Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 111 Globalization and Energy Consumption: Empirical Implications for Income Inequality in Developing Countries Muhammad Ali Gardezi a, Imran Sharif Chaudhry b a PhD Scholar, School of Economics, Bahauddin Zakariya University, Multan, Pakistan Email: mag_gardezi@yahoo.com b Dean faculty of Arts and Social sciences, Bahauddin Zakariya University, Multan, Pakistan Email: imran@bzu.edu.pk ARTICLE DETAILS ABSTRACT History: Accepted 30 April 2022 Available Online June 2022 The objective of this study is to envisage the impact of globalization and energy consumption on income inequality in developing countries. The specific objectives are to discover the impact of economic, social, political, and overall globalization along with energy consumption on income inequality. To attain the empirical outcomes, this study employed the System-GMM on a panel dataset from 1996 to 2018 in a sample of sixty-nine developing countries as per the classification of World Bank. To further validate the empirical outcomes different interaction terms between overall globalization and energy use, political globalization and energy consumption, social globalization and energy consumption are all regressed on income inequality. The main finding of this study is that income inequality is positively affected by globalization and is statistically significant. This research also discovered a negative correlation between energy consumption and income inequality. The study suggests that developing nations should adopt policies to open their markets to the entire world, make effective use of globalization, and promote policies that reduce income inequality. © 2022 The authors. Published by SPCRD Global Publishing. This is an open access article under the Creative Commons Attribution- NonCommercial 4.0 Keywords: Globalization, Energy Consumption, Income Inequality JEL Classification: F02, O13 DOI: 10.47067/reads.v8i2.440 Corresponding author’s email address: imran@bzu.edu.pk 1. Introduction World economies are achieving significant economic growth, but this success is not acquired in terms of equal income distribution (Shabaz et al., 2021). The increase in income inequality has becomes a matter of concern to all developed as well as developing nations. The rapid decline in equal distribution of income leads to more focus on the examination of the dynamics of income inequality in developing countries (Wildman, 2021). Extreme income disparities have gone out of control. Hundreds of millions of people live in poverty Hulme and Shepherd (2003) whereas the richest one percent of the population gets huge profits. Millionaires are in greater numbers than ever before, and their incomes have climbed to new heights. During this time, the world's poorest people grew poorer. Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 112 Globalization seems to have economic, political, as well as social effects on all people throughout the world and Globe is known as a globalized society, and people socially know each other through various forms of communication and globalization is a way through which the economy of the World is linked via foreign direct investment and trade (Mishkin, 2009). Through the influx of capital for economic development, globalization has brought the world's established and growing economies closer. Many countries have benefited from globalization, which has had a major impact on the socio- economic, environmental, and political elements of human life. By producing and investing in goods and services, technical change, financial convergence and information exchange all contribute to the increased interconnection between countries as a result of globalization. Globalization reduces the income gap in emerging countries by increasing labor’s demand because emerging countries are richer in unskilled labor (Ha, 2012). Globalization, on the other hand, promotes economic disparities in emerging countries (Bukhari & Munir, 2016). As a result, analyzing the influence of globalization on income disparity has become a significant topic in the political economy and internationally (Dixon & Boswell, 1996; Rudra, 2008; Lundberg & Squire, 2003; Milanovic & Squire, 2005). Energy consumption is a parameter for measuring the rate of economic growth and industrial development. In both industrialized and emerging countries, energy usage serves as a fuel for rapid economic expansion. Inequalities are frequently explored in-depth in terms of income, but differences in energy availability and usage might show income disparities as well. As a result, overall energy statistics can be utilized to uncover economic disparities at the worldwide, geographical, and national levels (Mainali et al., 2014). Oil, natural gas, coal, nuclear, solar, hydraulic, biomass and wind are primary energy sources that are utilized directly. Secondary sources of energy that are utilized after being converted include diesel oil, solar energy, gasoline, and coal-based electricity. Primary energy consumption makes it simpler to uncover income inequalities (Sonora, 2021). This is because the primary energy source is the most readily controllable kind of energy. Income inequality is affected by several variables such as GDP (Deininger & Squire, 1997; Hoffmann et al., 2020; Yang & Greaney, 2017; Zungu et al., 2021), population (Bor et al., 2017; Dong et al., 2018; Zhan et al., 2021; Zhong, 2011), and unemployment (Costantini et al., 2018; Mishchuk et al., 2018). This is an insufficient list, but it does indicate a general tendency in the existing literature. The influence of globalization on income disparity was scientifically explored by previous recent researchers and concluded that globalization has a positive impact on income disparity (Ha, 2012; Dorn et al., 