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nt. J. Energy Technology and Policy, Vol. 14, No. 1, 2018

Copyright © The Author(s) 2017. Published by Inderscience Publishers Ltd. This is an Open Access Article

distributed under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)

Residential electricity use effects of population in

Kazakhstan

Jeyhun Mikayilov*

King Abdullah Petroleum Studies and Research Center,

P.O. Box 88550, Riyadh 11672, Saudi Arabia

and

Department of Statistics,

Azerbaijan State University of Economics (UNEC),

Istiqlaliyyat Str., 6, Baku, Azerbaijan

and

Institute for Scientific Research on Economic Reforms,

H. Zardabi Avenue, 88a, AZ 1011, Baku, Azerbaijan

Email: jeyhun-mikayilov@unec.edu.az

*Corresponding author

Fakhri Hasanov

King Abdullah Petroleum Studies and Research Center,

P.O. Box 88550, Riyadh 11672, Saudi Arabia

and

Research Program on Forecasting, Economics Department

The George Washington University,

2115 G Street, NW, Washington DC, 20052, USA

and

Department of Socio-Economic Modelling,

Institute of Control Systems,

B. Vahabzade Street 9, Baku, AZ1141, Azerbaijan

Email: fakhri.hasanov@kapsarc.org

Sabuhi Yusifov

Department of Public Administration,

Azerbaijan Technology University,

Shah Ismayil Hatai Ave., 103, Ganja, Azerbaijan

and

The Institute of Economics, ANAS,

Azerbaijan Republic, Baku city,

av. H.Javid, 115, AZ1143, Azerbaijan

Email: s.yusifov@atu.edu.az

Residential electricity use effects of population in Kazakhstan 115

Abstract: We studied impacts of population groups of 15–64 and 65–above on

residential electricity use in Kazakhstan in the STIRPAT framework.

Unlike earlier studies for Kazakhstan in the STIRPAT framework, we applied

time series cointegration and error correction methods. Results from the

autoregressive distributed lags bounds testing approach indicate a significant

impact of the age group of 15–64 on the residential electricity use in long-run,

however, the age group of 65–above has only short-run effects and affluence

has no effect. Another finding is that, 21% of short-run disequilibrium can be

corrected towards long-run equilibrium during a year. Policymakers should

consider the trend of the population group of 15–64 in their decision about the

long-run stance of the residential electricity consumption. The trend suggests

an implementation of energy conservative policy and increasing efficiency of

its usage. Another policy implication is that household’s electricity

consumption is not income dependent maybe due to cheap electricity prices

subsidised by the government. In the short-run, policy makers should consider

the age group of 65–above among other factors in their implementations.

Moreover, they should be careful in making any policy shock to the residential

electricity consumption system, because convergence towards long-run

equilibrium path takes about six years.

Keywords: age groups; residential electricity consumption; STIRPAT;

Kazakhstan; cointegration; error correction modelling; income; Commonwealth

of Independent States; CIS.

Reference to this paper should be made as follows: Mikayilov, J., Hasanov, F.

and Yusifov, S. (2018) ‘Residential electricity use effects of population in

Kazakhstan’, Int. J. Energy Technology and Policy, Vol. 14, No. 1,

pp.114–132.

Biographical notes: Jeyhun Mikayilov is a researcher at the King Abdullah

Petroleum Studies and Research Center in Riyadh, Saudi Arabia. He holds a BS

and MA degrees in Mathematics from Baku State University, Baku/Azerbaijan

and a PhD in Applied Mathematics. His primary research interests include

applied econometrics and sustainable development.

Fakhri Hasanov is a Researcher at King Abdullah Petroleum Studies and

Research Center in Riyadh, Saudi Arabia. His research experience spans

building and applying energy-macroeconometric models for policy purposes,

energy economics with a particular focus on natural resource-rich countries. He

has served as a Deputy Director of the Research Institute at the Ministry of

Economic Development, and a Senior Economist at the Research Department

of the Central Bank, Azerbaijan. He is also affiliated with Department of

Socio-economic Modeling at Institute of System Controls, Azerbaijan. He

holds a PhD in Economics.

Sabuhi Yusifov is currently working at Azerbaijan Technology University

and is Vice Rector for Science and International Relations. He holds a

PhD in Economics. His teaching and research fields are good governance,

decentralisation, local government finance, intergovernmental fiscal relations

and rural development. He was a facilitator and organiser of number of

programs addressed to municipal capacity building in Azerbaijan. These

include local budgeting, municipal entrepreneurship, municipal corporations,

and public-private partnership mechanisms. He is the author of several books

and papers.

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1 Introduction

Energy is an indispensable part of our society and economy. Understanding the role of

economic growth and population on energy consumption is critical for policymaking and

economic development. This topic has been analysed by a vast number of studies. One of

the prominent questions in this field is relationship between energy use and

socio-economic factors, which has gained special attention with the pioneering study of

Kraft and Kraft (1978). One can find many different studies devoted to the divergent

aspects of this relationship both in national and in cross-national levels. However, little

attention has been paid to the oil-exporting economies of the Commonwealth of

Independent States (CIS), including Kazakhstan. In this regard, as one of the fast growing

economies of the Central Asia, it is valuable to investigate the topic in Kazakhstan.

