Panel Data Analysis of Financial Development,Trade Openness,and Economic Growth: Evidence from BRICS Countries

Abstract

The purpose of this article is to investigate the possible cointegration and direction of causality between foreign direct investment (FDI) inflow, trade openness, and economic growth in BRICS countries using panel data from 1993 to 2015. Besides these variables, money supply and domestic credit (DC) to private players are also added in the model to examine the impact of financial openness on economic growth. The Pedroni’s panel cointegration test is used to examine the existence of long-run relationship, and coefficients of cointegration are examined by fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS). Further panel Granger causality test is used to examine the direction of causality among the competing variables. The results of Pedroni’s panel cointegration test indicate that there exists a long-run relationship among the variables under considerations in BRICS countries. The coefficient of FMOLS and DOLS indicates that trade openness has a positive impact on economic growth in BRICS countries while FDI inflow has a negative impact in these nations. In addition, the results of panel Granger causality confirmed bidirectional causality between FDI inflow and economic growth in the short run. The study recommends that BRICS countries should liberalize trade openness as it strengthens the position of member countries in the world economy.

Keywords

Foreign direct investment trade openness economic growth BRICS countries

Introduction

An essential question as to whether financial openness has significant growth and stability benefits, especially for developing countries, is a matter of considerable attention and debate. Theoretically, financial globalization should improve efficient international allocation of capital, promote international risk sharing, guide macroeconomic policy, and facilitate institutional reforms, and these benefits should be much larger for emerging economies like BRICS. These countries tend to have less stable macroeconomic policy, more severe financial frictions, and lower institution capacity, so opening up their boundaries to global financial markets should help them improve productivity and grow faster (Kim, Lin, & Suen, 2012). The relationship between financial development and economic growth has received considerable attention in recent theoretical and empirical literature. It is most heavily researched topics in the recent years among the researchers. The theoretical underpinnings of this relationship can be traced back to the work (Galbis, 1977; Kapur, 1976; Mathieson, 1980; Shaw, 1973). Many res-earchers have tried to address the issue that how financial development and structure of the economy affects economic growth (Ahmed & Wahid, 2010; Khan & Senhadji, 2000; King & Levine, 1993; Levine, Loayza, & Beck, 2000; Sehrawat & Giri, 2016b; Uddin, Sjo, & Shahbaz, 2013; Xu, 2000). It is widely accepted that government restrictions on the banking system (such as interest rate ceilings, high reserve requirements, and restricted credit programs) hamper the process of financial development and, subsequently, reduce economic growth. Financial sector plays a critical role in facilitating economic growth by mobilizing saving, facilitating payments, and trading of goods and services across the border. The era of globalization and liberalization has brought about drastic changes in the outlook of manufactures and producers around the world. Further, they can explore the investment opportunities and financial intermediaries that increase the availability of the funds in the international market. This results in expansion of small business and generates employment and income. Thus, an efficient and robust financial system bec-omes a significant factor for the development of small and medium enterprises. In addition, many researchers found that trade openness leads to economic growth of the country (Anderson & Babula, 2008; Tahir & Norulazidah, 2014). Majority of these empirical studies recommended the significance of trade openness in economic growth through export-led hypothesis and import-led hypothesis (Balassa, 1978, 1985; Bhagwati, 1978; Feder, 1982; Greenaway, Morgan, & Wright, 2002; Michaely, 1977; Ram, 1987; Shahbaz, Azim, & Ahmad, 2011; Tyler, 1981).

In the last decade, BRICS countries played a very important role in the world economy. The BRICS is the acronym for an association of five major emerging national economies (Brazil, Russia, India, China, and South Africa). A quick glance at the statistics reveals that in 2011, the BRICS accounted for 25 percent of global GDP, 30 percent of global land area, and 45 percent of the world’s population (Singh, 2013). Further, the share of BRICS countries in global GDP has increased and reached at 30 percent. The BRICS countries accounted for over 17 percent of global trade, 13 percent of the global services market, and 45 percent of the world’s agricultural output in 2014 (Russian Minister of Economic Deve-lopment; Ulyukayev, 2015). Over the last two decades, BRICS economies have become the most emerging economies of the world because of continuously increasing share in world trade, trade openness, foreign reserves, economically active labor forces, and foreign direct investment (FDI) inflow and outflow.

