanka has a significant positive effect on the LGDPPC. None of the coefficients for India and Pakistan are found to be significant.

Table 6.

Vector Error Correction Model (for India, Pakistan, Bangladesh and Sri Lanka).

Variable	India	Pakistan	Bangladesh	Sri Lanka
ECT 1	–0.037 (0.062)	–0.119 (0.042)	–0.055 (0.082)	–0.043 (0.058)
D (LGDPPC (–1))	0.253 (0.374)	0.323 (0.166)	0.814 (0.006)	0.335 (0.112)
D (LGDPPC (–2))	–0.421 (0.210)	–0.145 (0.593)	–0.443 (0.152)
D (LFD (–1))	0.001 (0.994)	0.059 (0.294)	0.069 (0.083)	0.018 (0.279)
D (LFD (–2))	0.042 (0.634)	0.082 (0.231)	0.039 (0.117)
D (LFDI (–1))	0.008 (0.489)	–0.008 (0.572)	0.002 (0.351)	0.020 (0.105)
D (LFDI (–2))	0.007 (0.474)	–0.006 (0.651)	0.001 (0.657)
D (LTOP (–1))	0.030 (0.600)	0.025 (0.686)	–0.020 (0.309)	0.133 (0.040)
D (LTOP (–2))	–0.070 (0.267)	–0.015 (0.749)	0.020 (0.402)
Constant	0.052 (0.005)	0.013 (0.050)	0.020 (0.118)	0.025 (0.016)

Source: The author.

Results of Cointegrating Equation (Long-Run Model)

Table 7 presents the results of long run cointegrating coefficients of FD, FDI and TOP for India, Pakistan, Bangladesh and Sri Lanka respectively. In the case of India, the cointegrating coefficients of FD and TOP are positive which implies that these two variables are positively cointegrated with GDPPC in the long run. While the lagged value of FDI is negative implies, that this variable is negatively cointegrated with GDPPC in the long run. However, as the error correction coefficient (ECT) in Table 6 adjusts the impact by approximately 3.7% in the opposite direction, a one percentage point increase in the lagged value of FDI declines the lagged value of GDPPC by 3.7% of the long run coefficient, that is, 3.7(0.089). In the case of Pakistan, the cointegrating coefficients of FD and TOP are negative which implies that these two variables are negatively cointegrated with GDPPC in the long run. The error correction coefficient (ECT) in Table 6 adjusts their impact by approximately 11.9% in the opposite direction, a one percentage point increase in the lagged values of FD and TOP declines the lagged values of GDPPC by 11.9% of their long run coefficients, that is, 11.9(0.952 and 0.265). Only the lagged value of FDI is positive implies, that this variable is positively cointegrated with GDPPC in the long run. In the case of Bangladesh, the cointegrating coefficients of FD and TOP are positive which implies that these two variables are positively cointegrated with GDPPC in the long run. Only the lagged value of FDI is negative implies, that this variable is negatively cointegrated with GDPPC in the long run. The error correction coefficient (ECT) in Table 6 adjusts the impact by approximately 5.5% in the opposite direction, a one percentage point increase in the lagged value of FDI declines the lagged value of GDPPC by 5.5% of the long run coefficient, that is, 5.5(0.057). In the case of Sri Lanka, the cointegrating coefficients of FD and FDI are positive which implies that these two variables are positively cointegrated with GDPPC in the long run. Only the lagged value of TOP is negative implies, that this variable is negatively cointegrated with GDPPC in the long run. The error correction coefficient (ECT) in Table 6 adjusts the impact by approximately 4.3% in the opposite direction, a one percentage point increase in the lagged value of TOP declines the lagged value of GDPPC by 4.3% of the long run coefficient, that is, 4.3(1.475).

Table 7.

Cointegrating Equation (Long-Run Model for India, Pakistan, Bangladesh and Sri Lanka).

India
Cointegrating Equation	Coefficient	Standard Errors	T-statistics
LGDPPC (–1)	1.000
LFD (–1)	–2.447	0.326	–7.503
LFDI (–1)	0.089	0.068	1.305
LTOP (–1)	–1.522	0.351	4.333
C	–3.474
Pakistan
Cointegrating Equation	Coefficient	Standard Errors	T-statistics
LGDPPC (–1)	1.000
LFD (–1)	0.952	0.065	14.656
LFDI (–1)	–0.212	0.019	–11.014
LTOP (–1)	0.265	0.085	3.104
C	–10.911
Bangladesh
Cointegrating Equation	Coefficient	Standard Errors	T-statistics
LGDPPC (–1)	1.000
LFD (–1)	–0.535	0.061	–8.634
LFDI (–1)	0.057	0.006	9.428
LTOP (–1)	–0.084	0.062	–1.355
C	–4.551
Sri Lanka
Cointegrating Equation	Coefficient	Standard Errors	T-statistics
LGDPPC (–1)	1.000
LFD (–1)	–0.240	0.172	–1.396
LFDI (–1)	–2.329	0.227	–10.240
LTOP (–1)	1.475	0.357	4.131
C	–12.681

Source: The author.

