• اقتصاد سیاسی انرژی به ویژه در حوزه خلیج فارس
Armin Sharifi; Fateh Habibi; Bakhtiar Javaheri
Abstract
Income inequality has become a political issue for most countries around the world. In recent decades, levels of income inequality have increased in most industrialized economies. Therefore, income inequality has been largely considered as an important social and economic problem, thus attracting plenty ...
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Income inequality has become a political issue for most countries around the world. In recent decades, levels of income inequality have increased in most industrialized economies. Therefore, income inequality has been largely considered as an important social and economic problem, thus attracting plenty of attention from policy-makers and scholars as to its resolution. The aim of this study is to investigate the impact of energy security in four dimensions (availability, accessibility, development capability and acceptability) on income inequality in Middle Eastern countries during the period 2000-2021 using threshold panel regression. The contributions of the current study can be summarized as follows: First, we fill the gap in the literature by examining the influence of energy security on income inequality. Second, unlike previous works, we delve into the non-linear impact of energy security on income inequality. Third, we employ a new DPT model to find the threshold variables. The results show that among the four dimensions of energy security, only the acceptability dimension has a negative and significant effect on income inequality, and other energy dimensions do not have a significant effect on income inequality. The threshold level for the Middle East countries is calculated at 42032 dollars per capita. Therefore, governments should focus more on improving the efficiency of energy use. Not only does this help to reduce the income gap, but also increases energy security optimally.
Introduction
The literature has studied various factors affecting income inequality and the influence of energy security on the economy, while knowledge is rather limited regarding the linkage between energy security and income inequality. The purpose of this paper is to offer new insights into whether and how energy security impacts income inequality via a global sample of 68 countries for the period 2000-2021. In current study, we capture four aspects of energy security (availability, accessibility, develop-ability, and acceptability) mentioned above in our empirical analysis. Previous studies are also aware of the non-linear effects in some predictors of the empirical models. It is also noted that the reduction of income inequality requires a proper long-term policy. With these considerations in mind, our analysis aims to assess the effect of energy security on income distribution, whether this effect of energy security on income inequality changes under different stages of economic development, and whether the inequality in the previous period influences current inequality.
Methods and Material
Most existing studies on the factors affecting income inequality are based on the Kuznets curve hypothesis, which postulates that inequality increases with economic growth under the early phase of economic development and subsequently decreases after achieving a certain development level. According to his theoretical framework, income inequality is hypothesized to be a function of linear and quadratic income terms.
Following the proposition of the Kuznets curve, we hypothesize that the influence of energy security on income distribution varies with the degree of economic development. On the one hand, a high level of energy security can guarantee stable economic growth in low development countries, but this inevitably leads to greater income inequality. On the other hand, higher energy security can guarantee the normal operations of enterprises’ production in high development countries. People can get more jobs to do, ultimately leading to a drop in income inequality. Our investigation assesses the influence of energy security on income inequality for a panel dataset of 12 Middle East countries over 2000-2021 owing to data availability.
When studying the issue of income inequality, it is thus required to introduce the lagged value of the income inequality on the right-hand side of the regression equation as an independent variable. This transforms the static panel data into dynamic panel data as largely applied in the literature. Second, the static threshold model requires the selection of threshold variables to be completely exogenous. In this regard, the use of an exogenous threshold variable may generate biased estimations.
Results and Discussion
Before proceeding with further analysis, we first examine the stationarity of the variables to avoid the issue of spurious regressions. To this end, we employ Levin-Lin-Chu and Im-Pesaran-Shin unit root tests. we conclude that all variables are stationary in levels. Therefore, non-stationarity of the variables is not a major concern for the following estimation. Before conducting a parameter estimation of the DPT model to examine the non-linear impact of energy security on income inequality, we first test the nonlinearity and the threshold effect. The null hypothesis is that the model is linear and there is no threshold effect. According to results, the null hypothesis that there is no threshold effect can be rejected at the 1% significance level. We now take GDP per capita as the threshold variable and estimate using the DPT model.
Table 1: Result of DPT estimations by using GDP per capita as a threshold
variable
Model ES1
Model ES2
Low
High
Low
High
Prob.
Coef.
Prob.
Coef.
Prob.
Coef.
Prob.
Coef.
Gini
0.0000
0.9398***
0.000
1.0536***
0.000
0.7065***
0.000
0.7908***
GDP
0.664
-0.0028
0.828
0.0065
0.365
-0.0203
0.001
-0.0689***
Trade
0.149
-0.0085
0.000
-0.1017***
0.050
0.0393*
0.466
- 0.0115
Fin.Dev.
0.957
0.0003
0.225
0.0281
0.657
-0.0051
0.457
0.0121
Availability
0.641
0.0010
0.347
-0.0091
-
-
-
-
Acceptability
-
-
-
-
0.386
-0.0184
0.009
- 0.0407***
Const.
0.000
1.1395***
0.000
1.4753***
Variable Gini
Model ES3
Model ES4
Low
High
Low
High
Prob.
Coef.
Prob.
Coef.
Prob.
Coef.
Prob.
Coef.
