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Unemployment and Labour Market Institutions:
A Progress Report
Olivier Blanchard (MIT), Simon Commander (EBRD & LBS) and Axel
Heitmueller (LBS) 1
Introduction
A common assumption in the early literature on transition was that unemployment
would rise at the start of transition but would follow an inverted U shape with the speed
of increase in unemployment being driven primarily by the speed of restructuring and
firm closures. Indeed, data on job destruction and creation show that in Central Europe
and the Baltic states there were bursts of job destructions early in transition followed -
with a lag - by increases in job creation. By contrast, in much of the CIS job destruction
was very restrained early on, not least because of the continuation of soft budget
constraints. As such, unemployment has risen more gradually over time. In most
countries – whether of the CIS or Central Europe – unemployment has – contrary to
earlier predictions – stayed high and persistent.
This paper is –an attempt to understand why unemployment has remained high.
Our plan is to explore two hypotheses. The first is that changes in labour market
institutions account for high unemployment. In a manner similar to Western Europe,
this line of argument would require that benefits have become too generous and/or
labour taxation too high with the outcome being a high natural rate of unemployment.
The second is that high unemployment is due to changes in the composition of the
unemployment pool, with the share of workers least able to be matched to jobs rising
over time. At this stage, we have explored only the first hypothesis. . As such, the paper
is more in the nature of a progress report.
1 The authors thank the Japan-Europe Cooperation Fund and the EBRD for support
The paper is organised as follows. Section 2 provides information on the paths of
unemployment, focusing on six transition countries. Section 3 starts with a brief
overview of how labour market institutions could be expected to influence
unemployment, and then moves on to describe the institutions present in these transition
countries. Appendix 1 also provides more detailed information as to how various
measures used in the analysis have been constructed. Section 4 presents the results of our
initial estimates while Section 5 concludes.
2. Paths of unemployment
Figure 1 plots the path of aggregate unemployment in six transition countries between
1991-2005. There is significant variation in both levels and changes in unemployment
across countries. What is common, however, is the absence of an inverted U shape for
unemployment. Only in Hungary, did unemployment rise sharply at the start of
transition before falling steadily. In Poland the early rise was followed by an equally sharp
fall in the mid-1990s before a subsequent and sustained increase. In both the Czech and
Slovak Republics unemployment has tended to increase throughout the period, although
the level in the former has been far lower. In Romania after a sharp increase at the start,
unemployment has remained roughly constant since the mid-1990s at around 8%.
Finally, in Russia unemployment has increased more gradually before stabilising at
around 9% after 2000. Further, there have been very substantial declines in the
employment rate. In Central Europe by 2004 the employment rate was below 60%.
There has clearly been a large shift into non-participation
As unemployment has increased, there have been changes in the composition of
unemployment. In particular, there has been a sharp rise in share of long-term
unemployment (LTU). In Central Europe and Romania by 2004 LTU rates were around
50% or higher while in Russia the share was under 40%. In terms of educational
attainment, unemployment is characterised by relatively high shares of the unskilled.
Figure 2 provides unemployment rates for those with primary and tertiary education
respectively. With the exception of the Slovak Republic where unemployment is
particularly high among people with secondary education, unemployment rates for those
with primary education lie significantly above the mean rate and the reverse holds for
those with tertiary education. For example, in the Czech Republic the unemployment rate
for the primary educated is nearly ten times higher than for those with tertiary education.
3. Institutions & unemployment
The persistence of unemployment begs explanation. An obvious place to start is with
labour market institutions. In Western Europe and the OECD the available evidence
points to a major role being played by institutions, whether directly or in combination
with other factors or shocks 2. For example, Nickell, Nunziata and Ochel (2005) using
data for the OECD argue that over half of the upward shift in equilibrium
unemployment over the period 1960-1995 can be explained by changes in institutions. In
particular, they find that (in order of importance) the benefits system; labour taxes,
unions and changes in laws for employment protection have materially affected
unemployment. They find – contrary to Blanchard and Wolfers (2000) – that the
interaction of institutions and shocks adds no significant explanatory power.