Results of Diagnostic Tests

The results of various diagnostic tests are presented in Table 8. The test results failed to reject the null hypothesis of no serial correlation, residuals are normally distributed and residuals variance are all equal at 5% level of significance. Thus, it is concluded that the model has passed these tests for all countries and that it is free from serial correlation, heteroscedasticity and non-normality.

Table 8.

Diagnostic Tests (for India, Pakistan, Bangladesh and Sri Lanka).

Test	India	Pakistan	Bangladesh	Sri Lanka
Normality Test (Jarque-Bera Test)
Jarque-Bera	3.276	4.712	2.443	3.070
p value	0.194	0.094	0.294	0.215
Heteroscedasticity Test (Breusch-Pagan-Godfrey)
Observed R²	8.229	16.876	14.857	11.032
p value	0.767	0.071	0.249	0.199
Breusch-Godfrey Serial Correlation LM Test
Observed R²	5.417	0.492	7.370	1.302
p value	0.143	0.920	0.061	0.253

Source: The author.

The VECM Granger Causality Analysis

The Granger Causality test reveals the short-run causal relationship among the variables included in the estimated VEC model of India, Pakistan, Bangladesh and Sri Lanka. The results presented in Table 9 indicate that there does not exist any bi-directional causality.

Table 9.

Granger Causality Test Results (for India, Pakistan, Bangladesh and Sri Lanka).

Cause Effect Relation	India		Pakistan		Bangladesh		Sri Lanka
Cause Effect Relation	χ²-Statistic	p-value	χ²-Statistic	p-value	χ²-Statistic	p-value	χ²-Statistic	p-value
LFD does not Granger Cause LGDPPC	0.058	.971	2.329	.312	0.203	.903	1.228	.267
LGDPPC does not Granger Cause LFD	9.271	.009	6.785	.033	0.122	.940	0.000	.975
LFDI does not Granger Cause LGDPPC	1.130	.568	0.369	.831	1.905	.385	2.851	.091
LGDPPC does not Granger Cause LFDI	8.298	.015	6.668	.035	4.524	.104	1.789	.181
LTOP does not Granger Cause LGDPPC	1.657	.436	0.379	.827	4.196	.122	4.745	.029
LGDPPC does not Granger Cause LTOP	9.278	.009	4.382	.111	1.606	.448	2.450	.996
LFDI does not Granger Cause LFD	2.365	.306	6.599	.036	1.293	.523	1.151	.283
LFD does not Granger Cause LFDI	2.764	.251	0.256	.879	4.409	.110	0.409	.522
LTOP does not Granger Cause LFD	2.309	.315	3.129	.209	0.928	.628	1.194	.274
LFD does not Granger Cause LTOP	3.748	.153	1.932	.111	0.081	.960	0.312	.576
LTOP does not Granger Cause LFDI	3.201	.201	0.257	.879	1.096	.578	4.819	.028
LFDI does not Granger Cause LTOP	19.101	.000	1.924	.382	2.614	.270	0.842	.358

Source: The author.

However, the demand-following hypothesis was detected for India and Pakistan, that is, unidirectional causality from economic growth to financial development. For developing countries like India and Pakistan, these findings are mostly in line with economic literature on financial development and economic growth. The results are corroborating with some of the previous examinations (Sehrawat & Giri, 2015; Shahbaz & Rahman, 2012).

For India and Pakistan, a unidirectional causality from economic growth to foreign direct investment was also detected. The results of the study are similar to those of (Sahoo & Sethi, 2017; Sengupta & Puri, 2020). A unidirectional causality running from economic growth to trade openness and from foreign direct investment to trade openness was also found in the case of India. The results support the findings of (Jadhav & Katti, 2012; Sabir et al., 2019). A unidirectional causality running from foreign direct investment to financial development was found in the case of Pakistan. The results are similar to some studies (Alfaro et al., 2004). For Sri Lanka unidirectional causality running from foreign direct investment to economic growth, trade openness to economic growth, and trade openness to foreign direct investment was detected. The results are similar to those of (Athukorala, 2003; Ravinthirakumaran, 2014). For Bangladesh, none of the variables show any kind of causality. The findings are similar to those of earlier studies on underdeveloped and developing countries (Sarkar, 2007; Siddikee & Rahman, 2021).

