Discussion Papers In Economics
And Business

The relationship between an electricity supply ceiling and economic growth:
An application of disequilibrium modeling to Taiwan

Mototsugu Fukushige and Hiroshige Yamawaki

Discussion Paper 13-11

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe relationship between an electricity supply ceiling and economic growth:
An application of disequilibrium modeling to Taiwan

Mototsugu Fukushige and Hiroshige Yamawaki

Discussion Paper 13-11

June 2013

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe relationship between an electricity supply ceiling and economic growth:
An application of disequilibrium modeling to Taiwan

Mototsugu Fukushige1
Graduate School of Economics, Osaka University
1-7, Machikaneyama-cho, Toyonaka, Osaka 560-0043, Japan
Hiroshige Yamawaki
Graduate School of Economics, Osaka University
1-7, Machikaneyama-cho, Toyonaka, Osaka 560-0043, Japan

Abstract
Using a disequilibrium model, we investigate the relationship between the supply constraint ofelectricity generation capacity and electricity demand in Taiwan.

We find that electricity

consumption faced supply constraints in Taiwan between 1959 and 1972, but that after 1973
generation capacity grew rapidly, such that economic growth came to be the major determinantof electricity consumption. Our experience in fitting this disequilibrium model suggests that
simple causality tests are not a proper means to understand the relationship between electricity
consumption and economic growth. Our results also suggest, at least for developing countries,that an electricity supply constraint sometimes plays an important role when investigating therelationship between energy consumption and economic growth.

Keywords: Electricity Supply; Economic Growth; Disequilibrium Model; Taiwan

JEL Classification: Q43, C34, O11

1

Corresponding author:
E-mail address: mfuku@econ.osaka-u.ac.jp (M. Fukushige)
Phone & Fax: +81-6-6850-5248 (M. Fukushige).

1. Introduction
When considering the factors leading to economic growth in developing andmiddle-income countries, including Taiwan, the “export-led growth hypothesis” has been very
influential. The basis of this hypothesis is that exports provide the engine for rapid economic
growth. To investigate this, most studies apply Granger causality tests to time series data on
GDP and exports, including in Taiwan (Chu 1988). As an alternative, to understand better thesources of economic growth, and following Kim and Lau (1994), we can instead estimate thenational production function. We can then analyze the contribution of the separate factors ineconomic growth (so-called growth accounting) an approach taken by, for example, Hayashi
and Prescott (2002) in explaining Japan’s Lost Decade.

Sometimes, other explanatory

variables are also included to control for the effects of other factors in the production function.
For instance, Ghartley (1993) included the capital stock and terms of trade when investigating
the export-led growth hypothesis. Electricity (or energy) consumption, the focus of this paper,is also an intermediate input, so the production function or growth accounting approach is also a
candidate method when investigating the relationship between electricity consumption andeconomic growth
In terms of supply capacity or the constraints governing electricity, Rud (2012) recently
analyzed the effects of electricity supply on local industrial development in India.

This

approach shed light on the fact that energy supply facilities, including electricity, are part of an
economy’s essential infrastructure and that energy supply facilities therefore play an importantrole in countries aiming toward rapid economic growth.

Some studies in Taiwan already focus

on the impact of electricity supply capacity. Examples include studies providing long-term
forecasts, such as Huang et al. (2011), and analysis of the demand structures for electricity
across industrial sectors using input–output tables, including Chen and Wu (1994).
Following recent developments in time series analysis, most researchers first use unit root
tests and then proceed to estimate long-run (cointegrating) relationships when investigating therelationship between energy or electricity demand and GDP.

Soytas and Sari (2003), Chen etal. (2007), Lee and Chang (2008) and Ozturk et al. (2010) have applied this approach using data
for a number of countries. At least some have also employed panel data analysis to analyze thelong-run relationships across several countries.

