Dies ist die HTML-Version der Datei https://www.bsi.si/library/includes/datoteka.asp?DatotekaId=4696.
G o o g l e erzeugt beim Web-Durchgang automatische HTML-Versionen von Dokumenten.
Regional Disparities in Slovenia
Page 1
PRIKAZI IN ANALIZE
2/2012
REGIONAL
DISPARITIES IN
SLOVENIA
Biswajit Banerjee
Manca Jesenko
Klavdija Grm

Page 2
Izdaja BANKA SLOVENIJE
Slovenska 35
1505 Ljubljana
telefon: 01/ 47 19 000
fax: 01/ 25 15 516
Zbirko PRIKAZI IN ANALIZE pripravlja in ureja Analitsko-raziskovalni center Banke Slovenije
(telefon: 01/ 47 19 680, fax: 01/ 47 19 726, e-mail: arc@bsi.si).
Mnenja in zaključki, objavljeni v prispevkih v tej publikaciji, ne odražajo nujno uradnih stališč Banke Slovenije ali njenih
organov.
http://www.bsi.si/iskalniki/raziskave.asp?MapaId=234
Uporaba in objava podatkov in delov besedila je dovoljena z navedbo vira.
Številka 2, Letnik XVIII
ISSN 1581-2316

Page 3
REGIONAL DISPARITIES IN SLOVENIA
Biswajit Banerjee1*, Manca Jesenko2and Klavdija Grm
ABSTRACT
This paper finds that regional disparities in the levels of GDP per capita and labor utilization have widened in Slovenia since
1999. However, because of higher social transfers to the poorest regions and the growing incidence of inter-regional
commuting to work, regional gaps in per capita household disposable income have declined. Econometric analysis shows
that there is heterogeneity in steady-states across regions, and regional growth in per capita GDP and labor productivity are
converging to these region-specific steady states. Labor productivity growth has been driven by both capital deepening and
growing importance of TFP improvement mainly due to within-sector effects. The main policy priorities are to develop
transportation infrastructure, improve the structural and policy determinants of productivity, and strengthen competitiveness.
JEL Classification: O18, O47, O52
* Haverford College
Bank of Slovenia

Page 4
 
I. Introduction
Interest in the issue of regional disparities in the European Union (EU) is growing (e.g., Borys et
al, 2008; Funck and Pizzati, 2003; Marelli and Signiorelli, 2010c; OECD, 2010). An important
aim of the EU is to ensure economic and social cohesion between member states and within
them. The availability of Structural Funds and the Cohesion Fund aimed at achieving the
convergence goal has created new impetus for regional policy. However, as Marelli and
Signiorelli (2010a, 2010b) note, while the New Member States (NMS) of the EU have reduced
the gap in per capita GDP at the national level with Old Member States, within-country regional
disparities have increased. Eurostat data for NMS show two notable cross-country patterns: the
increase in within-country regional disparities1 has tended to be greater in countries that (i) had a
lower level of initial per capita GDP relative to the EU-15 average,2 and (ii) have been more
successful in reducing the gap between the national and EU-15 average per capita GDP.3
The co-existence of increasing within-country regional disparities and convergence with the Old
Member States can be explained within the traditional framework of the economic growth
literature. According to this framework, disparities in the level of income could widen even when
there was convergence in the growth rate of income, if steady state growth rates were
heterogeneous across regions and regions were converging to region-specific steady states
(Islam, 2003). The speed of transition to the steady state is commonly examined in the literature
                                                            
1 Eurostat measures disparity by the sum of absolute differences between regional and national
GDP per capita, weighted by the share of population and expressed in percent of national GDP.
Qualitatively, this measure should provide a picture similar to that shown by the coefficient of
variation or by the Gini index.
2 For a sample of nine NMS (Bulgaria, Czech Republic, Estonia, Hungary, Lithuania, Poland,
Romania, Slovakia, and Slovenia), the correlation between the change in disparity during
1999−2007 and initial level of per capita GDP relative to the EU-15 average is negative and
statistically significant (ρ = –0.77). The inclusion of Latvia (which is an outlier) in the sample
makes the relationship statistically insignificant.
3 For a sample of eight NMS (Czech Republic, Estonia, Hungary, Lithuania, Poland, Romania,
Slovakia, and Slovenia), the correlation between the change in disparity during 1999−2007 and
the absolute change in the level of per capita GDP relative to the EU-15 average is positive and
statistically significant (ρ = 0.72). The inclusion of Bulgaria and Latvia (which are outliers) in
the sample makes the relationship statistically insignificant.

Page 5
 
through the concept of β (beta)-convergence. As the marginal productivity of capital is generally
higher in poorer economies with lower levels of physical capital, it is expected that poorer
economies will grow at a faster rate than richer economies, leading to convergence of income
over time. If the steady state growth rate is the same for all economies (i.e., the structural
parameters of the underlying production function are the same for all economies), convergence
to the common steady state is characterized as unconditional or absolute convergence. However,
if the steady state level of income varies across economies because the structural parameters of
the underlying production function are different for the economies under consideration,
convergence to the economy-specific steady state is characterized as conditional convergence.
Many researchers have highlighted factors that may contribute to differences in growth rates and
the steady state level of income across regions in NMS: location advantages that facilitate the
development of growth poles, differences in human capital endowments, differentiated impact of
the restructuring process across regions, and uneven spatial coverage of technical progress
(Bogumił, 2009; Bruncko, 2003; OECD, 2011; Marelli and Signorelli, 2010a). The examination
of the dynamics of dispersion of income levels across economies is an alternative method of
investigating convergence, referred to as σ (sigma)-convergence. β-convergence is a necessary
but not sufficient condition of σ-convergence. As noted above, if economies are converging to
different economy-specific steady states, it is possible for β-convergence to take place together
with σ-divergence.
In this paper, we examine economic growth and various dimensions of regional disparities in
Slovenia during 1996−2008. Slovenia’s per capita GDP grew at an annual average rate
of 4.2 percent during this period, against a backdrop of prudent macroeconomic policies, a
gradualist approach to structural reform, and increasing integration with the EU. Adjusted for
differences in purchasing power, Slovenia’s per capita GDP was around 82 percent of the EU-15
average in 2008, much above the levels for other NMS. Eurostat data show that the dispersion in
regional per capita GDP in Slovenia is the lowest among NMS. During 1996–2004, the
dispersion in regional GDP per capita increased in Slovenia by a broadly similar magnitude to
that recorded in the majority of NMS. However, the dispersion in regional GDP per capita has

Page 6
 
increased only slightly since EU accession in 2004, in contrast to the experience of most NMS.4
Perhaps because of the low level of regional income disparity, Slovenia is still considered a
single NUTS2 programming area under the EU’s Cohesion Policy framework, although the
Slovene authorities have established twelve “development” or statistical regions corresponding
to NUTS3 units.5 OECD (2011) and Wostner (2002) have suggested that regional disparities in
Slovenia are low because of the country’s small size and good infrastructural connectedness, and
because of its long-standing regional policies that supported a scattering of industries across its
regions.
 
