Exploring the heterogeneous impact of technological innovation on income inequality: Formulating the SDG policies for the BRICS-T economies

Abstract

The present research evaluates the effect of technological innovation distribution on the distribution of income using data from between 1992Q1 and 2019Q4 for the BRICS-T (Brazil, Russia, India, China, South Africa, and Turkey) nations. The BRICS-T nations present a good case for this research since this problem could be more widespread in developing countries with high prospects for economic growth. The quantile causality and quantile-on-quantile regression techniques have been used to evaluate this association. The research findings provide a range of outcomes from different countries, which can be grouped into three categories; (i) Technological innovation impacts income inequality positively. (ii) Technological innovation distribution impacts income inequality distribution negatively. (iii) The effects of technology innovation on income distribution are not evenly distributed. Significant policy ramifications are deduced that might inspire sustainable development plans in the BRICS-T nations. This research is one of the first studies to demonstrate a direct connection between income inequality and technological innovation across various quantiles within a country. The study also effectively shows how these techniques are utilized to deduce the policy ramifications of the Sustainable Development Goals (SDGs).

Keywords

Technological innovation income inequality BRICS-T nations quantile-on-quantile regression quantile causality

Introduction

Economic growth is supported by technological innovation (TEC). Innovations brought about by this technology innovation support economic growth. While achieving this growth trajectory, innovation is likely not evenly distributed equally across a country. As a result, the wealth created during an economic growth is concentrated in the hands of a select few. This tendency is mirrored in the country's economic disparity, a problem that is related to the improper distribution of technology or the dissemination of innovation information. Among the first theoretical investigations on the connection between inequality and technology innovation were those by Katz and Murphy,¹ Karni and Zilcha,² and Rehme.³ According to Katz and Murphy,¹ technological innovation can raise worker productivity and income. As a result, income disparity may either decrease or increase. Changes in technology indirectly influence how income is distributed.²

Following these investigations, Acemoglu⁴ examined how technological innovation, income, and the labor market are related. The influence of TEC on the labor market and income/wage distribution is a crucial aspect of the investigation. Investment in TEC in the form of clean technologies in the energy industry causes the improvement in energy efficiency. These factors have gained particular significance during the past 10 years due to countries’ adherence to the Sustainable Development 2030 Agenda. The Sustainable Development Goals (SDGs) accepted by all United Nations member states were outlined in the framework to address economic, environmental, and social issues concurrently. Scholars also underlined the necessity for a multifaceted policy approach in light of this paradigm whilst addressing concerns relevant to achieving the SDG targets.^5–7 According to this perspective, combating climate change (SDG 13) may necessitate investments by countries in green technologies that are cost-effective for consumers (SDG 7), for which countries would inevitably need to concentrate on innovation and accompanying infrastructure development (SDG 9). Nevertheless, it is still unclear how these activities could affect a country's economic growth. In other words, the level of innovation that exists in a country may affect how money is distributed. This is particularly significant for developing countries because investing in technological innovation is necessary to combat climate change while also focusing on economic expansion.

On the one hand, these countries’ current uneven economic growth trajectories are likely to increase income inequality, given that raising income levels is the main aim in developing economies. Such developing countries frequently suffer from this unequal income distribution as a small proportion of the country's population typically receives the majority of the income. Since income disparity directly affects macroeconomic issues, governments work to implement policies that could reduce this disparity (SDG 10). Individuals who make between $10 and $100 daily are considered “middle class.” By 2030, it is anticipated that China alone will have one billion of such consumers, up from its present level of 150 million.¹ In Russia, over 20% of the national income went to the top 1% with the poorest receiving 50% in 2019. This indicates that the income of individuals in the top 1% was almost 50 times higher than that of those in the bottom 50%. In the case of India, significant growth in the middle class will likely start a little later in the 2020s. Nevertheless, Indians will be transitioning to the middle class at a significantly faster rate than Chinese people by the end of that decade. Furthermore, the middle class in South Africa has been diminishing for years; for example, from 2017 to June 2020, it fell from 6.1 to 2.7 million people, a decrease of 55.73%. Additionally, 6.6 million more people now live in extreme poverty and make less than the minimum wage (54%).² The middle class in Turkey has more than doubled from 18% to 40%, which is one of the main reasons for Turkey's expanding economy.³ The expansion of the middle class has aided Turkey's efforts to become an upper-middle-income economy.

Consequently, it is anticipated that income inequality would rank among the most crucial factors when suggesting any policy that aligns with the 2030 SDG goals. This paper aims to determine whether developing markets are effective at stimulating technological innovation while also taking into account the need to lessen inequality and prevent ecological deterioration. As a result, the study utilizes a bivariate model, focusing on how the level of technological innovation affects various income groups within a country. The rationale behind this research is that it as an augmentation of Schumpeter's Theory of Innovation, which implies that innovation contributes to the concentration of wealth in the hands of a select few.⁸ Although this theory was created in accordance with the entrepreneurial growth in developed countries, it can be extended within the parameters of innovation because, like all forms of innovation, technological innovation can be seen as a tool for sustainable development in developing markets. As a result, the theoretical paradigm of the paper can be seen as an augmentation of this theory.

