This study uses the Blinder-Oaxaca decomposition to investigate what are the main reasons that contribute to the changes of household electricity consumption. The household data in Taiwan over the period 1985-2015 are used. The empirical results indicate that the changes of household electricity consumption are driven by different factors across these three decades. The increase in household electricity consumption is mainly attributed to the changes in the coefficients effect of the determinants. In particular, the coefficients effect of household size plays the most important role. The declining of household size leads to electricity consumption per capita increases due to the loss of economies of scale. As for the contribution of the endowments effect, the number of air condition and household income are the most important factors. Moreover, the coefficients effect of household size is crucial both for high-income and low-income households. Therefore, the policy implication means that the electricity pricing policy should take household size into consideration so as to offer electricity-saving incentives for households with smaller family size. Besides, some strategies, such as improving energy efficiency of appliances and providing the subsidy for the investment in energy-efficient appliances, should have a higher priority.
With economic growth and industrial development, electricity consumption has increased rapidly in many countries over the past three decades. According to the statistics from the Bureau of Energy, Ministry of Economic Affairs of Taiwan, the share of electricity in final energy consumption rose from 35% to 49% from 1982 to 2017. In 2017, total electricity consumption was 261,308 MWh, which was more than 6.4 times greater than electricity use in 1982. Between 1982 and 2017, the average annual growth rate of total electricity consumption was 5.5%. In 2017, Taiwan’s per capita electricity consumption was 11.2 MWh, which was higher than the world average of 3.1 MWh per capita. It ranked first in Asia and the 12th in the world. The considerable growth trend of electricity consumption is also displayed in the residential sector. From 1982 to 2017, Taiwan’s residential electricity consumption has increased by 500%. The residential sector is responsible for 19% of total electricity use, which is only second to the industrial sector (50%). The continuous rise in household electricity consumption is adverse to the aims of energy conservation and environmental protection. Since Taiwan is highly dependent on imported energy and under the risk of insufficient power supply, seeking effective strategies for reducing electricity use is an important target for policymakers. Thus, it is necessary to investigate what are the main reasons that contribute to the change of household electricity consumption.
Many studies have explored the possible factors that affect household electricity consumption. In general, household electricity consumption is associated with various factors, such as socio-demographic characteristics [
Few studies have paid attention to the issues about household electricity consumption with the view of historical changes or intertemporal comparisons. Lacking the necessary data is the main barrier to studying household electricity consumption [
According to previous studies, it is necessary to consider the changes in the distributions of determinants when we explore household electricity consumption along the time dimension. The first reason is that the household structure and generations had changed over time. Some studies indicated that household size has gradually decreased [
This paper takes the advantage of the Blinder-Oaxaca decomposition to detect the critical reasons for the change of household electricity use. The results highlight that demographic structure and socio-economic characteristics would have critical influence on household electricity use. The increase in household electricity consumption is mainly attributed to the changes in the coefficients effect of the determinants. In particular, the coefficients effect of household size plays the most important role. This study can provide new findings on the issue of household electricity consumption with a historical perspective. The application of decomposition technique not only allows us to distinguish the change of household electricity consumption into difference sources, but also reveals the relative importance of driving sources.
