effects of socio-demographic factors on residential electricity...
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Master’s Thesis in Engineering
Effects of socio-demographic factors on
residential electricity consumption in Korea
February 2019
Yoori Kim
Environmental, Energy and Engineering Economics
College of Engineering
Seoul National University
Effects of socio-demographic factors on
residential electricity consumption in Korea
지도교수 허은녕
이 논문을 공학석사 학위논문으로 제출함
2019 년 2 월
서울대학교 대학원
에너지시스템공학부
김유리
김유리의 석사 학위논문을 인준함
2019 년 2 월
위 원 장 (인)
부위원장 (인)
위 원 (인)
i
Effects of socio-demographic factors on
residential electricity consumption in Korea
Yoori Kim
Department of Energy Systems Engineering
The Graduate School of Seoul National University
Abstract
This study conducts empirical analyses on how population aging and other socio-
demographic factors affect residential electricity consumption per capita in 16
municipalities, excluding Sejong City, in the Republic of Korea during the period of 2000-
2016. By doing so, the study proves how each socio-demographic factor affects differently
to the residential electricity consumption in the Republic of Korea, considering its current
trends of fast-growing aging society. The study uses the following components of socio-
demographic factors: the share of population aged 65 and above for population aging, the
number of households, the share of population aged 15~64 for working age population, the
number of households, female to male ratio for gender difference, per capita income,
education level, dwelling types including the ratio of apartments to houses and the ratio of
apartments to multi-houses, and homeownership rate. Also, the effects of electricity price,
ii
heating degree days (HDDs), and cooling degree days (CDDs) variables on the residential
electricity consumption are discussed to produce more thorough empirical results. For
empirical analysis, the study chooses fully modified least squares (FMOLS) and dynamic
least squares (DOLS) estimators with panel cointegration methods and fixed effects and
random effects models for panel data estimations. Using panel data has the benefits of
accounting for the regional differences and time-variant effects, effectively controlling for
the possibly existing multi-collinearity problems.
The following hypotheses are tested: 1) Population aging and residential
electricity consumption exhibit a negative relationship; 2) Increase in household size
reduces residential electricity consumption per capita; 3) Education level, gender difference,
working age population, and homeownership do not have significant effects on residential
electricity consumption; 4) Heating degree days (HDDs) and cooling degree days (CDDs)
have positive relationships with residential electricity consumption.
The study chooses the following statistically significant variables for analysis:
Income, price, population aging, household size, heating degree days, (HDDs) and cooling
degree days (CDDs). The results of the panel cointegration test confirm the presence of a
long-run equilibrium relationships among the variables, and using FMOLS and DOLS
approaches, the cointegrating vectors are estimated. The results of FMOLS and DOLS
indicate that as population aging, electricity price, and household size increase, residential
electricity consumption decreases in the long run; as personal income, heating degree days
(HDDs), and cooling degree days (CDDs) increase, the residential electricity consumption
increase in the long run. The panel estimation results of fixed effects and random effects
model show that as population aging, electricity price, and household size increase,
iii
residential electricity consumption decreases; as personal income, heating degree days
(HDDs), and cooling degree days (CDDs) increase, the residential electricity consumption
increases.
The study makes the following conclusions from the empirical results. First, as
population aging continues to unfold in the Republic of Korea, residential electricity
consumption per capita will likely be reduced; however, this does not mean that population
aging is a desirable condition in the Republic of Korea. Rapid population aging signals the
lack of accessibility to and affordability of energy for the vulnerable elderly households in
the Republic of Korea, possibly leading to the worsened energy equity problems of
especially the vulnerable elderly groups that are short of the access to energy. Further,
population aging in the Republic of Korea will rather have varying effects on the volatility
of residential electricity consumption in the future considering that population aging will
progress at an unprecedented rate. Second, the negative relationship between household
size and residential electricity consumption can be interpreted that as the number of people
per household increases, the frequency of household sharing and using the home appliances
increases. Third, the positive relationship between heating degree days (HDDs) and
residential electricity consumption can be explained by the recent increase in the number
of buildings which operate heating system with electricity or by the increase in the use of
electric auxiliary heating devices attributable to cheap electricity prices of the Republic of
Korea. Fourth, increase in the residential electricity consumption by the increase in cooling
degree days (CDDs) means that more households nowadays own and use air-conditioners
at homes than in the past. Lastly, in terms of the relationship between increasing disposable
income and increasing residential electricity consumption, this can be attributed to the
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increased purchasing power of consumers which enables them to purchase and continue to
use the electrical appliances for cooling.
Recently, the Republic of Korea has officially entered the era of an “aged society,”
and it is predicted that the country will get older at a much faster pace. Given that the
policies must be designed and implemented in comprehensive and precise manners in an
aged society, it seems that sticking solely to the price policy to reduce the future residential
electricity consumption possess some critical limitations. Hence, the government must
consider the number of influential factors, including socio-demographic aspects associated
with population aging and temperature effects when designing effective policies that ensure
stabilizations of the residential electricity demand and supply.
Keywords: Socio-demographic factors, population aging, residential electricity
consumption, cooling degree days, heating degree days, panel cointegration, panel data
analysis
Student Number: 2017-22870
v
Contents
Abstract ................................................................................................................................ i
Contents .............................................................................................................................. v
List of Tables ..................................................................................................................... vii
List of Figures .................................................................................................................... ix
Chapter 1. Introduction ................................................................................................. 11
1.1 Research background and research objective ................................................. 11
1.2 Research hypotheses ....................................................................................... 18
1.2.1 Hypothesis I: Population aging and residential electricity consumption exhibit a
negative relationship ....................................................................................................... 18
1.2.2 Hypothesis II: Increase in household size reduces residential electricity
consumption per capita ................................................................................................... 19
1.2.3 Hypothesis III: Education level, gender difference, working age population, and
homeownership do not have significant effects on residential electricity consumption . 20
1.2.4 Hypothesis IV: Heating degree days (HDDs) and cooling degree days (CDDs)
have positive relationships with residential electricity consumption .............................. 21
1.3 Research design .............................................................................................. 23
Chapter 2. Overview of population aging and residential electricity consumption ...... 25
2.1 Overview of population aging in the world and Korea ................................... 25
2.2 Overview of residential electricity demand in Korea ..................................... 34
2.3 Comparison with Germany and Sweden on the effects of population aging on
residential energy consumption ................................................................................. 44
Chapter 3. Literature Review ........................................................................................ 47
3.1 Theoretical studies .......................................................................................... 47
3.1.1 Houthakker (1951) and subsequent studies on the demand for electricity ...... 47
3.1.2 Residential electricity demand model considering socio-demographic
characteristics .................................................................................................................. 51
3.2 Population aging ............................................................................................. 59
3.3 Household size ................................................................................................ 75
vi
3.4 Education, gender, homeownership, working age population, and dwelling type
77
3.5 Heating degree days (HDDs) and cooling degree days (CDDs) ..................... 79
3.6 Studies using panel estimation methods ......................................................... 87
Chapter 4. Data and research methods .......................................................................... 90
4.1 Data ................................................................................................................. 90
4.2 Methodology ................................................................................................. 121
4.2.1 Panel cointegration approach ........................................................................ 122
4.2.2 Tests for fixed effects and random effects models ........................................ 129
Chapter 5. Empirical results ........................................................................................ 135
5.1 Panel cointegration results ............................................................................ 135
5.2 Fully modified ordinary least squares (FMOLS) and dynamic least squares
(DOLS) results ......................................................................................................... 139
5.3 Results for tests of hypotheses with panel data ............................................. 144
5.4 Fixed effects and random effects results ....................................................... 150
Chapter 6. Conclusions ............................................................................................... 159
6.1 Conclusions ................................................................................................... 159
Bibliography.................................................................................................................... 163
Appendix ......................................................................................................................... 173
Trends of all data for 16 municipalities in Korea during 2000-2016 presented in Figure
A.1 ~ Figure A.34. ................................................................................................... 173
Abstract (Korean) .......................................................................................................... 211
vii
List of Tables
Table 2.1. Compound annual growth rate (CAGR) of final energy consumption by energy
source in Korea (1990-2016) ............................................................................................ 38
Table 3.1. Summary of the literature studies for Section 3.1 ......................................... 50
Table 3.2. Summary of literature on the relationship between population aging and
residential electricity consumption ................................................................................. 73
Table 3.3. Summary of literature on the relationship of heating degree days (HDDs) and
cooling degree days (CDDs) on residential electricity consumption ................................ 86
Table 4.1. Definition of the variables (16 municipalities in Korea excluding Sejong City,
2000-2016) ...................................................................................................................... 117
Table 4.2. Descriptive statistics of the data .................................................................. 118
Table 4.3. Results from Pearson’s correlation analysis ................................................ 119
Table 4.4. Compounded annual growth rate (CAGR) of the variables from 2000 to 2016
by regions ........................................................................................................................ 120
Table 5.1. Results of Pesaran’s (2004, 2015) cross-sectional dependence tests .......... 136
Table 5.2. Results of Pesaran’s (2007) panel unit root tests......................................... 137
Table 5.3. Pedroni’s (1999, 2004) and Kao’s (1999) panel cointegration tests for
individual models without the deterministic trends ........................................................ 138
Table 5.4. Results from the estimations with Fully Modified OLS (FMOLS)............. 142
Table 5.5. Results from the estimations with Dynamic Least Squares (DOLS) .......... 143
Table 5.6. Results of the Breusch-Pagan Lagrange Multiplier (LM) test for random
effects .............................................................................................................................. 145
Table 5.7. Results of Wald test for fixed effects ........................................................... 146
Table 5.8. Results of Hausman test for fixed effects and random effects models ........ 147
Table 5.9. Results of modified Wald test for heteroscedasticity .................................. 148
Table 5.10. Results of Wooldridge test for autocorrelation ...................................... 149
Table 5.11. Results from the estimations with fixed effects model .............................. 155
Table 5.12. Results from the estimations with random effects model.......................... 156
viii
Table 5.13. Results from the estimations with LSDV model by region (Reference region:
Seoul) .............................................................................................................................. 157
Table 5.14. Confirmation of the research hypotheses with the estimated results ......... 158
ix
List of Figures
Figure 1.1. Conceptualization of the socio-demographic factors influencing residential
energy consumption .......................................................................................................... 14
Figure 1.2. Conceptual framework of the research ........................................................ 24
Figure 2.1. Comparison of the rate at which aged society becomes super-aged society in
major countries .................................................................................................................. 28
Figure 2.2. Comparisons between the population pyramids of the world and Korea in
1950 ................................................................................................................................... 29
Figure 2.3. Comparisons between the population pyramids of the world and Korea in
2000 ................................................................................................................................... 30
Figure 2.4. Comparisons between the population pyramids of the world and Korea in
2050 ................................................................................................................................... 31
Figure 2.5. Annual trends of final energy consumption by energy source in Korea, 1990-
2016 ................................................................................................................................... 39
Figure 2.6. Annual trend of final energy consumption structures by energy sources in
Korea, 1990-2016 .............................................................................................................. 40
Figure 2.7. Annual trend of final electricity consumption in Korea, 1981-2016 ........... 41
Figure 2.8. Annual trend of electricity consumption in residential sector in Korea, 1993-
2016 ................................................................................................................................... 42
Figure 2.9. Monthly trend of electricity consumption in residential sector in Korea,
1999:01-2015:01 ............................................................................................................... 43
Figure 2.10. Energy consumption for housing by gender and age differences in Germany
........................................................................................................................................... 45
Figure 2.11. Energy consumption for housing by gender and age differences in Sweden
........................................................................................................................................... 46
Figure 3.1. The framework of application of household production theory on residential
electricity considering socio-demographic characteristics ................................................ 52
Figure 3.2. Steps to derive the demand for residential electricity consumption in Deaton
x
and Muellbauer (1980) ...................................................................................................... 55
Figure 4.1. Trends of residential electricity consumption per capita (MWh/person) of 16
municipalities in Korea, 2000-2016 .................................................................................. 91
Figure 4.2. Trends of residential electricity price (KRW/kWh) of 16 municipalities in
Korea, 2000-2016 .............................................................................................................. 94
Figure 4.3. Trends of personal income per capita (1000 KRW) of 16 municipalities in
Korea, 2000-2016 .............................................................................................................. 96
Figure 4.4. Trends of the share of population aged 65 and above (%) of 16 municipalities
in Korea, 2000-2016 ......................................................................................................... 98
Figure 4.5. Trends of working age population (%) of 16 municipalities in Korea, 2000-
2016 ................................................................................................................................. 100
Figure 4.6. Trends of household size of 16 municipalities in Korea, 2000-2016 ...... 102
Figure 4.7. Trends of the share of population with degrees in higher education (%) of 16
municipalities in Korea, 2000-2016 ................................................................................ 104
Figure 4.8. Trends of heating degree days (HDDs) of 16 municipalities in Korea, 2000-
2016 ............................................................................................................................... 106
Figure 4.9. Trends of cooling degree days (CDDs) of 16 municipalities in Korea, 2000-
2016 ............................................................................................................................... 108
Figure 4.10. Trends of attached house to apartment ratio (%) of 16 municipalities in
Korea, 2000-2016 .......................................................................................................... 110
Figure 4.11. Trends of detached house to apartment ratio (%) of 16 municipalities in
Korea, 2000-2016 ............................................................................................................ 112
Figure 4.12. Trends of female to male ratio (%) of 16 municipalities in Korea, 2000-2016
......................................................................................................................................... 114
Figure 4.13. Trends of homeownership ratio (%) of 16 municipalities in Korea, 2000-
2016 ................................................................................................................................. 116
11
Chapter 1. Introduction
1.1 Research background and research objective
Research Background
Since Thompson (1929) first proposed the concept of demographic transition,
scholars have begun to illustrate how demographic transition provides grounds for
threatening the sustainability in the world. For instance, rapid population growth
throughout the world, which is noticeable in developing countries, has driven the energy
demand and exerted pressures on natural resources, endangering socio-economic
sustainability. The problems caused by population surge have further been compounded by
population aging, with the trend being more pronounced in developed countries; in fact,
population aging has now become a serious issue affecting the policy agenda of the entire
world. Considered as one of the fastest-aging countries in the world, South Korea (referred
to as “Korea” hereinafter) is expected to have far more threatening effects of population
aging on the society in contrast to the other advanced countries. As of 2017, Korea has
officially become “aged society,” and US Bureau of Statistics forecast that it will only take
27 years for Korea to become a super-aged society from an aging society at the fastest pace
in the world.
It is undeniable that the increased life expectancy dedicated to improved medical
12
system, nutrition, sanitation, enhanced socio-economic well-being, and better access to
education has led to the enriched quality of life for all. Notestein (1954), Kirkwood (2001),
and Cowgill (1970) have even acknowledged that population aging denotes a sign of
progress and socio-economic success, touting the trend as “a great triumph of civilization,”
“the greatest triumph that our species has achieved,” and “one of the truest measures of
progress.” However in the late 1900s and early 2000s, scholars have started to consider that
population aging will present a daunting burden on the socio–economic endurance for it
results in dwindling labor force participation (O’Neill et al., 2010), arouses fears among
national governments and international agencies about the tremendous expenditure costs
associated with an aging society (Walker, 1990), and transfers the financial obligations to
future younger generations, especially in the welfare states (Judt, 2010).
With these shifting viewpoints, academia has recently been focusing on how
population aging and other socio-demographic matters affect the structural changes in
energy consumption behaviors (Shin, 2018). O’Neill and Chen (2002), Liddle (2004), and
Prskawetz et al. (2004) argue that socio-demographic factors accompanied by population
aging significantly affect the changes in volatility of energy consumptions in transport and
residential sectors, especially those regarding the residential electricity consumption.
Further, not only the roles of population aging but the roles other socio-demographic factors
on residential electricity consumption have led to heated debates in the academic fields
(Liddle, 2004; Brounen et al., 2012; Hong et al., 2018).
Given such wide range of academic opinions, the scholars have yet to reach a
consensus as to whether population aging has negative or positive effects on the residential
electricity consumption. Moreover, relevant studies are confined to conceptual and
13
qualitative analyses which lack empirical frameworks. In order to correctly interpret the
impacts of population aging on the residential electricity consumption and develop suitable
policies to address the issues caused by population aging, empirical research with concrete
theoretical backgrounds must be conducted using appropriate data and methods. Further,
previous studies with different viewpoints must be organized and classified in a comparable
manner to yield to a comprehensive and satisfying analysis.
14
Figure 1.1. Conceptualization of the socio-demographic factors influencing residential
energy consumption
Source: Reorganized Figure 1 in Frederiks et al. (2015)
15
As mentioned earlier, the roles of socio-demographic factors and population aging
combined have attracted attention in the academic fields (Liddle, 2004; Brounen et al.,
2012; Hong et al., 2018); further, scholars have started to address and examine the effects
of socio-demographic factors on the residential energy consumption and have confirmed
that these factors individually produce different effects on energy consumption in
residential sectors. Figure 1.1 presents the conceptualization of the various classified socio-
demographic factors influencing residential energy consumption, reorganized from
Frederiks et al. (2015). As seen above, the socio-demographic factors that affect the
residential energy consumption vary in number, and these can be categorized into numerous
predictors including population aging, working age population, education, homeownership,
dwelling type (the type of living quarters in which a person resides), gender, income, and
so forth. Referring to this framework, we primarily focus on the impacts of these socio-
demographic factors on the residential electricity consumption in Korea.
16
Research Objective
This study seeks to empirically analyze how the arising phenomena of population
aging and other socio-demographic factors affect residential electricity consumption in 16
municipalities, excluding Sejong City, in Korea during the period of 2000-2016.
Residential electricity consumption is chosen as the scope to be analyzed since, along with
the energy consumption in the transportation sector, residential energy consumption is one
of the main areas known to be affected by the phenomena of population aging and other
socio-demographic factors (O’neill and Chen, 2002; Liddle, 2004; Prskawetz et al., 2004).
Further, the study focuses on examining the key predictors of socio-demographic factors,
along with population aging, which have influence on residential electricity consumption
in Korea. In specific, the study refers to the framework developed by Frederiks et al. (2015),
which classify a number of socio-demographic factors influencing the residential energy
consumption into several predictors. Assuming that these predictors pose some influence
on the residential electricity consumption in Korea, the study uses the following
components of socio-demographic factors for empirical analysis: the share of population
aged 65 and above, the share of population aged 15~64 for working age population, average
household size, female to male ratio, per capita income, education level, dwelling types
including the ratio of apartments to houses and the ratio of apartments to multi-houses, and
homeownership rate. The study also discusses the effects of residential electricity price,
heating degree days (HDDs), and cooling degree days (CDDs) on the residential electricity
consumption to lead to more concrete empirical results. For empirical methods, the study
applies fully modified least squares (FMOLS) and dynamic least squares (DOLS)
17
estimators with panel cointegration methods and fixed effects and random effects models
for panel data estimations.
18
1.2 Research hypotheses
This section describes several hypotheses to be tested in the research. The
following research hypotheses mainly refer to those related to socio-demographic factors.
1.2.1 Hypothesis I: Population aging and residential electricity
consumption exhibit a negative relationship
The key hypothesis to be tested in this research is about the impact of population
aging, the main component of the socio-demographic factors, on the residential electricity
consumption in Korea. The first hypothesis states that population aging and residential
electricity consumption exhibit a negative relationship, meaning that as the share of
population aged 65 and above increases, the amount of residential electricity per capita
consumes decreases, and vice versa. The results of existing studies support this argument
(Lim et al., 2013; Noh and Lee, 2013; Keum et al., 2018; Brounen et al. 2012). They argue
that the people in different age structures behave differently when consuming energy or
electricity in the residential sectors because the older people usually have strict perceptions
on energy conservation than the other age groups do, and these typical perceptions on
energy savings make them to use less amount of electricity at homes (Lee et al. 2011).
19
1.2.2 Hypothesis II: Increase in household size reduces residential
electricity consumption per capita
The second hypothesis states that the increase in family or household size reduces
the residential electricity consumption per capita. This is because energy efficiency per
household tends to improve as the size of a household gets larger in general. Ota et al.
(2017) has explained this situation by the fact that the increase in the number of people in
a household reduces the needs to duplicate the usage of shared appliances or electricity (i.e.,
televisions, refrigerators, living room lights, etc.).
Yohanis et al. (2008) in their empirical research have even found that the
electricity consumption per capita decreases as the number of people residing increases in
the UK. Also, Druckman and Jackson (2008) have found that per capita electricity
consumption in the UK is negatively correlated to household size, suggesting that a
household with more people is generally more efficient regarding per capita energy
consumption. Referring to these supporting arguments, we hypothesize in our empirical
research that the increase in household size reduces residential electricity consumption per
capita in Korea.
20
1.2.3 Hypothesis III: Education level, gender difference, working
age population, and homeownership do not have significant
effects on residential electricity consumption
The third hypothesis of our research states that education level, gender difference
ratio, working age population, and homeownership rate do not pose significant effects on
residential electricity consumption in Korea. Numerous studies have tried to examine
whether these factors have influence on residential electricity consumption. Although
differing effects of the education level on domestic electricity consumption have been
reported, several studies have confirmed that these factors do not pose significant effects
on residential electricity consumption. For instance, Bedir et al. (2013) and Cramer et al.
(1985) have found that education levels do not significantly affect electricity consumption
in Dutch and US dwellings, respectively. For gender difference ratio, which is
predominantly reflected by male to female or female to male ratio, it does not pose
significant effects on residential electricity consumption. Homeownership rate as well does
not significantly affect residential electricity consumption since one, regardless of owning
or renting a house, must pay its bills for using electricity at homes. Considering the
arguments of these studies, the research hypothesis states that education level is not much
influential on the residential electricity consumption per capita in Korea.
