In the paper we test the new Phillips curve for Central and Eastern European EU accession countries for the period from 1990 to 2002 and use it to compare the efficiency of the traditional Phillips curve. More specifically, we want to see whether real marginal cost, which includes labor productivity and real wage components, can account for inflation dynamics in the observed sample. Surprisingly, when observing all eight selected countries, the relation between real marginal cost and inflation is opposite than expected. On the other hand, inflation in Baltic States and Slovenia seems to be influenced by real marginal cost. The elasticity coefficient of real wages on inflation for Slovenia shows that inflation was quite responsive to movement in wages during the total period, however, inflation became quite inelastic with respect to wages after 2000. Thus, economic policies that were introduced in Slovenia after 2000 were quite efficient in wage regulation, although the real effect will be observed in a more advanced period.

1. The Relationship between Wage and Inflation: Case of Slovenia and
Selected Central and Eastern European Countries
1
Helios Padilla Mayer, PhD
Government of Republic of Slovenia
Institute for Macroeconomic Analysis and Research
Gregorčičeva 27,1000 Ljubljana
January, 2003
1
hpadillamayer@gmail.com

2. 2
Abstract:
In the paper we test the new Phillips curve for Central and Eastern European EU
accession countries for the period from 1990 to 2002 and use it to compare the efficiency
of the traditional Phillips curve. More specifically, we want to see whether real marginal
cost, which includes labor productivity and real wage components, can account for
inflation dynamics in the observed sample. Surprisingly, when observing all eight
selected countries, the relation between real marginal cost and inflation is opposite than
expected. On the other hand, inflation in Baltic States and Slovenia seems to be
influenced by real marginal cost. The elasticity coefficient of real wages on inflation for
Slovenia shows that inflation was quite responsive to movement in wages during the total
period, however, inflation became quite inelastic with respect to wages after 2000. Thus,
economic policies that were introduced in Slovenia after 2000 were quite efficient in
wage regulation, although the real effect will be observed in a more advanced period.

3. 3
1. Introduction
One of the main tasks of the Central Banks of EU accession countries is to achieve price
stability and therefore it is really important to determine sources and nature of inflation in
this area. Our research targets effects of wages on inflation. Or, more precisely, we try
to see whether there is an evidence of wage pressures on inflation.
The most conventional model used for testing this relation is the standard Phillips curve
approach. However, we follow Gali, Gertler and Lopez-Salido (2001) and test relation
between wages and inflation on the basis of so called “new” Phillips curve literature. As
the standard Phillips curve, also this one assumes that inflation varies positively with real
sector economic activity in the short run. However, the difference is that the new Phillips
curve relates inflation to movements in real marginal cost. Thus, real marginal cost is the
theoretically appropriate measure of real sector inflationary pressures, as opposed to the
cyclical measures used in traditional Phillips curve analysis, such as detrended output and
unemployment.
One of the advantages of the real marginal cost measure (which in our analysis
corresponds to real unit labor cost) is that it directly accounts for the influence of both
productivity and wage pressures on inflation. Thus, according to the new Phillips curve
theory, productivity, wages and inflation follow the similar path. We want to see whether
this relation holds for Central and Eastern European EU accession countries (Czech
Republic, Hungary, Estonia, Latvia, Lithuania, Poland, Slovakia and Slovenia). More
specifically, we want to test whether wages did have inflationary pressures in Slovenia
and if so, what policy measures can be implemented to avoid this effect.
In section 2 we first present analyze the connection between inflation and wages on the
basis of traditional Phillips curve approach. Then, in section 3 we discuss the new
Phillips curve. Section 4 develops the theoretical model used for estimation. Section 5
presents empirical results and draws comparisons between the full country sample and
two sub-samples (Baltic States including Slovenia and Central European Countries).
Section 6 concludes.