The results of the research on the relationship between energy use and

socio-economic factors are ambiguous. Therefore, there are four hypotheses in

energy-growth literature (Bozoklu and Yilanci, 2013; Damette and Seghir, 2013; Ozturk,

2010; among others). The growth hypothesis implies that energy consumption is one of

the drivers of economic growth while the conservative hypothesis argues for

unidirectional causality from economic growth to energy consumption. The feedback

hypothesis suggests a bi-directional causal relationship between energy consumption and

economic growth. The last view is neutrality hypothesis, which claims that there is no

causality between energy consumption and economic growth.

As Liddle (2013) expresses, many studies analysing energy use take it as a function

of per capita GDP and price (Holtedahl and Joutz, 2004; Halicioglu, 2007; Dergiades and

Tsoulfidis, 2008; Narayan et al., 2007). However, the impact of population age groups on

energy was not considered by any of the above-mentioned studies. In his work, Liddle

(2013) used panel data for 31 developed countries and 54 developing countries to analyse

the effect of population and its age groups on residential energy consumption. Despite

conducting a large panel analysis, Kazakhstan, the focus of this study, was not included

in his analysis. Meanwhile, numerous valuable studies investigated the relationship of

environmental effects of energy use and age structure of population. Two main directions

draw attention in the energy studies: energy-affluence and energy-population

relationships, which can/need to be combined by the unique model.

The STIRPAT model, developed by Dietz and Rosa (1994, 1997), allows for analysis

of the impacts of population and economic factors on energy use (Liddle, 2014). Almost

all of the energy studies based on the STIRPAT framework investigate the relationship of

the variables of interest using panel or cross-national data for a group of developed and

developing countries (York et al., 2003b; Poumanyvong et al., 2012; York, 2007; Liddle

and Lung, 2010). Regarding Kazakhstan, only a few studies analysed impacts of

economic growth and population or its age groups on energy use by employing STIRPAT

and other modelling frameworks (Brizga et al., 2013). In addition, many of these studies

employed cross-sectional or panel data (Shafiei, 2013; Scarrow, 2010; Fang et al., 2012;

Nouri et al., 2012). By applying STIRPAT framework, Hasanov et al. (2016) examined

the impact of population, age groups and GDP growth on energy use of Azerbaijan,

Kazakhstan and Russia. However, their dependent variable was total energy use and they

did not examine the effects on sectoral energy use, such as industrial or residential

consumption. To our knowledge, this is the first Kazakhstan focused study that examines

the effects of population and affluence on residential electricity consumption (REC)

using the STIRPAT framework, time series cointegration, and error correction modelling.

Residential electricity use effects of population in Kazakhstan 117

This study aims to reveal long- and short-run relationships between the

above-mentioned variables as well as convergence effects for Kazakhstan in the

STIRPAT modelling framework. In the cointegration context, convergence effects, also

known as speed of adjustment (SoA hereafter), provides useful information about the

timeframe needed for the short-run deviation of the relationship to converge towards the

long-run path. Therefore, it is of great importance for policymakers when they develop

measures for managing the growth of electricity use.

We applied the time series cointegration and error correction modelling approach of

the autoregressive distributed lags bound testing (ARDLBT hereafter) to the Kazakhstani

data over the period of 1999–2012. We found a significant impact of the population age

group 15–64 on the residential electricity use in long-run, however, the age group of

65–above has only short-run effects and affluence has no effect. Estimations also

revealed out 21.3% of SoA in the relationship between Kazakhstan’s residential

electricity use and population.

We would expect this study to contribute to the literature as follows: it is the pioneer

study dedicated to residential electricity effects of population and affluence for

Kazakhstan in a time-series analysis. Panel studies have some weaknesses (Kasprzyk

et al., 1989; Dietz and Rosa, 1994; Hsiao, 2003), and country specific features are more

easily discovered in studies using time series analyses as they offer better representation

of country specific features, thus enable more reliable policy recommendations.

Furthermore, as Liddle (2013) expresses, the non-stationarity properties of data were

not considered in earlier STIRPAT-based studies, except for Poumanyvong and Kaneko

(2010) and Liddle (2011). As Liddle (2014) puts it, earlier studies might possibly contain

spurious regression results as economic and population data are commonly

non-stationary.1 By taking non-stationarity of data into account, Liddle (2011, 2013,

2014) and Poumanyvong and Kaneko (2010) applied unit root (UR) and cointegration

tests and estimated long-run elasticities. However, they did not estimate error correction

models and SoA coefficients. The review of existing literature reveals that only Shafiei

(2013) employed panel error correction modelling and estimated SoA coefficient in the

STIRPAT framework. Her study focuses on renewable and non-renewable energy use

effects of population and affluence where she applied panel cointegration and ECM in the

STIRPAT modelling framework for 29 OECD countries. As her focus is on OECD

countries, Kazakhstan is not among the countries studied. Moreover, country specific

aspects are overlooked which, is common for panel analysis, as mentioned before. In the

light of the above-mentioned shortcomings, we accounted for the integration and

cointegration properties of the data and estimated SoA coefficient.