We start our analysis by using the ordinary least squares regression to estimate the effects of the predictors on household electricity consumption. The ordinary least squares regression can reflect the effects of individual factors on household electricity consumption. However, it cannot describe the relative importance of these factors in contribution to the change in household electricity consumption. Furthermore, we employ the Blinder-Oaxaca decomposition, based on linear regression and developed by Blinder [
Y g = X ′ β g + ε g , g = s , t , E ( ε g ) = 0 (1)
where Y g represents household electricity consumption of the two groups, and X is a vector of observable characteristics. β is the vector of parameter to be estimated, and ε is the error term. The mean outcome difference, denoted as D, can be expressed as follows:
D = E ( Y s ) − E ( Y t ) = E ( X s ) ′ β s − E ( X t ) ′ β t (2)
E(Y) is the expected value of the outcome variable. The contribution of group differences in predictors to the outcome difference can be represented as follows [
D = { E ( X s ) − E ( X t ) } ′ β t + E ( X t ) ′ ( β s − β t ) + { E ( X s ) − E ( X t ) } ′ ( β s − β t ) (3)
As the group means X ¯ s and X ¯ t are used as estimates for E(Xs) and E(Xt), the expression can be written as:
D = Y ¯ s − Y ¯ t = ( X ¯ s − X ¯ t ) ′ β ^ t + X ¯ ′ t ( β ^ s − β ^ t ) + ( X ¯ s − X ¯ t ) ′ ( β ^ s − β ^ t ) (4)
where β ^ s and β ^ t are the estimated coefficients, obtained separately from the two samples. The coefficients in the sample of the period t are used as the reference in Equation (4). If the coefficients in the sample of the period s are used as the reference, the decomposition can be expressed as another form:
D = Y ¯ s − Y ¯ t = ( X ¯ s − X ¯ t ) ′ β ^ s + X ¯ ′ s ( β ^ s − β ^ t ) + ( X ¯ s − X ¯ t ) ′ ( β ^ s − β ^ t ) (5)
The outcome difference is divided into three components. The first component is group differences in the mean values of the predictors, which can be referred as the endowments effect. For example, the distribution of household income and housing area may vary across time. The second component is differences in the coefficients of the predictors, which can be referred as the coefficients effect or marginal effect. For instance, household appliances may consume less electricity because of the improvement of energy efficiency. The third component is the interaction term that is due to the simultaneous effect of differences in endowments and coefficients, which is denoted as the interaction effect. Since this component is difficult to interpret, many researchers do not address it [
This study uses the household data which are obtained from Taiwan’s Family Income and Expenditure Survey (FIES). This is a nationwide cross-sectional survey that has been conducted annually by the Taiwanese government, but households are not tracked. There are approximately 15,000 households involved in this survey for each year. The database of Taiwan’s FIES contains household information such as demographic characteristics, property and facilities, income and expenditure. We use the household data in 1985, 1995, 2005, and 2015 so as to analyse the change of household electricity consumption for the periods: 1985-1995, 1995-2005, and 2005-2015. In particular, we aim to investigate what factors contribute to the change of household electricity consumption from 1985 to 2015.
However, the FIES database only collects household electricity expenditure, and household electricity use is not available. Therefore, the dependent variable needs to be calculated by using electricity prices, which are based on the progressive electricity tariff system. Thus, household electricity expenditure can be transformed into household electricity use. The electricity prices data are obtained from Taiwan Power Company, which is a state-owned company. The dependent variable can be defined as total annual electricity use per household and measured in kWh.
1985 database | 1995 database | 2005 database | 2015 database | |
