The document summarizes research on labor market flows in Poland from 1995 to 2015. It reviews literature on determinants of labor market changes in transition countries. The author analyzes data from the Polish Labor Force Survey to examine flows from employment to unemployment/inactivity, and from public to private sector and manufacturing to services. Multivariate models show flows are influenced by factors like age, education, gender, marital status, tenure and residence location. While these determinants do not seem to change over time, the analysis only covers one country from 1995 onward. More research is needed on additional countries and earlier periods of transition.

1. Labour Market Flows in Poland –
Did the Determinants Change?
Stanisław Cichocki
AIEL conference, 12.09.2019
Group for Research in Applied Economics

2. Outline
1. Motivation
2. Literature review
3. Data
4. Results
5. Conclusions
2

3. Motivation
• Two postulated features of labor reallocation during transition:
• Public vs. private sector
• Manufacturing vs. services
• Significant amount of literature for EE and FSU countries which
discusses the determinants of labour market flows but …
• Focuses on early transition period
• Mostly concerned with flows from employment to unemployment and
inactivity
• cycle
3

4. Motivation
• Our data is a decade longer than the longest period analyzed in the
literature
• We focus also on flows from public to private sector and from
manufacturing to services
• cycle
4

5. Literature review
• Theoretical background:
• Aghion and Blanchard (1994)
• Caballero and Hammour (1996, 1998, 2000)
• Empirical studies:
• Significant amount of literature on labour market flows in EE and FSU
countries (Bilsen and Konings, 1998; Haltiwanger and Vodopivec, 2002;
Faggio and Konings, 2003; De Loecker and Konings, 2006)
• Literature on determinants of these flows
5

6. Literature review
6
Table 1. Overview of the countries, time periods, data sources and methods used in the literature on the
determinants of labour market states change in transition countries
Source Country (-ies) Period Number of obs. Data source Method
Foley, 1997 Russia 1992-1996
9 684 (1992/93)
6 422 (1995/96) RLMS Multinominal logit
Jones, Kato, 1997 Bulgaria 1991-1992 463 BUS Multinominal logit
Orazem, Vodopivec, 1997 Slovenia 1987-1991
30 474 (1987)
21 198 (1991) LFS Multinominal logit
Lehmann, Wadsworth, 2000
Poland
Russia 1994-1995
12 000 (Poland)
7 000 (Russia)
LFS (Poland)
RLMS (Russia) Probit
Terrell, Sorm, 2000 Czech Republic 1992-1994 3 000 Dedicated survey Hazard model
Sorm, Terrell, 2000 Czech Republic 1994-1998 11 000 LFS Hazard model
Vodopivec, 2000 Estonia 1989-1997
9 608 (1995)
4 804 (1997)
4804 (1998r.)
LFS Multinominal logit
Bell, 2001 Poland 1994-1998 55 000 LFS Multinominal logit
Sabirianova, 2002 Russia 1985-1998
4 527 (1985)
4 704 (1991)
4 236 (1998)
RLMS Multinominal logit
Vodopivec, 2002 Estonia 1989-1995 9 608 (1995) LFS Multinominal logit

7. Literature review
7
Table 1. Overview of the countries, time periods, data sources and methods used in the literature on the
determinants of labour market states change in transition countries
Source Country (-ies) Period Number of obs. Data source Method
Bojnec, Dries, Swinnen, 2003 Slovenia 1993-1999 3 344 LFS Probit
Turunen, 2004 Russia 1992-1996
4 436 (1992/93)
1 994 (1994/95)
1 925 (1995/1996)
1925 (1995/96)
RLMS Multinominal logit
Bukowski, Lewandowski, 2005 Poland 1997-2004 78 690 LFS Multinominal logit
Lehmann, Philips, Wadsworth,
2005 Estonia 1989-1999
10 000 (1995)
5 000 (1997)
12 000 (1999)
LFS Multinominal logit
Orazem, Vodopivec, Wu, 2005 Slovenia 1987-1993 12 923 LFS Multinominal logit
Ingham, Ingham, 2009 Poland 1994-2001 24 136 LFS Multinominal logit
Jackson, Mach, 2009 Poland 1988-1998
5 854 (1988 r.)
2 268 (1993 r.)
1 775 (1998 r.)
Dedicated survey Multinominal logit
Earle, 2012 Romania 1993-1995 22 161 LFS Multinominal logit

8. Literature review
• Main determinants:
• Age
• Education
• Gender and marital status
• Tenure
• Residence
8

9. Data
• LFS for period: 1995q1-2015q4
• Types of flows
• Flows from employment to unemployment
• Flows from employment to inactivity
• Flows from public to private sector
• Flows from manufacturing to services
9

