Introduction

In recent years, there has been a heated debate on the effectiveness of financial sector development on promoting economic growth and reducing inequality, and particular attention has been paid to the issue of what role strong financial sector development plays on income inequality reduction. On the other side, a desirable economic growth and development levels is always required to push an economy and thus improve social welfare of the citizens. However, several empirical studies highlight the crucial role of finance and strong well-effective financial system on improving economic development and growth, as they contribute to boost total productivity and promote market-driven dynamics (McKinnon 1973; Shaw 1973; Levine 1997; Levine et al. 2000). Likewise, many researchers suggest that strong financial sector development can contribute largely to mitigate income inequality (Li et al. 1998; Beck et al. 2007; Agnello and Sousa 2012; Jalil and Feridun 2011; Clarke et al. 2013; Hoi and Hoi 2013; Nikoloski 2013; Shahbaz et al. 2015; Satti et al. 2015; Zhang and Cheng 2015). Consequently, it is emphasized in the literature that strong financial system will certainly stimulate desirable investment level and economic growth progress. However, an active and well-developed financial sector may provide cheaper credit and easing access to financial services to various individuals that helps to improve entrepreneurial activities which hence create job opportunities and enhance the welfare of the society. Therefore, the accessibility of credit at lower cost may offer decisive support to financially poor families by allowing them to invest in health and education, thereby improving human capital formation in the economy, which will certainly assist the income distribution and poverty alleviation.

A stable and effective financial system is also assumed to be a sign of healthy macroeconomic performance of every economy. Indeed, active and strong financial market adherents can perform a vital role in the enlargement of commerce and industry and thus strengthen the all-inclusive economy of a country. However, several studies show that scarce financial markets can be a source of income inequality and that financial sector imperfection generates income inequality, by assisting entrepreneurs and hurting lenders through its effects of decreasing the rental rate of capital (Westley 2001; Mookherjee and Ray 2003; Hye and Islam 2013; Daisaka et al. 2014; Satti et al. 2015). It is not surprising that income inequality has been on the upsurge worldwide and it affects almost all the developed, emerging, and developing countries, whereas social welfare of the people vary negatively with the country’s level of inequality. Furthermore, it has been argued that high inequality weakens the potency of the economy and promotes economic instability (Stiglitz 2012).

There are many ways to combat income inequality; one way is to encourage financial sector development as along with other benefits it also plays a central role in channeling private saving to investment. Therefore, the article’s goal is to examine the impacts of financial development and growth on income inequality reduction and validate the Kuznets hypothesis, which illustrates an inverted U-shaped linkage between economic development and income inequality, and between financial development and income inequality in the BRICS countries, namely, Brazil, Russia, India, China, and South Africa. To this end, we use annual panel data for BRICS countries covering the period 1990–2015. We contribute to the literature by using different techniques, time period, and portfolio of regressors, which are relatively different as compared to previous studies. In addition, to analyze the impact of financial development on income inequality, we use two different approaches; the first one is to analyze the impact of different proxies of financial development separately. The second is to construct a financial development index for each country by applying the principal component method on the major four proxies of financial development available in the literature, that is, domestic credit provided by banking sector, domestic credit provided to private sector, broad money supply, and stock market capitalization. We expect that the outcomes of this study may help the policy-makers to alleviate income inequality through the development of financial system.

The rest of the paper is organized as follows. Section 2 draws an overview of the literature on economic development, financial development, and income inequality nexus. Section 3 describes the data and the econometric methodology used. Section 4 depicts the empirical findings. Section 5 concludes and suggests some policy implications.

Literature Review

The study has divided the literature section into two parts. First is the review of the empirical literature that finds the relationship between economic growth and income inequality by taking cross-country-level studies. Second is the impacts of financial sector development on income inequality reduction.