Each of the BRICS countries has multiple and different attribute, and thus, each has a huge potential to develop. The inherent strength of the BRICS emanates strong domestic demand-based economies in the case of India and Brazil and significant outward linkage of China and Russia. South Africa benefits from its large resource base and proximity to untapped growth potential of the African continent. Persistent economic activities coupled with a growth-oriented strategy in BRICS nations have resulted in significant positive changes over the last two decades (1990–2010). The BRICS, now increasingly recognized as some of the fastest-growing countries and the engines of the global recovery process, plays a formidable role in shaping macroeconomic policy, as was observed after the financial crisis (Singh, 2013). The world seems to be more optimistic about their emergence based on their respective comparative advantages. This is the reason that the balance of global economic power is now shifting from the USA and Europe to a number of fast-growing and large-developing countries.

Theoretical Underpinning

Trade Openness and Economic Growth

The concept of absolute and comparative cost advantage theories emphasizes that international trade is important for trading countries because each country has specialization in the production of specific commodities and also countries differ in the resource endowments. The result of international trade is greater output and consumer welfare in both the trading countries (Murthy, Patra, & Samantaraya, 2014). The theoretical literature shows that trade openness affects the economic growth by different channels like capital accumulation, factor price equalization, and more employment. The debate on the role of trade openness on economic growth is still flourishing and still attracting several theoretical and empirical studies that examine the relationship between the two (Baltagi, Demetriades, & Law, 2009; Kim et al., 2010a, 2010b, 2011; Menyah, Nazlioglu, & Ruufael, 2014; Sehrawat & Giri, 2016b). Rajan and Zingales (2003) explained that when a country enters in to international trade, it is more likely to benefit from this dual openness. Law and Demetriades (2006) also support Rajan and Zingales hypothesis that financial development is enhanced when a country’s borders are simultaneously open to both capital flow and trade. Beck (2002) found that countries with better financial system have higher export share and trade balance in manufactured goods. Classical economists were in favor of free trade policy and advocated that free trade among different countries maximize the output and employment of all the participating countries (Salvatore, 2010). Edwards (1998) explained that countries that are more open to trade are benefited in capturing the better technology of developed nations as the costs of technological imitation is less than a cost of domestic developed nations. In addition, the growth rate will be faster if country remains opened to attract new ideas.

Financial Development and Economic Growth

Financial development affects economic growth mainly by increasing the marginal productivity of capital through mobilizing funds from relatively less to relatively more productive uses and also through the better utilization of funds (Bencivenga & Smith, 1991), thereby spawning capital formation, and hence leads to economic growth (King & Levine, 1993). This hypothesis is known as ‘supply-leading hypothesis’, which intends that causality is running from financial development to economic growth. Similarly, economic growth also encourages the development of financial system and markets by creating demand for different types of financial services. As economy grows, there will be greater variance in the growth rates among different sectors in the economy, thus increase the demand for financial intermediation to mobilize funds from slow growth sectors to fast-growing sectors. This hypothesis is usually referred as ‘demand following hypothesis’ that contends that finance is led by economic growth. According to the proponents of the ‘demand following hypothesis’, shortage of financial institutions in developing countries is an indication of the lack of demand for their services and, hence, hampers economic growth of such economies.

Apart from aforementioned hypotheses, many economists believe that economic growth and financial development have complementary relationship with each other and there would be bidirectional causality between economic growth and financial development (Blackburn, Bose, & Capasso, 2005; Blackburn & Hung, 1998; Greenwood & Smith, 1997). Still others advocate that there is no support for the view that financial development stimulates economic growth (Gregorio & Guidotti, 1995).