21
1.2.4 Hypothesis IV: Heating degree days (HDDs) and cooling
degree days (CDDs) have positive relationships with
residential electricity consumption
The last hypothesis of our research states that heating degree days (HDDs) and
cooling degree days (CDDs) have positive relationships with residential electricity
consumption in Korea. Simply put, heating degree days refer to measurements calculated
to quantify the demand for energy consumed to heat a building. In specific, heating degree
days are measured relative to balance temperature, the outside temperature above which a
building needs no heating. Similarly, cooling degree days (CDDs) are measurements
designed to quantify the demand for energy consumed to cool a building. In specific,
cooling degree days are measured relative to balance temperature, the outside temperature
above which a building needs no cooling.
Numerous of studies have analyzed the impacts of heating degree days and
cooling degree days on the residential electricity consumption and have conflicting results
as to whether cooling degree days or heating degree days affect the increase in the
residential electricity consumption. In this research, the hypothesis is established that both
heating and cooling degree days will significantly affect the increase in residential
electricity consumption. Nowadays, demand for heating has recently shifted from city gas
to electricity in Korea. Also, for cooling, more people today have air-conditioners installed
at their homes and use them during the summer; also, summer has become hotter today
than summer in the past due to climate change from global warming. With these reasons,
the study makes the fourth hypothesis heating degree days and cooling degree days exhibit
22
positive relationships with residential electricity consumption in Korea.
23
1.3 Research design
Order of the thesis
The order of the thesis is as follows: The first chapter begins with the introduction,
in which research background and objective are discussed. Then, the four main research
hypotheses to be tested in the analysis are described. For the last part of the introduction,
research design, which consists of the order of the thesis and the conceptual framework of
the research, is explained. In the second chapter, brief overviews of the phenomena of
population aging over the world and in Korea are discussed. Then, in the same chapter, the
trends and related policies of residential electricity consumption in Korea are presented for
the residential electricity consumption is the scope to be analyzed in our research. The third
chapter comprises literature review of the related theoretical and empirical studies on the
research topic. The fourth chapter considers the explanations of the data and the empirical
methods used in the analysis. Empirical results are discussed next in chapter five. Lastly,
conclusions of the research along with policy implications and suggestions for further
research will be discussed in chapter six. References, appendices for supplementary figures,
and abstract in Korean are presented in chapter seven, the last part of the thesis.
24
Figure 1.2. Conceptual framework of the research
25
Chapter 2. Overview of population aging
and residential electricity consumption
2.1 Overview of population aging in the world and Korea
Population aging in the world
Population aging is defined as a shift in the distribution of the population of a region
or a country towards older ages. This situation is usually reflected by the declining fertility
rates, which reduces the proportion of the population composed of the children, and
increasing life expectancy, which increases the proportion of the population composed of
the elderly. The United Nations (UN) have reported that among the countries currently
classified as more developed countries with a total population of 1.2 billion in 2005, the
overall median age rose from 28 to 40 in 1950 and 2010, respectively. Further this value is
forecasted to rise to 44 by 2050. For the less developed countries, the median age is
predicted to rise from 26 to 35 in 2010 and 2050, respectively. For the entire countries, the
figures are forecasted to rise from 29 to 36 in 2010 and 2050, respectively.
26
Population aging in Korea
Considered as one of the fastest-aging countries in the world, Korea is expected
to have far more threatening and substantial effects of population aging on the society in
contrast to the other developed countries. As of 2017, Korea has officially entered the era
of an “aged society”1 by reaching 14 percent of the total population aged 65 years and
above (2017 Population and Housing Census, Statistics Korea). In specific, it is forecasted
to take only 27 years for Korea to become a “super-aged society” from an “aging society,”
whereas it is predicted to take 34 years for China, 89 years for the United States, and 157
years for France (US Bureau of Statistics).
The problem is on the fact that the speed at which population aging is progressing
in Korea is incomparably faster than almost every other country in the world. For instance,
Korea’s total fertility rate, which is defined as the number of children who would be born
to a woman in her life time and often is served as a proxy for the predicting the rate at
which population aging progresses in a society, is the lowest in the world with the value at
1.17 (World Population Prospects: 2017 Revision, UN). With these matters behind, many
professionals predict that the aging of population in Korea will have detrimental effects on
many areas in the society, either directly or indirectly, in multi-faceted ways. Namely, the
areas typically affected by population aging of a country include labor markets,
governments, pensions, health care and social security, education, economy, and energy use
1 According to the definitions from the United Nations, aged society is when the share of total
population aged 65 years and above is 14%. For further information, aging society is when the
share of total population aged 65 years and above is 7%, and super-aged society is when the share
of aged 65 years and above is 21%.
27
in the residential sector.
28
Figure 2.1. Comparison of the rate at which aged society becomes super-aged society in
major countries
Source: US Bureau of Statistics
27 yrs.32 yrs.
37 yrs.
89 yrs.100 yrs.
157 yrs.
Korea China Japan USA UK France
29
Figure 2.2. Comparisons between the population pyramids of the world and Korea in 1950
Source: Rearranged from populationpyramid.net (United Nations Population Division, Department of Economic and Social
Affairs. World Population Prospects: The 2015 Revision)
30
Figure 2.3. Comparisons between the population pyramids of the world and Korea in 2000
Source: Rearranged from populationpyramid.net (United Nations Population Division, Department of Economic and Social
Affairs. World Population Prospects: The 2015 Revision)
31
Figure 2.4. Comparisons between the population pyramids of the world and Korea in 2050
Source: Rearranged from populationpyramid.net (United Nations Population Division, Department of Economic and Social
Affairs. World Population Prospects: The 2015 Revision)
32
Population pyramids of the World and Korea
Figure 2.2, 2.3, and 2.4 present the comparisons of the population pyramids of the
world and Korea in the year 1950, 2000, and 2050. Population pyramid. Population
pyramid is a graphical method of illustrating the age and sex structures of a population and
often serves as a useful tool for seeing the trends of changes in age and sex structures and
making comparisons with one country to the other countries. Population pyramid has three
main stages: Expansive, stationary, and contractive.
First, populations at the stage of expansive population pyramids usually have the
following characteristics: concave sides for both male and female, high fertility rates, and
low life expectancies. These types of populations are typically characteristic of developing
nations. Figure 2.2 shows that the world and Korea are at the expansive stage, in which a
large percentage of the populations is in younger age groups, reflecting high fertility rates;
a small proportion of the population in older age groups, reflecting low life expectancies.
Also, population pyramid of the world in 2000 in Figure 2.3 has the characteristic of
expansive population pyramid even though some proportions at the younger age cohorts
such as 0-4 and 5-0 are tapered off.
Second, populations at the stage of stationary population pyramids have the
following characteristics in general: rectangular shapes, non-increasing populations, and
relatively equal distributions across the age cohorts but tapered-off toward the top. These
population pyramid shapes are recognized as the characteristic of developed countries,
where the birth rates are relatively low, and the overall quality of life is high. The population
pyramid of the world illustrated in 2050 in Figure 2.4 is representative of the typical shape
33
of a stationary population pyramid.
Lastly, populations at the stage of constrictive population pyramids are illustrated
to describe the populations with the following characteristics: large proportions weighted
at the elderly age groups, shrinking number of populations, smaller percentages of
populations in the younger age groups, very high levels of the dependency ratio of the
population, and patterns tapering in at the bottom. Countries with constrictive population
pyramids exhibit very higher levels of social and economic development, in which access
to health care and quality education is available to a large percentage of the population. The
population pyramids of Korea in 2000 and 2050 in Figure 2.3 and 2.4, respectively,
represent the typical shapes of constrictive population pyramids. The difference between
the two is that the proportions of the population of Korea in 2050 are more towards the
older age cohorts than those in 2000. This means that the socioeconomic and health
developments are progressing in Korea, and that Korea is forecasted to be at even higher
stage of constrictive population pyramids in 2050. Further, these changes in the age
structures with the major proportions of the populations at the elderly age cohorts in Korea
denote the significant impacts which will be imposed on Korea’s national economy and the
society in the future.
34
2.2 Overview of residential electricity demand in Korea
Brief background of electricity consumption in Korea
Over the past few decades since 1970s, the cheap electricity prices in Korea have
driven the industries to gain competitiveness in domestic and global markets, supporting
its national economy to flourish at a very fast speed compared to any other country in the
world. Moreover, the individuals in Korea have gained their quality of life owing to the
cheap electricity prices which allow the people to freely use residential electricity. In 2016,
electricity consumption in residential and commercial sectors accounted for approximately
38.95% of the total electricity consumption in all sectors; electricity consumption in
residential sector accounted for 13.31% of the total electricity consumption (Korea Energy
Agency, Energy Statistics Handbook 2018).
However, Korea’s cheap electricity prices exhibit some downsides since they
provide incentives for the industries and households to consume more energy than
necessary. Figure 2.5 presents the annual trend of final energy consumption structures by
energy sources in Korea from 1990 to 2016. In 1990, the share of electricity consumption
has reached 10%, and since then, its percentage has almost doubled to approximately 20%
in 2016. The point that is worth to take a note hereinafter is that the rate at which the
electricity consumption increases has slowed down significantly since 2000s. This means
that currently in which the changes in demand for electricity are frequently occurring, it is
of utmost importance for the scholars to closely investigate on the factors that affect the
35
electricity consumption and thus able to urge the policymakers to implement the suitable
electricity policies in the right way.
Explanations on Table 2.1 and Figures 2.5 through 2.9
Table 2.1 presents the estimated results of compound annual growth rate (CAGR)
of final energy consumption by energy source in Korea during the periods of 1990-2016,
1981-1990, 1991-2000, 2001-2010, and 2011-2016. The results show that during 1981-
1990, the total energy consumption rate had reached 6.73%, and during 1991-2000, the
energy consumption had reached 6.05%. These results of fast energy consumption rate are
mainly due to the continued growth of the national economy led by high energy-consuming
industries such as petrochemicals and steel. As shown in Figure 2.5, however, the energy
consumption rate which had been on a steady rise until the late 1990s has slowed down
significantly since the early 2000s. For instance, during 2010-2010, the compound annual
growth rate of final energy consumption had reached 2.44%, and during 2011-2016, the
compound annual growth rate had reached 1.51%. This sharp decline is mainly caused by
the changes in industrial structures from relatively high energy-consuming industries to
low energy-consuming industries such as telecommunications and service industries.
In addition, although some differences exist by energy type, the rate at which the
energy consumption increases overall has slowed down significantly since 2000s,
especially the rate of petroleum consumption. As presented in Figure 2.6, although the rate
at which electricity consumption decreases is relatively slower than the rate at which
36
petroleum consumption decreases, the former is slowing down over time. For instance,
during 1981-1990, the compound annual growth rate of final electricity consumption had
reached relatively high percentage of 10.29%, and during 1991-2000, the rate had reached
8.66%. However, since 2000s, the growth rate had reached 5.35% during 2001-2010, and
1.48 % during 2011-2016.
Figure 2.7 presents the trend of final electricity consumption in Korea during the
period of 1981-2016. Except for in 1997 and 1998 during which the Asian financial crisis
occurred, total electricity consumption in Korea had consistently increased. In 2009, during
which the global financial crisis occurred, the electricity consumption rate slowed down
but did not decline. However, experts predict that there may be some possibilities of decline
in the rate of electricity consumption due to the enhancement in the management of demand
following the cyclical blackout in September 2011 and the decline in heating and air-
conditioning decreases following the global climate change.
Figure 2.8 presents the annual trend of electricity consumption in residential
sector in Korea during the period of 1993-2016. It can be seen from the graph that except
for the period of 1997-1999 during which the Asian financial crisis had occurred and posed
severe burden on the global and national economies, the trend of residential electricity
consumption in Korea has increased continuously. This may be explained as a result of
increased number of household appliances supplied to households due to increased income
levels.
Figure 2.9 presents the monthly trend of electricity consumption in residential
sector in Korea during January 1999 and January 2015. Clear seasonal trend for electricity
consumption is shown in the graph. The demand for electricity consumption in the winter
37
has increased since the mid-2000s, and since the 2010s, the number of peak consumptions
occurring in winter has increased. This is believed by many that while oil and gas prices
have risen significantly due to the rapid increase in the international oil prices, increase in
electricity price has been curtailed due to the government regulations, leading to the
increase in demand for heating electricity.
38
Table 2.1. Compound annual growth rate (CAGR) of final energy consumption by energy source in Korea (1990-2016)
Source: Korea Energy Statistical Information System (KESIS)
Period Coal Petroleum Natural
Gas City Gas Electricity
Heat
Energy Renewable Total
1981-1990 2.99% 9.12% - 45.98% 10.29% - -10.77% 6.73%
1991-2000 0.11% 5.94% - 23.35% 8.66% 30.74% 13.17% 6.05%
2001-2010 3.31% 0.71% - 4.72% 5.35% 4.52% 8.09% 2.44%
2011-2016 -0.31% 1.86% -7.43% -0.51% 1.48% 1.67% 11.04% 1.51%
1981-2016 2.25% 5.12% - 21.03% 7.61% - 4.19% 4.99%
39
Units: 1000 TOE
Figure 2.5. Annual trends of final energy consumption by energy source in Korea,
1990-2016
Source: Korea Energy Statistical Information System (KESIS)
0
20,000
40,000
60,000
80,000
100,000
120,000
Coal Petroleum Natural Gas City Gas
Electricity Heat Energy Renewable
40
Figure 2.6. Annual trend of final energy consumption structures by energy source in
Korea, 1990-2016
Source: Korea Energy Statistical Information System (KESIS)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
199
0
199
1
199
2
199
3
199
4
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
201
6
Coal (%) Petroleum (%) Natural Gas (%) City Gas (%)
Electricity (%) Heat (%) Renewable (%)
41
Figure 2.7. Annual trend of final electricity consumption in Korea, 1981-2016
Source: Korea Energy Statistical Information System (KESIS)
42
Figure 2.8. Annual trend of electricity consumption in residential sector in Korea, 1993-
2016
Source: Korea Power Statistics, Korea Electric Power Corporation (KEPCO)
43
Figure 2.9. Monthly trend of electricity consumption in residential sector in
Korea, 1999:01-2015:01
Source: Korea Power Statistics, Korea Electric Power Corporation (KEPCO)
44
2.3 Comparison with Germany and Sweden on the effects of
population aging on residential energy consumption
According to a comparison analysis conducted from Swedish Defence Research
Agency (FOI) in 2009, the amount of energy consumed differs by age cohorts in Germany
and Sweden. The report states that the youngest singles born after 1979 use energy the least,
and the oldest people born before 1949 use energy the most. The study has found that the
proportion of energy consumed in housing increases with age, and as shown in Figure 2.10
and 2.11, the disparities in energy consumption by age groups are more pronounced in
Sweden than in Germany. This can be explained by the fact that in Germany and Sweden,
the older people are relatively more economically stable than the younger people, and also
that the older people have similar patterns with younger people in using the home
appliances and the electronic devices. It is worth to take a note on the projections of some
professionals that, contrary to the current trend, the growing elderly population in Korea
may increase its residential energy consumption as Korea begins to possess the similar
characteristics of the advanced countries like Germany and Sweden in the future.
Also, Germany and Sweden are the two notable countries with high levels of
Energy equity index (12th and 17th, respectively), providing firm grounds for their
facilitated access to and affordability of energy among all groups of people; in contrast,
Korea ranks 26th in Energy equity index, respectively, which signals that energy is not
distributed equitably to all groups of people.
45
Units: Megajoule (MJ)
Figure 2.10. Energy consumption for housing by gender and age differences in
Germany
0
20000
40000
60000
80000
100000
120000
born before
1945
1959 1979 born after 1979
Men
Women
46
Units: Megajoule (MJ)
Figure 2.11. Energy consumption for housing by gender and age differences in
Sweden
0
10000
20000
30000
40000
50000
60000
70000
80000
born before
1945
1959 1979 born after 1979
Men
Women
47
Chapter 3. Literature Review
3.1 Theoretical studies
3.1.1 Houthakker (1951) and subsequent studies on the
demand for electricity
Houthakker (1951) has initiated the study on estimating the demand for electricity
in the UK using econometrics model. The study analyzes 42 British provincial cities for
the periods of 1938 and 1939, using cross-sectional data. The study applies generalized
least squares to address the issues of heteroscedasticity problem. The author assumes the
existence of a stable demand function and demonstrates that the demand for electricity is
responsive to changes in both price and income. The results of the study conclude that
electricity consumption is influenced by electricity price and household’s annual income.
Since this pioneering study of Houthakker (1951), vast number of studies have
attempted to estimate the residential electricity, incorporating the theoretical and empirical
concepts of Houthakker’s (1951) work. A few studies that estimate the residential
electricity demand following Houthakker (1951) are briefly summarized below.
Chern and Bouis (1982) two-stage least squares (2SLS) to estimate the structural
changes in demand for residential electricity in USA during 1955-1978. The study finds the
existence of significant structural changes associated with 1973 oil embargo or the reversal
48
of the declining trend of electricity prices during 1969-1972. Income and price elasticities
for residential electricity are found to be positive and negative, respectively. Silk and Joust
(1997) uses cointegration approach used by Bentzen and Engsted and error correction
model to estimate the short-run and the long-run elasticities of US residential electricity
during the period of 1949-1993. The empirical results of the study suggest that the short-
run elasticities for income and price exhibit the positive and negative signs, respectively;
the long-run income elasticity is found to be approximately 0.5. The evidence of a structural
shift in demand in the middle of the 1960s is found as well.
Holtedahl and Joutz (2004) use error correction model and Engle and Granger’s
cointegration methods to investigate the short-run and long-run elasticities of residential
electricity demand in Taiwan during 1955-1995, the time of rapid process of modernization
and economic development. The study in the empirical results find that both short-run
income and price effects are smaller than the long-run effects. For short-run and long-run
elasticities, coefficients for income are found to be 0.23 and 1.04 for income, respectively;
the price elasticity is found to be negative and inelastic with the coefficient of -0.15.
Narayan and Smyth (2005) uses autoregressive distributive lag (ARDL) method to
estimate the short-run and the long-run elasticities for the determinants of residential
electricity demand in Australia during the period of 1969-2000. The study finds that price
and income most significantly affect the demand for residential electricity; income
elasticity of demand is positive with 0.323~0.408, and price elasticity of demand in the
long run is negative with -.541.
Nakajima (2010) uses panel cointegration approach with dynamic ordinary least
squares (DOLS) estimators to examine the demand for residential electricity in Japan
49
excluding Okinawa-prefecture during the period of 1975-2005. The study concludes that
all variables, sales per household, electricity price, and income per household are appeared
to have a unit root and cointegrating relationships. The price effect is negative and elastic,
and the income effect is inelastic.
50
Table. 3.1. Summary of the literature studies for Section 3.1
Author Region Period Methodology
Relationship
with residential
electricity
consumption
Income Price
Houthakker (1951) UK 1938,
1939 GLS + -
Silk and Joust (1997) US 1949-
1993
ECM, Bentzen
and Engsted's
cointegration
+ -
Chern and Bouis
(1999) US
1955-
1978 2SLS + -
Holtedahl and Joutz
(2004) Taiwan
1955-
1995
ECM,
cointegration + -
Narayan and Smyth
(2005) Australia
1969-
2000 ARDL + -
Nakajima (2010) Japan 1975-
2005
panel
cointegration,
DOLS
+ -
51
3.1.2 Residential electricity demand model considering
socio-demographic characteristics
It has long been recognized that energy demand can change with demographic
characteristics, but of little interest to the scholars to analyze it empirically (Halvorsen,
1975; Flaig, 1990; Yamasaki and Tominaga, 1997). With this background, a stream of
research has started to apply the household production theory to estimate the residential
electricity demand, the demand derived from warming or cooling homes, cooking, using
home appliances, etc., considering demographic characteristics (Filippini, 1999).
The main idea of the household production theory is that the households are both
producers and consumers of goods and that the households try to maximize their utilities.
Applying this framework of household production theory, we can estimate the demand
for residential electricity. Filippini (1999) asserts that the households use electricity and
capital goods to produce and consume energy products; in other words, one’s utility is
not generated directly from the electricity itself but generated from consuming the
mixtures of energy-related services or products and capital goods.
52
Figure 3.1. The framework of application of household production theory on residential
electricity considering socio-demographic characteristics
Source: Reorganized from Filippini (1999)
53
First, the production function of the composite energy commodity S is written as
follows:
𝑆 = 𝑆(𝐸, 𝐶𝐺) (1)
where CG is the capital goods which consist of appliances and E is electricity consumption.
The output of the composite energy commodity is determined by the quantity of the capital
goods of appliances and the amount of electricity purchased. Further, the household is
assumed to have a utility function with normal properties of differentiability and curvature.
Then, the Equation (2) is set with the composite energy commodity S and a purchased
composite numeraire good X which directly yields a utility, and D and G which represent
demographic characteristics which determine the household’s preferences.
𝑈 = 𝑈(𝑆, 𝑋; 𝐷, 𝐺) (2)
where Equation (2) is satisfied with the following properties:
𝛿𝑆
𝛿𝐸≥ 0,
𝛿𝑆
𝛿𝐶𝐺≥ 0 ,
𝛿2𝑆
𝛿𝐸2 ≤ 0, 𝛿2𝑆
𝛿𝐶𝐺2 ≤ 0 (3)
Then, the household is assumed to maximize the utility of Equation (1) under the
54
budget constraint of the following equation:
𝑌 − 𝑃𝑆𝑆 − 1𝑋 = 0 (4)
where Y is income, 𝑃𝑆 is price of the composite energy commodity, and 𝑃𝑋 is the price
of composite numeraire good X, where the 𝑃𝑋 is assumed as 1 in Equation (4).