2018; Abakumova & Primierova,2018; Auguste, 2018; Elgindi, 2016; Asteriou et al., 2013; Mah, 2013; Choi et al., 2019). While On the other hand, the studies of globalization and income inequality found that globalization harms income inequality (Cabral et al., 2016; Ruana, 2020; Balanet et al., 2015; Zhou et al., 2011; Lee, 2010; Bechtel, 2014; Kinnaman, 2018; Mohanty, 2017). Related to this, Some studies discovered a positive link between income inequality and energy usage. (Topcu & Tugcu, 2019; Uzar, 2020; Asongu & Odhiambo,2020; Bianco et al., 2018; Ajmi et al., 2015; Salgado et al., 2020; Galvin & Sunikka, 2018). While some researchers found that energy use has a negative relationship with income inequality (Duan & Chen, 2018; Mohanty, 2017), the significant results are still unclear. The goal of this research is to find significant results by estimating globalization and energy use on income inequality in developing countries. Income inequality is a problem from the social point of view. For the economists and policymakers, rising income inequality among the developing countries is a major challenge. It is often assumed that Globalization offers countries more opportunities to pursue higher productivity growth. Globalization provides new chances for countries to improve their growth and development, but it also Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 113 creates conflicts for policymakers in regulating global, regional, and national economic systems, as well as imposing constraints on them. There is a lot of discussion over why certain countries profit more from globalization than others. Complementary policies and initial conditions in the host country have also resulted in a divergence of benefits from globalization among countries. Over the previous few decades, the gap between rich and poor countries has widened because of globalization. Despite the enormous potential afforded by globalization, its current patterns are exceedingly complicated and uneven, and detractors are always saying that as globalization increases, rich countries have more benefited at the expense of poor countries. Despite advances in technology, neoliberal reforms, and cross-national integration, the benefits of increased incomes and output growth have not been evenly distributed among the population which caste a serious doubt on the impact of globalization on the income inequality. The main questions arise how different type of globalization like political, social, and economic globalization along with the energy consumption impact the income inequality of the developing countries. This study is consisting of five sections. Section one contains introduction, second section establishes the review of existing studies, section three is data and econometric Strategy, section four presents the result and discussions and finally section five concludes the study. 2. Literature Review In this section, the connection between globalization, energy consumption, growth, population, and unemployment are reviewed. The researcher will try to pinpoint these connections with respect to theoretical and empirical research and reviewed a variety of macroeconomic channels. Several empirical studies have investigated the relationship between globalization and macroeconomic factors, as well as a few other topics such as globalization and economic growth. (Bataka, 2019; Kurniawati, 2020; Majidi, 2017; Radulovic & Kostic, 2020; Samimi & Jenatabadi, 2014; Zahonogo, 2018 among others); inflation (Ahmad & Civelli, 2016; Ali et al., 2019; Altansukh et al., 2017; Lv et al., 2019; Mazumder, 2017 among others); education (Ali et al., 2019; Malinetskiy & Sirenko, 2020; Poddubnaya et al., 2021; Spring, 2008; Zajda, 2020 among others); financial development (Hussain et al., 2020; Kandil et al., 2017; Muye & Muye, 2017; Kılıçarslan et al., 2018; Zameer et al., 2020 among others); (Fuinhas et al., 2019; Hassan et al., 2019) environment (Crawford-Brown, 2013; Gallagher, 2009; Kayikci, 2019; Pischke, 2018; Postolache et al., 2019; J. Yang et al., 2019 among others) globalization. However, only a few researchers have found a link between globalization and income inequality (Abakumova & Primierova, 2018; Adams, 2008; Balan et al., 2015; Heimberger, 2020 among others). The majority of literature supports that globalization has a positive impact on income inequality such as (Baddeley, 2006; Chordokrak & Chintrakarn, 2011; Khan & Faridi, 2008; Urata & Narjoko, 2017 among others) and others have a negative impact like (Adams, 2008). Asteriou et al., 2014 has empirically analyzed the association linking globalization and income disparity by using the panel data of twenty- seven EU countries. According to their findings, there is a positive relationship between globalization and income disparity. Dorn et al. (2018) examined the link between globalization and income disparities using panel data from 140 countries. They used instrumental variable estimates to conclude that globalization has a large positive impact on income disparity. Similarly, using data from both developed and developing nations, Atanasova and Tsvetkov (2021) evaluated the impact of globalization on income disparity. For data estimation, they employed ADF and PP Fisher approaches. Globalization has a detrimental influence on income disparity in emerging countries, according to the study's findings. On the other hand, GDP growth has no positive relationship with income disparity. Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 114 While on the other hand Munir and Bukhari (2020) analyzed the relationship between inequality and globalization by taking three aspects of globalization as financial globalization, trade globalization, and technological globalization. This study used HO and SS theorem as a theoretical base. They used panel data and found that financial globalization positively influences the income disparity, trade globalization negatively influences the income disparity and technological globalization also has a negative connection with income disparity. Heinze (2020) explored the association between globalization and inequality, and poverty. Finally, they reported that globalization has a positive impact on income disparity but has a negative impact on poverty. Several empirical kinds of research, on the other hand, have looked into the relationship between energy consumption and macroeconomic variables, as well as other variables such as energy consumption and growth. (Chen et al., 2020; Dat et al., 2020; Esen & Bayrak, 2017; Huang & Huang, 2020; Ivanovski et al., 2021; Magazzino et al., 2021; Shahbaz et al., 2020 among others); inflation (Bassey & Ekong, 2019; Naraghi et al., 2021; Sultan et al., 2020; Talha et al., 2021 among others); employment (Glasure & Lee, 1995; Muniyoor, 2020; Payne, 2009; Tiwari, 2010; Yu & Jin, 1992 among others); trade (Alkhateeb & Mahmood, 2019; Amri, 2019; ben Aïssa et al., 2014; Farhani et al., 2014 among others); health (Akbar et al., 2020; Caruso et al., 2020; Haseeb et al., 2019; Qu et al., 2017; Xing et al., 2019 among others); human capital (Akram et al., 2019, 2020; Alvarado et al., 2021; Asghar et al., 2020; Salim et al., 2017; Yao et al., 2019); financial development (Khan et al., 2020; Magazzino, 2018; Odhiambo, 2019; Raza et al., 2020; Tsaurai, 2020; Yang et al., 2020 among others) Duan and Chen (2018) used a graphical analysis of the Lorenz curve to investigate the relationship between energy usage and income disparity. Panel data of 121 countries were analyzed by the Multi-regional input out the model and found that energy consumption reduces income disparity in all three forms such as petroleum, coal, and natural gas. Topc and Tugcu (2020) examined the correlation between renewable energy consumption and income disparity and found that renewable energy use had a negative influence on income inequality. (Santillán-Salgado et al., 2020) used a sample of 134 countries to empirically investigate the impact of GDP growth and energy use on wealth disparities. They used the GINI index as a proxy if income inequality. The main findings of the study demonstrated that energy use has a long-term favorable impact on income disparities. While on the other hand Dong and Hao (2018) empirically analyzed the electricity consumption and income disparity in China using a panel from 1996 to 2013. They used the GMM approach for the estimation of panel data. Their findings revealed that the urban and rural effects of income inequality on electricity are dependent on income level. The study also revealed a substantial inverted U-shaped association between per capita power usage and per capita GDP. Galvin and Sunikka-Blank (2018) explored the relation of energy use with income disparity in high-income countries using data from the 1950s to the 1980s. The data revealed a negative link between income disparity and energy use. The majority of studies discovered a link between energy use with income inequality. The dependent variable was energy consumption, while the independent variable was income disparity. Only one study has looked into how energy consumption affects inequality. However, past empirical research implies that the actual influence of energy use on income inequality there may be needed to be investigated. As a result, the goal of this research is to find out how globalization and energy use affect economic inequality. While on the other hand Topcu and Tugcu (2019) used panel data from developed countries (1990-2014) to investigate the association between renewable energy use and inequality. The generalized methods of moments and the dynamic common effects estimator were both used in this work as panel data methodologies. According to their findings, increasing renewable energy usage leads to a reduction in income disparity. While on the other hand Globalization's impact on income inequality Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 115 in emerging and developed countries, according to Baekand and Shi (2016). They estimated panel data for 52 developing and 26 developed nations throughout the period 1990-2010. They used the Gini index, HO, and SST model for using the comprehensive sets for this study. The result of the study showed the positive relation of globalization with income inequality in developing countries. Heshmati and Lee (2010) used panel data by taking 61 developing and developed throughout 1995-2001. The finding of the study indicated that there has a negative relation between globalization with income disparity. 3. Data and Econometric Strategy 3.1 Data Source The effect of globalization and energy usage on income inequality in developing nations is investigated using panel data collected over a twenty-three-year period from 1996 to 2018.This study is based on secondary sources of information. Table 1 Description of variables and data sources Variables Symbol Measurement Data Sources GINI index GINI It is a statistical dispersion measure used to describe income disparity. SWIID* Globalization index GLOB Is measured by political, economic and social dimensions of globalization. KOF index (Dreher, 2006) Economic globalization index EGLOB Is measured by trade flow, FDI & portfolio investment, and restrictions on these outflows and inflows. KOF index (Dreher, 2006) Political globalization index PGLOB Is measured by the membership of international organizations, number of treaties signed by other countries, embassies numbers, and UN Security Council meetings. KOF index (Dreher, 2006) Social globalization index SGLOB Is measured by information flows, personal contact, and