Full text: The political economy of income distribution: industry level evidence from Austria (156)

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.
We measure the wage share as labour compensation over 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. Different concerns have been raised with regard to this imputation, 
as it is generally said to overestimate the wage share for sectors of predominantly low skilled 
workers while it underestimates high skilled sectors’ wage shares. Indeed we find the wage 
share to exceed 1 in a total of 588 out of 13796 cases (4.26%) for data at 2 digits and 324 out 
of 10245 observation (3.16%) for the 1-digit level.
 However, wage shares exceeding one are 
not generally a problem and can naturally arise for mainly two reasons which have nothing to 
do with overestimations of the imputed wages for self-employed workers: First, if a sector 
incurs heavy losses and second, if a sector receives significant subsidies (EU KLEMS, 2007). 
The second case arises because value added in KLEMS is calculated as compensation of 
employees plus operating surplus plus taxes minus subsidies (on labour and capital), i.e. at 
basic prices, and therefore can fall short of labour compensation if the subsidies exceed 
operating surplus and taxes in a particular period.
 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).
 Both series have a correlation of 0.91. We 
control for violent swings in the wage share by excluding years where the percentage change 
 The use of an international database is instructional for making the variables and estimations 
comparable between countries. See table A3 in the appendix for further information on sector definitions and the 
skill taxonomy.  
 This number excludes Agriculture, Fishing and Foresting. These sectors are repeatedly reported to 
have wage shares bigger than one because of poor data quality and because the imputation for self-employed 
workers largely overestimates the labour compensation for this low skilled sectors (EU Klems growth and 
productivity, p. 37).  
 It would be preferable to use value added at factor cost for the calculation of the wage share. 
Unfortunately, there are no long series on taxes minus subsidies on production in EU KLEMS.  
 Since self-employed are not included in the measure of labour compensation in OECD STAN we 
impute their wages by applying the same technique as in EU KLEMS. We exclude observations where the 
number of self-employed suddenly falls to zero, assuming that it must be related to a measurement error.

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