The paper is organized as follows. Section II examines the various dimensions of regional
disparities in Slovenia and their evolution over time. Section III presents the results of the
econometric analysis of β–convergence. Section IV applies the standard growth accounting
framework to identify the main determinants of growth of total output and productivity.
Section V looks at the sectoral patterns of productivity growth. Section VI concludes.
II. Dimensions of regional disparities in Slovenia
The eastern regions in Slovenia generally have lower per capita GDP than the western regions.
Osrednjeslovenska, the capital region, is the richest region with per capita GDP in 2008
equivalent to about 142 percent of the national average, and Pomurska in the extreme east the
poorest with per capita GDP about 65 percent of the national average.6 However, the east-west
divide is not a sharp one, as two of the three poorest regions―Zasavska and Notranjsko-
kraška― are nested next to the capital region (Figure 1).
The data indicate σ-divergence in the level of per capita GDP across regions since 1999
(Table 1). The widening dispersion is indicated by the increase in the coefficient of variation
                                                            
4 Income disparity has also remained broadly unchanged in Estonia since EU accession, but
decreased in Latvia. In all other NMS, the dispersion in per capita GDP continued to increase
after EU accession. 
5 The Nomenclature Units from Territorial Statistics (NUTS) is a geocode standard for
referencing the subdivision of countries for statistical purposes
6 The only other region to exceed the national average for per capita GDP is Obalno-kraška. 

Page 7
 
over time. A regression equation confirms a statistically significant quadratic relationship
between the coefficient of variation of regional GDP per capita and time during 1999–2008.7 The
regional differentials widened more during the pre-EU accession period (1999–2003), when
economic growth in Slovenia slowed down, than following EU membership in 2004 when
economic growth was stronger. The widening dispersion mainly reflects developments in three
regions: a slight improvement in the relative position of Osrednjeslovenska, and a marked
worsening of the relative position of Zasavska and Pomurska, the two poorest regions. The per
capita GDP of Pomurska relative to the national average fell from 75 percent to 65 percent
during 1995–2008, and the drop for Zasavska was much steeper, from 85 percent to 65 percent.
The divergence in per capita GDP does not necessarily imply a widening of regional income
inequalities. Regional differences in per capita household disposable income are considerably
smaller than the differences in per capita GDP (Table 2). The data also indicate σ-convergence or
decreasing disparities in per capita household disposable income since 1999.8 The small and
decreasing gaps in per capita household disposable income reflects two factors. First, as table 3
shows, expenditures on transfers and other social safety nets are greater for regions with lower
per capita GDP. In addition, transfer payments to the two poorest regions (Zasavska and
Pomurska) have increased over time while payments to the other regions have fallen or remained
unchanged as ratio to regional GDP. Second, as table 4 shows, commuting to work outside the
region of residence is sizeable and has become increasingly important over time. The volume of
inter-regional commuting and its increase are largest for residents of regions with the lowest per
capita GDP. Osrednjeslovenska, the capital region, is a major destination for commuting
workers. In 2008, nearly one-fourth of the workers in Osrednjeslovenska were commuters from
other regions. Slovenia being a small country with good transportation network, commuting is an
                                                            
7 0.1696 + 0.0095 t – 0.0003 t2
2 = 0.9381
(0.0051)***
(0.0020)*** (0.0002)*
*** significant at 1% level; * significant at 10% level. Standard errors are shown in parentheses. 
8 In an earlier study on Slovenia, Wostner (2002) noted that while regional disparities in terms of
economic activity were increasing, the overall level of dispersion in personal income (measured
by the personal income tax base per capita) had not changed.

Page 8
 
alternative to migration.9 Commuting, like migration, leads to regional concentration of
production and σ-divergence in the level of per capita GDP. However, unlike migration,
commuting does not increase regional disparities in disposable income as this income accrues in
the place of residence. In fact, if commuting is more prevalent among skilled workers, regional
disparities in disposable income are likely to diminish. Because of increased commuting, the
increase in the clustering of population in Slovenia has been less than that of production.
A large volume of economic activity is concentrated in Osrednjeslovenska, the capital region,
and the concentration has increased over time, albeit to a limited extent. The share of
Osrednjeslovenska in Slovenia’s GDP increased from 33.7 percent in 1996 to 36.1 percent in
2008 (Table 5).10 This mainly reflects growing concentration of business services activities,
while the share of this region in manufacturing value added declined. Manufacturing facilities
have tended to cluster toward Jugovzhodna Slovenija in recent years.
In order to gain more insight into the gaps in GDP per capita across regions, we can decompose
GDP per capita as follows:
where Y is gross domestic product, P is population, and L is total employment. The first term on
the right-hand side is labor productivity and the second term is labor utilization.
In Slovenia, productivity levels are generally higher in regions with higher levels of per capita
GDP. However, the regional differentials in productivity are smaller than in the case of per
capita GDP and have narrowed. The data indicate σ -convergence of labor productivity levels
                                                            
9 A regression equation shows that inter-regional commuting is negatively related to distance and
positively related to difference in average wages. Higher highway road density (highway roads
per square kilometers) in a region has a positive effect on commuting.
10 OECD (2011, p. 40) interprets the increase in concentration of economic activity against the
backdrop of little change in population shares as evidence of low labor force mobility and strong
connections to local economics. But, this interpretation is not correct. As we have noted, the
observed outcome can be explained by the large and increasing incidence of commuting.

