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Multi-Factor Productivity Results

Productivity Statistics User Group 2014. Multi-Factor Productivity Results. Simon Field and Joe Murphy. Accounting for growth within GVA. QALI technical background.

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Multi-Factor Productivity Results

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  1. Productivity Statistics User Group 2014 Multi-Factor Productivity Results Simon Field and Joe Murphy

  2. Accounting for growth within GVA

  3. QALI technical background • QALI is a way of measuring labour input into the production process which acknowledges that different types of workers contribute a different marginal productivity • It assumes that factors receive their marginal products, so earnings of different worker types are taken to represent the marginal productivity of these worker types- 360 types in total • It Produces a weighted index of hours worked, the growth in quality adjusted hours is represented using a Törnqvist index • From this we are able to subtract the effects of changes in unadjusted hours, leaving the change in the composition of labour

  4. Methodological changes • The 2013 QALI publication extends the time series to the end of 2012. Using the Sectional Unit Labour Cost methodology, it was possible to compile these using Blue Book 2012 income constraints • This change facilitates quarterly production of QALI, and the system now contains (not yet published) estimates for Q1 and Q2 2013 • Non respondents to education questions- previously added to GCSE category (Most numerous), now proportionally allocated to all other categories • Income constraints are no longer seasonally adjusted

  5. Results since recession

  6. QALI Future developments • Make better use of the underlying LFS data- coding rewrite could include imputation of hours and earnings data for some respondents, and outlier filtering • Medium term goal for QALI to undergo UKSA assessment and receive national statistic status- is this of value to users without VICS and MFP estimates undergoing the same process? • Desirable to users, and for improving the MFP work, to produce a further disaggregation by industry- from 10 to 16 categories, this is not currently possible using the LFS hours and earnings data- Could use ASHE

  7. Capital Services Capital services attempt to estimate the “service” flowing from capital. Productive capital stock weighted by a rent function or “user share cost” which are conditioned on profits, price changes and depreciation

  8. Deriving capital services Main input to capital services are long run data series of GFCF (Gross Fixed Capital Formation). However there are currently some known issues with the low level GFCF series. We revert back to pre SIC(2007) conversion data for all but software and other intangibles. We then use a combination of sources and assumptions to create data up to 2012.

  9. Accounting for growth within GVA

  10. Accounting for Labour Productivity Growth

  11. Growth Accounting by industry,1998 to 2012

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