55
Figure 3.2. Steps to derive the demand for residential electricity consumption in
Deaton and Muellbauer (1980)
56
According to Deaton and Muellbauer (1980), decisions by individual households
to maximize their utilities in the household production model take place in two stages.
The first step is to minimize the cost of obtaining energy products and to derive the
demand for power as an input. The second step is to derive the demand for electricity by
maximizing the utility with a cost function given.
In the first stage of optimization process, the consumer behaving as a firm can
obtain the optimized levels of the demands for inputs for electricity consumption (E) and
capital goods (CG) by minimizing the cost of producing S as the following equation:
𝑀𝑖𝑛(𝑃𝐸𝐸 + 𝑃𝐶𝐺𝐶𝐺)
𝑠. 𝑡. 𝑆 = 𝑆̅(𝐸, 𝐶𝐺) (5)
where 𝑃𝐸 represents the electricity price and 𝑃𝐶𝑆 represents the price of a composite
good. With the cost minimization process under the budget constraints in Equation (5),
the following Equation (6) is achieved:
𝐶 = 𝐶(𝑃𝐸 , 𝑃𝐶𝐺 , 𝑆) (6)
According to Varian (1992), Equation (6) has the following properties: (1) the
function is linear homogenous in S and in factor prices and (2) and increasing in S and
nondecreasing and concave in factor prices. With application of Shephard’s lemma, the
following input demand functions are derived:
57
𝐸 =𝛿𝐶(𝑃𝐸,𝑃𝐶𝐺,𝑆)
𝛿𝑃𝐸= 𝐸(𝑃𝐸 , 𝑃𝐶𝐺 , 𝑆) (7)
𝐶𝐺 =𝛿𝐶(𝑃𝐸,𝑃𝐶𝐺,𝑆)
𝛿𝑃𝐶𝐺= 𝐶𝐺(𝑃𝐸 , 𝑃𝐶𝐺 , 𝑆) (8)
In the second stage of optimization process, household maximizes its utility under
the budget constraint as follows:
𝑀𝑎𝑥 𝑈(𝑆, 𝑋; 𝐷, 𝐷)
𝑠. 𝑡. 𝐶(𝑃𝐸 , 𝑃𝐶𝐺 , 𝑆) + 𝑋 = 𝑌 (9)
The corresponding Lagrangian function is:
𝐿 = 𝑈((𝑆, 𝑋; 𝐷, 𝐷) + 𝜆(𝑌 − 𝐶(𝑃𝐸 , 𝑃𝐶𝐺 , 𝑆) − 𝑋)) (10)
The solution of Equation (8) leads to the following demand function for the energy
commodities S:
𝑆∗ = 𝑆∗(𝑃𝐸 , 𝑃𝐶𝐺 , 𝑌, 𝐷) (11)
Then finally, by substituting Equation (11) into Equation (7), the demand functions for
residential electricity of a household can be found as follows:
58
𝐸 = 𝐸(𝑃𝐸 , 𝑃𝐶𝐺 , 𝑆∗(𝑃𝐸 , 𝑃𝐶𝐺 , 𝑌, 𝐷))
= 𝐸(𝑃𝐸 , 𝑃𝐶𝐺 , 𝑌, 𝐷) (12)
where Equation 12 reflects the demand for residential electricity of a household considering
the demographic characteristics (D) in the function. In this case, population aging and other
demographic factors may be included, representing the features of demographic
characteristics (D).
Usually in the empirical analysis, the price of capital goods (𝑃𝐶𝐺) is not included
for the following reasons: First, it is difficult to find the fitting data representing the price
of capital goods; second, the changes in the prices of capital goods are relatively small and
so excluding these prices from the model does not cause distortions of the remaining
estimated values (Halvorsen, 1975; Flaig, 1990; Filippini, 1999).
59
3.2 Population aging
Many studies have long been analyzing the factors that influence the electricity
demand from different viewpoints. In this section, the literature which mainly discuss
various factors that influence the electricity demand will be discussed in the following
section.
Studies on the impact of population aging on the residential electricity consumption in
Korea
This section primarily examines the literature that investigate the effects of
demographic factors, such as population aging, on residential electricity consumption. The
first part of the two following sections will discuss the literature that specifically investigate
the conditions in Korea, and the last part will discuss the literature that analyze the effects
on countries other than Korea. Studies that analyze the effects of population aging on
residential electricity consumption in Korea are as follows.
60
1) Shin et al. (2016)
Shin et al. (2016) analyzes whether population aging has varying effects on
residential electricity consumption according to different income levels. The study analyzes
the heterogeneity of household electricity consumption caused by income level using
threshold regression methods with cross-sectional data for urban electricity consumption
in Korea in 2013. The data used are residential electricity consumption (kWh), average and
marginal prices of electricity (KRW/kWh, monthly income (1000 KRW), HDD, CDD,
number of residents, size of the residential area, and a dummy variable for aged person
within a household. The empirical results confirm that the effects of aging on residential
electricity consumption differ according to monthly income levels: For low income levels,
the coefficient for population aging is -0.010; for middle income levels, the coefficients for
population aging is 0.008; for high-income levels, population aging does not have
significant effect on residential electricity consumption.
Also, the study suggests that the size of price elasticity, the kind of perception price,
and the effects of other factors differ according to household income level. For instance,
households with monthly income less than 1.5 million won respond to average price of
elasticity for electricity usage; households with monthly income more than 1.5 million won
respond to marginal price instead of the average price for electricity consumption. Further,
the study finds that the households with low income is relatively price inelastic compared
to those with high income. the income levels are below 15 million won, the relationship
between aging and residential electricity consumption are negative; if the income levels are
in the range between 15 million ~ 27 million won, the relationship is positive; if the income
61
levels are above 27 million won, the relationship is not statistically significant.
2) Shin (2018)
Shin (2018) investigates population aging index that best explains household
electricity consumption in Korea and further predicts the volatility of household electricity
consumption according to aging trends. To conduct an analysis, the study uses Mallow’s
moving-average model and fixed effects threshold panel regressions on 16 municipalities
in Korea during 2003-2015. The equation for the fixed effects threshold panel regression
model is as follows:
𝑒𝑖,𝑡 = (𝜇𝑖 + 𝛣1𝑦𝑖,𝑡 + 𝛣2𝑝𝑖,𝑡 + 𝛣3𝑚𝑖,𝑡)𝐼[𝑎𝑖,𝑡 ≤ 𝛾]
+ (𝜇𝑖′ + 𝛣1
′ 𝑦𝑖,𝑡 + 𝛣2′ 𝑝𝑖,𝑡 + 𝛣3
′ 𝑚𝑖,𝑡)𝐼[𝑎𝑖,𝑡 ≥ 𝛾] + 𝑒𝑖,𝑡
where 𝐼[∙] is an indicator function in which a random variable takes the value 1 if the
expression inside the brackets is true or takes the value 0 if the expression inside the bracket
is false. Hence, if 𝑎𝑖,𝑡 or population aging level is greater than 𝛾, the price and income
elasticities can become from 𝛣1, 𝛣2 to 𝛣1′ , 𝛣2
′ . The study concludes that as a society
progresses towards an aging society, income elasticity for residential electricity demand
becomes more inelastic, whereas price elasticity for residential electricity demand becomes
more elastic. The study further gives policy insights for stabilization of volatility in
62
electricity consumption, emphasizing the importance of implementations of both price
policies and efficiency improvement policies.
3) Lim et al. (2013)
Lim et al. (2013) consider both the aging variables and the temperature variables
such as cooling degree days (CDDs) and heating degree days (HDDs) to investigate these
impacts on the residential electricity consumption in Korea using autoregressive distributed
(ARDL) model and error correction model. Time series data from 1966 to 2011 are used
for analysis. The following equation represents the long-run equilibrium function for the
residential electricity demand:
𝑙𝑛𝑄𝑡 = 𝛼0 + 𝛼1𝑙𝑛𝑌𝑡 + 𝛼2𝑙𝑛𝑃𝑡 + 𝛼3𝑙𝑛𝐷𝑡 + 𝛼4𝑙𝑛𝐶𝐷𝐷 + 𝛼5𝑙𝑛𝐻𝐷𝐷 + 𝜖𝑡
which is derived from ARDL model. In the above equation, 𝛼𝑚 represents the long-run
elasticity parameters of the demand for residential electricity. Y, P, D, CDD, and HDD
represent the variables for real income per capita, real price for residential electricity, aging
index, cooling degree days, and heating degree days, respectively.
The study concludes that, in terms of population aging, 1% increase in the levels of
aging index decreases the residential electricity consumption by 0.48%. For the
temperature effects, the study finds that cooling degree day variables have statistically
significant impacts on Korea’s residential electricity consumption, whereas heating degree
63
day variables do not have any impacts on the residential electricity consumption. The study
is significant for it analyzes not only the aging effects but also the global warming effects
due to climate change on the electricity demand.
4) Noh and Lee (2013)
Noh and Lee (2013) analyzes the factors affecting the energy consumption of the
household sector including oil and city-gas consumption and electricity consumption using
panel data for 7 metropolitan areas and 9 provinces in Korea for the period of 2001-2010;
the study excludes the province of Jeju considering the specialties of the islands. For
estimation, the study applies feasible generalized least squares (FGLS) method. The panel
model for estimations is as follows:
𝐸𝑖𝑡 = 𝛼 + 𝐿𝑖𝑡𝛣1 + 𝑃𝑖𝑡𝛣2 + 𝜖𝑖𝑡
(𝜖𝑖𝑡 = 𝜇𝑖𝑡 + 𝜆𝑡 + 𝜐𝑖𝑡)
where i represents 15 metropolitan areas and provinces, t represents time, E represents
energy (electricity, oil and city gas) consumption in residential and transport sectors, L
represents household characteristics (income, the ratio of population aged over 65, average
household size, and female economic participation rate) and consumption styles, and P
represents consumption environment (built environment and energy price).
The empirical results find that the increase in the ratio of population aged over 65
64
by 1% increases residential electricity consumption by 0.29% but decreases the oil and
city-gas consumption by 1.09%. Overall, the total residential energy consumption
decreases by 0.87% as the ratio of population aged over 65 increases by 1%. In conclusion,
the study asserts that residential electricity consumption is not only affected by the
geographical and physical environments, but also by the socio-demographic and economic
factors including household characteristics and consumption styles and environment.
5) Won (2012)
Won (2012) analyzes how population aging affects residential electricity
consumption in Korea using the annual data times series data from 1965 to 2010. The study
uses error correction model and Johansen cointegration methods to estimate the short-run
dynamics and long-run equilibrium relationships for the residential electricity demand. The
error correction model equation to estimate the long-run equilibrium relationships is as
follows:
𝑑𝑙𝑛𝐸𝑡 = 𝛣0 + 𝐵1𝑑𝑙𝑛𝑃𝐸𝑡 + 𝐵2𝑑𝑙𝑛𝑌𝑡 + 𝐵3𝑑𝑙𝑛𝐷𝑡 + 𝐵4𝑢𝑡−1 + 𝜐𝑡
where d represents the difference operator, 𝜐𝑡, an error term, and 𝑢𝑡−1, an error correction
term. For the variables, E, Y, P, and D represent residential electricity consumption (kWh)
per capita, real income per capita (10000 KRW/person), residential electricity price
(KRW/kW), and aging index, respectively. The empirical results show that population
65
aging significantly reduces residential electricity demand in the short run, but the effect
gradually decreases in the long run even though the relationship between the two variables
is still negative.
6) Noh (2014)
The study investigates the effects of household demographics on household
electricity consumption in 2008. The paper focuses on the impacts of numerous household
demographics on electricity consumption after controlling for the consumptions of various
household appliances, durable goods, and traditional factors influencing electricity
consumption such as electricity price and income. The controlled variables for the usages
of household appliances include televisions, refrigerators, Kimchi refrigerators, washing
machine, dryers, air purifier, rice cooker, stoves, fans, heaters, laptops, and desktop
computers, etc. Also, the price uses city gas consumption variables for controlling. The
study uses ordinary least squares (OLS) method for estimation on the following model:
𝑙𝑛𝑌𝑖 = 𝛼 + 𝛣1𝑙𝑛𝑃𝑖 + 𝛣2𝑙𝑛𝑋𝑖 + 𝛣3𝑍𝑖 + 𝛣4𝛿𝑟 + 𝜖𝑖
where 𝑌, 𝑃, 𝑋, 𝑍, 𝛿 each represents residential electricity consumption, unit price of
electricity, dwelling types, household characteristics of a household i, and dummy variables
reflecting the regional characteristics of 16 municipalities.
The study finds that the presence of toddlers under age 5 or elders over age 65 in
66
a household has negative relationship with the residential electricity consumption.
However, these effects are found to disappear when the consumptions of household
appliances and city gas consumption are controlled.
7) Keum et al. (2018)
The purpose of this study is to estimate the determinants of the demand for
electricity by using First Differenced Generalized Method of Moments (FD GMM) method
with the unbalanced panel data of 16 municipalities in Korea during 1the period of 1996-
2013. The following two models represent ARDL model and partial adjustment model,
respectively:
𝛥𝑙𝑛𝐸𝑖,𝑡 = 𝛣0 + 𝛣1𝛥𝑙𝑛𝐸𝑖,𝑡−1 + 𝛣2𝛥𝑃𝑖,𝑡 + 𝛣3𝛥𝑙𝑛𝑃(𝑖,𝑡−1) + 𝛣4𝛥𝑙𝑛𝑌𝑖,𝑡 + 𝛣5𝛥𝑙𝑛𝐴𝑖,𝑡
+ 𝛣6𝛥𝑙𝑛𝐶𝐷𝐷𝑖,𝑡 + 𝛣7𝛥𝑙𝑛𝐻𝐷𝐷𝑖,𝑡 + 𝛣8𝛥𝑙𝑛𝑇𝐷𝑖,𝑡 + 𝛥𝑢𝑖,𝑡
𝛥𝑙𝑛𝐸𝑖,𝑡 = 𝛣0 + 𝛣1𝛥𝑙𝑛𝐸𝑖,𝑡−1 + 𝛣2𝛥𝑃𝑖,𝑡 + 𝛣3𝛥𝑙𝑛𝑌𝑖,𝑡 + 𝛣4𝛥𝑙𝑛𝐴𝑖,𝑡 + 𝛣5𝛥𝑙𝑛𝐶𝐷𝐷𝑖,𝑡
+ 𝛣6𝛥𝑙𝑛𝐻𝐷𝐷𝑖,𝑡 + 𝛣7𝛥𝑙𝑛𝑇𝐷𝑖,𝑡 + 𝛥𝑢𝑖,𝑡
where E, Y, P, A, CDD, and HDD represent residential electricity consumption, real income
per capita, average real electricity price, aging population ratio, cooling degree days
(CDDs), and heating degree days (HDDs), respectively on region i and time series t. The
empirical results of the study suggest the following: 1) Partial adjust model gives better
67
results than ARDL model does in terms of statistical significance; 2) the elasticities of price
and income are found to be negative (-) and positive (+), respectively, and the long-run
elasticities are more elastic than the short-run elasticities; 3) the elasticities of CDDs and
aging population ratio are positive (+) and (negative), respectively; 4) since increase of
income elasticity dominates the increase of price elasticity, the government is
recommended to consider this in implementing the electricity policies.
68
Studies on the impact of population aging on the residential electricity consumption in
overseas countries
In the following section, the studies that analyze the effects of population aging on
residential electricity consumption in overseas countries other than Korea are discussed.
1) Liddle (2011)
Liddle (2011) examines consumption-driven environmental impact and age
structure on the residential electricity consumption in 22 OECD countries during 1960-
2007. For empirical analysis, the study uses cointegration modeling, FMOLS approach in
the framework of Stochastic Impacts by Regression on Population, Affluence, and
Technology (STIRPAT) on several variables including the share of residential energy
consumption from electricity, the share of population between ages 20-34, 35-49, 50-69,
and 70 and older, and the total mid-year population. The equation to be estimated for
residential electricity consumption is as follows:
𝑙𝑛𝐼𝑖𝑡 = 𝑎𝑖 + 𝑣𝑙𝑛𝑃𝑇,𝑖𝑡 + 𝑤𝑙𝑛𝑆ℎ𝑃20−34,𝑖𝑡 + 𝑥𝑙𝑛𝑆ℎ𝑃35−49,𝑖𝑡 + 𝑦𝑙𝑛𝑆ℎ𝑃50−69,𝑖𝑡
+ 𝑧𝑙𝑛𝑆ℎ𝑃70+,𝑖𝑡 + 𝑐𝑙𝑛𝐴𝑖𝑡 + 𝑑𝑙𝑛𝑆ℎ𝐸𝑖𝑡 + 𝜖𝑖𝑡
where ShE, I, 𝑃𝑇, ShP, and A are the share of electricity in residential energy consumption,
the aggregate environmental impact or residential electricity consumption, population total,
share of population in the four age cohorts, and GDP per capita, respectively. 𝜖 and 𝛼 are
69
error terms and constants, respectively.
The empirical results find a U-shaped relationship between residential electricity
consumption and age structure: the youngest cohorts (age cohorts of 20-34) and the oldest
cohorts (age cohorts of 70 and above) have positive coefficients, while the middle ones
(age cohorts of 35-49 and 50-69) have negative coefficients.
2) Mikayilov et al. (2018)
Mikayilov et al. (2018) investigate the impacts of population age groups of 15-64
and 65 above on residential energy consumption in Kazakhstan covering the period of
1999-2012. The study uses the modelling framework of Stochastic Regression on
Population, Affluence, and Technology (STIRPAT) developed by Dietz and Rosa (1994)
and autoregressive distributed lag (ARDL) approach for estimation. The equation in the
STIRPAT framework to be estimated is as follows:
𝑙𝑛𝐼 = 𝑞 + 𝑏𝑙𝑛𝑃 + 𝑐𝑙𝑛𝐴 + 𝑑𝑙𝑛𝑇 + 𝑤
In STIRPAT framework, the variables I, P, A, and T represent environmental impact,
population, affluence, and technology, respectively. For conceptual modelling in the study,
I, P, A, and T serve as proxies for the residential electricity consumption, population age
groups of 15-64 and 65 and above, GDP per capita (2015=100), GDP, respectively. The
study applies time series cointegration and error correction methods for empirical analysis,
70
and the results indicate a significant positive impact of the age groups of 15-65 on the
residential electricity use in the long run. Further, the study finds that 65 and above has
positive short-run effects, whereas affluence has no effect.
3) Brounen et al. (2012)
Brounen et al. (2012) focus on gas and electricity consumption, investigating the
impact of the physical structure of home (i.e., the features of dwellings) on variations in
energy consumption. Then, the study compares the importance of these structural
characteristics with the demographic characteristics of households in the energy
consumption of Dutch households with the observations on 300,000 in the Netherlands
collected from January 2008 to December 2009. For analysis, the study conducts OLS
regressions using datasets including gas and electricity consumption, period of construction,
number of persons in household, age of the head of households and elderly households.,
and household income. The empirical results suggest that the presence of elder person in a
household decreases residential electricity consumption, with the coefficients ranging from
-0.039 to -0.020.
71
4) Deutsch and Timpe (2013)
According to Deutsch and Timpe (2013), residential energy demand is influenced
by several factors including socio-demographic attributes. Deutsch and Timpe (2013)
investigate the effect of aging on residential energy demand in Germany in 2010. The study
uses several variables for analysis: Residential energy consumption (heating, gas,
electricity), social-demographic factors at the household level (number of household
members, age). For control variables, building types, building age, building renovation
status, and climatic factors such as heating degree days are used. The methodological
approach used in the study is one that has been developed by Prognos and AGFW (the
German District Heating Association). The results of the study confirm that the effects of
aging on the residential energy consumption is positive.
5) Mikayilov and Hasanov (2017)
The objective of this study is to investigate the effects of population age groups of
0-14, 15-64, and 65 and above on residential electricity consumption in Azerbaijan during
the period of 2000-2015. The study considers the STIRPAT framework and employs error
correction (ECM) model to ease the possible spurious estimation results caused by using
the non-stationary data and autoregressive distributed lag (ARDL) approach which is
suitable method to use with small samples. The equation to be estimated is as follows:
72
𝛥𝑟𝑒𝑐𝑡 = 𝑐0′ + 𝜃1
′𝑟𝑒𝑐𝑡−1 + 𝜃2′ 𝑔𝑑𝑝𝑝𝑐𝑡−1 + 𝜃3
′ 𝑝𝑜𝑝65𝑡−1+ 𝛴𝑖=1
𝑛 𝜔𝑖′𝛥𝑟𝑒𝑐𝑡−𝑖
+ 𝛴𝑖=0𝑛 𝜑𝑖
′𝛥𝑔𝑑𝑝𝑝𝑐𝑡−𝑖 + 𝛴𝑖=0𝑛 𝜏𝑖
′𝛥𝑝𝑜𝑝_65𝑡−𝑖 + 𝑢𝑡′
where 𝜃𝑖′ is a long-run coefficient, ωi
′ , 𝜑𝑖′ , and 𝜏𝑖
′ are short-run coefficients, ci′ is
constant term, and 𝑢𝑡′ is an error term. Pop_65 represents the age groups of 65 and above
measured in persons. The study concludes that there exist significant effects of different
population age groups and affluence on residential electricity consumption. The elasticities
with respect to population age groups of 15-64 and 65 and above are found to be 10.46 and
2.33, respectively.