Page 9
 
during 1996–2005 and a slight reversal of this trend during 2006–2008. As Table 6 shows, the
coefficient of variation of labor productivity declined in the first period, but settled at a
somewhat higher level in the second period. The relative performances in productivity varied
substantially between regions. Productivity levels relative to the national average fell in the two
richest and, to a lesser extent, in the two poorest regions (in terms of per capita GDP). However,
in two intermediate-production regions, Jugovzhodna Slovenija and Spodnjeposavska, relative
productivity levels increased markedly.
Regional disparities in labor utilization in Slovenia have widened over time, especially since
2000, as inter-regional commuting to work has increased. σ-divergence in labor utilization more
than offset the σ -convergence of labor productivity levels, and caused the σ-divergence in per
capita GDP. In the country as a whole, the ratio of employment to total population edged up only
slightly during 1996–2006, and rose sharply thereafter during 2007–2008. However, there were
differences in the regional patterns (Table 7). Labor utilization fell or increased by only a small
extent in regions from which the increase in commuting to other regions was greater (Figure 2).
In particular, three regions (Zasavska, Spodnjeposavska, and Pomurska) that figured among
those with the largest increase in commuting to other regions experienced a decline in labor
utilization. In contrast, labor utilization increased markedly in Osrednjeslovenska and Obalno-
kraška in the central and western part of the country and in Podravska in the east—regions which
attracted increasing number of commuters from other regions or recorded a low increase in
commuting to other regions.

Page 10
10 
 
III. Analysis of β–convergence
The growth path of per capita GDP in Slovenia was broadly U-shaped during 1996–2007, but
turned downward in 2008 with the onset of the global financial crisis. As Figure 3 shows, growth
fluctuated around an average of about 4¼ percent during1996–2000, slowed down to about
3 percent during 2001–03, but recovered quickly and continued at brisk pace before slowing
down in 2008. The U-shaped growth path was common to all regions except for
Osrednjeslovenska, where growth fluctuated around a relatively flat trajectory. Labor
productivity growth followed a cyclical path: it was on a declining trend during 1997–2001,
followed by a recovery during 2002–05 and a downward slide once again thereafter.
Being a small open economy, Slovenia’s economic growth was highly sensitive to the external
economic environment. Thus, the slowdown in growth during 2001–03 and in 2008 coincided
with unfavorable external conditions and restrictive monetary policy after inflationary shocks in
1999. A fall in government investment and weak business confidence were additional factors that
contributed to the slowdown in activity during 2001–03. The subsequent rebound in economic
growth was stimulated by buoyant foreign demand and a domestic demand boom following
Slovenia’s EU accession in May 2004 and entry into the Exchange Rate Mechanism II in June
2004.11 Abundant availability of credits, stable macroeconomic conditions, a gradual reduction in
the payroll tax12, and motorway construction stimulated investment during 2004–07.
As such, the regional pattern of per capita GDP growth does not provide clear support for β-
convergence. Contrary to expectations, Osrednjeslovenska, Jugovzhodna Slovenija and
Podravska―regions in the top half of per capita GDP rankings―generally grew at a faster pace
than Slovenia as a whole. Moreover, growth in the two regions with the lowest per capita
GDP―Pomurska and Zasavska―lagged the national average for most of the period. As for
growth of labor productivity, the three richest regions as well as the two poorest regions lagged
the national average. The results of more rigorous testing of β-convergence through an
econometric exercise are presented below.
                                                            
11 Slovenia adopted the Euro as its currency in January 2007.
12 The payroll tax was completely phased out in January 2009.

Page 11
11 
 
The presence of unconditional β-convergence is typically tested by regressing the growth rate of
any variable (e.g. output per capita or productivity) on the initial level of that variable:
it
it
it
y
y
ε
γ
γ
+
+
=
Δ
−1
1
0
ln
ln
(1)
If the growth rate is negatively related to the initial level of the variable—i.e., the sign for γ1 is
negative—there is said to be β-convergence. Conditional β-convergence is tested by regressing
the growth rate on the initial level of the variable and other structural variables. In its simplest
form, conditional β-convergence is estimated via a two-way fixed-effects (FE) method:
it
t
it
i
it
y
y
ε
γ
γ
γ
+
+
+
=
Δ
−1
1 ln
ln
(2)
where γi represents region-specific effects and γt captures time effects. The region-specific fixed
effect allows for heterogeneity in steady-states across regions. The time-fixed effects capture the
impact of changes in the external environment, technology, and policies over time. A more
rigorous method of testing conditional β-convergence is to estimate the growth models of Solow
(1956) and Mankiw, Romer and Weil (1992). The econometric specification of the Solow model,
with two-way fixed effects, is as follows:
(
)
[
]
it
t
it
it
it
i
it
g
n
s
y
y
ε
γ
δ
γ
γ
γ
+
+
+
+
+
+
+
=
Δ
ln
ln
ln
ln
2
1
1
.
(3)
where sit is the share of output invested in physical capital in region i at time t, nit is the growth
rate of employment in region i at time t, g is the rate of increase in technological progress, and 
δ is depreciation of capital. Similarly, the Mankiw-Romer-Weil model, which extended the
Solow model to incorporate the influence of human capital on output, can be specified as: 
(
)
[
]
(
)
[
]
,
ln
ln
ln
ln
ln
ln
3
2
1
1
it
t
it
H
it
it
K
it
it
i
it
g
n
s
g
n
s
y
y
ε
γ
δ
γ
δ
γ
γ
γ
+
+
+
+
+
+
+
+
+
+
+
=
Δ
(4)
where K
it
s and H
it
s represent the shares of output invested in physical and human capital in region i
at time t, respectively. In the specifications of both the Solow model and the Mankiw-Romer-
Weil model, it is assumed that the initial level of technologyis heterogeneous across regions but

Page 12
12 
 
that growth of technology, g, is homogenous across regions. It is further assumed, following the
literature (e.g. Bernanke and Gurkaynak, 2001 and Bosworth and Collins, 2003), that g+δ=0.05.  
To test the hypothesis of β-convergence, we estimated growth-initial level regressions separately
for the full sample period (1996–2008) and two sub-periods (1996–2003 and 2004–2008). β-
convergence is tested for per capita GDP and labor productivity. The regressions are based on
annual data because of the small sample size.13 Although results of several specifications are
presented, the estimations that use both fixed effects and control variables derived from the
Solow model and its extension by Mankiw et al are of particular interest. The region-specific
fixed effects allow for heterogeneity in steady states across regions. The time-fixed effects
capture the impact of changes in the external environment, technology, and policies over time.
In the specification for 1996–2008 where the initial level of per capita GDP is the only
explanatory variable (Table 8, column 1), the coefficient on this variable is positive and
statistically significant, suggesting the presence of absolute or unconditional divergence.
However, too much should not be made of this result as the overall explanatory power of the
equation is extremely low. When fixed effects for region and time are included in the
specification (column 4), the coefficient on the initial level of per capita GDP turns negative and
is statistically significant. There is also a substantial improvement in the goodness of fit. The
findings of this specification can be seen as evidence of conditional convergence to steady state
growth rates that differ across regions.
There is no evidence of unconditional convergence or divergence of per capita GDP growth in
the estimated equations for the two sub-periods. In the specifications without fixed effects
(columns 2 and 3), the coefficients on the initial level of per capita income are not statistically
significant. However, with the inclusion of fixed effects for region and time (columns 5 and 6),
                                                            
13 A standard methodology for the analysis of β-convergence of GDP and labor productivity
growth rates is to use data averaged over an interval of several years to reduce the influence of
short-term business fluctuations. However, the use of interval data would reduce the already
small sample size even further. In any event, an alternative econometric exercise based on 2-year
interval data yielded similar results to that based on annual data, confirming the robustness of the
results presented in Tables 8–10. Results of the alternative exercise based on interval data are
available from the corresponding author.