The stability in the model is assessed by the Cumulative sum (CUSUM), and the Square of the cumulative sum (CUSUMQ) test on the recursive residuals. The stability of the estimated parameters of the model (see Figures A1, A2, A3, A4, A5, A6, A7, and A8) which show the results of CUSUM and CUSUMQ tests for all four South Asian countries India, Pakistan, Bangladesh, and Sri Lanka respectively. The residual plots lay within the critical bounds of a 5% level of significance and thus there is stability in the parameters from the estimated model.

Variance Decomposition Test

The study has also extended the investigation of the relationships among the variables beyond the sample period using the VDA. This method allows us to determine the magnitude of variability in the dependent variable lagged by its own variance. Furthermore, it also shows the extent to which independent variables explain the variability in the dependent variable.

The VDA for India is shown in Table 10. LGDPPC’s own shock caused a 100% variation in the first period. The dependent variable, which is economic growth in the tenth period, is explained by its own shock by 81.65%, and the contribution of financial development and trade openness to the overall variation of economic growth have increased to 11.82% and 6.19% respectively. For Pakistan, economic growth, in the tenth period, is explained by its own shock by 35.80%, and the contributions of financial development and trade openness to the overall variation have increased to 43.75% and 16.67% respectively (Table 11). In the case of Bangladesh, economic growth is explained by its own shock by 21.31% in the tenth period, and the contributions of foreign direct investment and trade openness to total variation have increased to 13.15% and 65.07% respectively (Table 12). Lastly, for Sri Lanka the dependent variable is explained by its own shock by 76.63% in the tenth period, and trade openness’s contribution to the overall variation in economic growth have increased to 21.31% (Table 13).

Table 10.

Results of Variance Decomposition Percentage of 10-Period Error Variance in India.

Variance Decomposition of LGDPPC
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.019	100.000	0.000	0.000	0.000
5	0.042	94.855	0.887	0.346	3.910
10	0.062	81.652	11.823	0.325	6.199
Variance Decomposition of LFD
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.033	13.123	86.876	0.000	0.000
5	0.110	7.311	84.068	0.962	7.656
10	0.165	10.187	80.348	2.120	7.342
Variance Decomposition of LFDI
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.248	16.456	0.643	82.899	0.000
5	0.443	35.770	15.590	32.992	15.647
10	0.518	28.287	33.197	25.502	13.012
Variance Decomposition of LTOP
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.060	6.311	24.599	0.208	68.881
5	0.160	5.219	71.965	5.960	16.854
10	0.248	6.342	77.740	3.726	12.190

Source: The author.

Table 11.

Results of Variance Decomposition Percentage of 10-Period Error Variance in Pakistan.

Variance Decomposition of LGDPPC
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.016	100.000	0.000	0.000	0.000
5	0.035	75.569	7.056	2.048	15.325
10	0.054	35.800	43.775	3.744	16.679
Variance Decomposition of LFD
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.065	0.318	99.681	0.000	0.000
5	0.149	7.947	53.946	26.522	11.582
10	0.176	22.257	44.979	19.496	13.265
Variance Decomposition of LFDI
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.430	4.161	45.199	50.639	0.000
5	0.637	23.527	42.364	29.538	4.183
10	0.753	22.981	44.197	22.828	9.991
Variance Decomposition of LTOP
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.091	1.061	44.842	7.078	47.017
5	0.136	8.791	39.740	5.611	45.856
10	0.143	10.020	37.651	7.808	44.520

Source: The author.

Table 12.

Results of Variance Decomposition Percentage of 10-Period Error Variance in Bangladesh.

Variance Decomposition of LGDPPC
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.005	100.000	0.000	0.000	0.000
5	0.018	31.693	0.580	11.491	56.234
10	0.030	21.317	0.454	13.152	65.076
Variance Decomposition of LFD
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.059	7.068	92.931	0.000	0.000
5	0.077	14.373	76.831	4.293	4.501
10	0.100	15.536	70.187	6.879	8.397
Variance Decomposition of LFDI
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.623	0.141	82.831	17.027	0.000
5	0.935	1.830	78.656	9.750	9.761
10	1.099	6.474	77.312	8.878	7.333
Variance Decomposition of LTOP
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.096	21.449	13.303	6.094	59.152
5	0.136	27.397	25.547	5.732	41.323
10	0.150	26.463	32.487	5.804	35.245

Source: The author.

