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Empirical Economics
Published in: Empirical Economics 3/2022

Open Access 13-04-2021

Industry electricity price and output elasticities for high-income and middle-income countries

Authors: Brantley Liddle, Fakhri Hasanov

Published in: Empirical Economics | Issue 3/2022

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Abstract

Energy planning and climate policy require understanding long-run energy demand patterns. Electricity demand further is important because energy services derived from electricity typically do not have substitution possibilities from other fuels. By employing dynamic panel models, we estimate the long-run price and output elasticities of aggregate industrial electricity demand for high-income (mostly OECD) and middle-income (mostly non-OECD) countries. The unbalanced data span 1978–2016 and include 35 high-income countries and 30 middle-income countries. Our dynamic panel estimates address nonstationarity, heterogeneity, and cross-sectional dependence. We believe these are the first such panel estimates for middle-income/non-OECD countries and among the few such estimates for high-income/OECD countries to appear in the literature. The output elasticity for high-income countries typically was significantly below unity, around 0.5, and the price elasticity was around − 0.25 (and was statistically significant). For middle-income countries, the output elasticity was greater than unity and was likely significantly larger than the output elasticity for high-income countries, whereas the price elasticity was small and insignificant for middle-income countries.
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Appendix
Available only for authorised users
Footnotes
1
Electricity also played a prominent role in the so-called Second Industrial Revolution, which occurred over the last quarter of the nineteenth century and the beginning of the 20th (Rosenberg 1998).
 
2
Those countries with a high electricity share are Chile, Ecuador, El Salvador, Guatemala, Jordan, Lebanon, Mexico, Morocco, Peru, South Africa, and Turkey.
 
3
Van Benthem and Romani (2009) do analyze a panel of 17 mostly non-OECD countries and appear to consider both industry energy consumption and an index of end-use industry energy price. But they include (a polynomial of) GDP per capita, and thus, their estimations are not of an industry demand function per se.
 
4
In addition to the absence of previous non-OECD panel analyses, even meta-studies, like Labandeira et al. (2017), do not appear to have the resolution/available data to determine whether/how much elasticities vary between OECD and non-OECD countries at the industry sectoral level.
 
5
The preferred estimator of Csereklyei (2020)—between effects (BE)—essentially estimates a static, cross-section. However, there is some evidence that BE is robust to nonstationarity, and possibly, cointegration (e.g., Pesaran and Smith 1995). Adeyemi and Hunt (2007) considered only nonstationarity but not cointegration properties of their variables. Moreover, they performed unit root tests only for the industrial energy demand but not for price and income variables. Lastly, they used only the first-generation panel unit root tests, which do not account for cross-sectional dependency that most likely exists in the data used.
 
6
Some examples of multilevel modeling in those disciplines are Steenbergen and Jones (2002), Ronfeldt et al. (2013) and Islam et al. (2006).
 
7
Csereklyei estimated a lower price elasticity of − 0.4 when employing System Generalized Method of Moments (SGMM) with a time trend, but this does not appear to be Csereklyei’s preferred SGMM estimation.
 
9
This test is implemented via the Stata command xtcd, which was developed by Markus Eberhardt.
 
10
This test is implemented via the Stata command pescadf, which was developed by Piotr Lewandowski.
 
11
If we were employing a pure time series approach, rather than an (unbalanced) panel approach, we would consider a more comprehensive model, as in Hasanov and Mikayilov (2020), rather than a reduced form model.
 
12
In both cases, we follow the standard practice of robust regressions (see Hamilton 1992), in which outliers are weighted down in the calculation of averages.
 
13
The Dynamic Common Correlated Effects estimator of Chudik and Pesaran (2015) is implemented by using Stata command xtmg, which was developed by Markus Eberhardt.
 
14
Both an ARDL(1,2,2) and error correction model were rejected. (Considering even a larger number of lags would mean losing several additional countries from our dataset.)
 
15
Beck and Katz (2009) claimed that with at least 20 time observations, applying bias correction (e.g., Kiviet 1995) is counter-productive, whereas Judson and Owen (1999) were more conservative, recommending bias correction unless there are 30 time observations. However, Pesaran et al. (1999) cautioned that bias correction to the short-run coefficients can exacerbate the bias of the long-run coefficients.
 
16
The ARDL (1,0,0) model was rejected for the high-income/OECD panel.
 
17
Those countries are: Algeria, Azerbaijan, Bolivia, Ecuador, El Salvador, India, Indonesia, Iran, Mexico, South Africa, Thailand, and Venezuela.
 
18
Data on the value added in these sectors is from the United Nations Industrial Development Organization’s INDSTAT2 2016 CD-ROM: Industrial Statistics Database. There are missing observations for both countries and particular years.
 
19
That test is run by the Stata command xthst, which was written by Tore Bersvendsen and Jan Ditzen.
 
20
OECD-middle-income countries Mexico and Turkey have price data available from IEA.
 
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Metadata
Title
Industry electricity price and output elasticities for high-income and middle-income countries
Authors
Brantley Liddle
Fakhri Hasanov
Publication date
13-04-2021
Publisher
Springer Berlin Heidelberg
Published in
Empirical Economics / Issue 3/2022
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-021-02053-z

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