Literature Review

A review of previous literature provides an insight into the technical relationship between financial development, trade openness, and economic growth. Earlier studies explain that there is no generalized form of relationship between these concepts. The relationship between financial development, trade openness, and economic growth has been studied at both theoretical and empirical levels (Blackburn & Huang, 1998; Mckinnon, 1973; Schumpeter, 1911; Sehrawat & Giri, 2016b; Shaw, 1973). They found that there is bidirectional causality between economic growth and financial development. Beck and Levine (2004) applied panel data set for the period of 1976–1998 to investigate the impact of stock markets and banks on economic growth using generalized method of moments technique. The study explores the long-run relationship among the competing variables. Empirically, firm-level studies of particular countries often have found that FDI does not boost economic growth and mostly have not found positive spillovers running from foreign-owned to domestic-owned firms (e.g., Aitken & Harrison, 1999). Several studies of cross-sectional are found in the literature that provides evidence about the importance of well-functioning financial markets to obtain positive spillovers from FDI to speed up economic growth. However, Hsu and Wu (2009) argued that countries with better-developed financial markets are not necessary to obtain benefits from FDI to stimulate economic growth. Many economists, such as Prebish, Singer, and Miyrdal, argued that free trade between developed and developing countries shifts the gain from developing to developed economies. Odhiambo (2009) investigated the dynamic relationship between interest rate reforms, financial development, and economic growth. Causal relationship was found between financial depth and economic growth. Abdullahi (2010) found the bidirectional causality in five sub-Saharan countries and reverse causality from economic growth to financial development in two countries during the time period of 1976–2005. Uddin et al. (2013) found that development of financial sector has a positive impact on growth in Kenya over the period of 1971–2011. Sehgal, Ahmad, and Deisting (2013) determined the bidirectional causality between financial development and economic growth by using panel cointegration for 75 countries during 1990–2009. The estimated results of causality tests indicate that banks play a dominant role in promoting economic growth across all income groups. Adu, Marbuah, and Mensah (2013) revealed that both credit to the private sector as the percentage of GDP and total domestic credit (DC) to private sector are favorable for growth, while broad money stock to GDP ratio does not support economic growth. Recent studies on financial development, trade openness, and economic growth are summarized in Table 1.

Table 1.

Review of Recent Studies

Authors	Single/Multi-country Study	Sample Period	Methodology	Main Variables	Main Findings
Demetriads and Hussein (1996)	Multi (16 countries)	1960–1987	Granger causality test, Johansen cointegration	Financial development and real GDP	Not acceptance of the view that ‘finance leads growth’
Christopoulos and Tsionas (2004)	Multi (10 countries)	1970–2000	Panel cointegration, VECM, FMOLS	Financial depth and GDP	Equilibrium relationship between financial depth and growth
Butkiewicz and Yanikkaya (2008)	Multi (114 countries)	1970–1997	Seemingly Unrelated Regression(SUR) Technique	FDI, trade openness, GDP per capita	Trade with developed countries and FDI inflows is substitutes in developing countries and countries with higher flows grow faster
Yucel (2009)	Single (Turkey)	1989–2007	Johansen and Juselius cointegration, Granger causality test	Financial development, trade openness, and economic growth	Trade openness has positive impact while financial development has negative impact on growth
Hsu and Wu (2009)	Multi (71 countries)	1975–2005	Ordinary least squares (OLS) regression	FDI and economic growth	FDI does not affect economic growth
Kim et al. (2012)	Multi (90 developing countries)	1975–2007	Pooled mean group autoregressive distributed lag approach	Economic growth, financial openness, FDI	FDI impedes output growth, but in the short run, FDI has an average negative effect on economic growth
Murthy et al. (2014)	Single (India)	1971–2012	Principal component analysis, Johansen’s cointegration test, VECM	Economic growth, trade openness, and FDI	Long-run relationship between trade openness, financial development, and GDP
Menyah et al. (2014)	Multi (21 African Countries)	1965–2008	Panel Granger causality	GDP per capita, trade openness, financial openness	Financial development and trade liberalization do not have an impact on growth
Ulasan (2014)	Multi	1960–2000	First difference generalized method of moment (GMM)	GDP per worker, rates of accumulation of both physical and human capital, degree of openness	Openness is not associated with economic growth
Rahman, Shahbaz, and Farooq (2015)	Single (Australia)	1965–2010	ARDL (autoregressive distributive lag) model	Financial development, international trade, and economic growth	Financial development, international trade, and capital are drivers of growth
Bal, Dash, and Subhasish (2016)	Single (India)	1970–2012	ARDL model, ECM (error correction model)	economic growth, capital formation, trade openness	Capital formation and trade openness have positive significant impact on economic growth
Sehrawat and Giri (2016b)	Multi (SAARC)	1994–2013	Westerlund panel cointegration, FMOLS, PDOLS, Granger causality test	GDP, financial development index, trade openness, inflation	Cointegration between financial development and economic growth
Sehrawat and Giri (2016a)	Multi (SAARC)	1986–2012	Pedroni’s panel cointegration, FMOLS, panel Granger causality	Inequality, financial openness, GDP per capita, trade openness and CPI	Financial development increases rural urban inequality while trade openness reduces inequality

Source: Authors’ compilation.

