The nexus between access to electricity and economic

growth in Asian countries

Chamodi Jayalath a, Kavishka Deshan a, Pramodh Asanka a, Yasith Thilakshana a

Ruwan
Jayathilaka b,*

a
SLIIT Business School, Sri Lanka Institute of Information Technology, New Kandy Road,
Malabe, Sri Lanka, chamodijayalath98@gmail.com , 0009-0004-0624-9883

a
SLIIT Business School, Sri Lanka Institute of Information Technology, New Kandy Road,
Malabe, Sri Lanka, kavishkak99@gmail.com , 0009-0004-0896-6800

a
SLIIT Business School, Sri Lanka Institute of Information Technology, New Kandy Road,
Malabe, Sri Lanka, pramodhasanka@gmail.com, 0009-0004-6196-1080

a
SLIIT Business School, Sri Lanka Institute of Information Technology, New Kandy Road,
Malabe, Sri Lanka, wanasinghey@gmail.com , 0009-0006-4889-7090

b
Head - Department of Information Management, SLIIT Business School, Sri Lanka Institute
of Information Technology, New Kandy Road, Malabe, Sri Lanka, ruwan.j@sliit.lk,
ruwanips@gmail.com
*
Corresponding author
Prof. Ruwan Jayathilaka, PhD
Professor / Head - Department of Information Management,
SLIIT Business School,
Sri Lanka Institute of Information Technology,
New Kandy Road, Malabe, Sri Lanka.
E-mail: ruwan.j@sliit.lk , ruwanips@gmail.com
Tel.: +94-11-7544623; +94-71-5899199
Web: https://www.sliit.lk/faculty-of-business/staff/ruwan.j/
ORCID: 0000-0002-7679-4164

1 of 35
The nexus between access to electricity and economic
growth in Asian countries

Abstract
Electricity has been playing a vital role in driving economic growth and development
worldwide. It is obvious access to electricity is key for its consumption. Hence, this study
examines the relationship between access to electricity and economic growth in thirty-four
Asian countries using the World Development Indicators data from 2000 to 2021. The research
investigates the empirical gap, exploring the Granger causality model between access to
electricity and economic growth in Asian countries, in a single study. Results from these tests
expose bidirectional and unidirectional causality relationships between access to electricity and
economic growth proxied by population percentages and per capita gross domestic products,
respectively. The study observed long and short-term trends between electricity access and
economic growth. Empirical results suggest bidirectional relationships in Bangladesh and Iran.
Further unidirectional relationships from per capita gross domestic product to access to
electricity are seen in Cambodia, Iraq, Jordan, Lao PDR, Nepal, and Thailand. How, it is the
reverse in Azerbaijan, Bhutan, India, Kyrgyz Republic, Maldives, Myanmar, Sri Lanka,
Tajikistan, Turkey, and Yemen. Accordingly, effective decision -making can be achieved by
governments and policy -makers by utilising our findings.

Keywords: Access to electricity, Economic growth, Granger causality, Asian countries

2 of 35
1. Introduction
Electricity is the most utilised and desirable form of energy worldwide. It has become
irreplaceable due to its vast applications in industrial-based economies and the technological
boom that took place over the past few decades. Due to its convenience in generating, storing
and transmitting for very long distances, electricity plays a crucial role in the existing
economies (Zaman et al., 2015). The percentage of the population globally with access to
electricity (AEL) increased from 87% to 91% between 2015 and 2021, reaching over 800
million more individuals (UNSD, 2024).
Electricity is a major contributor in economic development worldwide. The economy
and daily lives of people heavily rely on electricity in numerous instances such as
telecommunications and utilities, commercial sectors, household sectors, agricultural sectors,
public services such as education, healthcare, transport systems, and other essential services of
the economy .. Millions of people yet suffer from lack of AEL and are instead dependent on
dangerous and harmful energies for cooking and lighting, despite the vital role electricity and
sustainable modern energy services play in supporting livelihoods, health, education, and
climate resilience (Acheampong, Erdiaw-Kwasie and Abunyewah, 2021; Burgess et al., 2020;
Oyuke, Penar and Howard, 2016). Nearly half of the people in the world who lived in South
Asia lacked access to electricity in early 2000. However, it has been rapidly decreasing over
the past few years (Ritchie, Rosado and Roser).
AEL rates are lower among lower-middle income countries such as Cambodia, Lao
PDR, Myanmar, Philippines. The lack of AEL is more likely in rural than urban areas.(Shi,
2016). Another study discusses that access to energy is recognised as a critical driver of
sustainable development (Mhaka et al., 2020). Therefore, reliable AEL acts as a catalyst for
people’s quality of life (Khandker et al., 2009; Simbarashe et al., 2020; Yang, 2003). AEL
involves, ensuring that households have the accessibility to enjoy the basic energy needs at a
limited level with few lightbulbs, phone charging, a radio and likely a television or a fan.(IEA,
2020; Lee, Miguel and Wolfram, 2020). The latest International Energy Agency data suggests
the number of people who live without electricity will rise to approximately 775Mn with a
significant increase of 20 Mn by the end of 2022. International Energy Agency has been
tracking these numbers for the past two decades and this is noted as the first worldwide rise in
the number of people who do not have access to electricity during that period.. It is noted that
the rise mostly affects people residing in Sub-Saharan Africa (IEA, 2022).

3 of 35
 This study’s objective is to examine the causal relationship between AEL and per capita
gross domestic product (PCGDP) for Asian countries using the Granger causality methodology.
The study contributes to existing literature four-fold.
First, , this study is perhaps one of the first to investigate the empirical gap, which
explores the causality between AELE and PCGDP in selected Asian countries in a single study.
Second, this study encompasses the latest data from the World Development Indicators for the
2000 -2021 period for Asian countries. Subsequently, some countries were excluded from the
initial pool due to insufficient data. This will be offset by selecting 34 Asian countries for
inclusion in this study. Researchers generalise the findings to all Asian countries since the
selected countries, encompass more than 70 % of the Asian countries in the world.
Third, the study shows how the two variables, AEL and PCGDP perform in selected
Asian countries according to the country-by-country analysis. Thus, the study's findings offer
valuable insights into the success of recent initiatives in Asian countries. Finally, there is a lack
of surplus literature related to the causal relationship between AEL and economic growth.
Studies had focused on different demographics, periods, variables and methodologies to arrive
at the results. The outcomes seem to be varied and found to be conflicting since studies have
used different proxies for AEL and economic growth thus making it challenging to draw
conclusions regarding the causal link between AEL and economic growth. Moreover
depending on the causal relationships that exist, policy implications can be significant.
This study is organised as follows. Section 1 introduces the paper. Section 2 discusses
the related literature review on the current topic. Section 3 presents the data and methodology
related to this investigation. Section 4 discusses the empirical results obtained by analysing the
data using the Granger causality model, along with the discussion of the results for the AEL
and PCGDP. Section 5 briefly presents the conclusion and policy implications, identifying
further research areas.

2. Literature Review
A systemic literature search was conducted, as shown in Figure 1 by using major databases
such as JSTOR, ScienceDirect, Wiley Online Library, Emerald Insight, Google Scholar and
Taylor Francis. Keywords such as electrification, electricity access, electricity availability,
energy access coupled with economic growth, per capita gross domestic product, and economic
development were used. By searching the above-mentioned research databases, eighty-eight
articles were found linked to the topic, keywords, and the abstract. In the screening phase,

4 of 35
forty-three articles were excluded due to overlapping publications, insufficient information,
and irrelevance to the selected title. For the eligibility step, forty-five articles were shortlisted
and thirty-seven contributing to the focused scope of this study were selected considering title,
keywords, abstract, conclusion, H index and the quartile of the published journal. The selected
articles were categorised into Global, Africa and Asia.

Figure 1: Literature search flow diagram

Search databases: ScienceDirect, JSTOR, Wiley online Library, Taylor Francis

Emerald Insight, Springer, and Google Scholar

Identification
Search strategy used for all databases:
(electrification, electricity access, electricity availability, energy access coupled
with economic growth, per capita gross domestic product, economic
development,)

88 publications were identified through the keywords and database search.

Total excluded = 45 publications were identified after screening for non-

Screening & Eligibility

45 relevance to access to electricity and economic growth.
-Overlapping Publications not fulfilling the criteria were removed.
publications = 12 (n=100, with 43 being excluded)

-Insufficient
information = 18 37 publications were assessed for eligibility based on
-Not relevant= 15 title, abstract and data type.
(n= 37, with 6 being excluded)

37 publications are included in the present study.

Inclusion

5 14 18
Global Africa Asia

Source: Based on authors’ observations.

5 of 35
 The Study discusses how existing studies have explored the variables thus far on a
global level, then moves on to literature related to country panels and lastly to individual
countries. The linkage between AEL and PCGDP is supported by various theoretical motives
explained in the literature. AELE positively influences economic growth, as reflected by
PCGDP and vice versa.

2.1 Global
It needs to be noted and emphasised that the relationship between electricity consumption and
economic growth has been extensively discussed in past studies (Altinay and Karagol, 2005;
Ghosh, 2002; Ozturk, Aslan and Kalyoncu, 2010; Shiu and Lam, 2004). However, a limited
number of studies have investigated the relationship between AEL and economic growth. Even
with the available literature, very few studies have examined the causality relationship between
the variables globally wisely.
The Bayesian Model Averaging was used in a study that investigated the important
factors related to AEL from twenty-six socioeconomic indicators in forty-eight developing
countries. Here, the results indicate that long-term AEL is positively correlated with not only
economic growth but also with the participation of the private sector, political resolve and
commitment from governments, financial availability, education, infrastructure, and
industrialisation, and integration with other development initiatives and poverty reduction.
Results suggest that a positive relationship exists between PCGDP and AEL. Further the study
extends the possibility of having a bidirectional causality relationship with v PCGDP, share of
the manufacturing industry and AEL with positive links. How ever this is not confirmed in the
study, rather an inclination. Theoretically, countries with higher incomes can enhance AELE
in the long run. Whereas increased AELE can boost income levels and economic growth
Furthermore, the government should pay attention to important factors to achieve long- term
solutions to electrification (Zhang et al., 2019). However, the study does not discuss individual
countries. Rather, it discussesthe macro picture, which might not suite every country. The time
frame has been obtained by averaging ten-year data for each variable, whereas the current study
explores twenty-one years of data as it is. Further, the study does not account for the causality
relationship of the variables.