Unlike previous studies on electricity use, we employed Pesaran’s (2001) ARDLBT

approach, to test for cointegration and then to estimate long-run and short-run elasticities

as well as SoA coefficient in the STIRPAT framework. One of the advantages of the

ARDLBT approach is that it works better with small sample sizes and brings about much

more consistent and unbiased estimates (Pesaran et al., 2001; Sulaiman and Muhammad,

2010; Oteng-Abayie and Frimpong, 2006).

The findings of this study may offer useful insights for Kazakhstan’s policymakers so

they can make better electricity market forecasts, and develop adequate measures to

manage the growth of REC. The policymakers should take into account future trend of

the population group aged 15–64 in their decision on the long-run stance of residential

energy consumption. They also should consider that REC in long-run is not income

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dependent, perhaps due to government subsidisation leading to cheap electricity prices. In

the short-run, the policy makers should consider the population age group of 65–above

among other factors in their REC related measures. Policy makers should also be careful

to avoid creating a policy shock to the REC system, since complete convergence towards

long-run equilibrium path will take about six years.2

The remaining sections of the paper are organised as follows. Section 2 briefly

reviews the existing literature on the STIRPAT analyses of energy use in selected

countries. Section 3 shortly introduces the STIRPAT modelling framework and Section 4

presents the data and describes the econometric method. Section 5 presents and discusses

the results of the empirical analysis. Finally, the main concluding remarks and policy

implications of the study are in Section 6.

2 Brief literature review

A great deal of literature is devoted to studying the energy consumption effects of

population and economic growth by employing STIRPAT modelling framework. Thus, in

this section, we will limit our review only to studies relevant to our research in terms of

methodology used and country chosen.3

As previously noted, some earlier studies have examined this relationship in the

oil-exporting economies of the CIS, including Kazakhstan. However, studies are either

cross-sectional (York et al., 2003a; Knight, 2008; Kick and McKinney, 2014; Lamb

et al., 2014; Mattos and Filippi, 2013), or panel studies (Fang and Miller, 2013;

Martínez-Zarzoso, 2009; York and Rosa, 2012; Brizga et al., 2013; Jorgenson, 2011;

Lankao et al., 2008; Grunewald and Martínez-Zarzoso, 2009a, 2009b, 2011; Prew, 2010;

Iwata and Okada, 2014; Martínez-Zarzoso and Maruotti, 2011) that investigate

environmental issues rather than energy use.4 Only Scarrow (2010), Liddle (2011), Fang

et al. (2012), Nouri et al. (2012), Shafiei (2013), Hasanov et al. (2016) studied the energy

use impacts. Although these energy studies, except Hasanov et al. (2016) and Mikayilov

and Hasanov (2015) are based on panel analysis and ignored country-specific features.

We still review them below.

Shafiei (2013) applied the STIRPAT modelling framework to examine the

determinants of renewable and non-renewable energy consumption for the panel of 29

OECD countries. She chose the period of 1980–2011 and used error correction and

cointegration modelling. Her study found a long-run relationship between the two energy

types and set of the variables: population, its density, GDP per capita, and the GDP share

of service and industry. Coefficients of all regressors were statistically significant in the

long-run elasticity estimations for the non-renewable energy use model. However,

urbanisation and population density were insignificant for the renewable energy use

model. The study results reveal that long-run elasticity with respect to population and

affluence was 1.763 and 0.710 for non-renewable energy, and 0.537, 0.268 for renewable

energy, respectively. Additionally, the estimated SoA coefficients were –0.91 and –0.92

for non-renewable and renewable energy use, which is an indication of rapid convergence

to an equilibrium path.

Scarrow (2010) studied energy consumption effects of population and affluence, with

other explanatory variables, over the period 1960–2007 for a panel of almost all countries

of the world, including Kazakhstan. Results of the employed STIRPAT model showed

that affluence has a statistically significant positive impact on total energy consumption

Residential electricity use effects of population in Kazakhstan 119

and per capita energy consumption. The estimated affluence elasticities of total and per

capita energy consumption varied from 0.03 to 0.2. Total population was found to have a

positive impact (with the coefficients within the interval 0.4–0.8) on total energy use and

negative effect (coefficient was around –0.1) on per capita energy use. However, the

results of the study may suffer from spurious regression problem, because

non-stationarity properties of the data were not taken into account.

Liddle (2011) also used STIRPAT modelling framework to example the

environmental impacts of transport carbon emissions and REC by applying panel FMOLs

to data for 22 OECD countries for the period of 1960–1970. The population variable was

divided into four age groups: 20–34, 35–49, 50–69, and 70 and older. Although

Kazakhstan is not included in this study, it is still relevant to our research since it

conducted a cointegration analysis in the STIRPAT framework. Impacts of population

age structure were statistically significant and different for the age groups. The study

reveals a U-shaped impact of age structure for REC, which means the youngest and

oldest groups have positive effects while middle groups have negative impact. REC

elasticities with respect to population age groups 20–34, 35–49, 50–69 and 70 and above

were 0.219, –0.418, –0.404 and 0.552, respectively.

