eyd
Ekonomik Yaklaşım
Derneği / Association
Ekonomik Yaklaşım 2015, 26(97): 1-22
www.ekonomikyaklasim.org
doi: 10.5455/ey.35714
The Impact of Economic Crisis and Tariff
Adjustment on Residential Electricity Demand in
Turkey
A. Ali KOÇ 1
Gülden BÖLÜK 2
Fatma Banu BEYAZ SİPAHİ 3
04 Şubat 2015’de alındı; 09 Temmuz 2014’de kabul edildi.
22 Ekim 2015’den beri erişime açıktır.
Received 04 February 2015; accepted 09 July 2015.
Available online since 22 October 2015.
Araştırma Makalesi/Original Article
Abstract
This study analyzes the welfare impact of the residential electricity tariff escalation initiated in July 2008
on households in Turkey. Empirical analysis consists of estimation of household electricity demand using TurkStat
“2007, 2008 and 2009 budget survey" data and Hicksian compensating variation of welfare. The estimated income
elasticities by income strata were found to be close to each other, confirming that electricity is a necessity good
and the magnitude of the elasticities decreases as income goes up (ranging from 0.74 to 0.91 for income strata).
The magnitude of the income elasticities by income strata was found to be very low compared to the long-run
income elasticities in previous time series studies. The estimated income elasticity by income strata and the mean
value of the Marshallian long-run own-price elasticity for residential electricity obtained from previous time series
models were used to obtain Hicksian own-price elasticities using Slutsky decomposition for each income strata.
The welfare measure which relied on Hicksian compensating variation indicated that welfare loss of households
from 2007 to 2009 was around 9-10 percent by income strata as compared to the situation before tariff adjustment.
It is expected that welfare losses would be greater if the analysis period were extended to 2012.
Keywords: Residential electricity demand, income elasticity of electricity demand, welfare impact of electricity
price escalation, Slutsky decomposition.
JEL Classification: D12, D13, D60.
© 2015 Published by EYD
1
Yazışmadan sorumlu yazar (Corresponding author).Akdeniz University, Department of Economics, Antalya, Turkey.
E-mail: alikoc@akdeniz.edu.tr
2
Akdeniz University, Department of Economics, Antalya, Turkey. E-mail: guldenboluk@akdeniz.edu.tr
3
Akdeniz University, Department of Economics, Antalya, Turkey. E-mail: banubeyaz@gmail.com
Ekonomik Yaklaşım ISSN 1300-1868 print © 2015 Ekonomik Yaklaşım Derneği / Association - Ankara
Her hakkı saklıdır © All rights reserved
A. Ali KOÇ, Gülden BÖLÜK, Fatma Banu BEYAZ SİPAHİ
2
Özet
Türkiye’de Tarife Düzenlemesi ve Ekonomik Krizin Hanehalkı Elektrik
Talebi Üzerine Etkisi
Bu çalışma, Türkiye’de Temmuz 2008 yılında tırmanmaya başlayan konut elektrik tarifesinin
hanehalkının refahı üzerindeki etkisini analiz etmektedir. Ampirik analiz, TÜİK “2007, 2008 ve 2009 bütçe anket”
verilerini kullanılarak hanehalkı elektrik talebinin ve Hicksgil telafi edici refah değişiminin tahmininden
oluşmaktadır. Gelir gruplarına göre hesaplanan gelir esnekliklerinin birbirlerine yakın bulunması elektriğin
zorunlu bir mal olduğunu ve gelir artarken (gelir grupları için 0.74-0.91 arası değişen) esneklik büyüklüğünün
azaldığını doğrulamaktadır. Gelir gruplarına göre hesaplanan gelir esnekliklerinin büyüklüğü, daha önceki zaman
serisi çalışmalarında hesaplanan uzun dönem gelir esnekliklerinden çok daha düşük bulunmuştur. Gelir gruplarına
göre hesaplanan gelir esneklikleri, önceki zaman serisi çalışmalarından elde edilen elektriğin Marshallian uzun
dönem fiyat esnekliği ve her bir gelir grubu için Slutsky ayrıştırması kullanılarak Hicksian fiyat esneklikleri elde
edilmiştir. Hicksgil telafi edici refah değişimine göre, tarife düzenlemesinden önceki durumla karşılaştırıldığında
gelir gruplarına göre 2007-2009 döneminde hanehalkının refahı % 9-10 civarında azalmıştır. Eğer analiz dönemi
2012 yılına uzatılırsa, refah kayıpları çok daha yüksek oranlara ulaşırdı.
Anahtar Kelimeler: Hanehalkı elektrik talebi, elektrik talebinin gelir esnekliği, elektrik fiyat artışının refah etkisi,
Slutsky ayrıştırması.
JEL Sınıflaması: D12, D13, D60.
© 2015 EYD tarafından yayımlanmıştır
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The Impact of Economic Crisis and Tariff Adjustment on Residential Electricity Demand in Turkey
http://dx.doi.org/10.5455/ey.35714
1. Introduction
The demand for electricity has increased due to the remarkable economic growth,
rapid urbanization, and relatively high population increase in Turkey over the last years.
Electricity production increased from 8,623 Gwh to 229,395.1 Gwh between 1970 and 2011.
Gross demand of electricity in Turkey reached 230,306 Gwh in 2011, with an average annual
growth rate of around 10 % in the last decade (TEIAS, 2012). Electricity market restructuring
process which was initiated in 2001 aimed to meet the rapidly increasing demand for
electricity in the country. The process was affected and accelerated by three main factors: (1)
a financial stabilization program which has been implemented under the supervision of the
World Bank and International Monetary Fund (IMF), (2) harmonization of the market with
the European Union, and (3) attracting private investments to the energy sector to meet the
The Impact of Economic Crisis and Tariff Adjustment on Residential Electricity Demand in Turkey
3
increasing electricity demand (Atiyas and Dutz, 2004). The restructuring process also
envisaged that electricity tariffs should be cost-based, and cross subsidies across segments
(residential, industry) and regions (East and West) should be removed (Bagdadioglu, 2007).