Of course, there are also many other studies

that have applied this approach to the single-country case. For example, Nakajima (2010)
analyzed regional electricity demand in Japan, Oh and Lee (2004) investigated the relationshipbetween GDP and energy demand in Korea, and Hondroyiannis et al. (2002) examined therelationship between energy demand, the price level, and GDP in Greece.
1

Elsewhere,Zachariadis and Pashourtidou (2007) analyzed the causal relationship between private income
and residential electricity demand in Cyprus and Altinay and Karagol (2004) consideredstructural change when investigating the long-term relationship between GDP and energy
consumption in Turkey.

As for Taiwan, Yang (2000) analyzed the causal relationship between

GDP and the consumption of coal, oil, gas, and electricity, while Holtedahl and Joutz (2004)
focused on the relationship between urbanization and residential electricity demand.
Following Yang (2000), Lee and Chang (2005) also considered structural change in thelong-term relationships between GDP and various types of energy consumption, including coal,oil, gas, and electricity.
From an economics point of view, when we observe causality from GDP to energy orelectricity consumption, economic growth effectively becomes a cause of the increase in energy
or electricity supply. In other words, the supply of energy or electricity increases in responseto economic growth.

Conversely, when the causal relationship is from energy or electricity

consumption to GDP, an abundance of energy or electricity induces rapid economic growth.
By implication, a shortage of energy or electricity can equally serve as an economic growthbottleneck.

The results of empirical studies on the exact direction of causality vary fromcountry to country and study to study. For some countries, the direction of causality is fromenergy consumption to GDP, for others there is evidence of a reverse causal relationship, while
for yet other countries, a feedback relationship is at play, suggesting bidirectional causality.

As

an example, recent work by Lee and Chang (2005) for Taiwan concluded that the causal
relationship between GDP and energy demand is unstable in the long term and therefore it isdifficult to identify causality clearly.
Besides these econometric analyses, Taiwan has been the subject of many economic
history studies that emphasize the role of public enterprise in the early stages of economic
growth following World War II.

Above all, economic history studies by Japanese researchers,

such as Kitaba (2003) and Minato (2011), conclude that the priorities of public companies inallocating electricity exerted a great effect on Taiwanese economic growth in the 1940s and the1950s.

In particular, Minato (2011, p. 223) declares that the complementary andwell-developed relationship between the electricity-intensive industry and the electricity
industry established in Taiwan in the late 1940s put Taiwan on the track to importsubstitution-led industrialization and rapid economic growth in the 1950s.

Similarly, Kitaba(2003, p. 145) states “…the electricity always ran short until the 1980s when nuclear power
plants started to work”, he subsequently explains the economic benefits of low cost electricity(special contract electricity) (Kitaba 2003, p. 149), and how this system lasted up until 1976
2

(Kitaba 2003, p. 156).

These accounts seemingly concur with the argument in Kuo and Myers(2012, p. 47): “From 1949 until the late 1950s, the administration was unable to generateadequate supplies of electrical energy…To supply energy at the low prices demanded byauthorities and general population, high-cost electricity plants faced rapidly accumulating
deficits.”
If these historical details are correct, and the electricity supply was indeed a bottleneck foreconomic growth in Taiwan, the estimation of a simple electricity demand function will notadequately capture the relationship between electricity consumption, GDP and the price ofelectricity. For instance, when we investigate a causal relationship using Granger causality, if
the electricity supply continued to be a bottleneck for economic growth, we may find a causal
relationship from electricity consumption to GDP. However, if the times when electricity
supplies had and had not been a constraint took place alternately, we could expect that theestimated relationship between GDP and electricity consumption would be unstable, and so theresults of any time-series analysis would not correctly capture their actual relationship.
Following this discussion, we investigate the determinants of electricity consumption in
Taiwan from 1950 to 2000 using a disequilibrium model encompassing both electricity demandand supply constraints.

This model assumes that GDP and the price of electricity determine

electricity consumption when there is sufficient supply capacity. However, when capacity is
insufficient to meet electricity demand, electricity generation capacity determines electricity
consumption. By estimating this type of disequilibrium model, we shed light on a dimensionof the relationship between electricity demand and economic growth in Taiwan not captured
through the estimation of a simple cointegration relationship.
The structure of the remainder of the paper is as follows.