cultural nearness. KOF index (Dreher, 2006) Energy Consumption ENC Energy use (kg of oil equivalent per capita). WDI** GDP growth rate GDPr GDP growth rate (annual %). WDI Total population POP The de facto definition of population, which includes all residents regardless of legal status or citizenship, is used to calculate the total population. WDI Total unemployment UEMP Unemployment, total (% of the total labor force) (modeled ILO estimate). WDI * Standardized World Income Inequality Data base, ** World Development Indicators Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 116 System GMM is used for the analysis of unbalanced panel data for developing countries. We used eight econometric models to find out the impact of globalization and energy consumption on income inequality in developing countries. Income disparity is described as a function of globalization, energy usage, income, population, and unemployment, as seen below: 3.2 Model Specification To unveil the impact of globalization and energy consumption on income inequality in developing nation the following models are to frame GINI = f(GLOB, ENC, GDPr, POP, UNEMP) (1) The squared term of GDP growth incorporates in the model because the Kuznets-type model is used to analyze the influence of globalization and energy consumption on income inequality in developing countries. GINI = f (GLOB, ENC, GDPr, GDPr2, POP, UNEMP) (2) All variables are in logarithm form, where GINI represents the GINI coefficient, GLOB indicates the globalization index, ENC is the energy consumption, GDPr is the GDP per capita growth rate, POP indicates the total population and UNEMP represents the unemployment rate. GINIit = β0it + β1GINIit−1 + β2GLOBit + β3ENCit + β4GDPrit + β5GDPr 2 it + β6POPit + β7UNEMPit + ɛit (3) GINIit = β0it + β1GINIit−1 + β2EGLOBit + β3ENCit + β4GDPrit + β5GDPr 2 it + β6POPit + β7UNEMPit + ɛit (4) GINIit = β0it + β1GINIit−1 + β2PGLOBit + β3ENCit + β4GDPrit + β5GDPr 2 it + β6POPit + β7UNEMPit + ɛit (5) GINIit = β0it + β1GINIit−1 + β2SGLOBit + β3ENCit + β4GDPrit + β5GDPr 2 it + β6POPit + β7UNEMPit + ɛit (6) EGLOB, PGLOB and SGLOB are economic globalization, political globalization and social globalization respectively. The interaction term might be expressed as follows to evaluate the contribution of globalization in moderating the impact of energy usage on income disparity. GINIit = β0it + β1GINIit−1 + β2GLOBit + β3ENCit + β4GDPrit + β5GDPr 2 it + β6POPit + β7UNEMPit + β8(GLOB ∗ ENCit) + ɛit (7) GINIit = β0it + β1GINIit−1 + β2EGLOBit + β3ENCit + β4GDPrit + β5GDPr 2 it + β6POPit + β7UNEMPit + β8(EGLOB ∗ ENCit) + ɛit (8) GINIit = β0it + β1GINIit−1 + β2PGLOBit + β3ENCit + β4GDPrit + β5GDPr 2 it + β6POPit + β7UNEMPit + β8(PGLOB ∗ ENCit) + ɛit (9) GINIit = β0it + β1GINIit−1 + β2SGLOBit + β3ENCit + β4GDPrit + β5GDPr 2 it + β6POPit + β7UNEMPit + β8(SGLOB ∗ ENCit) + ɛit (10) Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 117 To find out the relationship between globalization and energy consumption on income inequality, environmental quality, and economic growth, a total of sixty-nine countries are selected. The list of developing countries was taken by UNDP (2020). All the models were estimated by System GMM estimation technique. 3.3 Estimation Technique The first distinction is that Arellano and Bond developed the GMM estimator (1991). In the right- hand side of the model, the dependent variable's lag is added as a regressed. When the lag of the dependent variable is used as a regressor, the issue of correlation between the error term and the regressors arises. The difference GMM estimator is used with the lag of the regressor as an instrument. Even though the model contains autocorrelation, these instruments may be insufficient. GMM contains a variety of potential faults, according to Arellano and Bover (1995) and Bond and Blundell (1995). If the variables are persistent, the authors suggest that lag levels are insufficient instruments in first difference GMM estimation. According to the authors, using lag level and lagged difference variables as instruments in the model eliminates the problem of endogeneity and weak instruments. Arellano and Bover (1995) and Bond and Blundell (1995) created the two-step system GMM estimation approach (1998). This method is used to overcome the limitations of the panel and cross-sectional investigations. Due to heterogeneity and endogeneity, the first issue is omitted variable bias. System GMM equation is given below. ΔYit = α + β1Yi,t−1 + β2Xit + λi + φt + €it (11) Where Xit denotes control variable, β1 denotes the coefficient of lag of dependent variable λi denotes the cross country unobserved effect, φt denotes time effect €it denotes error term, i represents country and t represents time. 4. Results and Discussion 4.1 Descriptive statistics The following table shows the descriptive statistics that comprise mean, maximum, median, minimum, standard deviation, Kurtosis and skewness values. The values of GINI, GLOB, EGLOB, SGLOB, ENC, and POP are positively skewed while PGLOB, and GDPr are negatively skewed. Table 2 Descriptive Statistics Mean Median Minimum Maximum Std. Dev. Skewness Kurtosis GINI 43.20 42.10 5.90 68.00 8.41 0.23 3.10 GLOB 55.62 55.73 24.45 88.55 11.86 0.08 2.72 EGLOB 49.94 49.68 18.03 87.00 13.46 0.35 2.94 PGLOB 68.50 69.36 23.40 93.50 14.25 -0.38 2.52 SGLOB 48.26 48.62 9.78 88.22 17.38 0.02 2.26 ENC 1066.64 694.18 113.09 12172.41 1285.05 4.58 30.66 GDPr 26.21 26.32 0.01 41.68 3.88 -0.91 8.44 POP 7.06E+7 2.03E+7 1.11E+06 1.40E+09 2.03E+08 5.38 31.75 UEMP 7.53 5.75 0.21 33.29 5.70 1.39 4.98 Source: Author’s calculation Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 118 4.2 Correlation Matrix Table 3 shows the correlation matrix of some selected variables of the 69 developing nations, which will be used to determine