The retail price of electricity regulated by the government was constant in nominal terms
(declined in real terms) between 2003 and 2007 and has escalated since July 2008. However,
electricity tariff (in nominal terms) was adjusted in several steps and as a result, the nominal
price increased 59 percent from July 2008 to February 2013, even though the deflated price
still did not exceed the price range observed from January 2003 to February 2013 (Figure 1).
The state regulated retail price can be regarded as third degree price discrimination since
residential users pay a higher tariff than industrial/service users.
Figure 1: Monthly Residential Electricity Tariffs in Turkey (2003:01-2013:01)
Real Price (Kwh/TL)
Nominal Tariff (Kwh/TL)
Source: TurkStat, 2013.
The effect of electricity tariff changes on households’ consumption and welfare is a
very important for policy decisions. As of 2011, there were 32.6 million residential users of
electricity in Turkey. Approximately 24 percent of aggregate electricity was consumed by
residential users in the country (TEDAS, 2012; TurkStat, 2011). Although electricity market
has been very dynamic in Turkey in recent years, academic studies analyzing the impact of
various economic factors on residential demand is limited, and the existing studies have
4
A. Ali KOÇ, Gülden BÖLÜK, Fatma Banu BEYAZ SİPAHİ
focused solely on price escalation. However, Bagdadioglu, Basaran, and Waddams Price
(2007) analyzed the impact of electricity price changes on consumer welfare using the “2003
household budget survey" data conducted by TurkStat. In this study, the authors employed an
own-price elasticity of electricity demand of -0.23, which was borrowed from studies
conducted in other countries. Furthermore, the own-price elasticity was assumed to be the
same for different consumer segments in terms of income quintile, and the welfare
measurement was based on an ordinary Marshallian demand curve 4 . Akkemik (2009),
examined the liberalization of Turkish electricity market reform using computable general
equilibrium (CGE) model. The author found that market reform would enhance the efficiency
in electricity production and improve consumer's welfare because of the lower electricity
prices. Zhang (2011) estimated a short-run residential electricity demand function to evaluate
the distributional consequences of the tariff escalation using a national sample of 18,671
Turkish households from the “2008 and 2009 Household Budget Survey” of TurkStat. In this
study, the author estimated own-price and expenditure elasticities based on income strata
using an electricity demand model specified in double-log functional form including sociodemographic variables. The welfare changes of households by income strata were evaluated
with the own-price elasticities obtained by the demand model based on an almost constant
price variable from 2008 to 2009 (See Figure 1). During the data analysis period, however,
nominal electricity tariffs escalated only four times. Moreover, while price tariffs showed
even less variation in real terms between January 2008 and December 2009, it was almost
stable after October 2008 to December 2009. Therefore, price elasticity with non-variable real
price is questionable from the viewpoint of the assumption of OLS estimator. The author
found, however, electricity is inferior good among Turkish household which is not reasonable
at the economic viewpoint for a developing country and is not comparable with literature. In
addition to the less reliable price and income elasticity, the effect of economic crisis on
household expenditures was not taken into account to evaluate the impacts of price escalation
in a study conducted by Zhang (2011). Furthermore, both price and expenditure variables
were not deflated although there was an increase of almost 16 percent in consumer prices
during the period considered. Electricity price in real terms however increased 33 percent if
longer time period is considered such as January 2007 and December 2009 (See Figure 1).
4
It has been proved that the Marshallian demand curve was not suitable for estimating the welfare changes (Huang, 1993,
pp.217-218; Deaton and Muellbauer, 1980, pp.185-186).
The Impact of Economic Crisis and Tariff Adjustment on Residential Electricity Demand in Turkey
5
The main aim of this study is to evaluate the welfare effects of household electricity
price escalation by income strata in Turkey. For this purpose, the study estimates “a
residential electricity demand model” using the “2007, 2008 and 2009 Household Budget
Survey of TurkStat” in order to obtain expenditure elasticity by household income strata in
Turkey. The parameters obtained from different years enable us to verify whether household
electricity consumption differs during the economic cycles as well as expenditure (income)
elasticities. This study incorporated the mean value of own-price elasticity compiled from
various robust econometric studies for Turkey with time series data through the Slutsky
equation, with the expenditure elasticities estimated using the 2007, 2008 and 2009 household
budget data by income strata in order to obtain Hicksian compensating elasticities. Finally,
the impact of the residential electricity price escalation on households in different income
strata was measured by Hicksian price elasticity of demand instead of Marshallian elasticity.
The welfare impact of price escalation on various segments of households by income strata
was calculated by Hicks compensating variation method (Just, Hueth and Schmitz, 2004).
The remainder of this paper is organized as follows. Section 2 provides the literature
review for both the effects of price changes on consumer welfare and residential electricity
demand studies. Section 3 introduces the methodology of the study. Section 4 describes the
data set and descriptive statistics. Section 5 provides the results of the analysis and finally,
Section 6 summarizes the results.
2. Literature Review
There is an extensive body of literature examining residential electricity demand both
in developed and developing countries. Energy demand studies were primarily initiated by
Houthakker’s pioneering work (Houthakker, 1951). The literature on residential demand for
electricity can be grouped into two main categories: The first group of studies focuses on
traditional demand analysis, assumes homogeneous price elasticities and includes the income,
price and household characteristics. Halvorsen (1975) analyzed residential electricity demand
in America for the period of 1960-1970 using the 2SLS method. In this study, the real price of
electricity, average real income per capita, heating degree days, average temperature,
percentage of housing units and real price of households were used as explanatory variables
in the demand model. The price and income elasticities were estimated for each year.