Replicating this type of analysis is obviously problematic for the transition
countries. For a start the time series dimension of the data is small. Further, the concept
of a relatively stable equilibrium unemployment rate has less relevance given the initial
conditions and the need for large scale restructuring and reallocation. Nevertheless, the
apparent persistence of unemployment suggests that there have been factors at work that
have stopped an efficient reallocation of workers and jobs occurring. Given that the
transition countries started with relatively generous systems of social benefits – at least
compared with countries at comparable income levels – it is reasonable to ask whether it
is institutions that have, as in Western Europe, contributed to maintaining high
unemployment. In what follows, we pay particular attention to the level of
unemployment benefits, the duration of benefits, the coverage of the benefits system –
defined as the ratio of beneficiaries to total unemployed – as well as the strictness with
which the benefits system has been operated. In addition, we consider the weight and
strength of trade unions, the extent of employment protection operating in these
countries and the level of labour taxation. Wherever feasible, these measures have been
calculated in a way consistent with those used by the OECD. Appendix 1 gives a more
detailed description of how these variables have been constructed.
2 See, Blanchard and Wolfers (2000)
3.1 Unemployment benefits: net replacement rates
The level of benefits is measured as net replacement rates.3 Figure 3 & Figure 4 give net
replacement rates for both long-term and short-term unemployed in the six countries.
They show the average rate for ten different family types as well as the highest and
lowest net replacement rate for each year.4 It is clear that there are very substantial
differences in levels across countries and unemployment types. For long-term rates, for
example, Russia has had low and declining replacement rates – between 2-5 percent –
and these have been particularly low for individuals without a labour market history. By
contrast, the long-term unemployed in the Czech and Slovak Republics and Poland have
received mean net replacement rates of between 40-60 percent. For the short run
unemployed, the picture is somewhat different. Figure 4 shows relatively little variation in
generosity across countries – by 2005 mean replacement rates ranged between 40-60% -
and with a common declining trend (except for Romania). The minimum values typically
correspond to benefits for individuals without a labour market history. In most
countries, their treatment in the long term is not different from the short term, although
there are clear differences in the levels across countries. The maximum values mainly
comprise lone parents and couples with children. Again there is variation across
countries in the level of both short and long run replacement rates. However, over time
there is a clear tendency for short and long run rates within countries to converge at
lower levels of generosity than in the 1990s.
Figures 5-8 do the same exercise but now distinguish between workers at 100% of
the average production wage (APW) and those at 66%. There are some interesting
differences across and within countries. For the Czech Republic there is little difference
in the mean NRR for either type across long and short terms. There is however a clear
difference in the level between 100% and 66% of APW. In the other countries, there
tends to be an obvious difference between short and long term NRRs for both 100% and
66% categories. For example, in Poland short term NRRs were initially significantly
higher than for long term. Over time this difference has largely disappeared for the 100%
group although not for the 66% group. In Hungary, short term NRRs while similar for
100% and 66% also initially differed substantially from long term NRRs. While this
difference has been maintained, the gap has narrowed, particularly for the 66% group. In
3 Net replacement rate=[Benefit income of the family when unemployed-Tax on benefit income]/[Earned income + Benefit income of the family when employed –Tax on earnings and benefits]. 4 The ten family types are: single, couple, couple with two children, lone parent with two children, for people with and without employment history; two earner couple with and without children.
Russia, not only are there particularly large changes over time but the difference between
long and short term is huge for both 100% and 66% categories.
In short, these figures show that over time mean NRRs have tended to decline
for both long and short-term cases (with the exception of Romania). For the period
2000-2005 most countries report NRRs in the range of 0.4-0.6.