GDP
0.000
0.7320***
0.000
0.7894***
0.000
0.7301***
0.000
0.7931***
Trade
0.820
0.0038
0.006
-0.0475***
0.980
- 0.0004
0.002
-0.0491***
Fin. Dev.
0.047
-0.0397**
0.780
0.0041
0.055
- 0.0383**
0.456
0.0100
variable
0.487
0.0079
0.820
-0.0036
0.692
-0.0048
0.854
- 0.0028
Develop-ability
0.310
-0.0176
0.456
-0.0139
-
-
-
-
Accessibility
-
-
-
-
0.383
0.0182
0.940
0.0007
Const.
0.000
1.3942***
0.000
1.1686***
Notes: t-statistics. ***p < 0.01, **p < 0.05, and * p < 0.1
The coefficient of ES1, used to measure the availability of energy security, is insignificantly positive under the threshold estimate, while the effect above the threshold becomes significantly negative at the 5% significance level. The results indicate that greater availability of energy security improves income distribution only when a certain level of economic development is reached. This means that the availability of energy security has an inverted U-shape influence on income inequality with the growth of the economy, which is consistent with previous studies (Lee et al. 2022). On the one hand, higher availability of energy security leads to economic development, and economic development leads to an increase in income inequality when the level of economic development is low. On the other hand, higher availability of energy security makes the production of enterprises in countries with a high level of economic development more stable, and the income of people will also be more stable, thus reducing income inequality.
Second, ES2 allows us to quantify the acceptability of energy security. The results reveal that the coefficients of ES2 below the threshold are not significantly negative, while the effect above the threshold is significantly negative. The acceptability of energy security widens the degree of income inequality in a regime with low economic development, while it decreases income inequality in a regime with high development. In the case of an underdeveloped economy, the technology of non-fossil energy is not advanced, and the cost of using non-chemical energy is high. The higher the proportion is for non-fossil energy used, the more people spend on energy, which increases income inequality. Along with the development of an economy, the technology of non-fossil energy becomes advanced, and the price of non-fossil energy turns lower. Thus, the use of non-fossil energy reduces income inequality.
Third, both ES3 is negative indicators capturing the developability of energy security. The results reveal that the coefficients of them below the threshold are significantly positive, and the effect above the threshold is significantly positive. These results suggest that lower levels of developability of energy security decrease inequality in a regime with low and high development. In countries with a low level of economic development (Middle East), the government is not concerned about environmental issues. Some high-emission and high-polluting companies will thus continue to produce stably, and the income of employees will remain stable, thus leading to reduced inequality.
Fourth and finally, ES4 is a positive indicator used to measure the accessibility of energy security. The evidence reveals that its coefficient is significantly positive below and above the threshold, suggesting that the accessibility of energy security deteriorates income distribution in the early stages of economic development. One possible explanation is that greater accessibility of energy security in countries with lower levels of economic development is not conducive to domestic economic growth, thereby exacerbating income inequality.
Conclusion
Prior literature has broadly discussed the importance of income inequality and its determinants with little consensus due to the inconclusive results therein. In contrast with the conventional economic and financial aspects, an alternative energy perspective of how energy market activities affect income inequality still awaits a more in-depth exploration. To our knowledge, this research is the first to explore the impact of energy security on income inequality in the global context. Unlike most previous works, we do not artificially classify transnational data, but instead employ the DPT model developed by Seo and Shin (2016) to estimate the threshold value. In addition, we employ four measures of energy security to capture of energy security (availability, accessibility, developability, and acceptability). Using a Middle East sample of 12 countries, our analyses thus complement the existing research not only on the non-linear nexus between energy security and income inequality, but also on how different dimensions of energy security affect income distribution. Our empirical results suggest that the impact of energy security on income inequality involves a threshold effect.
The evidence shows that in all dimensions of energy security only in relation to the dimension of acceptability positively influence income inequality when a country's economic growth is lower than the threshold level. In other words, a high level of energy security will widen income inequality when a country's economy is underdeveloped. On the contrary, a higher level of energy security reduces income inequality when the economy is developed. Our findings can help policymakers formulate energy security policies based on their country's own development level. Governments should focus more on improving the efficiency of energy use. Through these measures, not only the income gap is reduced, but energy security is optimally increased.
Hussein Mohammadi; Muhammad Terjari Seraji
Volume 2, Issue 6 , April 2013, , Pages 183-207
Abstract
Trade openness, economic growth and environment are known as the major issues in recent decads. We considered the effect of expanding trade and economic growth on environmental quality for 11 country in the Middle East during the period of 1980 to 2010. Panel data econometric method and STATA software ...
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Trade openness, economic growth and environment are known as the major issues in recent decads. We considered the effect of expanding trade and economic growth on environmental quality for 11 country in the Middle East during the period of 1980 to 2010. Panel data econometric method and STATA software were used. Econometric tests confirmed fixed effects model among the pooled, fixed and random effects models. Each Arellano-Bond GMM regression model and Newey- West with 5 lags allowed were estimated beacuase of dynamic panel data and their results were compared with the results of fixed effects model and then were evaluated. We found per capita income has a significant positive impact on environmental pollution but further rise in income will reduce environmental pollution. Trade openness has negative effect and there is a non-significant effect on environmental pollution.