Accordingly, the direct income elasticity ranged from 0.47 to 0.54, and price elasticity ranged
6
A. Ali KOÇ, Gülden BÖLÜK, Fatma Banu BEYAZ SİPAHİ
from -1.0 to -1.21. Some studies in this group are based on the household production theory
and use micro models. In these studies, residential electricity demand is a conditional demand
derived from the demand for housing needs such as cooking food, hot water, washing, and so
on. Based on this theory, household purchases are input factors in order to produce the final
goods which appear in the households’ utility function (Shi, Zheng and Song, 2012). Hsiao
and Mountain (1985) estimated short-run conditional demand for electricity using the 1980
energy application survey for Ontario, Canada. In this study, electricity demand was assumed
to be derived demand of the services supplied by the stocks of electricity using appliances and
the short-run income elasticity was estimated to be 0.17. Barnes, Gillingham and Hugemann
(1981) analyzed the residential electricity demand as a function of income, price, appliancespecific factors and demographic variables, heating and cooling categories. The short-run
demand parameters were estimated using household data from the 1972-73 Consumer
Expenditure Survey covering 10,000 individuals in the USA. The price and income
elasticities of residential electricity were found to be -0.550 and 0.20, respectively.
Yoo, Lee and Kwak (2007) employed the bivariate model to estimate the residential
electricity demand for Seoul using the household survey in 2005. The study showed that
income, size of family, ownership of a plasma TV and air conditioning unit positively
affected residential electricity demand. The price and income elasticities were calculated to be
-0.25 and 0.06, respectively. Filippini and Pachauri (2002) analyzed the residential electricity
demand using 1993-1994 disaggregate survey data for the urban areas of India. Electricity
demand was assumed to be a function of the price of electricity and kerosene, stock
appliances, income, geographic and demographic variables such as the number of household
members and the size of houses. The elasticities were differentiated by seasons. Price
elasticities were estimated to be -0.32 for winter, -0.16 for summer and -0.39 for the monsoon
months. Income elasticities were estimated to be 0.68, 0.66 and 0.65 for the same seasons.
Moreover, it was found that regional differences and the house size had positive effects on
electricity demand, and LPG was a substitute for electricity during the summer time.
Petersen (1982) tried to identify the differences between rural and urban usage of
household electricity for Utah using a dataset consisting of 2.155 customers in 1980. The
study concluded that rural residents used significantly more electricity in the winter time, and
the differences between rural and urban electricity usage were primarily due to differences in
the stock of devices using electricity. Moreover, the income elasticity of electricity was
The Impact of Economic Crisis and Tariff Adjustment on Residential Electricity Demand in Turkey
7
estimated to be 0.005. Parti and Parti (1980) analyzed residential electricity demand using
cross-section data consisting of 5.286 individuals for San Diego in a conditional demand
framework. It was assumed that the total energy consumed by a household is the sum of the
energy used for household appliances. The price and income elasticities were estimated to be
-0.58 and 0.15, respectively. Tuzun (2002) estimated the residential electricity demand as a
function of electricity price, prices of substitutes, income level, weather (HDD and CDD),
population, and housing stock. Demand was estimated by 2SLS using pooled data for the
period of 1980-2000 in California. The elasticity of price and income was estimated to be 0.23 and 0.013, respectively. Reiss and White (2005) analyzed residential electricity demand
for California using the 1993 and 1997 household survey. The authors estimated the mean
price elasticity of electricity and income elasticity for space heating by households for all
income quintile to be-0.39 and 0.0, respectively.
The second group of residential electricity demand studies uses the advantages of a
great variety of time series and pooling techniques. These studies usually use the popular
error-correction models (ECM), cointegration methods, analyze the short-run and long-run
behavior of electricity and effects of shocks on demand. Furthermore, Holtedahl and Joutz
(2004) examined residential electricity demand in Taiwan using a dataset that covered the
1958-1995 periods. Short-run and long-run elasticities were differentiated using the ECM.
The estimated short-run and long-run elasticities were 0.23 and 1.04 for income and 1.63 and
3.91 for urbanization. The own price elasticity was estimated to be -0.15. Hondroyiannis
(2004) modeled residential electricity demand using the VECM estimation model and the
study employed monthly data for the 1986-1999 period. Long-run residential electricity
demand was estimated to be a function of real income, real price and weighted average
temperature. The estimated long-run elasticities of the income, price and temperature in this
study were 1.56, -0.41 and -0.19, respectively.
Narayan and Smyth (2005) estimated the long and the short-run elasticities of
residential electricity demand in Australia using annual time series data for the 1969-2000
periods. Income elasticity was estimated to be 0.0121 for the short run, and 0.0415 for the
long run. Price elasticity was calculated to be -0.263 and -0.541, respectively. Moreover, it
was found that temperature has a positive effect on energy consumption and is significant in
the long run. Narayan, Smyth and Pasarad (2007) estimated the long-run and short-run
income and price elasticities of residential electricity demand in 67 countries using panel
A. Ali KOÇ, Gülden BÖLÜK, Fatma Banu BEYAZ SİPAHİ
8
cointegration techniques. The study used annual data series for the 1978-2003 periods and
natural gas price was additionally included into the model as a substitute for energy price.
Short-run elasticity of residential electricity was computed to be -0.10. While the long-run
income and price elasticities of electricity were calculated to be 0.31, and -1.45, the price
elasticity of natural gas was calculated to be 1.77. Narayan (2007) analyzed the residential
electricity demand of G7 countries using annual data for the 1978-2003 periods using the
cointegration method. He found that the USA has the smallest elasticity among the G7
countries. Albertini and Filippini (2010) empirically analyzed residential electricity demand
using the annual aggregate data of 48 states for the 1995-2007 periods, and dynamic panel
data analysis. The estimated elasticities for price, income, price of natural gas, household size,
heating degree day and cooling degree day were -0.21, 0.28, 0.05, -0.74, 0.14 and 0.08,
respectively.