73
Table 3.2. Summary of literature on the relationship between population aging and
residential electricity consumption
Author Region(s) Period Methodology Relationship with residential
electricity consumption (REC)
Shin et al.
(2016) Korea 2013
threshold
regression
methods
- for low income households;
+ for middle income households;
not significant for high income
households
Shin (2018)
16
municipal
ities in
Korea
2003-
2015
threshold
panel
regressions
makes income elasticity of
residential electricity demand more
inelastic;
price elasticity of residential
electricity demand more elastic
Lim et al.
(2013) Korea
1966-
2011 ARDL -
Noh and
Lee (2013)
16
municipal
ities in
Korea
2001-
2010 FGLS -
Won
(2012) Korea
1965-
2010
ECM,
Johansen
cointegration
method
short-run: -;
long-run: -, but the effect gradually
decreases
Noh (2014) Korea 2008 OLS -
Keum et al.
(2018)
16
municipal
ities in
Korea
1996-
2013
FD GMM,
ARDL -
Liddle
(2011)
22 OECD
countries
1960-
2007
STIRPAT,
FMOLS,
panel
cointegration
age cohorts of 20-34, 70 and
above: +;
age cohorts of 35-49, 50-69: -
Mikayilov
et al.
(2018)
Kazakhst
an
1999-
2012
STIRPAT,
ARDL
age cohorts 15-65: +;
age cohorts 65 and above: +
Brounen et
al. (2012)
Netherlan
ds
Jan.
2008-
Dec.
2009
OLS -
74
Deutsch
and Timpe
(2013)
Germany 2010
Prognos and
AGFW
method
+
Mikayalov
and
Hasanov
(2017)
Azerbaija
n
2000-
2015
STIRPAT,
ECM, ARDL +
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3.3 Household size
In this section, the studies analyzing the effects of household size on the residential
electricity consumption in Korea and in overseas countries are discussed.
Studies on the impact of household size on the residential electricity consumption in Korea
1) Noh and Lee (2013)
Noh and Lee (2013) analyzes the factors affecting the energy consumption of the
household sector (e.g., residential electricity consumption, oil and city gas consumption)
using panel data for 7 metropolitan areas and 9 provinces in Korea for the period of 2001-
2010. For estimation, the study excludes Jeju province considering its specialties of the
islands and applies both fixed effects and random effects models. The estimated model for
panel analysis is as follows:
𝐸𝑖𝑡 = 𝛼 + 𝐿𝑖𝑡𝐵1 + 𝑃𝑖𝑡𝐵2 + 𝜖
(𝜖𝑖𝑡 = 𝜇𝑖 + 𝜆𝑖 + 𝜐𝑖𝑡)
where 𝐸𝑖𝑡 represents energy consumption or residential electricity consumption at time t
in region i, 𝐿𝑖𝑡 represents variables for consumption styles, and 𝑃𝑖𝑡 represents variables
for consumption environment such as built environment and electricity price. The study
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finds that household size and residential energy consumption exhibits negative relationship.
2) Hong et al. (2018)
The purpose of this study is to analyze whether single and elderly households have
different energy consumption patterns than the general household. In specific for energy
consumption, the study includes residential electricity consumption. The data are collected
from Household Energy Standing Survey conducted in 2011 on 16 municipalities, in which
2,520 household data are chosen in specific. For empirical analysis, the study applies
regional fixed effects model; the estimated panel model is as follows:
𝑙𝑛 𝐸𝑖𝑡 = 𝑎 + 𝐵𝑆𝑆𝑖𝑡 + 𝐵𝐻𝐻𝑖𝑡 + 𝐵1𝑙𝑛𝑀𝑃𝑖𝑡 + 𝐵2𝑙𝑛𝐷𝑃𝑖𝑡 + 𝐵𝑇𝑇𝑖𝑡 + 𝑎𝑅𝐷𝑅 + 𝜖𝑖𝑡
where t represents year, i represents household, 𝐸 represents energy consumption or
electricity consumption per capita, H represents variables that reflect household
characteristics, including household size, and gender, age, education level of head of
household. 𝑙𝑛𝑀𝑃 represents log-transformed variable for energy or electricity price, T
represents time dummy variables, and D represents variables that reflect regional
characteristics.
The results indicate that increase in household size or the number of people
residing per household decreases per capita electricity consumption by approximately 26%.
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3.4 Education, gender, homeownership, working age
population, and dwelling type
1) Lee et al. (2011)
The purpose of the study is to investigate the characteristics of energy use behaviors
and energy saving consciousness of multi-family housing residents. The energy usages
referred to in this study are electric energy, heating energy and water usages. This study is
conducted from a survey carried out in Seoul and Gyeonggi-do on 227 samples. The survey
questions are categorized into four sections: 24 questions on electric energy use behaviors,
4 questions on heating energy use behavior, 6 questions on water use behavior, and 8
questions on energy saving consciousness. The study performs t-tests, one-way ANOVA,
and simple statistical analyses in the study. Results of the study conclude that gender
difference does not significantly affect the residential energy consumption and households
with low education levels and older people have higher level of energy consciousness than
the other households, thus using lesser amount of residential energy.
78
2) Brounen et al. (2012)
Brounen et al. (2012) focus on gas and electricity consumption, investigating the
impact of the physical structure of home (i.e., the features of dwellings) on variations in
energy consumption. Then, the study compares the importance of these structural
characteristics with the demographic characteristics of households in the energy
consumption of Dutch households with the observations on 300,000 in the Netherlands
collected from January 2008 to December 2009. The dwelling types analyzed are
categorized into apartment, row house, semi-detached, detached, and corner. For analysis,
the study conducts OLS regressions using datasets including gas and electricity
consumption, period of construction, number of persons in household, age of the head of
households and elderly households., and household income. It is found that apartment,
semi-detached, and detached variables exhibit positive relationships with electricity
consumption per capita, whereas row house variables has negative relationship with
electricity consumption per capita.
79
3.5 Heating degree days (HDDs) and cooling degree days
(CDDs)
Numerous studies have confirmed that heating degree days and cooling degree days
serve as decent proxies for estimating the demand for residential electricity consumption.
In this section, a number of literature which investigate the effects of heating degree days
(HDDs) and cooling degree days (CDDs) on residential electricity consumption in overseas
countries and in Korea are discussed.
Heating degree days (HDDs) and cooling degree days (CDDs) in overseas countries
1) Eskeland and Mideksa (2010)
Eskeland (2010) uses panel data of 31 countries in Europe with selected years from
1994 to 2005 to investigate the relationships among the climatic factors and other factors
on the residential electricity consumption. For conceptual framework, the study uses
functions of residential electricity consumption before estimating the elasticities of demand
in residential electricity consumption. For empirical analysis, Arellano-Bond method, a
developed model from Generalized Method of Moment (GMM) method, is applied.
Arellano-Bond method uses time series dependent variables as dummy variables and has
benefits in addressing the issues of omitted variable bias when using the regional panel
80
data. The climatic variables used in the study are cooling degree days (CDDs) and heating
degree days (HDDs), where the values of CDDs and HDDs are calculated as 𝐶𝐷𝐷𝑖𝑡 =
𝑇𝑖𝑡 − 22 and 𝐻𝐷𝐷𝑖𝑡 = 18 − 𝑇𝑖𝑡, respectively, where 𝑇𝑖𝑡 is the average daily temperature
(in Celsius) of country i at time t.
The economy’s total revenue from the value with tax is used as an instrument
variable to address the potential problems of endogeneity and the measurement errors that
may arise from using the traditional income data, Further, panel data techniques with time-
and country-fixed effects are introduced to control for the possible omitted variable bias
problems that can occur when estimating the parameters that may be correlated with the
unobserved variables. The empirical results suggest that weather has a statistically
significant effect on electricity demand, with effects that are of plausible magnitude as in
the previous literature. In specific, a unit increase in HDD or CDD will raise electricity
consumption by 0.01% and 0.04%, respectively. In a simulation of climate change for the
next 100 years, holding other factors constant, the study finds that the demand for heating
will decrease in Northern Europe, while the demand for cooling will increase in Southern
Europe. In countries such as Cyprus, Greece, Italy, Malta, Spain, and Turkey, the net effects
of increased cooling outweigh the decreased heating consumption, whereas the opposite
holds in most of the European countries. Estimated elasticities with respect to income and
price are 0.8 and -0.2 respectively.
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2) Lim et al. (2013)
Lim et al. (2013) consider not only the aging variables but also the temperature
variables such as cooling degree days (CDDs) and heating degree days (HDDs) to
investigate these impacts on the residential electricity consumption in Korea using
autoregressive distributed (ARDL) model and error correction model. Time series data
from 1966 to 2011 are used for analysis. The following equation represents the long-run
equilibrium function for the residential electricity demand:
𝑙𝑛𝑄𝑡 = 𝛼0 + 𝛼1𝑙𝑛𝑌𝑡 + 𝛼2𝑙𝑛𝑃𝑡 + 𝛼3𝑙𝑛𝐷𝑡 + 𝛼4𝑙𝑛𝐶𝐷𝐷 + 𝛼5𝑙𝑛𝐻𝐷𝐷 + 𝜖𝑡
which is derived from ARDL model. In the above equation, 𝛼𝑚 represents the long-run
elasticity parameters of the demand for residential electricity. Y, P, D, CDD, and HDD
represent the variables for real income per capita, real price for residential electricity, aging
index, cooling degree days, and heating degree days, respectively.
The study concludes from the estimation results that cooling degree days (CDDs)
significantly influence the residential electricity demand, but heating degree days (HDDs)
do not significantly influence the residential electricity demand. In specific, CDDs increase
by 1%, the residential electricity consumption will increase by 0.35% in the long-run and
0.11% in the short-run. The study finds that the effect of CDD on the residential electricity
demand in the long-run is greater than in the short-run, giving insights that may enhance
the policies for stable electricity supply. The study is significant for it analyzes both the
82
aging and the global warming effects due to climate change on the electricity demand.
3) Fullerton et al. (2012)
This study examines the residential electricity consumption in Seattle,
Washington, which is the largest metropolitan economy in the northwestern region of the
United States. The study uses time series data from Seattle City Light (SCL) annual reports
during the period of 1960 and 2007. For the variables used in the analysis, consumption
data or kilowatt hours per customer, which is calculated with total residential consumption
in megawatt hours (MHW) and the number of customers, are used. Also, real income per
capita, heating degree days (HDD), real average electricity price, population of the SCL
service area, and non-agricultural wage and salary employment in the SCL service aria are
used. A unit root test conducted with the residuals confirms that a cointegrating
relationships exists among the variables. Further, to test for consistency of the long-run
multipliers, CSUMQ and CUSUM tests are conducted. As results from both these tests,
coefficient stability for the outcomes are achieved. The study makes the following
conclusions from the empirical results: 1) Heating degree days (HDD) and residential
electricity consumption has a positive relationship, in which 1 percent increase in annual
heating degree days (HDD) increases residential electricity consumption by 0.303% in the
long-run and 0.338% in the short-run.
83
4) Na and Ryu (2000)
This study performs both qualitative and quantitative analyses to estimate the
demand for energy consumption by energy types and sectors. The study applies
autoregressive distributed lag (ARDL) model using monthly and quarterly data in Korea
from the first quarter in 1983 to the second quarter in 1999. For estimation, lagged
independent variables and lagged dependent variables are included to handle the problems
of autocorrelation and endogeneity. Also, quarterly dummy variables are included to reduce
the issues of seasonality. The estimated ARDL model is as follows:
𝑙𝑛𝐸𝑡 = 𝛼0 + ∑ 𝑎1𝑖ln 𝐸𝑡−𝑖𝑛𝑖=1 + ∑ 𝛼2𝑖𝑙𝑛𝑌𝑡−𝑖 + ∑ 𝛼3𝑖𝑙𝑛𝑃𝑡−𝑖
𝑛𝑖=0
𝑛𝑖=0 + α4𝐻𝐷𝐷 +
α5𝐶𝐷𝐷 + 𝐴𝑋
where Q, Y, P, HDD, CDD, and X represent the residential electricity demand, quarterly
gross domestic product (GDP), quarterly electricity price by sectors, heating degree days
(HDD), and cooling degree days (CDD), and other variables, respectively. The results
suggest that income elasticity and price elasticity for residential demand are found to be
positive with the coefficients of 0.17 and negative with the coefficient of -.042, respectively.
Also, HDD and CDD are found to exhibit positive relationships with residential electricity
demand with the coefficients of .0001 and .0002, respectively.
.
84
5) Keum et al. (2018)
The purpose of this study is to estimate the determinants of the demand for
electricity by using First Differenced Generalized Method of Moments (FD GMM) method
with the unbalanced panel data of 16 municipalities in Korea during the period of 1996-
2013. The following two models represent ARDL model and partial adjustment model,
respectively:
𝛥𝑙𝑛𝐸𝑖,𝑡 = 𝛣0 + 𝛣1𝛥𝑙𝑛𝐸𝑖,𝑡−1 + 𝛣2𝛥𝑃𝑖,𝑡 + 𝛣3𝛥𝑙𝑛𝑃(𝑖,𝑡−1) + 𝛣4𝛥𝑙𝑛𝑌𝑖,𝑡 + 𝛣5𝛥𝑙𝑛𝐴𝑖,𝑡
+ 𝛣6𝛥𝑙𝑛𝐶𝐷𝐷𝑖,𝑡 + 𝛣7𝛥𝑙𝑛𝐻𝐷𝐷𝑖,𝑡 + 𝛣8𝛥𝑙𝑛𝑇𝐷𝑖,𝑡 + 𝛥𝑢𝑖,𝑡
𝛥𝑙𝑛𝐸𝑖,𝑡 = 𝛣0 + 𝛣1𝛥𝑙𝑛𝐸𝑖,𝑡−1 + 𝛣2𝛥𝑃𝑖,𝑡 + 𝛣3𝛥𝑙𝑛𝑌𝑖,𝑡 + 𝛣4𝛥𝑙𝑛𝐴𝑖,𝑡 + 𝛣5𝛥𝑙𝑛𝐶𝐷𝐷𝑖,𝑡
+ 𝛣6𝛥𝑙𝑛𝐻𝐷𝐷𝑖,𝑡 + 𝛣7𝛥𝑙𝑛𝑇𝐷𝑖,𝑡 + 𝛥𝑢𝑖,𝑡
where E, Y, P, A, CDD, and HDD represent residential electricity consumption, real income
per capita, average real electricity price, aging population ratio, cooling degree days
(CDDs), and heating degree days (HDDs), respectively on region i and time series t. The
empirical results of the study suggest the following: 1) Partial adjust model vies better
results than ARDL model does in terms of statistical significance; 2) the elasticities of price
and income are found to be negative (-) and positive (+), respectively, and the long-run
elasticities are more elastic than the short-run elasticities; 3) the elasticities of CDDs and
aging population ratio are positive (+) and (negative), respectively
85
6) Kamerschen and Porter (2004)
This study estimates the total, residential, and industrial electricity demand using
partial adjustment approach and simultaneous equation approach in US during 1973-1998
and suggests that simultaneous equation approach is suitable in estimating the electricity
demand since they provide negative estimates for the price elasticity. In specific for the
estimating models, the study uses three-stage least squares (3SLS) model on the following
equations:
𝑙𝑛𝑄 = 𝛼1 + 𝛼2𝑙𝑛𝑃 + 𝛼3𝑙𝑛𝑋 + 𝛼4𝑙𝑛𝐺 + 𝛼6𝑙𝑛𝐶 + 𝜖
𝑙𝑛𝑃 = 𝐵1 + 𝐵2𝑙𝑛𝑄 + 𝐵3𝑙𝑛𝐿 + 𝐵4𝑙𝑛𝐹 + 𝐵5𝑙𝑛𝐾 + 𝑢
where Q, P, X, G, D, C, L, F, and K are average annual electricity sales per customer, real
marginal price of residential electricity, real annual GDP, real price for residential natural
gas, HDD, CDD, cost of labor, composite fuel cost, and cost of capital, respectively. Of the
parameter estimates on HDD and CDD, only CDD estimates are found to be statistically
significant with the coefficients of 0.145 and 0.137 for equation with CDD only and with
both CDD and HDD, respectively.
86
Table 3.3. Summary of literature on the relationship of heating degree days (HDDs) and
cooling degree days (CDDs) on residential electricity consumption
Author Region(s) Period Methodology Relationship with residential
electricity consumption (REC)
Eskelan
d and
Mideks
a
(2010)
31
countries in
Europe
1994-
2004
Arellano-
Bond method
HDD: positive, with coefficient
of 0.01
CDD: positive, with coefficient
of 0.04
Lim et
al.
(2013)
Korea 1966-
2011 ARDL
CDD: positive, with coefficients
of 0.35 in the long-run and 0.11
in the short-run
HDD has no significant effect
Fullerto
n et al.
(2012)
Seattle,
Washingto
n, US
1960-
2007
Cointegration
test
HDD: positive, with coefficients
of 0.303 in the long-run and
0.338 in the short-run
Na and
Ryu
(2000)
Korea 1983-
1999 ARDL
HDD: positive, with coefficient
of 0.001
CDD: positive, with coefficient
of 0.002
Keum
et al.
(2018)
16
municipalit
ies in
Korea
1996-
2013 ARDL
CDD: positive, with coefficient
of 0.018
HDD has no significant effect
Kamers
chen
and
Porter
(2004)
US 1973-
1998 3SLS
CDD: positive, with the
coefficients of 0.145 and 0.137,
respectively
HDD has no significant effects
87
3.6 Studies using panel estimation methods
1) Shin (2017)
Shin (2017) uses panel data for 16 regions in Korea to investigate the effects of
population aging on the residential electricity consumption. The study argues that using
regional panel data has some benefits over using time-series data for analysis.
First, using panel data for analysis can effectively solve the issues of multi-
collinearity of time series data. If time-series data is used for analyzing the effects of
population aging in a study, income and population aging variables may suffer from the
problems of multi-collinearity because economic growth leads population aging, and the
two conditions are usually happening at the same time. This multi-collinearity problem can
lead to inaccurate parameter estimations and cause severe loss of statistical power. Hence,
addressing this issue is important to avoid misleading interpretations for the analysis.
Second, using panel data can effectively investigate the changes in volatility of
electricity consumption without the usage of microeconomic data. If micro data are used
for analysis, multi-collinearity problem may be somewhat addressed since time series
correlation between income and aging is weak. However, it may be difficult to capture the
changes in income or price elasticities which cause changes in volatility of electricity
consumption over time.
88
2) Noh and Lee (2013)
Noh and Lee (2013) analyzes the factors affecting the energy consumption of the
household sector (i.e., residential electricity consumption) using panel data for 7
metropolitan areas and 9 provinces in Korea for the period of 2001-2010. For estimation,
the study excludes the province of Jeju considering the specialties of the islands. Several
results are presented in the study: First, household size, the ratio of the old-aged, female
economic participation rate, net population ratio, and the ratio of the apartment influence
household energy consumption. Second, electric energy consumption is affected by energy
price and the level of income. Third, electricity price, average household size, and the ratio
of expenditures on restaurants/hotels and the residential electricity consumption exhibit
negative relationships; the ratio of population aged over 65, the ratio of apartment house,
and income and the residential electricity consumption exhibit positive relationships.
Before estimating the coefficients of the panel data, assumptions of homogeneity
and no serial correlations must hold. Estimating the coefficients by fixed and random
effects models without verifying the former assumptions may result in biased and
inefficient estimates. Hence, Wooldridge test for time-series autocorrelation and Wald test
for heteroscedasticity are conducted. The null hypotheses of Wooldridge test and Wald test
are no autocorrelations among the panel data and homoscedastic characteristics among the
panel data sets, respectively. In the study, the test statistics reject the null hypotheses of
both Woolridge and Wald tests. In this case, panel Generalized Least Square (GLS) is a
better method for estimation. Hence, the study panel uses panel GLS under the assumptions
89
of heteroscedasticity and autocorrelations and Feasible Generalized Least Square (FGLS)
which is used when the structures of variance and covariance of the error terms are
unknown.
The study states that estimations via panel models have several benefits: First, with
panel models, regional differences and time-variant effects can be controlled to estimate
the effects of independent variables on the dependent variables. Second, although the cross-
sectional samples (in this case, the municipalities of Korea) are not enough in number, the
data can be integrated with the corresponding time series data, thus increasing the
significance levels and efficiency of the estimated parameters
90
Chapter 4. Data and research methods
4.1 Data
Dependent variable
1) Residential electricity consumption per capita (MWh/person)
The dependent variable used in this analysis is residential electricity consumption
per capita (MWh/person). The value is calculated by dividing the residential electricity
sales by the number of populations by region at a certain point of time. The equation is as
follows:
𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎
=𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑡𝑦 𝑠𝑎𝑙𝑒𝑠 𝑏𝑦 𝑟𝑒𝑔𝑖𝑜𝑛
𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑏𝑦 𝑟𝑒𝑔𝑖𝑜𝑛
Residential electricity data for 16 municipalities excluding Sejong City during
2000-2016 are collected from Electric Power Statistics Information System (EPSIS), and
population data are collected from Statics Korea (KOSTAT). Figure 4.1 shows that per
capita residential electricity consumption in all 16 municipalities in Korea has increased
sharply since 2000 until it reaches the peak in 2013, which is followed by the sudden drop
in 2014. The declined electricity consumption is rebounded back in 2015.