The study chooses the BRICS-T (Brazil, Russia, India, China, South Africa, and Turkey) as the case study for the following reasons. First of all, these nations are among the developing economies that have the potential to become the globe's biggest economies. As a result, they not only constitute a challenge to the current leading countries but also provide a solid starting point for discourse about the relationship between climate change and economic growth. It is clear that policymakers in developing countries are more concerned with achieving economic growth than preserving ecological quality.⁹ Secondly, these nations have become less energy efficient as they are primarily concentrated on economic activities, making them more vulnerable to ecological issues in the near future. The reality that the BRICS-T countries contribute more to global carbon emissions than any other developed or emerging nation further supports this argument. The energy poverty challenges that frequently affect the BRICS-T countries might have a negative impact on manufacturing operations and resulting economic growth. The expanding problem of unmet energy demand is caused by these countries’ reliance on fossil fuel-based energy and growing energy demand. The prevalence of this problem provides a good foundation for these countries to develop technological innovation. Thirdly, the BRICS-T nations’ exposure to the level of investments in technological innovation is uneven. As we continue our discussion on infrastructure development, it is important to recall that the economic growth trajectory in these countries has led to the problem of energy poverty, which is a significant contributor to the widening wealth gap in these countries. As a result, it can be argued that these countries’ innovations aimed at increasing energy efficiency could responsible for the waves of sustainable development in these countries. Since technological innovation can be seen as a generator of sustainable development in these countries, where energy is a significant engine of economic growth, the energy façade of the innovation may seem more pertinent to these countries. Recognizing how different levels of technology innovation affect income inequality in the BRICS-T nations is crucial because policy-level myopia may prevent decision-makers from investing in technological innovation by forgoing short-term gains in favor of long-term gains.

To the best of the authors’ knowledge, very few scholars have sought to analyze the connection between income distribution and technological innovation in developing economies from a policymaking perspective. For instance, Josifidis and Supic¹⁰ reported a positive innovation-income association. Furthermore, the research of Sinha et al.⁶ on the energy innovation-income nexus association reported mixed findings. Since the influence of technological innovation on income distribution in emerging economies has not been explicitly covered in previous studies, there is a gap in terms of how policies are developed to ensure sustainable development. By examining the effect of technological innovation on income disparities in developing economies such as the BRICS-T nations, this research attempts to close this gap. This research is necessary because it aims to determine whether the level of technological innovation contributes to a uniform change in income creation across these markets, with a particular focus on the BRICS-T nations. This assumes significance since, in such countries, the pursuit of economic expansion and clean energy production are frequently at odds. The SDG agenda that the study proposes to design by examining the relationship between technological innovation and income inequality among the BRICS-T economies yields the policy-level implications of this research. In this schema, the primary level of policy design will be geared toward achieving SDGs 7, 9, and 13. In contrast, the secondary and tertiary levels will be intended to accomplish SDGs 8 and 10. The research's contribution at the policy level necessitates the use of an appropriate methodological framework that considers the entire range of data distribution. Additionally, it is crucial to keep in mind that the influence of the control policy variables must be evaluated across the whole range of the target policy variable. For this purpose, the study uses the Troster¹¹ causality method and the quantile-on-quantile regression (QQR) developed by Sim and Zhou.¹² The capacity to assess the influence of the whole distribution of one parameter on the quantile distribution of the other parameter is one of the most significant benefits of employing advanced quantile approaches. This enables us to provide a complete picture of the effect of technological innovation on inequality. Furthermore, these techniques allow the formulation of policy across each quantile. The benefits of these methods match the research's policy merit, and therein lies its analytical contribution.

The research is structured as follows: The literature is examined in section “Literature review.” The model and data are the focus of section “Data and methods.” The empirical results and discourse are covered in section “Findings and discussion.” The policy recommendations and conclusion are provided in the final section.

Literature review

It is widely acknowledged that technological innovation has a significant impact on macroeconomic factors. Technological innovation can influence energy efficiency.¹³ A recent study has concentrated on how technological innovation (TEC) affects income inequality (IE). Some studies established a positive innovation-IE association. For instance, Cetin et al.¹⁴ examined the innovation-inequality nexus in Turkey using data from 1987 to 2018. The study using the ARDL bounds test and VECM Granger documented that technological innovation has a beneficial impact on income disparity, whereas economic expansion has a negative effect on inequality. Furthermore, feedback causality exists between income inequality and technological advancement. Similarly, the study of Law et al.¹⁵ using patent applications and patents granted as a proxy of technological innovation, reported that technological innovation impact inequality positively. Likewise, the study of Aghion et al.¹⁶ reported the positive innovation-inequality association in the USA using data between 1975 and 2018. The skill-biased technology theory is used by Breau et al.¹⁷ to evaluate the interconnection between innovation income disparity in a Canadian city between 1996 and 2006. The study reported that in all the cities, the increase in income inequality is caused by an intensification of technological innovation.

Moreover, Lin and Ma¹⁸ study in China using data from 1995 to 2011scrutinised the effect of technology on inequality. The findings indicate a U-shaped link between innovation and income, indicating that while modest levels of innovation can contribute to and reduce economic disparity, more significant amounts of innovation may do the opposite. Furthermore, income disparity rises as a result of both industrialization and urbanization. The findings also indicate an inverse U-shaped association between the population's high skill level percentage and innovation. Similarly, Josifidis and Supic¹⁰ scrutinized the innovation-inequality nexus in the United States. As a consequence of the change in R&D spending from the public to the private sectors, econometric research shows that the income level of the richer classes increased at the cost of the lower-income groups. According to an institutionalist viewpoint, these findings were supported by corporate capital's ceremonial confinement of innovation, which inhibits the rate of social advancement. This situation allows for the treatment of innovation dissemination as a gradual institutional transformation.

On the flip side, some research has concluded that an upsurge in innovation results in lessening income disparity. Utilizing quantile regression, Antonelli and Gehringer¹⁹ assess the impact of innovation on income for a group of developed nations. They discover that the effect of creative destruction is how an increase in innovation decreases income inequality. Similar to this, Karagiannis et al.²⁰ investigate the inequality-innovation nexus on a larger scale, looking at 126 spatial units within the European Union. They find that while an increment in innovation increases the income of high-income groups, it also lowers complete inequality by increasing the income of other income groups. Similar findings were made by Włodarczyk,²¹ who discovered that in 30 European nations, an upsurge in technological innovation contributes to the reduction in income disparity. According to a study conducted by Tchamyou et al.,²² between 1996 and 2014, the income inequality in 46 African states decreased due to technological innovation.