Page 13
13 
 
the evidence swings in favor of conditional convergence during both 1996–2003 and 2004–2008,
as in the case of the entire sample period.
While the econometric exercise indicates evidence of unconditional β-convergence of labor
productivity for the full sample and the two sub-periods (Table 9, columns 1, 2, and 3), the
evidence of conditional β-convergence is stronger. In the specifications with fixed effects
(columns 4, 5, and 6), the goodness of fit is better and the coefficients on the initial level of
productivity variable are more negative than in the corresponding equations without fixed
effects, suggesting faster convergence. F-tests indicate that region- and time-fixed effects are
individually and jointly statistically significant at the 1 percent level.
The estimates of the Solow model for labor productivity growth with region- and time-fixed
effects for 1997–2008 as well as the two sub-periods also reinforce the evidence on conditional
β-convergence (Table 10, columns 1, 2, and 3). The coefficients on the initial level of labor
productivity are similar in size during the two sub-periods, suggesting similar pace of
convergence. F-tests indicate that region- and time-fixed effects are individually and jointly
statistically significant at the 1 percent level. In accordance with expectations, in all the three
sample periods, the results show that the faster is the growth in employment the slower is the
growth in productivity. However, the coefficient on the investment ratio is not statistically
significant in any of the equations, suggesting that investment played little role in productivity
growth. A similar result was obtained in studies on transition and EU candidate countries by
Banerjee and Jarmuzek (2010), Havrylyshyn and Wolf (2001), and Borys et al. (2008). One
reason for the lack of a significant relationship for overall investment may be that during the
economic restructuring process new investment was accompanied by a lot of disinvestment.
Another reason could be the relatively short sample period. As the growth literature emphasizes,
investment is a major engine of growth in the medium to long term.
In the Mankiw–Romer–Weil model (columns 4, 5, and 6), the initial level of productivity
variable and employment growth have negative signs and are statistically significant, as in the
Solow model. However, the estimates do not show the expected result on the impact of human
capital on productivity growth. The coefficient on the variable education measuring the number
of graduates over 1000 residents of a region is not statistically significant.

Page 14
14 
 
Since an examination of the data series indicated a strong correlation between education and time
controling region (ρ = 0.88), an alternative specification was estimated without time-specific and
region-specific fixed effects. In this equation, the education variable has the expected positive
sign and is statistically significant (column 7).14
Following Marelli and Signiorelli (2010b), in a separate specification (not reported in table 10),
we also included a variable―Krugman’s specialization index (KSI)―to take into account the
influence of regional differences in the structure of employment.15 However, the coefficient on
the KSI variable was not statistically significant.16
IV. Growth accounting
A supplementary perspective on the driving forces of growth can be obtained by utilizing the
growth accounting framework. The decomposition of growth of total output can be expressed by
the following equation:
L
L
y
y
Y
Y
Δ
+
Δ
=
Δ
,
(5)
                                                            
14 In a study cross-country study on EU–27 countries, Marelli and Signiorelli (2010b) obtained a
positive and statistically significant relationship between education and productivity. However,
their specification included only education and dummy variables for eight NMS as the
explanatory variables. Marelli and Signiorelli suggest caution when using formal education
measures as a proxy for human capital as these measures do not capture the effect of other
factors in accumulation of human capital.
15 Krugman’s specialization index (KSI) is computed as
∑ | ,
, |  
where , is the share of sector i out of total employment in region j, and , is the corresponding
share for the country as a whole. Its numerical value may range from 0 (the region has the same
structure as the country average) to 2 (the sector structure is totally different).
 
16 In a cross-country study on EU-27 countries, Marelli and Signiorelli (2010b) obtained a
negative and statistically significant coefficient on the KSI variable. However, their explanatory
variables were limited to education, a global competitiveness index, KSI, and country fixed
effects.

Page 15
15 
 
where
Y
Y
Δ
is growth of total output,
y
yΔ
is growth of labor productivity, and
L
L
Δ
is growth of labor
input. In turn, growth of labor productivity can be decomposed as follows:
A
A
k
k
y
y
Δ
+
Δ
=
Δ
α
,
(6)
where
k
kΔ
is growth of capital17 per worker and
A
A
Δ
is growth of total factor productivity (TFP).
As Table 11 shows, GDP growth in all regions was driven primarily by labor productivity
growth throughout the period under consideration. The contribution of employment growth was
negative during 1997–98 and in two or more years during 2002–05 in all regions except
Osrednjeslovenska. These episodes mainly reflected job losses associated with the restructuring
process in agriculture and industry and not so much an increased tendency for inter-regional
commuting, since the contribution of employment growth for Slovenia as a whole also was
negative during these two periods. The influence of employment growth on GDP growth picked
up substantially in all regions from 2006 onward (in line with the business cycle and partly in
response to the gradual easing of payroll tax), with the exception of Zasavska where the
contribution continued to be negative. In Osrednjeslovenska, the contribution of employment
growth on GDP growth rose over time, reflecting progressive increase in employment in
construction, business services, and public administration that more than offset a decline in
industrial employment. As noted earlier, a sizeable proportion of the increase in employment in
Osrednjeslovenska was owing to workers commuting from other regions.
The growth of labor productivity was driven by both capital deepening and TFP growth, but their
relative importance changed over time and there were notable regional differences. The
contribution of capital deepening was significant throughout the period in all regions. It followed
a U-shaped or L-shaped path in eight of the twelve regions, fluctuated around a horizontal path
                                                            
17 Capital stock was calculated by the authors following the perpetual inventory method,
assuming a depreciation coefficient of 0.04. For a comprehensive discussion of the perpetual
inventory method see OECD (2001). 

