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How ICT investment influences energy demand in South Korea and Japan

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Abstract

This empirical study examines substitute/complementary relationships in the demands for ICT capital, non-ICT capital, energy, materials, and labor in the industrial sectors in Japan and South Korea during 1973–2006 and 1980–2009, respectively. In doing so, a dynamic factor demand model is applied to link intertemporal production decisions by explicitly recognizing that the level of certain factors of production (referred to as quasi-fixed factors: ICT and non-ICT capital) cannot be changed without incurring so-called adjustment costs, defined in terms of forgone output from current production. Special emphasis is on the effects of ICT investment on energy use through the substitute/complementary relationships. This study quantifies how ICT capital investment in South Korea and Japan affects industrial energy demand. We find that ICT and non-ICT capital investment serve as substitutes for the inputs of labor and energy use. The results also demonstrate significant cost differences across industries in both countries.

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Notes

  1. These data are publicly available at “http://ecos.bok.or.kr/EIndex_en.jsp/.”

  2. These data are publicly available at “http://ecos.bok.or.kr/EIndex_en.jsp/.”

  3. The dataset is publicly available at “http://www.stat-search.boj.or.jp/index_en.html/.”

  4. This variable is measured by accounting for heterogeneity in the labor force and the productivity of various types of labor (based on skill, gender, education, etc.).

  5. There has been some confusion in the literature concerning the price and its use for intermediate goods. Most studies agree on using the consumer purchase price which includes payable taxes on goods and the margins on trade and transportation (when trade and transportation are included as separate products), but excludes the subsidies on goods. However, as clearly explained by O’Mahony and Timmer (2009), the EU KLEMS was not able to collect the necessary data to cover the mentioned issues above, and instead used the purchase price to value intermediate inputs in all cases except for the USA.

  6. Generally, when computing standard errors for estimated parameters within the maximum likelihood estimator (ML) framework, the diagonal elements of the observed information matrix are used which is obtained directly by the Hessian argument. For details, see Gallant (2008).

  7. The aim here is to reflect the structural changes in the Korean economy because of the implementation of the country’s economic development plan described in the “Introduction” section.

  8. The estimated coefficients for the industries’ dummy variables are not reported to save space.

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Authors and Affiliations

  1. Technology Management, Economics, and Policy Program, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, South Korea

    Nabaz T. Khayyat & Jongsu Lee

  2. Department of Energy Resources Engineering, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Republic of Korea

    Eunnyeong Heo

Authors
  1. Nabaz T. Khayyat
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  2. Jongsu Lee
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  3. Eunnyeong Heo
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Corresponding author

Correspondence to Nabaz T. Khayyat.

Appendices

Appendix 1

Data sources and construction of the variables

Table 3 Definition of variables
Full size table
Table 4 Constructed variables
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Table 5 Industry sectors’ classification
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Table 6 Summary statistics of the raw data, in 2005 prices—Korea, no. of obs. = 900
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Table 7 Summary statistics of the raw data, in 2005 prices—Japan, no. of obs. 1020
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Appendix 2

Parameter estimates

Table 8 Korea’s nonlinear FIML estimates—dynamic factor demand, 30 sectors (1980–2009)
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Table 9 Japan’s nonlinear FIML estimates—dynamic factor demand, 30 sectors (1973–2006)
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Table 10 Korea’s short- and long-run price and output elasticities for the studied three decades
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Table 11 Korea’s short- and long-run price and output elasticities of knowledge- and nonknowledge-based industries
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Table 12 Japan’s short- and long-run price and output elasticities for the three studied decades
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Table 13 Japan’s short- and long-run price and output elasticities of knowledge- and nonknowledge-based industries
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Khayyat, N.T., Lee, J. & Heo, E. How ICT investment influences energy demand in South Korea and Japan. Energy Efficiency 9, 563–589 (2016). https://doi.org/10.1007/s12053-015-9384-9

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