the strength of factors. The correlation matrix may also be used to check for multicollinearity between variables; if the correlation coefficient is larger than 0.8, there is a lot of multicollinearities. Table 3: Correlation Matrix Correlation GINI GLOB EGLOB PGLOB SGLOB ENC GDP POP UEMP GINI 1 GLOB -0.19 1 EGLOB -0.09 0.31 1 PGLOB -0.11 0.18 0.04 1 SGLOB -0.23 0.40 0.37 0.13 1 ENC -0.35 0.19 0.19 0.05 0.28 1 GDPr -0.02 -0.04 -0.02 -0.04 -0.07 -0.05 1 POP -0.12 -0.03 -0.05 0.10 -0.05 -0.02 0.06 1 UEMP 0.31 0.08 0.08 -0.02 0.13 0.01 -0.10 -0.05 1 4.3 Panel Regression Results and Discussions Table 4 presents the results of System-GMM of developing countries. The usage of lagged terms in all models is statistically significant, indicating that the study was conducted using a dynamic model, and indicates that one year of income inequality is highly affected by the previous year of income inequality. Table 4 Dependent Variable : Income Inequality (Two-step S-GMM results of developing countries) Dependent Variable: Natural logarithm of Gini coefficient index VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) L.LNGINI 1.038** * 1.005*** 1.059*** 1.036*** 1.037*** 1.041*** 1.018*** 1.037*** (0.010) (0.009) (0.008) (0.007) (0.009) (0.007) (0.001) (0.001) LNGLOB 0.047** 0.076*** (0.023) (0.006) LNENC - 0.017** * (0.004) - 0.021* (0.011) - 0.018** (0.009) - 0.011*** (0.003) - 0.068** * (0.007) - 0.063** * (0.012) - 0.034** * (0.001) - 0.043** * (0.002) LNGDPr 0.171** 0.160*** 2.070*** 0.170** 0.158** 0.183** 0.005** 0.013* (0.081) (0.049) (0.187) (0.069) (0.080) (0.074) (0.002) (0.007) 𝐋𝐍𝐆𝐃𝐏𝐫𝟐 - 0.167*** (0.055) - 0.156*** (0.041) - 1.687* (0.871) - 0.158*** (0.057) - 0.148** (0.066) - 0.168*** (0.060) - 0.025*** (0.001) - 0.022** (0.011) LNPOP 0.004** 0.004** 0.005** 0.004** 0.002** 0.005** 0.004** 0.003** Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 119 * * * (0.002) (0.001) (0.001) (0.002) (0.001) (0.002) (0.002) (0.001) LNUEMP 0.010** 0.008** 0.010** 0.007** * 0.005** 0.006** 0.007* 0.008** (0.005) (0.004) (0.005) (0.001) (0.002) (0.003) (0.004) (0.004) LNEGLOB 0.028** * 0.078** (0.039) (0.005) LNPGLOB 0.011*** (0.003) 0.036** * (0.001) LNSGLOB 0.013 0.044 (0.012) (0.052) LN(GLOB*ENC) -0.022* (0.012) LN(EGLOB*ENC ) - 0.018*** (0.002) LN(PGLOB*ENC ) -0.019* (0.011) LN(SGLOB*ENC ) -0.020 (0.021) Constant 0.571*** 0.595*** 5.803*** 0.591*** 0.488** 0.586** * 0.212*** 0.111*** (0.201) (0.139) (0.509) (0.203) (0.229) (0.202) (0.006) (0.004) Groups 69 69 69 69 69 69 69 69 Instrument 44 49 52 52 49 49 66 67 Observations 1,225 1,225 1,225 1,225 1,225 1,225 1,225 1,225 AR (1) p-value 0.0894 0.0785 0.00022 1 0.0807 0.0646 0.0873 0.001 0.000 AR (2) p-value 0.112 0.109 0.343 0.117 0.112 0.117 0.147 0.157 Hansen p-value 0.594 0.727 0.491 0.808 0.735 0.499 0.577 0.451 Source: Author’s calculation; Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Results are presented in table 4 and indicate a significant positive effect of globalization on income disparity in selected developing countries (𝛼2 = 0.047, 𝑝 < 0.05). Therefore, it is concluded that globalization worsens the distribution of income in selected developing countries. Model one further shows a significantly negative relationship between energy consumption and income inequality (𝛼3 = −0.017, 𝑝 < 0.01). It means energy use has a positive effect on income disparity. The overall results reveal that energy consumption has a negative association with income inequality. All the production sectors are entirely dependent on energy use because of excessive use of energy due to this, production increases which leads to generating employment, as results income inequality decreases in Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 120 overall developing countries. The coefficient value of GDP growth showed a positive relationship between growth and income disparity (𝛼4 = 0.171, 𝑝 < 0.05). It implies that growth has a significantly favorable impact on income inequality. Additionally, the estimated GDP square coefficient demonstrates a negative relationship between income inequality and growth (𝛼5 = −0.167, 𝑝 < 0.01). It means growth square has a significantly negative impact on income disparity. The Inverted U-shaped relation between GDP growth and income disparity is exposed by this result. This supports the Kuznets hypothesis (1955), which states that when a country's per capita income rises, income disparity rises to a certain level and then income inequality starts reducing as per capita income rises. The coefficient value of population shows a positive association between population and income disparity (𝛼6 = 0.004, 𝑝 < 0.05). It means the population has a statistically positive relation to income inequality. The coefficient value of unemployment shows the positive relationship between unemployment and income inequality (α7 = 0.010, p < 0.01). It means that unemployment has a statistically significant positive association with income inequality. Economic globalization shows a positive relationship with income inequality in selected developing countries (𝛼8 = 0.028, 𝑝 < 0.01). It means that economic globalization is positively associated with income inequality. Political globalization also shows a positive association with income inequality in selected developing countries (𝛼9 = 0.011, 𝑝 < 0.01). Political globalization is positively affecting income inequality. It means that political globalization has a worse effect on income inequality in selected developing countries. Social globalization has a positive but insignificant correlation with income disparity in developing nations (𝛼10 = 0.013, 𝑝 > 0.1). It means that social globalization has an insignificant effect on income disparity in all developing nations. Four interaction terms are introduced in this analysis. First, in model