2.2 African Region

Kenya’s study investigated the long-run relationship between gross domestic product, AELE,
and remittances for the period of 1987 to 2018 by using the autoregressive distributed lag
6 of 35
bounds test and the Granger causality method. The results indicate a positive impact on AEL
and remittances to economic growth in the long run. Granger causality analysis suggests a
unidirectional relationship between gross domestic product and AEL. According to the long -
run coefficient, economic growth will increase by a small percentage if the availability of
AELE increases by a certain percentage There is no causality between remittances and AELE.
However, the government should maintain implementing policies that will boost economic
growth and AELE (Onuonga, 2020) However, the study has focused on a 40 -year framework.
The study is oriented towards Kenya and discusses only he long-term relationship between the
variables without considering the short-term relationships between the variables.
The Ghanaian study indicates the impact of AEL on e-commerce adoption in Ghanaian
small- and medium-sized businesses , with a focus on electricity availability for the period
o2007 to 2017 using tests of multicollinearity, autocorrelation, and multiple regression models.
The study found that AEL had a robust positive relationship with e-commerce adoption in the
country. To encourage businesses to conduct business online, the government, legislators, and
other stakeholders should work hard to resolve the nation's power problems (Twi-Brempong et
al., 2020). Moreover, greater effort should be made to encourage enterprises to conduct
business online. Although the study shows that AEL uplifts e-commerce for small- and
medium-sized businesses in Ghana, it doesn’t necessarily link to economic growth in every
scenario.
Another study of Sub-Saharan Africa reveals that providing AEL for poor urban and
rural residents is very poor due to various reasons for high connection charges and limited
electrification rates limiting the growth of the region's economy, healthcare, education, and
overall quality of life. Also, they have provided various reasons for high charges for small
consumers, and it is a major factor for developing countries (Golumbeanu and Barnes).
However, by prioritising adequate AEL, Sub-Saharan Africa can create opportunities for social
advancement and financial success for all its citizens.
Furthermore, another study compared electricity access initiatives for Sub-Saharan
African countries of Ghana, Nigeria, Tanzania, Kenya, South Africa, Zambia, Congo, and
Cameroon. This study highlights the importance of effective policies and investments in
expanding AEL. AEL is essential to achieving economic development and improving welfare.
Also,, achieving universal AEL is important for the United Nation’s sustainable development
goals, especially for improving livelihood, gender, and education (Zigah and Creti, 2023). Most
authors have explored a wide range of subjects related to the electrification program in Sub-
Saharan Africa, including electrification planning (Bekker et al., 2008; Eberhard et al., 2016;
7 of 35
Trotter, 2016; Trotter, Mcmanus and Maconachie, 2017). Furthermore, it is necessary to
provide a comprehensive comparative analysis of eight countries, emphasizing centralized
coordination, decentralized approaches, and geospatial information systems in rural
electrification. However, it has a limited focus on the impact of electrification initiatives on
poverty alleviation and sustainable development.

2.3 Asian Region

The study found that combining grid extension is more effective for improving rural electricity
access in South Asian countries than relying on choosing one approach of grid or off-grid. This
enhances reliable AEL for rural communities. (Palit and Bandyopadhyay, 2016).Another study
based in South Asia discusses improving household connections and emphasises the
requirement to enhance electricity supply using local renewable sources (Palit and Chaurey,
2011). Moreover, it highlights the diverse approaches and outcomes of each South Asian
country of India, Bangladesh, Nepal, and Sri Lanka. However, certain aspects of rural
electrification, such as the economic effects of electrification and the long-term sustainability
of off-grid systems, receive limited attention.
Electricity access and economic development are linked in rural and urban areas in
China. The autoregressive distributed lag method was used for short-term and long-term
connections between the variables of electricity access of the total population, rural population,
urban population, and energy usage. In the long run, all the variables are positively associated
with economic growth. However, in the short run, there is a negative relationship between
economic growth and the rural population. Overall, energy use and economic development
seem to have a positive association. Furthermore, the Granger causality vector error correction
model techniques indicate that all variables have a unidirectional relationship (Milin et al.,
2022). However, the study is confined to a singular country. In contrast the current study
explores Asian es, which allow researchers generalise the findings for larger segment that
represents a significant portion of the global economy.
In addition, another study of Pakistan investigates the relationship between AEL,
population growth and economic growth in the region for the period 1990 to 2016 using World
Development Indicators derived from the World Bank data. Here, the variables' stationarity
was tested using the Phillips–Perron (PP) unit root test and the causality link between the study
variables was investigated using an autoregressive distributed lag bounds testing approach. The
long-run and short-run relations examined the autoregressive distributed lag representation of
the unrestricted error correction model. The main results demonstrate that the AEL to the urban
8 of 35
population and the urban population growth has a positive influence on Pakistan’s economic
growth in the long run, and the rural population growth has a negative influence on Pakistan’s
economic growth in the long run. Furthermore, Pakistan's government needs to pay attention
to increasing production to fulfil the country’s expanding demand. In addition, creating and
extending new strategies is beneficial to the development of renewable energy sources
(Rehman, 2019). Although the study does not necessarily show causality between AEL and
economic growth and the data used, it is relatively old compared to the current study and is
limited to a single country.
Additionally, research of Southeast Asian countries investigated for the period 2000 to
2020 period how AEL has improved over the time span and make recommendations for policy
implications to increase AEL against energy poverty by using existing statistical data and
literature. Over the period South Asian countries have achieved approximately more than 90%
electrification rates however still 45 million people lack electricity in Cambodia and Myanmar
while having other poverty related challenges. . Having AEL improves living standard relating
to education levels, income-generation, health and hygiene conditions, entertainment, family
interaction, etc. The energy role of Southeast Asia is increasing in the overall world energy
structure. Cambodia has limited access to energy and a relatively low per-capita energy
demand. Indonesia can be identified as the largest energy consumer in the Association of
Southeast Asian Nations. Myanmar has a low level of energy access. The Philippines has a
rapid increase in electricity demand and requires expanded supplies. Thailand aims to diversify
electricity generation. Indonesia, Philippines, Vietnam, and Lao PDR countries have adopted
more ambitious policy targets and measures to improve AEL. Indonesia has an interaction with
Malaysia to export electricity, Vietnam exports electricity to Cambodia, and Lao PDR exports
electricity to Cambodia, Malaysia, Thailand, and Vietnam. However, people who live in rural
areas in Cambodia and Myanmar, without AEL suffer from poverty issues and with the need
for financial support from the government. Hence, the government should implement their
requirements to afford electricity use (Shyu, 2020). However, the study has arrived at such
policies based on existing literature without accounting statistics related to economic growth
or testing any hypotheses. This gives rise to queries such as whether the suggested policies still
apply to those countries since the study has only focused on enhancing AEL, which might not
always be a catalyst for economic growth.
A study across seven South Asian countries for the period 1980 to 2020 and a study of
a rural-urban perspective in India, indicate that both positive and negative influences on
globalisation ,suggests that Afghanistan, Bangladesh, India, Pakistan, Nepal, Sri Lanka, and
9 of 35
Bhutan can accelerate their transition to sustainable energy sources by enhancing institutional
quality, promoting technical innovation, enhancing physical infrastructure, improving labour
productivity, and improving financial openness (Rehman et al., 2023). Due to the rise in trade
openness and foreign direct investment inflows, India's economic growth has enhanced and
increased AELE (Shahbaz et al., 2024). Moreover, these factors create a favourable
environment for renewable energy projects, improve energy security, promote growth in the
economy, create job opportunities, and fix environmental problems.
Studies that were found focused on different demographics, periods, variables, and
methodologies to arrive at the results. The results seem to be varied and found to be
contradictory. At the same time, the current study intends to fill the empirical gap, which is the
causality between AEL and economic growth in Asian countries, using the latest data.

3. Data and methodology

This study has used the secondary data sources available on the World Bank’s open annual data
to measure the variables for the 2000 to 2021period. Here, we examine the causal relationship
between the AEL and the economic growth of thirty-four selected Asian countries by using the
Granger causality method. This study uses a unit root test to find the integration of the variables.
After establishing the order of integration, this study tests if there is a causal relationship
between AEL and PCGDP.

Table 1: Data sources of the variables

Variable Measure Source

PCGDP Current US ($) The World Bank
https://data.worldbank.org/indicator/NY.GDP.PCAP.CD
AELE % of Total The World Bank
Population https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS
Source: Authors’ compilation based on The World Bank data.

This study aims to examine the causal relationship between AELE and the economic
growth for selected Asian countries. PCGDP (as an annual percentage) was used to measure
economic growth. In addition, AELE was measured using the population (as a percentage of
the total population). It is possible to gain three results: bidirectional, unidirectional or no
interdependence relationships between AELE and economic growth. STATA software is used
in the study, and two stationary tests, Dickey-Fuller (DF) and PP tests, are employed to ensure
the data are stationary prior to deploying the vector autoregression model. The optimal lag

10 of 35
length is determined based on the lag selection criteria. The Granger causality formula is as
follows:

𝐴𝐸𝐿𝐸𝑡 = 𝐶0 + ∑𝛼𝑖 𝐴𝐸𝐿𝐸𝑡−𝑖 + ∑ 𝛽𝑖𝑃𝐶𝐺𝐷𝑃𝑡−𝑖 + 𝜀𝑡 (Eq. 1)

𝑃𝐶𝐺𝐷𝑃𝑡 = 𝐶0 + ∑𝛼𝑖 𝑃𝐶𝐺𝐷𝑃𝑡−𝑖 + ∑ 𝛽𝑖𝐴𝐸𝐿𝐸𝑡−𝑖 + 𝜀𝑡 (Eq. 2)

We selected the Granger causality method due to its ability to assess the directionality
of causality between variables. It requires identifying causal relationships between variables
and examining historical relationships, which is very useful for examining the relationship
between PCGDP and AELE.
The causal relationship between the variables is examined using the Granger causality
method. The study observes two stationary variables, X and Y, over the time span (t periods).
It can be argued that X Granger causes Y if lagged values of X with a significant influence in
a Y regression model that consists not only of X but also its lagged values suggest that X
Granger causes. Furthermore, it shows the possibility that changes in the Y variable could also
cause X (Jayawardhana et al., 2023).
The above-mentioned equation shows that if the sum of estimated coefficients on
lagged values PCGDP is significantly different from zero and the sum of estimated coefficients
on lagged values of the AELE is not significantly different from zero (Eq. 1), it indicates a
one-way causation from PCGDP to AELE. Conversely, if the opposite is true (Eq. 2), it
suggests an inverse relationship from AELE to PCGDP. If both coefficients are statistically
different from zero, it indicates bidirectional causation, that both variables influence each other.