10. Data
10
Table 2. Descriptive statistics in the sample
Variable All years 1995-2004 2004-2015
Employment 51.7% 50.8% 52.4%
Wage employed 38.6% 36.6% 40.2%
Unemployed 7.5% 8.8% 6.6%
Inactive 40.8% 40.4% 41.0%
Working in manufacturing 12.3% 12.4% 12.1%
Working in services 15.9% 13.7% 17.5%
Working in public sector 16.9% 20.5% 14.1%
Working in the private sector 21.8% 16.1% 26.2%
Flows from employment to
unemployment 1.2% 1.3% 1.0%
Flows from employment to inactivity 1.3% 1.5% 1.2%
Flows from public to private sector 2.4% 2.0% 2.9%
Flows from manufacturing to
services 0.7% 0.8% 0.6%
Gender (1=female) 51.5% 51.7% 51.3%
Age 15-24 18.2% 19.4% 17.3%
Age 25-34 18.9% 17.9% 19.7%
Age 35-44 18.4% 20.2% 17.0%
Age 45-54 18.6% 18.8% 18.5%
Age 55-59/64 14.6% 12.2% 16.4%
Age 60/65+ 11.2% 11.5% 11.0%

11. Data
11
Variable All years 1995-2004 2004-2015
Tertiary education 13.8% 8.8% 17.5%
High school education 11.8% 10.6% 12.7%
Vocational high school education 19.7% 20.3% 19.2%
Vocational education 27.3% 28.3% 26.6%
Elementary education 26.0% 32.0% 21.4%
Single 27.7% 26.3% 28.8%
Married/Divorced/Widowed 72.3% 73.7% 71.2%
Big city (>100 000 inhabitants) 29.7% 30.1% 29.4%
Small city (>2 000 <100 000
inhabitants) 32.6% 33.1% 32.2%
Rural areas 37.7% 36.8% 38.4%
Tenure (years) 11.41 11.86 11.07
No of observations 4 425 733 1 692 082 2 733 651
Note: Polish LFS, 1995q1-2015q4, shares in total population, flows relative to the relevant population.
To compute flows, population weights were utilized. For tenure mean in the sample is presented.

12. Results
12
Table 3. Multinominal logit for flows out of employment – Relative Risk Ratio
Unemployment Inactivity
Gender (1=female) 1.072*** 1.399***
Age 15-24 1.231*** 1.845***
Age 35-44 1.090*** 0.605***
Age 45-54 1.199*** 0.816***
Age 55-59/64 0.983 2.427***
Age 60/65+ 0.26*** 5.021***
High school education 1.754*** 1.169***
Vocational high school education 1.727*** 0.906***
Vocational education 2.575*** 1.094***
Elementary education 3.126*** 1.675***
Married/Divorced/Widowed 0.698*** 0.755***
Small city (>2 000 <100 000
inhabitants) 1.338*** 1.036*
Rural areas 1.067** 1.023
Tenure (years) 0.822*** 0.969***
Pseudo 𝑅2 0.076
No of observations 1 031 207
Note: Polish LFS, 1995q1-2015q4. Base category: male, 25-34 years, tertiary education, single, living
in big city.*** denote significance at 1% level, ** at 5% and * at 10%.

13. Results
13
Table 4. Logit models for flows from public to private and manufacturing to
service sector – Odds Ratio
From public to
private
From
manufacturing to
service
Gender (1=female) 0.715*** 1.051
Age 15-24 1.388*** 1.455***
Age 35-44 0.922** 1.000
Age 45-54 1.131*** 0.975
Age 55-59/64 1.309*** 0.829
Age 60/65+ 1.232* 1.508
High school education 1.587*** 1.025
Vocational high school education 1.674*** 0.849**
Vocational education 2.338*** 0.595***
Elementary education 1.953*** 0.648***
Married/Divorced/Widowed 0.777*** 1.058
Small city (>2 000 <100 000
inhabitants) 0.833*** 0.650***
Rural areas 0.935** 0.478***
Tenure (years) 0.982*** 0.954***
Pseudo 𝑅2 0.022 0.029
No of observations 321 539 231 074
Note: Polish LFS, 1995q1-2015q4. Base category: male, 25-34 years, tertiary education, single, living
in big city.*** denote significance at 1% level, ** at 5% and * at 10%.

14. Conclusions
• Theory: Two features of labor reallocation in EE and FSU
countries
• Empirics: Determinants of flows from employment to
unemployment and inactivity
• Determinants: Age, Education, Gender, Marital status, Tenure,
Residence
• These determinants don’t seem to change over time
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15. Conclusions
• Shortcomings:
• one country
• data from 1995 on
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16. Thank you for your attention!
Author: Stanisław Cichocki
e-mail: scichocki@wne.uw.edu.pl
More about our research on
http://grape.org.pl
Twitter: @GrapeUW