Based on the studies reviewed above, three perspectives emerged on the technological innovation and income inequality nexus. First, some studies have found that an increase in technological innovation contributes to income inequality. Secondly, some studies have established a negative income equality and technological innovation nexus. Lastly, other studies have established an insignificant nexus between income inequality and technological innovation. Based on these results, there is conflicting and ambiguous evidence about how innovation affects income inequality. Therefore, there is a need for further study on the technological innovation and income inequality nexus. From the standpoint of the SDGs, this influence is significant since focusing on technological innovation may pave the way for a socio-environmentally sustainable society. Technological innovation is now more prevalent when the economic growth patterns of developing nations are taken into account since the impact of innovation on income is an issue of increased relevance as it meets SDGs 10 and 7. Thus, no research studies have focused on this topic in the context of the BRICS-T nations; therefore, this paper fills this knowledge vacuum. This research intends to build an appropriate policy framework for the BRICS-T nations, contributing to the literature from a policymaking standpoint. This connection may be significant for reaching the SDGs from the sustainable development perspective.

Data and methods

Data

The influence of technological innovation distribution on income distribution has been examined for the BRICS-T nations between 1992 and 2019. In this research, the post-tax/transfer (net) Gini index data was initiated by Solt.²³ Standardized World Income Inequality Database (SWIID)⁴ was utilized to signify income inequality (IE). In this empirical investigation, the SWIID inequality data is vital because it standardizes and combines inequality data from different inequality databases, including World Income Inequality, World Income Distribution Data, Luxembourg Income Studies, and others.²³ Prior studies, including Acheampong et al.,^24,25 have utilized the SWIID. The data for technological innovation (TEC) is gathered from the World Bank database and calculated as the addition of patents (nonresident and resident). The data were converted from yearly to quarterly frequency utilizing the quadratic-match-sum approach.^5,26

Brief information on both IE and TEC is presented in Table 1. The skewness values reveal that the data are extremely skewed, while the kurtosis statistics indicate that almost all of the series have a fat-tailed distribution. The kurtosis and skewness values show that the data are not distributed normally, which is supported by the Jarque-Bera (JB) statistic. Moreover, the JB statistics results demonstrate that $H_{o}$ of normality can be rejected at a significance level of 1%. Examining the JB results, it is clear that both IE and TEC for each BRICS-T nation exhibit nonlinear attributes, implying that using linear estimators to evaluate this connection will produce incorrect results (see Table 1). The BDS nonlinearity test²⁷ is then used to assess whether nonlinearity exists in each nation's IE and TEC. Table 2 displays the empirical evidence. It shows that the Ho of linearity is rejected for all the series, indicating that nonlinearity exists and suggesting that a nonlinear technique should be used for the empirical investigation. The QQR technique is used to solve the issue of non-normality.

Table 1.
Descriptive statistics.

Brazil China India Russia South Africa Turkey

Income inequality (IE)

Mean 50.66667 39.79583 45.97917 35.53750 62.37917 41.12500

Median 51.08438 41.42656 47.17813 36.18750 62.70000 41.45781

Maximum 54.11562 43.00625 49.83750 37.04063 63.60625 42.40625

Minimum 46.20625 33.37813 41.19375 28.18125 60.78438 39.49375

Std. Dev. 2.871131 3.086862 3.074880 1.671708 0.977885 1.035625

Skewness −0.254567 −0.651320 −0.247661 −2.282318 −0.346637 −0.326778

Kurtosis 1.514959 1.875587 1.485229 8.708940 1.609191 1.512178

Jarque-Bera 9.858259 11.84471 10.15950 213.7116 9.659922 10.56301

Probability 0.007233 0.002679 0.006221 0.000000 0.007987 0.005085

Technological innovation (TEC)

Mean 19817.00 427491.9 25863.57 35757.96 6655.071 3283.893

Median 19167.19 192561.1 26703.91 36429.25 6932.766 2626.031

Maximum 30920.78 1552623. 54988.16 61073.66 8372.000 8695.531

Minimum 6336.031 11195.63 3387.500 23017.41 2509.844 698.0938

Std. Dev. 7687.493 499440.3 17291.31 7367.473 1209.522 2288.831

Skewness −0.320106 1.081347 0.060119 0.168044 −1.659032 0.931532

Kurtosis 2.131574 2.690819 1.352345 3.058856 5.837284 2.767312

Jarque-Bera 5.432161 22.27323 12.73638 0.543289 88.94542 16.45069

Probability 0.066133 0.000015 0.001715 0.762125 0.000000 0.000268

	Brazil	China	India	Russia	South Africa	Turkey
	Income inequality (IE)
Mean	50.66667	39.79583	45.97917	35.53750	62.37917	41.12500
Median	51.08438	41.42656	47.17813	36.18750	62.70000	41.45781
Maximum	54.11562	43.00625	49.83750	37.04063	63.60625	42.40625
Minimum	46.20625	33.37813	41.19375	28.18125	60.78438	39.49375
Std. Dev.	2.871131	3.086862	3.074880	1.671708	0.977885	1.035625
Skewness	−0.254567	−0.651320	−0.247661	−2.282318	−0.346637	−0.326778
Kurtosis	1.514959	1.875587	1.485229	8.708940	1.609191	1.512178
Jarque-Bera	9.858259	11.84471	10.15950	213.7116	9.659922	10.56301
Probability	0.007233	0.002679	0.006221	0.000000	0.007987	0.005085
	Technological innovation (TEC)
Mean	19817.00	427491.9	25863.57	35757.96	6655.071	3283.893
Median	19167.19	192561.1	26703.91	36429.25	6932.766	2626.031
Maximum	30920.78	1552623.	54988.16	61073.66	8372.000	8695.531
Minimum	6336.031	11195.63	3387.500	23017.41	2509.844	698.0938
Std. Dev.	7687.493	499440.3	17291.31	7367.473	1209.522	2288.831
Skewness	−0.320106	1.081347	0.060119	0.168044	−1.659032	0.931532
Kurtosis	2.131574	2.690819	1.352345	3.058856	5.837284	2.767312
Jarque-Bera	5.432161	22.27323	12.73638	0.543289	88.94542	16.45069
Probability	0.066133	0.000015	0.001715	0.762125	0.000000	0.000268

Table 2.