Halicioglu (2007) investigated the income and price elasticities of residential
electricity demand by cointegration method and Granger causality test, using the annual series
of the 1968-2005 periods for Turkey. Short-run income, price and urbanization elasticities of
electricity were estimated to be 0.40, -0.46 and 0.67, respectively. Moreover, long-run
income, price and urbanization elasticities of electricity were estimated to be 0.54, -0.63 and
0.02, respectively. Dilaver (2009) empirically analyzed income and price elasticities of
residential electricity demand by structural time series modeling using annual data for the
period of 1971-2006 in Turkey. Short- run income and price elasticities of electricity were
estimated to be 0.41 and -0.10. Long-run income and price elasticities of electricity were
estimated to be 2.21 and -0.57. Furthermore, Dilaver and Hunt (2011) revised their residential
electricity demand model using longer and updated annual series (1960-2008) for Turkey.
Short-run income and price elasticities of electricity were found to be 0.38 and -0.09. Longrun income and price elasticities of electricity were estimated to be 1.57 and -0.38.
Nakajima and Hamori (2010) estimated the elasticities of residential electricity for 48
states in the USA using quarterly data for the period of 1993-2008 and panel cointegration
techniques. Electricity demand was modeled as a function of price, income, cooling and
heating degree day (CDD, HDD). Estimated elasticities were differentiated for the periods
before and after the deregulation process. Elasticities of income, price, HDD and CDD after
deregulation were calculated to be 0.85, -0.14, 0.17 and 0.64, respectively. Elasticities before
deregulation were 0.38, -0.33, 0.19 and 0.56, respectively.
The Impact of Economic Crisis and Tariff Adjustment on Residential Electricity Demand in Turkey
9
Dergiades and Tsoulfidis (2011) examined the long-run and the short-run residential
demand for electricity in Greece using ARDL cointegration method over the period of 1964
to 2006. Short-run income and price elasticities of electricity were estimated to be 0.64 and 0.092 for Greece. Long-run income and price elasticities of electricity were estimated to be
1.79 and -0.61. Table 1 provides the results of some selected residential electricity demand
studies. Additionally, an extensive literature review on residential energy demand was further
reported by Taylor (1975), Dahl (1993) and Madlener (1996).
Blazquez, Boogen and Filippini (2012) analyzed residential demand for electricity
using aggregate panel data for 47 provinces for the period from 2000 to 2008 in Spain. They
estimated the short and the long run own price elasticities lower than unity. Moreover, authors
found that weather variables have a significant impact on electricity demand. Fell, Li and Paul
(2010) estimated residential electricity demand using household electricity expenditure data
based on Consumer Expenditure Survey (7,500 households) in USA. Authors found that price
and income elasticities ranges between -0.82 to -1.02 and 0.061 to 0.072, respectively.
Table 1:Empirical Results of Selected Residential Electricity Demand Studies
Source
Country
Study Period
Income Elasticity
Price Elasticity
Halvorsen (1975)
USA
1961-1969
0.54
(-1) to (-1.21)
Parti and Parti (1980)
San Diego
1975
0.15
-0.58
Barnes et al. (1981)
USA
1972-73
0.20
-0.55
Dergiades and Tsoulfidis (2011)
Greece
1964-2006
0.64-0.79
-0.092 to -0.61
Tuzun (2002)
California
1980-2000
0.0125
-0.23
Filippini and Pachauri (2002)
India
1993-1994
0.64
-0.29 to -0.51
Holtedahl and Joutz, (2004)
Taiwan
1958-1995
1.57
-0.15
Hondroyiannis (2004)
Greece
1986-1999
1.56
-0.41
Narayan and Smith(2005)
Australia
1969-2000
0.04
-0.263 to -0.541
Reiss and White (2005)
California
1993-1997
0.01
-0.39
Narayan et al.(2007)
67 countries
1978-2003
0.31
-0.10 to -1.45
Halıcioglu (2007)
Turkey
1968-2005
0.70
-0.52
Dilaver (2009)
Turkey
1971-2006
0.41 to 2.21
-0.10 to -0.57
Dilaver and Hunt (2011)
Turkey
1960-2008
0.38 to 1.57
-0.09 to -0.38
Yoo et al.(2007)
Seoul
2005
0.10
-2.245
Nakajima and Hamori (2010)
USA
1993-2008
0.38 to 0.85
-0.14 to -0.33
Blazquez et.al.(2012)
Spain
2000-2008
0.14-0.30
-0.11 to -0.24
Fell et.al.((2010)
USA
2004-2006
0.061 to 0.072
-0.82 to -1.12
Source: Compiled by the authors.
A. Ali KOÇ, Gülden BÖLÜK, Fatma Banu BEYAZ SİPAHİ
10
3. Measuring Welfare Impact of Price Change
Reform efforts to restructure electricity markets have raised interest in assessing the
consumer response to price changes. Knowledge of price and income elasticities is very
useful and important in setting up pricing and energy policy (Hasio and Mountain, 1985).
Consumers’ response to energy price change is additionally a very important issue due to its
impact on consumer and social welfare (Freund and Wallich, 1997). Energy price increases
have direct and indirect effects 5 on household energy usage. Household energy demand may
be directly affected by energy price changes through complementary mechanisms. Higher
energy prices will reduce discretionary income as households have less money to spend after
paying their energy bill. In the case of less elastic demand for energy, this discretionary
income would be larger. Increasing energy prices may create uncertainty about the future path
of the price of energy and leads consumers to postpone irreversible purchases of consumer
durables. Even these purchase decisions are reversible, as consumption can fall due to energy
price shocks as households increase their precautionary savings. Moreover, household
consumption of durables (operates with energy) will decrease, since consumers postpone the
purchase of energy durables (operating cost effect) (Kilian, 2008). Indirectly, higher energy
prices will cause reallocation of production inputs such as labor and capital away from energy
intensive industries, as households tend to move towards more energy efficient durables
(Zhang, 2011) 6.