3.2 Strictness of benefits
The impact of the generosity of the unemployment benefit system on labour market
outcomes depends very much on the enforcement of the benefit rules. For example, a
generous system does not necessarily lower employment incentives if the access rules are
strict and targeted at the most deserving. Our strictness indicator has been collected over
time and is based on eight questions capturing aspects such as job search requirements,
requirements on occupational and geographical mobility, acceptability of jobs, and
sanctions attached to job refusals. The index ranges from 1 (low) to 5 (high) and is
unweighted. An important limitation of the indicator is that it relates to the strictness of
the rules rather than the strictness of actual enforcement. For example, local
employment offices may have the ability to enforce rules differently, depending on local
circumstances 5. If so, the variation in the strictness indicator may underestimate or
overestimate the actual changes in enforcement over time.
Figure 9 gives reports the index for the six transition countries between 1992 and
2005. There are significant differences in levels of enforcement. For example, Hungary
has relatively strict rules throughout while the index is lowest for Russia. The other
countries exhibit quite similar levels of strictness, except for the Slovak Republic which
has seen a sharp increase over the period. However, there are clear differences in the
underlying factors driving the differences in levels. For example, in Hungary the
strictness measure is mainly driven by sanctions for rule violations rather then
requirements on occupational or geographical mobility or proof of job search, while in
Russia there are higher requirements for job search but lower sanctions and requirements
for mobility. With the exception of the Slovak Republic, there has been a high degree of
persistence in strictness over time. Where changes have occurred they have generally
been through more stringent requirements to provide evidence of job search activity
5 There is some evidence for Hungary that the frequency of visits to local employment offices varies substantially by region and is often left to the discretion of the employment office (Micklewright and Nagy, 2005)
(Czech and Slovak Republics and Romania) or the requirements for occupational
mobility (Poland and Russia).
3.3 Unions & bargaining
It is well established that the way in which wages are set in the economy can have a
bearing on unemployment rates. Trade unions’ role in wage setting can have a significant
impact on both wages and employment 6. Further, it has been argued that the locus of
bargaining can be important - a high degree of centralisation or coordination in wage
setting on the one hand or plant level bargaining on the other being viewed as having
superior employment implications than intermediate loci of bargaining (such as industry
level bargaining) 7. Given that wages in Eastern Europe are generally set by collective
wage bargaining for the majority of employees, we construct three different measures; a
co-ordination index8, a centralisation index9, and an index of the number of central union
confederations10. We have also collected information on union density and union
coverage11.
Figure 10 plots the three indices over time. Several things stand out. First, there is
significant variation across countries in both centralisation and coordination scores. In
the majority of countries the level of co-ordination and centralisation has been extremely
low. Yet, in Hungary, co-ordination has substantially increased over time and is currently
high despite its low degree of centralisation for collective wage bargaining. Second, the
number of trade union confederations – between 2-5 union confederations and/or
private sector organisations - is similar in most countries, with the exception of the
Slovak Republic where wage bargaining is dominated by one union confederation.
Third, while in Poland, Slovak Republic and Russia there have been limited changes over
time, in both Hungary and the Czech Republic changes have been quite frequent. With
6 See Layard, Nickell and Jackman (1995) 7 There is a large literature on this topic; see, inter alia, Calmfors and Driffill (1988) 8 Co-ordination index (characterising the degree of consensus between the actors in the collective bargaining system. 1:low, 2:medium, 3:high) 9 Centralisation index (characterising the degree of centralisation of the collective bargaining system according to the privileged level of bargaining within national union organisation and national employer organisation: 1:firm level, 2:industry level, 3:national level) 10 Number of central union confederations (3:one dominating union confederation and one dominating private sector employer organisation, 2:the existence of 2-5 union confederations and/or 2-5 central employer organisations, 1:the absence of a central organisation on one or both sides of the labour market) 11 This information is not available on a consistent basis for most countries from official sources. Survey data is often unreliable as many employees are not aware of whether they are covered by a union wage agreement.
respect to the locus of bargaining, until recently collective bargaining has tended to occur
at firm level. In the Czech Republic and Hungary, which started out with collective
bargaining at the national level, they have gradually moved towards less centralised
systems in the mid and late 1990s. Other available evidence also suggests that in general,
there has been an increase in wage flexibility over time12.