Rationale of the Study

Over the last few decades, though studies were conducted on trade–growth relationship or financial development–growth relationship based on a specific country or a group of countries, research on trade–growth relationship and financial development-growth relationship is rare for BRICS nations. Also, the effects of financial development and trade openness on economic growth and the causal relationship among them remain unclear in the existing literature (Katircioglu, Kahyalar, & Benar, 2007). The main objective of this study is to examine the effect of financial development and trade openness on economic growth in BRICS countries.

Data Source and Definition of the Variables

The annual data used in the study cover the period from 1993 to 2015 for the BRICS nations. The data has been taken from World Development Indicators (2016) and Handbook of Statistics of Indian Economy published by Reserve Bank of India. In order to measure the financial development, two proxy variables are used. These are: the ratio of private sector credit to GDP, that is, DC (Demirguc & Levine, 1999; Kar, Nazilioglu, & Agir, 2011; Khan & Senhadji, 2000; Levine, 1992; Sehrawat & Giri, 2016a) and the ratio of broad money to GDP (M3) (Bittencourt, 2012; Sehrawat & Giri, 2016b). Further trade openness (ratio of sum of export and import to GDP) is used to represent the trade openness in the economy (Sehrawat & Giri, 2016a; Tan & Law, 2009; Tiwari, Shahbaz, & Islam, 2013). The openness of the country is calculated as the proportion of foreign trade volume to GDP besides the usage of the individual proportion of export or import to GDP (Anorua & Ahmad, 2000; Awokuse, 2008; Burange, Ranadive, & Karnik, 2013; Chow, 1987; Hye & Lau, 2015; Kakar & Khilji, 2011; Kwan & Cotsomitis, 1991; Mercan, Gocer, Bulut, & Dam, 2013; Okuyan, Ozun, & Erbaykal, 2012; Pilinkiene, 2016; Romer, 1993). The FDI inflow as percentage of GDP and gross capital formation as the percentage of GDP is also added in the study as it has significant impact on economic growth (Bakare, 2011; Chen, 2006; Kakar & Khilji, 2011). Gross capital formation represents the additional investment of the economy. The GDP per capita in PPP (constant 2011 international $) is taken to measure the economic growth.

Econometric Methodology

Panel Unit Root Test

Unit root tests are traditionally used to check the order of integration and to confirm the stationarity of the variables. The study uses Levin, Lin, and Chu (2002) test (LLC) and Im, Pesaran, and Shin (2003) panel unit root test (IPS). These tests are based on augmented Dickey–Fuller (ADF) test. The LLC test assumes homogeneity in the dynamics of the autoregressive (AR) coefficients for all panel members. In particular, the LLC test assumes that each individual member in the panel shares the same AR(1) coefficient, but allows for individual effects, time effect and possibly a time trend. The LLC test is known as a pooled Dickey–Fuller test or an ADF test when lags are included by assuming the null hypothesis that of non-stationarity (Sehrawat & Giri, 2016a). The model only allows for heterogeneity in the intercept and is given by the following Equation 1.

Δ Y_{i, t} = a_{i} + β Y_{i, t - j} + \sum_{j = 1}^{k_{i}} a_{j} Δ Y_{i, t - j} + ε_{i, t}

(1)

where ∆Yi,t is a series for panel member (countries) i, over period t ((i = 1, 2, 3 … N); t = 1, 2, 3, … t)), ki represents the number of lags in the ADF regression and the error term ei,t are assumed to be independent and normally distributed random variables for all i and t zero mean and finite heterogeneous variance. The lag order ki in Equation 1 is allowed to vary across panel members. Here, it is assumed that H0: b = 0 meaning that panel series contains a unit root and the alternate hypothesis is that all individual series in the panel are stationary, that is, H1: b < 0 (Sehrawat & Giri, 2016a).

Further, Im et al. (1997, 2003) test is not as restrictive as the LLC test, as it permits heterogeneous coefficient. Hence, it is also called as a ‘heterogeneous panel unit root test’. In addition of the earlier, the IPS test allows individual effects, time trend, and common time effects. The model for IPS test is given as follows:

Δ Y_{i, t} = a_{i} + β_{ι} Y_{i, t - 1} + \sum_{j = 1}^{k_{i}} a_{j} Δ Y_{i, t - j} + ε_{i, t}

(2)

