Friday, 23 October 2015

Various teaching links: broadband and innovation

1. Broadband raises productivity and demand for the skilled, lowers for the unskilled (Quarterly Journal of Economics (2015), 1781–1824).

This is an interesting paper that uses matched employee and employer data for Norway, using variation in Broadband availability across regions to measure the broadband effects on various meaures, including skilled and unskilled output elasticities.   Broadband lowers (raises) the output elasticity of the unskilled (skilled) with an overall effect on total factor productivity of (p.1809) a 10 point rise in availability of 0.4%.  The overall change in availability is a bit hard to see, but figure 1 suggests that most areas of Norway had zero availability in 2001 but 75% or above in 2005.  So if availability rose by, say 80 percentage points in 4 years, roughly TFP rose by 0.8% per year.

A new paper by Rosa Sanchis and co-authors does not however find such good results for Broadband speed on learning by kids.(summary here)

The abstract
Governments are making it a priority to upgrade information and communication technologies (ICT) with the aim to increase available internet connection speeds. This paper presents a new empirical strategy to estimate the causal effects of these policies, and applies it to the questions of whether and how ICT upgrades affect educational attainment. We draw on a rich collection of microdata that allows us to link administrative test score records for the population of English primary and secondary school students to the available ICT at their home addresses. To base estimations on exogenous variation in ICT, we notice that the boundaries of usually invisible telephone exchange station catchment areas give rise to substantial and es-
sentially randomly placed jumps in the available ICT across neighboring residences. Using this design across more than 20,000 boundaries in England, we find that even very large changes in available broadband connection speeds have a precisely estimated zero effect on educational attainment. Guided by a simple model we then bring to bear additional microdata on student time and internet use to quantify the potentially opposing mechanisms underlying the zero re-duced form effect. While jumps in the available ICT appear to increase student consumption of online content, we find no significant effects on student time spent studying online or offline, or on their learning productivity.

2. McKinsey have a new report on China Innovation.  They focus on innovation, measured by TFP growth, this from the Executive Summary.

Without labor force expansion and investment to propel growth, China must rely more
heavily on innovation that can improve productivity. We use multifactor productivity—growth that does not come from factors of production such as labor and capital investment—as a proxy for the macroeconomic impact of innovation broadly defined (including productivity gain from catch-up). The contribution to GDP of multifactor productivity has been falling in China, from nearly half of yearly GDP growth in the 1990 to 2000 decade to 30 percent in the past five years. To reach the growth target of 5.5 to 6.5 percent per year (the current consensus view from five leading economic institutions), multifactor productivity growth will need to contribute 35 to 50 percent of GDP growth, or two to three percentage points per year of GDP (Exhibit E1).

Thursday, 15 October 2015

Thursday, 1 October 2015

Have ONS data revisions solved the productivity puzzle?

(This is a corrected post following my earlier post, for readers of the earlier post, please see Update note below)

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 (all data, output per hour CAGRs)

Years old data new data
1995-00 2.17 2.29
2000-07 2.02 1.97
2007-10 -0.13 -0.37
2010-14 -0.08 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 16.8 on the old data, but 16.1 on the new, hardly reducing 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 2.02% giving a gap of 15.7.  But the new data trend is 1.97% giving a gap of 14.6%, a reduction in the gap of 7%.    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 21.4 to 20.1 points, a fall of 6%).  (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 7%.  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.

Mea culpa and apologies to earlier readers of this post.  After kindly getting some comments, I checked my spreadsheet. The original ONS data contained an error in the market sector data  (the data were displaced by a cell) and I had done the revised gap calculations incorrectly: I had calculated the gap for the service sector.  The implied gap due to revisions should be about 7% less than the original one and not 20% as I estimated earlier.  I have therefore revised the data in the table and three paragraphs above.