Nouri et al. (2012) analysed demographic and economic determinants of energy use,

measured in kiloton of oil equivalent for the Economic Cooperation Organization (ECO)

countries, including Kazakhstan over the period of 1960–2012. Panel regression

estimations showed that the driving factors of energy use in the ECO members are total

population, urbanisation and affluence.

Using the STIRPAT framework, Hasanov et al. (2016) examined impacts of total

population, its age groups and affluence on the use of energy in oil-exporting economies

of the CIS: Azerbaijan, Kazakhstan and Russia over the period 1990–2011. An

Autoregressive Distributed Lags Bounds Testing approach was employed in the study.

The study found significant impact of population and its age groups as well as affluence

on the energy use in selected countries. The long-run elasticities of energy use with

respect to the population age group 15–64 in Azerbaijan, Kazakhstan and Russia were

1.92, 0.13 and 8.59, while for the age group of 65 and above the elasticities were 1.71,

0.14, and –1.23, respectively.

Mikayilov and Hasanov (2015) examined impacts of affluence and age groups on

REC in Azerbaijan employing ARDL Bounds Testing approach in the STIRPAT

framework for the period 2000–2012, and concluded that there are significant effects of

population age groups and affluence. The elasticities of REC with respect to population

age group of 15–64 and 65 and above were 10.46 and 2.33, respectively.

In sum, the review of existing literature for the CIS oil-exporting economies shows a

significant gap in this research area. The studies of the economies are mainly panel

studies, which neglect specific features of countries. With the exception of Shafiei

(2013), Hasanov et al. (2016) and Mikayilov and Hasanov (2015), none of these studies

applied cointegration and ECM, and thus have not estimated SoA in the STIRPAT

framework. Finally, except the above mentioned three studies none of them applied the

ARDLBT approach to the time series data of the countries considering that data for these

countries spans for a short period. Hasanov et al. (2016) examined the impact of

population and growth rate on total energy use, but not on residential or industrial energy

use. As a result, their policy suggestions remain too general and do not address specific

energy-type issues. Mikayilov and Hasanov (2015) studied the impacts only in the case of

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Azerbaijan but not for Kazakhstan. Yet, Shafiei (2013) did not include Kazakhstan in her

analysis.

Saleheen et al. (2012) studied the effect of per capita electricity consumption among

capital, labour and trade openness on economic growth in the production function

framework, applying ARDL BT approach in case of Kazakhstan. They found that 1%

increase in per capita electricity consumption leads to 0.28% increase in per capita GDP.

However, they did not find any casualty from economic growth to electricity

consumption. Moreover, their main interest was economic growth rather than electricity

consumption and therefore, STIRPAT modelling framework have not been applied.

In this study, we consider all of the above-mentioned aspects of the REC in the

STIRPAT framework for Kazakhstan.

3 Framework of analysis: STIRPAT

This section briefly describes the STIRPAT modelling framework employed in our

empirical analysis. STIRPAT is a popular and widely used approach developed by Dietz

and Rosa (1994, 1997). It is based on IPAT, which was first offered by Ehrlich and

Holdren (1971). IPAT assumes that environmental impacts (I) are multiplicative product

of population (P), affluence (A) and technology (T):

I

PAT= (1)

Since IPAT is an accounting identity and therefore assumes proportionality, there is no

space for hypothesis testing. However, the impacts of population, affluence and

technology are certain to differ in magnitude. Primarily because energy use,

environmental, demographic and economic characteristics of the countries differ from

each other. Consequently, IPAT was not featured much in empirical studies. Dietz and

Rosa (1994, 1997) added stochastic terms in equation (1) and thus, the STIRPAT was

developed. The STIRPAT formula can be expressed as the following:

bcd

I

aP A T e= (2)

where a, b, c and d are the coefficients to be econometrically estimated, and e is a

stochastic error term.

Equation (3), which is natural logarithmic expression of equation (2) can be estimated

empirically as:

() * ( ) * ( ) * ( )Ln I q b Ln P c Ln A d Ln T w=+ + + + (3)

where Ln expresses the natural logarithm. q and w are natural logarithm of a and e.

4 Data

In line with the STIRPAT modelling framework, our dataset for Kazakhstan covers the

following indicators:

• REC: The dependent variable in our analysis, measured as the total kilowatt-hours

consumed by residential sector. The data was retrieved from the International Energy

Association (IEA).

Residential electricity use effects of population in Kazakhstan 121

• Gross domestic product (GDP): The sum of gross value added by all resident

producers in the Kazakhstani economy, plus any product taxes, and minus any

subsidies not included in the value of the products (World Bank, 2015). It is

measured in constant 2005 US$.

• Population age group of 15–64 years old (POP_15_64): It is calculated as the share

of the population in the interval of 15–64 years old multiplied by total population,

and measured in persons.

• Population age group of 65 years and older (POP_65): It is calculated as the

population share of 65 years and older multiplied by total population, and measured

in persons.

• Affluence (GDPPC): Measured as GDP per person in constant 2005 US$.

With the exception of REC, the data was retrieved from the World Bank Development

Indicators Database and cover the period of 1999–2012.

Figure 1 illustrates time profile of the variables over the period 1999–2012.