BDS test.

	Brazil	China	India	Russia	South Africa	Turkey
	Income inequality (IE)
M2	50.9519*	30.3357*	44.9108*	24.6056*	41.9757*	49.6172*
M3	54.5540*	32.4326*	47.8086*	25.7617*	44.7806*	52.7883*
M4	59.0767*	35.0071*	51.5750*	27.3529*	48.1844*	56.9543*
M5	65.5854*	38.7083*	57.1564*	29.8526*	53.0460*	63.0043*
M6	74.4550*	43.7419*	64.8787*	33.3575*	59.6192*	71.3443*
	Technological innovation (TEC)
M2	45.2290*	25.6237*	48.4414*	33.7005*	17.1970*	26.2841*
M3	48.1054*	26.8977*	51.4226*	35.3559*	17.8599*	27.2855*
M4	51.5713*	28.6941*	55.3581*	37.1841*	18.8015*	29.0290*
M5	56.5751*	31.5211*	61.3272*	39.9003*	20.2274*	31.7637*
M6	63.40590*	35.54811*	69.76752*	43.81956*	22.09778*	35.62210*

BDS: Brock, Dechert, and Scheinkman.

* denotes 1% level of significance.

Methodology

Quantile-on-quantile regression

This research uses the QQR technique initiated by Sim and Zhou.¹² This approach combines ordinary least squares (OLS) with quantile regression. The influence of the TEC on the different quantiles of the IE model parameters is shown using the standard quantile regression technique. Furthermore, typical OLS considers the effect of a certain quantile of TEC on the IE. The QQR approach combines both techniques to investigate the relationship between quantiles of TEC and IE. The QQR model debated upon based on the parameters of the model of this research is as follows:

I E_{t} = σ^{q} (T E C_{t}) + ε_{t}^{q}

(1)

The qth quantile of the constrained principal model parameter band is depicted by q. The regression residual with sporadic qth quantile projected to be null is shown by

ε_{t}^{q}

. Since no previous information on the parametric inference can be found, σq(.) is an undetermined function.

Quantiles causality

We use a causality approach known as “Granger-causality in quantiles” initiated by Troster¹¹ to detect the causality in the various grids of quantiles between the IE and TEC parameters. A factor y_i does not Granger cause x_i, if the prior y_i does not corroborate the prediction of x_i.²⁸ For instance, there is an elucidation vector m_i = (m_i^x, m_i^y)′ ∈ ℝ ^e , e = 0 + q, where m_i^y are the prior results of y_im_i^y = (y_i ₋ ₁, …, y_i ₋ _q )′ ∈ ℝ ^q . The current article established a $H_{o}$ in this situation, as described below:

H_{o}^{X ↛ Z} : F Z (Z | M_{i}^{Z}, M_{i}^{x}) = F Z (Z | M_{i}^{Z}) for all X ϵ ℜ

(2)

if Fx(M_ix, M_iy) depends on equation (2), then Fx(.IM_ix, M_iy) is the X_i function of conditional distribution. The following is the

H_{o}

which is “there is no Granger causality”:

Q A R (1) : m_{1} (M_{i}^{Z} | \partial (π)) = λ 1 (π) + λ 2 (π) Z_{i - 1} + μ t ψ_{X}^{- 1} (π)

(3)

Here

ψ_{X}^{- 1} (.)

is the contrary of a conventional main conditional distribution function, λ1(π), λ2(π), and μ_t are calculated with maximum probability in the same range of different quantiles.

Q_{π}^{Z} = (Z_{i} | M_{i}^{Z}, M_{i}^{x}) = λ 1 (π) + λ 2 (π) Z_{i - 1} + η (π) X_{i - 1} + μ t ψ_{X}^{- 1}

(4)

Findings and discussion

Quantile-quantile (QQ) results

QQ can provide an insight into the quantile distribution before starting the study, allowing the researcher to understand the desired impact better. Figure 1 shows the QQ diagrams created for IE and TEC across the BRICS-T countries as part of this endeavor. Following that, we examine each country's specific plans.

Figure 1.

QQ plot of income inequality and technological innovation for BRICS-T nations. (a) IE QQ plot for Brazil, (b) TEC QQ plot for Brazil, (c) IE QQ plot for Russia, (d) TEC QQ plot for Russia, (e) IE QQ plot for India, (f) TEC QQ plot for India, (g) IE QQ plot for China (h) TEC QQ plot for China, (i) QQ plot for South Africa, (j) TEC QQ plot for South Africa, (k) IE QQ plot for Turkey, and (l) TEC QQ plot for Turkey