91
Figure 4.1. Trends of residential electricity consumption per capita (MWh/person) of 16 municipalities in Korea, 2000-2016
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.62000
2005
2010
2015
2003
2008
2013
2001
2006
2011
2016
2004
2009
2014
2002
2007
2012
2000
2005
2010
2015
2003
2008
2013
2001
2006
2011
2016
2004
2009
2014
2002
2007
2012
2000
2005
2010
2015
2003
2008
2013
2001
2006
2011
2016
2004
2009
2014
2002
2007
2012
2000
2005
2010
2015
2003
2008
2013
Busan ChungbukChungnam Daegu Daejeon entire Gangwon GwangjuGyeongbukGyeonggiGyeongnam Incheon Jeju Jeonbuk Jeonnam Seoul Ulsan
92
Independent variables
2) Residential electricity price (KRW/kWh)
Residential electricity price variable (KRW/kWh) is included as an independent
variable. The data is in real average retail price of residential electricity, which is attained
by using the data of average price of electricity considering the regional consumer price
index (CPI) with base year 2015=100. The study uses the average retail price for residential
electricity as the proxy for residential electricity price following Jo and Jang (2015), Ito
(2014), and Shin (2017). These studies have used the average retail price for residential
electricity to represent the residential electricity price under the current progressive billing
system in Korea. In specific, the average retail price is calculated as follows:
𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑝𝑟𝑖𝑐𝑒
=𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑟𝑒𝑡𝑎𝑖𝑙 𝑝𝑟𝑖𝑐𝑒 𝑜𝑓 𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦
𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑝𝑟𝑖𝑐𝑒 𝑖𝑛𝑑𝑒𝑥 (𝐶𝑃𝐼) × 100
where
𝑟𝑒𝑡𝑎𝑖𝑙 𝑝𝑟𝑖𝑐𝑒 𝑓𝑜𝑟 𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦
=𝑟𝑒𝑡𝑎𝑖𝑙 𝑝𝑟𝑖𝑐𝑒 𝑓𝑜𝑟 𝑟𝑒𝑠𝑖𝑑𝑛𝑒𝑖𝑡𝑎𝑙 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑏𝑦 𝑟𝑒𝑔𝑖𝑜𝑛
𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑠𝑎𝑙𝑒𝑠 𝑣𝑜𝑙𝑢𝑚𝑒 𝑏𝑦 𝑟𝑒𝑔𝑖𝑜𝑛
93
The data for the average retail price of residential electricity and regional CPI are
attained from Electric Power Statistics Information System (EPSIS) and Korean Statistical
Information Service (KOSIS), respectively. The data cover 16 municipalities excluding
Sejong City during the period of 2000-2016. Figure 4.2 shows that the real price of
residential electricity in Korea has declined from 2000 to 2016 from the values at
approximately 160 KRW/kWh to 120 KRW/kWh, respectively.
94
Figure 4.2. Trends of residential electricity price (KRW/kWh) of 16 municipalities in Korea, 2000-2016
0
20
40
60
80
100
120
140
160
1802000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
95
3) Personal income per capita (1000 KRW)
For income variable, the data for real personal income per capita in 1000 South
Korean Won (KRW) are used. Real personal income refers to disposable income or the total
annual disposable earnings of a citizen from all income sources including salaries and
wages, dividends and investment interest, and other ventures. Personal income well reflects
the socio-demographic characteristic for income since it captures the ability of the people
to spend money in their pockets to meet the consumption needs and to participate in the
society in which they live. Real personal income per capita is calculated from the nominal
personal income per capita with using the regional CPI with the base year 2015=100 as
follows:
𝑅𝑒𝑎𝑙 𝑝𝑒𝑟𝑠𝑜𝑛𝑎𝑙 𝑖𝑛𝑐𝑜𝑚𝑒 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 =𝑛𝑜𝑚𝑖𝑛𝑎𝑙 𝑝𝑒𝑟𝑠𝑜𝑛𝑎𝑙 𝑖𝑛𝑐𝑜𝑚𝑒 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎
𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝐶𝑃𝐼
The data for nominal personal income per capita and regional CPI, which are used
to calculate the real personal income per capita, are obtained from Korean Statistical
Information Service (KOSIS). The annual panel data consist of the 16 municipalities
excluding Sejong City from 2000 to 2016. Figure 4.3 presents personal income capita by
region. All municipalities in Korea have experienced gradual increase in personal income
per capita since 2000; Seoul and Ulsan are shown to be the cities with first and second
highest income per capita among all municipalities in Korea.
96
Figure 4.3. Trends of personal income per capita (1000 KRW) of 16 municipalities in Korea, 2000-2016
0
5000
10000
15000
20000
25000
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnamDaegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
97
4) Share of population aged 65 and above (%)
The share of population aged 65 and above is used as an independent variable
which represents population aging, a key socio-demographic factor analyzed in this
research. Numerous studies have used the share of different age groups to explain how the
people in certain age groups have varying influences on the residential electricity
consumption; in specific, they have used the share of population aged 65 and above as a
proxy variable for population aging (Pessanha and Leon, 2015; Kelly, 2011; Zhou, 2016;
Noh and Lee, 2013). Collected from Korean Statistical Information Service (KOSIS), the
data is the annual panel data of 16 municipalities excluding Sejong City during the period
of 2000-2016. Figure 4.4 shows that Jeonnam has the highest share of population aged 65
and above, and Ulsan, the lowest. This may be explained with the regional characteristics
of Jeonnam and Ulsan, in which the former has large portions with pastoral areas and the
latter has huge industrial hub operating with many plants, factories, facilities, et cetera. In
general, the elder populations tend to live in the rural areas (e.g., Jeonnam), and the younger
populations tend to live in the industrial (e.g., Ulsan) and metropolitan (e.g., Seoul) cities.
98
Figure 4.4. Trends of the share of population aged 65 and above (%) of 16 municipalities in Korea, 2000-2016
0
5
10
15
20
252000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
99
5) Working age population (%)
The independent variable, the share of population aged in the range of 15-64 is
used to represent the working age population in Korea during 2000-2016. OECD defines
working age population as those who are aged 15 to 64 and considers it as a typical indicator
for potential labor supply. The data is the annual panel data of 16 municipalities excluding
Sejong City during the period of 2000-2016, collected from Korean Statistical Information
Service (KOSIS). Figure 4.5 shows that currently, the trends of working age populations
are decreasing in almost all municipalities in Korea starting from 2010. Also, it is shown
that Jeonnam, Seoul, and Ulsan have the high shares of working populations compared to
other cities in Korea, and Jeonbuk, the lowest.
100
Figure 4.5. Trends of working age population (%) of 16 municipalities in Korea, 2000-2016
0
10
20
30
40
50
60
70
80
902000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
101
6) Household size
Household size is defined as the number of persons residing in a household in a
region. This often the time represents the number of persons that are financially responsible
within a family. The values are calculated by dividing the number of populations by the
number of households in a region at a specific time as the following equation:
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑠𝑖𝑧𝑒 =𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑠 𝑏𝑦 𝑟𝑒𝑔𝑖𝑜𝑛
𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠 𝑏𝑦 𝑟𝑒𝑔𝑖𝑜𝑛
The data, collected from Korean Statistical Information Service (KOSIS), is the
annual panel data of 16 municipalities excluding Sejong City during the period of 2000-
2016. Figure 4.6 represents the trends of average household size in Korea from 2000 to
2016. It is clearly shown in the graph that the household size has been rapidly decreasing
with the values of approximately 3~3.3 in 2000 to 2.25~2.5 in 2016. Gangwon, Chungbuk,
Chungnam, Gyeongbuk, Jeonnam, Jeonbuk, and Seoul have the low household size
relatively to other municipalities such as Deagu, Daejeon, Gwangju, Gyeonggi, Incheon,
and Ulsan.
102
Figure 4.6. Trends of household size of 16 municipalities in Korea, 2000-2016
0
0.5
1
1.5
2
2.5
3
3.52000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
103
7) Share of population with high education level (%)
Share of populations with high education level consists of the share of populations
with degrees in college or university level or with higher degrees (such as Master’s or
PhD’s degrees). Variable related to education level is used as one of the socio-demographic
factors focused on this study. The data are obtained from Korean Statistical Information
Service (KOSIS), Population and Housing Census. KOSIS conducts surveys and provides
the data once every five years (i.e., 2000, 2005, 2010, and 2015); in the study, the data used
in the analysis are approximated through interpolation methods. Figure 4.7 shows that the
shares of population with higher education level are increasing for all the municipalities in
Korea. In specific, Seoul has the highest percentage of population with high education level,
and Jeonnam, the lowest.
104
Figure 4.7. Trends of the share of population with degrees in higher education (%) of 16 municipalities in Korea, 2000-2016
0
0.2
0.4
0.6
0.8
1
1.22000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
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2009
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2004
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2005
2011
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2006
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2001
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2013
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2004
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2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
105
8) Heating degree days (HDDs)
Heating degree days (HDDs) are measurements designed to quantify the demand
for energy needed to heat a building. In specific, heating degree days are measured relative
to balance temperature, the outside temperature above which a building needs no heating.
Usually, the balance temperature differs by countries; in Korea, the balance temperature of
heating degree days is 18℃. Numerous ways exist in which heating degree days can be
calculated. One popular method for approximating heating degree days is to take the
average temperature on any given day and subtract it from the given balance temperature
(Lim et al. 2013). Conceptually, the higher the value of heating degree days means the
colder the climate and the higher the fuel cost to heat a building.
𝐻𝑒𝑎𝑡𝑖𝑛𝑔 𝐷𝑒𝑔𝑟𝑒𝑒 𝐷𝑎𝑦𝑠 = 𝛴[(18℃) − (𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑜𝑓 𝑎 𝑑𝑎𝑦)]
In this study, the estimated values for heating degree days are obtained by region
during the months on a seasonal basis from October to April. The balance temperature for
heating degree days is set as 18℃ following Lim et al. (2013). The data are obtained from
National Climate Data Service System (NCDSS) provided by Korean Meteorological
Administration (KMA). It is shown in Figure 4.8 that overall trends of HDDs in all
municipalities are shown to be gradually declining with large fluctuations. Also, it is
presented that Busan, Daegu, Ulsan, and Gyeongnam have relatively low HDDs, and
Chungbuk and Gyeonggi have relatively high HDDs than other municipalities.
106
Figure 4.8. Trends of heating degree days (HDDs) of 16 municipalities in Korea, 2000-2016
0
20
40
60
80
100
1202000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
107
9) Cooling degree days (CDDs)
Cooling degree days (CDDs) are measurements designed to quantify the demand
for energy needed to cool a building. In specific, cooling degree days are measured relative
to “balance temperature,” the outside temperature above which a building needs no cooling
an average daily-mean temperature threshold for human thermal comfort. Usually, the
balance temperature differs by countries and is commonly set to a value between 18~24℃
(Glickman, 2000; MRCC, 2007); in Korea, the “balance temperature” of HDDs is in the
range of 18~24℃. Numerous ways exist in which CDDs can be calculated. One popular
method for approximating CDDs is to subtract the balance temperature from the average
temperature on any specific day (Lim et al. 2013). In concepts, the higher the value of
cooling degree days means the hotter the climate and the higher the fuel costs to cool a
building.
𝐶𝑜𝑜𝑙𝑖𝑛𝑔 𝐷𝑒𝑔𝑟𝑒𝑒 𝐷𝑎𝑦𝑠 = 𝛴[(18℃) − (𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑜𝑓 𝑎 𝑑𝑎𝑦)]
In this study, the approximated values for cooling degree days are obtained during
the months on a seasonal basis from May to September. Following the values used in Lim
et al. (2013), the balance temperature is set as 18℃. The data are obtained from National
Climate Data Service System (NCDSS) provided by Korean Meteorological
Administration (KMA).
108
Figure 4.9. Trends of cooling degree days (CDDs) of 16 municipalities in Korea, 2000-2016
0
5
10
15
20
25
30
35
40
45
502000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
109
10) Attached house to apartment ratio (%)
Attached house to apartment ratio refers to the ratio of multi-family residences and
townhouses to apartments in Korea. This variable is representative of one of the factors of
dwelling types used in this study. The data are obtained from Korean Statistical Information
Service (KOSIS) and consist of the unbalanced panel data of 16 municipalities excluding
Sejong City in Korea in the years 2006, 2008, 2010, 2012, 2014, and 2016. It is illustrated
in Figure 4.10 that the shares of attached houses to apartments are decreasing throughout
the years for all 16 municipalities in Korea. This can be interpreted that the people
nowadays tend to reside more in the apartments than the attached houses including
townhouse and multi-family residences.
110
Figure 4.10. Trends of attached house to apartment ratio (%) of 16 municipalities in Korea, 2000-2016
0
0.5
1
1.5
2
2.5
3
3.5
42000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
111
11) Detached house to apartment ratio
Detached house to apartment ratio refers to the ratio single-detached to apartments
in Korea. This variable represents one of the factors of dwelling types used in the study.
The data are obtained from Korean Statistical Information Service (KOSIS) and consist of
the unbalanced panel data of 16 municipalities excluding Sejong City in Korea in the years
2006, 2008, 2010, 2012, 2014, and 2016. As shown in Figure 4.11, the shares of detached
houses to apartments are decreasing throughout the years for all 16 municipalities in Korea.
This can be interpreted that the people nowadays tend to reside more in the apartments than
in the detached houses.
112
Figure 4.11. Trends of detached house to apartment ratio (%) of 16 municipalities in Korea, 2000-2016
0
0.5
1
1.5
2
2.5
3
3.5
4
4.52000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
113
12) Female to male ratio
Female to male ratio is one of the socio-demographic factors considered in the
study. The data are obtained from Korean Statistical Information Service (KOSIS) and
Ministry of the Interior and Safety using Resident Registry Population Census. Figure 4.12
presents the trends of the female to male ratio in 16 municipalities excluding Sejong City
in Korea during 2000-2016.
The 6 municipalities with female to male ratio greater than 100 are Busan, Daegu,
Gwangju, Jeonbuk, Jeonnam, and Seoul; this means that these cities have more female
citizens than male citizens residing. The other 10 municipalities, Chungbuk, Chungnam,
Daejeon, Gangwon, Gyeongbuk, Gyeonggi, Gyeongnam, Incheon, Jeju, and Ulsan, have
female to male ratio smaller than 100; this means that the populations consist of more male
citizens than female citizens in these municipalities. The directions to which the trends of
female and male ratio are increasing or decreasing vary by region: the 8 municipalities (i.e.,
Busan, Daegu, Daejeon, Gwangju, Gyeonggi, Incheon, Jeonbuk, and Seoul) have
increasing trends and the 8 municipalities (i.e., Chungbuk, Chungnam, Gangwon,
Gyeongbuk, Gyeongnam, Jeju, Jeonnam, and Ulsan) have decreasing trends.
114
Figure 4.12. Trends of female to male ratio (%) of 16 municipalities in Korea, 2000-2016
88
90
92
94
96
98
100
102
104
1062000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
115
13) Homeownership rate (%)
The data for homeownership rate for 16 municipalities in Korea during the period
of 2000 to 2016 are attained from Korea Housing Surveys published by Ministry of Land,
Infrastructure, and Transport (MOLIT). Homeownership rate is defined as the percentage
of homes that are owned by their occupants, and this is one of the socio-demographic
factors considered in the analysis. Figure 4.13 presents the trends of homeownership ratio
of 16 municipalities in Korea from 2000 to 2016. It is shown that Seoul and Jeonnam have
the lowest and highest homeownership rates, respectively. The low homeownership rate in
Seoul can be dedicated to its exceptionally high housing prices compared to the housing
prices in other cities. This situation can make people who do not have the financial ability
to purchase a house to live on rent instead. Also, the low homeownership rate in Korea can
be explained by the investors owning numerous houses but not residing for the speculative
and pecuniary purposes.
116
Figure 4.13. Trends of homeownership ratio (%) of 16 municipalities in Korea, 2000-2016
0
10
20
30
40
50
60
70
802000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
2005
2011
2000
2006
2012
2001
2007
2013
2002
2008
2014
2003
2009
2015
2004
2010
2016
Busan ChungbukChungnam Daegu Daejeon entire GangwonGwangjuGyeongbukGyeonggiGyeongnamIncheon Jeju Jeonbuk Jeonnam Seoul Ulsan
117
Table 4.1. Definition of the variables (16 municipalities in Korea excluding Sejong City, 2000-2016)
Variable Data Unit Reference Period Regions Classifications
Dependent 𝑦𝑒𝑙𝑒𝑐 Residential electricity
consumption per capita MWh/person
KOSIS,
EPSIS 2000-2016
16
municipalities
Independent
𝑥𝑝𝑟𝑖𝑐𝑒 Residential electricity
price KRW/kWh
KOSIS,
EPSIS 2000-2016
16
municipalities Others
𝑥𝑖𝑛𝑐𝑜𝑚𝑒 Personal income per
capita 1000 KRW KOSIS 2000-2016
16
municipalities Socio-demographic
𝑥𝑓𝑒𝑚𝑎𝑙𝑒 Female to male ratio % KOSIS 2000-2016 16
municipalities Socio-demographic
𝑥𝑤𝑜𝑟𝑘 Share of population aged
15~64 % KOSIS 2000-2016
16
municipalities Socio-demographic
𝑥𝑜𝑙𝑑 Share of population aged
65 years and above % KOSIS 2000-2016
16
municipalities Socio-demographic
𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒 Household size Total population /
# of household KOSIS 2000-2016
16
municipalities Socio-demographic
𝑥𝑒𝑑𝑢𝑐 Share of population with
high education level % KOSIS 2000-2015
16
municipalities Socio-demographic
𝑥𝑐𝑑𝑑 Cooling degree days
(CDDs) °C day NCDSS 2000-2016
16
municipalities Climatic
𝑥ℎ𝑑𝑑 Heating degree days
(HDDs) °C day NCDSS 2000-2016
16
municipalities Climatic
𝑥𝑎𝑡𝑡𝑎𝑐ℎ𝑒𝑑 Attached house to
apartment ratio % KOSIS
2006, 2008,
2010, 2012,
2014, 2016
16
municipalities Others
𝑥𝑑𝑒𝑡𝑎𝑐ℎ𝑒𝑑 Detached house to
apartment ratio % KOSIS
2006, 2008,
2010, 2012,
2014, 2016
16
municipalities Others
𝑥ℎ𝑜𝑚𝑒𝑜𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝 % of homes owned by
their occupants % MOLIT
2006, 2008,
2010, 2012,
2014, 2016
16
municipalities Others
118
Table 4.2. Descriptive statistics of the data
Variable # of observations Mean Standard
deviation Minimum Maximum
Dependent 𝑦𝑒𝑙𝑒𝑐 272 1.082 0.159 0.763 1.351
Independent
𝑥𝑝𝑟𝑖𝑐𝑒 272 137.187 12.242 119.972 163.949
𝑥𝑖𝑛𝑐𝑜𝑚𝑒 272 14353.94 2032.037 10490.780 20266.850
𝑥𝑓𝑒𝑚𝑎𝑙𝑒 272 99.387 1.722 94.008 103.630
𝑥𝑤𝑜𝑟𝑘 272 71.308 3.149 63.800 77.000
𝑥𝑜𝑙𝑑 272 11.069 3.783 4.000 20.900
𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒 272 2.679 0.228 2.240 3.222
𝑥𝑒𝑑𝑢𝑐 256 0.628 0.143 0.310 0.992
𝑥𝑐𝑑𝑑 272 31.183 3.379 22.625 43.500
𝑥ℎ𝑑𝑑 272 78.188 12.757 46.300 102.800
𝑥𝑎𝑡𝑡𝑎𝑐ℎ𝑒𝑑 96 2.779 0.365 2.223 3.666
𝑥𝑑𝑒𝑡𝑎𝑐ℎ𝑒𝑑 96 1.071 0.568 0.371 3.843
𝑥ℎ𝑜𝑚𝑒𝑜𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝 96 58.979 6.891 40.200 73.400
119
Table 4.3. Results from Pearson’s correlation analysis
𝒚𝒆𝒍𝒆𝒄 𝒙𝒊𝒏𝒄𝒐𝒎𝒆 𝒙𝒑𝒓𝒊𝒄𝒆 𝒙𝒇𝒆𝒎𝒂𝒍𝒆 𝒙𝒘𝒐𝒓𝒌 𝒙𝒐𝒍𝒅 𝒙𝒉𝒐𝒖𝒔𝒆𝒉𝒐𝒍𝒅𝒔𝒊𝒛𝒆 𝒙𝒅𝒆𝒕𝒂𝒄𝒉𝒆𝒅 𝒙𝒂𝒕𝒕𝒂𝒄𝒉𝒆𝒅 𝒙𝒄𝒅𝒅 𝒙𝒉𝒅𝒅 𝒙𝒆𝒅𝒖𝒄 𝒙𝒉𝒐𝒎𝒆𝒐𝒘𝒏
𝒚𝒆𝒍𝒆𝒄 1.000
𝒙𝒊𝒏𝒄𝒐𝒎𝒆 0.740* 1.000
𝒙𝒑𝒓𝒊𝒄𝒆 -0.911* -0.621* 1.000
𝒙𝒇𝒆𝒎𝒂𝒍𝒆 0.027 -0.176* -0.013 1.000
𝒙𝒘𝒐𝒓𝒌 0.384* 0.622* -
0.162* -0.049 1.000
𝒙𝒐𝒍𝒅 0.344* -0.066 -
0.503* 0.217*
-
0.638* 1.000
𝒙𝒉𝒐𝒖𝒔𝒆𝒉𝒐𝒍𝒅 -0.819* -0.468* 0.876* -0.110* 0.080 -
0.741* 1.000
𝒙𝒅𝒆𝒕𝒂𝒄𝒉𝒆𝒅 -0.542* -0.393* 0.242* -0.068 -
0.669* 0.417* -0.162 1.000
𝒙𝒂𝒕𝒕𝒂𝒄𝒉𝒆𝒅 -0.717* -0.341* 0.925* -0.089 0.050 -
0.579* 0.875* 0.096 1.000
𝒙𝒄𝒅𝒅 0.278* 0.293* -
0.189* 0.202* 0.189* -0.029 -0.083 -0.107 -0.196 1.000
𝒙𝒉𝒅𝒅 0.106 -0.116 0.001 -0.111 -0.018 0.080 -0.146* -0.334* -0.135 -0.106 1.000
𝒙𝒆𝒅𝒖𝒄 0.663* 0.718* -
0.552* 0.253* 0.714*
-
0.261* -0.332* -0.406* -0.151 0.366*
-
0.057 1.000
𝒙𝒉𝒐𝒎𝒆𝒐𝒘𝒏 -0.236* -0.474* -0.115 -0.231* -
0.722* 0.674* -0.274* 0.264* -0.168 -0.150
-
0.137 -0.819* 1.000
120
Table 4.4. Compounded annual growth rate (CAGR) of the variables from 2000 to 2016 by regions
Region REC per capita Personal income
per capita
Real electricity
price
Working age
population Aged population Household size HDD CDD
Entire 2.95% 1.97% -1.75% 0.15% 3.86% -1.37% 0.55% -0.53%
Busan 2.97% 2.11% -1.86% -0.13% 5.74% -1.69% 0.54% -0.42%
Chungbuk 2.62% 2.08% -1.67% 0.21% 2.70% -1.64% 0.53% -0.56%
Chungnam 2.66% 2.00% -1.74% 0.20% 1.83% -1.57% 0.49% -0.69%
Daegu 2.42% 2.00% -1.74% 0.06% 5.06% -1.40% 0.23% -0.16%
Daejeon 2.58% 2.47% -1.69% 0.22% 4.49% -1.45% 0.96% -0.65%
Gangwon 2.83% 2.12% -1.68% 0.03% 3.50% -1.77% 0.07% -0.52%
Gwangju 2.75% 2.04% -1.76% 0.21% 4.60% -1.49% 0.80% -0.88%
Gyeongbuk 2.98% 1.86% -1.61% 0.09% 2.79% -1.61% 0.49% -0.48%
Gyeonggi 2.24% 1.74% -1.82% 0.38% 3.95% -1.07% 0.83% -0.72%
Gyeongnam 2.89% 2.02% -1.78% 0.24% 2.83% -1.49% 0.55% -0.46%
Incheon 2.77% 2.37% -1.75% 0.35% 4.37% -1.25% 0.02% -0.21%
Jeju 2.80% 1.73% -1.67% 0.14% 3.35% -1.49% 0.87% -0.89%
Jeonbuk 2.93% 2.16% -1.73% 0.03% 3.07% -1.68% 0.67% -0.63%
Jeonnam 3.07% 2.19% -1.66% -0.04% 2.82% -1.64% 0.70% -0.43%
Seoul 2.21% 1.71% -1.93% -0.02% 5.56% -1.28% 0.94% -0.44%
Ulsan 3.18% 1.70% -1.87% 0.46% 5.20% -1.39% -0.02% -0.31%
121
4.2 Methodology
Panel estimation methods are used for empirical analysis in this research since
using panel data has some benefits over other estimation methods. First, using panel data
for analysis can effectively solve the multi-collinearity problems that may occur when
using time series data. The multi-collinearity problem can lead to inaccurate parameter
estimations and cause severe loss of statistical power. Hence, addressing this issue is
important to avoid misleading interpretations and conclusions in the analysis. Second,
using panel data can effectively investigate the changes in volatility of electricity
consumption without using the microeconomic data. If microeconomic data are used for
analysis, multi-collinearity problem may be somewhat addressed by easing the problems
of time series correlations among the explanatory variables. However, it may be difficult
to capture the changes in income or price elasticities which can cause the changes in
volatility of electricity consumption over time. With these advantages, the study applies
panel cointegration methods and panel estimations for the empirical analysis.