3.4 Employment protection
We construct an index of employment protection based on hiring and firing practices.
Figure 11 indicates some differences in the extent of flexibility across countries: Russia is,
for example, more flexible in terms of hiring and firing rules than most of the other
countries. There are also some clear changes over time with the Slovak Republic
becoming more flexible as its neighbour – the Czech republic - has tended to become
less flexible.
To put this in context, Riboud et al (2000) and Micevska (2003) have estimated
that in Central Europe there was generally less or comparable employment protection to
that existing in the pre-Accession EU countries. Hiring temporary workers has generally
been easier in Central Europe (but harder in Romania) than in the EU while collective
dismissal (>5 workers) has everywhere been more difficult. Suranyi and Korosi (2003)
have used firm level data for Hungary to estimate adjustment costs. They found that such
costs amounted to around 3.5 months’ wages: a level considerably lower than in most of
the Western European economies.
3. 5 Payroll taxes
Obviously the main taxes of interest are those that determine the wedge between product
and consumption wages. In principle, these include income and consumption taxes as
well as payroll taxes. Although empirical evidence of the impact of labour taxes on
employment has been ambiguous, Nickell et al (2005) find for the OECD not only have
they increased sharply since the 1960s but they are also a significant explanatory factor in
explaining the rise in unemployment.
Figure 12 plots the payroll tax burden and the employer contribution. What is
clear is that payroll tax rates have generally been high in the transition countries; they
have – with the notable exception of Romania – been mostly stable. In Romania we
observe a very strong increase between the mid 1990s and now. Figure 13 distinguishes
12 For example, responses from employers – the Executive Opinion Survey – reported in the World
further by type of worker – 100% or 66% of APW using OECD data (2005). Figure 13
shows that the tax burden is generally lower for the low wage category but the margin of
difference is also small. There seems to be no difference by type in either Romania or
Russia.
To put this in context, the average tax wedge for single people in France would
be 38.5%, in Germany 45.5%. The wedge in these six transition countries is mostly
roughly comparable to France and below Germany.
3.6 Real Interest Rates
A regression of unemployment on institutions should also include the other relevant
determinants of unemployment, initial conditions and shocks. For the time being, we
have introduced only one “shock’’ variable, the real interest rate. A number of f
empirical papers have found interest rates to be relevant in explaining unemployment in
Western Europe. The channel is typically argued to be through capital accumulation and
hence to labour demand 13. Figure 10 plots the evolution of real interest rates in the six
countries. There is large variation across countries – Russia, for example, has only
infrequently run a positive real interest rate – with in addition significant variation over
time within countries. Polish real interest rates, for example, jumped from around 2.5
percent in 1995/96 to average over 8 percent between 1997-2001.
4. Estimation – some initial results
We now explore the relationship between unemployment rates, the labour market
institution variables and the real interest rate variable. In order to distinguish types of
unemployment we disaggregate unemployment into two categories - namely
unemployment for those with low and high educational attainment respectively. Table 1
brings the results together. In the first instance, country and time dummies are excluded.
The idea is to see whether institutions can explain time and cross country evolutions.
Country dummies are then included to see whether institutions can explain time
evolutions. Finally, both country and time dummies are included to see whether
institutions can explain specific country evolutions beyond common time evolutions and
differences in levels.
In the base runs (1-3), the unemployment measure is related to the short and
long term net replacement rates alone with and without country and time dummies.