Effect of Economic Growth on Income Inequality

The relationship between economic development and income inequality has been widely discussed in the literature. The leading theory of economic development and income inequality is the Kuznets inverted U-shaped hypothesis. Kuznets (1955) posited that at the early stages of economic development, income inequality worsens as per capita income increases and at the advanced stages of economic development income inequality lessens as per capita income increases. Kuznets’ inverted U-shaped hypothesis has been debated since the last few decades and confirmed by a number of empirical studies (e.g., Ahluwalia 1976; Papanek and Kyn 1986; Deininger and Squire 1996). So, several studies have found some contradictions. For example, Piketty and Saez (2003) found that income inequality was more pronounced in the USA since the 1970s. Moreover, International Monetary Fund (IMF (2007) found that income inequality is worsening in most advanced countries than in less advanced countries. Likewise, the Organization for Economic Cooperation and Development, OECD (2008) found that income inequality is rising in the most advanced countries. Another study conducted by OECD (2015) has shown a significant negative relationship between inequality and economic growth in OECD countries over the last 30 years. Shahbaz and Islam (2011) showed that economic growth worsens income distribution in Pakistan during 1971–2005 thereby rejecting Kuznets’ (1955) inverted U-shaped hypothesis. Herzer and Vollmer (2012) found a negative linear relationship between income inequality and economic growth in 46 developed and developing countries over the period 1970–1995. Malinen (2012) found a negative effect of income inequality on economic growth in developed economies. Similar support for the negative relationship between inequality and economic growth was found by Stewart and Moslares (2012) in the case of Indian states. Focusing on the income–inequality nexus in Latin America, Delbianco et al. (2014) found that income inequality worsens economic growth but that this effect dampens with economic development of each country. The results of Park and Shin (2015) confirmed an inverted U-shaped relationship between per capita income and income inequality in Asian economies. Shahbaz et al. (2017) found that economic growth worsens income inequality in Kazakhstan during 1991–2011. Their findings further suggested the decreasing effect of financial development on inequality, while inflation and trade openness increase income distribution.

Effect of Financial Development on Income Inequality

There is a growing body of empirical literature that examines the linkage between financial development and income inequality. The following review explains the impact of financial development on income inequality.

Clarke et al. (2006) examined the impact of financial development on income inequality in 83 countries over the period 1960–1995 and showed that financial development significantly lessens income inequality. Beck et al. (2007) found that income inequality has been declining faster in countries with a well-developed financial system. Their results further suggested that about 40% of the long-run effect of financial development on the income growth of the poorest quintile is due to the declines in income inequality, whereas 60% is the results of the effect of financial development on overall economic growth. Rehman et al. (2008) found that financial development reduces income inequality in 51 countries at different stages of economic growth. However, their empirical findings support for Kuznets’ inverted U-shaped hypothesis. Ang (2010) found that developed financial sector development extensively lessens income inequality in India. In contrast, Sehrawat and Giri (2015) found that financial development worsens income inequality rather widen the gap between poor and rich. Batuo et al. (2010) failed to find any evidence of an inverted U-shaped relationship between financial development and income inequality in African countries. Jalil and Feridun (2011) found that financial development significantly lessens income inequality in China. Shahbaz and Islam (2011) found that financial development reduces income inequality, while economic growth worsens income distribution in Pakistan.

Hoi and Hoi (2013) examined the effect of financial development on income inequality in Vietnam over the period 2002–2008; they have not found evidence supporting the inverted U-shaped hypothesis on financial development–income inequality nexus. Nikoloski (2013) examined the linear and non-linear relationships between financial development and income inequality for both developing and developed countries, and found strong evidence supporting Greenwood and Javanovic’s (1990) inverted U-shaped hypothesis. Baligh and Piraee (2012) and Shahbaz et al. (2015) showed that financial development significantly lessens income inequality in Iran. Their findings lend support for the inverted U-shaped hypothesis proposed by Greenwood and Javanovic (1990). Law and Tan (2009) and Mansur and Azleen (2017) failed to find any statistically significant effect of financial development on income inequality in Malaysia.