---|---|---|---|---|
Mean | 2307.57 | 4507.42 | 5226.74 | 5019.21 |
Standard deviation | 1186.04 | 2619.29 | 2557.09 | 1797.37 |
10th percentile | 1220.83 | 2023.82 | 2598.57 | 2854.18 |
50th percentile | 2072.26 | 4117.99 | 4869.93 | 4839.32 |
90th percentile | 3627.10 | 7188.94 | 8004.57 | 7182.37 |
Notes: Household electricity consumption is measured in kWh.
The explanatory variables are classified into three categories: household head characteristics, household characteristics, and dwelling attributes. Household head characteristics include age, gender, and education. The age of household head is a continuous variable, which captures the life-cycle stage of a household and generation effects. Gender is represented by a dummy variable that takes value 1 for male and 0 for female. Education level is an ordinal variable, measured by the highest degree that household heads obtain. We assign scores 1, 2, 3, and 4, respectively, to the four levels: less than junior high school, junior high school, senior high school, and bachelors or graduate degree. Household characteristics include household size, the number of elderly members, the number of wage earners, and household income. Household size is defined as the number of household members. Some studies have shown that electricity consumption per capita may decline since family members can share appliances. The effect of economies of scale may exist in electricity consumption [
Dwelling attributes contain ownership status, whether the house is used for business, the number of floors, housing area, and household appliance ownership. Ownership status is measured by dummy variable, valued at 1 if the house is owner occupied and 0 otherwise. Ownership status may influence households’ incentive to invest in energy-efficient appliance and the willingness to buy appliances. We also consider that residential houses may be used for business if the member of households is self-employed. The house used for business may consume more electricity. We use a dummy variable, taking value 1 if the house is not used for business and 0 otherwise. In addition, we use dummy variables for the number of floors, which are classified into four categories: 1 floor, 2 to 3 floors, 4 to 5 floors, and more than 5 floors. The first category served as the reference category is omitted. Housing area is a continuous variable, measured by total housing area in square meters. Lastly, we also include the number of household appliances in the model. We consider the number of televisions, water heaters, air conditioners, and washing machines. The descriptive statistics for explanatory variables are reported as
Variables | 1985 dataset | 1995 dataset | ||||||
---|---|---|---|---|---|---|---|---|
Mean | SD | Max | Min | Mean | SD | Max | Min | |
Continuous/Numeric variables | ||||||||
Age of household head | 41.85 | 12.29 | 90 | 16 | 44.44 | 13.21 | 94 | 15 |
Household size | 4.60 | 1.94 | 21 | 1 | 3.93 | 1.73 | 16 | 1 |
Number of elderly members | 0.22 | 0.51 | 3 | 0 | 0.34 | 0.62 | 4 | 0 |
Number of wage earners | 1.82 | 1.08 | 9 | 0 | 1.71 | 1.05 | 8 | 0 |
Household income (thousand NT dollars) | 514.76 | 297.89 | 5061 | 1.59 | 981.10 | 624.73 | 13,413 | 5.69 |
Housing area (m2) | 99.71 | 52.70 | 892 | 1.50 | 122.56 | 68.84 | 2148 | 3.31 |
Number of televisions | 1.05 | 0.33 | 5 | 0 | 1.29 | 1.02 | 8 | 0 |
Number of water heaters | 0.72 | 0.53 | 4 | 0 | 1.02 | 0.39 | 5 | 0 |
Number of air conditioners | 0.30 | 0.62 | 7 | 0 | 1.13 | 1.12 | 10 | 0 |
Number of washing machines | 0.79 | 0.43 | 10 | 0 | 0.94 | 0.29 | 3 | 0 |
N | % | N | % | |||||
Dummy/Categorical variables | ||||||||
Gender of household head | ||||||||
Male | 14,814 | 90.5 | 12,585 | 85.7 | ||||
Female | 1562 | 9.5 | 2103 | 14.3 | ||||
Education level of household head | ||||||||
Less than junior high school* | 7691 | 47.0 | 4809 | 32.7 | ||||
Junior high school | 2587 | 15.8 | 2553 | 17.4 | ||||
Senior high school | 3477 | 21.2 | 4024 | 27.4 | ||||