4. 4
2. Traditional Phillips Curve
The traditional Phillips curve relates inflation to some cyclical indicator (either level of
unemployment of real GDP) and lagged values of inflation. In our analysis, we are going
to use the real GDP as the cyclical indicator. Thus, if πt denotes inflation and the log
deviation of GDP from its long run trend, then the common specification of the Phillips
curve is:
, (1)
where is a random disturbance. This equation can be understood as a demand-side
explanation of inflation; aggregate demand increases following a rise in money supply.2
It is sometimes assumed that the sum of weights on lagged inflation equals to one so that
there is no long-run tradeoff between inflation and output. We did not impose this
assumption in our analysis because coefficients on lagged inflation variables in the
regression equation added up close to one.
We run the generalized least squares (GLS) regression controlling for fixed effects3
based
on equation (1) for the full country sample, and then (due to their differences) two sub-
samples, (a) Central and Eastern European countries, and (b) Baltic States and Slovenia.4
We use quarterly data from 1990 to 2002.5
We run several regressions and the one where
we use four lags for inflation fits best the quarterly data. To measure inflation we use log
difference of the CPI index, which we obtain from national statistic databases on a
quarterly basis. The output term is HP filtered real GDP on a quarterly basis. We also
obtain data on quarterly real GDP from national statistic databases. Figure 2.1 shows
inflation rate for Slovenia and Central and Eastern European EU candidate countries on a
2
For example, an increase in price level can be caused by a rise in aggregate demand, which is due to an
expansionary fiscal policy.
3
Due to heteroskedasticity of data and autocorrelation in the data series, ordinary least square (OLS)
estimates are not most efficient, so instead we choose to run the GLS regression and obtain unbiased as
well as most efficient coefficients.
4
We also run the regression for Slovenian data only, but since the size and sign of coefficients do not
change significantly, we only report regression results on the two sub-samples.
5
We do not include data for first two quarters of 2003 because it is not available for the full country
sample.
tyˆ
tt
h
i
tit y edpgp ++= -
=
-å 1
1
1 ˆ
te

5. 5
yearly basis (average inflation rate).6
Figure 2.2 plots the real GDP growth rates for the
same country sample on a yearly basis.
The corresponding equation for the full data set is:
where standard errors are shown in parentheses. The estimates for the two sub-samples
are as follows:
(1) Baltic States with Slovenia
(2) Central European Countries
Surprisingly, in all three cases, there is a negative relation between the HP filtered output
and current inflation. Coefficients on output for the full sample and a sub-sample for
Baltic States and Slovenia are significant at 5 percent level, but the coefficient on Central
European countries is not significant. Gali, Gertler and Lopez-Salido (2001) on the other
hand show that there was a positive relation between the detrended output and inflation,
6
For the purpose of presentation, inflation rates are expressed in logarithmic form.
u_i)toduevarianceof(fraction0.431rho
0.00value-p61.17,statisticF
(0.073)
yˆ0.129-0.112045.0239.0567.0
(0.951)(0.095)(0.132)(0.137)
1-t4321
=
==
+-+= ---- ttttt ppppp
u_i)toduevarianceof(fraction0.498rho
0.00value-p52.28,statisticF
(0.148)
yˆ0.376-0.157105.0302.0347.0
(0.172)(0.143)(0.193)(0.219)
1-t4321
=
==
-++= ---- ttttt ppppp
u_i)toduevarianceof(fraction0.109rho
0.00value-p12.84,statisticF
(0.667)
yˆ0.026-0.161046.0044.0618.0
(0.093)(0.115)(0.160)(0.145)
1-t4321
=
==
+-+= ---- ttttt ppppp

6. 6
which actually corresponds to the real essence of the traditional Phillips curve. Thus, the
traditional Phillips curve is not really able to explain the stability of relation between
inflation and output over the observed period nor account for the policy reforms that were
introduced in these countries in order to suppress inflation. Moreover, coefficients on
lagged inflation may very well embed expectations of future inflation. Hence, it is
necessary to remodel the traditional Phillips curve in order to observe the whether
inflation can be better explained through the supply side of the economy.
3. The New Phillips Curve
The new Phillips curve is based on staggered nominal price setting (Taylor, 1980). The
price setting behavior is based on optimization by monopolistically competitive firms.
The basic equation that we are going to test is the one that relates inflation πt to the
anticipated future inflation πt+1 and real marginal cost:
(2)
where is the average real marginal cost, in percent deviation from its steady state
level, β is subjective discount factor, and λ is the slope coefficient that depends on the
parameters of the model. This equation shows direct effects of the frictions in the price
adjustment process that are the key aspects of the theory (Gali,Gertler,Lopez-Salido,
2001).
Nevertheless, the new Phillips curve literature (Goodfriend and King, 1997) relates
inflation to an output gap variable.7
Assuming restrictions on technology and labor
market structure, real marginal cost are proportionally related to the output gap
(Rotemberg and Woodford, 1997),
, (3)
( ) tttt ME ˆ
1 lpbp += +
tMˆ
)(ˆ *
ttt yyM -= d