Data and Method

Data

The study employs a panel annual dataset covering five emerging countries, namely, Brazil, Russia, India, China, and South Africa, from 1990 to 2015. Data on GINI, per capita GDP growth, inflation, domestic credit provided by banking sector (DCB) to GDP ratio, domestic credit provided to private sector (DCP) to GDP ratio, broad money supply (M2) to GDP ratio, and stock market capitalization (SMC) to GDP ratio are taken from the World Bank World Development Indicators (WDI 2016) online database (http://data.worldbank.org/indicator). In this framework, we use the last four ratios, i.e., DCB (% of GDP), DCP (% of GDP), M2 (% of GDP), and SMC (% of GDP), to construct the financial development index for each country. Table 9 (refer to Appendix) summarizes the description of the included variables.

Empirical Methodology

The question we seek to address in this study arises what role economic growth and financial development play on income inequality reduction over time and across countries. In doing so, we consider the income inequality model by endogenizing per capita income, inflation, and financial development that was written as follows:

$$ {GINI}_{it}={\alpha}_i+{\beta}_1{GDP}_{it}+{\beta}_2{GDP}_{it}^2+{\beta}_3{INF}_{it}+{\beta}_4{FD}_{it}+{\beta}_5{FD}_{it}^2+{v}_i+{\varepsilon}_{it} $$
(1)

where i = 1,…, N represents countries observed over the time periods t = 1,…, T, Gini is the Gini coefficient which measures income inequality, GDP is per capita GDP growth rate, GDP2 is its squared term, INF is inflation rate, FD is financial development which is represented by five different proxies, FD2 is a squared term of the different proxies of financial development for examining Kuznets’ inverted U-curve hypothesis for BRICS countries, vi is an unobserved individual effect/homoscedastic country specific effect, and εit is an unobserved white noise disturbance/the stochastic disturbance term. To validate the Kuznets curve for our model, we expect β1 > 0, β4 > 0, β2 < 0, and β5 < 0.

In order to examine the linkage between income inequality, financial development, and its major components, we consider the model equations with different proxies of financial development that takes the following form:

$$ {GINI}_{it}={\alpha}_i+{\beta}_1{GDP}_{it}+{\beta}_2{GDP}_{it}^2+{\beta}_3{INF}_{it}+{\beta}_4{CDB}_{it}+{\beta}_5{CDB}_{it}^2+{v}_i+{\upvarepsilon}_{it} $$
(2)
$$ {GINI}_{it}={\alpha}_i+{\beta}_1{GDP}_{it}+{\beta}_2{GDP}_{it}^2+{\beta}_3{INF}_{it}+{\beta}_4{CDP}_{it}+{\beta}_5{CDP}_{it}^2+{v}_i+{\varepsilon}_{it} $$
(3)
$$ {GINI}_{it}={\alpha}_i+{\beta}_1{GDP}_{it}+{\beta}_2{GDP}_{it}^2+{\beta}_3{INF}_{it}+{\beta}_4M{2}_{it}+{\beta}_5M{2}_{it}^2+{v}_i+{\varepsilon}_{it} $$
(4)
$$ {GINI}_{it}={\alpha}_i+{\beta}_1{GDP}_{it}+{\beta}_2{GDP}_{it}^2+{\beta}_3{INF}_{it}+{\beta}_4{SMC}_{it}+{\beta}_5{SMC}_{it}^2+{v}_i+{\varepsilon}_{it} $$
(5)
$$ {GINI}_{it}={\alpha}_i+{\beta}_1{GDP}_{it}+{\beta}_2{GDP}_{it}^2+{\beta}_3{INF}_{it}+{\beta}_4{FDI}_{it}+{\beta}_5{FDI}_{it}^2+{v}_i+{\varepsilon}_{it} $$
(6)

where DCB is a total domestic credit provided by banking sector, DCP is total domestic credit provided to private sector, M2 is broad money supply which represents the circulation money in each country’s economy, SMC is stock market capitalization which represents the development of capital market of any economy, FDI is financial development index, and FDI2 is a squared term of financial development index.