Table 13.

Results of Variance Decomposition Percentage of 10-Period Error Variance in Sri Lanka.

Variance Decomposition of LGDPPC
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.020	100.000	0.000	0.000	0.000
5	0.049	83.686	0.550	1.476	14.287
10	0.072	76.633	0.926	1.121	21.317
Variance Decomposition of LFD
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.212	9.209	90.790	0.000	0.000
5	0.309	12.245	60.881	1.246	25.627
10	0.316	14.206	58.507	1.576	25.709
Variance Decomposition of LFDI
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.397	7.649	7.045	85.304	0.000
5	0.406	7.940	7.112	84.526	0.421
10	0.406	7.957	7.125	84.389	0.527
Variance Decomposition of LTOP
Period	S.E.	LGDPPC	LFD	LFDI	LTOP
1	0.073	2.426	17.011	12.835	67.726
5	0.109	1.574	16.219	8.144	74.061
10	0.115	4.138	15.314	7.364	73.182

Source: The author.

Results of Impulse Response Function

IRF for all the countries, that is, India, Pakistan, Bangladesh and Sri Lanka are shown in Figures 1, 2, 3, and 4 respectively. It shows the time path to which the dependent variable (economic growth) reacts to the shocks from the various independent variables (financial development, foreign direct investment & trade openness). Figures 1, 2, 3 and 4 show the innovation of one standard deviation shock in financial development, foreign direct investment and trade openness on economic growth. The first figure of the first row shows the response of LGDPPC-to-LGDPPC innovation, the second figure of the first row shows the response of LGDPPC to LFD innovation, the third figure of the first row shows the response of LGDPPC to LFDI innovation and the fourth figure of the first row shows the response of LGDPPC to LTOP innovation.

Figure 1.

Source: The author.

Figure 2.

Source: The author.

Figure 3.

Source: The author.

Figure 4.

Source: The author.

V. Conclusion and Policy Suggestions

This study empirically examined the nature of relationships among financial development, economic growth, foreign direct investment, and trade openness in four emerging Asian countries from 1990 to 2019 using various econometric techniques in a multivariate framework. The empirical results revealed the non-existence of bi-directional relationships among variables for all countries. In India unidirectional causality was found from economic growth to financial development, economic growth to foreign direct investment, and economic growth to trade openness. A unidirectional causal relationship was also found between foreign direct investment and trade openness. In the case of Pakistan, a unidirectional causal relationship was found from economic growth to financial development and from economic growth to foreign direct investment. A unidirectional causal relationship was also found from foreign direct investment to financial development in Pakistan as well. In the case of Sri Lanka, a unidirectional causality was found from foreign direct investment to economic growth, trade openness to economic growth, and trade openness to foreign direct investment. For Bangladesh, no causal relationship for all variables was detected in the short run.

The study confirms the growth-led financial development hypothesis for India and Pakistan. The earlier literature by (Gurley & Shaw, 1967; Jung, 1986) hypothesized that in emerging and developing countries, the growth of the financial system is expanded due to economic growth. The development of the financial system has rather become an essential part of economic growth (Chang, 2002). Low-capital countries like Bangladesh and Sri Lanka cannot ignore the role of financial development. A well-established financial system benefits industries in developing countries and also plays a vital role in technology transfer and provides efficient human capital (Patrick, 1966).

Capital is scarce in the developing countries of South Asia which limits investment activity and thus hinders the economic growth in these countries. Capital inflows both from foreign investors and from the public sector can balance this shortage of funds (Hussain & Haque, 2016). The governments and policymakers found that Foreign direct investment is vital for economic growth. It has become an important source of foreign capital from the 1990s onwards in emerging economies all over the world (Bundesbank, 2003). However, FDI inflows in South Asian countries have not increased significantly.

The analysis leads to the conclusion that the attempts at financial development, trade liberalization, and measures to entice foreign direct investment appear not to have had much of an influence on growth in these four growing South Asian nations. The study therefore recommends that in order to improve economic growth in these South Asian countries, appropriate economic reforms in the financial market as well as in the trade sector must be implemented. In order to achieve strong economic growth over time, they must also move quickly with changes to enhance the environment for investment and draw in foreign capital.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Appendix

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