7. 7
where are the logarithms of real output and the natural level of real output,
respectively. Combining equations (2) and (3) results in the standard output gap based
for relations of the new Phillips curve.
, (4)
where .
However, in the literature equation (4) has been the subject of a considerable discussion.
According to the traditional Phillips curve, inflation varies positively with the output gap.
On the other hand, in the new Phillips curve context, inflation is an entirely forward-
looking phenomenon. By iterating equation (4) forward we get:
, (5)
This equation shows that if a central bank can commit to stabilizing the output gap
, it can reach price stability. On the other side this equation also suggest
that inflation should anticipate movements in the output gap. So far, as the
approximation of the traditional Phillips curve recommend, the output gap tends to lead
inflation. Gali, Gertler, and Lopez-Salido, 2001, Sbordone, 1999, and Gali and Gertler,
1999 have shown that this relation holds for the US as well as for the EU countries.
Thus, the output gap formulation of the new Phillips curve cannot account for the
persistence of inflation either for the US or the EU countries.
Nevertheless, above mention authors find that the relation between inflation and real
marginal cost given by equation (2) is some how consistent with the data. Thus, the link
between the real marginal cost and the output gap corresponds with data. It seems that
7
For a detailed discussion on output gap refer to Bovha Padilla and Padilla Mayer, 2002.
*
and tt yy
( ) )( *
1 ttttt yykE -+= +pbp
ld=k
)( *
0
ktktt
k
k
t yyEk ++
¥
=
-= åbp
)( *
ktkt yy ++ -

8. 8
real marginal cost response sluggishly to output gap movements, the same way as
inflation does. The first possible explanation for that is that conventional measures of the
output gap are not adequate. It may not be appropriate to use detrended output to
estimate the output gap if there are significant real shocks to the economy. The second
explanation could be that there are rigidities in the labor market (either in the form of real
or nominal wage rigidities), which break down the relation between marginal cost and
output gap (equation 3). These rigidities offer an explanation for inactive behavior of
real marginal cost.
4. The Baseline Model
In this section we present relation between inflation and real marginal cost across firms.
As Gali, Gertler and Lopez-Salido, 2001, we assume that firms face increasing real
marginal cost.8
We assume that there is a continuum of firms indexed by . Each firm is a
monopolistic competitor, it produces a differentiated good , and sells it at nominal
price . The demand curve that each firm faces is then , where
and are aggregate output and aggregate price, respectively. The production
function for firm is given by , where is a technological
parameter, is employment, and a is the share of labor in income.
Firms set nominal prices on a reel basis as in Calvo, 1983. Each firm resets its price with
probability each period, while a fraction of producers leave their prices
unchanged. Thus, the expected time that the price remains fixed is and the
parameter then shows the degree of price rigidity.
[ ]1,0Îj
( )jYt
( )jPt ( )
( )
t
t
t
t Y
P
jP
jY
e-
÷÷
ø
ö
çç
è
æ
=
tY tP
j ( ) ( ) a-
= 1
jLAjY ttt tA
( )jLt
q-1 q
q-1
1
q