five, the interaction term of overall globalization and energy consumption is introduced. The coefficient value of that interaction showed a significantly negative correlation with income inequality (𝛼11 = −0.022, 𝑝 < 0.1). So, the interaction term of model five concluded that the joint effect of globalization and energy use that is used as an interaction term is helpful for income inequality in selected developing countries. Second, in model six, the interaction term of economic globalization and energy consumption is introduced. The coefficient value of that interaction term showed a significantly negative relationship with income inequality (𝛼12 = −0.018, 𝑝 < 0.01). Therefore, the interaction term of model six concluded that the joint effect of economic globalization and energy use that is used in the interaction term is helpful for income inequality in selected developing countries. Third, in model seven, the interaction term of political globalization and energy consumption is introduced. The coefficient value of that interaction term showed a significantly negative correlation with income inequality (𝛼13 = −0.019, 𝑝 < 0.1). hence, the interaction term of model seven concluded that the combined effect of political globalization and energy consumption which are used in the interaction term showed a positive effect on income disparity in selected emerging countries. Fourth, in model eight, the interaction term of social globalization and energy consumption is introduced. The coefficient value of that interaction term showed an insignificantly negative relationship with income inequality (𝛼14 = −0.020, 𝑝 > 0.1). The interaction term of model eight concluded that the combined effect of social globalization and energy consumption which are used in an interaction term has no significant impact on income disparity in selected emerging countries. All model's diagnostic tests on System-GMM reveal the results. The findings of Arellano and Bond's (1991) serial correlation test are presented in this analysis. The presence of first-order AR (1) serial correlations does not mean that the estimates are inconsistent. All models succeeded the AR (1) Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 121 and AR (2) tests, as evidenced by their p-values. The existence of second-order AR (2) autocorrelation, on the other hand, indicates that the estimates are inconsistent. As a result, AR (2) is more genuine. The results of AR (2) show that there is no serial correlation in this investigation. Extra instruments have been used as a necessity for System-GMM, the p-values of the Hansen tests reflect the total instruments' validity. When the p-value is significant, we reject the null hypothesis and conclude that the instruments are invalid. But in these models, we don’t reject the null hypothesis of the Hansen test meaning that the instrument is valid in all models. As a consequence of these tests, it is possible to conclude that the estimated model is correctly specified. 5. Conclusion and Policy Recommendations This study's main goal is to examine how globalization and energy consumption affect income inequality in developing nations. The purpose of the research is to achieve the hypothesis that “globalization and energy consumption improves economic growth but deteriorates income inequality”. Sixty-nine developing countries are selected for this analysis. The estimation method used in this analysis is System-GMM (S-GMM) two-step method. Finally, the study has concluded the whole findings regarding the impact of globalization, energy consumption on income inequality. According to the finding of this study, income inequality in developing nations is positively and statistically associated with globalization. The positive association between globalization and income inequality indicates that an increase in globalization leads to increasing income inequality is due to, it increases the demand for labor, the high-skilled labor earned more as compared to low-skilled labor, which creates income inequality. Income inequality and energy consumption are negatively and significantly correlated, as energy consumption increases it reduces income inequality. Because most production sectors depend on energy consumption, which leads to increased employment as a result, income inequality decreases. Moreover, in this empirical research, the interaction term of globalization and energy consumption, economic globalization and energy consumption, and political globalization and energy consumption all have a significant and favorable influence on lowering income inequality in developing countries. while the interaction term of social globalization and energy consumption shows negative and insignificant relationship income inequality in developing countries. Furthermore, population and unemployment have a positive link with income inequality. Additionally, there is a positive correlation of population and unemployment with income inequality. Based on the findings of panel data analysis, it is recommended that globalization and industrial growth should be designed to minimize income disparity in all developing nations. Because globalization does not contribute to the notion of sustainable development, developing nations should adopt policies to open their markets to the entire world, make effective use of globalization, and promote policies that reduce income inequality. According to the findings, developing countries should use applicable policy instruments to channel investment inflows and trade-induced technological change to achieve the sustainable development goals (SDGs). Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 122 References Abakumova, J., & Primierova, O. (2018). Economic growth, globalization and income inequality: the case of Ukraine. 