For Brazil, TEC has the propensity to be skewed in the middle and upper quantiles, whilst IE is skewed towards the lower tails (see Figure 1(a) and (b)). As a result, it can be concluded that the TEC distributional model, which does not benefit people from lower socioeconomic strata, is to blame for the skew in IE distribution. The IE for Russia is evenly distributed throughout all quantiles, but the TEC is skewed towards the lower quantiles (see Figure 1(c) and (d)). This demonstrates that TEC may affect the higher quantiles of IE equally. In the lower and higher quantiles of TEC, India displays a bullish pattern, and virtually the same pattern is also discernible for IE (see Figure 1(e) and (f)). Therefore, it can be argued that TEC's effect on IE may be evenly distributed across quantiles. In the case of China, IE is equitably spread across all quantiles, but TEC has a bullish trend in both the lower and upper quantiles (see Figure 1(g) and (h)). This pattern reveals the likelihood of a higher TEC impact at its higher quantiles. Both TEC and IE are bullish concerning South Africa's lower quantiles (see Figure 1(i) and (j)). This trend reveals that the distribution of TEC favors the poor and may not be in the best interests of those in the upper-income strata. As a bullish trajectory is observed in the lower and higher quantiles of IE and a roughly identical pattern in the case of TEC, Turkey's situation is comparable to Russia's (see Figure 1(k) and (l)). Therefore, it is reasonable to presume that the effect of TEC on IE may be equal across quantiles.

Quantile unit root outcomes

We use the Quantile unit root test to determine whether the model's parameters are stationary, and the empirical findings are presented in Table 3. The critical values for the grid of 19 quantiles, which range from 0.05–0.95, are provided by the quantile unit root. We also use 10 lags of an endogenous variable to overcome the problem of serial correlation. The findings show that at the level, there is a unit root for the variable's quantiles in the preliminary distribution. According to the quantile unit root test, the variables are stationary at the first-order difference. Nonetheless, after the first difference is taken, evidence of stationarity is confirmed.

Table 3.

Quantile autoregressive unit root test results.

Countries	Variables	0.05	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	0.95
Brazil	IE	−2.1581	−2.0587	−1.7587	−1.3587	−0.9587	−0.8587	−0.7587	−1.1587	−0.6255	−0.7711	−0.5584
Brazil	TEC	−0.9427	−0.9042	−0.8142	−0.7942	−1.1942	−1.5942	−2.1942	−1.2549	−1.4549	−1.5678	−1.3563
Russia	IE	−0.3825	−0.4976	0.3835	−1.6371	−1.7373	−2.1373	−2.0374	−1.3493	−0.8973	−0.6373	−1.1373
Russia	TEC	−2.6693	−2.4294	−2.3734	−1.8342	−1.6594	−0.9020	−1.2020	−0.5259	−0.8925	−0.7259	−0.5943
India	IE	−2.1294	−2.0485	−1.9402	−1.7448	−1.5675	−0.8679	0.3557	−1.4248	−1.3355	−0.8372	−0.6389
India	TEC	0.2785	−1.4855	−1.7226	−1.5868	−0.9448	0.3496	−1.3993	−0.7605	−0.6842	−0.8326	−0.5809
China	IE	−2.4865	−2.1241	−1.9258	−1.8753	−0.9629	−0.9331	−1.2898	−1.6932	−0.7820	−0.8976	−0.9648
China	TEC	−1.7795	−2.1916	−2.1144	−2.4640	−1.3523	−1.1734	−0.8230	−0.7896	−0.6239	−0.6708	−0.5329
South Africa	IE	0.3111	−1.1657	−1.6335	−1.0900	−0.7018	−0.6214	0.2848	−0.6904	−0.8614	−0.7893	−0.5689
South Africa	TEC	−0.8602	−1.3446	−1.6344	−1.8547	−0.8117	−0.9811	0.1788	−1.2788	−0.8034	−0.5614	−0.4757
Turkey	IE	−2.5803	−2.3139	−1.4084	−1.2853	−0.9513	−0.7753	−1.4220	−1.0184	−1.7287	−1.3216	−1.1555
Turkey	TEC	−2.1466	−1.9488	−1.5019	−2.0942	−1.8942	−1.5942	−1.8956	1.4549	−0.8151	−0.6151	−0.9151

Note: Bold values signify stationarity.

IE: income inequality; TEC: technological innovation.

Quantile cointegration outcomes

Utilizing the innovative quantile cointegration test developed and proposed by Xiao,²⁹ we investigate the likelihood of cointegration between the variables. Similar applications of the cointegration model are made in the evenly spaced grid of 19 quantiles (0.05–0.95). Furthermore, two leads and lags of $(Δ Z_{t}, Δ Z_{t}^{2})$ are used in the provided quantile cointegration framework. Table 4 displays the results of the quantile cointegration method. The observed estimations show that IE and TEC quantiles have a long-term, nonlinear connection.

Table 4.

Results of quantile cointegration.

Countries	Model	Coeff	$S u p τ \| V n (τ) \|$	CV1	CV5	CV10
Brazil	$I E_{t} versus T E C_{t}$	Β	4272	2617	1891	1022
Brazil	$I E_{t} versus T E C_{t}$	γ	2739	1706	1190	875
Russia	$I E_{t} versus T E C_{t}$	Β	8140	5712	4519	2984
Russia	$I E_{t} versus T E C_{t}$	γ	3295	2840	22016	1833
India	$I E_{t} versus T E C_{t}$	Β	639	412	362	240
India	$I E_{t} versus T E C_{t}$	γ	266	183	127	93
China	$I E_{t} versus T E C_{t}$	Β	922	737	536	408
China	$I E_{t} versus T E C_{t}$	γ	627	413	305	263
South Africa	$I E_{t} versus T E C_{t}$	Β	1707	1447	1085	832
South Africa	$I E_{t} versus T E C_{t}$	γ	860	547	365	269
Turkey	$I E_{t} versus T E C_{t}$	Β	8317	6915	4026	3943
Turkey	$I E_{t} versus T E C_{t}$	γ	2279	1526	1079	549

Note: This table shows the outcomes of the quantile cointegration test for the log of income inequality and technological innovation. CV10, CV5, and CV1 are the critical levels of significance, with values of 10%, 5%, and 1%, correspondingly.