9. 9
The price level can be expressed as a function of the newly set price and the lagged
price .9
(6)
All firms that change price in period choose the same value of . A firm that is able
to reset price in period chooses price to maximize expected discounted profits given
technology, factor prices and the constraint on price adjustment (defined by the reset
probability ). An optimizing firm will set according to the following log-linear
rule:
, (7)
where is a discount factor, is the logarithm of nominal marginal cost in
period of a firm that last reset its price in period , and is the firm´s
desired gross markup.
We need to find an expression for inflation in terms of an observable measure of
aggregate marginal cost. By cost minimizing rule the firm´s real marginal cost equals the
real wage divided by the marginal product of labor. Given the Cobb-Douglass
production function, the real marginal cost is given by ,
where and are output and employment for a firm that has set price in at the
8
Allowing marginal cost to vary across firms gives more reliable estimates of the degree of price rigidity in
the Euro area as showing in Gali, Gertler and Lopez-Salido, 2001.
9
This expression is obtained after log-linearizing the price index around zero inflation steady state and it is
taken in a log form.
tp *
tp
1-tp
1
*
)1( -+-= ttt ppp qq
t
*
tp
t
q-1 *
tp
( ) ( ) ( )n
kttt
k
k
t MEp +
¥
=
å-+= ,
0
*
1log bqbqµ
b ( )n
kttM +,
kt + t
1-
=
e
e
µ
( )
( )( )kttktt
ktkt
ktt
LY
PW
M
++
++
+
-
=
,,
,
/1
/
a
kttY +, kttL +, t

10. 10
optimal value . Since we cannot observe individual firm marginal cost (absence of
firm level data), we define “average” marginal cost, which depends only on aggregates:
(8)
By exploiting the assumptions of Cobb-Douglas production function and isoelastic
demand curve, we obtain the following log-linear relation between and :
(9)
where and are the log deviations of and from their respective
steady state values.
We obtain the formulation of the new Phillips curve that relates inflation to the real
marginal cost by combining equations (6), (7), and (9),
, where (10)
(11)
Slope of the coefficient depends of parameters of the model. is decreasing in the
degree of price rigidity, , which shows the fraction of firms that keep their prices
constant. Thus, the smaller the fraction of firms that adjust prices, the less sensitive
inflation will be to movements in marginal cost. Moreover, is also decreasing in the
slope of production function, measured by , and in the elasticity of demand, . The
larger and , the more sensitive is the marginal cost of an individual firm to
deviations of its price from the average price level. Finally, the model suggests that
inflation should equal a discounted stream of expected future average real marginal costs.
*
tP
( )
( )( )tt
tt
t
LY
PW
M
/1
/
a-
=
kttM +, tM
)(
1
ˆˆ *
, kttktktt ppMM +++ -
-
-=
a
ea
kttM +,
ˆ
ktM +
ˆ
kttM +, tM
( ) tttt ME ˆ
1 lpbp += +
[ ])1(1
)1)(1)(1(
-+
---
=
eaq
abqq
l
l l
q
l
a e
a e

11. 11
5. Empirical Results
In this section we present estimates of the baseline model (equation 10) for the sample of
selected EU candidate countries. Since we are interested to observe whether Slovenia
follows the full sample, we also perform analysis of Slovenia inflation dynamics
separately.
We use quarterly data from 1990 to 2002.10
Data for inflation and real GDP are obtained
from the Countries in Transition CD-Rom (on a yearly basis), WIIW, and quarterly series
are obtained from the national statistical offices. Inflation is given as a percentage
change in prices with respect to the previous year, and the real GDP data are given in
1995 prices.
We calculate average real marginal cost (equation 8) in the following way. First, we
obtain data on gross monthly real wages per capita, quarterly data on real GDP and labor
force. Since we calculate quarterly marginal cost, we set up the nominator as a sum of
the three month gross real wages per capita. We also need data on labor income share,
which we calculate according to Dobrinsky (2001). He presented a measure of capital
income share for countries in transition. According to him, the capital income share can
be calculated as the share of gross operating surplus and gross mixed income compared to
total factor incomes.11
Thus, the labor income share is simply calculated as a share of
compensation of employees compared to total factor incomes. Since we cannot obtain
quarterly data on compensation of employees and total factor income, we use yearly data
and therefore calculate labor income share on a yearly basis. However, we believe that
this share is not varying significantly during the year and we use it for calculating
quarterly real average marginal cost. Thus, the denominator is calculated as a product of
annual labor income share and ratio of a real GDP and labor force on a quarterly basis.
Then, we use the log deviation of real marginal cost from its mean as a measure of .
10
We do not include data for first two quarters of 2003 because it is not available for the full country
sample.
11
Total factor incomes can be calculated as the difference between the nominal GDP and net indirect taxes.
tMˆ

