Wage inequality did not increase in the last 30 years, both in transition and non-transition economies, after first shock in early 90’s. Wage inequality is larger in transition countries, but when we take into account only inequality due to characteristics the difference disappears. Indicators of globalization and skill biased technological change correlate positively or do not correlate with wage inequality (partial CONFIRMATION of the theory) Surprisingly, the effect is smaller for transition economies The only exception is a share of high-skilled workers that decreases wage inequality attributable to characteristics.

1. RESEARCH POSTER PRESENTATION DESIGN © 2015
www.PosterPresentations.com
Theoretical literature suggests that due to:
• GLOBALIZATION
Feenstra and Hanson (1996)
• SKILL BIASED TECHNOLOGICAL CHANGE
Acemoglu and Autor (2011)
WAGE INEQUALITY should increase, but
empirical literature does not provide strong evidence on
WAGE INEQUALITY INCREASE in the last 30 years.
WE FOCUS on large sample of transition countries that
experience both – STRUCTURAL shock and EDUCATIONAL
boom to test relation between WAGE INEQUALITY and
STRUCTURAL CHANGE.
RESEARCH OBJECTIVES
DATA
How to measure wage inequality?
• ENTROPY MEASURES:
• Gini
• Mean log deviation
• RATIOS OF POSITION MEASURES:
• Total: 9th to 1th decile ratio
• Top: 9th to 5th decile ratio
• Bottom: 5th to 1st decile ratio
METHODS
WAGE INEQUALITY AND STRUCTURAL CHANGE
CONCLUSIONS
• Wage inequality did not increase in the last 30 years, both
in transition and non-transition economies, after first
shock in early 90’s.
• Wage inequality is larger in transition countries, but when
we take into account only inequality due to characteristics
the difference disappears.
• Indicators of globalization and skill biased technological
change correlate positively or do not correlate with wage
inequality (partial CONFIRMATION of the theory)
• Surprisingly, the effect is smaller for transition economies
• The only exception is a share of high-skilled workers that
decreases wage inequality attributable to characteristics.
REFERENCES
Literature:
Feenstra and Hanson, 1996, “Globalization, Outsourcing
and Wage Inequality,” American Economic Review 86(2),
240-245.
Acemoglu and Autor, 2011, “Skill, tasks and technologies:
Implications for employment and earnings”, Handbook of
Labor Economics 4, 1043-1171.
Dinardo, Fortin and Lemiuex, 1996, “Labor market
institutions and the distribution of wages, 1973-1992: A
semiparametric approach” Econometrica, 64 (5), 1001–1044.
Data:
LFS – Labor Force Survey – LFS Poland, LFS Serbia, LFS
France, LFS UK (by Central Statistical Offices)
SES – Structure of Earnings Survey – EU SES (by EUROSTAT
and Central Statistical Offices)
ECHP – European Community Household Panel (by
EUROSTAT)
LSMS - Living Standards Measurement Study (by The World
Bank)
ISSP – International Social Survey Programme (by GESIS)
CONTACT DETAILS
GRAPE website: grape.org.pl
Magdalena Smyk, GRAPE | University of Warsaw
m.smyk@grape.org.pl
Joanna Tyrowicz , GRAPE | University of Warsaw
j.tyrowicz@grape.org.pl
Magdalena Smyk | Joanna Tyrowicz
GRAPE | University of Warsaw
Wage inequality and structural change
WHY TRANSITION ECONOMIES?
We selected several indicators of
GLOBALIZATION and SKILL BIASED
TECHNOLOGICAL CHANGE in the
economy:
• TRADE (as a percentage of GDP)
• EMPLOYMENT IN SERVICES (share in
total employment)
• R&D EXPENDITURES (as a percentage
of GDP )
• HIGH-SKILLED WORKERS (share of
hours worked by high-skilled workers
in total hours worked )
How to disentangle changes in wage inequality that are due to changes in characteristics
and not due to prices?
We constructed COUNTERFACTUAL DISTRIBUTIONS of wages based on wage distribution
in USA in 1990’s.
• Parametric approach – fitted values from Mincerian wage regression on USA labor
force population are attributed to European workers with the same characteristics
(gender, education, age group and occupation)
• Non-parametric approach – (Dinardo, Fortin and Lemiuex, 1996) USA 1990 wage
distribution is weighted by characteristics from European sample
TIME TRENDS IN WAGE INEQUALITY
Transition economies experienced several labor market
transformations faster and with greater intensity than other
European countries:
• shift to a market economy
• opening up to a global economy
• educational boom
We collected 404 micro-level datasets from transition
economies and (as a baseline) Western European economies:
TRANSITION economies: LFS SES ECHP LSMS ISSP
Bulgaria 3 3 12
Croatia 7
Czech Republic 3 5
Estonia 3 1
Latvia 3 16
Lithuania 3
Poland 19 3 16
Romania 3
Russia 15 16
Serbia 12 1
Slovakia 3 4
Slovenia 16
Ukraine 3 2
NON-TRANSITION economies:
Austria, Cyprus, Denmark, Finland, France,
Germany, Greece, Ireland, Italy Luxembourg,
Netherlands, Norway, Portugal, Spain, Sweden,
Switzerland, UK
33 35 101 63
GINI 9th to 1th DECILE RATIO
GINI 9th to 5th DECILE RATIO 5th to 1th DECILE RATIO
Raw Para DFL Raw Para DFL Raw Para DFL
Trade
0.027 0.025** 0.026** 0.041 0.110** 0.048 0.029 0.069 0.156***
Trade * Transition
-0.055 -0.024* 0.005 0.031 -0.081* 0.0323 -0.125* -0.174*** 0.0381
Employment in Services
0.483** 0.217** -0.093 0.113 0.008*** -0.235 0.640 0.942** -0.600
Employment in Services
* Transition
-0.45*** -0.018 0.163** -0.074 -0.333 0.481** -0.594** -0.517** 0.522
R&D
0.040 0.010 0.0076 0.097** 0.041 0.018 0.037 0.106*** 0.417
R&D * Transition
-0.069** -0.013 0.006 -0.163*** -0.070* 0.002 -0.103** -0.145*** 0.088
High-skilled
0.039 -0.224*** -0.233*** 0.171 -0.789*** -0.609** 0.609* 0.338 -0.337
High-skilled * Transition
0.179 0.0826 0.349*** 0.591* 0.532** 1.050*** 0.0332 -1.009*** 1.499***
Year and Year*Transition coefficients from OLS regressions with country and source fixed effects.
95% confidence intervals on the graph.
Year and Year*Transition coefficients from OLS regressions with country and source fixed effects.
95% confidence intervals on the graph.
1 = 100%
*** p<0.01, ** p<0.05, * p<0.1