Bachelors or graduate degree | 2621 | 16.0 | 3302 | 22.5 | ||||
Ownership | ||||||||
Own | 12,687 | 77.5 | 12,247 | 83.4 | ||||
Rent | 3689 | 22.5 | 2441 | 16.6 | ||||
Business use | ||||||||
No | 14,738 | 90.0 | 13,765 | 93.7 | ||||
Yes | 1638 | 10.0 | 923 | 6.3 | ||||
Number of floors | ||||||||
1 floor* | 5650 | 45.8 | 2967 | 20.2 | ||||
2 - 3 floors | 6633 | 35.7 | 6307 | 42.9 | ||||
4 - 5 floors | 3611 | 16.9 | 3948 | 26.9 | ||||
>5 floors | 482 | 1.6 | 1466 | 10.0 | ||||
Total observations | 16,376 | 14,688 | ||||||
Variables | 2005 dataset | 2015 dataset | ||||||
Mean | SD | Max | Min | Mean | SD | Max | Min | |
Continuous/Numeric variables | ||||||||
Age of household head | 48.96 | 14.39 | 96 | 16 | 52.27 | 14.81 | 101 | 16 |
Household size | 3.39 | 1.58 | 13 | 1 | 3.09 | 1.48 | 15 | 1 |
Number of elderly members | 0.46 | 0.71 | 3 | 0 | 0.59 | 0.77 | 4 | 0 |
---|---|---|---|---|---|---|---|---|
Number of wage earners | 1.48 | 1.03 | 7 | 0 | 1.41 | 1.06 | 7 | 0 |
Household income (thousand NT dollars) | 963.95 | 669.34 | 11,185 | 3.47 | 928.56 | 665.39 | 21,216 | 10.08 |
Housing area (m2) | 140.56 | 75.48 | 1322 | 6.31 | 146.49 | 76.56 | 1124 | 9.92 |
Number of televisions | 1.49 | 0.72 | 7 | 0 | 1.57 | 0.79 | 9 | 0 |
Number of water heaters | 1.03 | 0.35 | 4 | 0 | 1.07 | 0.33 | 6 | 0 |
Number of air conditioners | 1.82 | 1.32 | 11 | 0 | 2.29 | 1.35 | 11 | 0 |
Number of washing machines | 0.98 | 0.23 | 4 | 0 | 1.01 | 0.21 | 4 | 0 |
N | % | N | % | |||||
Dummy/Categorical variables | ||||||||
Gender of household head | ||||||||
Male | 10,655 | 78.0 | 11,632 | 70.4 | ||||
Female | 3011 | 22.0 | 4888 | 29.6 | ||||
Education level of household head | ||||||||
Less than junior high school* | 3410 | 25.0 | 2973 | 18.0 | ||||
Junior high school | 2210 | 16.2 | 2458 | 14.9 | ||||
Senior high school | 4033 | 29.5 | 5026 | 30.4 | ||||
Bachelors or graduate degree | 4013 | 29.4 | 6063 | 36.7 | ||||
Ownership | ||||||||
Own | 11,921 | 87.2 | 13,892 | 84.1 | ||||
Rent | 1745 | 12.8 | 2628 | 15.9 | ||||
Business use | ||||||||
No | 13,024 | 95.3 | 15,916 | 96.3 | ||||
Yes | 624 | 4.7 | 605 | 3.7 | ||||
Number of floors | ||||||||
1 floor* | 1828 | 13.4 | 1630 | 9.9 | ||||
2 - 3 floors | 6013 | 44.0 | 6717 | 40.7 | ||||
4 - 5 floors | 3386 | 24.8 | 4749 | 28.7 | ||||
>5 floors | 2439 | 17.8 | 3424 | 20.7 | ||||
Total observations | 13,666 | 16,520 |
Notes: SD means standard deviation. * is used as the reference category.
trend, while household size and the number of wage earners gradually declined. As for dwelling attributes, housing area and the number of household appliances showed an increasing trend. It is worth noting that household income increased from 515 thousand NT dollars in 1985 to 964 thousand NT dollars in 2005, and then decreased to 929 thousand NT dollars in 2015. This phenomenon reflected the stagnation of real income in Taiwan for the past decade.
We first use the ordinary least squares regression to estimate the effects of possible factors on household electricity consumption. We focus on the household datasets in 1985, 1995, 2005, and 2015, which represent four time points from different decades.
The effects of household size on household electricity consumption are significant and positive for the four datasets. The effects of household size have increased with time. This result reflects the fact that household size had decreased from 4.6 persons in 1985 to 3.1 persons in 2015. Since the decline of household size would result in a loss of economies of scale, the effects of household size on
Variables | 1985 | 1995 | 2005 | 2015 | ||||
---|---|---|---|---|---|---|---|---|
Coefficient | S.E. | Coefficient | S.E. | Coefficient | S.E. | Coefficient | S.E. | |
Intercept | 757.13** | 52.19 | 658.80** | 146.37 | 793.80** | 2.68 | 1624.67** | 109.40 |
Age | 0.71 | 0.63 | 2.18 | 1.62 | 2.68 | 1.68 | −1.86* | 0.95 |
Gender | 12.17 | 24.02 | −16.51 | 49.98 | 85.66* | 43.11 | 73.00** | 22.70 |
Education | −10.95 | 7.30 | 8.18 | 18.37 | 32.70 | 19.81 | 39.83** | 12.05 |