4. Empirical Results
This section will discuss the empirical results of PCGDP and AEL in Asian countries,
descriptive statistics, the unit root test, and the lag length criteria, respectively.
Figure 2 shows the values of PCGDP in eleven Asian countries. Syrian Arab Republic,
Iran, Azerbaijan, Maldives, Kazakhstan, China, Malaysia, Armenia, Sri Lanka, Yemen, and
Tajikistan countries were found to fluctuate at different points throughout the period under
discussion. All countries, except for the Syrian Arab Republic, show moderate growth
throughout the selected period. After the all-time high PCGDP in 2010, the Syrian Arab
Republic has been facing multiple economic challenges due to the continuing civil war that
officially began in March 2011. However, the country had undergone severe economic changes

11 of 35
before the declared time, which is evident with the massive drop shown during the years of
2010 to 2011. Most of the countries show a subtle drop in PCGDP during 2008 and 2009 due
to the global financial crisis. In the context of the changing global environment, PCGDP in
Mongolia showed a sharp increase after the global crisis in 2008 due to their exports of mineral
resources but later faced a downward trend mainly due to political unrest. However, the country
has recovered the same through reforms suggested by the International Monetary Fund.

Figure 2: PCGDP in Asian countries.

14000

12000

10000
PER CAPITA GDP US($)

8000

6000

4000

2000

0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

YEAR
Syrian Arab Republic Iran Azerbaijan
Maldives Kazakhstan China
Malaysia Armenia Sri Lanka
Yemen Tajikistan Mongolia
Timor-Leste

Source: Authors’ Compilation based on World Bank data.

Timor-Leste shows significant growth in the latter part of 2020, due to moderate
contribution from private and public sectors, especially in exports led by coffee and the
decrease in imports. In the early 2000s, Armenia's GDP expanded rapidly because of economic
reforms and increased foreign investment. A notable observation is seen in Yemen, where
economic growth has an erratic and negative relationship economic activity declined, and
economic growth sharply decreased in 2011 due to political unrest, internal conflicts, and the
Arab Spring uprising.
The World Development Indicators provide a review of potential macro socio-
economic factors that are either important or are impacted by AEL. Figure 3 shows the values
of AELin eighteen Asian countries: Sri Lanka, Philippines, Pakistan, Mongolia, India, Bhutan,

12 of 35
Lao PDR, Nepal, Bangladesh, Yemen, Myanmar, Timor-Leste, Cambodia, Georgia, Vietnam,
Thailand, Indonesia, and the Syrian Arab Republic.

Figure 3: AELE in Asian countries.

105
ACCESS TO ELECTRICITY (% OF POPULSTION)

95

85

75

65

55

45

35

25

15

5
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

YEAR

Sri Lanka Philippines Pakistan Mongolia

India Bhutan Lao PDR Nepal
Bangladesh Yemen Myanmar Timor-Leste
Cambodia Georgia Thailand Vietnam
Indonesia Syrian Arab Republic

Source: Authors’ Compilation based on World Bank data.

Those countries have generally maintained a trend of constant growth trend with
fluctuations in AEL for the selected period. Cambodia has the lowest percentage for the AEL
of all countries due to urban-rural gaps; investment and trade shifted away from agriculture
toward higher value-added activities. However, Cambodia has maintained continuous growth
in AEL. Looking further, in Siriya AEL gradually increased till 2006 and then witnessed a
decline. AEL in this country was 86% in 2018, the lowest. This was mainly due to the civil war.
It disrupted production, trade, and investment and resulted in an economic downturn. There
was a decline in 2005 due to the 2004 Indian Ocean tsunami. However, after 2005, Indonesia
maintained steady growth. Vietnam shows a temporary slowdown in 2007 and 2008 due to
global financial crisis. In Sri Lanka, the average percentage of the population having AEL for
the selected period was 86.72%. The maximum is displayed as 100% in 2021, while the
minimum was 63.6% in 2001. Low rainfall and the high cost of petroleum have been identified
as reasons for this. There was an eight-hour power cut in 2001, seven hours longer than in 1996.
By 2021, the percentage of power supplied in Cambodia had increased from 6.6% in 2000 to
97.5%. It is among the nations that has achieved rapid electrification in the world, according
to the World Bank. However, 350 Cambodian villages won’t have electricity by late 2021.

13 of 35
 The empirical findings of descriptive statistics are represented in Appendix 1. Among
selected Asian countries in this study, Tajikistan exhibited the lowest PCGDP value, while
Turkey had the highest. Additionally, Colombia had the lowest AELE, and Kazakhstan had the
highest.

4.1 Unit root test

Researchers employed -to-unit root tests, the DF and PP tests, to identify the order of
integration of the two variables. The two unit-to-unit root tests ensure robustness of results.
Both tests were done for each country individually. Appendix 2 shows the results of the DF
test, and Appendix 3 shows the results of the PP test on the integration of the two series. This
indicates that the variables are individually integrated at the 1%, 5%, and 10% significant
levels.
DF test was performed first for both variables. Except for Bangladesh, Lao PDR,
Timor-Leste, Uzbekistan and China, the other twenty-nine countries became stationary in the
first difference PCGDP, whereas Bangladesh, Lao PDR, Timor-Leste and Uzbekistan went for
second difference, and lastly, China became stationary in both second difference PCGDP. AEL
became stationary in the first attempt in Armenia, Georgia, Jordan, Kazakhstan, Kyrgyz
Republic, Lebanon, Maldives, Syrian Arab Republic, Tajikistan, Thailand and China, while the
rest of the countries became stationary in the first different AEL. PP test was then performed,
and twenty-nine countries except Bangladesh, Lao PDR, Timor-Leste, Uzbekistan and China
became stationary in the first difference PCGDP. No country became stationary in PCGDP for
the first time in both tests.

4.2 Lag length criteria

Determining the optimal lag length is mandatory before the Granger causality test. Deciding
the lag length criteria is based on the final prediction error, Akaike’s information criterion
(AIC), Schwarz’s Bayesian information criterion (SBIC), and the Hannan and Quinn
information criterion (HQIC). In the optimal VAR specification, lag length was decided by
AIC, SBIC, and HQIC. The AIC, SBIC, and HQIC in every country had the same lag order.
Hence, lag length was chosen as per the criteria with the majority weightage.
Appendix 4 shows that the default maximum number of lags is ten. In Azerbaijan, and
Yemen, the AIC, HQI and SBIC criteria are lower when selecting six lags. Selecting seven lags
for Maldives, Tajikistan, and Turkmenistan lowers the AIC, HQIC, and SBIC criteria. Further,
the AIC, HQIC, and SBIC criteria is lower when selecting eight lags for Bangladesh, Iran, Iraq,
14 of 35
and Pakistan. Selecting nine lags for Bhutan, India, Kyrgyz Republic, Malaysia, Mongolia,
Myanmar, Nepal, Thailand, and Vietnam are lower in the AIC, HQIC, and SBIC criteria.
Armenia, Cambodia, China, Georgia, Indonesia, Jordan, Kazakhstan, Lao PDR, Lebanon,
Philippines, Sri Lanka, Syrian Arab Republic, Timor-Leste, Turkey, Uzbekistan, and West
Bank Gaza are lower when selecting ten lag criteria.
Stationarity and stability are two fundamental assumptions of the selected two variables
and the Stationarity refers to the statistical features of the variables remaining stable throughout
time.

Figure 4: Stability test graph.

Source: Authors’ illustrations based on STATA software.

Figure 4 confirms the stationarity of the selected two variables, AELE and economic growth.
In contrast, stability refers to how the system behaves as stated by the vector autoregression
model. The stability condition is satisfied when it is confirmed that all of the vector
autoregression model's eigenvalues are contained within the unit circle. Therefore, Figure 4’s
stationarity and stability requirements are fulfilled.

4.3 Granger causality test

The Granger test is used to investigate the causal relationship between variables. One time
series is used to predict another time series. In this scenario, does AEL promote PCGDP, or
does PCGDP promote AEL?

15 of 35
 The Granger results are depicted in Table 2 The results suggest that Granger causality
exists between PCGDP and AELE in the selected Asian countries except Armenia, China,
Georgia, Indonesia, Kazakhstan, Lebanon, Malaysia, Mongolia, Pakistan, Philippines, Syrian
Arab Republic, Timor-Leste, Turkmenistan, Uzbekistan, Vietnam, and West Bank and Gaza.
Table 2: Granger causality test results.

Country PCGDP → AELE AELE → PCGDP

DPCGDP → AELE AELE → DPCGDP
Armenia 3.2158 4.2568
Georgia 5.2543 0.45895
Jordan 26.667*** 0.77681
Kazakhstan 5.4257 3.9219
Kyrgyz Republic 1.3303 9.7011**
Maldives 1.4832 15.975***
Syrian Arab Republic 0.39222 0.66922
Tajikistan 2.2204 8.1506**
Thailand 7.0721* 1. .335
DDPCGDP → AELE AELE → DDPCGDP
Lebanon 2.3937 1.2042
DPCGDP → DAELE DAELE → DPCGDP
Azerbaijan 1.0926 10.45**
Bhutan 2.1653 7.4713*
Cambodia 4.4946*** 1.9602
India 2.5867 8.1185**
Indonesia 0.55754 1.0076
Iran 8.8749** 7.0543*
Iraq 10.429** 4.7327
Malaysia 5.5842 3.116
Mongolia 3.9053 5 .016
Myanmar 1.729 9.0841**
Nepal 7.7404* 0.30417
Pakistan 0 .75838 5.5844
Philippines 1.1413 6.2233
Sri Lanka 1.6305 6.7604*
Turkey 0.11804 36.006***
Turkmenistan 2.9409 3.7446
Viet Nam 0.74899 4.325
West Bank and Gaza 0.44943 1.9642
Yemen 5.5473 12.017***
DDPCGDP → DAELE DAELE → DDPCGDP
Bangladesh 13.185*** 1 7.073***
Lao PDR 15.275*** 2.3761
Timor-Leste 2.335 0.6218
Uzbekistan 2.2906 3.1207
DDDPCGDP → AELE AELE → DDDPCGDP
China 1.666 3.6962

16 of 35
Notes: * denotes significance at the 10% level, ** denotes significance at the 5% level, ***
denotes significance at the 1% level.