In Section 2, we first describe

the relationship between electricity consumption and GDP in Taiwan after World War II. We
also introduce and estimate a disequilibrium model to investigate the effect of a supply limit inelectricity generation on Taiwanese economic growth. In Section 3, we summarize the resultsof the analysis and discuss some possible future research.

2. Empirical results
2.1 Taiwan’s economic growth after World War II
Fig. 1 plots the trends in GDP and electricity generation capacity in Taiwan from 1949 to2000. We describe the sources of the data in the following section. As shown, between thestarting point in 1949 and the ending point in 2000, rates of change of GDP and generation
capacity move up and down at various times and the lines intersect in about the middle of 1974.
3

Of course, the location of the intersection depends on the scales of the left and right verticalaxes used plot GDP and generation capacity.

However, we can appreciate clearly thedifferences in the changes in GDP and electricity consumption using this figure. This type ofcomparison is very similar to the energy coefficient method that Ang (1987, 1988) and Huang(1993) applied to the geometric mean of the growth rate of GDP and energy consumption in
Taiwan.
With this in mind, the fact that the plot for electricity consumption crosses that for GDP
from below in 1974 indicates that the growth rate of GDP is higher than that of electricity
consumption in the first half of the sample period from 1949 to 2000 and vice versa in the last
half of the sample period. In other words, the supply capacity of electricity may have been“tight” or reached some ceiling in the 1950s or the 1960s, whereas generation capacity may
have become sufficient to meet electricity demand toward the middle of the 1970s and thereafter.
To analyze such a situation with an econometric techniques, we have available so-called“disequilibrium models”. In the next subsection, we introduce a disequilibrium model forelectricity consumption.

2.2 Disequilibrium model
We first specify the demand and supply limit functions.

In this analysis, a supply side

restriction is not a supply function. We instead assume that there is a supply ceiling based
upon generation capacity, so we specify the ceiling for supply as follows:
lnC𝑡 = lnS𝑡 = α0 + α1 lnE𝑡 + ϵ𝑡𝑤ℎ𝑒𝑛 S𝑡 < D𝑡

ϵ𝑡 ～ 𝑁(0, 𝜎22 )where E𝑡 is generation capacity.

As the equation is of log-linear form, lnC𝑡 = lnS𝑡 infers

that the supply of electricity equals the consumption of electricity. We include a normal error
term ϵ𝑡 as there may be observational errors or metrological disturbances in reported electricity
consumption. As for the demand function for electricity, following the literature we specify
real GDP (Y𝑡 ) and the electricity price (P𝑡 ) as independent variables, again in log-linear form:
lnC𝑡 = lnD𝑡 = β0 + β1 lnP𝑡 + βlnY𝑡 + u𝑡
ρut-1 +ωt
𝑢𝑡 = {
vt

when
when

Dt-1 <St-1
St-1 ≤Dt-1

ω𝑡 ～ 𝑁(0, 𝜎12 )4𝑤ℎ𝑒𝑛 D𝑡 < S𝑡

2 )v𝑡 ～ 𝑁(0, 𝜎12 + 𝜎01

where lnC𝑡 = lnD𝑡 indicates that the demand for electricity equals the consumption ofelectricity. We introduce a serially correlated error term in this function when the generationof the preceding observation is from the demand function and a serially uncorrelated error termwhen the preceding observation is set from the supply ceiling.

These error terms have2
different variances, which we denote 𝜎12 and 𝜎12 + 𝜎01, respectively.

In theory, if we assumeserially correlated error terms, we should apply Quandt’s (1981) estimation method. However,this method involves some limitations and difficulties when estimating parameters.

Moreover,several market adjustments were made when demand faced a supply ceiling, and this served tolower electricity demand.

We consider that these adjustments affected the error term for thedemand function by removing any serial correlation but that it introduced a new serially
uncorrelated error term when the ceiling became freer.