This study relies on Hicksian compensating variation measure (CV) in order to
analyze the consumer welfare effects of electricity tariff changes. The correct measure of
welfare is an integral of Hicksian demand curve rather than the Marshallian demand curve
(Varian, 2004, pp.168). However, the use of the Marshallian concept as an analytical tool for
measuring consumers’ surplus leads to errors and confusion. Taking the area under a
compensated or Hicksian demand curve over a price change would be a more appropriate
welfare measure, as Hicksian demand functions are the derivatives of the cost function, and
the integration of demand functions provides the differences in the costs of reaching the same
indifference curve at different price vectors (Huang, 1993, pp.217-218; Deaton and
Muellbauer, 1980, pp.185-186). If welfare changes are analyzed for a normal good, the
5
Energy price increases also have macro-economic impacts (Kilian, 2008) but this study only concentrates on the direct effects of
electricity prices on households.
6
Kilian (2008) for detailed information about direct and indirect effects of energy price shocks on consumers and economy.
The Impact of Economic Crisis and Tariff Adjustment on Residential Electricity Demand in Turkey
11
derivative of the Hicksian demand curve will be larger than the derivative of the Marshallian
demand curve. Let's consider a situation where only the price of electricity moves from p0 to
pı, income fixed at m=m0=mı and an expenditure function, E(p, u), defined as the minimum
amount of expenditure necessary to get to a given level of utility u and a vector of electricity
prices p. Supposing that consumer achieves utility u0, CV is given by (Huang and Huang,
2009);
𝐶𝑉 = 𝐸(𝑝1 , 𝑢0 ) − 𝐸(𝑝0 , 𝑢0 )
(1)
where, the expenditure functions 𝐸(𝑝1 , 𝑢0 ) and 𝐸(𝑝0 , 𝑢0 ) are the minimum
expenditure necessary to maintain the level of utility (𝑢0 ) given prices 𝑝1 and 𝑝0 .
A positive CV implies a requirement for more spending to obtain the same utility level as
before tariff changes, and thus there is a decrease in consumer welfare. By contrast, a
negative CV implies a decline in spending, and thus a gain in consumer welfare.
Let 𝑞 ℎ (𝑝1 , 𝑢0 ) be a vector of Hicksian compensated demand, a given price vector 𝑝1 and at
the same initial utility level 𝑢0 . The CV can be expressed as the following implicit products
of price and quantity vectors:
𝐶𝑉 = 𝑝1 ●𝑞ℎ (𝑝1 , 𝑢0 ) − 𝑝0 ●𝑞 0
(2)
By further defining the 𝑑𝑝 = 𝑝1 − 𝑝0 as a vector of tariff changes, and 𝑑𝑞 ℎ = 𝑞 ℎ (𝑝1 , 𝑢0 ) −
𝑞 0 as a vector of compensated quantity changes, the CV equation can be transformed into
𝑑𝑝
𝑝0
𝐶𝑉 = (𝑝0 𝑞 0 )●(
+
𝑑𝑞ℎ
𝑞0
+
𝑑𝑝 𝑑𝑞ℎ
● 𝑞0 )
𝑝0
(3)
Then the change in compensated demand can be approximated as 𝑑𝑞 ℎ = 𝑞 ℎ (𝑝1 , 𝑢0 ) − 𝑞 0 by
applying the first-order differential form as follows;
𝑑𝑞ℎ
𝑞𝑖
∗ 𝑑𝑝𝑗�
= ∑ 𝑒𝑖𝑗
(
𝑝𝑗 )
𝜕𝑞ℎ
𝑝
(4)
Where 𝑒𝑖𝑗 = ( 𝜕𝑝𝑖 )( 𝑞𝑗) is a compensated price elasticity of ith commodity with respect
𝑗
𝑖
to a price change in the j commodity. Moreover, 𝑑𝑞𝑖ℎ is a change in Hicksian demand for ith
th
good, such as electricity demand (Huang and Huang, 2009). Given the initial prices of
electricity, a change in compensated quantities demanded could be calculated based on the
information given on the compensated price elasticities and the price changes. The
compensated price elasticities can be computed using the following Slutsky identity which
decomposes the income and substitution effects of price changes (Huang, 1993).
A. Ali KOÇ, Gülden BÖLÜK, Fatma Banu BEYAZ SİPAHİ
12
∗
𝑒𝑖𝑗
= 𝑒𝑖𝑗 + 𝑤𝑗 𝑛𝑖
𝑖, 𝑗 = 1,2,3 … . . , 𝑛
(5)
The compensated price elasticities obtained by the Slutsky equation can be used to
calculate the Hicksian compensated variation. As seen from equation (5), in order to calculate
Hicksian compensated price elasticity, Marshallian own-price and income elasticity of
𝜕𝑞
𝑚
𝑞𝑖
residential electricity demand is needed. In Equation 5, 𝑛𝑖 = � 𝑖 � � � is an expenditure
𝜕𝑚
elasticity of ith commodity. This demand system can be estimated by incorporating the
following parametric constraints of homogeneity, symmetry and Engel aggregation:
∑𝑗 𝑒𝑖𝑗 = −𝑛𝑖 , 𝑒𝑖𝑗 /(𝑤𝑗 + 𝑛𝑗 ) = 𝑒𝑖𝑗 /(𝑤𝑗 + 𝑛𝑖 ) and ∑𝑖 𝑤𝑖 𝑛𝑖 = 1 , where 𝑤𝑖 = 𝑞𝑖 𝑝𝑖 /𝑚 is the
expenditure weight of ith commodity (Huang, 1993). The available residential demand models
are estimated with time series data, however they can only provide aggregated elasticities for
both price and income. Therefore, it is necessary to decompose elasticities by income strata in
order to measure the differential welfare impact of electricity price changes. To accomplish
decomposition or obtain disaggregated elasticities, Marshallian own-price elasticity is
calculated by taking the mean value of the own-price elasticities for residential electricity
demand from the models estimated using time series data. It is assumed that household by
income strata have similar Marshallian price responses. In addition, Hicksian price response
is differentiated using equation (5) and disaggregated income elasticities obtained from the
household demand model estimates with cross-section data by income strata. In the empirical
literature, the household demand model for electricity can be specified either based on the
household production theory which assumes that electricity is a derived demand of household
stocks of devices such as appliances or neo-classical consumer theory which assumes that
demand is a function of prices and budget constraints. There are also some studies in which
both approaches for model specification have been combined.