Panel Cointegration

To determine whether a cointegrating relationship exits, the recently developed methodology proposed by Pedroni (1999a) is employed. Basically, it employs four panel statistics and three group panel statistics to test the null hypothesis of no cointegration against the alternative hypothesis of cointegration. Conventional cointegration tests, such as Engle and Granger (1987) and Johansen and Juselius (1990), have low power of estimation when numbers of data points are very less. This method is an extension of traditional Engle and Granger two-step residual biased methods. The technique allows for heterogeneity among individual countries (panel members) and an improvement over traditional cointegration test. The estimated cointegration relationship by using Pedroni’s method is specified as follows:

Model (A)

L G D P C = a_{0} + λ_{i} t + η_{1} L F D I I_{i t} + η_{2} L M 3_{i t} + η_{3} L T O P E N_{i t} + η_{4} L G C F_{i t} + ε_{i t}

(3)

Model (B)

L G D P C = a_{0} + λ_{i} t + η_{1} L F D I I_{i t} + η_{2} L D C_{i t} + η_{3} L T O P E N_{i t} + η_{4} L G C F_{i t} + ε_{i t}

(4)

The LGDPC is proxy of economic growth, LFDII, LM3, and LDC are the variables of financial development, LTOPEN is the trade openness, LGCF is gross capital formation (all in log form); t = 1, 2, 3, … T refers to the time period; i = 1, 2, 3, … N are for the member country in the panel; a₀ represents country specific effect, li is the deterministic time trend, and eit is the estimated errors. The estimated errors indicate the deviation from the long-run relationship. All the competing variables in the study are presented in natural logarithm so the hi parameters can be interpreted in terms of elasticities. Pedroni (1999b) indicates seven different statistics for cointegration. They are panel n-statistic, panel rho-statistic, panel PP-statistic, panel ADF-statistic, group rho-statistic, group PP-statistic, and group ADF-statistic. The first four statistics are called as panel cointegration statistics (within dimension), and the last three are group panel statistics and are based on the between dimension approach (Sehrawat & Giri, 2016a).

Panel Dynamic OLS (DOLS) and FMOLS Analysis

Once identified that there is a linear combination that keeps the panel variables in proportion to one another in the long run, then the next step is to investigate the panel relationship among the competing variables. In order to check the extent of relationship, the panel dynamic ordinary least square (DOLS) method provided by Kao and Chiang (2001) and fully modified ordinary least square (FMOLS) suggested by Phillips and Hansen (1990) are used. The panel DOLS includes advanced and delayed values in the cointegrated relationship to eliminate the correlation between regressors and error term. On the other hand, an FMOLS regression is used to present optimal estimates of cointegrating regressions. By keeping the fact that the OLS estimator is a biased and inconsistent estimator when applied to cointegrated panels, the study also used the ‘group-mean’ panel FMOLS estimator developed by Pedroni (1999b, 2001a, 2001b, 2004). Estimator of the FMOLS model not only generates consistent estimates of the β parameters in relatively small samples but also controls for the likely endogeneity of the regressors and serial correlation.

Panel Granger Causality Test

The Pedroni’s cointegration analysis suggests the presence of long-run relationship among the variables, but it does not determine the direction of causality among variables. Following the Holtz-Eakin, Newey, and Rosen (1988) and Arellano and Bond (1991) estimation procedure, we can establish the causal relationship among the competing variables by using vector error correction model (VECM) as follows:

\begin{array}{l} (1 - L) [\begin{array}{l} L Y_{i t} \\ L X 1_{i t} \\ L X 2_{i t} \\ L X 3_{i t} \\ L X 4_{i t} \end{array}] = [\begin{array}{l} λ_{1} \\ λ_{2} \\ λ_{3} \\ λ_{4} \\ λ_{5} \end{array}] + \sum_{i = 1}^{k} (1 - L) \\ [\begin{matrix} β_{11, i} & β_{12, i} & β_{13, i} & β_{14, i} & β_{15, i} \\ β_{21, i} & β_{22, i} & β_{23, i} & β_{24, i} & β_{25, i} \\ β_{31, i} & β_{32, i} & β_{33, i} & β_{34, i} & β_{35, i} \\ β_{41, i} & β_{42, i} & β_{43, i} & β_{44, i} & β_{45, i} \\ β_{51, i} & β_{52, i} & β_{53, i} & β_{54, i} & β_{55, i} \end{matrix}] \\ [\begin{array}{l} L Y_{i t} \\ L Y_{t -1} \\ L X 1_{t -1} \\ L X 3_{t -1} \\ L X 4_{t -1} \end{array}] + [\begin{array}{l} η_{1} \\ η_{2} \\ η_{3} \\ η_{4} \\ η_{5} \end{array}] (E C T_{t -1}) + [\begin{array}{l} μ_{1} \\ μ_{2} \\ μ_{3} \\ μ_{4} \\ μ_{5} \end{array}] \end{array}

The term (1 − L) represents first difference, and k is the optimal lag length determined by the Schwarz information criteria (SIC) and Y, X1, X2, X3, and X4 are the variables of the model (all in log form). m is the serially uncorrelated error term. b represents the short-run dynamics that exists between the variables while h represents the long-run dynamics of the model. Further, long-run causality is determined by the statistical significance of the respective error terms using a t-test.