In addition, if we apply Maddala and

Nelson’s (1974) estimation method, which does not utilize prior information about excess
demand or excess supply to assign the observations a priori to excess demand or excess supply
regimes, serially correlated error terms in the demand function make estimation difficult.
Therefore, we utilize the ratio of electricity consumption to generation capacity to assign theobservations to each regime:t ∈ 𝑇1 : Dt-1 <St-1

whent ∈ 𝑇2 : St-1 ≤Dt-1

when

𝐶𝑡
>k0
𝐸𝑡
𝐶𝑡
≤k0
𝐸𝑡

where k0 is unknown and so we estimate it using a grid search method to maximize thelikelihood and introduce the following dummy variable:

1
Dumt = {0in each k0 .

whent ∈ T1whent ∈ T2The model is now the same as Model B in Quandt (1988, p. 22) with a seriallycorrelated error term in the demand function.
observation becomes as follows:

5

Then, the likelihood function for each1

lt =Dumt *√2π(σ21 +(1-Dumt-1 )*σ201 )2

1 (lnCt -β0 -β1 lnPt -βlnYt -Dumt-1 *ρ*(lnCt-1 -β0 -β1 lnPt-1 -βlnYt-1 ))× exp ()
2
σ21 +(1-Dumt-1 )*σ201
lnCt − α0 − α1 lnE𝑡
× (1 − Φ ())
𝜎2
1+(1-Dumt )*exp (-√2πσ22× (1 − Φ (1 (lnCt − α0 − α1 lnE𝑡 )2)2
σ22

lnCt -β0 -β1 lnPt -βlnYt -Dumt-1 *ρ*(lnCt-1 -β0 -β1 lnPt-1 -βlnYt-1 )))𝜎1 +(1-Dumt-1 )*𝜎01

where Φ(∗) is the cumulative density function of the standard normal distribution and thelikelihood function for the observations becomes:
L= ∏Tt=1 lt .
We maximize this likelihood function to estimate the parameters with k0 given and then search
for that k0 which maximizes the likelihood for a given k0 between 3055 and 3557. The
lower bound is equal to the mean while the upper limit for convergence in the estimation
process for maximum likelihood estimation determines the upper bound.

2.3 Data and estimation
Table 1 provides the sources and definitions of the data used in the estimation. As
shown in Table 1, we calculate the nominal price of electricity by dividing total electricity sales
by the volume of electricity sold. We then obtain the real price by deflating this estimatednominal price by the GDP deflator at 1960 prices. Fig. 2 depicts the estimated real price ofelectricity obtained using this method.

As shown, the real electricity prices increased

temporarily in the 1980s, outside of which the overall trend was gently increasing withrelatively small increases and decreases. In Table 2, we provide summary statistics for the logs
of each data series.
Table 3 provides the estimation results obtained using the maximum likelihood method.
The standard errors of the equations for the supply constraint and demand function and the6estimated coefficients are significant at the 1% level of statistical significance with the
exception of the estimated coefficient for GDP in the demand function and the increase in thestandard error of the demand function following the resolution of the supply constraint.

Theestimated coefficient for GDP is significant at the 10% level using a two-sided test. When we
consider the expected sign of the coefficient and apply a one-sided test in a positive direction,
the estimate for GDP is statistically significant at the 5% level of significance.
As for the increment in the standard error of the demand functions following theresolution of the supply constraints, we estimate this to be ten times more than that without
increments.

However, and as discussed later, because we had only two observations (1961 and1973) with which to estimate this value between 1950 and 2000, the estimate of the parameter is
not stable. We estimate the ratio of electricity consumption to generation capacity, which
determines whether the supply constraints held or not, to be between 3.360 and 3.397. We
obtained this result using a grid search with integer values between 3.055 and 3.557 tomaximize the likelihood. When we set the ratio between 3.360 and 3.397, the separation of theperiod whether the supply constraints are present or not is the same and the estimation result forthe disequilibrium model given by the maximum likelihood method is unchanged between theseratios. Because of space limitations and in the interests of brevity, we have not conducted theunit root and cointegrating relationship tests usually found in previous studies in this area.
However, we do employ a test for cointegrating relationships using McKinnon’s (1991) tableconcerning the coefficient of the lagged residual from the estimated demand function. The
calculated test statistic for the unit root in error terms becomes:

z =

̂ −1
𝜌
̂)
√𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒(𝜌= −4.996.