Page 16
16 
 
in three regions (Zasavska, Notranjsko-kraška, and Podravska), and rose over time in Pomurska.
In contrast, gains in TFP were generally small or negative in all regions during 1997–2003, and
its contribution to productivity growth during this period was much smaller than that of capital
deepening, except in Koroška. TFP growth gained momentum during 2004–07, with utilization
of superior production and organization techniques as Slovenia’s integration into the global
economy deepened (OECD, 2011, pp. 28–29). As the process of structural change intensified,
the importance of high- and medium-high technology activities increased and the share of labor-
intensive activities in value added continued to decline. Thus, the contribution of TFP growth to
productivity growth exceeded that of capital deepening in six regions (Osrednjeslovenska,
Goriška, Savinjska, Podravska, Gorenjska, and Zasavska) but continued to remain below in the
other six regions. However, with the onset of the global financial crisis in 2008, the contribution
of TFP growth fell sharply, turning negative in nine of the twelve regions, and capital deepening
became the dominant driver of productivity growth in all regions.
.
V. Sectoral patterns of productivity growth
Further insight into labor productivity growth can be gained by examining whether it was driven
by sectoral shifts or by within-sector productivity gains. Following Timmer and Szirmai (2000)
and World Bank (2008), aggregate labor productivity growth can be decomposed as follows:
1
1
1
1
1
1
1
1
1
=
=
=
Δ
Δ
+
Δ
+
Δ
=
Δ
t
it
n
i
it
t
it
n
i
it
t
it
n
i
it
t
t
y
yS
y
yS
y
Sy
y
y
(7)
where i denotes sector (i=1, …n, with n number of sectors), t-1 and t are time subscripts denoting
the beginning and end of period (t-1,t), Si is the share of sector i in total employment. The first
component of equation (7) is the within-sector effect, which captures the impact of productivity
growth within individual sectors on overall productivity growth. The second component is the
static reallocation or between effect, which reflects the impact of changes in the sectoral
composition of employment; i.e., the impact of employment shift from less productive to more
productive sectors. The third component, is the dynamic reallocation or cross effect, which
captures the joint effect of changes in employment shares and sectoral productivity; i.e.,

Page 17
17 
 
contribution arising from whether expanding sectors have above-average or below-average
productivity growth.
In all regions, within-sector productivity improvements were the most important driver
of aggregate productivity growth during 1997−2008 and their relative importance increased
over time in ten of the twelve regions18 (Table 12). Productivity improvements were associated
with labor shedding as well as adoption of new technologies and managerial techniques. Entry of
new firms, which tend to show higher productivity, were also a likely contributory factor.
Although the within-sector effect was the dominant driver, the static reallocation or between
effect accounted for a sizeable amount of productivity improvements during 1997–2003. In six
regions that included the richest and the two poorest regions (Osrednjeslovenska, Gorenjska,
Podravska, Spodnjeposavska, Zasavska, and Pomurska), the static reallocation or between effect
initially accounted for as much as one fourth to one half of productivity improvements, reflecting
a shift of labor away from agriculture and industry toward services.19 However, subsequently
during 2004–08 the importance of static reallocation or between effect declined markedly. The
dynamic reallocation or cross effect was negative in all regions throughout the period, since
services had below-average productivity growth but higher productivity level than in agriculture
and manufacturing.
VI. Conclusions
                                                            
18 Within-sector productivity improvements fell in Obalno-kraška and Goriška.
19 The calculations shows in table 12 are based on a broad level of aggregation of NACE
industrial classification (e.g., AB, CD , E, F, G, H and so on). Thus, the estimates of within-
sector effect are subject to upward bias and the estimates of between-sector effect are subject to
downward bias, because they do not capture the structural shifts that took place within
manufacturing. Within manufacturing, there was a pronounced decrease in employment in labor
intensive sectors and sectors most affected by entry to the EU (textiles, wood, leather products,
and food processing), while an increase in employment occurred in sectors where sales to foreign
markets grew significantly (vehicle manufacturing, machinery, rubber and plastic products).
Unfortunately, data constraints do not allow us to calculate sectoral productivity decomposition
at the regional level using a more disaggregated NACE industrial classification.

Page 18
18 
 
This paper finds that regional disparities in the levels of GDP per capita and labor utilization
have widened in Slovenia since 1999, mainly reflecting greater dynamism of the capital region
and underperformance of the two poorest regions. However, the widening dispersion in per
capita GDP has not been accompanied by a widening of household income inequalities. Because
of higher social transfers to the poorest regions and the growing incidence of inter-regional
commuting to work, regional gaps in per capita household disposable income have declined.
Econometric analysis shows that there has been conditional β-convergence in the growth rates of
GDP per capita and labor productivity. There is heterogeneity in steady-states across regions,
and regional growth is converging to these region-specific steady states. Labor productivity
growth has been driven by both capital deepening and growing importance of TFP improvement.
Within-sector effects have been the key driver of labor productivity gains throughout the period.
Static reallocation effects were initially sizeable but have faded in recent years.
According to the OECD (2011), based on international comparison, there is scope for further
increase in geographic concentration of economic activity in Slovenia. The increase in
concentration of value added in the capital region thus far has taken place against a backdrop of
increasing incidence of commuting to work. However, increase in traffic congestion and
infrastructural bottlenecks to commuting could slow down the process of further concentration of
economic activity. Improving within-region and inter-region rail and road networks will help to
fully exploit the potential for agglomeration economies and enhance Slovenia’s aggregate
growth performance. Such a pattern of development would likely further widen the regional
disparities in per capita GDP but it would not worsen regional inequalities in household
disposable income.
Despite the increase in concentration of value added in the capital region, a substantial part of
Slovenia’s growth has been generated in the non-capital regions. This is likely the result of
Slovenia’s long-standing policy focus on ensuring balanced regional development, and suggests
the presence of multiple growth poles in the country. The challenge ahead is to ensure that each
region develops and reaps its growth potential by boosting investment to spur an increase in the
labor utilization rate, improving the structural and policy determinants of productivity, and by