12. 12
Figures 5.1 and 5.2 show our measure of real marginal costs along with inflation for
Central and Eastern European EU Candidate Countries. Surprisingly, when we take into
account the full sample, variables do not move closely together. Although correlation
between inflation and real marginal costs is positive, it is extremely small and non-
significant. Thus, it is hard to say that inflation is really related to movements in
marginal costs as suggested by the theory explained in Section 4.
Moreover, Table 5.1 shows correlation between inflation and real marginal cost for all
countries that are in the sample. Inflation could be best explained by variation in real
marginal cost in Baltic region (Estonia, Latvia and Lithuania). Correlation between both
variables is relatively high also for Slovenia. Moreover, elasticity coefficient for
Slovenia shows that inflation is relatively responsive on real wages. Increasing wages by
1 percent would increase inflation by 2.76 percent. Thus, according to the real data from
the observed period, wages did have inflationary pressures in Slovenia. On the other
hand, there is strong negative correlation between inflation and real marginal cost in
Poland, Czech Republic, Slovakia and Hungary.
Table 5.1: Correlation between Real Marginal Cost and Inflation
We then present empirical estimates of coefficients in equation (9). First, we present
estimates of the baseline model for the full sample, and then for two sub-samples: (1)
Country Correlation
Czech Republic -0.73
Estonia 0.73
Hungary -0.06
Latvia 0.66
Lithuania 0.63
Poland -0.83
Slovakia -0.55
Slovenia 0.52
Average 0.04

13. 13
Baltic States with Slovenia, and (2) Central European countries. We show that in
contrast with findings for the Euro area and the USA (Gali, Gertler, Lopez-Salido, 2001)
this model does not explain the dynamics of inflation in the Central and Eastern European
EU candidate countries in expected way.
5.1. Baseline Model Estimates
We estimate the coefficients in equation (9) by using generalized method of moments
(GMM). Under rational expectations, equation (9) defines the set of orthogonality
conditions:
,
where zt denotes a vector of variables observed in time t, that is, instrumental variables.
The optimal choice of instrumental variables are lags of explanatory variables already
presented in the model: lags of inflation, lags of real marginal cost and detrended output.
Since the data series consists only from 52 observations (per country), we choose
relatively small number of lags for instruments in order to minimize the potential
estimation bias that can arise in small samples. As Gali, Gertler and Lopez-Salido, 2001,
we use four lags of inflation, two lags of the real marginal costs, and detrended output.12
The corresponding equation for the full data set is:
where standard errors are shown in parentheses. The estimates for the two sub-samples13
are as follows:
12
We tried to estimate coefficients with using more than four lags for inflation and more than two lags for
the real marginal costs, but results were not affected.
( )( ) 01 =-- + ttttt zmcE lbpp
0.01value-p5.24,statisticJ
0.00value-p16.20,statisticFˆ271.4)(2215.0
(1.132)(0.073)
1
==
==-= + tttt ME pp