PCGDP denotes per capita gross domestic product, AELE denotes access to electricity, DAELE
= first difference of AELE, DDAELE = second difference of AELE, PCGDP; DPCGDP = first
difference of PCGDP., DDPCGDP = second difference of PCGDP, DDDPCGDP = third
difference.
Source: Authors’ computation based on the data.

Economic growth seems to drive AELin Cambodia, Iraq, Jordan, Lao PDR, Nepal, and
Thailand. In contrast, economic growth is driven by AELE in Azerbaijan, Bhutan, India,
Kyrgyz Republic, Maldives, Myanmar, Sri Lanka, Tajikistan, Turkey, and Yemen.
Bidirectional relationships between economic growth and AEL are shown in both
Bangladesh and Iran, as well as the Islamic Republic. Bangladesh and Iran might be due to the
continuous growth in AELE and PCGDP. One way causality relationship observed for sixteen
countries (Azerbaijan, Bhutan, Cambodia, India, Iraq, Jordan, Kyrgyz Republic, Lao PDR,
Maldives, Myanmar, Nepal, Sri Lanka, Tajikistan, Thailand, Turkey, Yemen). Moreover, no
causality relationship was observed for sixteen countries (Armenia, China, Georgia, Indonesia,
Kazakhstan, Lebanon, Malaysia, Mongolia, Pakistan, Philippines, Syrian Arab Republic,
Timor-Leste, Turkmenistan, Uzbekistan, Vietnam, West Bank and Gaza).
Furthermore, Table 3 represents causality directions in all selected individual Asian
countries contribution.
Table 3: Granger causality test results.

Country PCGDP AELE PCGDP → AELE

Armenia DPCGDP AELE No way
Azerbaijan DPCGDP DAELE One way
Bangladesh DDPCGDP DAELE Bidirectional
Bhutan DPCGDP DAELE One way
Cambodia DPCGDP DAELE One way
China DDDPCGDP AELE No way
Georgia DPCGDP AELE No way
India DPCGDP DAELE One way
Indonesia DPCGDP DAELE No way
Iran DPCGDP DAELE Bidirectional
Iraq DPCGDP DAELE One way
Jordan DPCGDP AELE One way
Kazakhstan DPCGDP AELE No way
Kyrgyz Republic DPCGDP AELE One way
Lao PDR DDPCGDP DAELE One way
Lebanon DDPCGDP AELE No way
Malaysia DPCGDP DAELE No way
Maldives DPCGDP AELE One way

17 of 35
Mongolia DPCGDP DAELE No way
Myanmar DPCGDP DAELE One way
Nepal DPCGDP DAELE One way
Pakistan DPCGDP DAELE No way
Philippines DPCGDP DAELE No way
Sri Lanka DPCGDP DAELE One way
Syrian Arab DPCGDP AELE No way
Republic
Tajikistan DPCGDP AELE One way
Thailand DPCGDP AELE One way
Timor-Leste DDPCGDP DAELE No way
Turkey DPCGDP DAELE One way
Turkmenistan DPCGDP DAELE No way
Uzbekistan DDPCGDP DAELE No way
Vietnam DPCGDP DAELE No way
West Bank and Gaza DPCGDP DAELE No way
Yemen DPCGDP DAELE One way
Notes: and denote unidirectional relationships to left and right directions.
and represents bidirectional and no interdependence relationships
respectively. 1st, 2nd and 3rd differences are presented in three arrow sizes small, medium and
large correspondingly.

DAELE = first difference of AELE, DDAELE = second difference of AELE, PCGDP;

DPCGDP = first difference of PCGDP., DDPCGDP = second difference of PCGDP,
DDDPCGDP = third difference of PCGDP.
Source: Authors’ computation based on the data.

Overall results suggest that a complex relationship exists between AELE and PCGDP.
Thus, no substantial evidence is available to convince us that AEL and economic growth go
hand in hand in selected Asian countries. Most results suggest a short-term trend in PCGDP
and AELE since the variables become stationary in the first difference in most instances except
for a few countries. Regarding PCGDP, only China reaches the third difference, implying a
long-term trend during the time series. China seems to have maintained continuous growth
throughout the years compared to other Asian countries. However, AELE doesn’t show much
of a trend while maintaining a somewhat stable pace since the data becomes stationary in the
first attempt. It is to be noted that PCGDP seems to have short and long-term trends in every
country, whereas AEL shows short-term trends or no significant trend at all.
AEL and PCGDP are found in different trends because PCGDP is being manipulated
by multiple factors compared to AELE (Best and Burke, 2018). Furthermore, the study of China
discusses the positive relationship between AELE and economic growth in the short run and
long run (Milin et al., 2022). However, in our study, we are unable to confirm the same, which
might be due to the time frame of our study, methodology and obvious external forces such as

18 of 35
COVID-19. Enhancing AEL, ly via electrification, has a significant impact on the health,
education, economy, and other aspects of rural Indian households' lives (Kanagawa and Nakata,
2008). Vietnam, China, and Mongolia have highest electricity intensity (Hien, 2019). The
Granger test results summary is reported in the Table 4. Generally, this table has categorised
the relationship based on their causality directions.

Table 4: Granger results test summary.

Causality relationship Number of Asian countries As a percentage

Bidirectional 2/34 5.88%
Unidirectional 16 /34 47.06%
• PCGDP (→) AELE • 6/34 • 17.65%
• AELE (→) PCGDP • 10/34 • 29.41%
No way 16/34 47.06%
Sources: Authors’ illustrations based on Granger results.

In two out of thirty-four Asian countries, which is 5.88%, there are bidirectional causal
relationships between AEL and economic growth. This means that one variable can affect
another variable and vice versa. Unidirectional causal relationships in sixteen Asian countries
as a percentage of 47.06% are observed Six countries show a unidirectional relationship
between PCGDP and AEL with a percentage of 17.65%. Whereas AEL of ten countries Granger
causes PCGDP as a percentage of 29.41%. That means the relationship is only valid in one
direction. The remaining sixteen countries (47.06%) show no interdependence between AEL
and PCGDP.

5. Conclusion and policy implications

The purpose of this study aimed to examine the causal relationship between the AEL and the
economic growth in Asian countries using the World Development Indicators data from 2000
to 2021. From the test results, we conclude that unidirectional Granger causality runs from
PCGDP to AELE for six Asian countries, while bidirectional Granger causality runs for
Bangladesh and Iran. In the long run, there are unidirectional Granger causality runs from
AELE to PCGDP for ten countries.
This research hopes that future studies focus on identifying the causal effects of
electricity reliability, infrastructure, and AEL on economic growth, testing the Granger
causality and how the availability of electricity can be a constraint to growth. Our empirical

19 of 35
findings provide an alternative viewpoint on the relationship between AEL and economic
growth compared to previous research on this topic. Based on the results, we propose
policymakers should aim to enhance economic conditions raise AEL, establishing awareness
sessions for the community to ensure the use of advanced technology. The governments should
implement policies that would enhance economic growth and increase AELE and focus on
essential factors to achieve long term solutions to electrification.
In the DF test, except for Bangladesh, Lao PDR, Timor-Leste, Uzbekistan, and China,
29 countries achieved first-difference stationarity in PCGDP. China achieved second difference
stationarity. In the PP test, twenty-nine countries, excluding Bangladesh, Lao PDR, Timor-
Leste, Uzbekistan, and China, achieved stationarity in DPCGDP. No country achieved
stationarity in PCGDP in either test. Most lag selection criteria suggest that the optimum lag
length is between six and ten. As a result, this estimated lag length will be utilised for all further
research.
This study investigates the Granger causality relationship between AELE and PCGDP.
Granger causality is observed bidirectionally for Bangladesh and Iran countries. Sixteen
countries show a unidirectional relationship between PCGDP and AEL an AEL and PCGDP.
The remaining 16 countries have no interdependence between AELE and PCGDP.
The significant problems of the electricity sector are low consumption levels, high
electricity costs, unequal access, unreliable supply, and power shortages. Insufficient
infrastructure also leads to limited availability, hindering economic productivity. The
availability of electricity to the rural population displayed a negative link with economic
growth in China, and Pakistan. Notably, most of the countries have developed AEL more than
80% over the period, except Yemen (74.88%) and Myanmar (72.47%). When taking control of
these economic challenges, there must be a need of vital government and political participation
needed to implement public services and infrastructure for rural electrification.
In the short run, Azerbaijan’s manufacturing, services, and information technology
heavily rely on AEE. AEL encourages both domestic and foreign investment. It enhances
productivity, attracts investment, and supports station, directly benefiting households and
businesses. It also improves living standards supported by the government and tourism sector
in Cambodia. It drives manufacturing and urbanisation and enhances industrial output with the
support of tourism and services in China. As evidenced in Indonesia, AELE improves quality
of life.
In the long run, Armenia has grown remarkably over the past few years. Armenia wants
to diversify its energy sources and reach a substantial solar PV capacity by 2030, contributing
20 of 35
15% of generation. Also, expanding relations with Georgia and Iran requires new market rules
for international trading. AEL leads to creation of jobs, fosters innovation, and supports other
industries. By diversifying energy sources and infrastructural investment, the country can
decrease risks and foster sustainable development in Azerbaijan. AEL diversify energy sources
and infrastructural investment in Bangladesh, India, Cambodia, etc. Investment in renewables
can boost growth, as evidenced in Iran. Looking at the past years, we can see that there has
been a massive infrastructure investment that has fuelled growth in China. As a
recommendation, countries with strong bidirectional causality should give priority to
investment in economic development and electricity infrastructure in the high-electrification
countries of Bangladesh and Iran. As infrastructure improves, businesses can operate more
effectively, leading to economic growth.

6. Limitations
This study has collected secondary data for the selected Asian countries for 22 years of the
selected period to examine the relationship between AEL and PCGDP. It also comes with
certain limitations. First, the study may only partially capture the diversity and dynamics of
part of the continent. Second, while the Granger causality method is a commonly used
statistical tool, it may not consider all the variables that might influence economic growth.
Despite these limitations, the study provides a basis for more research in this field and valuable
information about the relationship between AEL and PCGDP in Asian countries. Future
research should expand the study's scope by covering more Asian countries using the Granger
causality method. Investigating the impact of adopting renewable energy sources and
technological improvements in the relationship between PCGDP and AELE would also be
useful.