The first econometric issue that we need to deal with when we estimate the regression equation is to analyze the stationary properties of the relevant variables. To this end, we begin our econometric analysis by testing the presence of a unit root using the two-panel unit root tests suggested by Levin et al. (2002), (LLC thereafter), and Im et al. (2003), IPS thereafter. The starting point of LLC is to assume that the stochastic process ∆yit is generated by the first-order autoregressive process as:

$$ {\Delta y}_{it}={\alpha y}_{it-1}+{\sum}_{j=1}^{p_i}{\beta}_{ij}{\Delta y}_{it-1}+X{\prime}_{it}\delta +{\varepsilon}_{it} $$
(7)

where Δyit is the corresponding panel data series in difference term, α = ρ − 1, ρ is the lag order for Δyit that may fall and rise for cross section, and X′it is the exogenous variable in the model. The LLC unit root test considers the different autoregressive parameters as homogeneous across all individuals, i.e., βi = β for all i. The null hypothesis under the LLC is that each series has a unit root, H0 : βi = 0 for all i against the alternative hypothesis that some of the individual series have a unit root, H1 : βi < 0 for all i. The asymptotic distribution of these statistics follows a standard normal distribution. The individual unit root procedure is allowed in IPS panel unit root test. Hence, the IPS unit root test combines the individual unit root test to derive a panel specific result.

After confirming that all series in our panel are integrated with order one, the next step is to test for the presence of cointegration among them. In doing so, Pedroni (1999) panel cointegration technique is used to examine the long-run relationship between the considered variables. This technique is preferred over other cointegration methods of its class because it is usefully effective for controlling the country’s size bias and solving the heterogeneity issue through parameters that may differ among individuals. However, to test for cointegration in a heterogeneous panel data, Pedroni (1999) considers the following cointegrating regression:

$$ {Gini}_{it}={\alpha}_i+{\delta}_{it}+{\beta}_1{GDP}_{it}+{\beta}_2{GDP}_{it}^2+{\beta}_3{INF}_{it}+{\beta}_4{FD}_{it}+{\beta}_5{FD}_{it}^2+{\varepsilon}_{\mathrm{i}t} $$
(8)

where αi is the country-specific fixed effects and δit represents country-specific time trends, which capture any country-specific omitted variables. The slope coefficients β1, β2, β3, β4, and β5 can differ from one individual to another allowing the cointegrating vectors to be heterogeneous across countries. The estimated residuals are as the following form:

$$ {\varepsilon}_{it}={\varphi}_i{\varepsilon}_{it-1}+{u}_{it} $$
(9)

Under the null hypothesis of no cointegration, that is, H0 : φi = 1. , there are two alternative hypotheses. First, the homogenous alternative (within dimension or panel statistics) is to be tested as H1 : φi = φ < 1,   ∀ i. Second, the heterogeneous alternative (between dimension or group statistics) is to be tested is H1 : φi < 1, ∀ i.

Pedroni’s (1999) panel cointegration approach is based on seven different statistics, four of which are based on pooling along the within-dimension test, while the other three statistics are based on the group statistics approach by using the appropriate mean and variance; the asymptotic distribution of these statistics follows a normal distribution. We also employ Kao’s (1999) residual panel cointegration test to determine the long-run relationship between the relevant variables. The null hypothesis of Kao residual panel cointegration test is that there is no cointegration between the series. The desirable probability to have a valid long-run relationship is must be less than 10%, which implies that there exist valid long-run relationships at the 10% significance level.