QQR outcomes

The study analyzes the influence of TEC distribution on the distribution of EI in the BRICS-T nations using the QQR technique. The research evaluates the findings for each of the BRICS-T countries individually to determine how TEC affects IE.

The impact of TEC on EI in Brazil can be seen in Figure 2(a). The region between the lower and higher quantiles of TEC (0.05–0.95) and IE (0.05–0.95) continues to be where the influence of the distribution of TEC on EI distribution is present. The effect of TEC on IE is weak and negative in all quantiles (0.05–0.95) of a combination of both TEC and IE. As a result, it is feasible that the TEC distribution is unfavorable for all IE quantiles (poor and lower–middle–higher class). Additionally, preserving innovative capacities results in a gap between the wealthiest individuals in the income tiers in all quantiles (0.05–0.95). These outcomes correspond with the studies of Karagiannis et al.²⁰ and Włodarczyk,²¹ who reported that technological innovation has a negative effect on income inequality.

Figure 2.

The effect of technological innovation on income inequality. The graphs display the slope coefficient β₁(θ) estimate in the z-axis against the income inequality quantile in the y-axis and the technological innovation quantiles in the x-axis. (a) Brazil, (b) Russia, (c) India, (d) China, (e) South Africa, and (f) Turkey.

On the contrary, the impact of TEC on EI in Russia is shown in Figure 2(b). The region between the lower and higher quantiles of TEC (0.05–0.95) and IE (0.05–0.95) continues to be where the influence of the distribution of TEC on EI distribution is present. In all quantiles (0.05–0.95) of a combination of both TEC and IE, the influence of TEC on IE is strong and positive. This infers that in all tails, an increase in IE is caused by an intensification of TEC in all quantiles. As a result, it is feasible that innovation is distributed favorably across all income quantiles. These results corroborate with the studies of Breau et al.¹⁷ and Tchamyou et al.,²² who reported a positive innovation-income association. Similarly, for India, the impact of TEC on EI is shown in Figure 2(c). The region between the lower and higher quantiles of TEC (0.05–0.95) and IE (0.05–0.95) continues to be where the influence of the distribution of TEC on the EI distribution is evident. In all quantiles (0.05–0.95) of the combination of TEC and IE, the influence of TEC on IE is strong and positive. As a result, it is feasible that innovation is distributed favorably across all income quantiles in India. These results corroborate the findings of Aghion et al.¹⁶ and Josifidis and Supic,¹⁰ who reported a positive innovation-income association.

The impact of TEC on EI for China is seen in Figure 2(d). The region between the lower and higher quantiles of TEC (0.05–0.95) and IE (0.05–0.95) continues to be where the influence of the distribution of TEC on the EI distribution is evident. The effect of TEC on IE is weak and positive in all quantiles (0.05–0.95) of TEC and the lower quantiles (0.05–0.40) of IE. However, an adverse effect of TEC in all of the quantiles of TEC (0.05–0.95) and the middle and upper quantiles (0.45–0.95) of IE emerged. In summary, the effect of TEC on IE is positive in the lower quantiles (0.05–0.40) and negative in the middle and higher quantiles (0.45–0.95). The distribution of technological innovation may thus be beneficial at lower-income quantiles and unfavorable at intermediate and upper-income quantiles. Although innovation has assisted China in disseminating technological breakthroughs to those from lower socioeconomic strata, the dissemination of technological innovation across sectors is hampered by corruption and high transaction costs, which leads to an unequal distribution of income at the middle- and upper-income echelons. Similar findings were reported in the studies of Aghion et al.,¹⁶ and Law et al.¹⁵

The impact of TEC on EI for South Africa is shown in Figure 2(e). The region between the lower and higher quantiles of TEC (0.05–0.95) and IE (0.05–0.95) continues to be where the influence of the distribution of TEC on the EI distribution is present. In the lower tails (0.05–0.45) and higher tails (0.65–0.95) of a combination of both TEC and IE, the influence of TEC on IE is positive. However, in the middle tails (0.50–0.60), TEC impacts IE negatively. This indicates that in the lower and higher tails, an increase in IE is caused by an intensification of TEC in all quantiles, while TEC negatively influences IE in the middle tails. This diffusion, which may be an initial outcome of the expenditures on clean energy, is responsible for the favorable influence in the lower and upper tails. Nevertheless, the effect of innovation on income is negative in the middle quantiles, indicating that policy-level efforts to disseminate the advantages of technological innovation to the industrial sector or to the disadvantaged population have been unsuccessful. Similar findings were reported in the studies of Aghion et al.¹⁶ and Law et al.¹⁵

Lastly, the impact of TEC on EI for Turkey is depicted in Figure 2(f). The region between the lower and higher quantiles of TEC (0.05–0.95) and IE (0.05–0.95) continues to be where the influence of the distribution of TEC on the distribution of IE is evident. In the majority of the tails (0.05–0.85) of the combination of both TEC and IE, the influence of TEC on IE is negative and weak. However, in the higher tails (0.85–0.95), TEC impacts IE positively. This demonstrates how poorly national technological innovation initiatives are disseminated at the policy level. Additionally, Turkey's production capacity has been severely hampered by the energy crisis and corruption, which has led to an unequal income distribution amongst the populace, particularly in the highest stratum of income. Increasing inequality due to this situation has led to the concentration of wealth in a small number of hands among those in higher income quantiles. The study of Sinha et al.⁶ reported a similar result.

