Income and electricity consumption relationship

Determining the Relationship Between Consumption and Household Income

income and electricity consumption relationship

The study of relationship between residential electricity demand and income is important as it helps to better understand welfare implications of. This paper investigates how per capita income influences energy demand and CO2 picture of the relationship between income, consumption and emissions. This study investigates the inter-temporal causal relationship between energy consumption and economic growth in Bangladesh during the period

Typically these studies were done about long-term and short-term effects of energy prices and GDP or other variables of total income on total consumption of one or more kind of fuel in private sector or countries economy.

Atkinson and Manning conducted a study on the sensitivity of price and income for developed countries. The sensitivity analysis was done to the year for developed countries; recently similar analysis is done for developing countries too.

As seen in the literature of energy economy, the existence of unit root in economic time series variables explicitly or implicitly is refers to the removal of ARDL model as a framework to estimate the relationship of energy demand. Instead, convergence and error correction of vector technique be used for calculating the non-static variables and determine the economy variables.

For example Bentzen [ 2 ] found that the energy consumption and real income and energy prices for the Britain and Denmark countries while they are converging are non-static.

income and electricity consumption relationship

So scholars should be careful in applying of their ARDL model for the non-static variables, because when the variables are not converge, regression can be established between these variables is pseudo regression. Even the variables are converging too, standard statistical inferences such as t and F are not established about them [ 2 ].

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An interesting point that exists about the vector error correction model is; this model includes only variables that these variables have been converted to a static mode. Therefore with using this property, Standard statistical inference can be applied in the asymptotic state for the null hypothesis of these variables.

For example, some times for analysis of long-term and short-term sensitivity, hypothesis tests using standard statistical method for non-static variables are still preserved its statistical value.

Presence of unique convergent long-term relationship between variables for preserving these results is necessary. Theory Energy carriers are demanded as the marginal products by consumers and as the production inputs by the economic firms. Determination of demand measure for that part of energy carriers which are used marginal products is done according to the theory of consumer behavior and the utility maximization based on the consumer's budget constraint.

With forming the first and second condition and assuming that consumer utility function is strictly quasi concave, the demand for energy carriers like demand for other consumption goods will be a function of prices and incomes n-dimensional vector. So the energy consumption that here include different carriers such as oil, gas, coal and electricity is itself inverse function of the level of energy carriers prices.

income and electricity consumption relationship

In other words, increasing energy prices causes that reduced energy consumption. Manufacturing firms may seek to maximize production given a certain amount of cost or minimizing the cost given a certain amount production or maybe they are looking profit maximization. The results of the first and second order conditions indicate that in each of the three modes firm's demand for energy input depends on the price of desired carrier and other input prices, product price and production quantity.

So the most important economic factors affecting energy demand can be considered price variable and a variable of activity like real national income or real GDP. Conducted studies Anderson has reviewed the function of energy demand in both domestic and industrial sectors over the period in America.

Investigation of Energy Consumption, Energy Price and Income in Iran in the Long-Term

In addition to the long-run parameters, estimation of short-run parameters also gives some valuable information. In the final stage of the study, PMGE and MGE methods will be used to estimate the short-term and long-term relationship between the variables for each unit. MGE estimates the long-run parameters by using the means of the long-run parameters of the autoregressive distributed lag ARDL models for the estimation of the individual units.

Therefore, it allows the long-run parameters to change for the units. On the other hand, PMGE holds the long-run parameter constant and allows the short-run parameters, and even the variance, to change for the units.

Therefore, the series used in the model must first be tested for stationarity by using unit root tests. In this study, it is necessary to first determine the presence of crosssection dependence to determine which generation root test should be applied.

This is important when choosing the unit root and cointegration tests to be performed. Crosssection independence is an important issue for today's markets, as they are becoming increasingly more integrated Herzer, as a result of various common factors, such as countries, global financial crises and fluctuating oil prices, all of which can be affected in such an environment Cavalcanti et al.

These tests examine whether the cross-section units are dependent on each other and whether they are equally affected by a shock to the series. Table II presents the results of the cross-section dependence tests performed on the series. The results summarized in Table II indicate that there is cross-section dependence in the panel data used to estimate the model at the 1 per cent significance level for all related sub-categories as well as for the whole data set.

This means that any shock to one of the countries affects the other countries. Therefore, while developing energy policies, these countries should take into account the policies adopted by the other countries in the panel, as well as the shocks that affect their energy consumption. For this reason, the stationarity of the series is tested using the CADF test developed by Pesaranwhich is one of the second-generation unit root tests that takes cross-section dependence into consideration.

This method uses the ADF regression augmented by the lagged cross-section means.

income and electricity consumption relationship

As shown in Table IIIthe results of the Pesaran CADF panel unit root test performed on the economic growth and energy consumption variables indicate that the series are not stationary at the 5 per cent significance level, which means that they have a unit root. Therefore, we take the first difference of each series, thus getting a stationary series.

According to these results, we can say that the variables are integrated at the same level and it is possible to examine a relationship in the long term, which can be done through cointegration tests. For this reason, the panel cointegration test developed by Westerlundwhich takes into account cross-section dependence, is used to analyze the long-term relationship between variables.