122
4.2.1 Panel cointegration approach
4.2.1.1 Panel unit root test: Testing the stationarity of the panel series
Famous studies on panel unit roots test (Maddala and Wu, 1999; Im et al., 2003;
Levin et al., 2002) have suggested that the tests are not robust in the presence of cross-
sectional dependence among the variables. In order to diagnose this problem, cross-
sectional dependence test for panel data proposed by Pesaran (2004) is performed.
Pesaran’s (2004) cross-sectional test is based on the average of the correlation coefficients
of the ordinary-least-squares (OLS) residuals, obtained from augmented Dickey-Fuller
(ADF) regressions. The null hypothesis is cross-sectional independence and is asymptotic
distribution as a two-tailed standard normal distribution (Boubtane et al., 2012). If the p-
values are small enough to reject the null hypothesis of cross-sectional independence
among the panel series, panel unit root test must be employed to handle the problem of
cross-sectional dependence.
After proving the existence of cross-section dependence among the panel series,
the next step to take is determining whether the variables are integrated of the same order.
If a variable is integrated of order k: I(k), the variable is said to be integrated of order k and
thus is non-stationary. The stationary variable can be defined as I(0), and a non-stationary
variable, as I(1); an I(1) variable must be first-differenced to achieve stationarity (Harris
and Sollis, 2013). When conducting panel cointegration test, addressing the issues of non-
stationary of panel time-series data is crucial because using standard regression methods,
123
such as OLS, on non-stationary time-series with a unit root can produce the problem of
spurious regression, making the results statistically insignificant.
Hence in this study, panel unit root test developed by Pesaran (2007) is conducted.
Pesaran (2007) proposes a simple procedure for testing the unit roots in dynamic panel
series addressing the issues of cross-sectional dependence and serial correlations with error
terms. Let 𝑦𝑖𝑡 be the observations on the ith cross-section unit at time t that is generated
according to a simple dynamic panel data model:
𝑦𝑖𝑡 = (1 − 𝜌𝑖)𝜇𝑖 + 𝜌𝑖𝑦𝑖𝑡−1 + 𝑢𝑖𝑡 (1)
where yi0 has a given density function with a finite mean and variance, and uit is an error
term with the single-factor structure
𝑢𝑖𝑡 = 𝛾𝑖𝑓𝑡 + 𝜖𝑖𝑡 (2)
where 𝑓𝑡 is the unobserved common effect and 𝜖𝑖𝑡 is the idiosyncratic error.
124
Consider the following assumptions necessary for attaining the consistency of the
panel unit root tests: First, the idiosyncratic shocks, 𝜖𝑖𝑡 are independently distributed both
across i and t with mean zero and variance 𝜎𝑖2 and finite fourth order moment; second, 𝑓𝑡
is serially uncorrelated with mean zero and a constant variance σ𝑓2 which is set equal to
one without loss of generality and finite fourth order moment; third, 𝑒𝑖𝑡, 𝑓𝑡, and 𝛾𝑖 are
independently distributed for all i. More conveniently, eq. (1) can be written as the
following:
𝛥𝑦𝑖𝑡 = (1 − 𝜌𝑖)𝜇𝑖 − (1 − 𝜌𝑖)𝑦𝑖𝑡−1 + 𝜆𝑖𝑡𝑓𝑡 + 𝜖𝑖𝑡 (3)
where 𝛥𝑦𝑖𝑡 = 𝑦𝑖𝑡 − 𝑦𝑖𝑡−1. Pesaran’s (2007) unit root tests the following null hypothesis:
𝐻0: 𝜌𝑖 = 1 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑖
against the alternative hypotheses:
Ha: {
𝜌𝑖 < 1 𝑓𝑜𝑟 𝑖 = 1, … , 𝑁1
𝜌𝑖 = 1 𝑓𝑜𝑟 𝑖 = 𝑁1, … , 𝑁
Such that N1
𝑁→ 𝑘 as 𝑁 → ∞ with 0 < 𝑘 ≤ 1.
125
4.2.1.2 Panel cointegration test: Testing the convergence of panel series to long-
run equilibrium
If the variables are found to be non-stationary with unit roots, panel cointegration
test can be applied to investigate whether the series converge to the long-run equilibrium.
In the analysis, panel cointegration tests developed by Pedroni (1999, 2004) and Kao (1999)
are performed.
As an extension to the panel framework of Engle-Granger, Pedroni’s (1999, 2000)
heterogenous panel cointegration test considers regressing the variables along with cross-
section specific intercepts and tests the null hypothesis of no cointegrating relationships.
The two sets of test statistics for alternative hypotheses are proposed: 1) a test based on the
within-dimension approach, with which the four panel cointegration statistics are
calculated (i.e., panel v-statistic, panel rho-statistic, panel PP-statistics, and panel ADF-
statistic); 2) a test based on between-dimension approach, with which the four group mean
panel cointegration statistics are calculated. A consensus of these statistics is often
interpreted as a sign in favor of cointegration, but having majority of these statistics
rejecting the hypothesis is sufficient to confirm the presence of cointegration relationships
(Liddle, 2011).
126
Kao is an Engle-Granger based test which follows the same basic approach as
Pedroni cointegration test, but specifies the homogenous coefficients and the cross-section
specific intercepts on the firs-stage regressors. Under the null hypothesis of no
cointegration, the augmented version of test statistic
𝐴𝐷𝐹 =𝑡�̂�+
√6𝑁�̂�𝜐2�̂�0𝜐
√�̂�0𝜐2 /(2�̂�𝜐
2)+3�̂�𝜐2/(10�̂�0𝜐
2 )
(4)
converge to N(0,1) asymptotically.
127
4.2.1.3 Fully Modified Ordinary Least Squares (FMOLS) and Dynamic
Ordinary Least Squares (DOLS): Estimating long-run cointegrating
vectors
Once the cointegrating relationships of the variables in panel data sets are found,
we can estimate the long-run cointegrating vectors and find the signs or the values of
coefficients on the variables. The long-run parameters are estimated using fully modified
OLS (FMOLS) and dynamic OLS (DOLS) estimators developed by Pedroni (2001).
FMOLS is a non-parametric approach, while DOLS is a parametric approach, where lagged
first-differenced terms are explicitly estimated (Harris and Sollis, 2003). Both FMOLS and
DOLS address the issues of endogeneity and serial correlation. There are conflicting views
as to whether FMOLS or DOLS is preferred, with one side that views FMOLS to be more
biased than DOLS and the other side that regards DOLS has a smaller distortion in size
than FMOLS (Kao and Chiang, 2000; Pedroni, 2001). Hence in this study, both FMOLS
and DOLS are applied to test for statistical significance of the coefficients. The regression
model to be estimated with FMOLS and DOLS is as follows:
𝑙𝑛(𝑦𝑒𝑙𝑒𝑐)𝑖𝑡 = 𝛽0 + 𝛽1 𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒)𝑖𝑡 + 𝛽2 𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒)𝑖𝑡
+ 𝛽3 𝑥𝑜𝑙𝑑𝑖𝑡+
𝛽4 𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒)𝑖𝑡 + 𝛽5 𝑙𝑛(𝑥𝑐𝑑𝑑)𝑖𝑡 + 𝛽6 𝑙𝑛(𝑥ℎ𝑑𝑑)𝑖𝑡 + 𝑒𝑖𝑡 (5)
where i (i = 1, …, 16) indicates 16 municipalities in Korea, t (t = 1, …, 16) indicates annual
period from 2000 to 2016, 𝑒𝑖𝑡 is an idiosyncratic error term. The variables 𝑙𝑛(𝑦𝑒𝑙𝑒𝑐) ,
128
𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒) , 𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒) , 𝑥𝑜𝑙𝑑 , 𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒) , 𝑙𝑛(𝑥𝑐𝑑𝑑) , and 𝑙𝑛(𝑥ℎ𝑑𝑑) represent
logged term of residential electricity consumption per capita, logged term of personal
income per capita, percentage of population aged 65 and above, logged term of household
size, logged term of cooling degree days (CDDs), and logged term of heating degree days
(HDDs).
The FMOLS and DOLS estimators are given as the following:
�̂�𝐹𝑀𝑂𝐿𝑆 =1
𝑁𝛴𝑖=1
𝑁 (𝛴𝑡=1𝑇 (𝑥𝑖𝑡 − �̅�𝑖)2)−1(𝛴𝑡=1
𝑇 (𝑥𝑖𝑡 − �̅�𝑖)𝑦𝑖𝑡∗ − 𝑇𝛾𝑖) (6)
�̂�𝐷𝑂𝐿𝑆 =1
𝑛𝛴𝑖=1
𝑛 (𝛴𝑡=1𝑇 𝑧𝑖𝑡𝑧𝑖𝑡
′ )−1(𝛴𝑡=1𝑇 𝑧𝑖𝑡𝑦𝑖𝑡) (7)
129
4.2.2 Tests for fixed effects and random effects models
4.2.2.1 Breusch-Pagan Lagrange Multiplier (LM) test for random effects
and Wald test for fixed effects
The model for panel data estimations can be chosen based on the results of testing
the assumptions about the error terms 𝑢𝑖 in a panel linear regression model. Assume the
following linear panel regression model which considers the heterogeneity properties of
the panel data series, i:
𝑦𝑖𝑡 = 𝛼 + 𝛽𝑥𝑖𝑡 + 𝑢𝑖 + 𝑒𝑖𝑡 (1)
where the model includes time-invariant error term 𝑢𝑖, an error term which does not reflect
the changes over time but reflects the changes in regional characteristics, and time-variant
error term 𝑒𝑖𝑡, an error term which reflects both the changes over time and the changes in
regional characteristics. Fixed effects model considers the error term 𝑢𝑖 as the parameter
to be estimated, and random effects model assumes that the expected value of error term
𝑢𝑖 equals zero with consistent variance, 𝑢𝑖~𝑁(0, 𝜎𝑒2). In contrast, pooled ordinary least
squares (OLS) model assumes that there are no error terms that reflect heterogeneity in
regions, ignoring the characteristics of panel data, and estimates the model with the typical
OLS method.
The first step to choosing the suitable model for panel estimations is by comparing
both fixed effects model and random effects model to pooled OLS with Wald test and
130
Breusch-Pagan LM test.
The Wald test tests the significance of the fixed effect model. To determine
whether is suitable to select fixed effects model for estimation, Wald test conducts
hypothesis testing on 𝐻0: 𝑎𝑙𝑙 𝑢𝑖 = 0. Failure to reject the null hypothesis means that the
constant terms in the model becomes 𝛼 , and thus is acceptable to use pooled OLS
estimation which does not consider the heterogeneity across the regions. Rejection of the
null hypothesis means that estimating with fixed effects model is a suitable method.
Breusch-Pagan LM test tests the significance of the random effects model. To
confirm whether random effects model is suitable for estimating the panel data, the test
conducts hypothesis testing on 𝐻0: 𝑣𝑎𝑟(𝑢𝑖) = 𝜎𝑢2 = 0. Failure to reject the null hypothesis
means that pooled OLS is suitable method to estimate the panel data since the variance of
the error terms as a whole simply becomes (σu2 + 𝜎𝑒
2) = 𝜎𝑒2 ; rejection of the null
hypothesis implies the presence of heteroscedastic variances of the error terms and means
that the estimation with random effects model which considers the regional characteristics
of a panel data is an appropriate method.
131
4.2.2.2 Hausman test
Panel data estimation method is chosen depending on whether the error term 𝑢𝑖
is presented as fixed effects or random effects. Consider the following linear panel
regression model:
𝑦𝑖𝑡 = (𝑎 + 𝑢𝑖) + 𝛽𝑥𝑖𝑡 + 𝑒𝑖𝑡 (2)
From eq. (2), fixed effects model considers (𝑎 + 𝑢𝑖) as a fixed parameter for
each panel data object, and random effects model considers (𝑎 + 𝑢𝑖) as a random variable
following random distribution with (𝑎 + 𝑢𝑖)~𝑁(𝛼, 𝜎𝑢2). It is possible to select the fitting
model for panel estimations between fixed and random effects models by conducting
Hausman test. The null and alternative hypotheses for Hausman test are as follows:
𝐻0: 𝑐𝑜𝑣(𝑥𝑖𝑡 , 𝑢𝑖) = 0 (3)
𝐻1: 𝑐𝑜𝑣(𝑥𝑖𝑡 , 𝑢𝑖) ≠ 0 (4)
Failure to reject the null hypothesis means that random effects model is suitable
for estimating the panel data; rejection of the null hypothesis means that fixed effects model
is more efficient model to obtain consistent estimates in panel series.
132
4.2.2.3 Modified Wald test for heteroscedasticity and Wooldridge test for auto-
correlations within panel units
Since panel data has the characteristics of both cross-sectional and time-series
data, the problems of heteroscedasticity among panel data sets and autocorrelations among
the error terms may occur. Estimating without addressing these issues can produce
inconsistent and inefficient estimates. Hence, it is of utmost importance to detect and
address these issues prior to conducting panel data estimations (Wooldridge, 2010).
First, modified Wald test tests for the panel-level heteroscedasticity among the
variances of all cross-sectional unit i. The null hypothesis for the test is 𝐻0: 𝜎𝑖2 = 𝜎𝑢
2 ,
which presents the homoscedastic properties of the panel data. To account for the presence
of heteroscedasticity among the variances, standard errors robust to heteroscedasticity must
be computed and applied. Second, Wooldridge test tests for autocorrelation among the error
term in panel groups against the null hypothesis of no first-order autocorrelation, 𝐻0 =
𝑐𝑜𝑣(𝑒𝑖𝑡 , 𝑒𝑖𝑠) = 0. If the error term 𝑒𝑖𝑡 exhibits first-order autocorrelation, it is denoted as
AR(1) process, 𝑒𝑖𝑡 = 𝜌𝑒𝑖𝑡−1 + 𝑣𝑖𝑡. Consider the following linear one-way model:
𝑦𝑖𝑡 = 𝑎 + 𝑋𝑖𝑡𝐵1 + 𝑍𝑖𝐵2 + 𝜇𝑖 + 𝜖𝑖𝑡 (5)
133
Wooldridge test uses the residuals from the regression with first-differences:
𝑦𝑖𝑡 − 𝑦𝑖𝑡−1 = (𝑋𝑖𝑡 − 𝑋𝑖𝑡−1)𝐵1 + 𝜖𝑖𝑡 (6)
𝛥𝑦𝑖𝑡 = 𝛥𝑋𝑖𝑡𝐵1 + 𝛥𝑒𝑖𝑡 (7)
where 𝛥 is the first-difference operator.
The procedure of Wooldridge starts by estimating the parameter 𝐵1 by regressing
𝛥𝑦𝑖𝑡 on 𝛥𝑋𝑖𝑡 and attaining the residuals 𝜖�̂�𝑡 . If 𝜖𝑖𝑡 is not serially correlated, then
𝑐𝑜𝑟𝑟(𝛥𝜖𝑖𝑡 , 𝛥𝜖𝑖𝑡−1) = −0.5. To adjust for the panel correlations in the regression of 𝜖�̂�𝑡
on 𝜖�̂�𝑡−1, robust standard errors or clustering at the panel level must be applied, which is
also robust to heteroscedasticity issues.
134
4.2.2.4 Equation to be estimated with fixed effects and random effects models
The following equation considers the panel regression model with seven statistically
significant variables to be estimated and further interpreted in the results:
𝑙𝑛(𝑦𝑒𝑙𝑒𝑐)𝑖𝑡 = 𝛽0 + 𝛽1 𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒)𝑖𝑡 + 𝛽2 𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒)𝑖𝑡
+ 𝛽3 𝑥𝑜𝑙𝑑𝑖𝑡
+ 𝛽4 𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒)𝑖𝑡 + 𝛽5 𝑙𝑛(𝑥𝑐𝑑𝑑)𝑖𝑡 + 𝛽6 𝑙𝑛(𝑥ℎ𝑑𝑑)𝑖𝑡 + 𝑢𝑖 + 𝑒𝑖𝑡
where i (i = 1, …, 16) indicates 16 municipalities in Korea, t (t = 1, …, 16) indicates annual
period from 2000 to 2016, 𝑢𝑖 is an individual-specific effect error term, and 𝑒𝑖𝑡 is an
idiosyncratic error term. The variables 𝑙𝑛(𝑦𝑒𝑙𝑒𝑐) , 𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒) , 𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒) , 𝑥𝑜𝑙𝑑 ,
𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒) , 𝑙𝑛(𝑥𝑐𝑑𝑑) , and 𝑙𝑛(𝑥ℎ𝑑𝑑) represent logged term of residential
electricity consumption per capita, logged term of personal income per capita, percentage
of population aged 65 and above, logged term of household size, logged term of cooling
degree days (CDDs), and logged term of heating degree days (HDDs). In fixed effects
model, 𝑢𝑖 is considered as a parameter to be estimated, whereas in random effects model,
𝑢𝑖 is considered as a random variable following the distribution 𝑢𝑖~𝑁(0, 𝜎𝑢2) along with
the distribution of an error term 𝑒𝑖𝑡~𝑁(0, 𝜎𝑒2).
135
Chapter 5. Empirical results
5.1 Panel cointegration results
First, the presence of cross-sectional dependence among the variables is checked in
order to confirm the robustness of the test. Table 5.1 reports the results of cross-sectional
dependence test proposed by Pesaran (2004, 2015). All the test statistics of the results
strongly reject the null hypothesis of cross-sectional independence among the series at the
1% significance levels, indicating that cross-sectional dependence exists in the panel units.
Table 5.2 summarizes the results of panel unit root tests and show that all the variables
appear to be non-stationary integrated of order one I(1) variables. This holds true because
the test statistics of the variables at level fail to reject the null hypothesis of non-stationarity
of the variables but reject the null hypothesis with the variables at first-difference. Table
5.3 reports the results of Pedroni’s (1999, 2004) and Kao’s (1999) panel cointegration tests.
