Quality-Adjusted Labour Input LAbour MArket Statistics working group Luxembourg, 10-11 October 2017

Overview Introduction QALI Project Methodology of QALI Focus on Törnqvist index Data sources for QALI Preliminary tasks QALI calculation step by step Annual QALI growth In Euro Area Labour Quality average growth rates by period and by country Hours average growth rates by period and by country Publication as experimental statistics

1. Introduction Usually, labour productivity is compiled by dividing a measure of output (e.g. GDP or gross value added expressed in volumes) by the quantity of labour input, e.g. expressed as number of persons employed or, more precisely, total hours actually worked.  All hours worked have the same productivity The Quality-adjusted Labour input (QALI) aims to improve the measure of labour input by weighting these hours with qualification (estimated by the age of the workers) and skill (estimated by the highest educational level attained according the International Standard Classification of Education)

2. QALI Project Joint projects of DG JRC- Joint Research Center and Eurostat Quality-Adjusted Labour Index (QALI) in the EU Fixed capital indicators for the EU Member States Next step : Collection of feedback from NSI and main users Dissemination of QALI as experimental statistics

3. Methodology of QALI (2003-2014) Törnqvist index: weights are labour income shares National Accounts aggregates benchmarked hours worked and compensation of employees Workers groups Age: 15-29; 30-49; +50 Skill: High (ISCED97 0-2); Medium (3-4); Low (5-6) Industry breakdown: 10 or 21 Same methodology for all EU Member States (macro level not micro data linking)

4. Focus on Törnqvist index Törnqvist index definition : weighted geometric average of growth rates of hours worked (h), where the weights are labour income shares (w) across the different groups (i) Labour income are estimated by earnings (compensation of employees)

5. Data sources for QALI Micro data from Labour Force Survey (LFS), Structure of Earnings Survey (SES) and Statistics on Income and Living Conditions (EU-SILC) Macro data from National Accounts aggregates : hours worked (HW) and compensation of employees (D1)

6. Preliminary tasks Missing Hours worked from National accounts estimated with a bottom-up approach or a top-down approach (when the sections were the same in A*10 and A*21) and homogenised across different vintages of data Data converted from NACE Rev.1.1 to NACE Rev.2 using bridge matrices from A17 breakdown to A*10 Geometric Interpolation of Earnings structure data as SES waves are made every four years Homogenisation of EU-SILC/SES data: Earnings for primary (agricultural) industries are estimated with the EU-SILC survey data and have been made consistent with the SES standards

7. QALI calculation step by step Item 14 NAWG 12 May '16

7. QALI calculation step by step Step 1: The hours worked from LFS are scaled up to National Accounts totals (named HW) into different age groups and skill levels. Step 2: Earnings per hour are calculated from SILC/SES Step 3: Earnings per hour from step 2 are multiplied by the hours worked from step 1 to calculate annual earnings. Step 4: The earnings from step 3 are scaled up to the National Accounts totals (named D1) into different age groups and skill levels. Step 5: QALI and chained QALI are calculated using hours worked from step 1 and annual earnings from step 4.

8. Annual QALI growth in Euro Area QALI growth  hours growth + labour quality growth

9. Labour Quality by country and period - Average growth rates (in %) -

10. Hours worked by country and period - Average growth rates (in %) -

11. Publication as experimental statistics QALI combines micro and macro data to derive an indicator that has value added for productivity analysis, but: Confidential and reliability of data sources constrain publishable level of detail; The weighing by earning shares relies on the neoclassical assumption of remuneration reflecting marginal productivity under (perfectly) competitive markets, which is not an assumption used in core national accounts.  disseminate QALI as experimental statistics (i.e. separated from regular NA, with appropriate metadata) and investigate the feasibility of regular updates.