21 of 35
7. References
Acheampong, A. O., Erdiaw-Kwasie, M. O. & Abunyewah, M. (2021) Does energy
accessibility improve human development? Evidence from energy-poor regions.
Energy Economics [online], v.96, pp. 105165.Available at:
doi.org/10.1016/j.eneco.2021.105165 Accessed: [10th March 2024]
Altinay, G. & Karagol, E. (2005) Electricity consumption and economic growth: evidence
from Turkey. Energy Economics [online], v.27 (6), pp. 849-856.Available at:
doi.org/10.1016/j.eneco.2005.07.002 Accessed: [25th March 2024]
Bekker, B., Eberhard, A., Gaunt, T. & Marquard, A. (2008) South Africa's rapid
electrification programme: Policy, institutional, planning, financing and technical
innovations. Energy Policy [online], v.36 (8), pp. 3125-3137.Available at:
doi.org/10.1016/j.enpol.2008.04.014 Accessed: [13th March 2024]
Best, R. & Burke, P. J. (2018) Electricity availability: a precondition for faster economic
growth? Energy Economics [online], v.74, pp. 321-329.Available at:
10.1016/j.eneco.2018.06.018 Accessed: [20th February 2024]
Burgess, R., Greenstone, M., Ryan, N. & Sudarshan, A. (2020) The consequences of treating
electricity as a right. Journal of Economic Perspectives [online], v.34, pp. 145-
169.Available at: 10.1257/jep.34.1.145 Accessed: [14th March 2024]
Eberhard, A., Gratwick, K., Morella, E. & Antmann, P. 2016. Independent power produces in
Sub-Saharan Africa Lessons from five key countries.
Ghosh, S. (2002) Electricity consumption and economic growth in India. Energy Policy
[online], v.30 (2), pp. 125-129.Available at: doi.org/10.1016/S0301-4215(01)00078-7
Golumbeanu, R. & Barnes, D. Connection charges and electricity access in Sub-Saharan
Africa.
Hien, P. D. (2019) Excessive electricity intensity of Vietnam: evidence from a comparative
study of Asia-Pacific countries. Energy Policy [online], v.130, pp. 409-417.Available
at: doi.org/10.1016/j.enpol.2019.04.025 Accessed: [16th March 2024]
IEA. (2020). Defining energy access: 2020 methodology [Online]. Available:
https://www.iea.org/articles/defining-energy-access-2020-methodology Accessed:
[13th January 2024]
IEA. (2022). For the first time in decades, the number of people without access to electricity
is set to increase in 2022 [Online]. Available: https://www.iea.org/commentaries/for-
the-first-time-in-decades-the-number-of-people-without-access-to-electricity-is-set-to-
increase-in-2022 Accessed: [13th January 2024]
Jayawardhana, T., Anuththara, S., Nimnadi, T., Karadanaarachchi, R., Jayathilaka, R. &
Galappaththi, K. (2023) Asian ageing: the relationship between the elderly population
and economic growth in the Asian context. PLoS One [online], v.18 (4), pp.
e0284895.Available at: doi: 10.1371/journal.pone.0284895 Accessed: [12th January
2024]
Kanagawa, M. & Nakata, T. (2008) Assessment of access to electricity and the socio-
economic impacts in rural areas of developing countries. Energy Policy [online], v.36
(6), pp. 2016-2029.Available at: doi.org/10.1016/j.enpol.2008.01.041 Accessed: [15th
March 2024]
Khandker, S., Barnes, D., Samad, H. & Minh, N. (2009) Welfare impacts of rural
electrification : evidence from Vietnam. The World Bank, Policy Research Working
Paper Series [online].Available at, Accessed: [6th March 2024]
Lee, K., Miguel, E. & Wolfram, C. (2020) Does household electrification supercharge
economic development? The Journal of Economic Perspectives [online], v.34 (1), pp.
122-144.Available at, Accessed: [24th March 2024]

22 of 35
Mhaka, S., Runganga, R., Nyagweta, D. T., Kaseke, N. & Mishi, S. (2020) Impact of rural
and urban electricity access on economic growth in Zimbabwe. International Journal
of Energy Economics and Policy [online], v.10 (6), pp. 427-434.Available at: doi:
10.32479/ijeep.10141 Accessed: [06th January 2024]
Milin, I. A., Mungiu Pupazan, M. C., Rehman, A., Chirtoc, I. E. & Ecobici, N. (2022)
Examining the relationship between rural and urban populations’ access to electricity
and economic growth: a new evidence. Sustainability [online], v.14 (13), pp.
8125.Available at: doi: 10.3390/su14138125 Accessed: [06th January 2024]
Onuonga, S. M. (2020) Economic growth, electricity access, and remittances in Kenya.
Management and Economics Research Journal [online], v.6 (2).Available at: doi:
10.18639/MERJ.2020.1100554 Accessed: [06th January 2024]
Oyuke, A., Penar, P. & Howard, B. Off-grid or 'off-on':lack of access, unreliable electricity
supply still plague majority of Africans. 2016.
Ozturk, I., Aslan, A. & Kalyoncu, H. (2010) Energy consumption and economic growth
relationship: evidence from panel data for low and middle income countries. Energy
Policy [online], v.38 (8), pp. 4422-4428.Available at:
doi.org/10.1016/j.enpol.2010.03.071 Accessed: [18th March 2024]
Palit, D. & Bandyopadhyay, K. R. (2016) Rural electricity access in South Asia: Is grid
extension the remedy? a critical review. Renewable and Sustainable Energy Reviews
[online], v.60, pp. 1505-1515.Available at: doi.org/10.1016/j.rser.2016.03.034
Accessed: [9th March 2024]
Palit, D. & Chaurey, A. (2011) Off-grid rural electrification experiences from South Asia:
status and best practices. Energy for Sustainable Development [online], v.15 (3), pp.
266-276.Available at: doi.org/10.1016/j.esd.2011.07.004 Accessed: [15th March
2024]
Rehman, A. (2019) The nexus of electricity access, population growth, economic growth in
Pakistan and projection through 2040: An ARDL to co-integration approach.
International Journal of Energy Sector Management [online], v.13.Available at:
10.1108/IJESM-04-2018-0009 Accessed: [10th March 2024]
Rehman, F. U., Islam, M. M., Ullah, M., Khan, S. & Rehman, M. Z. (2023) Information
digitalization and renewable electricity generation: Evidence from South Asian
countries. Energy Reports [online], v.9, pp. 4721-4733.Available at:
doi.org/10.1016/j.egyr.2023.03.112 Accessed: [12th March 2024]
Shahbaz, M., Siddiqui, A., Sinha, A. & Bigerna, S. (2024) Does financial development
enhance access to electricity? a rural-urban perspective in India. Energy [online],
v.291, pp. 130464.Available at: doi.org/10.1016/j.energy.2024.130464 Accessed:
[28th February 2024]
Shi, X. (2016) The future of ASEAN energy mix: a SWOT analysis. Renewable and
Sustainable Energy Reviews [online], v.53, pp. 672-680.Available at:
doi.org/10.1016/j.rser.2015.09.010 Accessed: [20th March 2024]
Shiu, A. & Lam, P.-L. (2004) Electricity consumption and economic growth in China.
Energy Policy [online], v.32 (1), pp. 47-54.Available at: doi: 10.1016/S0301-
4215(02)00250-1 Accessed: [07th January 2024]
Shyu, C.-W. (2020) Energy poverty alleviation in Southeast Asian countries: policy
implications for improving access to electricity. Journal of Asian Public Policy
[online], v.15, pp. 1-25.Available at: doi: 10.1080/17516234.2020.1770426 Accessed:
[08th January 2024]
Simbarashe, M., Raynold, R., David T, N., Nyasha, K. & Syden, M. (2020) Impact of rural
and urban electricity access on economic growth in Zimbabwe. International Journal

23 of 35
 of Energy Economics and Policy [online], v.10 (6), pp. 427-434.Available at,
Accessed: [12th January 2024]
Trotter, P. A. (2016) Rural electrification, electrification inequality and democratic
institutions in sub-Saharan Africa. Energy for Sustainable Development [online],
v.34, pp. 111-129.Available at: doi.org/10.1016/j.esd.2016.07.008 Accessed: [03rd
March 2024]
Trotter, P. A., McManus, M. C. & Maconachie, R. (2017) Electricity planning and
implementation in sub-Saharan Africa: a systematic review. Renewable and
Sustainable Energy Reviews [online], v.74, pp. 1189-1209.Available at:
doi.org/10.1016/j.rser.2017.03.001 Accessed: [10th March 2024]
Twi-Brempong, C., Gu, H., Oppong-Baah, T., Owusu, I., Gyamfi, E., Pinas, J., Kim, E.,
Nsaful, A., Boateng, F., Ofosu, D., Nketiah, E., Adjei, M., Fianko, S. K., Gyamerah,
K. & Owusu, J. (2020) The impact of access to electricity on E-commerce adoption in
Ghanaian SMEs, an empirical analysis. Open Journal of Business and Management
[online], v.8, pp. 245-261.Available at: 10.4236/ojbm.2020.81015 Accessed: [25th
March 2024]
UNSD (2024) Affordable and clean energy. [online].Available at,
https://unstats.un.org/sdgs/report/2023/Goal-07/ Accessed: [8th March 2024]
Yang, M. (2003) China's rural electrification and poverty reduction. Energy Policy [online],
v.31 (3), pp. 283-295.Available at: doi.org/10.1016/S0301-4215(02)00041-1
Accessed: [5th March 2024]
Zaman, M., Shaheen, F., Haider, A. & Qamar, S. (2015) Examining relationship between
electricity consumption and its major determinants in Pakistan. International Journal
of Energy Economics and Policy [online], v.5 (4), pp. 998-1009.Available at,
Accessed: [15th March 2024]
Zhang, T., Shi, X., Zhang, D. & Xiao, J. (2019) Socio-economic development and electricity
access in developing economies: a long-run model averaging approach. Energy Policy
[online], v.132, pp. 223-231.Available at: doi: 10.1016/j.enpol.2019.05.031 Accessed:
[08th January 2024]
Zigah, E. & Creti, A. 2023. A comparative analysis of electricity access initiatives in Sub-
Saharan Africa.