Page 19
19 
 
strengthening competitiveness. It would be important to direct efforts to increase efficiency in
the services sector, where productivity growth in recent years has been limited.
REFERENCES
Banerjee, B. and M. Jarmuzek (2010). Economic Growth and Regional Disparities in the Slovak
Republic. Comparative Economic Studies vol. 52, pp. 379–403.
Bernanke, B. and R. Gurkaynak (2001). Is Growth Exogenous? Taking Mankiw, Romer, and
Weil Seriously. In: Bernanke, B.S. and Rogoff, K. (eds). NBER Macroeconomics Annual 2001
vol. 16, The MIT Press: Cambridge MA. pp. 11–72.
Bogumił, P. (2009). Regional disparities in Poland. ECFIN Country Focus, vol. VI, Issue 04,
May.
Borys, M. M., E. K. Polgar, and A. Zlate (2008). “Real Convergence and the Determinants of
Growth in EU Candidate and Potential Candidate Countries: A Panel Data Approach. European
Central Bank Occasional Paper Series No.8, Frankfurt.
Bosworth, B.P. and S. M. Collins (2003). The Empirics of Growth: An Update. Brookings
Papers on Economic Activity, No. 2, pp. 113–206.
Bruncko, M., (2003). Regional Convergence in the Slovak Republic and European Union
Regional Funds. In B. Funck and L. Pizzati. (eds) European Integration, Regional Policy, and
Growth, World Bank: Washington D.C., pp.183–194.
Havrylyshyn, O. and T. Wolf (2001). Growth in Transition Countries, 1990–1998: The Main
Lessons. In: O. Havrylyshyn and S. Nsouli (eds) A Decade of Transition: Achievements and
Challenges, International Monetary Fund: Washington D.C., pp. 83–128.
Funck, B. and Pizzati, L. Editors (2003) European Integration, Regional Policy, and Growth,
World Bank: Washington D.C.
Islam, N. (2003). What have we learnt from the convergence debate. Journal of Economic
Surveys, vol. 17, No. 3, pp. 309–362.
OECD (2001). Measuring Capital--OECD Manual, Paris.
OECD (2010). Regional Development Policies in OECD Countries, OECD Publishing, Paris.
OECD (2011). OECD Territorial Reviews: Slovenia 2011, OECD Publishing, Paris.

Page 20
20 
 
Mankiw, G., D. Romer, and D. Weil (1992). A Contribution to the Empirics of Economic
Growth. Quarterly Journal of Economics vol. 107, pp. 407-437.
Marelli, E. and M. Signorelli (2010a). Transition, Regional Features, Growth and Labour Market
Dynamics. In: F. E. Caroleo and F. Pastore (Editors) The Labour Market Impact of the EU
Enlargement, Springer-Verlag: Berlin, pp. 99-147.
Marelli, E. and M. Signorelli (2010b). Productivity, Employment and Human Capital in Eastern
and Western EU Countries. In: E. Marelli and M. Signorelli, (Editors). Economic Growth and
Structural Features of Transition, Palgrave Macmillan: New York, pp. 84–103.
Marelli, Enrico and Marcello Signorelli, Editors (2010c). Economic Growth and Structural
Features of Transition, Palgrave Macmillan, New York.
Smarzynska-Javorick, B. (2004). Do Foreign Direct Investment Increase the Productivity of
Firms? In Search of Spillovers through Backward Linkages. American Economic Review vol. 94
No. 3, pp. 605–627.
Solow, R. (1956). A Contribution to the Theory of Economic Growth. Quarterly Journal of
Economics 70, pp. 65–94.
Timmer, M. and A. Szirmai (2000). Productivity growth in Asian manufacturing: the structural
bonus hypothesis examined. Structural Change and Economic Dynamics, vol. 11, No.4,
pp. 371–392
World Bank (2008). Unleashing prosperity: Productivity Growth in Eastern Europe and the
Former Soviet Union, Washington D.C.
Wostner, Peter (2002). Regional Disparities in Transition Economies—The Case of Slovenia.
Paper presented at the European Regional Association Conference, Dortmund, Germany,
August.

Page 21
Figure 1. Slovenia: Regional Map

Page 22
Figure 2. Slovenia: Changes in net commuting flows and labor utilization rate
-2,00
0,00
2,00
4,00
6,00
8,00
10,00
-10,00
-5,00
0,00
5,00
10,00
15,00
20,00
25,00
30,00
nge in
labor utilzation
rate, 2000-2008
(%
age points)
Notranjsko-kraška
Spodnjeposavska
Koroška
Osrednjeslovenska
Obalno-kraška
Gorenjska
Podravska
Savinjska
Jugovzhodna Slovenija
Goriška
-6,00
-4,00
-2,00
0,00
2,00
4,00
6,00
8,00
10,00
-10,00
-5,00
0,00
5,00
10,00
15,00
20,00
25,00
30,00
Ch
an
ge in
labor utilzation
rate, 2000-2008
(%
age points)
Change in net commuting as % of workers working in region, 2000-2008 (%age points)
Zasavska
Notranjsko-kraška
Spodnjeposavska
Pomurska
Koroška
Osrednjeslovenska
Obalno-kraška
Gorenjska
Podravska
Savinjska
Jugovzhodna Slovenija
Goriška

Page 23
Figure 3. Regional Patterns in growth of GDP per capita and labor productivity
0,0
1,0
2,0
3,0
4,0
5,0
6,0
7,0
8,0
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Percen
t
GDP per capita
Labor productivity
Slovenia: Growth of GDP per capita and labor productivity
0,00
1,00
2,00
3,00
4,00
5,00
6,00
d
slove
n
ska
a
lno
-kraška
Go
riška
o
. Slo
ven
ija
Savin
jska
Po
d
ravska
Go
re
n
jska
d
. posa
vska
Ko
ro
ška
tra
n. kraška
Za
sa
vska
Po
m
u
rska
Slo
ven
ia
Pe
rce
n
Average annual growth of GDP per capita, 1996-2008
0,0
1,0
2,0
3,0
4,0
5,0
6,0
7,0
8,0
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Percen
t
GDP per capita
Labor productivity
Slovenia: Growth of GDP per capita and labor productivity
0,00
1,00
2,00
3,00
4,00
5,00
6,00
Osre
d
slove
n
ska
Oba
lno
-kraška
Go
riška
Ju
go
. Slo
ven
ija
Savin
jska
Po
d
ravska
Go
re
n
jska
Sp
o
d
. posa
vska
Ko
ro
ška
Notra
n
. kraška
Za
sa
vska
Po
m
u
rska
Slo
ven
ia
Pe
rce
n
Average annual growth of GDP per capita, 1996-2008
0,00
1,00
2,00
3,00
4,00
5,00
6,00
Osre
d
slove
n
ska
Oba
lno
-kraška
Go
riška
Ju
go
. Slo
ven
ija
Savin
jska
Po
d
ravska
Go
re
n
jska
Sp
o
d
. posa
vska
Ko
ro
ška
Notra
n
. kraška
Za
sa
vska
Po
m
u
rska
Slo
ven
ia
Pe
rce
n
Average annual growth of labor productivity, 1997-2008