As illustrated in panel A of Figure 1 REC shows an upward trend over the 2000–2012

periods with a level shift in 2005. This level shift may be explained with the economic

developments described below. Strong economic growth observed in Kazakhstan, due to

the record export prices for its energy, minerals, and agricultural goods in 2005.

Government’s efforts to create a good investment environment, especially in the energy

sector, through economic liberalisation and privatisation resulted cumulative 38.4 billion

USD of foreign investment by September 2005. In February 2002, Kazakh Government

established a national energy conglomerate, KazMunaiGaz, combining the national oil

company with the national oil and gas transport company for the stated purpose of being

able to compete with the international oil companies. In 2002, agreement was reached

among Azerbaijan, Georgia, Turkey, Turkmenistan, and Kazakhstan on the route for the

Baku-Tbilisi-Ceyhan (BTC) pipeline, and construction began in May 2003. The first

stage of the pipeline was officially inaugurated in 2005. After re-election in 2005,

President Nazarbaev announced his new economic reforms, which also contributed

significantly to the economic development of the country. Moreover, the construction of

the new energy pipeline between Kazakhstan and China started in 2005.

During the period 2000–2012, population group ages 15–64 and GDP per capita in

Kazakhstan also exhibit increasing trends.

The POP_65 can be characterised by some cyclicality over the period 1990–2012.

The unconventional trend in population age group of 65 and above might be due to the

following reasons. The population began declining in the 1990s due to emigration,

declining fertility rates, and lower life expectancy. Then, the government encouraged

Kazakhs who lived abroad to return. During the ‘90s, there was an organised return

of 70,000 Kazakhs from Mongolia, Iran and Turkey and almost 82,000 Ukrainians,

16,000 Belarussians, 614,000 Russians and 480,000 ethnic Germans returned to

Germany. In addition, many Kazakhs had been displaced internally or had left for other

CIS countries.

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Figure 1 Time profile of the variables, (a) Panel A: log level of the variables (b) Panel B: growth

rate of the variables (see online version for colours)

4,000,000,000

5,000,000,000

6,000,000,000

7,000,000,000

8,000,000,000

9,000,000,000

10,000,000,000

2000 2002 2004 2006 2008 2010 2012

rec

960,000,000

1,000,000,000

1,040,000,000

1,080,000,000

1,120,000,000

1,160,000,000

2000 2002 2004 2006 2008 2010 2012

pop

_

15

_

64

100,000,000

104,000,000

108,000,000

112,000,000

116,000,000

2000 2002 2004 2006 2008 2010 2012

pop_65

2,000

3,000

4,000

5,000

6,000

2000 2002 2004 2006 2008 2010 2012

gdppc

(a)

-.2

-.1

.0

.1

.2

.3

.4

2000 2002 2004 2006 2008 2010 2012

Diffe

r

enced

r

ec

-.005

.000

.005

.010

.015

.020

.025

.030

2000 2002 2004 2006 2008 2010 2012

Diffe

r

enced pop

_

15

_

64

-.03

-.02

-.01

.00

.01

.02

.03

.04

2000 2002 2004 2006 2008 2010 2012

Differenc ed pop_65

-.04

.00

.04

.08

.12

.16

2000 2002 2004 2006 2008 2010 2012

Differenc ed gdppc

(b)

Note that in the empirical analysis, we used the natural logarithm expressions of the

variables, which are denoted with small letters: rec, gdppc, pop_15_64, pop_65.

Residential electricity use effects of population in Kazakhstan 123

4.1 Econometric method

In the following sub-sections, we discuss the UR tests of Augmented Dickey-Fuller

(ADF) and cointegration methods of the autoregressive distributed lag bounds testing

(ARDLBT).

4.1.1 UR test

It is essential to examine the integration order of variables through UR tests before

conducting a cointegration analysis. To do that, we use the ADF (Dickey and Fuller,

1981) method. Advantages and disadvantages of univariate UR tests, in particular ADF,

have been discussed by Dickey and Fuller (1981), Stock and Watson (1993), Dolado

et al. (1990), Brouwer and Ericsson (1998) and Enders (2010, pp.237–239) among others.

The test takes the null hypothesis of non-stationarity of a given time series.

For a variable y, the ADF statistics are the t-ratio on b1 in the regression below:

011

1

k

ttitit

i

ybψtrend b y y ε

−−

=

Δ= + + + Δ +

∑

α

(4)

Here, Δ and k represent the first difference operator and number of the lags, respectively.

b0 is a constant term, trend and εt are linear time trend and white noise residuals, and i is

lag order. We will skip discussing this test here because of space limitation.

4.1.2 ARDLBT approach

We apply the ARDLBT cointegration approach developed by Pesaran et al. (2001) and

Pesaran and Shin (1999). It is a powerful approach when samples are small, and is easy to

perform by using OLS. Moreover, there is no endogeneity problem in the method, and it

is possible to estimate long and short-run coefficients simultaneously. One of the

advantages of the method is that it can be employed regardless of whether regressors are

I(1), I(0), or a mixture of both (Pesaran et al., 2001; Oteng-Abayie and Frimpong, 2006;

Sulaiman and Muhammad, 2010). This approach is more suitable for our empirical

assessment since the number of observations in our study is relatively small.