Cholesky Impulse Response Function to One SD Innovations

Figure 1.

Results and Interpretation

The results of the LLC and IPS panel unit root tests at levels and first differences are shown in Table 2, which confirms that all the variables in the study are non-stationary at the level but stationary at first difference. This reveals that all the variables are first difference stationary. The descriptive statistics of all the variables are reported in Table 3. Having established that all variables are integrated at an order one, the next step is to apply cointegration test. The Pedroni’s panel cointegration technique is applied to examine the cointegration among the variables. The results of the panel cointegration using seven-test statistics are presented in Table 4. The results confirmed that there is long-run relationship among inward FDI, trade openness, broad money, DC to private investors, gross capital formation, and GDP per capita in both the model. The long-run relationship between financial development, trade openness, and economic growth is supported by several researchers (Kakar & Khilji, 2011; Kar et al., 2011; Murthy et al., 2014; Odhiambo, 2009; Rahman et al., 2015; Sehgal et al., 2013; Sehrawat & Giri, 2016a; Uddin Bidisha, & Ozturk, 2016). In addition, the study used FMOLS and DOLS methods to investigate the degree of long-run coefficient. The results of FMOLS and DOLS are presented in Table 5. The coefficient of FMOLS suggests that all the coefficients (Model A and Model B) are positively associated with economic growth at 5 percent level of significant except FDII which is negatively influencing economic growth. Inward FDI dampens the economic resources in long run by the mode of repercussion and this is not good for developing countries. Whereas the results of DOLS suggests that money supply is not significant at 5 percent in Model A and DC to private investors also found insignificant in Model B. Otherwise, all the variables are significant in Model A and Model B. The negative sign of FDI inflow indicates that it reduces economic growth in these nations.

Table 2.

Unit Root Test Results

		At Level			At First Difference
Variables		NIT	OT	IT	NIT	OT	IT	Inference
LGDPC	LLC	3.3729	0.2506	0.3615	−1.6277	−1.6354	−1.6264	I (1)
	IPS	—	2.6876	0.6784	—	−1.8478	−2.6008	I (1)
LFDII	LLC	−2.5947	−2.3800	0.6700	−7.2243	0.8958	3.3652	I (1)
	IPS	—	−2.8727	−0.7151	—	−4.1598	−3.0319	I (1)
LM3	LLC	4.3399	−0.7040	1.2762	−6.2881	−5.9240	−6.7651	I (1)
	IPS	—	1.3031	−1.3742	—	−6.8569	−5.9846	I (1)
LTOPEN	LLC	−3.3875	−1.9430	0.9863	−6.9113	−5.0224	−3.5943	I (1)
	IPS	—	−0.5102	0.3120	—	−4.6324	−3.2066	I (1)
LGCF	LLC	3.1914	−0.8634	1.1556	−3.5716	0.0700	1.6669	I (1)
	IPS	—	1.2269	0.4853	—	−2.5605	−0.8096	I (1)
LDC	LLC	3.1074	0.2310	−1.0092	−7.0813	−6.4417	−5.7975	I (1)
	IPS	—	1.3872	−1.6079	—	−6.0462	−4.5299	I (1)

Source: Authors’ own calculations.

Notes: NIT indicates no trend and intercept, OT only trend, and IT represents both trend and intercept.

Table 3.