Robustness check

The research used QR to analyze the effect of the distribution of technological innovation on the income inequality distribution in the BRICS-T nations in order to check the reliability of the QQR outcomes. Since QQR breaks down QR assessments, QQR assessments give more information than QR assessments. Furthermore, because QR takes the quantiles of the explanatory variable into account, its indexation is one level lower than that of QQR. The results of the QR method are shown in Figure 3. When the QQR assessment results are plotted against the parameters slope of the QR assessment for all the BRICS-T nations, it is clear that the variances in these two features are essentially the same for all the nations, regardless of the quantile distribution.

Figure 3.

The comparison of QR and QQR estimates. The graphs show the QR (red dot line) estimates and the averaged QQR parameters (continuous purple line) at various quantiles of technological innovation and income equality for all nations evaluated. (a) Brazil, (b) Russia, (c) India, (d) China, (e) South Africa, and (f) Turkey.

For instance, we can see in Figure 3(a) that the averages of the QQ and QR estimations of the coefficient slope are similar. In the bulk of the quantiles for Brazil, the weak and negative impact of TEC on IE is very clear. This suggests that while the numbers are considerably different, the QQR and QR line trends are comparable. Figure 3(b) displays the QQR and QR estimates for the effect of TEC on IE in Russia. It is evident that at all tails, the effect of TEC on IE is positive, with similar coefficients for both QR and QQR. Moreover, Figure 3(c) presents the trend of the QQR and QR estimates for India. The results show that TEC influences IE positively in all quantiles. Thus, the evidence for the QQR and QR coefficients is clearly comparable, although the values differ slightly. Similarly, Figure 3(d) shows that the averages of the QQ and QR estimations of the coefficient slope are similar for China with regard to the effect of TEC on IE. TEC's weak and positive impact on IE is obvious in the bulk of the quantiles. The results suggest that while the numbers are considerably different, the QQR and QR line trends are comparable. Figure 3(e) displays the QQR and QR estimates for the effect of TEC on IE in South Africa. It is evident that at all tails, the TEC effect on IE is positive with the coefficients of both QR and QQR. Lastly, Figure 3(f) displays the QQR and QR estimates for the impact of TEC on IE in Turkey. It is evident that in the lower and higher tails, the TEC effect on IE is negative, while a positive effect of TEC on IE is noticeable in the middle quantile. The findings lead us to the conclusion that the QQR results are reliable for all the nations under investigation.

Quantile causality outcomes

Table 5 provides the Granger-causality test in quantiles results between IE and TEC. Table 5 clearly illustrates the consistency of the estimates under various QAR framework conditions that describe the $H_{o}$ hypothesis of “no causality.” Disregarding the median quantile, it is clear that causality exists from IE to TEC in the majority of the quantiles and vice versa in all the BRICS-T nations. The results of the quantiles Granger Causality test strongly support the theory that changes in TEC and IE have a significant impact on each other at the 5% level of statistical significance in each BRICS-T nation. The empirical findings show that IE and TEC have a bidirectional causal relationship in both the high and low tails of the quantile distribution for all nations. This demonstrates that TEC and IEC Granger cause each other in the BRICS-T countries. Therefore, any policy formulated for income inequality will have an effect on technological innovation and vice versa. The result confirms the findings of Avink et al.,³⁰ who reported a causal association between innovation and income inequality.

Table 5.

Troster¹¹ causality.

	Brazil		Russia		India		China		South Africa		Turkey
	$I E \to T E C$	$T E C \to I E$	$I E \to T E C$	$T E C \to I E$	$I E \to T E C$	$T E C \to E$	$I E \to T E C$	$T E C \to I E$	$I E \to T E C$	$T E C \to I E$	$I E \to T E C$	$T E C \to E$
0.05–0.95	0.013	0.013	0.018	0.018	0.018	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.05	0.104	0.090	1.000	1.000	1.000	1.000	1.000	0.039	0.514	0.471	1.000	0.039
0.10	0.026	0.013	0.037	0.203	0.037	0.203	0.013	0.013	0.014	0.014	0.013	0.013
0.15	0.013	0.013	0.018	0.018	0.018	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.20	0.013	0.013	0.018	0.018	0.018	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.25	0.013	0.013	0.018	0.018	0.018	0.018	0.0130	0.013	0.014	0.014	0.013	0.013
0.30	0.013	0.013	0.018	0.018	0.018	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.35	0.013	0.013	0.037	0.018	0.037	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.40	0.013	0.013	0.259	0.018	0.259	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.45	0.013	0.883	0.018	0.055	0.018	0.055	0.389	0.077	0.057	0.057	0.389	0.077
0.50	0.935	0.039	0.740	0.333	0.740	0.333	0.688	0.129	0.728	0.328	0.688	0.129
0.55	0.013	0.013	0.648	0.333	0.648	0.333	0.285	0.363	0.485	0.257	0.285	0.363
0.60	0.013	0.013	0.685	0.018	0.685	0.018	0.181	0.026	0.314	0.014	0.181	0.026
0.65	0.013	0.013	0.055	0.018	0.055	0.018	0.584	0.013	0.028	0.014	0.584	0.013
0.70	0.013	0.013	0.018	0.018	0.018	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.75	0.013	0.013	0.018	0.018	0.018	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.80	0.013	0.013	0.018	0.018	0.018	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.85	0.299	0.013	0.018	0.018	0.018	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.90	0.506	0.013	0.092	0.018	0.092	0.018	0.013	0.013	0.014	0.014	0.013	0.013
0.95	0.085	0.350	0.037	0.166	0.037	0.166	0.039	0.116	0.028	0.128	0.039	0.116

Note: IE and TEC denote the income inequality and technological innovation, respectively. The bold value illustrates the null hypothesis dismissal of no causality at the 5% significance level.