The results for Pedroni panel cointegration test indicate that two out of four within-
dimension statistics and two out of three between-dimension statistics strongly reject the
null hypothesis of no cointegration.
136
Table 5.1. Results of Pesaran’s (2004, 2015) cross-sectional dependence tests
Variable Test statistic p-value
𝑙𝑛(𝑦𝑒𝑙𝑒𝑐𝑡) 44.770 0.000
𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒) 43.782 0.000
𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒) 44.454 0.000
𝑥𝑜𝑙𝑑 44.694 0.000
𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒) 45.080 0.000
𝑙𝑛(𝑥ℎ𝑑𝑑) 41.299 0.000
𝑙𝑛(𝑥𝑐𝑑𝑑) 34.687 0.000
Note: ***, ** and * denote rejection of the null hypothesis of cross-sectional independence among the series in the panel at 1%, 5% and 10%
significance levels, respectively.
137
Table 5.2. Results of Pesaran’s (2007) panel unit root tests
Note: ***, ** and * denote rejection of null hypothesis of non-stationarity of the variables at 1%, 5% and 10% significance levels, respectively.
Figures in the parentheses are p-values.
Variable
Test statistic at level Test statistic at first difference
Without trend With trend Without trend With trend
𝑙𝑛(𝑦𝑒𝑙𝑒𝑐𝑡) -0.784
(0.217)
1.167
(0.878)
-7.563***
(0.000)
-5.791***
(0.000)
𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒) 0.044
(0.518)
-1.243
(0.107)
-10.565***
(0.000)
-9.316***
(0.000)
𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒) 2.416
(0.992)
2.562
(0.995)
-3.212***
(0.001)
-1.500*
(0.067)
𝑥𝑜𝑙𝑑 3.389
(1.000)
3.510
(1.000)
-0.050
(0.480)
-6.957***
(0.000)
𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒) -0.758
(0.224)
0.769
(0.779)
-3.217***
(0.001)
-1.556*
(0.060)
𝑙𝑛(𝑥ℎ𝑑𝑑) 0.927
(0.823)
1.572
(0.942)
-9.952***
(0.000)
-7.592***
(0.000)
𝑙𝑛(𝑥𝑐𝑑𝑑) 2.134
(0.984)
4.391
(1.000)
-11.237***
(0.000)
-10.031***
(0.000)
138
Table 5.3. Pedroni’s (1999, 2004) and Kao’s (1999) panel cointegration tests for individual models without the deterministic trends
Note: ***, ** and * denote rejection of the null hypothesis of no cointegration at 1%, 5% and 10% significance levels, respectively. Figures in
the parentheses are p-values. Lag length are determined using Akaike Information Criterion (AIC).
Pedroni’s (1999, 2004) residual cointegratrion test
Within-dimension test statistics Between-dimension test statistics
Panel v-statistic -0.999 (0.841) Group rho-statistic 4.66 (1.000)
Panel rho-statistic 3.352 (0.999) Group-PP statistic -3.036*** (0.001)
Panel PP-statistic -1.976*** (0.024) Group ADF statistic -2.913*** (0.001)
Panel ADF-statistic -2.111*** (0.017)
Kao’s (1999) residual cointegration test
ADF -4.751*** (0.000)
139
5.2 Fully modified ordinary least squares (FMOLS) and
dynamic least squares (DOLS) results
𝑙𝑛(𝑦𝑒𝑙𝑒𝑐)𝑖𝑡 = 𝛽0 + 0.191 𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒)𝑖𝑡 − 0.600 𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒)𝑖𝑡
− 0.018𝑥𝑜𝑙𝑑𝑖𝑡−
1. 406 𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒)𝑖𝑡 + 0.193 𝑙𝑛(𝑥ℎ𝑑𝑑)𝑖𝑡 + 0.088 𝑙𝑛(𝑥𝑐𝑑𝑑)𝑖𝑡 + 𝑒𝑖𝑡 (1)
𝑙𝑛(𝑦𝑒𝑙𝑒𝑐)𝑖𝑡 = 𝛽0 + 0.213 𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒)𝑖𝑡 − 0.565 𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒)𝑖𝑡
− 0.017𝑥𝑜𝑙𝑑𝑖𝑡−
1.410 𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒)𝑖𝑡 + 0.199 𝑙𝑛(𝑥ℎ𝑑𝑑)𝑖𝑡 + 0.077 𝑙𝑛(𝑥𝑐𝑑𝑑)𝑖𝑡 + 𝑒𝑖𝑡 (2)
Having determined the cointegrating relationship in the panel series, the study
conducts FMOLS and DOLS methods proposed by Pedroni (2001) to find the long-run
parameters. In Table 5.4 and Table 5.5, the results of FMOLS and DOLS estimates are
reported, respectively. Eq. 1 and Eq. 2 show the estimated equations for FMOLS and DOLS
estimators, respectively. The coefficients on logged personal income per capita or price
elasticity of the demand for residential electricity are found to be 0.191 and 0.213 at 1%
and 5% significance levels for FMOLS and DOLS, respectively. This means that 1%
increase in disposable or personal income per capita increases the residential electricity
consumption per capita by 0.191 or 0.213% in the long run. This is consistent with the
previous studies which confirm the positive relationship between income and residential
electricity consumption. The coefficients on logged residential electricity price is estimated
to be -0.600 and -0.565 for FMOLS and DOLS, respectively, and are statistically
significant at 1% level. This can be interpreted that 1% increase in the prices of residential
140
electricity decreases residential electricity consumption per capita by 0.600% or 0.565% in
the long run. Also, price elasticity for residential electricity demand is found to be negative
and inelastic, consistent with the results of the previous studies.
The share of population aged 65 and above is shown to exhibit negative relationship
with residential electricity consumption, with the coefficients of -0.018 and -0.017 at 1%
significance level for FMOLS and DOLS, respectively. This means that 1% increase in the
share of population aged 65 and above will reduce the residential electricity consumption
per capita by 1.8% or 1.7% in the long run. This result is consistent with the previous
studies that have confirmed the negative aging effects on residential electricity
consumption in Korea.
The coefficients on the logged variable of household size is estimated to be -1.406
and -1.410 at 1% significance level for FMOLS and DOLS, respectively. This implies that
1% increase in household size reduces residential electricity consumption by 1.406% or
1.410% in the long run. This is consistent with the results of previous studies that state that
the increase in the number of people in a household reduces residential electricity
consumption since the energy efficiency per household improves as the size of a household
gets larger in general.
Logged variables for heating degree days (HDDs) have the coefficients of 0.193 and
0.199 at 1% significance level for FMOLS and DOLS, respectively. This means that 1%
increase of HDDs increases residential electricity consumption per capita by 0.193% or
0.199% in the long run. Logged variables for cooling degree days (CDDs) have the
coefficients of 0.088 and 0.077 at 1% significance level for FMOLS and DOLS,
141
respectively. This can be interpreted that as 1% of cooling degree days increase, 0.088% or
0.077% of residential electricity consumption rises in the long run.
142
Table 5.4. Results from the estimations with Fully Modified OLS (FMOLS)
Variable Coefficient Standard error Test statistic p-value Direction
𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒) 0.191*** 0.039 4.821 0.000 +
𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒) -0.600*** 0.053 -11.243 0.000 -
𝑥𝑜𝑙𝑑 -0.018*** 0.002 -8.169 0.000 -
𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒) -1.406*** 0.078 -17.798 0.000 -
𝑙𝑛(𝑥ℎ𝑑𝑑) 0.193*** 0.017 10.838 0.000 +
𝑙𝑛(𝑥𝑐𝑑𝑑) 0.088*** 0.013 6.318 0.000 +
R-squared 0.960
Adjusted R-squared 0.957
Note: *** denotes rejecting the hypothesis at 1% significance level.
143
Table 5.5. Results from the estimations with Dynamic Least Squares (DOLS)
Variable Coefficient Standard error Test statistic p-value Direction
𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒) 0.213** 0.093 2.280 0.023 +
𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒) -0.565*** 0.127 -4.441 0.000 -
𝑥𝑜𝑙𝑑 -0.017*** 0.005 -3.198 0.001 -
𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒) -1.410*** 0.182 -7.717 0.000 -
𝑙𝑛(𝑥ℎ𝑑𝑑) 0.199*** 0.043 4.561 0.000 +
𝑙𝑛(𝑥𝑐𝑑𝑑) 0.077** 0.033 2.307 0.021 +
R-squared 0.966
Adjusted R-squared 0.963
Note: *** and ** denote rejecting the hypothesis at 1% and 5% significance levels, respectively.
144
5.3 Results for tests of hypotheses with panel data
The following section briefly describes the results for tests of hypotheses with panel
data which are considered prior to conducting panel estimations with fixed effects and
random effects models. These tests are important for they confirm the suitability of the
models used for estimations.
145
Table 5.6. Results of the Breusch-Pagan Lagrange Multiplier (LM) test for random
effects
Chi-squared statistics p-values
χ2(1) = 599.48 Prob>χ2 = 0.000
𝑯𝟎: 𝒗𝒂𝒓(𝒖𝒊) = 𝝈𝒖𝟐 = 𝟎
Table 5.6 presents the results from the Breusch-Pagan Lagrange Multiplier (LM) test
for random effects. The null hypothesis is rejected with the p-value of 0.000 at the
significance level of 1%. This result implies the presence of heteroscedastic variances of
the error terms and means that the estimation with random effects model which considers
the regional characteristics of a panel data is an appropriate method.
146
Table 5.7. Results of Wald test for fixed effects
Chi-squared statistics p-values
χ2(6) = 5906.52 Prob.>χ2 = 0.000
𝑯𝟎: 𝒂𝒍𝒍 𝒖𝒊 = 𝟎.
Wald test is conducted to choose whether fixed effects model or pooled OLS
estimation is a better tool for estimation. In specific for the testing of the null hypothesis,
failure to reject the null hypothesis means that the constant terms in the model becomes 𝛼,
and thus is acceptable to use pooled OLS estimation which does not consider the
heterogeneity across the regions; rejection of the null hypothesis means that estimating with
fixed effects model is a suitable method. Since the null hypothesis is rejected at the
significance level of 1%, using fixed effects model which considers heterogeneity across
the regions is a better method.
147
Table 5.8. Results of Hausman test for fixed effects and random effects models
Chi-squared statistics p-values
χ2(6) = 42.84 Prob>χ2 = 0.000
𝑯𝟎: 𝒄𝒐𝒗(𝒙𝒊𝒕, 𝒖𝒊) = 𝟎
Failure to reject the null hypothesis means that random effects model is suitable for
estimating the panel data; rejection of the null hypothesis means that fixed effects model is
more efficient model to obtain consistent estimates in panel series.
148
Table 5.9. Results of modified Wald test for heteroscedasticity
Chi-squared statistics p-values
χ2(16) = 127.18 Prob>χ2 = 0.000
𝑯𝟎: 𝝈𝒊𝟐 = 𝝈𝟐 𝒇𝒐𝒓 𝒂𝒍𝒍 𝒊
Table 5.9 presents that the null hypothesis of groupwise homoscedasticity is
strongly rejected at 1% significance level. Rejection of the null hypothesis for modified
Wald test means that cross-sectional units in across the panel data exhibit heteroscedasticity.
To relax the presence of heteroscedasticity, the final model is estimated using robust
standard errors which are robust to heteroscedasticity issues.
149
Table 5.10. Results of Wooldridge test for autocorrelation
F-statistics p-values
𝐹(1, 15) = 31.225 Prob>F = 0.000
𝑯𝟎: 𝒄𝒐𝒓𝒓(𝜟𝝐𝒊𝒕, 𝜟𝝐𝒊𝒕−𝟏) = −𝟎. 𝟓
Table 5.10 reports the results for Wooldridge test for autocorrelation. The F-
statistics is 152.97, which strongly reject the null hypothesis of no first order correlation in
panel data at 1% significance level. This means that the panel data are under the issues of
autocorrelation. To ease these issues, the final model is estimated considering the robust
standard errors or clustering at panel level.
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5.4 Fixed effects and random effects results
Through the process of eliminating the statistically insignificant variables one by one,
the equation with the variables which are statistically significant for every single one is
achieved. This leads to the interpretation on the estimated coefficients more robust. The
following equations present the estimated coefficients with fixed effects and random effects
models, respectively, for seven statistically significant variables that are chosen ultimately
for empirical analysis in the study:
𝑙𝑛(𝑦𝑒𝑙𝑒𝑐)𝑖𝑡 = 1.251 + 0.212 𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒)𝑖𝑡 − 0.564 𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒)𝑖𝑡
− 0.017𝑥𝑜𝑙𝑑𝑖𝑡
− 1.412 𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒)𝑖𝑡 + 0.200 𝑙𝑛(𝑥ℎ𝑑𝑑)𝑖𝑡 + 0.077 𝑙𝑛(𝑥𝑐𝑑𝑑)𝑖𝑡
+ 𝑢𝑖 + 𝑒𝑖𝑡
(3)
𝑙𝑛(𝑦𝑒𝑙𝑒𝑐)𝑖𝑡 = 2.365 + 0.178 𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒)𝑖𝑡 − 0.734 𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒)𝑖𝑡
− 0.014𝑥𝑜𝑙𝑑𝑖𝑡
− 1.165 𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒)𝑖𝑡 + 0.123 𝑙𝑛(𝑥ℎ𝑑𝑑)𝑖𝑡 + 0.108 𝑙𝑛(𝑥𝑐𝑑𝑑)𝑖𝑡
+ 𝑢𝑖 + 𝑒𝑖𝑡
(4)
where i (i = 1, …, 16) indicates 16 municipalities in Korea, t (t = 1, …, 16) indicates annual
period from 2000 to 2016, 𝑢𝑖 is an individual-specific effect error term, and 𝑒𝑖𝑡 is an
151
idiosyncratic error term. The variables 𝑙𝑛(𝑦𝑒𝑙𝑒𝑐) , 𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒) , 𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒) , 𝑥𝑜𝑙𝑑 ,
𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒) , 𝑙𝑛(𝑥𝑐𝑑𝑑) , and 𝑙𝑛(𝑥ℎ𝑑𝑑) represent logged term of residential
electricity consumption per capita, logged term of personal income per capita, percentage
of population aged 65 and above, logged term of household size, logged term of cooling
degree days (CDDs), and logged term of heating degree days (HDDs). In fixed effects
model, 𝑢𝑖 is considered as a parameter to be estimated, whereas in random effects model,
𝑢𝑖 is considered as a random variable following the distribution 𝑢𝑖~𝑁(0, 𝜎𝑢2) along with
the distribution of an error term 𝑒𝑖𝑡~𝑁(0, 𝜎𝑒2).
Table 5.11. and Table 5.12. present the estimated results with fixed effects and
random effects models. The coefficients on logged personal income per capita or price
elasticity of the demand for residential electricity are found to be 0.212 and 0.178 at 1%
significance level for fixed effects and random effects, respectively. This means that 1%
increase in disposable or personal income per capita increases the residential electricity
consumption per capita by 0.212 or 0.178%. This is consistent with the previous studies
which confirm the positive relationship between income and residential electricity
consumption. The coefficients on logged residential electricity price is calculated to be -
0.564 and -0.734 for fixed effects and random effects, respectively, and are statistically
significant at 1% level. This can be interpreted that 1% increase in the prices of residential
electricity decreases residential electricity consumption per capita by 0.564% or 0.734%.
Also, price elasticity for residential electricity demand is found to be negative and inelastic,
consistent with the results of the previous studies.
152
The share of population aged 65 and above is shown to exhibit negative relationship
with residential electricity consumption, with the coefficients of -0.017 and -0.014 at 1%
significance level for fixed effects and random effects models, respectively. This means
that 1% increase in the share of population aged 65 and above will reduce the residential
electricity consumption per capita by 1.7% or 1.4%. This can be explained in part by the
general propensities of older people towards energy savings and preference of analogue
styles to digital styles; with these, older people use much less home appliances and
electricity than younger generations. The result is consistent with the previous studies that
have confirmed the negative aging effects on residential electricity consumption in Korea.
The coefficients on the logged variable of household size is estimated to be -1.412
and -1.165 at 1% significance level for fixed effects and random effects models,
respectively. This implies that 1% increase in household size reduces residential electricity
consumption by 1.412% or 1.165%, which is consistent with the previous studies that state
that the increase in the number of people in a household reduces residential electricity
consumption since the energy efficiency per household improves as the size of a household
gets larger in general.
Logged variables for heating degree days (HDDs) have the coefficients of 0.200 and
0.123 at 1% significance level for fixed effects and random effects models, respectively.
This means that 1% increase of HDDs increases residential electricity consumption per
capita by 0.200% or 0.123%. Logged variables for cooling degree days (CDDs) have the
coefficients of 0.077 and 0.108 at 1% significance level for fixed effects and random effects
153
models, respectively. This can be interpreted that as 1% of cooling degree days increases,
0.077% or 0.108% of residential electricity consumption rises.
Table 5.13 presents the results from the panel estimations with Least Square Dummy
Variable (LSDV) model considering the characteristics that are inherent in a certain region.
With the reference group being Seoul city, the equation for LSDV model with the estimated
coefficients is as follows:
𝑙𝑛(𝑦𝑒𝑙𝑒𝑐)𝑖𝑡 = ∑ 𝐴𝑖15𝑖=1 + 1.185 + 0.212 𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒)𝑖𝑡 − 0.564 𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒)
𝑖𝑡−
0.017𝑥𝑜𝑙𝑑𝑖𝑡− 1.412 𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒)𝑖𝑡 + 0.200 𝑙𝑛(𝑥ℎ𝑑𝑑)𝑖𝑡 + 0.077 𝑙𝑛(𝑥𝑐𝑑𝑑)𝑖𝑡 + 𝑢𝑖 +
𝑒𝑖𝑡
(5)
where 𝐴𝑖 represents the constant term for each regional dummy which demonstrates the
inherent regional characteristics affecting the residential electricity consumption per capita
in Korea, in comparison to the reference group, Seoul. It is shown in Table 5.13 that the six
major metropolitan cities in Korea consisting of Busan, Incheon, Daegu, Daejeon, and
Gwangju exhibit statistically significant coefficients with the values 0.165, 0.060, 0.124,
0.041, and 0.085, respectively. This means that due to their inherent regional characteristics,
these six metropolitan cities consume more residential electricity per capita than Seoul does
by about 0.165%, 0.060%, 0.124%, 0.041%, and 0.085% more for Busan, Incheon, Daegu,
Daejeon, and Gwangju, respectively. This may be explained from the fact that these
154
metropolitan cities consist of people more than one million and possess somewhat lesser
population densities and thus larger residential area per person than Seoul does; the larger
the residential area per capita, the greater the residential electricity consumption per capita
may be. On the other hand, it is shown that the provinces which mainly consist of the rural
cities with less than 500,000 people and low population densities such as Chungbuk,
Chungnam, Gangwon, Gyeongbuk, and Jeju-do do not have significantly different effects
than Seoul does. However, further investigations must be conducted for more concrete
interpretations.
155
Table 5.11. Results from the estimations with fixed effects model
Variable Coefficient Standard error Test statistic p-value Direction
𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒) 0.212*** 0.068 3.100 0.002 +
𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒) -0.564*** 0.093 -6.070 0.000 -
𝑥𝑜𝑙𝑑 -0.017*** 0.003 -4.370 0.000 -
𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒) -1.412*** 0.133 -10.570 0.000 -
𝑙𝑛(𝑥ℎ𝑑𝑑) 0.200*** 0.032 6.250 0.000 +
𝑙𝑛(𝑥𝑐𝑑𝑑) 0.077*** 0.024 3.150 0.002 +
constants 1.251 0.879 1.420 0.156 +
R-squared (within) 0.963
R-squared (between) 0.409
R-squared (overall) 0.878
Note: *** denotes rejecting the hypothesis at 1% significance level.
156
Table 5.12. Results from the estimations with random effects model
Variable Coefficient Standard error Test statistic p-value Direction
𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒) 0.178*** 0.061 2.900 0.004 +
𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒) -0.734*** 0.098 -7.490 0.000 -
𝑥𝑜𝑙𝑑 -0.014*** 0.003 -4.200 0.000 -
𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒) -1.165*** 0.138 -8.430 0.000 -
𝑙𝑛(𝑥ℎ𝑑𝑑) 0.123*** 0.042 2.910 0.004 +
𝑙𝑛(𝑥𝑐𝑑𝑑) 0.108*** 0.025 4.230 0.000 +
constants 2.365** 0.930 2.540 0.011 +
R-squared (within) 0.962
R-squared (between) 0.397
R-squared (overall) 0.905
Note: *** and ** denote rejecting the hypothesis at 1% and 5% significance levels, respectively.
