Thursday, 1 October 2015

Has ONS solved 25% of the productivity puzzle?

The ONS yesterday revised growth up with new GDP data.  Today, they have released new productivity data which uses this new output data.  What difference does it make to the productivity puzzle?  Answer: it changes the dates of it and solves some of it, but not all.

  1. The revisions are to real output, mostly of the service sector, says the ONS. Very little revisions to hours/jobs.
  2. The figure shows annual average growth in real output per hour, whole economy, using the new and the older data.  You can see the following
    • with the new data productivity growth was lower in the 2000-07. 
    • with the new data, the downturn in productivity came earlier, in 2008.
    • with the new data, the dip in productivity and recovery in 2009/10 was not as large
    • with the new data, there is a dip down in 2012, but recovery since then.

All this means that the averaged periods look like this

Years old data new data
1995-00 2.14 2.27
2000-07 2.09 1.95
2007-10 -0.10 -0.37
2010-14 -0.03 0.30

Finally, the productivity gap, that is the productivity we would have expected in 2014 had the trend 1985-2007 (2.17%pa) continued after 2007  was 16.5 on the old data, but 14.4 on the new, reducing then about 1/8th of the gap.

But we might do another calculation, which is to project forward productivity on the basis of trends 2000-07.  If we do that the old data trend was 2.09% giving a gap of 15.5.  But the new data trend is 1.95% giving a gap of 12.6%, a reduction in the gap of 25%.    So the gap is reduced, but mostly because we were doing worse before the recession than we thought we were (if we use market sector data, which might be better measured the gap falls from 18 to 14.7 points, a fall of 22%) .  (The source of this reduction is basically a fall in the pre-recession productivity growth of the service sector, from 2.1%pa to 1.9%pa).

So the revisions look like a better representation of the immediate timing and do reduce the gap by around 20-25%%.  In our earlier work we found the things like structural change, utilisation and scrapping can account (using pre Blue Book 2015 data) for around 50% of the puzzle, see here.  Perhaps we are getting closer to a solution.