Pesaran et al. (2001) describe the following stages of the approach:

a Construction of an unrestricted error correction model (ECM).

01 1

10

nn

ttyxxtitiitit

ii

ycθyθxyφxu

−− − −

==

Δ= + + + Δ + Δ +

∑∑

ϖ

(5)

where y is a depended variable and x is explanatory variable; u indicates white noise

errors; c0 is a drift coefficient; θi denotes long-run coefficients, while i

ϖ

and φi are

short-run coefficients. It is worth mentioning that correct specification of lag length

of the first differenced right-hand side variables in the ARDLBT estimations is one

of the main issues since finding cointegration relationships between variables are

sensitive to lag length [Pesaran et al., (2001), p.23].

Following Pesaran et al. (2001), among others, optimal lag length can be specified

by minimising the Akaike and Schwarz information criteria while removing the

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serial autocorrelation of residuals. It is advisable to rely on the Schwarz information

criterion when samples are small (Pesaran and Shin, 1999; Fatai et al., 2003).

b After constructing an unrestricted ECM, one can test if cointegrating relationships

exist. The Wald-test (or the F-test) on the θi coefficients above is performed for this

purpose.

The null hypothesis of no cointegration is H0: θ1 = θ2 = θ3 = 0, while an alternative

hypothesis of cointegration is: H1: θ1 ≠ θ2 ≠ θ3 ≠ 0.

If in the given significance level, the computed/sample F-statistic is greater than the

upper bound of the critical value, then one can reject the null hypothesis of no

cointegration. Similarly, the null of no cointegration cannot be rejected if at a given

significance level, the sample F-statistic is smaller than the lower bound of the

critical value. As a third option, the test results will be inconclusive when the sample

value falls between critical values of the upper and low bands.

It is worth mentioning that in the ARDLBT cointegration test, the F-statistics have

non-standard distribution. Thus, F-distribution’s conventional critical values are no

longer valid. Hence, the table of critical values developed by Pesaran and Pesaran

(1997) or Pesaran et al. (2001) must be used.

The cointegrating relationship is stable if θ is statistically significant and negative. In

other words, short-run deviations from the long-run equilibrium path are temporary

and correct towards it.

c If a cointegrating relationship is found among the variables, then the long-run

coefficients can be estimated. We calculate these coefficients based on equation (5)

by either applying a Bewley transformation (Bewley, 1979) or manually setting

c0 + θyt–1 + θyxxt–1 to zero and solving for y as follows.

0yxx

θ

c

yxu

θθ

=− − + (6)

4.2 Small sample bias correction in the ARDLBT approach

Pesaran and Pesaran (1997) used large sample sizes of 500 and 1,000 as well as 20,000

and 40,000 replications, respectively to calculate the F-distribution’s upper and lower

critical values. However, Narayan (2005) mentions that these critical values are

calculated based on large sample points so they are not accurate for small sample sizes

(Narayan, 2004, 2005). Indeed, he compared the critical value generated based on 31

observations with those value reported in Pesaran et al. (2001) at the 5% significance

level and in the case of four regressors. He found that the critical value (3.49) from

Pesaran and Pesaran (1997) is 18.3% lower than his critical value of (4.13). Narayan,

thereby, calculated critical values for small sample sizes ranging from 30 to 80 data

points (see Narayan, 2005). To correct for small sample bias, we employ Narayan’s

critical values in our ARDLBT cointegration test.

Residential electricity use effects of population in Kazakhstan 125

5 Empirical results and discussion

By following the methodological section, we checked integration properties of the

variables using the ADF test. Note that equation (4) is used in all testing exercises. In

other words, a trend and intercept are in all the ADF test specifications regardless of

whether we tested the level or difference of the variables. Our justification is that, as

econometrically explained, if a trend is a part of data generating process and we miss it,

then we will have biased results, which is a serious problem. On the other hand, if a trend

is not a part of data generating process and we have it redundantly, then we will only lose

one degree of freedom.

Table 1 reports the ADF test results.

Table 1 The ADF test results

Variable Panel A: at the level Panel B: at the first

difference Panel C: at the

second difference Conclusion

k Actual value k Actual value k Actual value

rec 0 –2.512278 0 –3.911778** I(1)

gdppc 1 –1.882518 1 –2.917351 1 –4.292989** I(2)

pop_15_64 0 –3.199770 0 –2.073872 1 –5.985103* I(2)

pop_65 2 –5.105226* 2 –5.230702 I(0)

Notes: Maximum lag order is set to two and optimal lag order (k) is selected based on

Schwarz criterion; *, ** and *** indicate statistical significance at the 1%, 5%

and 10% significance levels respectively. The critical values are taken from

MacKinnon (1996). Estimation period: 1999–2012.

As Table 1 concludes, rec is integrated at the order of one, i.e., it is an I(1) process. In

other words, it is non-stationary in its logarithm level, but stationary in its first difference

of logarithm level, which is its growth rate.