157
Table 5.13. Results from the estimations with LSDV (Reference group: Seoul)
Variable Coefficient Standard
error
Test
statistic p-value Direction
𝑙𝑛(𝑥𝑖𝑛𝑐𝑜𝑚𝑒) 0.212*** 0.068 3.100 0.002 +
𝑙𝑛(𝑥𝑝𝑟𝑖𝑐𝑒) -0.564*** 0.093 -6.070 0.000 -
𝑥𝑜𝑙𝑑 -0.017*** 0.005 -6.480 0.000 -
𝑙𝑛(𝑥ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠𝑖𝑧𝑒) -1.412*** 0.133 -10.57 0.000 -
𝑙𝑛(𝑥ℎ𝑑𝑑) 0.200*** 0.032 6.250 0.000 +
𝑙𝑛(𝑥𝑐𝑑𝑑) 0.077*** 0.024 3.150 0.002 +
constants 1.185 0.895 1.320 0.187
Chungbuk 0.020 0.030 0.680 0.495
Chungnam 0.010 0.034 0.310 0.755
Daegu 0.124** 0.023 5.220 0.000
Daejeon 0.041** 0.019 2.180 0.030
Gangwon 0.042 0.035 1.200 0.233
Gwangju 0.085*** 0.022 3.800 0.000
Gyeongbuk 0.007 0.037 0.190 0.851
Gyeonggi 0.037** 0.015 2.380 0.018
Gyeongnam 0.108** 0.027 3.910 0.000
Incheon 0.060*** 0.019 3.020 0.003
Jeju 0.045 0.030 1.480 0.140
Jeonbuk 0.116*** 0.038 3.030 0.003
Jeonnam 0.120** 0.050 2.350 0.019
Busan 0.165*** 0.024 6.660 0.000
Ulsan 0.074*** 0.019 3.740 0.000
R-squared 0.9663
Adjusted R-squared 0.9634
Note: *, ** and *** denote rejecting the hypothesis at 1%, 5%, and 1% significance levels,
respectively
158
Table 5.14. Confirmation of the research hypotheses with the estimated results
Research
hypotheses
Estimated
results from
FMOLS
Estimated
results from
DOLS
Estimated
results from
fixed effects
model
Estimated
results from
random
effects model
Hypothesis I:
Population aging
and residential
electricity
consumption exhibit
a negative
relationship
Yes, with the
coefficient of
-0.018
Yes, with the
coefficient of
-0.017
Yes, with the
coefficient of
-0.017
Yes, with the
coefficient of
-0.014
Hypothesis II:
Increase in
household size
reduces residential
electricity
consumption per
capita
Yes, with the
coefficient of
-1.406
Yes, with the
coefficient of
-1.410
Yes, with the
coefficient of
-1.412
Yes, with the
coefficient of
-1.165
Hypothesis III:
Education level,
gender difference,
working population,
and homeownership
do not have
significant effects on
residential electricity
consumption
Yes. Hypothesis III is confirmed via the process of eliminating
the statistically significant variables. Education level, female
to male ratio, working population, and homeownership rate are
found to have no effects on residential electricity consumption,
at least for the empirical analysis.
Hypothesis IV:
Heating degree days
(HDDs) and cooling
degree days (CDDs)
have positive
relationships with
residential electricity
consumption
Yes, with the
coefficient of
0.193 for
HDDs and
0.088 for
CDDs
Yes, with the
coefficient of
0.199 for
HDDs and
0.077 for
CDDs
Yes, with the
coefficient of
0.200 for
HDDs and
0.077 for
CDDs
Yes, with the
coefficient of
0.123 for
HDDs and
0.108 for
CDDs
159
Chapter 6. Conclusions
6.1 Conclusions
The study has conducted empirical analyses on how population aging and other
socio-demographic factors affect residential electricity consumption per capita in 16
municipalities, excluding Sejong City, in Korea during the period of 2000-2016. For
empirical methods, the study has chosen Fully Modified OLS (FMOLS) and Dynamic OLS
(DOLS) with panel cointegration approach and fixed effects and random effects models for
panel estimations. Then, the study has applied least squares dummy variables (LSDV)
models considering each regional characteristic that may differently affect the residential
electricity consumption per capita in Korea.
By classifying several socio-demographic factors, the study has proposed the
following four research hypotheses for in-depth analysis: 1) Population aging and
residential electricity consumption exhibit a negative relationship; 2) increase in household
size reduced residential electricity consumption per capita; 3) Education level, gender
difference, working age population, and homeownership do not have significant effects on
residential electricity consumption; and 4) Heating degree days (HDDs) and cooling degree
days (CDDs) have positive relationships with residential electricity consumption.
From the empirical results, the study draws conclusions that all four research
hypotheses presented above are valid. For the first hypothesis, population aging has a
negative impact on the residential electricity consumption in the long-run with the
160
parameter estimates ranging from -0.018 to -0.014. This means that population aging
reduces the residential electricity consumption per capita in Korea, but the aging society is
not a desirable condition nationwide. From the energy-related perspective, rapid population
aging in Korea suggests the widening gap in energy equity in Korea; growing number of
elderly generations may lead to elderly households that lack accessibility to or affordability
of energy at homes. As clearly exemplified in the World Energy Council’s Energy
Trilemma Index, Korea is ranked 26th in Energy equity, an index which measures
accessibility to or affordability of energy; it is worth to take note that the other advanced
countries such as Netherlands, Switzerland, Austria, and Denmark are ranked 6th, 7th, 8th,
and 12th, respectively. The government must devise effective plans to make sure that the
elderly and the vulnerable groups in Korea are under the access to energy safely and freely.
Further, since Korea's population aging is progressing at an unprecedented rate, this
suggests that its impact on residential electricity consumption will be much different than
now in the future. It is recommended that the government set up plans for electricity supply
and demand in preparation for the upcoming changes in population structure and their
effects on the volatility of residential electricity consumption.
For the second hypothesis, as the size of households grows, the residential electricity
consumption per capita decreases. This result parallels that of Ota et al. (2018) which states
that energy efficiency per household improves as the size of a household grows, in general,
considering that the increase in the number of people in a household reduces the needs to
duplicate usage of shared appliances or electricity. Policy-wise, it is important to introduce
R&D policies to encourage the development of technologies for large home appliances to
improve their energy efficiency and product qualities.
161
For the third hypothesis, heating degree days (HDDs) and cooling degree days
(CDDs) positively affect residential electricity consumption in Korea. This can be
interpreted that for heating, more people are using their means to heat a house by using
electricity than city gas. For cooling, more people today than in the past have air-
conditioners installed at their homes, and these generations have propensity to freely use
air-conditioners and other cooling devices which use electricity. Hence, it is recommended
for the government to devise energy plans considering the demand for electricity for
heating and cooling in accordance with the rise and decline in temperatures due to climate
change.
For the fourth hypothesis, through the process of eliminating the statistically
insignificant variables, education level, gender difference, working age population, and
homeownership are found have no significant effects on residential electricity consumption.
The differences in education level and gender do not pose significant effects on residential
electricity consumption because. Homeownership rate as well does not significantly affect
residential electricity consumption since a person, regardless of owning or renting a house,
must pay its bills for using electricity at homes.
Price elasticities and income elasticities of demand for residential electricity
consumption are found to be negative and positive, respectively. This means that as
electricity price increases, residential electricity consumption per capita decreases; as per
capita income increases, residential electricity consumption increases. This result gives
further insights for policy designs that while the plans that tackle electricity price rises may
help reduce the residential electricity consumption in Korea, solely considering the price
of electricity may pose some constraints as per capita personal income is expected to
162
increase.
In this study, brief overviews of Germany and Sweden regarding the effects of
population aging on the residential energy consumption are discussed; however, more
detailed and in-depth analyses consisting of cross-country comparisons considering multi-
faceted indicators are recommended to provide for Korea more cutting-edge and effective
academic and policy insights. Also, due to insufficiency of data, the study has not
considered other socio-demographic factors that may well affect residential electricity
consumption. More dynamic results are expected if the variables for the consumer behavior
factors are included in the analysis. Also, conducting thorough research by carrying out
surveys to obtain more precise data on socio-demographic and consumer-behavior factors
is recommended.
163
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Appendix
Trends of all data for 16 municipalities in Korea during 2000-2016 presented in Figure
A.1~Figure A.34.
Figure A.1. Trends of all variables excluding population aging for the entire municipalities,
2000-2016 ....................................................................................................................... 176
Figure A.2. Trends of the population aging and residential electrity consumption variables
for the entire municipalities, 2000-2016 ......................................................................... 177
Figure A.3. Trends of all variables excluding population aging for Busan, 2000-2016 . 178
Figure A.4. Trends of the population aging and residential electrity consumption variables
for Busan, 2000-2016 ...................................................................................................... 179
Figure A.5. Trends of all variables excluding population aging for Chungbuk, 2000-2016
......................................................................................................................................... 180
Figure A.6. Trends of the population aging and residential electrity consumption variables
for Chungbuk, 2000-2016 ............................................................................................... 181
Figure A.7. Trends of all variables excluding population aging for Chungnam,
2000-2016 ....................................................................................................................... 182
Figure A.8. Trends of the population aging and residential electrity consumption variables
for Chungnam, 2000-2016 .............................................................................................. 183
Figure A.9. Trends of all variables excluding population aging for Daegu,
2000-2016 ....................................................................................................................... 184
Figure A.10. Trends of the population aging and residential electrity consumption variables
for Daegu, 2000-2016 ..................................................................................................... 185
Figure A.11. Trends of all variables excluding population aging for Daejeon,
2000-2016 ....................................................................................................................... 186
Figure A.12. Trends of the population aging and residential electrity consumption variables
for Daejeon, 2000-2016 .................................................................................................. 187
Figure A.13. Trends of all variables excluding population aging for Gangwon,
2000-2016 ....................................................................................................................... 188
174
Figure A.14. Trends of the population aging and residential electrity consumption variables
for Gangwon, 2000-2016 ................................................................................................ 189
Figure A.15. Trends of all variables excluding population aging for Gwangju, 2000-2016
......................................................................................................................................... 190
Figure A.16. Trends of the population aging and residential electrity consumption variables
for Gwangju, 2000-2016 ................................................................................................. 191
Figure A.17. Trends of all variables excluding population aging for Gyeongbuk,
2000-2016 ....................................................................................................................... 192
Figure A.18. Trends of the population aging and residential electrity consumption variables
for Gyeongbuk, 2000-2016 ............................................................................................. 193
Figure A.19. Trends of all variables excluding population aging for Gyeonggi, 2000-2016
......................................................................................................................................... 194
Figure A.20. Trends of the population aging and residential electrity consumption variables
for Gyeonggi, 2000-2016 ................................................................................................ 195
Figure A.21. Trends of all variables excluding population aging for Gyeongnam,
2000-2016 ....................................................................................................................... 196
Figure A.22. Trends of the population aging and residential electrity consumption variables
for Gyeongnam, 2000-2016 ............................................................................................ 197
Figure A.23. Trends of all variables excluding population aging for Incheon, 2000-2016
......................................................................................................................................... 198
Figure A.24. Trends of the population aging and residential electrity consumption variables
for Incheon, 2000-2016 ................................................................................................... 199
Figure A.25. Trends of all variables excluding population aging for Jeju, 2000-2016 ..... 20
Figure A.26. Trends of the population aging and residential electrity consumption variables
for Jeju, 2000-2016 ......................................................................................................... 201
Figure A.27. Trends of all variables excluding population aging for Jeonbuk, 2000-2016
......................................................................................................................................... 202
Figure A.28. Trends of the population aging and residential electrity consumption variables
for Jeonbuk, 2000-2016 .................................................................................................. 203
Figure A.29. Trends of all variables excluding population aging for Jeonnam,
175
2000-2016 ....................................................................................................................... 204
Figure A.30. Trends of the population aging and residential electrity consumption variables
for Jeonnam, 2000-2016 ................................................................................................. 205
Figure A.31. Trends of all variables excluding population aging for Seoul, 2000-2016 206
Figure A.32. Trends of the population aging and residential electrity consumption variables
for Seoul, 2000-2016 ....................................................................................................... 207
Figure A.33. Trends of all variables excluding population aging for Ulsan, 2000-2016 208
Figure A.34. Trends of the population aging and residential electrity consumption variables
for Ulsan, 2000-2016 ...................................................................................................... 209
176
Figure A.1. Trends of all variables excluding population aging for the entire
municipalities, 2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
177
Figure A.2. Trends of the population aging and residential electrity consumption
variables for the entire municipalities, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
16.000
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
178
Figure A.3. Trends of all variables excluding population aging for Busan,
2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
179
Figure A.4. Trends of the population aging and residential electrity consumption
variables for Busan, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
16.000
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
180
Figure A.5. Trends of all variables excluding population aging for Chungbuk, 2000-
2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
181
Figure A.6. Trends of the population aging and residential electrity consumption
variables for Chungbuk, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
16.000
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
182
Figure A.7. Trends of all variables excluding population aging for Chungnam,
2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
183
Figure A.8. Trends of the population aging and residential electrity consumption
variables for Chungnam, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
16.000
18.000
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
184
Figure A.9. Trends of all variables excluding population aging for Daegu,
2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
185
Figure A.10. Trends of the population aging and residential electrity consumption
variables for Daegu, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
186
Figure A.11. Trends of all variables excluding population aging for Daejeon,
2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
187
Figure A.12. Trends of the population aging and residential electrity consumption
variables for Daejeon, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
188
Figure A.13. Trends of all variables excluding population aging for Gangwon,
2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
189
Figure A.14. Trends of the population aging and residential electrity consumption
variables for Gangwon, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
16.000
18.000
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
190
Figure A.15. Trends of all variables excluding population aging for Gwangju,
2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
191
Figure A.16. Trends of the population aging and residential electrity consumption
variables for Gwangju, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
192
Figure A.17. Trends of all variables excluding population aging for Gyeongbuk,
2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
193
Figure A.18. Trends of the population aging and residential electrity consumption
variables for Gyeongbuk, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
16.000
18.000
20.000
5.75
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
194
Figure A.19. Trends of all variables excluding population aging for Gyeonggi, 2000-
2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
20002001200220032004200520062007200820092010201120122013201420152016
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
195
Figure A.20. Trends of the population aging and residential electrity consumption
variables for Gyeonggi, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
196
Figure A.21. Trends of all variables excluding population aging for Gyeongnam,
2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
197
Figure A.22. Trends of the population aging and residential electrity consumption
variables for Gyeongnam, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
16.000
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
198
Figure A.23. Trends of all variables excluding population aging for Incheon, 2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
199
Figure A.24. Trends of the population aging and residential electrity consumption
variables for Incheon, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
200
Figure A.25. Trends of all variables excluding population aging for Jeju, 2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
201
Figure A.26. Trends of the population aging and residential electrity consumption
variables for Jeju, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
16.000
5.75
5.8
5.85
5.9
5.95
6
6.05
6.1
ly_elec x_old
202
Figure A.27. Trends of all variables excluding population aging for Jeonbuk, 2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
203
Figure A.28. Trends of the population aging and residential electrity consumption
variables for Jeonbuk, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
16.000
18.000
20.000
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
204
Figure A.29. Trends of all variables excluding population aging for Jeonnam,
2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
205
Figure A.30. Trends of the population aging and residential electrity consumption
variables for Jeonnam, 2000-2016
0.000
5.000
10.000
15.000
20.000
25.000
5.75
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
206
Figure A.31. Trends of all variables excluding population aging for Seoul, 2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
207
Figure A.32. Trends of the population aging and residential electrity consumption
variables for Seoul, 2000-2016
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
208
Figure A.33. Trends of all variables excluding population aging for Ulsan, 2000-2016
0
1
2
3
4
5
6
7
0
0.5
1
1.5
2
2.5
lx_price lx_householdsize lx_cdd
lx_hdd ly_elec lx_income
209
Figure A.34. Trends of the population aging and residential electrity consumption
variables for Ulsan, 2000-2016
0.000
1.000
2.000
3.000
4.000
5.000
6.000
7.000
8.000
9.000
10.000
5.75
5.8
5.85
5.9
5.95
6
6.05
6.1
6.15
ly_elec x_old
210
Note: For Figure A.1 through Figure A.17, the variables are logged first to see the trends in
the fitted range. In specific, before logging the values of residential electricity consumption
𝑦𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 variables, they are converted from Megawatts hour per person to Watt hours
per person by multiplying by 106 to make them fit into the corresponding range.
211
Abstract (Korean)
한국의 고령화 속도는 주요 선진국들의 고령화 속도와 비교하여 매우
빠른 편으로, 미국 통계국은 한국이 고령사회에서 초고령사회로 진입하는데
세계에서 가장 빠른 수준인 27년이 걸린다고 전망하였다. 인구고령화 현상은
성장, 고용, 복지 등 경제 및 사회 전반에 영향을 미칠 뿐만 아니라 에너지
소비 행태에 구조변화를 일으킬 가능성이 높기 때문에 최근 학계에서는
인구고령화를 포함한 사회·인구학적 요인들이 에너지 소비 변동에 미치는
영향에 대한 분석을 활발히 진행하고 있다. 특히 대부분의 관련 연구들은
주로 수송부문과 가정부문에 집중되어 있는데, 이는 사회·인구학적 현상이
산업 및 발전 부문보다 수송 및 가정부문에 더 직접적인 영향을 끼치기
때문이다. 고령화가 수송부문과 가정부문의 에너지소비에 미치는 영향에 대한
연구가 급증하고 있는 가운데, 아직까지 사회·인구학적 요인들을 세분화하여
지역적 특성을 고려한 패널분석을 실시한 연구는 미미한 실정이다.
본 연구의 목적은 고령화를 포함한 사회·인구학적 요인들이 국내
1인당 주거용 전력수요에 미치는 영향을 실증분석 하는것이다. 사회·인구학적
요인을 크게 고령화, 소득, 생산가능인구, 교육수준, 가구원크기, 성별,
자가점유율, 그리고 주택형태로 세분화하여 분석을 진행한다. 이 외에
전력소비에 유의미한 영향을 미칠 것으로 판단되는 전력가격, 냉방도일,
그리고 난방도일 변수를 분석에 포함한다. 분석범위는 세종시를 제외한 16개
광역자치단체이며, 분석기간은 2000년부터 2016년까지인 지역별 패널자료를
사용한다. 지역별 패널자료를 사용할 경우 변수들간의 다중공선성(multi-
collinearity) 문제의 해결과 시간에 따른 전력소비의 변동성을 포착할 수
있다는 이점이 있다.
실증분석을 실시하기에 앞서 관련 선행연구의 조사와 정리를 통해
이론적 근거 및 관련 연구들을 소개한다. 그 뒤, 다음과 같은 네 가지 연구
212
가설을 설정하여 검정한다. 첫째, 인구 고령화가 증가하면 1인당 주거용 전력
소비량이 감소한다. 둘째, 가구원의 크기가 증가하면 1인당 주거용 전력
소비량이 감소한다. 셋째, 교육수준, 성별차이, 생산가능인구, 자가점유율은
주거용 전력 소비량에 유의한 영향을 미치지 않는다. 넷째, 난방도일(HDDs:
Heating Degree Days)과 냉방도일(CDDs: Cooling Degree Days)이 증가하면
주거용 전력 소비량이 증가한다.
통계적으로 유의한 소득, 가격, 고령화, 가구원크기, 냉방도일,
난방도일 변수들을 채택하여 분석을 실시하였다. 패널 공적분 검정 결과,
변수들 간에 장기균형관계가 성립함을 확인하였으며, FMOLS와 DOLS
분석기법을 사용하여 변수들의 장기균형 벡터를 추정하였다. FMOLS와 DOLS
분석결과 고령화, 전력가격, 가구원크기가 증가하면 주거용 전력소비가
장기적으로 감소하고, 개인소득, 난방도일, 냉방도일이 증가하면 주거용
전력소비가 장기적으로 증가하는 것을 확인하였다. 패널 고정효과 및
확률효과 모형 추정 결과 고령화, 전력가격, 가구원크기가 증가하면 주거용
전력소비가 감소하고, 개인소득, 난방도일, 냉방도일이 증가하면 주거용
전력소비가 증가하는 것을 확인하였다.
본 연구는 국내 고령화 현상이 주거용 전력소비 감소의 요인임을
확인하였으나, 고령화 사회는 국가적으로 보면 매우 큰 부담이다. 한국의
인구고령화가 전례 없는 속도로 진행되고 있기 때문에 이에 따른 인구구조
변화가 향후 주거용 전력 소비에 미치는 영향은 크게 변동할 것으로 판단된다.
또한 가구원크기가 증가할수록 주거용 전력 소비가 줄어드는데, 이는 세대당
인구수가 증가할수록 한 가정에서 가전기기를 공유하여 사용하는 빈도가
늘어나기 때문이다. 가전기기를 대상으로 한 에너지 효율적인 설계와 보급을
장려하는 정책의 도입이 바람직해 보인다.
난방도일이 증가할수록 주거용 전력소비가 증가한다는 것은, 최근
전력으로 난방 설비를 가동하는 건물이 많아지면서 가스난방의 대체로 겨울철
전기난방이 사용되거나, 값싼 전기요금으로 인해 전기를 사용하는 보조 난방
213
기구의 사용이 증가하는 것에서 설명될 수 있다. 냉방도일이 증가할수록
주거용 전력소비가 증가하는 현상은 과거에 비해 에어컨과 냉방기기를
보유하고 사용하는 가정이 늘어난 데 기인한다. 소득이 증가할수록 주거용
전력소비가 증가한다는 것은, 가처분소득의 증가로 인해 개인의 구매력이
증가하면서, 이들이 전기를 사용하는 가전제품을 더 많이 구매하고 사용하는
현상에서 비롯된다.
최근 한국은 공식적으로 고령사회에 진입하였으며, 국제사회는 한국의
고령화가 훨씬 더 빠르게 진행될 것으로 전망하고 있다. 고령사회에서 정책은
훨씬 더 신중하게 수립되어야 한다는 점을 비추어 보았을때, 향후 주거용
전력소비를 줄이기 위하여 가격정책만을 고수하는 것은 한계가 있어 보인다.
안정적인 주거용 전력 수급을 위하여 고령화를 포함한 여러 요인들을
고려하는 것이 바람직할 것이다.
주요어 : 사회·인구학적 요인, 고령화, 주거용 전력 소비, 냉방도일, 난방도일,
패널공적분, 패널분석
학 번 : 2017-22870