4. Residential Electricity Demand Model and Data
In this study, Working-Leser type Engel function for electricity demand was specified
to obtain income elasticities by income strata. Working-Leser specification is one of the
widely used methods for studying relationship between expenditure share of goods (food,
electricity, transportation, health, etc) and household characteristics (Deaton and Muelbauer,
1980). This specification meets the following criteria: i) it is suitable for multiple types of
goods, ii) it allows for increasing, decreasing and constant marginal properties to spend over a
The Impact of Economic Crisis and Tariff Adjustment on Residential Electricity Demand in Turkey
13
wide range expenditure levels and iii) it satisfies the additivity criterion, in other words, the
sum of marginal propensities for all goods should equal unity (Bandyopadhyay, Guzman and
Lendelvo, 2010). Since incomes vary substantially across individuals or households, therefore
income elasticity may vary across goods. The income effect for individuals at different space
on the income distribution must be fully captured for a demand model to predict responses to
price increases (Banks, Blındell and Lewbel, 1997). The Engel Curve analysis has been
accepted as an important tool in understanding the dynamics of houshold welfare. For
example it has proved useful in the modelling of income distribution and the tax applications
on prices (Castaldo and Reilly, 2007; Dai, Masui, Matsuoaka and Fujimori, 2012; Brau and
Florio, 2001). Moreover, a complete description of consumer behavior sufficient for welfare
analysis requires a specification of both Engel curve and relative price effects consistent with
utility maximization (Banks et al, 1997). In Working-Leser model, each share of the goods
and services item is simply a linear function of the log of total expenditure on all the goods
and services. This model can be extended to include household demographic characteristics
and household location which have an influence on all goods and services items in the model
(Sirirotjanaput, 2012). The general functional form of Working-Leser can be expressed as
follows:
𝑤𝑖𝑗 = 𝛼0 + 𝛽𝑖 𝑙𝑛𝑋𝑗 + ∑𝑘 𝛾𝑖𝑘 𝑍𝑘𝑗 + 𝜀𝑖
(6)
where 𝑤𝑖𝑗 is the share of the ith good or service within total goods and services expenditure
budget of the jth household, Xj is the log of the jth household total expenditure adjusted by
adult equivalence scale , Zkj are set of jth household characteristics that may influence demand
and 𝜀𝑖 is random disturbances assumed with zero mean and constant variance. β and γ are
estimated regression coefficients (Sirirotjanaput, 2012). The income elasticity (or expenditure
as a proxy), is generally differentiated through the shifting slope of household income by
dummy variables for all income strata. In this study, the general forms of the household
demand model in the form of Engel functions are specified as follows;
𝑤𝑖𝑗 = 𝛼0 + 𝛽𝑖 𝑙𝑛𝑌𝑗 + 𝛾1 ∑5𝑗=2 𝐿𝑛𝑌 ∗ 𝐷𝑗 + 𝛾2 𝐿𝐴𝐸𝑆 + 𝛾3 ∑5𝑗=2 𝐸𝐷𝑗 + 𝛾4 𝐷𝑊 + 𝛾5 𝐿𝐶 + 𝛾6 𝑆𝐷 + 𝜀𝑖
(7)
where; 𝑤𝑖𝑗 is the budget share of electricity expenditure within jth household monthly
consumption expenditure, LnY is the log of jth household monthly income, LnY*D stands for
14
A. Ali KOÇ, Gülden BÖLÜK, Fatma Banu BEYAZ SİPAHİ
LnY*D2, LnY*D3, LnY*D4 and LnY*D5 which are interaction variables of household
income with lowest, second lowest, medium, medium-high and higher income strata,
respectively (i.e. D2 stands for the second lowest), LAES is the log of household size adjusted
to the adult equivalent scale (OECD Equivalence Scale) 7, EDj is the dummy variable for the
education level of jth household head (ED1, ED2, ED3, ED4 and ED5 indicate illiterate,
primary school, secondary school, high school and higher education degrees owner
respectively), DW is the dummy variable of dishwasher owner status of the jth household
(DW=1, if the household owned a dishwasher), LC is the dummy variable for location where
jth household is living (urban=1), SD is the dummy variable for seasonality (winter months
=January, February, December=1). The household budget survey data (2007, 2008 and 2009)
obtained from TurkStat was used to estimate the residential electricity demand model
specified in the equations given above (Equations 6 and 7). It was not possible to estimate the
price responses since the data did not include price variables for the household level.