On the other hand, the test statistics suggest that gdppc and pop_15_64 are stationary

only after second differencing. Common sense about the integration order of these

variables, however, does not support these statistical results. Moreover, the ADF test

statistics indicate that pop_65 is trend stationary. However, the Phillips-Perron (Phillips

and Perron, 1988) UR test statistics, as well as graphical inspection of the variable

indicates that the series is not trend stationary at level form. We think that such

contrasting results for gdppc and pop_15_64 and pop_65 are mainly caused by the small

number of observations. We have only 14 observations at the best case, which is still

insufficient to get accurate critical values and probabilities. Note that Hasanov et al.

(2016) and Mikayilov and Hasanov (2015) also faced similar problems due to small

sample sizes. In spite of the fact that the test results are quite disappointing, as a research

decision and by relying on the conventional view about integration orders of socio-

economic variables, we consider that all the variables are non-stationary in their log level

and stationary in their growth rate.

We estimated equation (5) for Kazakhstan in two different specifications (i.e., one

with pop_15_64 and another one with pop_65, respectively) as expressed below:5

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01 12 13 1

10

0

_

15 _ 64

_15_64

tt t t

nn

iti i ti

ii

n

itit

i

rec c θrec θgdppc θpop

ωrec φgdppc

τpop u

−− −

−−

==

−

=

Δ=+ + +

+Δ+ Δ

+Δ +

∑∑

∑

(7)

01 12 13 1

10

0

_

65

_65

tt t t

nn

iti i ti

ii

n

itit

i

rec c θrec θgdppc θpop

ωrec φgdppc

τpop u

−− −

−−

==

−

=

′

′′ ′

Δ=+ + +

′′

+Δ+ Δ

′′

+Δ +

∑∑

∑

(8)

We set the maximum lag order to be one for equations (7) to (8) since the small number

of observations does not allow for the serial correlation LM test to run in more than one

lag order. The test results together with the Schwarz information criterion are tabulated in

Table 2.

Table 2 Statistics for choosing optimal lag size

K SBC FSC(2)

Equation (7) 0* –4.366756 1.987364

[0.2317]

1 –4.563904 31.43241

[0.0308]

Equation (8) 0 –3.859767 0.317201

[0.7381]

1* –4.087104 0.264622

[0.7800]

Notes: k is a lag order while SBC denotes Schwarz information criterion. FSC(2) is the

LM statistics for testing no residual serial correlation against lag orders 1.

Probabilities are in brackets.

We chose zero lag for equation (7) and one lag for equation (8) based on the Schwarz

information criterion and the F-statistics of the serial correlation LM test.

We checked for the existence of long-run (cointegrating) relationships among the

lagged level variables in equations (7) and (8) in the second stage of the ARDLBT

approach. Table 3 reports the cointegration test results.

Two kind of critical values were used in the testing process: Pesaran et al. (2001)

critical values and those from Narayan (2005), with the latter used to avoid potential

biases caused by small sample size of the estimations. Results of the cointegration test

show there is no cointegrating relationship among the lagged level variables in equation

(7). We additionally investigated cointegration properties of REC, population and GDP

per capita. It can be seen from Table 3, that there is a cointegrating relationship at 10%

significance level, between rec and pop_15_64, when we excluded gdppct–1 from the

cointegration space in equation (7). Equation (8) was a similar case, which demonstrated

no long-run co-movement among the lagged level variables of the equation. Furthermore,

detailed investigation of the cointegration relationship among the variables showed that

there is no cointegration relationship even in the combination of any two lagged level

variables in equation (8).

Residential electricity use effects of population in Kazakhstan 127

Table 3 Cointegration test statistics

Equation (7) Equation (8)

Fsample F

sample

3.757330a 1.593500b

Narayan (2005) Pesaran et al. (2001)

At the 1% significance level: 6.760 5.580

At the 5% significance level: 4.663 4.160

At the 10% significance level: 3.797 3.510

At the 1% significance level: 6.265 5.000

At the 5% significance level: 4.428 3.870

At the 10% significance level: 3.695 3.350

Notes: aIn the case of one regressor, restricted intercept and no trend.

bIn the case of two regressors, restricted intercept and no trend.

The first three upper bound critical values of Narayan (2005) and Pesaran et al.

(2001) are in the case of two regressors, restricted intercept and no trend, while

the last three upper bound critical values are in the case of one regressor, restricted

intercept and no trend.

Thus, we found cointegrating relationship between rec and pop_15_64 in equation (7)

and only short-run relationship for the first differenced regressors in the equation (8).

As a next stage of the ARDLBT approach, we specified the final ECM specifications.

The final specifications are presented in Table 4.

Table 4 The final ARDL specification

Regressor Coef. (std. error)

Panel A: the estimated final ARDL specification of equation (7)

rect–1 –0.212997 (0.070586)

pop_15_64t–1 0.972618(0.295416)

Intercept –15.35966 (4.777056)

Δpop_15_64t 0.931658 (1.154106)

Δgdppct 0.323134 (0.196360)

DP05 0.261101 (0.022297)

DP00 –0.164320 (0.022519)

Panel B: the estimated final ARDL specification of equation (8)

Δpop_65t 0.562013 (0.287682)

Δrect–1 –0.158072 (0.063311)

Intercept 0.039739 (0.006894)

DP05 0.273428 (0.022991)

DP00 –0.171509 (0.022916)

Note: Dependent variable is Δrect; method: least squares; estimation period: 1999–2012.