Competitiveness Report
Without either country or time dummies, the coefficient on the short term NRR is
positive and significant at the 10% level while the long term NRR coefficient is negative
and significant at the 5% level. The explanatory power of the estimate is very low. In (2)
we add country dummies. The sign, size and significance of the coefficient on the short
term NRR remains very similar, but that for the long term NRR switches sign and is
significant.(3) repeats the exercise but this time including both time and country
dummies. Estimates (4-6) then include the other institutional variables, namely the
strictness index, employment protection and tax wedge. None enter significantly with the
exception of the tax wedge measure that is actually negatively signed. With country
and/or time dummies, the coefficient on short term benefits increases in size and is
highly significant. In Table 2 the same exercise is performed but this time the real interest
rate variable – specified in logs - has been added. In all instances it enters positively and
highly significantly. Finally, there is a large degree of inter-country heterogeneity – the
country dummies are mostly significant. By contrast, there are no significant time effects.
5. Preliminary conclusions
What can we conclude from these results? First, we need to be cautious - there
are still gaps in the dataset and this has limited the number of observations. Second, we
do not adequately cover a number of institutional features, such as the presence or
absence of extension agreements. Third, we offer no robustness checks at this stage.
Fourth, we have experimented with a number of other variables – such as the political
orientation of the ruling party, centralisation of wage bargaining, a wage flexibility index
– but they were all insignificant.
The main storyline that emerges at this point is that unemployment in this group
of transition countries cannot be explained by the evolution of labour market institutions
alone. If institutions matter, it must be in combination with other factors in explaining
unemployment.
An open issue is whether we would have more success explaining non-
employment rather than unemployment. For example, disability and early retirement
pensions –may well have provided incentives to leave employment and ultimately the
labour force. For example, in Hungary disability benefits have been in place throughout
the 1990s and there has been little change in entitlement rules over time
13 Blanchard and Wolfers (2000)
Appendix 1 Data appendix
Unemployment rate: Aggregate unemployment rate from the EBRD database as well as
disaggregated unemployment rates by education and where possible by previous earnings.
Unemployment rates by educational attainment come from Labour Force Surveys and Statistical
Offices.
Annual net replacement rates: NRR=[Benefit income of the family when unemployed-Tax on benefit
income]/[Earned income + Benefit income of the family when employed –Tax on earnings and
benefits]. Gross earnings in work for an Average Production Worker (APW) defined as an adult
full-time production worker in the manufacturing sector whose wage earnings are equal to the
average wage earnings of such workers. It is assumed that the worker is fully employed during the
year (no sick leave or unemployment). White-collar workers are excluded. Manufacturing is
defined as in Division D of the International Standard Industrial Classification of All Economic
Activities (ISIC Revision 3, UN). Income includes average amounts of overtime and regular cash
supplements (such as Christmas bonuses, thirteenth month payments, vacation month
payments). Regular annual bonuses are to be included unless they are dividend payments. Fringe
benefits are to be excluded. Where possible annual earnings have been calculated by referring to
the average of hourly earnings in the manufacturing sector in each quarter or month, weighted by
the hours worked during each period, multiplied by the average number of hours worked during
the year, assuming that the worker is neither unemployed nor sick (see OECD Taxing Wages,
Table V3, p 438 for various examples for different countries). NRR are available for six different
family types (single, couple, lone parent, two-earner couples with and without 2 children). They
are also available at 100% and 66.7% of the APW. We also have information on the initial
(unemployment insurance or unemployment assistance benefits) and long-term (social assistance)
rates. NRRs are based on the OECD definitions where possible. The NRR is calculated for an
overall category with a full employment record assumed to qualify for the full amount of benefits
and a ‘youth’ category without any employment record.
Strictness indicator: Information on the strictness with which unemployment benefit rules are
enforced (on paper) based on a questionnaire by the Danish Ministry of Finance, used by Nickell
et al. (2005). However, there is a potential problem as the enforced strictness is likely to differ
from the legislative strictness in some countries (e.g. Hungary). The index ranges from 1 to 5.
