*2.3. Data*

For the productivity analysis in the 1st step, this study uses four data variables in energy sector data from 42 countries between 2000 and 2014 (Table 1). In this study, the energy sector is defined as the electricity, gas, steam and air conditioning supply sectors following the WIOD and the United Nations Statistics Division [33].


**Table 1.** The description of data variables for the 1st step of estimation (productivity analysis).

Spain,Source: Figure created by the author using the World Input-Output Database (WIOD) [34].

 Sweden, Switzerland, Turkey, Taiwan, United Kingdom,

Luxembourg,

 Mexico, Netherlands,

 Norway, Poland, Portugal,

The analysis includes observations on gross output, labor compensation, capital stock, and intermediate input data from the World Input-Output Database (WIOD) [34]. This study uses the following four data variables from WIOD: (1) the gross output by industry at current basic price (in millions of national currency), (2) intermediate inputs at current purchasers' price (in millions of national currency), (3) compensation of employees (in millions of national currency), and (4) nominal capital stock (in millions of national currency).

All financial data are in 2010 dollars (\$ U.S.), applying the currency exchange and price deflation factors from the WIOD. Using the dataset for the 1st step of the analysis, the four productive performance indicators are calculated. It should be noted that the DDF model for TFP estimation requires a large sample size to identify the production frontier line [35]. To estimate TFP change using a large dataset, the data of 42 countries are included in the 1st step of the analysis.

For the 2nd step of the analysis, this study uses four productivity indicators estimated as dependent variables and seven data variables as independent variables (Table 2). Seven independent variable datasets are obtained from three different databases. The first database is the EU KLEMS database, which provides capital stock data by type of usage [36]. Data on 14 countries from 2000 to 2014 are obtained from the EU KLEMS database. The data variables include IT capital stock, CT capital stock, software capital stock, R&D capital stock, and gross capital stock. This study estimates each capital stock share using the gross capital stock as the denominator.

The second database is the Renewable Energy Information 2017 published by the International Energy Agency (IEA). To investigate the ICT capital effect on distributed energy systems, energy production by solar photovoltaic and wind generation are used to estimate data on the share of renewable energy. Additionally, the total data on all energy sources are used as the denominator.

Finally, the electricity price index is obtained from the Energy Price and Taxes 2018 database published by the IEA. The electricity price index is used as the proxy of the market environment in the energy sector (e.g., a feed-in tariff policy makes the electricity price increase).

This study combines two datasets: the financial dataset for productivity analysis and the dataset for the determinant analysis. Data on 14 countries are available from both datasets; thus, these data are

used for the 2nd step of the analysis. Table 2 shows the average value of the data variables for the 14 countries in the determinant analysis.


**Table 2.** The description of data variables for the 2nd step of estimation (determinant analysis).

According to Stiroh [15], the share of ICT capital stock in total capital stock is the preferred way to measure ICT capital intensity. Thus, this study estimates the share of each type of capital stock in gross capital stock. Notably, the share of each capital type of stock in gross capital stock can reflect the relative priority of the accumulation of capital stock compared with other types of capital stock, including non-ICT capital. To conduct the determinant analysis of productive performance with ICT capital shares, this study clarifies the impact of ICT capital stock on productive performance.

This research uses three types of capital stock (IT capital, CT capital, and software capital) as data on ICT capital. This categorization follows the definition of ICT investments reported by the OECD. According to the OECD [24], ICT investment is defined as the acquisition of equipment and computer software, and ICT has three components: IT equipment (e.g., computers and related hardware), CT equipment (e.g., telecommunications equipment), and software (e.g., packaged software and customized software).

Notably, the sector integration method of the EU KLEMS database is different from that of the WIOD. The EU KLEMS database provides data only on the utility sector; such data integrate data on the energy sector and on the water supply sector. Therefore, it is difficult to distinguish the ICT capital data in the energy sector from the EU KLEMS database, which is a limitation of this research. To overcome this limitation, this study assumes that the ICT capital share in the energy sector is broadly similar to that in the utility sector. This assumption is based on the fact that the capital stock data on the energy sector are much higher than those on the water supply sector based on the WIOD database. For example, in 2014, the energy sector accounted for a 92% share and an 80% share of the capital stock in the utility sector in the U.S. and Italy, respectively. This evidence supports our assumption that the trend of capital stock formation between the energy sector and the utility sector is similar.

Tables 1 and 2 describe the countries and the variables in the 1st and 2nd steps of the analysis. Because of the limited availability of data on ICT capital stock from the EU KLEMS database, the data sample was decreased from 42 countries in the 1st step of the analysis to 14 countries in the 2nd step of the analysis.

It should be noted that ICT capital utilization is just one dimension of the productive performance improvement in the energy sector; there are other ways to promote this improvement (e.g., fossil fuel combustion efficiency and distribution efficiency). One limitation of this study is that the data on R&D capital stock are limited to the total value and do not reveal the type of technology. Thus, this study assumes that technological innovation related to resource utilization (e.g., fuel combustion and

distribution technology) is reflected in the R&D capital stock value. Based on this assumption, the R&D capital stock is applied as an innovation factor of resource utilization technology in the 2nd step of the analysis.
