Causality between Electricity Consumption and ...

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Causality between Electricity Consumption and Economic Growth in Macao T.M. Lai*

W.M. To

School of Business Macao Polytechnic Institute, Rue de Luis Gonzaga Gomes, Macao Special Administrative Region, People’s Republic of China e-mail: [email protected] * corresponding author

School of Business Macao Polytechnic Institute, Rue de Luis Gonzaga Gomes, Macao Special Administrative Region, People’s Republic of China e-mail: [email protected]

Abstract— Macao's booming gaming industry has contributed to a rapid increase in its gross domestic product (GDP) per capita GDP. In 2007, Macao’s government collected US$10.4 billion in direct tax revenue from the gaming industry and. Macao’s per-capita GDP was estimated at US$36,357, making it one of the highest in the world. Many Asian countries/cities were excited by changes in Macao and started to allow gaming in their territories. However, the relationship between economic growth and electricity consumption in this type of service-oriented territories has yet to be investigated. Specifically, our study focuses on determining this relationship in Macao. The empirical results show that there was no causal relationship between the overall electricity consumption and economic growth in Macao using the quarterly data from 1999Q1 to 2007Q4. However, vector error correction models reveal that unidirectional causality ran from the seasonal component in electricity consumption to the seasonal component in economic growth. Our results shed light on how economic growth affects electricity consumption in a serviceoriented territory and we will discuss practical implications for policy makers.

II. LITERATURE REVIEW ON MODELING OF ELECTRICITY CONSUMPTION AND THE CAUSAL RELATIONSHIP BETWEEN ENERGY CONSUMPTION AND ECONOMIC GROWTH In the past fifty years, researchers have focused on the relationships between electricity consumption and various economic indicators. In the 1950s, Houthakker [2] analyzed electricity demand on domestic two-part tariffs for 42 provincial towns in Britain. He found that the average annual electricity consumption per consumer was a function of the average income per household, the marginal price of elasticity, and the marginal price of competing forms of energy, such as gas. He also reported that the monthly electricity consumption of families had a strong seasonal variation, depending on the average temperature and the average hours of daylight per day for each month. Foss [3] studied the relationship between electricity consumption and the utilization of capital equipment. He suggested that electricity consumption was an indicator of capital usage, especially in an industrial country such as the United States. Foss’s idea was followed by Jorgenson and Griliches [4] and Heathfield [5] to measure capital usage using electricity consumption data. Mount et al. [6] analyzed both the shortrun and long-run demand for electricity for three classes of consumers, namely residential, commercial and industrial. They demonstrated that long-run electricity demand was generally price elastic and became increasingly elastic as prices increased. In contrast, demand was general inelastic with respect to income, especially for residential and industrial classes that approached zero as income increased. In the past decade, Ranjan and Jain [7], Egelioglu et al. [8], Nasr et al. [9], Ozturk and Ceylon [10], Pao [11], and Lai et al. [12] studied the modeling of electricity consumption in different countries or cities. When annual data were used, researchers found that gross domestic product (GDP) or its derivative such as the number of tourists in a tourist center was the major determinant of electricity consumption (Egelioglu et al. [8], Ozturk and Ceylon [10]). When monthly data were used, researchers found that population (POP), temperature (TEMP) and other economic or industrial factors are the major determinants of electricity consumption (Ranjan and Jain [7], Nasr et al. [9], Pao [11], Lai et al. [12]). Moreover, Pao [11] reported that economic indicators such as gross domestic product (GDP) and consumer price index (CPI) have a very weak instantaneous

Keywords: Electricity consumption; Economic growth; Granger-causality

I.

INTRODUCTION

Macao, the only city in China having legalized gaming, has experienced phenomenal growth since its return to China in 1999. According to the government statistics [1], gross revenue from Macao’s casinos amounted to US$10.4 billion in 2007, representing an increase of 46.6% on year-to-year basis. This figure had already surpassed gross income of US$6.8 billion from Las Vegas Strip’s casinos as reported by the Nevada Gaming Commission. Besides, the annualized GDP per capita in Macao was estimated at US$36,357 as at the end of 2007, higher than that of Hong Kong and Singapore. Many Asian cities including Singapore and Penghu of Taiwan have decided or proposed to jump on the bandwagon. However, this type of service industry, like the manufacturing sector, consumes substantially amount of electricity to operate and brighten its facilities including casinos, hotels, and meeting and convention venues on 24x7 basis. To cope with the increasing demand of electricity consumption, public policy makers in those cities need to understand the relationships between economic activities, electricity consumption, and economic growth for policy formulation. 1

where Xt and Yt are stationary time series. He mentioned that when b0 = c0 = 0, Equations 1 and 2 are the expression for a simple causal model.