24 of 35
8. Appendices
Appendix 1: Descriptive Statistics of the AELE and PCGDP.
Country Variable Mean Std. Dev. Min Max
Armenia PCGDP 2996.22 1435.003 603.298 4972.783
AELE 99.50101 0.492851 98 100
Azerbaijan PCGDP 4158.427 2425.011 655.0972 7890.84
AELE 99.67267 0.469549 98.598 100
Bangladesh PCGDP 1049.95 676.2258 407.963 2457.924
AELE 61.59076 20.76309 32 98.99103
Bhutan PCGDP 2134.005 974.3341 722.8268 3564.599
AELE 75.29783 22.96332 31.15 100
Cambodia PCGDP 902.2339 462.2045 301.5166 1671.385
AELE 43.85813 27.65399 9.528574 89.07
China PCGDP 5405.942 3660.654 959.3604 12617.51
AELE 99.00561 1.189914 96.74506 100
Georgia PCGDP 3150.571 1515.766 749.9085 5023.274
AELE 99.8773 0.437781 97.93505 100
India PCGDP 1264.756 579.4257 442.0348 2238.127
AELE 78.80141 12.6086 60.29284 99.57253
Indonesia PCGDP 2640.674 1272.162 739.0039 4334.216
AELE 93.41632 4.697022 84.77742 99.21
Iran PCGDP 4488.923 1942.698 1672.023 8329.002
AELE 99.21418 0.797461 97.9 100
Iraq PCGDP 3871.633 1854.155 809.8457 6612.902
AELE 98.51338 1.148877 96.80883 100
Jordan PCGDP 3333.597 1026.067 1673.358 4477.618
AELE 99.58986 0.407679 98.8 100
Kazakhstan PCGDP 7598.219 3944.783 1229.001 13890.63
AELE 99.93127 0.125613 99.58387 100
Kyrgyz Republic PCGDP 905.1485 399.3015 279.6196 1451.516
AELE 99.47367 0.453082 98.32 100
Lao PDR PCGDP 1377.405 889.4484 313.6184 2598.506
AELE 71.80022 19.35607 42.5835 100
Lebanon PCGDP 6433.84 1889.923 3994.861 9225.845
AELE 99.52573 0.469952 97.8 100
Malaysia PCGDP 8187.187 2680.379 3941.123 11134.62
AELE 99.35338 0.56076 98.58813 100
Maldives PCGDP 6913.02 2797.962 2209.988 11349.86
AELE 95.61825 5.358809 83.8 100
Mongolia PCGDP 2669.064 1583.914 463.8539 4566.14
AELE 83.36177 9.938891 67.3 100
Myanmar PCGDP 828.7741 493.9208 140.7762 1479.614
AELE 54.56836 9.414526 41.87319 72.46682
Nepal PCGDP 660.2999 354.2724 223.7119 1229.394
AELE 64.96117 22.58166 24.6 93.92
Pakistan PCGDP 1111.2 328.4999 599.7894 1620.743
AELE 85.04443 7.542566 72.81689 94.92286
Philippines PCGDP 2230.373 893.7821 991.148 3460.539
AELE 86.15402 6.951031 74.6991 97.48997
Sri Lanka PCGDP 2667.566 1360.922 832.4273 4388.202
AELE 86.71897 10.38966 63.6 100
Syrian Arab Republic PCGDP 4326.635 3667.78 420.6227 11304.64
AELE 90.97616 2.694128 86 99.50576
Tajikistan PCGDP 656.7952 311.9927 137.1819 1094.43
AELE 99.00048 0.454096 98 100
Thailand PCGDP 4747.323 1867.845 1889.971 7628.576

25 of 35
 AELE 96.51185 4.757387 82.1 100
Timor-Leste PCGDP 982.3354 561.9993 417.7379 2741.951
AELE 53.86562 25.21238 17.83436 100
Turkey PCGDP 8861.774 2848.035 3100.459 12578.19
AELE 99.88549 0.140189 99.66225 100
Turkmenistan PCGDP 4449.166 2611.49 635.7145 7885.3
AELE 99.8054 0.188156 99.53642 100
Uzbekistan PCGDP 1464.713 830.6608 383.3431 2753.971
AELE 99.71047 0.200568 99.4497 100
Vietnam PCGDP 1830.025 1173.216 394.5831 3756.489
AELE 96.01391 4.114965 88.2351 100
West Bank and Gaza PCGDP 2517.141 947.6743 1156.217 3678.636
AELE 99.69891 0.34945 98.8 100
Yemen PCGDP 933.9648 351.9063 514.6923 1557.601
AELE 61.58627 9.434957 49.23923 79.2
Note: PCGDP denotes per capita gross domestic products, AELE denotes access to
electricity.
Sources: Authors’ computation based on the data.

26 of 35
Appendix 2: Dickey-Fuller test results.

Country PCGDP ELE DPCGDP DELE DDPCGDP DDDPCG

DP
Armenia -1.029 -3.752*** -4.340***
Azerbaijan -1.438 -2.580* -2.832 * -8.769***
Bangladesh 3.420 0.250 -2.494 -6.537*** -6.891***
Bhutan -0.447 -2.018 -5.485*** -5.796***
Cambodia - 0.527 - 0.063 -3.852*** -5.861***
China 2.144 -3.525*** -2.200 -2.923** -3.137**
Georgia -1.043 -4.493*** - 3.941***
India 0.098 0.233 -5.259*** -9.291***
Indonesia -0.676 -1.107 - 3.584*** -7.512***
Iran -1.603 - 1.390 -2.968** -5.216***
Iraq -1.199 -0.856 -3.606*** -8.251***
Jordan -1.651 -3.754*** -2.755*
Kazakhstan -1.520 -3.636*** -3.206**
Kyrgyz -1.113 -4.133*** -4.605***
Republic
Lao PDR 0.114 -0.482 -1.391 -6.692*** -5.498***
Lebanon -1.180 -4.014*** -2.060 -4.765***
Malaysia -1.124 -0.754 -4.518*** -4.982***
Maldives -1.435 -2.659* -6.309***
Mongolia -0.807 -1.083 -3.257** -7.711***
Myanmar -1.005 -0.595 -2.632* -10.150***
Nepal 0.367 -1.141 -3.881*** -8.555***
Pakistan -0.859 -1.243 -3.439*** -4.103***
Philippines -0.166 -1.056 -4.660*** -7.627***
Sri Lanka -0.927 -1.255 -3.123** -10.537***
Syrian Arab -0.956 -3.459*** -4.035***
Republic
Tajikistan -1.569 -4.040*** -3.093**
Thailand -0.814 -4.025*** -3.839***
Timor-Leste 2.157 0.535 -1.379 -6.710*** -6.197***
Turkey -1.648 -0.886 -3.848*** -3.432***
Turkmenistan -0.699 -1.651 -3.693*** -7.110***
Uzbekistan -0.982 -0.471 -2.575* -3.853***
Viet Nam 1.428 1.744 -3.420** -4.197***
Yemen -0.974 -2.042 -2.744*** -9.398***
Notes: * denotes significance at the 10% level, ** denotes significance at the 5% level, ***
denotes significance at the 1% level.
PCGDP denotes per capita gross domestic products, AELE denotes access to electricity,
DAELE = first difference of AELE, DDAELE = second difference of AELE, PCGDP;
DPCGDP = first difference of PCGDP., DDPCGDP = second difference of PCGDP,
DDDPCGDP = third difference.
Source: Authors’ computation based on the data.

27 of 35
Appendix 3: Philips Perron test results.

Country PCGDP ELE DPCGDP DELE DDPCGDP DDDPCGDP

Armenia -1.004 -3.816*** -4.343***
Azerbaijan -1.504 -2.445 -2.789* -9.980***
Bangladesh 3.144 0.871 -2.389 - 6.877*** -7.490***
Bhutan -0.328 -2.628* -5.618*** -5.899***
Cambodia -0.520 -0.129 -3.825*** -5.864***
China 2.447 -2.648* -1.721 -1.895 -2.662**
Georgia -1.033 -4.492*** -3.884***
India 0.401 0.756 -5.413*** -10.015***
Indonesia -0.697 -0.957 -3.587*** -7.847***
Iran -1.683 -1.432 -2.948** -5.222***
Iraq -1.259 -0.745 -3.571*** -9.280***
Jordan -1.503 -3.794*** -2.777*
Kazakhstan -1.529 -4.114*** -3.195**
Kyrgyz Republic -1.106 -4.182*** -4.649 ***
Lao PDR 0.096 -0.403 -1.399 -7.611*** -5.433***
Lebanon -1.459 -4.079*** -2.105 -6.441***
Malaysia -1.107 -0.748 -4.536*** -4.958***
Maldives -1.358 -2.848* -6.309***
Mongolia -0.852 -0.832 -3.231** -8.006***
Myanmar -1.012 -0.025 -2.578* - 10.736***
Nepal 0.452 -1.317 -3.835*** -8.132***
Pakistan -0.829 -1.264 -3.294** -4.103***
Philippines -0.161 -0.696 -4.672*** -9.644***
Sri Lanka -0.908 -1.028 -3.102** -10.999***
Syrian Arab -1.126 -3.475*** -4.051***
Republic
Tajikistan -1.519 -4.042*** -3.092**
Thailand -0.814 -4.565*** -3.816***
Timor-Leste 1.983 1.482 -1.946 -7.021*** -6.4060***
Turkey -1.644 -1.134 -3.870*** -3.426**
Turkmenistan -0.723 -1.402 -3.690*** -8.226***
Uzbekistan -1.120 -0.603 -2.563 -3.946*** -4.975***
Vietnam 1.151 1.828 -3.399** -4.205***
Yemen -1.292 -1.758 -2.781*** -15.909***
Notes: * denotes significance at the 10% level, ** denotes significance at the 5% level, ***
denotes significance at the 1% level.

PCGDP denotes per capita gross domestic products, AELE denotes access to electricity,
DAELE = first difference of AELE, DDAELE = second difference of AELE, PCGDP;
DPCGDP = first difference of PCGDP., DDPCGDP = second difference of PCGDP,
DDDPCGDP = third difference.
Source: Authors’ computation based on the data.