Page 24
Table 1. Slovenia: Regional differences in real GDP per capita
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Osrednjeslovenska
138
137
137
139
139
140
140
144
143
143
144
144
142
Obalno-kraška
109
108
108
106
105
104
105
104
103
102
102
104
106
Goriška
100
101
99
100
99
99
97
96
96
96
96
96
96
Jugovzhodna Slovenija
89
90
91
91
92
92
91
90
92
93
93
93
93
Savinjska
92
92
92
92
91
89
90
89
89
90
89
88
90
Podravska
82
82
82
83
84
83
84
84
84
84
84
85
85
Gorenjska
89
90
90
88
88
89
88
87
86
85
84
85
84
Spodnjeposavska
81
82
86
83
85
85
84
80
80
83
81
80
82
Koroška
80
79
80
80
83
82
80
78
77
79
77
77
77
Notranjsko-kraška
79
80
81
79
80
80
80
78
77
76
75
75
74
Zasavska
84
84
83
82
79
75
73
71
71
70
68
66
65
Pomurska
75
74
74
71
70
70
69
68
68
67
66
65
65
Slovenia
100
100
100
100
100
100
100
100
100
100
100
100
100
Memorandum item:
Coefficient of variationa (annual)
All regions
0,188
0,182
0,180
0,192
0,19
0,198
0,204
0,222
0,221
0,222
0,231
0,232
0,230
Excluding Osrednjeslovenska
0,110
0,107
0,104
0,109
0,107
0,110
0,115
0,119
0,120
0,122
0,131
0,137
0,144
Coefficient of variationa
(3-year moving average)
All regions
0,183
0,185
0,187
0,193
0,197
0,208
0,216
0,222
0,225
0,228
0,231
Excluding Osrednjeslovenska
0,107
0,107
0,107
0,109
0,111
0,115
0,118
0,120
0,124
0,130
0,137
a Coefficient of variation is defined as standard deviation of the regional distribution divided by Slovenia average.
Sources: Statistical Office of the Republic of Slovenia; and authors' calculations.
Regional GDP per capita in percent of Slovenia average

Page 25
Table 2. Slovenia: Regional differences in per capita household disposable income
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Osrednjeslovenska
120
114
113
112
112
111
111
111
110
112
Obalno-kraška
103
103
103
102
103
103
104
105
106
106
Goriška
104
106
106
105
107
109
106
105
105
105
Jugovzhodna Slovenija
85
97
99
98
98
99
100
99
99
98
Savinjska
97
96
97
97
99
99
97
96
96
96
Podravska
87
88
88
90
90
90
90
91
91
90
Gorenjska
99
102
102
101
101
102
101
100
101
100
Spodnjeposavska
93
94
100
95
91
91
93
95
96
95
Koroška
100
99
99
98
97
97
98
99
99
96
Notranjsko-kraška
95
95
96
96
98
100
101
99
99
101
Zasavska
100
99
100
105
103
98
96
97
97
96
Pomurska
80
80
80
81
79
79
80
83
83
82
Slovenia
100
100
100
100
100
100
100
100
100
100
Memorandum item
Coefficient of variation
a
(annual)
All regions
0,103
0,085
0,081
0,078
0,083
0,084
0,078
0,071
0,069
0,076
Excluding Osrednjeslovenska
0,080
0,074
0,075
0,071
0,077
0,079
0,071
0,063
0,064
0,067
Coefficient of variation
a
(3-year moving average)
All regions
0,090
0,081
0,081
0,082
0,082
0,078
0,073
0,072
Excluding Osrednjeslovenska
0,076
0,073
0,074
0,076
0,076
0,071
0,066
0,065
a
Coefficient of variation is defined as standard deviation of the regional distribution divided by Slovenia average.
Sources: Statistical Office of the Republic of Slovenia; and authors' calculations.
Regional per capita household disposable income in percent of Slovenia average

Page 26
1999
2004
2008
(In percent of regional GDP)
Osrednjeslovenska
16,0
14,9
13,5
Obalno-kraška
19,7
19,2
17,2
Goriška
20,1
20,3
19,2
Jugovzhodna Slovenija
18,9
19,6
17,7
Savinjska
20,4
22,1
20,5
Podravska
20,9
21,3
19,5
Gorenjska
21,7
21,8
20,7
Spodnjeposavska
19,6
21,5
19,6
Koroška
22,7
24,4
23,3
Notranjsko-kraška
23,9
24,7
24,0
Zasavska
24,9
30,2
30,6
Pomurska
20,5
23,1
24,5
Slovenia
19,1
19,2
17,8
Sources: Statistical Office of the Republic of Slovenia; and
authors' calculations.
Table 3. Slovenia: Regional differences in expenditures on social benefits
and other transfers

Page 27
Table 4. Slovenia: Inter-regional commuting to work
2000
2008
2000
2008
2000
2008
Osrednjeslovenska
4,4
6,7
16,0
23,2
-12,1
-17,7
Obalno-kraška
10,5
14,8
8,4
13,8
2,4
1,3
Goriška
8,0
12,4
5,0
8,8
3,3
4,2
Jugovzhodna Slovenija
17,9
26,3
6,9
13,6
13,4
17,2
Savinjska
7,4
13,0
7,9
10,9
-0,6
2,4
Podravska
8,3
12,6
6,5
9,5
1,9
3,5
Gorenjska
17,4
20,1
7,4
10,8
12,1
11,6
Spodnjeposavska
16,3
28,2
7,2
12,3
10,9
22,2
Koroška
11,0
20,5
12,3
16,4
-1,4
5,2
Notranjsko-kraška
22,5
33,7
13,1
14,4
12,2
29,2
Zasavska
21,4
38,1
7,8
12,7
17,3
41,1
Pomurska
10,1
17,3
3,1
5,5
7,7
14,2
a
A negative number means that outflow of commuters to other regions was less than inflow of
commuters from other regions.
Sources: Statistical Office of the Republic of Slovenia; and authors' calculations.
(in percent of workers
living in the region)
(in percent of workers
working in the region)
(in percent of workers working
in the region)
Outflow of commuters to
other regions
Inflow of commuters
from other regions
Net commuting from region
a