Descriptive Statistics of Variables

Summary Statistics	LGDPC	LFDII	LDC	LTOPEN	LGCF	LM3
Mean	9.032627	0.471189	4.022905	−0.963096	28.52416	4.160074
Median	9.307194	0.746826	3.955147	−0.864161	29.10561	4.151382
Maximum	10.13238	1.826615	5.075954	−0.430679	31.27424	5.326627
Minimum	7.530756	−4.77877	2.120343	−1.762816	25.86971	2.878937
Std. Dev.	0.710148	1.008478	0.768668	0.362996	1.771702	0.557510
Skewness	−0.61083	−1.58427	−0.39903	−0.423109	−0.17093	−0.02876
Kurtosis	2.244635	7.881068	2.201445	1.958977	1.441500	2.835205
Jarque–Bera	9.885358	162.2675	6.107329	8.624118	12.19859	0.145979
Probability	0.007135	0.000000	0.047186	0.013406	0.002244	0.02123
Sum	1038.752	54.18675	462.6341	−110.756	3280.279	478.4085
Sum Sq. Dev.	57.49141	115.9412	67.35702	15.02137	357.8378	35.43314
Observations	115	115	115	115	115	115

Source: Authors’ own calculations.

Table 4.

Pedroni’s Residual Cointegration Test

	Model A			Model B
	NIT	OT	IT	NIT	OT	IT
Panel v-Statistic	2.3051*	−0.9969	4.3091*	1.5945*	−1.4617	3.5323*
Panel rho-Statistic	−2.3051	0.0322	0.9643	−0.7467	0.5344	0.5706
Panel PP-Statistic	−5.2010*	−1.6306*	−4.2458*	−3.4218*	−2.6850*	−2.6037*
Panel ADF-Statistic	−2.7136*	−0.0956	−2.5888*	−0.5515	−1.5097**	−0.0890*
Group rho-Statistic	0.5126	1.2053	1.5952	0.3800	1.1517	1.1471
Group PP-Statistic	−5.7218*	−4.1714*	−3.5059*	−2.9610*	−1.4918**	−2.9226*
Group ADF-Statistic	−2.0833*	−0.5170	−1.4959*	−0.3605	−0.1544	−0.2361

Source: Authors’ own calculations.

Notes: * and ** indicate significant at 5 and 10 percent levels, respectively, NIT indicates no trend and intercept, OT only trend, and IT represents both trend and intercept.

Table 5.

Panel Long-run Elasticity

		FMOLS		DOLS
	Regressors	Coefficient	t-ratio	Coefficient	t-ratio
	LFDII	−0.1402*	−4.8414	−0.2973*	−8.0765
Model A	LM3	0.3959*	4.0738	0.0623	0.5603
	LTOPEN	0.1250*	3.1387	0.1844*	2.1681
	LGCF	0.2694*	16.9430	0.3330*	16.4832
	LFDII	−0.2306*	−6.6103	−0.3871*	−11.6423
Model B	LDC	0.2676*	1.9977	−0.1574	−1.0079
	LTOPEN	0.0554*	0.8562	0.2032*	1.9311
	LGCF	0.2986*	14.1576	0.3727*	13.6037

Source: Authors’ own calculations.

Note: * indicates significant at 5 percent level.

After having the existence of cointegration among variables, the study also employed a panel Granger causality test. The results of panel short-run and long-run Granger causality of both the models are demonstrated in Table 6. The panel Granger causality test indicates that there is bidirectional causality between LGDPC and LFDII in short run. Further, bidirectional causality was also found between LGDPC and LGCF (Bakare, 2011; Beddies, 1999; Chen, 2006; Perkins, Fedderke, & Luiz, 2006; Shahbaz & Rahman, 2012). The results demonstrate that unidirectional causality is running from LGDPC to LM3 (Model A) supporting demand following hypothesis which argues that when an economy grows, demand for financial services increases. In Model B, bidirectional causality was found between LGDPC and LDC which explains that financial development and economic growth are complement to each other (Blackburn et al., 2005; Blackburn & Huang, 1998; Greenwood & Smith, 1997). In long run, only the estimated coefficient of ECT in the GDP per capita equation is significant (Model A and Model B). The estimated results are also supported by the Cholesky impulse response functions.

Table 6.

Panel Causality Test Results

	Sources of Causation
Dependent Variables	Short Run (t-values)					Long Run
Model (A)
	ΔLGDPC	ΔLFDII	ΔLM3	ΔLTOPEN	ΔLGCF	ECT (t-value)
ΔLGDPC	—	−1.9428*	1.5156	0.7291	6.1769*	−2.3343*
ΔLFDII	2.4435*	—	1.4915	1.2313	0.4434	5.8178
ΔLM3	3.0788*	−0.2349	—	0.0774	−3.2135*	1.1244
ΔLTOPEN	1.1553	0.52903	−0.1188	—	0.4453	2.3477
ΔLGCF	1.7301**	−0.039	1.5187	0.4682	—	0.7091
Model (B)
	ΔLGDPC	ΔLFDII	ΔLDC	ΔLTOPEN	ΔLGCF	ECT (t-value)
ΔLGDPC	—	−1.9503**	1.6906*	0.7733	6.5355*	−3.1417*
ΔLFDII	2.2055*	—	1.4035	1.2661	0.7856	5.54246
ΔLDC	3.0727*	−1.2056	—	−0.9635	−1.3633	1.2333
ΔLTOPEN	1.227	0.43037	0.5983	—	0.5661	2.5251
ΔLGCF	1.3572*	−0.1243	1.8436*	0.49891	—	0.3623