Furthermore, nominal prices started to adjust from the beginning of 2008 and then only
changed a few times during the period considered (2007-2009). Therefore, the variability of
prices is limited to obtain price responses if it is included in the demand models. The budget
survey data of TurkStat consist of 8548, 8550 and 10046 observations for the 2007, 2008 and
2009 surveys, respectively. The reporting rates of electricity expenditures by household were
81, 76 and 76 percent for the same years, respectively. In this study, since the remaining
observations are at a high percent of total observations, the omitted variables have been
treated as if they do not cause bias in the model (see Table 2). However, Zhang (2011) tested
whether omitted variable bias existed due to non-electricity expenditure reporting household
with TurkStat “2008 and 2009 budget survey data” and rejected null hypothesis. The
descriptive statistics and ANOVA test for the variable used in the demand model are reported
in Table 2 and Table 3. As seen in Table 3, F statistics confirm that the mean electricity
expenditure is statistically different according to the income strata, location, household size,
education level of household head and dishwasher owner status. Therefore, these variables
can be used as explanatory variables in the demand model specified in equations 6 and 7.
7
The number of adults for each household size is calculated by using the coefficients which are called equivalence scale. The
OECD measure which is 1 for the reference person of the household, 0.5 for household members aged 14 and over, 0.3 for
household members less than age 14 is used. The equivalence measure enables comparisons between households with different
size and structure (TurkStat, 2012).
The Impact of Economic Crisis and Tariff Adjustment on Residential Electricity Demand in Turkey
15
5. Model Results and Discussion
The model associated with the 2007 survey data indicates that 8 coefficients including
income are significant at the 1 percent level and two variables are significant at the 10 percent
level. Similar results are obtained with 2008 and 2009 survey data. However, 9 and 12
estimated coefficients are significant at the 1 percent level for 2008 and 2009, respectively.
Therefore, it can be said that majority of estimated coefficients of the model for each year are
statistically significant. The results obtained with the different surveys conducted in different
years are very similar in terms of magnitude and sign of coefficients. The income elasticities
of electricity calculated from the model estimated using the survey conducted in 2007, 2008
and 2009 confirm the necessity of electricity at each income strata (range from 0.74 to 0.91)
and decrease as the economic status of household increases. On the other hand, income
elasticity obtained from the model estimated using the 2009 budget survey is moderately
higher than the income elasticity for the “2007 budget survey data”. Estimated income
elasticities are comparable with elasticities which are reported in electricity demand literature
(Table 1).
As frequently encountered in empirical econometric studies with cross-section data,
error variance is not homoscedastic among observations, which seriously affects the result of
the tested hypothesis. Whether or not the variance of estimated models is homoscedastic was
examined using the White General Heteroscedasticity test. Since the chi-square test statistics
were found to be higher than the critical value (see Table 4), the White General
Heteroscedasticity test rejects the null hypothesis. Hence, the variance of the estimated
models is not homoscedastic. One of the remedies applied in empirical studies to alleviate the
heteroscedasticity issue is White’s heteroscedasticity-corrected standards errors procedure
(Gujarati, Porter and Gunasekar 2011, pp.434). In this study, the models are estimated with
the White covariance correction procedure. As expected, the determination coefficients are
low which is a common result obtained with cross-section data. Studies with cross-section
data rely on the significances of estimated parameters rather than determination coefficients
for goodness of fit statistics. Since majority of estimated coefficients are significant at either
the 1 percent, 5 percent or 10 percent level (very few), we can use the estimated model
parameters for policy analysis. In the following part of the paper, the impact of residential
electricity price (tariff) escalation on household welfare for different income strata is
16
A. Ali KOÇ, Gülden BÖLÜK, Fatma Banu BEYAZ SİPAHİ
calculated using the compensated variation employing the formula given in Eq.4 (Huang and
Huang, 2009).
Table 4: Household Electricity Demand Model Estimation Results
Explanatory Variables
Constant
Log of Household Monthly Income (LY)
LPCY*D2 for Second Income Strata
LPCY*D3 for Third Income Strata
LPCY*D4 for Fourth Income Strata
LPCY *D5 for Fifth Income Strata
Log of Household Adult Equivalent Size
(LAES)
Dummy for Dishwasher Owner Status
Education Level of Household Head
(ED2)
Education Level of Household Head
(ED3)
Education Level of Household Head
(ED4)
Education Level of Household Head
(ED5)
Dummy for Location (LC=1 Urban)
Dummy for Seasonality
(Winter Months;1,2,12)
R2
Observations (n)
White’s general heteroscadasticity test
2007
2008
2009
Household Monthly Electricity Expenditure Share (Wi)
0.0098
0.0931
0.0991
(12.58)*
(8.52)*
(9.47)*
-0.0086
-0.0045
-0.0052
(6.99)*
(2.62)*
(3.16)*
-0.0008
-0.0001
-0.0010
(3.49)*
(3.32)*
(3.49)*
-0.0008
-0.0012
-0.0013
(3.04)*
(3.60)*
(4.12)*
-0.0007
-0.0016
-0.0016
(2.62)*
(4.11)*
(4.39)*
-0.0005
-0.0019
-0.0019
(1.72)***
(3.93)*
(4.25)*
0.0033
0.0019
0.0026
(2.89)*
(1.60)
(2.43)**
0.0021
0.0037
0.0046
(3.52)*
(0.92)
(1.28)
0.0006
-0.0030
-0.0037
(0.51)
(2.08)**
(2.66)*
-0.0022
-0.0036
-0.0041
(1.62)
(2.20)**
(2.57)*
-0.0017
-0.0032
-0.0040
(1.39)
(1.96)**
(2.59)*
-0.0018
-0.0045
-0.0050
(1.30)
(2.69)*
(3.21)*
-0.0093
-0.0140
-0.015
(10.96)*
(11.94)*
(13.71)*
0.0013
0.0049
0.0061
(1.89)***
(4.96)*
(6.99)*
0.115
0.129
0.144
6891
6489
7642
200.9
153.0
7591.5
Income Elasticities by Income Strata
Lowest
Second Lowest
Medium
Medium High
Highest
0.797
0.702
0.680
0.635
0.552
0.880
0.850
0.785
0.739
0.660
0.912
0.864
0.839
0.807
0.744
*, ** and *** indicate that the coefficients are significant at 1%, 5% and 10% level. Critical value of Chi-square is 23.7 and
27.6 for 14 and 17 degree of freedom at 5% level. The values in the parenthesis are t statistics.