Household size | 130.74** | 4.66 | 320.06** | 13.30 | 461.49** | 15.25 | 510.85** | 9.83 |
Number of elderly members | −21.35 | 14.19 | −51.80 | 30.38 | −60.49* | 29.14 | 59.85** | 15.43 |
Number of wage earners | −49.21** | 8.36 | −50.21* | 21.72 | 101.97** | 23.57 | 70.64* | 13.84 |
Income | 0.92** | 0.03 | 0.71** | 0.04 | 0.50** | 0.03 | 0.28** | 0.02 |
Owner occupied | −9.93 | 17.55 | 26.64 | 47.40 | 86.45 | 53.48 | 173.32** | 28.49 |
Without business use | −267.64** | 23.37 | −328.71** | 70.15 | −445.80** | 81.99 | −176.80** | 53.77 |
2 - 3 floors | 48.02** | 18.11 | 145.04** | 50.47 | 250.15** | 63.24 | 173.34** | 37.45 |
4 - 5 floors | 226.86** | 21.44 | 701.83** | 54.91 | 583.87** | 63.08 | 277.63** | 39.22 |
>5 floors | 463.33** | 45.21 | 709.66** | 71.66 | 607.04** | 68.48 | 266.01** | 41.56 |
Housing area | 1.87** | 0.16 | 0.62* | 0.29 | 1.69** | 0.27 | 0.60** | 0.16 |
Number of televisions | 152.82** | 22.44 | 334.90** | 33.24 | 290.92** | 26.98 | 181.81** | 14.34 |
Number of water heaters | 75.27** | 15.34 | 122.10** | 47.69 | 81.81 | 52.93 | 63.00* | 33.14 |
Number of air conditioners | 639.89** | 13.77 | 753.21** | 19.24 | 434.61** | 16.43 | 289.30** | 9.29 |
Number of washing machines | 156.40** | 17.78 | 404.03** | 63.17 | 424.38** | 77.62 | 205.69** | 49.64 |
Adjusted R2 | 0.45 | 0.39 | 0.39 | 0.49 |
Notes: S.E. means standard error. *, and ** represent 5% and 1% significance levels, respectively.
household electricity use would be stronger as the number of household members decreased. As expected, household income had significantly positive effects on household electricity use. The effects of household income were significant for the four datasets and gradually decreased with time. This result was consistent with the findings of Park and Heo [
This study employs the Blinder-Oaxaca decomposition to investigate what factors contribute to the change of household electricity consumption from 1985 to 2015. We also analyse the change of household electricity consumption for the three periods: 1985-1995, 1995-2005, and 2005-2015. The change of household electricity consumption is decomposed into three parts: endowments effect, coefficients effect, and interaction effect. Specifically, when we focus on the change of household electricity consumption from 1985 to 2015, the endowments effect reflects the increase of household electricity consumption in 1985 if the group in 1985 had the same the characteristics with the group in 2015. The coefficients effect reflects the change of household electricity consumption in 1985 if the estimated coefficients in 2015 were applied to the sample in 1985.
Variables | (A). Differences between 1985 and 1995 | (B). Differences between 1995 and 2005 | ||||
---|---|---|---|---|---|---|
Endowments effect | Coefficients effect | Interaction effect | Endowments effect | Coefficients effect | Interaction effect | |
Intercept | −93.34 | 135.00 | ||||
Age | 1.85 | 61.34 | 3.79 | 9.87 | 22.34 | 2.28 |
Gender | −0.58 | −25.95 | 1.37 | 1.27 | 87.55 | −7.88 |
Education | −3.65 | 39.45 | 6.38 | 1.94 | 58.78 | 5.81 |
Household size | −88.68** | 871.61** | −128.41 | −170.66** | 555.20** | −75.41** |
Number of elderly members | −2.61 | −6.71 | −3.72 | −6.11 | −2.98 | −1.02 |
Number of wage earners | 5.40** | −1.82 | 0.11 | 11.38* | 260.08** | −34.50** |
Income | 429.78** | −111.49** | −101.00** | −12.09* | −202.82** | 2.30 |
Owner occupied | −0.59 | 28.33 | 2.16 | 1.03 | 49.87 | −1.86 |
Without business use | −9.95** | −54.97 | −2.27 | −5.21** | −109.73 | 1.11 |
2 - 3 floors | 1.17* | 39.29* | 2.36 | 1.54 | 45.15 | 2.47 |
4 - 5 floors | 10.95** | 104.73** | 22.93** | −14.75** | −31.71 | −8.07 |
>5 floors | 32.61** | 7.25** | 17.34** | 55.82** | −10.24 | 19.37** |
Housing area | 42.89** | −125.69** | −28.81** | 11.09* | 131.93** | −8.07 |
Number of televisions | 36.00** | 192.33** | 42.89** | 67.11** | −56.82 | 19.37** |
Number of water heaters | 22.50** | 33.73 | 13.99 | 0.70 | −41.07 | −8.81 |
Number of air conditioners | 529.29** | 34.46** | 93.73** | 521.34** | −360.42** | −0.23 |