Source: Authors’ own calculations.

Notes: * and ** indicate significant at 5 and 10 percent levels, respectively.

Conclusions and Policy Implications

The article investigated the relationship between financial development, trade openness and economic growth of BRICS countries over the period of 1993–2015. Financial development is measured by money supply (percentage of GDP) and DC to private sector (percentage of GDP) while trade openness is measured as sum of export and import to GDP. Besides these variables, gross capital formation and inward FDI (percentage of GDP) is also taken to examine the impact of domestic and foreign investment on economic growth. The study employed Levin et al. (2002) and Im et al. (2003) panel unit root test which suggests that variables are stationary at first difference. Further, Pedroni’s cointegration test was applied to examine the long-run relationship among variables. The empirical results of Pedroni’s cointegration confirmed that there exists a long-run relationship among the variables. The FMOLS and DOLS results indicate that trade openness, money supply, and gross capital formation are positively related to economic growth. Our findings support (Bal et al., 2016; Kar et al., 2011; Murthy et al., 2014; Odhiambo, 2009; Rahman et al., 2015; Sehgal et al., 2013; Sehrawat & Giri, 2016a; Uddin et al., 2016). Other interesting result derived from the study is that inward FDI negatively associated with economic growth in BRICS nations. In addition, the panel Granger causality test indicates that there is bidirectional causality between LGDPC and LFDII in short run. Further, bidirectional causality was also found between LGDPC and LGCF. In long run, only the estimated coefficient of ECT in the GDP per capita equation is significant. All the results are significant at 5 percent level of significance.

In the view of these findings, it is recommended that BRICS countries should liberalize the process of trade openness and more FDI in financial sector by opening multinational banks and other institution should be encouraged to strength financial market in these nations. The development of financial system will increase the economic growth of BRICS by providing financial innovation to private sector. In addition, structural and institutional restriction should be reduced for effective financial system. A strong financial sector will induce private players to export more, and it will increase foreign reserve of these nations. These reserves can be used to import new technology from developed nations, which will stimulate economic growth through capital formation in BRICS.

On the basis of empirical findings, it is suggested that policy related to easy availability of credit to private sector should be adopted in these nations because it will spur the needed innovation and expand the domestic market. The policy implications that can be drawn from the aforementioned conclusion is that because inward FDI has negative effect on economic growth in BRICS, it would be better to save the domestic market from foreign completion as it creates unemployment (Brazil (9%), India (8.4%), and South Africa (25%; World Factbook, 2016). Further FDI inflows also discourage small business and firms particularly in Brazil, India, and South Africa. Government should contribute to address domestic economic and social challenges, including job creation and promotion of social inclusion. Growth in production and export of value-added goods would benefit BRICS and raise the level of their competitiveness. Moreover, government of these nations should make policy for improving the transparency of trade and investment climate in the framework of international obligations and national legislation. A strong financial system will directly enhance economic growth and indirectly improve the trade operations. Economic policies of the government should be made toward trade openness, which will improve capital formation and strengthen the positions of the member countries in the global economy. In addition, BRICS nations should focus on simplification of customs clearance procedures, the exchange of information on national custom laws, as well as the exchange of best practices.

Footnotes

Authors’ Biography

Ritu Rani is an Assistant Professor in Government College for Women, Sampla, Rohtak, Haryana. Her primary research areas include managerial economics, macroeconomics, public economics, and development economics. Her current work involves impact of fiscal deficit on selected macroeconomic variables in India.

Naresh Kumar is an Assistant Professor in the Institute of Management Studies and Research, Maharshi Dayanand University, Rohtak, Haryana, India. His primary research areas include macroeconomics, managerial economics, public economics, and development economics. He received his PhD and Master Degrees in Management and Economics from the Maharshi Dayanand University, Rohtak.

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