The Impact of Economic Crisis and Tariff Adjustment on Residential Electricity Demand in Turkey
17
As explained before, one of the key parameters for measuring welfare change with
CV is compensated own-price elasticity, which can be obtained by the Slutsky decomposition
of price changes. The key parameter in this study is income elasticity, since Marshallian ownprice elasticity is computed with meta analysis from time series electricity demand model
studies reported in the Table 1, assuming all households in all income strata have the same
Marshallian price response. However, the mean value of own-price elasticity computed from
different demand studies with time series data for residential electricity demand in Turkey is 0.49 which indicates that electricity is a moderately inelastic good in Turkey. The estimated
models have been used to compute differentiated income elasticity by income strata.
Therefore, the key parameter in this study is income elasticity. The income elasticities were
obtained from the share dependent model from the viewpoint of aforementioned theoretical
basis of the model (Bandyopadhyay et al, 2010).
Table 5: Welfare Impact of Residential Electricity Price Tariff Changes
Income
Si*
Si
Si
(2007)
(2008)
(2009)
Strata
Welfare
Welfare
%
of
Hicks
(CV)
(CV)
Expend
Electricity
Price
2009/08
2009/07
2007**
Expend***
Elasticity
First
4.2
3.7
5.9
0.71
1.6
16.9
9.2
-0.45
Second
3.1
3.1
4.6
0.89
1.8
18.2
9.8
-0.46
Third
2.9
2.7
4.0
1.05
2.2
22.0
10.1
-0.47
Fourth
2.5
2.3
3.5
1.17
2.6
24.8
10.3
-0.47
Fifth
2.0
1.9
2.8
1.40
3.2
29.6
10.7
-0.48
Means
3.0
2.7
4.2
1.04
2.2
22.3
10.1
-0.46
* Si is percentage of electricity expenditure within total household monthly expenditure.
** Electricity expenditure is calculated with deflated price (CPI, 2003=100).
** *indicates that the welfare loss (CV) in 2009 as a percentage of electricity expenditure with constant price in 2007.
The welfare impact result of electricity tariff escalation on households in the income
strata is given in Table 5. As seen, welfare change is remarkable when compared with the
baseline average monthly electricity expenditure. However, from 2007 to 2009, welfare
declined due to price escalation to be 9.2 %, 9.8 %, 10.1 %, 10.3 % and 10.7 %, according to
the ascending order of income strata from the lowest to the highest. It can be expected that the
impact will be higher as a percentage of baseline expenditure with deflated prices if the
A. Ali KOÇ, Gülden BÖLÜK, Fatma Banu BEYAZ SİPAHİ
18
analysis is extended up to the end of 2012. However, nominal prices have increased around
sixty percent since July 2008. Our results are in line with the findings of the previous studies
pointing out the welfare loss in case of positive price shocks (See Bagdadioglu, Basaran, and
Waddams Price, 2007; Zhang, 2011; Akkemik, 2009).
6. Conclusion
The restructuring process of electricity market initiated in 2001 also envisaged
rationalization in electricity tariff rates and removing cross subsidies. The rationalization in
electricity tariffs has not followed the restructuring process simultaneously. Although the
government-regulated retail price of electricity remained constant in nominal terms (declined
in real terms) during 2003-2007, it has been escalating since July 2008. However, the nominal
retail tariff of electricity was adjusted in several steps and the nominal price increased 59
percent (13.6 % in real terms) from July 2008 to February 2013. The real price change would
be higher (33%) if January 2007 and December 2009 period was considered. This study
analyzed the welfare impact of residential electricity tariff adjustment on households in
different income strata. Empirical analysis consists of the estimation of the household
electricity demand model with TurkStat 2007, 2008 and 2009 budget survey data by income
strata and Hicksian compensating measures of welfare due to price change. The income
elasticities by income strata obtained from the demand models with the budget survey
conducted in different years are found to be close to each other, confirming that electricity is a
necessity good in Turkey. Furthermore, these elasticities decrease as income goes up (ranging
from 0.74 to 0.91 for income strata). The magnitude of the income elasticities by income
strata is much lower than the long-run income elasticities obtained by previous time series
econometric studies.
The income elasticity by income strata derived from the estimated
residential demand model and the mean value of Marshallian long-run own-price elasticity of
residential electricity obtained by previous time series econometric model studies for Turkey
were used to obtain Hicksian own-price elasticities via Slutsky decomposition for each
income strata. The welfare impact relies on Hicks compensated variation (CV), which
indicated that welfare loss of households from 2007 to 2009 was about 9-10 percent by
income strata as compared with the pre-tariff adjustment period. It is expected that the welfare
losses would be larger if the analysis periods extended to 2012. The main contribution of this
study is to combine time series and cross section parameters, and use it to differentiate
The Impact of Economic Crisis and Tariff Adjustment on Residential Electricity Demand in Turkey
19
elasticities and calculate welfare impact by income group. It will be possible to estimate direct
price elasticities by income strata using pooling or panel data if this data is provided by
TurkStat which will enrich results and increase reliability of welfare measure. The data and
empirical results indicate that electricity price adjustment increased budget share of electricity
in total expenditure of the households. Moreover price changes caused significant welfare
losses in all income groups, particularly lowest income segment of society. Gradual
adjustment of price instead of radical change would be more appropriate for social and
political stability which is essential for economic stability.
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