Construction of Financial Development Index

To analyze the importance of financial reforms on the performance of any economy, researchers applied two different methods. The first group of researchers used different proxies of financial development to analyze the utilization of different financial reforms features and characteristics on the performance of the economy (e.g., Ahmed 2007; Bittencourt 2010; Hye 2011). The second group of researchers deals with constructing a composite financial development index by applying the principal component analysis (PCA) method on the major measures of financial development (e.g., Batuo et al. 2010; Hye and Islam 2013). Our novelty in this study is that we use both approaches. Firstly, we analyze the impact of the different proxies of financial sector development independently in different models. Secondly, we construct the financial development index for Brazil, Russia, India, China, and South Africa by applying the PCA method on the major four proxies of financial development available in the literature, namely, domestic credit to private sector to GDP ratio, domestic credit given by banks sector to GDP ratio, broad money supply to GDP ratio, and stock market capitalization to GDP ratio. The PCA is a multivariate statistical technique which is usually used for analyzing the inter-correlation linking several quantitative variables. In terms of methodology, for each dataset with p quantitative variables, we can evaluate at most p principal components (PC), each being a linear combination of the original variables, where the coefficients are equal to the eigenvectors of the correlation covariance matrix. The PC is then arranged by descending order of the eigenvalues which are equal to the variance of the components.

The results of construction of the composite of financial sector development index for Brazil, Russia, India, China, and South Africa through PCA method are reported in Appendix Table 10. The PC analysis for Brazil indicates that the first PC explains about 70.21%, the second PC explains 27.18%, the third PC explains 1.96%, and the fourth PC explains 0.65% of the standardized variance. This implies that we select the first PC to compute the financial development index. The first PC is a linear combination of the four measures of financial development with weights provided by the first eigenvector. After rescaling, the individual contributions of each series DCB, DCP, M2, and SMC to the standardized variance of the first PC are around 59.99%, 58.81%, 36.97%, and 41.15%, respectively. We use further these weights to construct the composite financial sector development index for the economy of Brazil. The same interpretation of the results is found to be true regarding the economy of Russia, India, China, and South Africa.

Empirical Results

Descriptive Statistics

Table 1 reports the descriptive statistics of our main variables for BRICS countries during the period 1990–2015. It is worth noting that the sample countries depict a wide gap with regard to their income inequality and financial development measures. However, the highest average value of Gini coefficient (61.02) is shown in South Africa, while the lowest value is shown in India (33.51) with a standard deviation of 1.23. The highest average level of per capita GDP growth (9.35) is shown in China, while the lowest average level is shown in Brazil (2.46) with a standard deviation of 2.82. In addition, the highest level of inflation (24.60) is in Russia, while the lowest level is for China (3.59) with a standard deviation of 3.74. Thereafter, China is the highest volatility country in Gini coefficient (0.06), followed by Russia in GDP per capita growth (1.75) and in inflation (1.25), respectively. For the financial development measures, the highest means of DCB (% of GDP) (166.98), DCP (% of GDP) (135.76), and SMC (% of GDP) (204.96) are shown in South Africa, while the lowest CDB (% of GDP) (33.17), CDP (% of GDP) (30.66), M2 (% of GDP) (36.89), and SMC (38.78) are shown in Russia. The comparison between these countries shows that Russia is the highest volatility country in terms of CDB (0.30), CDP (0.53), and M2 (0.38). The same pattern is found for SMC (0.63) for China.

Table 1 Descriptive statistics
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Results of Panel Unit Root and Cointegration Tests

The first step in our econometric analysis is to check the stationary properties of the relevant variables. The existence of a unit root is tested using LLC and IPS panel unit root tests. Table 2 reports the results of panel unit root tests. These tests are first applied on the level of variables, then on their first difference. The null hypothesis of non-stationarity based on both LLC and IPS tests is rejected against the alternative hypothesis at the 1% significance level. This implies that all series in our panel sets are stationary and integrated at their first difference, I(1), with intercept, and with intercept and trend. The results of both LLC and IPS panel unit root tests confirm that the series of variables may exhibit no unit root problem and we can then use them to analyze the long-run relationship.

Table 2 Panel unit root tests results
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Since the stationary results from unit root tests prove that all variables are non-stationary and integrated with order 1, we further proceed to the cointegration test panel suggested by Pedroni (1999) in order to analyze the long-run relationship among the considered variables. The results of Pedroni panel cointegration tests presented in Table 3 show that in all five models, the augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) test statistics based on both within-dimension and group-based approach statistics reject the null hypothesis of no cointegration. This implies that all the considered variables are cointegrated and exhibited a valid long-run relationship.