Given the number of independent variables (N = 3) and the sample size (T = 50) in our analysis,
the calculated critical value at the 5% significance level is:Ĉ(𝑝, 𝑇) = 𝛽̂∞ + 𝛽̂1 𝑇 −1 + 𝛽̂2 𝑇 −2 = −3.7429 − 8.352⁄50 − 13.41⁄(50)2 = −3.9153.
Given the test statistic is smaller than the critical value, we consider that the estimated demand
function does not represent spurious regression.

Of course, this is not an exact testingprocedure for this type of disequilibrium model, but it can be considered as a roughapproximation.
7

2.4 Estimated regimes and relationship between the supply ceiling and demand
In Fig. 3, we illustrated the ratios of actual electricity consumption and the estimatedelectricity supply ceiling given by the supply limit function.

The shaded regions in Fig. 3

represent periods when electricity demand reached the ceiling. As shown, it is clear that withthe exception of 1961, between 1959 and 1972 the supply ceiling held.

As discussed earlier,the observation points used to estimate the increase in the standard error following theresolution of the supply constraints comprise only two time points: 1962 and 1973. After 1973
and until 2000, the last year of our estimation period, electricity consumption did not reach the
supply ceiling.

This suggests that electricity generation capacity was not a constraint toeconomic growth in Taiwan after 1973. This result is somewhat different from Kitaba’s (2002)
earlier findings that electricity shortages persisted in Taiwan up until the 1980s.

However,

until 1972, we consider that electricity consumption had reached the ceiling of supply and sosome sort of restriction on economic growth in Taiwan must have existed.
Compared with existing studies employing the estimation of cointegrating relationships
and the use of causality tests, our result suggests that we should identify a causal relationshipfrom electricity consumption to GDP up until 1973, while after 1973 we would instead identify
a causal relationship from GDP to electricity consumption.

Were we to consider structuralchange within the sample period, we would locate it in 1973 and it would be associated with achange in the direction of causality. On this basis, we argue the finding in Lee and Chang(2005), of difficulty in identifying a clear cause-and-effect relationship between GDP andelectricity consumption in Taiwan, is the result of the regime switching we found by fitting a
disequilibrium model.

3. Conclusion
Using a disequilibrium model, we investigate the relationship between the supply
constraints of electricity generation capacity and electricity demand in Taiwan. Between 1959
and 1972, electricity consumption faced supply constraints, but after 1973, generation capacity
grew rapidly such that economic growth came to be the major factor in determining power
consumption in Taiwan.

Fitting this disequilibrium model suggests that applying simple

causality tests is not a proper approach for understanding the relationship between electricity
consumption and economic growth in Taiwan.

Our analysis therefore provides a meaningful

economic interpretation for the historical changes in the relationship between economic growthand electricity consumption in Taiwan, which has hitherto been a “black box” in the estimation8

of the long-term relationship and causality tests using time-series model in existing studies.
This result also provides evidence that the econometric approach (disequilibrium model) used inthis paper can play a complementary role to conventional time series analysis. It also suggests
that, at least in developing countries, that electricity supply constraints sometimes play animportant role in determining the relationship between energy consumption and economic
growth.
As a final matter, we note some problems remaining in our analysis, all of which suggest
future directions of research.

To start with, in estimating our disequilibrium model, thesplitting of the estimation period according to the regime switch makes it difficult to conduct
unit root tests and to test for cointegrating relationships because of the shortened time series. In
addition, we cannot stably estimate the standard error for the demand function after the regime
switch. At present, there is no means available to improve this situation and thereby provide amore robust estimation method, but we trust that novel analytical methods or models to addressthese deficiencies will be available in the future.

Acknowledgments
The Global COE Program “Human Behavior and Socioeconomic Dynamics” supported thisresearch.

The financial support of the Japan Society for the Promotion of Science(Grants-in-Aid for Scientific Research A(2) 16203020 and A 22243026) is also gratefulacknowledged.