18th International Scientific Conference Globalization and Its Socio- Economic Consequences, 18(October). Acheampong, A. O., Dzator, J., & Shahbaz, M. (2021). Empowering the powerless: Does access to energy improve income inequality? Energy Economics, 99. https://doi.org/10.1016/j.eneco.2021.105288 Adams, S. (2008). Globalization and income inequality: Implications for intellectual property rights. Journal of Policy Modeling, 30(5). https://doi.org/10.1016/j.jpolmod.2007.10.005 Adnen, B. N., Balcilar, M., & Gupta, R. (2017). Kuznets Curve for the US: A Reconsideration Using Cosummability. Aghaei, M., & Lin Lawell, C. Y. C. (2020). ENERGY, ECONOMIC GROWTH, INEQUALITY, and POVERTY in IRAN. Singapore Economic Review. https://doi.org/10.1142/S0217590820500198 Aimon, H., Kurniadi, A. P., & Satrio, M. K. (2020). ANALYSIS OF INCLUSIVE GROWTH IN POVERTY, UNEMPLOYMENT AND INCOME INEQUALITY IN WEST SUMATERA PROVINCE: PANEL ERROR CORRECTION MODEL APPROACH. Jurnal Benefita, 5(1). https://doi.org/10.22216/jbe.v5i1.4901 AKINBODE, S. O., BOLARINWA, T. M., & HASSAN, O. O. (2020). ECONOMIC GROWTH AND INCOME INEQUALITY IN SUB-SAHARAN AFRICA. Journal of Humanities, Social Science and Creative Arts, 14(1). https://doi.org/10.51406/jhssca.v14i1.2053 Baek, I., & Shi, Q. (2016). Impact of Economic Globalization on Income Inequality: Developed Economies vs Emerging Economies. Global Economy Journal, 16(1), 49–61. https://doi.org/10.1515/gej-2015-0047 Balan, F., Torun, M., & Kilic, C. (2015). Globalization and Income Inequality in G7: A Bootstrap Panel Granger Causality Analysis. International Journal of Economics and Finance, 7(10). https://doi.org/10.5539/ijef.v7n10p192 Baten, J., & Mumme, C. (2010). Globalization and educational inequality during the 18 th to 20 th centuries: Latin America in global comparison. Revista de Historia Economica - Journal of Iberian and Latin American Economic History, 28(2). https://doi.org/10.1017/S021261091000008X Bergh, A., & Nilsson, T. (2010). Do liberalization and globalization increase income inequality? European Journal of Political Economy, 26(4). https://doi.org/10.1016/j.ejpoleco.2010.03.002 Bor, J., Cohen, G. H., & Galea, S. (2017). Population health in an era of rising income inequality: USA, 1980–2015. In The Lancet (Vol. 389, Issue 10077). https://doi.org/10.1016/S0140- 6736(17)30571-8 Borraz, F., & Lopez-Cordova, J. E. (n.d.). Has Globalization Deepened Income Inequality in Mexico? Butler, J., Wildermuth, G. A., Thiede, B. C., & Brown, D. L. (2020). Population Change and Income Inequality in Rural America. Population Research and Policy Review, 39(5). https://doi.org/10.1007/s11113-020-09606-7 Carvalho, L., & di Guilmi, C. (2020). Technological unemployment and income inequality: a stock- flow consistent agent-based approach. Journal of Evolutionary Economics, 30(1). https://doi.org/10.1007/s00191-019-00628-9 Castells-Quintana, D., & Royuela, V. (2012). Unemployment and long-run economic growth: The role of income inequality and urbanisation. Investigaciones Regionales - Journal of Regional Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 123 Research, 24. Chang, J. J., & Hung, H. W. (2014). Trade unions, unemployment, economic growth, and income inequality. Macroeconomic Dynamics, 20(1). https://doi.org/10.1017/S1365100514000443 Chordokrak, C., & Chintrakarn, P. (2011). Globalization, technology, and income inequality: New evidence. International Research Journal of Finance and Economics, 62. Costantini, M., Meco, I., & Paradiso, A. (2018). Do inequality, unemployment and deterrence affect crime over the long run? Regional Studies, 52(4). https://doi.org/10.1080/00343404.2017.1341626 Crouch, C. (2019). Inequality in post-industrial societies. Structural Change and Economic Dynamics, 51. https://doi.org/10.1016/j.strueco.2019.07.011 Curran, M., & Mahutga, M. C. (2018). Income Inequality and Population Health: A Global Gradient? Journal of Health and Social Behavior, 59(4). https://doi.org/10.1177/0022146518808028 Deininger, K., & Squire, L. (1997). Economic growth and income inequality: reexamining the links. Finance and Development, 34(1). Destek, M. A. (2018). Dimensions of globalization and income inequality in transition economies: Taking into account crosssectional dependence. Eastern Journal of European Studies, 9(2). Dong, Z., Tang, C., & Wei, X. (2018). Does population aging intensify income inequality? Evidence from China. Journal of the Asia Pacific Economy, 23(1). https://doi.org/10.1080/13547860.2017.1354270 Dorn, F., Fuest, C., & Potrafke, N. (2018). Globalization and Income Inequality Revisited. www.RePEc.org Dreher, A., & Gaston, N. (2008). Has globalization increased inequality? Review of International Economics, 16(3). https://doi.org/10.1111/j.1467-9396.2008.00743.x Duan, C., & Chen, B. (2018). Analysis of global energy consumption inequality by using Lorenz curve. Energy Procedia, 152, 750–755. https://doi.org/10.1016/j.egypro.2018.09.240 Firebaugh, G., & Goesling, B. (2004). Accounting for the recent decline in global income inequality. American Journal of Sociology, 110(2). https://doi.org/10.1086/421541 Galvin, R., & Sunikka-Blank, M. (2018). Economic Inequality and Household Energy Consumption in High-income Countries: A Challenge for Social Science Based Energy Research. Ecological Economics, 153. https://doi.org/10.1016/j.ecolecon.2018.07.003 Heimberger, P. (2020). Does economic globalisation affect income inequality? A meta-analysis. World Economy, 43(11). https://doi.org/10.1111/twec.13007 Hoffmann, F., Lee, D. S., & Lemieux, T. (2020). Growing income inequality in the United States and other advanced economies. Journal of Economic Perspectives, 34(4). https://doi.org/10.1257/JEP.34.4.52 Hulme, D., & Shepherd, A. (2003). Conceptualizing chronic poverty. World development, 31(3), 