14. 14
(1) Baltic States with Slovenia
(2) Central European Countries
In all three equations, standard errors are modified using a Newey-West correction to
account for serial correlation in the error terms. Also, we take care of fixed effects in all
three regressions.
In order to check for potential weakness of the instruments, we perform an F-test applied
to the first stage regression. Results show that instruments that we use are relevant (see F
statistic in above regressions). We also test for overidentifying restrictions of the
model.14
On the basis of Hansen test, we reject overidentifying restrictions for all three
regressions (see J statistic in above regression).
In general, the empirical model does not work as expected in the full sample and in the
sub-sample of Central European countries (Czech Republic, Slovakia, Poland). The
coefficients on marginal cost are negative and significant at 5 percent level in both cases.
The only case that coefficient is positive and significant as implied by the theory is in the
case of Baltic States including Slovenia. This result is in contrast with findings of Gali,
Gertler, and Lopez-Salido, 2001, where they estimate positive and highly significant
effect of marginal cost on inflation in the Euro area and the USA.15
13
Again, we run an individual regression analysis for Slovenia, but since the size and significance of
coefficients do not change, we report results only on the two sub-samples.
14
The model is overidentified is there is more than one way to calculate its parameters from the reduced
form parameters, leading to restrictions on the reduced form parameters.
15
However, we need to take into the account that the findings for the Euro are and the USA are based on
the quarterly data set from 1970 to 1998. After that time there has been a decline in unemployment and a
J(1.738)(0.103) 0.01value-p4.22,statistic
0.00value-p13.73,statisticFˆ649.3)(314.0 1
==
==+= + tttt ME pp
J(0.772)(0.082) 0.01value-p4.65,statistic
0.00value-p18.65,statisticFˆ695.3)(178.0 1
==
==-= + tttt ME pp

15. 15
6. Final Remarks
Our results indicate that neither traditional nor a marginal cost-based Phillips curves do
not provide a satisfactory explanation of Central and Eastern European area inflation
from 1990 to 2002. First, due to policy reforms that were oriented toward reduction of
inflation in the post transition period, the traditional Phillips curve did not show the
positive relation between the output and price level neither in the full sample nor in the
two sub-samples. The new Phillips curve model suggests that marginal cost is negatively
related to inflation for the full sample. On the other hand, results for Baltic States and
Slovenia are in accordance with the model prediction.
According to this information, wages in Slovenia did have inflationary pressures.
However, one should be aware that this result is obtained on the basis of quarterly data
from 1990 to 2002. Since 2000 there have been wage policies introduced which allowed
the increase of wages only with respect to an increase in productivity and thus should not
have inflationary pressures. Indeed, if one checks the elasticity of wages with respect to
inflation on the basis of data from 2000 on, the coefficient drops to 0.67 percent (with
respect to 2.76 percent when full period data is taken into account). However, if one runs
the regression on the basis of the new Phillips curve with the data from 2000 on, results
do not change: there is still positive and significant relation between real marginal cost
and inflation.
Therefore, at this point it is hard to say how marginal cost really affects inflation
dynamics and thus give more precise policy recommendations. It would be appropriate
to decompose the movement in real marginal cost in order to isolate the factors that drive
this variable. One possible decomposition is into a component that accounts for labor
market frictions (measured as the wage markup) and the other one that would correspond
to the frictionless competitive equilibrium.
rise in the output growth in the Euro area, without any corresponding rise in inflation. Thus, including more
recent period in the analysis of Euro inflation dynamics may alter results.

16. 16
Moreover, to determine whether wages really have inflationary pressures on selected
countries in the observed sample, it is necessary to understand the determinants of the
wage markup. Nevertheless, it will be necessary to include in the analysis additional
factors, such as union pressures, that would account for variation in the wage markup and
thus explain its effect on inflation dynamics of Central and Eastern European EU
candidate countries.
7. References
1. Bovha Padilla, S. and H. Padilla Mayer, 2002. “Sources of GDP Growth, Potential
Output and Output Gap for Slovenia: Mid-Term Analysis”, IB Review, Institute for
Macroeconomic Analysis and Development, Ljubljana, Slovenia, June 2002.
2. Bovha Padilla, S. and H. Padilla Mayer, 2002. “Sources of Growth in Selected
Central and Eastern European Countries”, Countries in Transition International
Conference, Kranjska Gora, Slovenia, June 2003.
3. Calvo, G., 1983. “Staggered Prices in a Utility Maximizing Framework”, Journal of
Monetary Economics, 12, 383-398.
4. Eurostat, 1997-2003. “Statistical Information”, various issues.
5. Eurostat, 1997-2002. “Statistical Yearbook for Central and Eastern European
Countries”, various issues.
6. Gali, J. and M. Gertler, 1999. “Inflation Dynamics: A Structural Econometric
Analysis”, Journal of Monetary Economics, 44, 195-222.
7. Gali, J., M. Gertler and J. Lopez-Salido, 2001. “European Inflation Dynamics”,
NBER Working Paper Series, Working Paper 8218, 39 pp.
8. Goodfriend, M. and R. King, 1997. “The New Neoclassical Synthesis”, in NBER
Macroeconomic Annual, Ben Bernanke and Julio Rotemberg, eds., MIT Press.
9. Intriligator, M., R. Bodkin, and C, Hsiao, 1978. “Econometric Models, Techniques
and Applications”, Prentice Hall, Upper Saddle River, NJ.
10. Rotemberg, J. and M. Woodford, 1997. “An Optimization-based Economic
Framework for the Evaluation of Monetary Policy”, in NBER Macroeconomic
Annual, Ben Bernanke and Julio Rotemberg, eds., MIT Press.
11. Sbordone, A., 1999. “Prices and Unit Labor Costs: A New Test of Price Stickiness”,
mimeo, Rutgers University.
12. Taylor, J., 1980. “Aggregate Dynamics and Staggered Contracts”, Journal of
Monetary Economics, 44, 293-335.
13. WIIW: Countries in Transition 2002, CD-Rom.

