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- BANCO DE ESPAÑA 23 DOCUMENTO OCASIONAL N. 1808 A The EUKLEMS and TED databases Our growth accounting exercises exploit two different data sources, namely, EU KLEMS and TED. Although the patterns arising from these two sources are very similar, it is worth describing the nuances in the methods of collating the data. This section gives more details about the construction of the main variables used in section 2, which are Total Output, Labor Services, Labor Quality and Capital Services from EU KLEMS, and Total Output, Labor Quantity, Labor Quality and Total Capital from TED. A.1 EU KLEMS In EU KLEMS, data on Total Output and Capital Services is drawn from Eurostat while data on Labor Services and Labor Quality are sourced from the European Labor Force Survey (EULFS) and the Structure of Earning Survey (SES).
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- BANCO DE ESPAÑA 24 DOCUMENTO OCASIONAL N. 1808 A.2 TED In the TED database, Total Output is sourced from Eurostat, Labor Quantity is drawn from the OECD, and Labor Quality is taken from the EU KLEMS database. Total Capital is constructed combining all the three databases, EU KLEMS, OECD and Eurostat. For Labor Quantity, TED gives a clear deï¬Ânition. The employment ï¬Âgures cover all persons engaged in some activity that falls within the production boundary of the system of national accounts. In line with the GDP, it includes all workers employed domestically but excludes any nationals working abroad. The measure used in TED is actual hours worked, so it includes paid overtime and excludes paid hours that are not worked due to sickness, vacation and holidays etc.
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- BANCO DE ESPAÑA 27 DOCUMENTO OCASIONAL N. 1808 The contribution of labor input to GDP growth in TED is split into the contribution of employment quantity (H) and labor composition or quality (LQ), and the contribution of capital services is split into ICT capital services (Kit) and non-ICT capital services (Knit): Δ ln(GDP) = s̄K,itΔ ln(Kit) + s̄K,nitΔ ln(Knit) + s̄LΔ ln(H) + s̄LΔ ln(LQ) + Δ ln(TFP) (18) where s̄K,it and s̄K,nit are respectively the shares of ICT capital and non-ICT capital income in nominal GDP. To summarize, this equation decomposes the growth in GDP into contribution from labor and capital inputs (weighted by their respective shares in nominal GDP) and a residual labeled TFP growth. Under neoclassical assumptions, this refers to technological change or the overall efficiency of the economy.
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- For Total Output, volumes of gross value added (denoted as V A QI in EU KELMS) are denoted in 2010 prices. Also, data on output is consistent with Eurostat at the corresponding industry levels. For constructing labor services series for the period 2008-2015, the main source is the microdata underlying the European Labor Force Survey (EULFS) provided by the National Institute of Economic and Social Research (NIESR). Years prior 2008 have been extrapolated using the trend in labor services from former versions of EU KLEMS.
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- The Capital Services variable is estimated using different depreciation rates depending on the type of asset. Depreciation rates for computing equipment, communications equipment, software and databases, transport equipment, other machinery, total non-residential investment and other assets are taken form previous EU KLEMS releases, the depreciation rate for research and development is taken from the SPINTAN project, and depreciation rates for other asset types stems from Montinari et al. (2016).
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- Turning to the estimation of Labor Quality, it combines different sources of information. First, information on the employment structure of the workforce, such as age, gender and educational attainment level is obtained from EULFS. In particular, two gender categories (male, female), three age categories (15-29, 30-49, 50 and above) and three educational qualiï¬Âcation levels (high, medium and low) are considered. Second, data on wages are drawn from the Structure of Earning Survey (SES). Since the micro data underlying SES is not yet available for the most recent survey, EU KLEMS uses the available SES tabulation from Eurostat to obtain wage ï¬Âgures for 2010 and 2014.
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