28 of 35
Appendix 4: Results of Lag length criteria.
Country Lag P FPE AIC HQIC SBIC
0 4344.36* 14.0514 14.0058 14.1237
1 0.198 5349.16 14.2321 14.0953 14.4492
2 0.135 6568.71 14.3223 14.0943 14.6841
3 0.363 12618.4 14.656 14.3367 15.1624
4 0 6563.43 13.1326 12.7222 13.7837
Armenia 5 0 . -105.241 -105.742 -104.445
6 . . -103.603 -104.105 -102.807
7 0.14 . -104.232 -104.733 -103.436
8 0 . -106.311 -106.813 -105.515
9 . . . . .
10 . . -112.15* -112.652* -111.354*
0 1779.1 13.1586 13.113 13.231
1 0.04 1523.36 12.9761 12.8393 13.1931
2 0.121 1822.37 13.0402 12.8122 13.4019
3 0.08 2433.56 13.0102 12.6909 13.5166
4 0 9.52854* 6.5976 6.18717 7.2487
Azerbaijan 5 0 . -111.941 -112.443 -111.145
6 0 . -122.575* -123.077* -121.779*
7 . . -121.629 -122.131 -120.833
8 . . -118.962 -119.464 -118.166
9 0.13 . -119.608 -120.11 -118.813
10 0 . -122.505 -123.007 -121.709
0 432640 18.6521 18.5857 18.7126
1 0.148 507522 18.775 18.5758 18.9565
2 0.294 808688 19.0817 18.7498 19.3843
3 0.016 856340 18.667 18.2023 19.0906
4 . 0* . . .
Bangladesh 5 . . -110.649 -111.313 -110.044
6 0.476 . -111 -111.664 -110.395
7 0 . -114.914 -115.578 -114.309
8 0.045 . -115.889* -116.553* -115.284*
9 . . -115.108 -115.772 -114.503
10 . . -114.91 -115.573 -114.304
0 1.3e+06* 19.7406 19.695 19.8129
1 0.422 1.90E+06 20.1146 19.9778 20.3316
2 0.987 4.30E+06 20.8107 20.5827 21.1725
3 0.016 4.10E+06 20.4353 20.1161 20.9417
4 0.418 1.40E+07 20.8072 20.3968 21.4583
Bhutan 5 0 . -114.184 -114.686 -113.388
6 . . -114.099 -114.6 -113.303
7 0.674 . -114.311 -114.813 -113.515
8 . . -112.329 -112.831 -111.534
9 0 . -115.434* -115.936* -114.638*
10 . . -108.864 -109.366 -108.068
0 246024 18.0879 18.0423 18.1603
1 0.719 432665 18.6252 18.4883 18.8422
2 0 96367.4* 17.0082 16.7802 17.3699
3 0.453 196583 17.4019 17.0827 17.9083
4 0.001 169009 16.381 15.9706 17.0321
Cambodia 5 0 . -116.317 -116.818 -115.521
6 0.096 . -117.034 -117.536 -116.238
7 0.875 . -117.145 -117.647 -116.349
8 . . -116.986 -117.487 -116.19
9 . . -116.709 -117.21 -115.913
10 0 . -119.167* -119.669* -118.372*

29 of 35
 0 903947 16.5515 16.5042 16.5734
1 0.821 1.10E+06 16.768 16.6734 16.8118
2 0.001 456403 15.8425 15.7007 15.9083
3 0.189 490001 15.8734 15.6843 15.9611
4 0.696 648515 16.0787 15.8422 16.1882
China 5 0.916 925293 16.2996 16.0159 16.4311
6 0.599 1.40E+06 16.4911 16.1601 16.6445
7 0 404468* 14.6928 14.3144 14.8681
8 0 . -47.2934 -47.719 -47.0962
9 0.001 . -48.5636 -48.9892 -48.3664
10 0.152 . -48.7917* -49.2173* -48.5945*
0 219.819* 11.0675 11.0219 11.1399
1 0.957 440.395 11.7351 11.5983 11.9521
2 0.392 703.975 12.089 11.861 12.4507
3 0.024 719.415 11.7915 11.4723 12.2979
4 0.586 1101.12 11.8973 11.5324 12.476
Georgia 5 0.193 1578.05 11.7073 11.2968 12.3584
6 0 . -99.5621 -100.064 -98.7663
7 . . . . .
8 . . . . .
9 . . . . .
10 . . -109.875* -110.377* -109.08*
0 28092.9 15.918 15.8724 15.9904
1 0.094 29065.2 15.9247 15.7879 16.1418
2 0.174 37841.9 16.0734 15.8454 16.4352
3 0.473 78184.1 16.4799 16.1607 16.9863
4 0 10290.4* 13.5823 13.1718 14.2334
India 5 0 . -116.771 -117.273 -115.975
6 . . -116.451 -116.952 -115.655
7 0.014 . -117.593 -118.095 -116.797
8 0 . -121.193 -121.695 -120.397
9 0.001 . -122.824* -123.326* -122.029*
10 . . -117.899 -118.401 -117.103
0 48250.2 16.4589 16.4133 16.5312
1 0.101 50720.7 16.4815 16.3447 16.6986
2 0.128 61509.1 16.5592 16.3312 16.9209
3 0.038 69577 16.3632 16.044 16.8697
4 0 14823.6* 13.9473 13.5369 14.5984
Indonesia 5 0 . -113.318 -113.819 -112.522
6 0.026 . -114.32 -114.821 -113.524
7 0.862 . -114.438 -114.939 -113.642
8 0.039 . -115.357 -115.859 -114.562
9 . . -113.756 -114.258 -112.96
10 0 . -119.906* -120.408* -119.11*
0 12524 15.1101 15.0645 15.1825
1 0.409 18548.3 15.4756 15.3388 15.6926
2 0.007 12072.8 14.931 14.703 15.2927
3 0.009 10172* 14.4404 14.1212 14.9469
4 0.36 33800.2 14.7715 14.3611 15.4226
Iran 5 0 . -116.556 -117.058 -115.761
6 . . -116.513 -117.014 -115.717
7 0 . -120.131 -120.632 -119.335
8 0.001 . -121.76* -122.261* -120.964*
9 . . -121.7 -122.202 -120.904
10 . . -116.522 -117.024 -115.726
0 191611* 17.838 17.7924 17.9103
1 0.159 223894 17.9664 17.8296 18.1834
2 0.251 318850 18.2048 17.9767 18.5665

30 of 35
 3 0.015 296851 17.814 17.4948 18.3205
4 0.11 737930 17.8549 17.4445 18.506
Iraq 5 0 . -115.706 -116.208 -114.91
6 0 . -118.626 -119.128 -117.83
7 . . -116.816 -117.318 -116.02
8 0 . -119.36* -119.861* -118.564*
9 . . -118.762 -119.263 -117.966
10 . . -102.316 -102.818 -101.521
0 560.093 12.0029 11.9572 12.0752
1 0.002 256.844 11.1959 11.0591 11.4129
2 0.058 260.505 11.0949 10.8669 11.4566
3 0 37.5584* 8.83895 8.51973 9.34536
4 0.137 98.3664 8.93201 8.52158 9.58311
Jordan 5 0 . -99.6631 -100.165 -98.8674
6 . . -99.6537 -100.155 -98.8579
7 0 . -103.539 -104.04 -102.743
8 0.017 . -104.637 -105.139 -103.842
9 0.059 . -105.464 -105.966 -104.668
10 0 . -110.001* -110.502* -109.205*
0 9607.11* 14.845 14.7994 14.9174
1 0.543 15427.1 15.2913 15.1545 15.5083
2 0.232 21541.3 15.51 15.282 15.8717
3 0.051 26038.4 15.3804 15.0612 15.8868
4 0.041 51856.6 15.1995 14.7891 15.8507
Kazakhstan 5 0 . -76.3188 -76.8205 -75.523
6 0 . -94.0169 -94.5186 -93.2211
7 . . . . .
8 . . . . .
9 . . -99.5621 -100.064 -98.7663
10 0 . -109.304* -109.806* -108.509*
0 6591.93* 14.4683 14.4227 14.5407
1 0.931 12973.9 15.1181 14.9813 15.3352
2 0.525 22518.2 15.5544 15.3263 15.9161
3 0.238 38827.9 15.7799 15.4607 16.2864
Kyrgyz Republic 4 0.172 107259 15.9263 15.5159 16.5774
5 0 . -104.99 -105.491 -104.194
6 0.011 . -106.175 -106.677 -105.379
7 . . . . .
8 . . . . .
9 . . -110.652* -111.154* -109.857*
10 . . . . .
0 16760.4 15.4012 15.3348 15.4617
1 0.027 12915 15.1038 14.9047 15.2854
2 0.167 17654.3 15.2573 14.9253 15.5598
3 0.49 44725.3 15.7148 15.2501 16.1385
4 0 3.1e-11* -20.8141 -21.4115 -20.2694
Lao PDR 5 0 . -118.55 -119.214 -117.945
6 0.001 . -120.411 -121.075 -119.806
7 0.988 . -120.443 -121.107 -119.838
8 . . -120.397 -121.061 -119.792
9 . . -118.373 -119.037 -117.768
10 0 . -121.315* -121.979* -120.71*
0 49823 16.4906 16.4243 16.5512
1 0.05 44479.1 16.3405 16.1413 16.522
2 0.136 57679.7 16.4412 16.1093 16.7438
3 0.038 74583.3 16.2262 15.7615 16.6498
4 . 0* . . .
Lebanon 5 . . -100.227 -100.891 -99.622