17. 17
Figure 2.1: Inflation in Slovenia and Central and Eastern European EU Candidate Countries
from 1990 to 2002 (log form)
Source: Countries in Transition 2002, WIIW, Eurostat, selected publications.
Figure 2.2: Real GDP Growth Rates in Slovenia and Central and Eastern European EU
Candidate Countries from 1990 to 2002, in percent
Source: Countries in Transition 2002, WIIW, Eurostat, selected publications.
-­5.5
3.22.9
4.6
5.2
3.8
4.6
3.5
4.1
5.3
2.8
-­8.9
-­4.7
-­12
-­10
-­8
-­6
-­4
-­2
0
2
4
6
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
in %
Slovenia
Average
1.97
2.132.19
1.81
2.07
2.12
2.29
2.60
3.04
3.49
5.33
4.74
6.31
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
ln Inflation
Average
Slovenia

18. 18
Figure 5.1: Inflation and Real Marginal Cost in Central and Eastern European EU Candidate
Countries*
* Data are in logarithmic forms. Left-side scale measures inflation and the right-side scale measures
real marginal costs as explained in Sections 3 and 4.
Source: Authors´calculations.
Figure 5.2: Inflation and Real Marginal Cost in Central and Eastern European EU Candidate
Countries*
a) Czech Republic
0
1
2
3
4
5
6
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-­0.04
-­0.02
0
0.02
0.04
0.06
0.08
Inflation
Real Marginal Cost
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-­0.5
-­0.4
-­0.3
-­0.2
-­0.1
0
0.1
0.2
Inflation
Real Marginal Cost

19. 19
b) Estonia
c) Hungary
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-­0.1
-­0.05
0
0.05
0.1
0.15
0.2
Inflation
Real Marginal Cost
0
0.5
1
1.5
2
2.5
3
3.5
4
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-­0.1
-­0.05
0
0.05
0.1
0.15
Inflation
Real Marginal Cost

20. 20
d) Latvia
e) Lithuania
0
1
2
3
4
5
6
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-­0.25
-­0.2
-­0.15
-­0.1
-­0.05
0
0.05
0.1
0.15
Inflation
Real Marginal Cost
-­2
-­1
0
1
2
3
4
5
6
7
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-­0.2
-­0.1
0
0.1
0.2
0.3
0.4
Inflation
Real Marginal Cost

21. 21
f) Poland
g) Slovak Republic
0
1
2
3
4
5
6
7
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-­0.4
-­0.3
-­0.2
-­0.1
0
0.1
0.2
0.3
Inflation
Real Marginal Cost
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-­0.2
-­0.15
-­0.1
-­0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
Inflation
Real Marginal Cost

22. 22
h) Slovenia
* Data are in logarithmic forms. Left-side scale measures inflation and the right side scale measures
real marginal costs as explained in Sections 3 and 4.
Source: Authors´calculations.
0
1
2
3
4
5
6
7
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
-­0.08
-­0.06
-­0.04
-­0.02
0
0.02
0.04
0.06
Inflation
Real Marginal Cost