Number of washing machines | 24.47** | 194.83** | 38.74** | 16.38** | 19.19 | −220.52** |
Total | 1030.85** | 1187.40** | −18.41 | 490.64** | 549.28** | −320.60** |
Sum of three components | 2199.84 | 719.32 | ||||
Variables | (C). Differences between 2005 and 2015 | (D). Differences between 1985 and 2015 | ||||
Endowments effect | Coefficients effect | Interaction effect | Endowments effect | Coefficients effect | Interaction effect | |
Intercept | 830.87** | 872.53** | ||||
Age | 8.88 | −222.69* | −15.05* | 7.45 | −107.97* | −26.88* |
Gender | −6.47* | −9.87 | 0.96 | −2.44 | 55.02 | −12.20 |
Education | 7.37 | 18.76 | 1.61 | −8.71 | 104.75** | 40.40** |
Household size | −139.82** | 167.47** | −14.96** | −198.00** | 1750.02** | −575.67** |
Number of elderly members | −7.79* | 55.39** | 15.49** | −7.87 | 17.88** | 29.94** |
Number of wage earners | −6.63** | −46.44 | 2.04 | 19.75** | 217.98** | −48.11** |
Income | −17.63** | −211.22** | 7.75** | 381.36** | −330.69** | −265.83** |
Owner occupied | −2.71 | 75.78 | −2.73 | −0.66 | 141.97** | 12.13** |
Without business use | −4.62** | 256.37** | 2.79* | −16.97** | 81.76 | 5.76 |
2 - 3 floors | −8.35** | −33.79 | 2.57** | 0.07 | 50.76** | 0.19 |
4 - 5 floors | 23.18** | −75.88** | −12.16** | 15.19** | 11.20* | 3.39 |
>5 floors | 17.48** | −60.86** | −9.82** | 82.79** | −5.81** | −35.09** |
Housing area | 10.04** | −156.44** | −6.60** | 87.78** | −129.34** | −60.68** |
Number of televisions | 24.15** | −162.82** | −9.06** | 79.31** | 30.62 | 15.04 |
Number of water heaters | 3.80 | −148.44* | −6.73* | 26.43** | −99.61** | −48.56** |
Number of air conditioners | 201.86** | −264.96** | −67.49** | 1269.40** | −106.62** | −695.49** |
Number of washing machines | 10.39** | −215.14* | −5.35* | 34.64** | 38.78 | 10.92 |
Total | 113.12** | −203.91** | −116.74** | 1769.13** | 2593.23** | −1650.73** |
Sum of three components | −207.53 | 2711.64 |
Notes: *, and ** represent 5% and 1% significance levels, respectively.
The results show that the coefficient effect is greater than the endowments effect for each period, suggesting that the increase in household electricity consumption is mainly attributed to the changes in the coefficients effect of the determinants. For instance, since the household electricity consumption had increased 2712 kWh from 1985 to 2015, the differences in coefficients resulted in an increase of 2593 kWh, which was greater than the endowments effect (1769 kWh) and interaction effect (−1651 kWh). Even though the mean value of household electricity consumption declined slightly from 5227 kWh in 2005 to 5019 kWh in 2015, the decrease in household electricity use was still primarily driven by the coefficient effect.
We further observe the coefficients effect of these determinants. We find that the coefficient effect of household size plays the most important role, except for the period 2005-2015. The decrease of household size leads to electricity consumption per capita increases due to the loss of economies of scale. Thus, the coefficients effect of household size is significantly positive, reflecting that one additional household member may make household electricity use increase much more than before. Another important factor is the number of air conditioners. During the period 2005-2015, the number of air conditioners had the largest coefficient effect that brought about the decrease of household electricity use. In addition, other household appliances also exhibited significantly negative coefficients effects. It is obvious that, during the period 2005-2015, the decrease of electricity use was mainly induced by the differences in coefficients effects of household appliances. This result may be related to two possible reasons. One reason is that newly purchased appliances are more energy efficient. Another reason is that the additional household appliances may be non-primary use or used as supplements. Thus, the marginal effects of the increase of household appliances decline.