Table 3 Pedroni panel cointegration tests
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The Kao residual cointegration test is reported in Table 4. The test results, in all five models, indicate the rejection of the null hypothesis of no cointegration at the 10% significance level, which implies that there exists a long-run cointegration relationship between all the considered variables.

Table 4 Kao residual cointegration test
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Estimation Result

One important issue in a panel data analysis is to take into account possible both the cross-section dimension and the time-series dimension. In line with this and before running regression, some specification tests have been performed. The first one is the Wald test (Greene 2000) to test whether there are a cross-section effects and period effects in our considered models. The first null hypothesis is that the cross-section effects are absent. The second null hypothesis is that the period effects are absent. The Wald test results confirm that both the null hypotheses are rejected at the 1% significance level in all five models, and hence there exists a strong difference in considered variable between countries and over time.

Hausman (1978) test is performed in order to identify the appropriateness method between fixed-effect (FE) model and random-effect (RE) model in the empirical estimations. The null hypothesis is that there is no systematic difference in coefficients of FE and RE estimation. The rejection of the null hypothesis implies that there is a systematic difference in the coefficients; therefore, we need to apply FE model rather than RE model and vice versa (Jeffrey 2009). The Hausman test results (refer to Table 5) confirm that in all five models, the null hypothesis is rejected at the 1% significance level and hence FE model is preferable to RE model. Moreover, Wu–Hausman test is used to check the exogenous properties of the estimated models. The rejection of the null hypothesis implies that there is endogeneity in the model. The endogeneity is an issue when there is a correlation between the parameters and the error term. The Wu–Hausman test results (refer to Table 5) indicate that the null hypothesis is accepted. This implies that there is no simultaneity that exists between the considered variables and the estimators are unbiased and consistent (Greene 2000).

Table 5 Results of fixed-effect (FE) models
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The empirical result of fixed-effect models, pooled ordinary least square (POLS), and generalized methods of moments (GMM) panel estimations are presented in Tables 5, 6, and 7, respectively.

Table 6 Results of pooled ordinary least square (POLS) models
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Table 7 Results of generalized method of moment (GMM) models
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Fixed-Effect Estimation Result

Table 5 reports the results of FE models. Columns 1–5 discuss the static effects of economic growth and financial development on income inequality in which the different proxies of financial development (i.e., DCB, DCP, M2, SMC, and FDI) are estimated separately. In all specifications, we find that the different proxies of financial development have a positive and statistically significant impact on income inequality, while the coefficients of their squared terms have a negative and statistically significant impact on income inequality. We find also that FDI has a positive and statistically significant impact on income inequality at the 1% significance level, while the coefficient of its squared term has a negative and statistically significant impact on income inequality at the 5% significance level. It shows that 1% increase in financial development leads to 0.195% decrease in income inequality. This implies that financial development can significantly help to reduce income inequality for BRICS countries. The results further suggest that sound and well-developed financial system is essential for fighting income inequality and promoting development by increasing the financial services availability to the poor for financing their capital investments.

Similarly, we find that GDP per capita has a positive and statistically significant impact on income inequality at the 5% significance level, while its squared term has a negative and statistically significant impact on income inequality at the 1% significance level in all model specifications. It can be inferred that when per capita income continues to increase, income inequality starts to decrease. Further, inflation has a positive and statistically significant impact on income inequality at the 1% significance level in all specifications, indicating that when macroeconomic stability is improved by reducing inflation in BRICS countries, financial sector development leads to mitigate largely income inequality. These empirical findings support Kuznets’ inverted U-shaped relationship between economic growth and income inequality, and between financial development and income inequality in BRICS countries.