31 of 35
 6 0 . -104.734 -105.398 -104.129
7 . . -103.903 -104.567 -103.298
8 0.002 . -105.561 -106.225 -104.956
9 . . . . .
10 . . -112.862* -113.526* -112.257*
0 11651.6* 15.0379 14.9923 15.1103
1 0.246 15128.6 15.2718 15.135 15.4888
2 0.164 19436.8 15.4072 15.1792 15.7689
3 0.019 18872.7 15.0585 14.7393 15.5649
4 0.012 28947.8 14.6166 14.2061 15.2677
Malaysia 5 0 . -120.402 -120.903 -119.606
6 . . -120.322 -120.823 -119.526
7 0.086 . -121.063 -121.565 -120.267
8 0.039 . -121.979 -122.481 -121.184
9 0.001 . -123.666* -124.167* -122.87*
10 . . -122.007 -122.508 -121.211
0 401340 18.5773 18.5317 18.6497
1 0.003 199575 17.8514 17.7146 18.0684
2 0.037 183245 17.6509 17.4228 18.0126
3 0.249 319476 17.8875 17.5683 18.3939
4 0 388.42* 10.3054 9.89497 10.9565
Maldives 5 . . . . .
6 . . -103.696 -104.198 -102.901
7 0 . -105.618* -106.12* -104.822*
8 . . -105.464 -105.966 -104.668
9 . . . . .
10 . . . . .
0 5.10E+06 21.1255 21.0799 21.1979
1 0.039 4.40E+06 20.9375 20.8006 21.1545
2 0.072 4.60E+06 20.8845 20.6565 21.2463
3 0.04 5.30E+06 20.7011 20.3818 21.2075
4 0 809827* 17.9479 17.5375 18.599
Mongolia 5 0 . -109.799 -110.301 -109.003
6 0.03 . -110.776 -111.278 -109.981
7 0.235 . -111.282 -111.783 -110.486
8 0.001 . -112.887 -113.388 -112.091
9 0 . -115.485* -115.987* -114.69*
10 . . -115.349 -115.85 -114.553
0 564250 18.918 18.8724 18.9903
1 0.019 408982 18.5689 18.432 18.7859
2 0.359 638885 18.8998 18.6717 19.2615
3 0.055 783143 18.7841 18.4649 19.2905
4 0 12793.7* 13.8 13.3896 14.4511
Myanmar 5 0 . -104.211 -104.713 -103.416
6 0 . -112.244 -112.746 -111.449
7 0 . -115.774 -116.275 -114.978
8 0.009 . -117.006 -117.507 -116.21
9 0.005 . -118.341* -118.842* -117.545*
10 . . -116.183 -116.684 -115.387
0 156838 17.6377 17.5921 17.7101
1 0.053 142534 17.5148 17.378 17.7318
2 0.01 97967.8 17.0247 16.7967 17.3864
3 0.044 114422 16.8607 16.5415 17.3671
4 0 2756.01* 12.2649 11.8544 12.916
Nepal 5 0 . -113.511 -114.013 -112.715
6 0.008 . -114.773 -115.274 -113.977
7 . . -113.854 -114.355 -113.058
8 0 . -115.936 -116.438 -115.141

32 of 35
 9 0 . -117.878* -118.38* -117.082*
10 . . -115.277 -115.779 -114.481
0 252.602 11.2066 11.161 11.2789
1 0.168 298.961 11.3477 11.2109 11.5648
2 0.002 154.851* 10.5747 10.3467 10.9365
3 0.357 296.173 10.904 10.5848 11.4104
4 0.588 1131.67 11.3748 10.9643 12.0259
Pakistan 5 0 . -121.206 -121.707 -120.41
6 0.956 . -121.266 -121.768 -120.47
7 0.694 . -121.469 -121.97 -120.673
8 0.031 . -122.435* -122.937* -121.64*
9 . . -122.103 -122.604 -121.307
10 . . -115.155 -115.656 -114.359
0 19831.7 15.5698 15.5242 15.6421
1 0.223 25122.7 15.779 15.6422 15.996
2 0.036 22892.2 15.5708 15.3428 15.9326
3 0 6360.17 13.9709 13.6516 14.4773
4 0 408.837* 10.3566 9.9462 11.0077
Philippines 5 0 . -112.841 -113.342 -112.045
6 0 . -119.97 -120.471 -119.174
7 . . -117.493 -117.995 -116.697
8 0 . -121.24 -121.741 -120.444
9 . . -119.339 -119.841 -118.543
10 0 . -122.51* -123.012* -121.714*
0 91394.9* 17.0977 17.0521 17.17
1 0.183 110354 17.2589 17.1221 17.4759
2 0.446 182768 17.6482 17.4202 18.01
3 0.062 230071 17.5592 17.24 18.0656
4 0.024 409714 17.2665 16.8561 17.9176
Sri Lanka 5 0 . -111.485 -111.987 -110.689
6 0 . -116.379 -116.881 -115.584
7 . . -115.583 -116.085 -114.788
8 0 . -120.023 -120.524 -119.227
9 0.533 . -120.309 -120.811 -119.513
10 0.036 . -121.244* -121.745* -120.448*
0 1.10E+07 21.8509 21.8053 21.9232
1 0.065 1.00E+07 21.7741 21.6373 21.9912
2 0.66 1.90E+07 22.2821 22.0541 22.6438
3 0.254 3.30E+07 22.5236 22.2044 23.03
4 0 449415* 17.359 16.9486 18.0101
Syrian Arab Republic 5 0 . -97.1031 -97.6047 -96.3073
6 0 . -104.221 -104.723 -103.426
7 . . . . .
8 . . -105.5 -106.002 -104.705
9 . . . . .
10 . . -109.541* -110.043* -108.746*
0 1688.04* 13.1061 13.0605 13.1784
1 0.123 1856.12 13.1737 13.0369 13.3907
2 0.068 1946.7 13.1062 12.8782 13.4679
3 0.726 4599.62 13.6468 13.3276 14.1532
4 0 3160.6 12.4018 11.9914 13.0529
Tajikistan 5 0 . -99.3118 -99.8134 -98.516
6 0 . -104.017 -104.519 -103.221
7 0 . -107.855* -108.357* -107.059*
8 . . . . .
9 . . . . .
10 . . . . .
0 16507.8 15.3863 15.3407 15.4587

33 of 35
 1 0.008 9989.79 14.8568 14.7199 15.0738
2 0.009 6860.49 14.3658 14.1378 14.7275
3 0 1881.2* 12.7527 12.4335 13.2591
4 0.714 7662.77 13.2874 12.877 13.9385
Thailand 5 0 . -102.639 -103.141 -101.843
6 . . . . .
7 . . . . .
8 . . . . .
9 . . -106.85* -107.352* -106.054*
10 . . . . .
0 560350 18.9107 18.8443 18.9712
1 0.099 592054 18.929 18.7299 19.1106
2 0.085 682354 18.9118 18.5799 19.2144
3 0.447 1.70E+06 19.341 18.8763 19.7646
4 0 6.0e-12* -22.4521 -23.0496 -21.9074
Timor-Leste 5 0 . -115.544 -116.208 -114.939
6 . . -113.995 -114.659 -113.39
7 0.583 . -114.28 -114.944 -113.675
8 0.112 . -115.029 -115.692 -114.423
9 . . -114.266 -114.93 -113.661
10 0 . -117.858* -118.522* -117.253*
0 627330 16.1866 16.1638 16.2228
1 0.245 667648 16.2453 16.1997 16.3177
2 0.843 805998 16.4236 16.3551 16.5321
3 0.917 985964 16.6044 16.5132 16.7491
4 0.206 1.10E+06 16.6406 16.5266 16.8215
Turkey 5 0.703 1.30E+06 16.8092 16.6723 17.0262
6 0.597 1.70E+06 16.9656 16.806 17.2188
7 0.012 1.40E+06 16.5746 16.3922 16.864
8 0.004 1.00E+06 16.0093 15.8041 16.3348
9 0 577264* 14.8776 14.6496 15.2393
10 0 . -49.9792* -50.2301* -49.5814*
0 3594.85 13.862 13.8164 13.9343
1 0.034 2968.54 13.6433 13.5064 13.8603
2 0.307 4449.94 13.9329 13.7049 14.2946
3 0.615 9943.56 14.4177 14.0985 14.9241
4 0 729.141* 10.9352 10.5248 11.5863
Turkmenistan 5 0 . -119.784 -120.285 -118.988
6 0 . -122.477 -122.979 -121.681
7 0 . -125.119* -125.621* -124.323*
8 . . -124.48 -124.982 -123.684
9 . . -123.522 -124.024 -122.726
10 . . -113.208 -113.71 -112.412
0 338.362 11.4985 11.4321 11.559
1 0.001 117.555 10.4046 10.2054 10.5861
2 0.04 112.518 10.2016 9.86971 10.5042
3 0 47.5567 8.86848 8.40377 9.29209
4 . -9.8e-15* . . .
Uzbekistan 5 . . -105.793 -106.457 -105.188
6 0 . -120.531 -121.195 -119.926
7 0 . -124.077 -124.741 -123.472
8 . . -123.199 -123.863 -122.594
9 . . -112.713 -113.377 -112.108
10 0 . -125.462* -126.126* -124.857*
0 3198.57 13.7452 13.6996 13.8176
1 0.307 4392.94 14.0352 13.8984 14.2522
2 0.051 4319.24 13.9031 13.6751 14.2648
3 0 1151.59* 12.262 11.9427 12.7684

34 of 35
 4 0.011 1722.73 11.795 11.3845 12.4461
Vietnam 5 0 . -117.14 -117.642 -116.344
6 0.182 . -117.708 -118.209 -116.912
7 0 . -120.291 -120.793 -119.495
8 0.005 . -121.633 -122.135 -120.838
9 0.002 . -123.203* -123.705* -122.407*
10 . . -122.939 -123.44 -122.143
0 586.239 12.0485 12.0029 12.1208
1 0.513 925.54 12.4778 12.341 12.6948
2 0.018 728.446 12.1232 11.8952 12.4849
3 0.001 397.694 11.1987 10.8795 11.7051
4 0 178.17* 9.52605 9.11562 10.1772
West Bank and Gaza 5 0 . -111.848 -112.349 -111.052
6 0 . -116.727 -117.229 -115.931
7 0 . -121.822 -122.324 -121.027
8 . . -121.757 -122.259 -120.962
9 . . -121.44 -121.942 -120.644
10 0.01 . -122.644* -123.145* -121.848*
0 3.50E+06 20.7325 20.6869 20.8049
1 0.036 2.90E+06 20.5252 20.3883 20.7422
2 0.218 4.00E+06 20.729 20.501 21.0907
3 0 1.50E+06 19.4489 19.1297 19.9553
4 0.001 1.4e+06* 18.5239 18.1135 19.175
Yemen 5 0 . -111.779 -112.281 -110.983
6 0.002 . -113.29* -113.791* -112.494*
7 . . -112.874 -113.376 -112.078
8 . . -111.397 -111.898 -110.601
9 0.008 . -112.639 -113.14 -111.843
10 . . -110.962 -111.463 -110.166
Notes: Final prediction error (FPE), Akaike’s information criterion (AIC), Schwarz’s
Bayesian information criterion (SBIC), and the Hannan and Quinn information criterion
(HQIC).

35 of 35