As for the contribution of endowments effects, the number of air conditioners is the most important factors. For each period, the endowments effects of number of air conditioners are significant and greater than that of other factors. However, the endowments effects of household income are divergent over these three segmented periods. Household income has a significantly positive endowments effect for the period 1985-1995, whereas the effects become significantly negative for the periods 1995-2005 and 2005-2015. The negative endowments effect of household income may reflect the declining trend of real household income. As shown in
On the whole, our results reveal that the changes of household electricity consumption are driven by different factors across these three periods. During the period 1985-1995, household electricity use exhibited a more substantial increase. The most important endowments effects were originated from the increase in the number of air conditioners and household income. Regarding to the coefficients effect, the decrease of household size played the most critical role for triggering the rising of household electricity use, since electricity consumption per capita would increase. During the period 1995-2005, household electricity use showed a more moderate increase. The main endowments effect was caused by the number of air conditioners, while the coefficients effect derived from the decrease of household size dominated over that of other factors. However, the endowments effect of household income became negative due to the declining trend of real household income. During the period 2005-2015, household electricity use displayed a downward trend. A more notable phenomenon was the negative coefficients effects of household appliances, which may be related to the improvement of energy efficiency.
The results of the endowments effects show that the increase of household income had induced residential demand for electricity between 1985 and 2015. Our results are consistent with the findings of previous studies, such as Vassileva et al. [
The results reveal that the change of household electricity use is mainly attributed to the coefficient effects both for the high-income and low-income households, indicating that the marginal effects of the determinants differ greatly over the past three decades. It is remarkable that the greatest contribution to the coefficient effects in terms of the absolute value is household size. For the high-income and low-income households, household size explains 60% (=1650.39/2767.33) and 76% (=1545.98/2043.57) of total coefficients effect, respectively. This finding is consistent with our previous results. The decrease of
Variables | High-income households | Low-income households | ||||
---|---|---|---|---|---|---|
Endowments effect | Coefficients effect | Interaction effect | Endowments effect | Coefficients effect | Interaction effect | |
Intercept | 1331.86** | 889.54** | ||||
Age | 37.16 | −273.47 | −35.03 | −33.90* | −131.50 | −51.38 |
Gender | −4.15 | 47.23 | −7.40 | 3.46 | 33.88 | −11.61 |
Education | −12.48 | 66.84 | 18.21 | −4.61 | 20.94 | 5.71 |
Household size | −203.96** | 1650.39** | −389.97** | −157.01** | 1545.98** | −751.56** |
Number of elderly members | 1.51 | 29.21* | 28.51* | −8.04 | 28.51* | 48.15* |
Number of wage earners | 30.78** | 430.36** | −39.95** | −3.09 | −61.32 | 39.06 |
Income | 455.99** | −395.36** | −371.03** | 144.24** | −59.12 | −23.37 |
Owner occupied | −21.27** | 314.42** | 31.98** | 1.45 | 66.85 | 2.12 |
Without business use | −44.19** | 344.56* | 28.84** | −2.55 | −184.89 | −8.01 |
2 - 3 floors | −3.50 | 13.89 | −2.50 | 5.31 | 10.12 | 4.88 |
4 - 5 floors | 22.69** | −70.55** | −13.38 | 10.02** | −0.31 | −0.28 |
>5 floors | 127.81** | −38.67** | −93.74* | 4.08 | 0.43 | 10.95 |
Housing area | 142.62** | −285.61** | −114.10** | 53.90** | −73.35* | −44.74* |
Number of televisions | 87.95* | −98.33 | 59.59 | 78.46** | −115.50* | −36.71* |
Number of water heaters | 15.41 | −127.20 | −25.71 | 39.28** | 24.53 | −35.09 |
Number of air conditioners | 1613.41** | −298.41** | −990.18** | 616.55** | −6.86* | −155.54** |
Number of washing machines | 18.98** | −70.50* | −9.14 | 57.46** | 55.65 | 42.21 |
Total | 2264.77** | 2767.33** | −1925.00** | 805.00** | 2043.57** | −895.02** |
Sum of three components | 3107.10 | 1953.55 |
Notes: *, and ** represent 5% and 1% significance levels, respectively. We focus on the period 1985-2015.
household size is adverse to the aim of electricity conservation no matter for high-income or low-income groups. In addition, some demographic and socioeconomic characteristics, such as the number of wage earners, household income, owner-occupied and business use variables, only exert significant effects for the high-income group but not significant for the low-income group. The results reflect the fact that the high-income group are usually related to a higher level of electricity use, and high electricity consumption would be more likely attributed to the change in the marginal effects of demographic characteristics.