9References
Altinay, G., Karagol, E., 2004. Structural break, unit root, and the causality between energy
consumption and GDP in Turkey. Energy Economics 26, 985-994.
Ang, B.W., 1987. Electricity-output ratio and sectorial electricity use: The case of Southeast
Asian developing countries. Energy Policy 15, 262-282.
Ang, B.W., 1988. Electricity-output ratio and sectorial electricity use: The case of East andSoutheast Asian developing countries. Energy Policy 16, 115-121.
Chen, C.Y., Wu, R.H., 1994. Source of change in industrial electricity use in the Taiwan
economy, 1976-86. Energy Economics 16, 115-120.
Chen, S.T., Kuo, H.I., Chen, C.C., 2007. The relationship between GDP and electricity
consumption in 10 Asian countries. Energy Policy 35, 2611-2621.
Chu, W.W., 1988. Export-led growth and import dependence: The case of Taiwan 1969-1981.
Journal of Development Economics 28, 265-276.
Ghartley, E.E., 1993. Causal relationship between exports and economic growth: Someempirical evidence in Taiwan, Japan and the US. Applied Economics 25, 1145-1152.
Hayashi, F., Prescott, E.C., 2002. The 1990s in Japan: A lost decade. Review of Economic
Dynamics 5, 206-235.
Holtedahl, P., Joutz, F.L., 2004. Residential electricity demand in Taiwan. Energy Economics 26
201-224.
Hondroyiannis, G., Lolos, S., Papapetrou, E., 2002. Energy consumption and economic growth:
Assessing the evidence from Greece. Energy Economics 24, 319-336.
Huang, J.P., 1993. Electricity consumption and economic growth: A case study of China. Energy
Policy 23, 717-720.
Huang, Y., Bor, Y.J., Peng, C.Y., 2011. The long-term forecast of Taiwan’s energy supply anddemand: LEAP model application. Energy Policy 39, 6790-6803.
Kim, J.I., Lau, L., 1994. The source of economic growth of the East Asian newly industrializedcountries. Journal of the Japanese and International Economies 8, 235-271.
Kitaba, M., 2003. Kouhatsu Kougyou-koku no Keizai-hattenn to Denryoku Jigyou (Economic
Development of Less Developed Countries and Power Industry). Yoko-shobo: Kyoto.
Kuo, T.C., Myers, R.H., 2012. Taiwan’s Economic Transformation: Leadership, Property Rights
and Institutional Change 1949-1965. Routledge: New York.
Lee, C.C., Chang, C.P., 2005. Structural breaks, energy consumption, and economic growthrevisited: Evidence from Taiwan. Energy Economics 16, 857-872.
Lee, C.C., Chang, C.P., 2008. Energy consumption and economic growth in Asian economies: A
10more comprehensive analysis using panel data. Resource and Energy Economics 30,
50-65.
McKinnon, J.G., 1991. Critical values for cointegration tests, in Engle, R.F. and Granger, C.W.J.
(Eds.). Long-Run Economic Relationships: Readings in Cointegration, Oxford Univ.
Press: Oxford, pp. 267-276.
Maddala, G.S., Nelson, F.D., 1974. Maximum likelihood methods for models of market
equilibrium, Econometrica 42, 1013-1030.
Minato, T., 2011. Kindai Taiwan no Denryoku Sangyo (Power Industry in Modern Taiwan).
Ochanomizu-shobo: Tokyo.
Mizoguchi, T., 2008. Ajia Choki Keizai-Toukei 1: Taiwan (Asian Historical Statistics: Taiwan).
Toyokeizai, Tokyo.
Nakajima, T., 2010. The residential demand for electricity in Japan: An examination using
empirical panel analysis techniques. Journal of Asian Economics 21, 412-420.
Oh, W., Lee, K., 2004. Energy consumption and economic growth in Korea: Testing thecausality relation. Journal of Policy Modeling 26, 973-981.
Ozturk, I., Aslan, A., Kalyoncu, H., 2010, Energy consumption and economic growth
relationship: Evidence from panel data for low and middle income countries. Energy
Policy 38, 4422-4428.
Quandt, R.E., 1981. Autocorrelation disequilibrium models. Economics Letters 7, 55-62.
Quandt, R.E., 1988. The Econometrics of Disequilibrium. Basil Blackwell: New York.
Rud, J.P., 2012. Electricity provision and industrial development: Evidence from India. Journalof Development Economics 97, 352-367.
Soytas, U., Sari, R., 2003. Energy consumption and GDP: Causality relationship in G-7
countries and emerging markets. Energy Economics 25, 33-37.
Yang, H.Y., 2000. A note on the causal relationship between energy and GDP in Taiwan. Energy
Economics 22, 309-317.
Zachariadis, T., Pashourtidou, N., 2007. An empirical analysis of electricity consumption in
Cyprus. Energy Economics 29, 183-198.