Union density: Union members as percentage of employees
Collective bargaining coverage: Percentage of employed labour force whose pay is determined by
collective bargaining
Co-ordination index: Characterising the degree of consensus between the actors in the collective
bargaining system. 1: low, 2: medium, 3: high, intermediate scores are possible
Centralisation index: The degree of centralisation of the collective bargaining system according to
the level of bargaining within national union organisation and national employer organisation: 1:
firm level, 2: industry level, 3: national level, intermediate scores are possible
Number of central union confederations: 3: one dominating union confederation and one dominating
private sector employer organisation, 2: the existence of 2-5 union confederations and/or 2-5
central employer organisations, 1: the absence of a central organisation on one or both sides of
the labour market, intermediate scores are possible
Tax Burden: Taken from the OECD publication Taxing Wages 2004 and other local sources.
Consist of average tax rates including income tax, employer and employee contribution less cash
benefits. This differs from other definitions that have used either marginal tax rates or/and
included consumption taxes (see e.g. Nickell, 2005). Currently we have this information from
1995-2004 for Hungary, Czech, Poland and Slovak (2000-2004). Furthermore, the information is
available at 100% and 66.7% of APW.
Employer Contribution: Taken from the OECD publication Taxing Wages 2004 and other local
sources. For the Slovak Republic 1996-1999 Czech Republic values have been used.
Hiring and Firing index: Constructed from responses by executives to questionnaires sent out by
the World Economic Forum and reported in various years in the World Competitiveness Report
Log real interest rate: Taken from the EBRD database mostly using the money market rate and
consumer prices.
Largest Government Party Policy Direction: Taken from the Database of Political Institutions (DPI)
and refers to the political direction of the largest party in government (right, central, left).
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Figure 1 : Aggregate unemployment rates
0.1
.20
.1.2
1990 1995 2000 20051990 1995 2000 20051990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
aggr
egat
e un
empl
oym
ent r
ate
yearGraphs by country
Figure 2: Unemployment rates by education level and overall
0.1
.2.3
0.0
5.1
.15
0.1
.2.3
0.1
.2
.05
.1.1
5.2
0.1
.2
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
primary tertiaryaggregate unemployment rate
year
Graphs by country
Figure 3: Net replacement rates long term (averages, max and min)
.2.4
.6.8
0.2
.4.6
.2.4
.6
.2.4
.6.8
0.0
5.1
.15
.2
0.5
11.
5
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
Mean long-term Max long-termMin long-term
year
Graphs by country
Figure 4: Net replacement rates initial phase (averages, max and min)
.2.4
.6.8
1
0.5
1
.2.4
.6.8
.2.4
.6.8
1
0.5
1
.4.6
.81
1.2
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
Mean short-term Max short-termMin short-term
year
Graphs by country
Figure 5: Net replacement rates (100%) long term
.2.2.4.4
.6.6.8.8
00.2.2
.4.4.6.6
.2.2.4.4
.6.6
.2.2.4.4
.6.6.8.8
00.0
5.0
5.1.1
.15
.15
.2.2
00.5.5
111.
51.
5
19901990 19951995 20002000 20052005 19901990 19951995 20002000 20052005 19901990 19951995 20002000 20052005
19901990 19951995 20002000 20052005 19901990 19951995 20002000 20052005 19901990 19951995 20002000 20052005
CzechCzech HungaryHungary PolandPoland
RomaniaRomania RussiaRussia SlovakSlovak
Mean NRR long-termMean NRR long-term Max NRR long-termMax NRR long-termMin NRR long-termMin NRR long-term
Figure 6 Net replacement rates (66%) long-term
.4.6
.81
0.2
.4.6
.2.4
.6.8
.2.4
.6.8
1
0.1
.2.3
0.5
11.
5
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
Mean NRR long-term Max NRR long-termMin NRR long-term
year
Graphs by country
Figure 7 Net replacement rates (100%) short-term
.2.4
.6.8
1
0.5
1
.2.4
.6.8
.2.4
.6.8
1
0.5
1
.4.6
.81
1.2
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
Mean NRR short-term Max NRR short-termMin NRR short-term
year
Graphs by country
Figure 8 Net replacement rates (66%) short-term
.4.6
.81
0.5
1
.4.6
.81
1.2
.2.4
.6.8
1
0.5
1
.4.6
.81
1.2
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
Mean NRR short-term Max NRR short-termMin NRR short-term
year
Graphs by country
Figure 9: Strictness indicator of the enforcement of unemployment benefit rules
2.5
33.
