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TheevolutionoflabourshareinPoland.
Newevidencefromfirm-leveldata
Sebastian Zalas Hubert Drążkowski
FAME|GRAPE & University of Warsaw FAME|GRAPE and Warsaw University of Technology
Motivation
Worldwide decline of labour share. Evidence for
Poland? Working with aggregate has important
drawbacks. Growiec (2009) provides firm-level es-
timates from Statistic Poland data, but:
• covers data until 2008 only
• covers firms with 49+ employees only
• dataset is not available
=⇒ We add to the existing literature by:
1. extending to present times;
2. comparing small & large firms;
3. comparing new & widely available data
to (unavailable) registry data from Statistics
Poland.
What do we do?
We prepare firm level data set compilled from 10 waves of Orbis data
1. Show how to construct database using Orbis data.
2. We present evolution of labour share of value added, together with labour productivity and wages.
3. Next we compare our estimates to those presented by Growiec (2009) and prepared with national
accounts from OECD STructural ANalysis Database (STAN).
4. Propose how to improve coverage with imputation methods.
The evolution of labour share of value added.
=⇒ level of labour share ↓ 10ppt in
years 2000-2005
=⇒ this decline was stronger for
companies with 49+ employees.
=⇒ since 2005, labour share is ↑
in all categories, whereas in other coun-
tries it was ↓ profoundly.
=⇒ labour share among small compa-
nies is ↑, notably dynamically in first 10
years.
Orbis vs. Growiec (2009) & STAN data & mean values
Comparison of levels:
=⇒ labour share from Orbis fluctu-
ates around 0.6, higher than Growiec
(2009) by over 10ppt.
=⇒ difference between Orbis and
OCED STAN amounts to 20ppt.
=⇒ decline in years 2000-2005 alike
to Growiec (2009); increase after 2005
comparable to STAN data.
Comparison to unweighted mean:
=⇒ unweighted mean labour share
fluctuates around (weighted mean)
labour share.
=⇒ labour share leave confidence
intervals of unweighted mean labour
share → differences between (weighted
mean) labour share and unweighted
mean are significant in certain years.
The database properties
To construct final database we:
• use 10 waves of Orbis data between 2000
and 2020
• keep observations with reported added value
and either employment or labour compensa-
tion
• drop agriculture, mining and non-market ser-
vices
• trim the sample by one percentile from both
sides of distribution of capital-to-labor ratio
and average compensation.
Final database covers:
• years from 1995 to 2019
• roughly 750 thousands observations
• approximately 186 thousands of unique com-
panies
Imputation exercise
We have direct information on employment only
for half of the sample. Thus we implement impu-
tation methods to improve employment coverage.
We impute years between which we observe em-
ployment for a given company.
We compare a few methods
• naive imputation resorting to economic
identities and production function.
• idiosyncratic firm-year level regression
• linear interpolation within a firm
We test the methods looking at
• characteristics of prediction errors made for
the observable years
• train test split prediction errors
The most robust way to impute the missing data
is to create an ensemble of three methods, being
interpolation, regression and average sector wage.
Conclusions:
• We report ↓ in 2000-2005 and since then stably ↑ labour share.
• Among companies with 49- employees labour share is continuously ↑.
• The level of labour share from Orbis is different than Growiec (2009) and OCED STAN, however
its shape is similar in all data.
Acknowledgements
We thank J. Tyrowicz for valuable comments. We are
grateful to J. Growiec for sharing his data. This re-
search was supported by National Research Center (grant #
2019/34/H/HS4/00481). The remaining errors are ours.

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Evolution of labor share in Poland. New evidence from firm level data.

  • 1. TheevolutionoflabourshareinPoland. Newevidencefromfirm-leveldata Sebastian Zalas Hubert Drążkowski FAME|GRAPE & University of Warsaw FAME|GRAPE and Warsaw University of Technology Motivation Worldwide decline of labour share. Evidence for Poland? Working with aggregate has important drawbacks. Growiec (2009) provides firm-level es- timates from Statistic Poland data, but: • covers data until 2008 only • covers firms with 49+ employees only • dataset is not available =⇒ We add to the existing literature by: 1. extending to present times; 2. comparing small & large firms; 3. comparing new & widely available data to (unavailable) registry data from Statistics Poland. What do we do? We prepare firm level data set compilled from 10 waves of Orbis data 1. Show how to construct database using Orbis data. 2. We present evolution of labour share of value added, together with labour productivity and wages. 3. Next we compare our estimates to those presented by Growiec (2009) and prepared with national accounts from OECD STructural ANalysis Database (STAN). 4. Propose how to improve coverage with imputation methods. The evolution of labour share of value added. =⇒ level of labour share ↓ 10ppt in years 2000-2005 =⇒ this decline was stronger for companies with 49+ employees. =⇒ since 2005, labour share is ↑ in all categories, whereas in other coun- tries it was ↓ profoundly. =⇒ labour share among small compa- nies is ↑, notably dynamically in first 10 years. Orbis vs. Growiec (2009) & STAN data & mean values Comparison of levels: =⇒ labour share from Orbis fluctu- ates around 0.6, higher than Growiec (2009) by over 10ppt. =⇒ difference between Orbis and OCED STAN amounts to 20ppt. =⇒ decline in years 2000-2005 alike to Growiec (2009); increase after 2005 comparable to STAN data. Comparison to unweighted mean: =⇒ unweighted mean labour share fluctuates around (weighted mean) labour share. =⇒ labour share leave confidence intervals of unweighted mean labour share → differences between (weighted mean) labour share and unweighted mean are significant in certain years. The database properties To construct final database we: • use 10 waves of Orbis data between 2000 and 2020 • keep observations with reported added value and either employment or labour compensa- tion • drop agriculture, mining and non-market ser- vices • trim the sample by one percentile from both sides of distribution of capital-to-labor ratio and average compensation. Final database covers: • years from 1995 to 2019 • roughly 750 thousands observations • approximately 186 thousands of unique com- panies Imputation exercise We have direct information on employment only for half of the sample. Thus we implement impu- tation methods to improve employment coverage. We impute years between which we observe em- ployment for a given company. We compare a few methods • naive imputation resorting to economic identities and production function. • idiosyncratic firm-year level regression • linear interpolation within a firm We test the methods looking at • characteristics of prediction errors made for the observable years • train test split prediction errors The most robust way to impute the missing data is to create an ensemble of three methods, being interpolation, regression and average sector wage. Conclusions: • We report ↓ in 2000-2005 and since then stably ↑ labour share. • Among companies with 49- employees labour share is continuously ↑. • The level of labour share from Orbis is different than Growiec (2009) and OCED STAN, however its shape is similar in all data. Acknowledgements We thank J. Tyrowicz for valuable comments. We are grateful to J. Growiec for sharing his data. This re- search was supported by National Research Center (grant # 2019/34/H/HS4/00481). The remaining errors are ours.