effect on Taiwan’s electricity consumption. Lai et al. [12] used multiple regression, artificial neural work, and wavelet-ANN to model electricity consumption in Macao using monthly data between January 2000 and December 2006. They found that the total monthly electricity consumption depended on economic factors such as the number of visitors and their hotel-room occupancy rate, demographic and climatic conditions in Macao. In pioneering the study of causal relationship between energy consumption and economic growth, Kraft and Kraft [13] used annual data on gross energy inputs and gross national product (GNP) for the period of 1947 - 1974 in the United States and utilized the test for unidirectional causality as proposed by Sims [14]. They found evidence of a unidirectional causality running from GNP to energy consumption. Yu and Hwang [15] reexamined the causality between energy consumption and GNP over the time period of 1947-1979 using annual data. However, they did not find evidence to support any causality between energy consumption and GNP over the entire sample period. When the time period was changed to 1973-1981 and quarterly data were used, Yu and Hwang [15] reported that unidirectional causality ran from GNP to energy consumption. A year later, Yu and Choi [16] studied the causal relationship between energy consumption and GNP using Sims and Granger causality tests for five countries. They reported that there was (i) no causal relationship between energy consumption and GNP in the United States, Britain and Poland; (ii) unidirectional causality from GNP to energy consumption in South Korea; and (iii) unidirectional causality from energy consumption to GNP in the Philippines. Nachane et al. [17] used Engle-Granger cointegration approach [18] to test the energy-GDP relationship over the time period 1950-51 to 1984-85 for 16 countries. They reported that there was long-run relationship between energy consumption and GDP for 11 developing countries and 5 developed countries. Since then, many researchers adopted Engle-Granger co-integration approach or its modified version to study the causal relationship between energy consumption or electricity consumption and economic growth such as GDP or GNP. However, there is still no conclusive agreement on this issue. III.

B. Vector error correction model If two variables are co-integrated, there is causality between these two variables in at least one direction [20]. If co-integration does not exist between variables, standard cointegration test (vector autoregression) is applied [19]. If cointegration exists between variables, Engle and Granger [18] propose that the vector error correction model (VECM) can be used to test Granger causality for at least one direction. Short run causality can be tested using the standard Wald test on the joint significance of the lagged explanatory variables. Optimal lag length of the co-integration and vector error correction model is determined by Schwartz Information Criteria. IV.

A. Data sources and definition of variables To test whether there was a causal relationship between electricity consumption and economic growth, the values of quarterly electricity consumption per capita and economic output per capita for the period 1999Q1 – 2007Q4 were obtained from the Principle Statistical Indicators of Macao published by published by Macao’s Statistics and Census Service. Electricity consumption (ELEC) was expressed in terms of 106 kWh per capita while economic output was characterized by per-capita gross domestic product (GDP) in 2002 prices, using GDP deflectors. These variables were transformed to logarithmic values, namely LELEC and LGDP, before tests were performed. As seasonality may affect the analysis of short/long-run relationship between the two variables, the time-series data were seasonally adjusted with X12-ARIMA and logarithmic transformation and tests were repeated using seasonally adjusted data, namely LELECsa and LGDPsa. LELEC & LELECsa and LGDP & LGDPsa were plotted in Figures 1 and 2, respectively. In order to investigate the seasonal effects of ELEC and GDP, the seasonal components ELECson and GDPson in electricity consumption and GDP in Figure 3 were calculated, respectively: ELECson = ELEC - ELECsa (3) GDPson = GDP - GDPsa (4) where ELECsa and GDPsa are seasonally adjusted data of electricity consumption (ELEC) and gross domestic product (GDP). The computer software employed in this study is Eviews 6.0 (Quantitative Micro Software, Irvine, CA.).

METHODOLOGY

A. Granger-causality In his 1969 classic paper on causality, Granger [19] stated that a causal model with two variables is expressed as: m

m

X t + b0Yt = ∑ a j X t − j + ∑ b j Yt − j + ε t' j =1

j =1

m

m

Yt + c0 X t = ∑ c j X t − j + ∑ d j Yt − j + ε t'' j =1

DATA AND THE EMPIRICAL FINDINGS

(1)

(2)

j =1

2

determined using minimum Schwartz Information Criterion (SIC) of unconstrained VAR estimation. Wald test was applied in the series of vector autoregression and vector error correction to test whether short-run causality effects occurred or not, respectively. Tables I and II show the findings for the endogeneity of the two variables by the Wald test.

7.500

e alc s go ll ar ut an ni h) W G ( yit cri cte lE

7.400

LELEC LELECsa

7.300 7.200 7.100 7.000 6.900 6.800 6.700

TABLE I. TEST STATISTICS OF SHORT-TERM CAUSALITY BETWEEN LELEC AND LGDP, BETWEEN LELECSA AND LGDPSA USING THE VECTOR AUTOREGRESSION (VAR) MODEL BASED ON WALD TEST.

6.600 6.500

99 99 00 00 01 01 02 02 03 03 04 04 05 05 06 06 07 07 -19 -19 -20 -20 -20 -20 -20 -20 -20 -20 -20 -20 -20 -20 -20 -20 -20 -20 er 1 er 3 er 1 er 3 er 1 er 3 er 1 er 3 er 1 er 3 er 1 er 3 er 1 er 3 er 1 er 3 er 1 er 3 art uart uart uart uart uart uart uart uart uart uart uart uart uart uart uart uart uart u Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q

χ2 statistic Implication 21.918 LELEC does granger cause LGDP (