Westerlund proposes comparing the test statistics calculated for cross-section dependence with the bootstrap critical values recommended by Chang and Westerlund Table IV presents the results of the panel cointegration analysis. According to the results of the Westerlund panel cointegration test presented in Table IVH0, which states that there is no cointegration between the cross-section units in the panel with respect to either the country groups or the relevant sub-categories, is rejected by each of the four test statistics.

The results further reveal that there is a statistically significant cointegration relationship in the panel data set. Therefore, according to the results of the cointegration analysis of the net energy-importing countries, we conclude that there is a strong relationship between energy consumption and economic growth over the long term.

In this study, the coefficients in the cointegrating vector are analyzed using the dynamic OLS estimator and the fully modified OLS estimator. Table V displays the estimation results of a long-term relationship between energy consumption and economic growth. According to the results presented in the table, t-statistics for the common long-term coefficients are statistically significant at the 1 per cent level for all country groups and for other sub-categories, except for low-income net energy-importing economies.

Duncker & Humblot - Berlin

The results obtained from the DOLS and FMOLS estimation techniques indicate that there is a long-run, positive relationship between energy consumption and economic growth. Thus, a 1 per cent increase in energy consumption in the long term will increase economic growth by 0. However, in addition to the long-run parameters, estimation of short-run parameters also gives some valuable information.

Therefore, we will use PMGE and MGE as a panel error correction model to examine both the long-term and short-run relationship between energy consumption and economic growth.

income and electricity consumption relationship

Table VI shows the results of the analysis. According to the PMGE and MGE results, the error correction parameter is negative and significant for all of the net energy-importing countries and subgroups. The error correction parameter represents the adjustment speed of the short-run deviations caused by the nonstationarity of the series to equilibrium in the next period.

According to the PMGE method, approximately 69 per cent of the disequilibrium in a period can be corrected in the following period, and thus, long-term equilibrium can be attained.

This rate was 64 per cent for countries with import dependence less than 50 per cent and 74 per cent for countries with import dependence greater than 50 per cent.

Furthermore, with respect to the economic subgroups, the rate of disequilibrium was 80 per cent for the low-income economies, 66 per cent for the lower-middle-income economies, 66 per cent for the upper-middle-income economies and 69 per cent for the high-income economies. Additionally, the long-term parameters of the energy consumption variable in the model were significant and positive for all groups, except for the low-income economies, a result that is consistent with a priori expectations.

In other words, a 1 per cent increase in energy consumption increases the GDP per capita by 0. If we categorize the net energy importers by income levels, this increase is 0. The short-run parameter of energy consumption is statistically insignificant, except for the upper-middle-income economies and high-income economies. According to the MGE results shown in Table VI, about 77 per cent of the disequilibrium in a period can be corrected in the following period and the long-term equilibrium can be reached.

This rate is found to be 72 per cent for the countries with import dependence less than 50 per cent and 84 per cent for those with import dependence greater than 50 per cent and to be 90 per cent for the low-income economies, 76 per cent for the lower-middle-income economies, 76 per cent for the upper-middle-income economies and 72 per cent for the highincome economies.

Besides, the long-term parameters of the energy consumption variable in the model were found to be significant and positive for all groups, which is a result consistent with a priori expectations. The short-run parameter was found to be statistically significant only in the group formed based on the dependence level of the countries.

Only the short-term parameters of the country group with high import dependence and the country group with high income levels are statistically significant. In this stage, PMGE and MGE methods are used to estimate both the short-term and long-term relationship between energy consumption and economic growth variables for each unit. As the PMGE results for the low-income economies show, only one long-term parameter is estimated 0.

According to the results of the analysis, the error correction parameter calculated for each net energy-importing low-income economy is statistically significant and negative.

This indicates the presence of a long-term relationship between the variables. In addition, the long-run and short-run parameters of energy consumption, except for Ethiopia, are found to be statistically insignificant. The error correction parameters of all countries, except for Tanzania, are considerably high.

Therefore, the adjustment speed of short-run deviations to long-run equilibrium is extremely high in these countries. Besides, the error correction parameter and the short-run coefficient change depending on the units. According to the MGE results obtained for each unit of the low-income economies, the error correction parameter is statistically significant and negative. This indicates the presence of a long-term relationship between the variables for the country group analyzed.

However, the long-run parameters, except for Nepal and Tanzania, and the short-run coefficients were found to be insignificant. As the PMGE results for the lower-middle-income economies show, only one long-term parameter is estimated 0. According to the results of the analysis, the error correction parameter calculated for each net energy-importing lower-middleincome economy is statistically significant, except for Bangladesh, and negative.

In addition, the long-run coefficient of energy consumption is found to be statistically significant and positive for all countries. However, the short-run parameters are found to be statistically insignificant for the countries, except for Armenia, Guatemala, The Philippines, Senegal, Tajikistan and Ukraine. According to the MGE results shown in Table VIII and obtained for each unit of the lower-middle-income economies, the error correction parameter is statistically significant and negative for all countries, except for Bangladesh, Georgia and Senegal.

The effect of energy consumption on economic growth is strong in El Salvador, India, Kenya, Moldova, Tajikistan and Ukraine, as the long-run coefficients were estimated to be high in these countries. On the other hand, this effect is weak in Armenia, Honduras, Kyrgyz Rep.

The longrun coefficients were found to be statistically insignificant for the other countries. Besides, the short-run coefficients are also insignificant for the countries, except for Armenia, Kenya, The Philippines, Senegal and Ukraine.