54
2.5
33.
54
1990 1995 2000 20051990 1995 2000 20051990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
stric
tnes
s in
dex
yearGraphs by country
Figure 10: Measures of wage determination
11.
52
2.5
31
1.5
22.
53
1990 1995 2000 20051990 1995 2000 2005
Czech Hungary
Poland Slovak
Co-ordination index Centralisation indexNumber of central union confederations
Year
Graphs by Country
Figure 11: Hiring and firing indices 3
45
63
45
6
1990 1995 2000 20051990 1995 2000 20051990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
Hiri
ng a
nd F
iring
yearGraphs by country
Figure 12 Tax burden (Overall and employer)
2030
4050
2030
4050
1020
3040
3040
5060
2530
3540
45
2025
3035
40
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
Overall tax burden Employer tax burden
year
Graphs by country
Note: Values 1996-1999 for Slovak are from Czech
Figure 13 Tax burden by APW type 41
4243
44
4045
5055
4142
4344
45
3040
5060
2530
3540
45
3940
4142
43
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
Tax burden Tax burden 66%
year
Graphs by country
Note: Values 1996-1999 for Slovak are from Czech
Figure 14 Real interest rates
-20
-10
010
05
1015
-50
510
-200
-100
010
0
-600
-400
-200
020
0
-10
010
20
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
Czech Hungary Poland
Romania Russia Slovak
Rea
l int
eres
t rat
e
yearGraphs by country
22
Table 1 (1) (2) (3) (4) (5) (6) Mean NRR short-term
0.0580 0.0726 0.1881 0.0035 0.2606 0.2882
(1.67)* (2.46)** (4.00)*** (0.05) (2.89)*** (3.13)*** Mean NRR long-term
-0.0467 0.1163 0.0346 0.0112 0.0186 0.0457
(2.04)** (2.15)** (0.60) (0.21) (0.17) (0.40) Strictness index -0.0273 0.1242 0.0356 (0.99) (1.00) (0.25) Hiring and Firing -0.0114 0.0090 0.0075 (1.21) (1.28) (0.94) Tax burden -0.0019 -0.0044 -0.0004 (1.82)* (2.93)*** (0.18) Constant 0.0712 -0.0467 -0.1092 0.3092 -0.3627 -0.2795 (4.14)*** (1.51) (2.78)*** (2.92)*** (0.84) (0.60) Country dummies No Yes Yes No Yes Yes Time dummies No No Yes No No Yes Observations 130 130 130 81 81 81 R-squared 0.02 0.45 0.54 0.09 0.60 0.64 Robust t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Note: Dependent variable is aggregate unemployment rate for average over primary and vocational educated or secondary and tertiary educated individuals. See data appendix for variable definitions and explanations
23
Table 2 (1) (2) (3) Mean NRR short-term 0.1383 0.2156 0.2271 (1.91)* (2.41)** (2.44)** Mean NRR long-term -0.0716 0.1008 0.1019 (1.22) (0.83) (0.84) Strictness index -0.0000 0.2506 0.2724 (0.00) (1.63) (1.48) Hiring and Firing 0.0062 0.0111 0.0127 (0.66) (1.56) (1.51) Tax burden -0.0025 -0.0041 -0.0037 (2.36)** (3.01)*** (1.46) Log real interest rate 0.0465 0.0266 0.0334 (3.34)*** (2.30)** (2.53)** Constant 0.1046 -0.8281 -0.9490 (0.97) (1.54) (1.61) Country dummies No Yes Yes Time dummies No No Yes Observations 77 77 77 R-squared 0.28 0.63 0.66 Robust t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Note: Dependent variable is aggregate unemployment rate for average over primary and vocational educated or secondary and tertiary educated individuals. See data appendix for variable definitions and explanations