As for the endowments effects, number of air conditioners and household income are the most important factors contributing to the increase of household electricity use. As shown in
Since household size plays an important role both in the high-income and low-income groups, we further estimate the Pearson’s correlation coefficients so as to observe how the relationships between household size, income and electricity use would differ across the household groups. We focus on the four subsamples, including the high-income group in 1985, the high-income group in 2015, the low-income group in 1985, and the low-income group in 2015.
In sum, our results indicate that the increase of household electricity consumption in Taiwan over the past three decades can be explained by two important reasons. The first reason is that the decline of household size has weakened economies of scale in electricity consumption. An additional family member induces a higher level of household electricity use than before. To some extent, the basic subsistence level of electricity consumption per capita has increased
Subsample | household income versus household size | household electricity use versus household size | household income versus household electricity use |
---|---|---|---|
High-income group in1985 | 0.101* | 0.073* | 0.279* |
High-income group in 2015 | 0.007 | 0.441* | 0.088* |
Low-income group in 1985 | 0.435* | 0.413* | 0.410* |
Low-income group in 2015 | 0.375* | 0.448* | 0.328* |
Notes: * represents 1% significance levels.
over time. The second reason is the increase of the number of air conditioners. Because households pursue a better material life and the temperature continues to rise due to global warming, air conditioners become the necessary appliances for households. Therefore, when we make strategies for reducing electricity consumption, these two critical factors should be taken into account.
In the aspect of electricity demand-side management, it is important to make use of the pricing strategies. In Taiwan, the electricity pricing policy is based on increasing-block pricing. The electricity prices are specified into different blocks and increases with blocks according to the amount of electricity use [
This study employs the Blinder-Oaxaca decomposition to investigate what are the main reasons that contribute to the change of household electricity consumption in Taiwan over the period 1985-2015. The change of household electricity consumption is decomposed into three parts: endowments effect, coefficients effect, and interaction effect. Our results verify that the changes of household electricity consumption are driven by different factors across these three decades.
During the period 1985-1995, household electricity use exhibited an upward trend. The main endowments effect was caused by the number of air conditioners and household income. The coefficients effect originated from the decrease of household size played the most critical role. During the period 1995-2005, household electricity use showed a more moderate increase. The main endowments effect was caused by the number of air conditioners, while the coefficients effect derived from the decrease of household size dominated over that of other factors. However, the endowments effect of household income became negative due to the declining trend of real household income. During the period 2005-2015, household electricity use displayed a downward trend. A more notable phenomenon was the negative coefficients effects of household appliances, which may be related to the improvement of energy efficiency.
Apparently, the increase in household electricity consumption is mainly attributed to the changes in the coefficients effect of the determinants. In particular, the coefficients effect of household size plays the most important role. The decrease of household size leads to electricity consumption per capita increases due to the loss of economies of scale. As for the contribution of the endowments effect, household income and the number of air condition are the most important factors. When we observe the decomposition results of the high-income and low-income households, it is also evident that the coefficients effects of household size are crucial to the increase of electricity use. The decrease of household size is adverse to the aim of electricity conservation no matter for high-income or low-income groups. Moreover, the correlation coefficients between household size and electricity use are greater than before. Thus, high electricity consumption would be more likely attributed to the change in the marginal effects of demographic characteristics. Therefore, with the changes of demographic structure and socio-economic characteristics, identifying the characteristics that affect household electricity consumption would be an important issue.
From a historical view, our results highlight that demographic structure and socio-economic characteristics would have critical influence on household electricity use. In Taiwan, as the government makes policies for reducing household electricity use, the main obstacles would be the decline of household size and the high dependency on air conditioning. Therefore, the policy implication means that the electricity pricing policy should take household size into consideration so as to offer electricity-saving incentives for these households with smaller family size. Besides, some strategies, such as enhancing energy efficiency of air conditioners, improving housing structure with better insulation, and providing the subsidy for the investment in energy-efficient appliances should have a higher priority for the policymakers.
The author declares no conflicts of interest regarding the publication of this paper.
Huang, W.-H. (2019) Analysis on the Changes of Household Electricity Consumption over the Past Three Decades. Modern Economy, 10, 1487-1506. https://doi.org/10.4236/me.2019.105099