Full text: Wirtschaft und Gesellschaft - 2016 Heft 4 (4)

42. Jahrgang (2016), Heft 4

Wirtschaft und Gesellschaft

cussed in the IMF study, the labour share of skilled workers is also falling in
some major economies. Lin and Tomaskovic-Devey (2013) and Onaran
(2011, 2012) are closest to our analysis, but while these studies focus on a
single country, the US and Austria respectively, we perform our analysis
for selected OECD countries and are therefore able to account for country
specific differences in industrial relations. Furthermore, we incorporate a
broader range of explanatory variables.

3. Data and stylised facts
3.1 Data
We have compiled a comprehensive database for nine OECD economies drawing on six publicly available international databases for sectoral
data which we augmented by country level data.25
We measure the wage share as labour compensation as a ratio to value
added with data obtained from the EU KLEMS database. Labour compensation includes the wage of self-employed workers, imputed based on the
assumption that their wage is equal to the average hourly wage of the sector.26 Since data from EU KLEMS is only available until 2009 we extrapolate through splicing. More specifically, we link the wage share from
KLEMS with the growth rate of the wage share obtained from the OECD
Structural Analysis database (OECD STAN).27 Both series have a correlation of 0.91. We control for violent swings in the wage share by excluding
years where the percentage change in the wage share exceeds 30% in absolute values, which mostly appear in Denmark, the UK and Sweden, but
our results are robust to all these cleaning procedures.
In order to see how our results differ if we use the after-tax wage share as
the dependent variable in our estimations we had to obtain measures for
implicit tax rates on labour income, indicating the share of taxes paid out of
wage income. The series are not readily available for many countries and
for long periods; therefore we reconstructed the series using the technique
proposed by Carey and Tchilinguirian (2000) with data from several
sources of the OECD database.
We obtain measures of capital stock from the EU KLEMS database. Unfortunately only aggregated capital stock data is available at the 2-digit
level.28 We extrapolate capital stock from KLEMS using the growth rate of
the same measure from STAN. At the 1-digit level we are able to disaggregate ICT and non-ICT capital. ICT and non-ICT capital is reported as
services (measured as an index) rather than stock in the newer versions of
KLEMS.
Our globalisation variables are obtained from the OECD. Import data
disaggregated for intermediate import and other imports is from OECD
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