Each of the final specifications in Table 4 succeeded residual diagnostics tests of the

serial correlation, autocorrelation, heteroscedasticity, normality, and Ramsey reset’s

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misspecification test. We do not report the results of tests here. However, they can be

obtained upon request.

According to the results, the population group ages 15–64 is statistically significant in

the final specification of equation (7). The elasticity of the REC with respect to this age

group is 4.568 (0.973/0.213). Putting it differently, a 1% increase in the age group leads

to a 4.56% increase in REC in the long-run. Despite the higher magnitude of the

coefficient, the sign of it is consistent with the conventional interpretation of the

STIRPAT framework. Note that the growth rates of the age group and affluence are

positive in the final specification of equation (7), which is consistent with the STIRPAT

framework. However, they are statistically insignificant most probably due to small

number of observation.

As panel A of Table 4 demonstrates, SoA coefficient is 0.213, which means 21.3% of

disequilibrium in the short-run can be corrected towards long-run equilibrium path during

a year.

Panel B of Table 4 reports that the growth rate of the population group ages 65 and

above has a positive impact on the growth rate of REC in the short-run. Numerically, a

1% increase in the growth rate of this population group causes a 0.56% increase in the

growth rate of electricity consumption. The positive impact of the population age group

on the electricity consumption is also consistent with the STIRPAT framework.

Moreover, there is a dynamic relationship in REC as its one year lagged growth rate has a

statistically significant impact on the current year’s growth rate.

Additionally, as reported in the both panels of Table 4, dummy variables for

capturing extraordinary decrease and increase in REC in 2000 and 2005 respectively are

statistically significant.

6 Concluding remarks

A great deal of studies has examined energy consumption effects of population, its age

groups, and affluence in developed and developing countries. However, the number of

studies focusing on oil-exporting countries of the CIS, in particular for Kazakhstan, is

limited to either panel or cross sectional analyses. In the absence of time series studies, it

is difficult to discover country specific features of energy effects of population and

affluence. To our knowledge, there is one time series analysis for Kazakhstan, Hasanov

et al. (2016) in the STIRPAT framework, where the dependent variable is aggregated

energy use, which does not allow using specific energy types and offer related policy

suggestions. With these considerations in mind, we examined the impacts of population

age groups and affluence on REC in Kazakhstan by applying an ARDLBT cointegration

method, a powerful method in the case of small sample in the STIRPAT framework. As

the number of observations is small, results of our empirical estimations and conclusions

are presented cautiously. We found that one of the driving forces of the REC in the long-

run is the population age group of 15–64, whereas the age group of 65 and above exhibit

only short-run effects. The affluence was not found to have any statistically significant

influence on the REC. Moreover, the analysis revealed that 21.3% speed of convergence

towards long-run equilibrium path in the relationship of REC and the 15–64 population

age group.

Findings of this study may offer useful insights for policymakers in making better

electricity demand forecasts and taking adequate measures on residential electricity use in

Residential electricity use effects of population in Kazakhstan 129

Kazakhstan. The policymakers should take into account trend of the population group

aged 15–64 in their decision on the long-run stance of residential energy consumption. As

Figure 1 illustrates population age group 15–64, which has a significant long-run effect

on REC, has a strict upward trend over the period of analysis. Such a strong upward trend

in the group is related to national and traditional customs of the Kazakhstan, and

therefore may not easily be curbed in future. Further, the SoA coefficient is as small as

21.3%. Combining these two findings, we can conclude that, in the long-run, adequate

policy measures should include increase in the efficiency of electricity consumption and

applying energy conservation measures. As Liddle (2011) notes, the size of the family is

proportional with the age of family head in general, which is also the case for

Kazakhstan. Promoting more efficient (and economically viable) electricity appliances

with these household is a suggestion for increasing efficiency of electricity usage.

Policymakers should also pay attention to the finding that REC is not income dependent,

perhaps due to cheap electricity prices subsidised by the government. In the short-run, the

policymakers should consider the population age group of 65–above among other factors

in their REC related measures. Furthermore, policymakers should be careful not to create

a policy shock to the REC system, since convergence towards the long-run equilibrium

path takes about six years.

Acknowledgements

The authors are deeply thank to the editor of this journal and anonymous referees for

their comments and suggestions. Proofreading of Patrick Bean and Kenneth White are

greatly appreciated. All remaining errors and omissions are our sole responsibility. Please

note that views expressed in this paper are those of the authors and do not necessarily

represent the views of their affiliated institutions.

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Notes

1 According to econometric theory, obtained regression results are spurious if a linear

combination of non-stationary variables is not stationary, i.e., there is no cointegrating

relationship among them (see Engle and Granger, 1987; inter alia).

2 Half the distance to equilibrium will take 2.89 years [ln(0.5) / ln(1 + adjustment coefficient) =

ln(0.5) / ln(1 + (–0.213)) = 2.89].

3 Time series studies for Kazakhstan have been preferred to review. In the case of absence of

such studies, cross-sectional and or panel studies for Kazakhstan have been reviewed.

4 Dependent variables of these studies were either CO2 emission or ecological footprint.

5 The reason for running two different specifications (i.e., not including the both age groups in

one specification) is small number of observations.