Sensitivity Analysis (POLS and GMM Estimation Results)

In the FE static panel model, the country-specific effect is treated as fixed rather than random and it may be correlated with the regressors. Therefore, to examine whether our initial results are robust for endogeneity, we applied two different sensitivity analyses, that is, pooled ordinary least square (POLS) and generalized methods of moments (GMM), in order to find efficient estimators. First, the robustness of empirical results is checked by employing POLS estimators. The results of POLS presented in Table 6 show that, in all specifications, the linear terms of both GDP per capita and the different financial development proxies have positive signs and are statistically significant, while their squared terms have negative signs and are statistically significant even if the magnitude is also almost the same as in FE models.

Second, the robustness of empirical results is examined by employing GMM estimators, which is the most commonly used method to eliminate fixed effects in the case of small time series and large cross-sectional units. This technique uses a set of instrumental variables in order to solve the endogeneity problem (Arellano and Bond 1991; Blundell and Bond 1998). We used Hansen (1982) J-test over-identifying restrictions statistics for the validity of GMM instruments’ variables. The null hypothesis of Hansen J-test is that all instruments are uncorrelated with the error term. The results of GMM estimations are presented in Table 7. The Hansen test (p value) suggests that all the result specifications did not reject the null hypothesis and hence our over-identified restriction is valid. The regression results show that income inequality is influenced positively and significantly by GDP per capita and inflation. The results also show that the different proxies of financial development have a positive and significant relationship with income inequality, while the squared terms of the different financial development proxies have a negative and significant relationship with income inequality. It shows that in all specifications, the coefficients of all considered variables and their squared terms have retained the same signs and significance even if the magnitude is also almost the same as in FE models. This means that our initial results are robust and offer strong support to the Kuznets inverted U-shaped hypothesis.

Granger Causality Analysis

To examine the causality direction between economic growth, financial development, and income inequality, we used panel Granger (1969) causality test. Granger causality is a statistical hypothesis test which verifies whether one time series is skilled of forecasting another. It is a powerful tool for analyzing the causal effect and functional link from various panel data. We determine the causality analysis of the income inequality equations on lag one. According to Jones (1989), ad hoc selection technique for lag length in Granger causality test is most preferred to other statistical methods to determine the optimal lag length.

Table 8 reports the results of Granger causality test. The results confirm that there is a unidirectional causality running from all financial development proxies to income inequality. On the other side, a bidirectional causality running from inflation to income inequality and from income inequality to inflation is found. This implies that inflation and income inequality both are significantly affecting each other. The results also confirm a unidirectional causal relationship between economic growth and income inequality. The direction of causality runs from income inequality to economic growth, indicating that income inequality negatively affects economic growth in BRICS countries.

Table 8 Panel Granger causality results
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Conclusions and Policy Implications

This paper seeks to strengthen the literature by examining the relationship between economic growth, financial development, and income inequality in BRICS countries, namely, Brazil, Russia, India, China, and South Africa, by using annual panel data covering the period 1990–2015. Our empirical findings provide strong evidence for Kuznets’ inverted U-shaped relationship between economic growth and income inequality. We find that the linear term of GDP per capita growth has a positive sign and squared term has a negative sign and statistically significant at the 1% significance level in all specifications. In addition, our findings support for Kuznets’ inverted U-shaped relationship between financial development and income inequality in BRICS countries. We find that the linear terms of all financial development proxies have positive signs and squared terms have negative signs and statistically significant at the 5% significance level in all specifications.

The main findings of the paper suggest some policy implication to fighting income inequality in BRICS countries. First, the policy-makers forcefully need to devise fiscal policy and thereby progressive taxation system in the true sense. Second, they should be vigorously encouraging inclusive development covering rural development policies, comprising income tax policies and publicly financed services. Additionally, progressive redistribution of asset ownership plan is also a good option for income inequality alleviation. Finally, efforts should be directed at improving institutional quality, maintaining low levels of inflation, and strengthening further the financial systems in order to combat inequality and achieve sustainable economic growth for a long span of time in these countries.