Conclusion and policy recommendations

Conclusion

The present research evaluates the effect of technological innovation distribution on the distribution of income using data from between 1992Q1 and 2019Q4 for the BRICS-T nations. The research's goal was to examine how the technological innovation distribution within a country may affect income inequality distribution, or how innovation may contribute to income inequality. The BRICS-T nations present a good case for this research since this problem could be more widespread in developing and frontier countries with high prospects for economic growth. The QQR created by Sim and Zhou¹² and the Troster¹¹ causality techniques have been used to evaluate the impact. The research's findings provide a range of outcomes from different countries, which may be grouped into three categories; (i) technological innovation impacts income inequality positively, (ii) technological innovation distribution impacts income inequality distribution negatively, (iii) the effects of technological innovation on income distribution are not evenly dispersed. The research deduced significant policy ramifications that might inspire sustainable development plans in the BRICS-T nations.

Policy recommendations

The policy recommendations start with nations where the allocation of technological innovation has been equal and positive for income distribution. When it comes to achieving the SDGs, these nations are ahead of other emerging nations. Nevertheless, it should be highlighted that the nations included in this echelon exhibit some degree of skewing in their impacts, which might develop into a greater problem in the future. To reduce this risk, nations must first encourage people–public–private partnerships, which would make residents aware of the numerous energy breakthroughs developed domestically and enable them to be duplicated.^5,6 The government should encourage business cooperation to facilitate technology transfer, establish new companies, and make innovations more accessible. This strategy could assist these countries in expanding employment opportunities, thus reducing income inequality (SDGs 10 and 8).

For the nations in the second strata, where there is evidence of a negative influence of the technological innovation distribution on the distribution of income, these remedies might not be realistic. Since these nations’ economies still mostly rely on fossil fuel-based technologies, it is challenging for technological improvements to replicate and spread there. Additionally, instances of corruption may be another obstacle preventing adoption. The regulatory and legislative agenda for encouraging technological innovation has to be enhanced in order to solve this problem. The financial institutions should be instructed to make loans and advances to the organizations depending on their ecological or carbon footprint, that is, a discriminatory interest rate structure should be used for granting loans and advances to formalize this. This will drive the firms to choose energy technologies in order to lessen their negative ecological externality. This can be seen as the initial stage of the policy structure for these countries, guaranteeing innovation in the energy sector by implementing infrastructure change (SDG 9 objective).

The decision to spread these technologies across the country should be considered by policymakers when this step is established. Only raising awareness of environmental issues via public–private–people collaborations may not be adequate in this endeavor since these innovations might not be copied without appropriate business prospects. As a result, for new business prospects to arise, governments should concentrate on relaxing the business climate and reducing bureaucratic processes. Once this procedure has been finalized, an increase in employment prospects will start to increase residents’ standards of living and enable them to earn a respectable income (SDG 8 objective), while innovations will assist these countries in coping with climate change (SDG 13 goals). Once this stage is complete, policymakers should begin revising the educational curricula to emphasize the role of innovation and alternative energy sources. The implementation of these policies will be continued at the grassroots level owing to this educational reform, which will ensure educational quality (SDG 4 objective).

For nations with an uneven influence of innovation distribution on income distribution, these reform-oriented initiatives might not be required. These countries should concentrate on distributing technological innovation effectively across the country so that residents from all socioeconomic strata can profit. The appropriate spread of innovations to individuals in both the upper and lower tails of income should be the emphasis of policymakers to achieve this. The government should provide financial support for ideas presented by individuals from lower income quantiles to implement such innovations. The government should promote public–private partnerships to inspire others to duplicate such inventions and disseminate knowledge of them. Furthermore, the idea creators must be anchors for spreading the concepts at the grassroots level. On the other hand, businesses should be incentivized to deploy energy innovations to replace the energy sources they now use, which depend on fossil fuels. Loans given to them should be charged differently in this endeavor according to the harmful ecological externality they have caused. At this point, the government must guarantee that the innovations promoted by these companies do not directly displace workers, as doing so may jeopardize the socioeconomic sustainability of these countries. The countries will be able to stimulate innovation via infrastructure improvements, tackle climate change (SDG 13's goal), and provide a sufficient income and means of survival for their populations if these stages are executed (SDG 9's goal).

Limitation of study and future directions

Before concluding the research, it is crucial to remember that no policy framework can be considered absolute because it may not be feasible to include all pertinent target and control policy variables in one framework. The policy framework proposed in this research is no exception. This research's drawback stems from the fact that it was conducted using a bivariate approach. The more in-depth analysis could be conducted if industrial factors are considered. Future research on this topic may contribute to the baseline methodology established by the current paper in estimating the influence of technological innovation distribution on income distribution by considering spatial dimensions to clarify regional reliance. Furthermore, future studies may consider eco-innovation instead of technological innovation in modeling. Lastly, other emerging and developed blocs should also be considered in future studies.

Footnotes

Data availability

Data is readily available at request from the author.

Declaration of conflicting interests

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

Ethical approval

This study follows all ethical practices during writing.

Funding

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

ORCID iD

Tomiwa Sunday Adebayo

Notes

Tomiwa Sunday Adebayo was born in Lagos Nigeria. He currently works at the Faculty of Economics and Administrative, Cyprus International University. His enlisted in the updated 2022 edition of the for global scientists top 2% for scientists list by Elsevier BV Sandford University. He has taught and/or is teaching Microeconomics, macroeconomics, advanced business statistics and econometrics in the undergraduate and graduate programs in the Department of economics, Cyrus International University. He is also a research fellow at AKFA University, Tashkent, Uzbekistan. He has published in renowned journals such as Resource Policy, Sustainable Production and Consumption, Journal of Environmental Management, Renewable Energy, Business Strategy and the Environment, International Journal of Finance and Economics, Energy Journal, Science of the Total Environment, and Current Issue in Tourism. His research interests include Environmental Economics, International Economics, Macroeconomics, Time series Econometrics, Panel data models; and Energy Economics.

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