11

Table 1. Data Sources
Data

Definition

SourceRevenue from electricity sales

Revenue from electricity sales

Tai Power’s home page (access at /07/02/2013/)

in New Taiwan Dollars

http://www.taipower.com.tw/content/govern/govern01.aspx?MType=8

Sales of electricity in kWh

Tai Power’s home page (access at /07/02/2013/)

Consumption of electricity

http://www.taipower.com.tw/content/govern/govern01.aspx?MType=8

Price of electricity

Capacity

Authors’ calculations

Revenue from sale of Electricity
Consumption of electricity

Tai Power’s home page (access at /07/02/2013/)

Electricity generation capacity in kWh

http://www.taipower.com.tw/content/govern/govern01.aspx?MType=8

Real GDP

Aggregated real GDP (in 1960 prices).

Table 7.2 on p. 327 in Mizoguchi (2008)

Estimated using two alternative methods
before adjustments for discrepancies in
GDP/GDE
Nominal GDP

AggregatednominalGDP

beforeTable 7.1 on p. 325 in Mizoguchi (2008)

adjustments for GDP/GDE discrepancies
GDP deflator

Authors’ calculations

Nominal GDP/Real GDP

12

Table 2. Summary Statistics
Variable

Definition (before logarithmic transformation)

MeanStandard Deviation

Minimum

MaximumlnC

Consumption of electricity

23.19329

1.5360319.97254

25.2747lnP

10000 × Price of electricity
GDP deflator

8.712250.20094

8.372139.14169

lnY

Real GDP

12.45521

1.26746

10.3897

14.49585

lnE

Capacity

15.18123

1.52278

12.52717.20445

13

Table 3. Estimated Results
Demand function
Variables

Supply limits

Coefficientt-value

Coefficientt-valueConstant

22.8999

7.165**−−

lnP–0.2472–5.983**

−

−

−

−

+

lnY

0.35971.721

ρ

0.9623127.693 **

−

−

𝜎1

0.0397

10.630**

−

−

𝜎01

1.6714

1.471

−

−

Constant−−

8.203823.370**

lnE

−

−

0.998940.906**𝜎2−−0.03883.382**

log likelihood

81.1492k0[ 3360 , 3397 ]Note: ** and + indicate statistical significance at the 1% and 10% level, respectively.
Log-likelihood reported from the results of the ML command in TSP 5.0.

14

30,000,000

1,800,000
25,000,000
1,600,000

Real GDP (left-hand side axis)
1,400,000
20,000,000

Generation capacity (right-hand side axis)
1,200,000

1,000,000

15,000,000

800,000
10,000,000
600,000

400,000
5,000,000
200,000

0
1949

1952

1955

1958

1961

1964

1967

1970

1973

1976

1979

Year
Fig. 1 Trends in Real GDP and Electricity Generation Capacity
15

1982

1985

1988

1991

1994

1997

0
2000

Electricity generation capacity in kW

Real GDP in millions of New Taiwan Dollars, 1960 prices

2,000,000

Real price of electricity per kWh in New Taiwan Dollars, 1960 prices

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0
1949

1952

1955

1958

1961

1964

1967

1970

1973

1976

Year
Fig. 2 Price of Electricity
16

1979

1982

1985

1988

1991

1994

1997

2000

Fig. 3

Electricity Demand Exceeding Electricity Supply Ceiling17