# **5. Electricity Consumption and Economic Activity**

# _5.1 Cointegration and Causality_

First, we determine the long-run association between electricity consumption and economic activity based on annual data. We perform the Johansen cointegration test on two bivariate annual models: (i) real GDP and electricity consumption, and (ii) industrial production and electricity consumption. To uncover the long-run equilibrium and causal relationships between the selected variables, data from 2000 to 2020 are analyzed. Each of the estimated models contains one cointegrating vector, suggesting that an equilibrium relationship holds in the long run. The existence of a cointegration relationship also implies that at least one causal relationship exists among the variables. To check pairwise causality, we employ the TodaYamamoto (1995) Granger causality test, which is robust to the identified cointegration relationship.<sup>5</sup> It uncovers unidirectional causality from GDP to electricity consumption ( = 6.24, p = 0.04) and bidirectional causality between industrial production and electricity χχ<sup>2</sup> consumption.<sup>6</sup> Second, we determine the short-run association between electricity consumption and economic activity based on monthly data. Since Bangladesh publishes only annual GDP data, we can only use industrial production. For the analysis, we employ monthly data on industrial production and electricity consumption from August 2012 to April 2021. In line with the annual data, we find bidirectional Toda-Yamamoto Granger causality for electricity consumption and industrial production with monthly data. Growth in industrial production Granger causes growth in electricity consumption ( = 7.52, p = 0.01) and vice versa ( = 3.414, p = 0.07). The results suggest that electricity consumption can provide statistically significant χχ<sup>2</sup> χχ<sup>2</sup> information about economic activity.

# _5.2 Regression Models_

To confirm the meaningful relationship between electricity consumption and economic activity in Bangladesh, we next estimate the following linear regression model with annual data: e e e e T (1) tt tt tt<sup>+ εε</sup> tt<sup>,</sup> log xx = ββ log e ee + TTe

> 5 The Toda and Yamamoto Granger causality test takes care of some of the shortcomings of two-variable Granger causality. The traditional Granger causality test is sensitive to model specification and the number of lags. When the variables are integrated, the F-test procedure is not valid, as the test statistics do not have a standard distribution. Toda and Yamamoto (1995) propose an interesting yet simple procedure requiring the estimation of an augmented vector autoregression, which is robust to the integration and cointegration properties of the process. 6 The results indicate strong evidence of causality running from industrial production to electricity consumption  The results indicate strong evidence of causality running from industrial production to electricity consumption

> an augmented vector autoregression, which is robust to the integration and cointegration properties of the process. 6 The results indicate strong evidence of causality running from industrial production to electricity consumption  The results indicate strong evidence of causality running from industrial production to electricity consumption ( = 12.92, p = 0.02) and vice versa ( = 24.79, p = 0.00). More detailed results are available upon request. χχ<sup>2</sup> χχ<sup>2</sup>

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