403-423. Hung, H. F. (2021). Recent Trends in Global Economic Inequality. In Annual Review of Sociology (Vol. 47). https://doi.org/10.1146/annurev-soc-090320-105810 Hwang, S., Choe, C., & Choi, K. (2021). Population ageing and income inequality. Journal of the Economics of Ageing, 20. https://doi.org/10.1016/j.jeoa.2021.100345 KARACAN, R., YARDIMCI, M. E., & İŞLEYEN, A. (2021). The Effect of Regime on the Economic Growth and the Income Inequality. OPUS Uluslararası Toplum Araştırmaları Dergisi. https://doi.org/10.26466/opus.927567 Lee, C. C., Lee, C. C., & Lien, D. (2020). Income inequality, globalization, and country risk: A cross- country analysis. Technological and Economic Development of Economy, 26(2), 379–404. https://doi.org/10.3846/tede.2019.11414 Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 124 Mainali, B., Pachauri, S., Rao, N. D., & Silveira, S. (2014). Assessing rural energy sustainability in developing countries. Energy for Sustainable Development, 19(1). https://doi.org/10.1016/j.esd.2014.01.008 Mishchuk, H., Samoliuk, N., Bilan, Y., & Streimikiene, D. (2018). Income inequality and its consequences within the framework of social justice. Problemy Ekorozwoju, 13(2). Mocan, H. N. (n.d.). STRUCTURAL UNEMPLOYMENT, CYCLICAL UNEMPLOYMENT, AND INCOME INEQUALITY. Molero-Simarro, R. (2017). Growth and inequality revisited: The role of primary distribution of income. A new approach for understanding today’s economic and social crises. Cambridge Journal of Economics, 41(2). https://doi.org/10.1093/cje/bew017 Muryani, Esquivias, M. A., Sethi, N., & Iswanti, H. (2021). Dynamics of Income Inequality, Investment, and Unemployment in Indonesia. Journal of Population and Social Studies, 29. https://doi.org/10.25133/JPSSv292021.040 Nam, Y. (2020). Do welfare benefits compensate for globalization among affluent democracies? Journal of European Social Policy, 30(2). https://doi.org/10.1177/0958928719886796 Ningsih, C., & Choi, Y.-J. (2019). An Effect of Internet Penetration on Income Inequality in Southeast Asia Countries. Information Society & Media, 20(3). https://doi.org/10.52558/ism.2019.12.20.3.121 Odusola A, M. F. W. Y. R. W. (n.d.). Income Inequality and Population Growth in Africa. Rougoor, W., & van Marrewijk, C. (2015). Demography, Growth, and Global Income Inequality. World Development, 74. https://doi.org/10.1016/j.worlddev.2015.05.013 Sehrawat, M. (2021). Modelling the nexus between human capital, income inequality, and energy demand in India: new evidences from asymmetric and non-linear analysis. Environmental Science and Pollution Research, 28(3). https://doi.org/10.1007/s11356-020-10733-z Serin Oktay, D. (2019). Asymmetric Impact of Inflation and Unemployment on Income Inequality: The Case of Turkey. International Conference on Eurasian Economies 2019. https://doi.org/10.36880/c11.02309 Shayegh, S. (2017). Outward migration may alter population dynamics and income inequality. Nature Climate Change, 7(11). https://doi.org/10.1038/nclimate3420 Sheng, L. (2015). Theorizing income inequality in the face of financial globalization. Social Science Journal, 52(3). https://doi.org/10.1016/j.soscij.2014.06.003 Sheng, Y. (2012). Unemployment and Income Inequality: A Puzzling Finding from the US in 1941- 2010. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2020744 Sonora, R. J. (2021). A panel analysis of income inequality and energy use. Contemporary Economic Policy. https://doi.org/10.1111/coep.12550 Tamasauskiene, Z., & Žičkienė, S. (2021). An investigation of the nexus between globalisation dimensions and income inequality. Entrepreneurial Business and Economics Review, 9(2). https://doi.org/10.15678/EBER.2021.090203 Topcu, M., & Tugcu, C. T. (2020). The impact of renewable energy consumption on income inequality: Evidence from developed countries. Renewable Energy, 151, 1134–1140. https://doi.org/10.1016/j.renene.2019.11.103 Tumba Henry, J., & Panotani, P. A. (2019). Relationship between per Capita National Income and Income Inequality: Is the Kuznets Hypothesis True for Nigeria? In International Journal of Innovative Science and Research Technology (Vol. 4, Issue 4). www.ijisrt.com198 Yang, W., & Kanavos, P. (2012). The less healthy urban population: income-related health inequality in China. BMC Public Health, 12. https://doi.org/10.1186/1471-2458-12-804 Yang, Y., & Greaney, T. M. (2017). Economic growth and income inequality in the Asia-Pacific region: A comparative study of China, Japan, South Korea, and the United States. Journal of Review of Economics and Development Studies, Vol. 8 (2) 2022, 111 - 125 125 Asian Economics, 48. https://doi.org/10.1016/j.asieco.2016.10.008 York, M. (2018). Revolutionary Love and Alter-globalisation: Towards a New Development Ethic. Social Change, 48(4). https://doi.org/10.1177/0049085718801402 Younsi, M., & Bechtini, M. (2020). Economic Growth, Financial Development, and Income Inequality in BRICS Countries: Does Kuznets’ Inverted U-Shaped Curve Exist? Journal of the Knowledge Economy, 11(2). https://doi.org/10.1007/s13132-018-0569-2 Zhan, P., Ma, X., & Li, S. (2021). Migration, population aging, and income inequality in China. Journal of Asian Economics, 76. https://doi.org/10.1016/j.asieco.2021.101351 Zhong, H. (2011). The impact of population aging on income inequality in developing countries: Evidence from rural China. China Economic Review, 22(1). https://doi.org/10.1016/j.chieco.2010.09.003 Zucman, G. (2019). Global Wealth Inequality. In Annual Review of Economics (Vol. 11). https://doi.org/10.1146/annurev-economics-080218-025852 Zungu, L. T., Greyling, L., & Mbatha, N. (2021). Economic growth and income inequality: a non- linear econometrics analysis of the SADC region, 1990–2015. African Journal of Economic and Management Studies, 12(2). https://doi.org/10.1108/AJEMS-09-2020-0465