Page 28
1996
2003
2008
Population
24,5
24,7
25,5
GDP
33,7
35,7
36,1
Agriculture value added
13,7
13,9
14,2
Manufacturing value added
26,3
24,6
23,9
Business Services value added
39,3
43,4
42,7
Population
5,2
5,3
5,3
GDP
5,6
5,5
5,6
Agriculture value added
2,8
3,3
3,4
Manufacturing value added
3,0
3,3
3,1
Business Services value added
4,6
4,9
4,8
Population
6,1
6,0
5,9
GDP
6,0
5,7
5,6
Agriculture value added
6,8
6,7
6,9
Manufacturing value added
6,2
6,4
6,4
Business Services value added
5,0
4,2
4,8
Population
6,9
7,0
6,9
GDP
6,1
6,3
6,4
Agriculture value added
11,2
11,1
10,8
Manufacturing value added
8,4
9,7
10,7
Business Services value added
5,0
5,0
5,8
Sources: Statistical Office of the Republic of Slovenia; and authors' calculations.
Osrednjeslovenska
Obalno-kraška
Goriška
Jugovzhodna Slovenija
Table 5. Slovenia: Concentration of population and economic activity in four richest regions
Share of region in national aggregate (in percent)

Page 29
Table 6. Slovenia: Regional differences in labor productivity
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Osrednjeslovenska
122
121
120
121
121
122
119
120
118
116
116
115
115
Obalno-kraška
115
112
110
109
109
109
109
107
107
107
106
107
109
Goriška
101
102
100
100
100
100
99
98
100
99
100
100
98
Jugovzhodna Slovenija
88
89
89
90
91
91
92
93
94
95
96
97
97
Savinjska
89
90
90
90
88
88
90
89
90
90
90
89
89
Podravska
90
90
90
91
90
89
89
88
90
91
91
91
91
Gorenjska
95
98
99
98
99
99
99
98
97
99
99
100
100
Spodnjeposavska
84
84
89
87
88
90
91
89
91
92
92
94
96
Koroška
84
84
85
84
88
87
88
88
88
89
87
87
87
Notranjsko-kraška
87
90
91
89
91
90
92
90
89
88
87
89
88
Zasavska
97
98
99
98
95
93
94
95
94
92
92
92
93
Pomurska
79
78
77
74
73
74
75
75
76
77
76
76
77
Slovenia
100
100
100
100
100
100
100
100
100
100
100
100
100
Memorandum item
Coefficient of variationa (annual)
All regions
0,136
0,129
0,122
0,130
0,127
0,129
0,117
0,122
0,115
0,108
0,112
0,110
0,110
Excluding Osrednjeslovenska
0,104
0,100
0,092
0,096
0,093
0,093
0,086
0,084
0,081
0,077
0,081
0,085
0,085
Coefficient of variationa
(3-year moving average)
All regions
0,129
0,127
0,126
0,129
0,124
0,123
0,118
0,115
0,112
0,110
0,111
Excluding Osrednjeslovenska
0,099
0,096
0,094
0,094
0,091
0,088
0,084
0,081
0,080
0,081
0,084
a Coefficient of variation is defined as standard deviation of the regional distribution divided by Slovenia average.
Sources: Statistical Office of the Republic of Slovenia; and authors' calculations.
Regional productivity in percent of Slovenia average

Page 30
Table 7. Slovenia: Regional differences in labour utilization
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Osrednjeslovenska
51,0
50,3
50,5
51,5
51,9
52,4
54,6
55,2
56,1
56,9
57,9
59,4
60,2
Obalno-kraška
43,0
42,7
43,3
43,8
44,1
43,5
44,7
44,9
44,6
43,7
44,9
46,2
47,5
Goriška
44,7
43,8
44,3
45,0
45,0
45,4
45,3
44,7
44,3
44,7
44,7
45,8
47,7
Jugovzhodna Slovenija
45,3
45,0
45,5
45,5
45,9
46,1
45,6
44,7
45,2
45,0
45,0
45,6
46,8
Savinjska
47,0
45,7
45,5
46,0
46,5
46,2
46,1
45,7
45,5
45,6
46,1
47,1
49,1
Podravska
40,9
40,4
40,4
41,2
42,1
42,6
43,6
43,6
43,4
42,3
43,0
44,6
46,0
Gorenjska
42,1
40,9
40,2
40,4
40,4
40,8
41,0
40,7
40,6
39,8
39,8
40,5
41,3
Spodnjeposavska
43,7
43,4
43,0
43,0
43,8
43,0
42,7
41,2
40,7
41,1
40,7
40,5
42,2
Koroška
42,8
42,2
41,9
42,8
43,0
43,0
42,3
41,0
40,8
40,5
41,0
42,1
42,9
Notranjsko-kraška
40,9
39,7
39,4
39,8
40,1
40,2
40,1
39,7
39,8
39,5
39,8
40,3
41,3
Zasavska
39,0
38,0
37,3
37,8
38,1
36,7
35,9
34,5
34,8
34,8
34,3
34,2
34,4
Pomurska
43,2
42,6
42,6
42,8
43,1
43,1
42,1
41,8
41,2
39,8
40,0
40,8
41,4
Slovenia
45,2
44,4
44,4
45,0
45,5
45,6
46,3
46,0
46,2
46,0
46,5
47,7
48,9
Memorandum item
Coefficient of variationb (annual)
Labor utilization rate a by regions, in percent
All regions
0,075
0,078
0,083
0,085
0,085
0,093
0,108
0,120
0,125
0,131
0,139
0,145
0,146
Excluding Osrednjeslovenska
0,050
0,052
0,059
0,057
0,058
0,063
0,068
0,075
0,073
0,074
0,080
0,087
0,095
Coefficient of variation
(3-year moving average)
All regions
0,079
0,082
0,084
0,088
0,095
0,107
0,118
0,125
0,132
0,138
0,143
Excluding Osrednjeslovenska
0,054
0,056
0,058
0,059
0,063
0,069
0,072
0,074
0,076
0,080
0,087
a  Labor utilization rate measured as total employment (ESA definition) divided by total population.
b Coefficient of variation is defined as standard deviation of the regional distribution divided by Slovenia average.
Sources: Statistical Office of the Republic of Slovenia; and authors' calculations.

Page 31
Table 8. Regression analysis of β(beta)-convergence of GDP per capita growth (Dependent variable: ∆ln(GDP per capita))
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
(standard error)a
(standard error)a
(standard error)a
(standard error)a
(standard error)a
(standard error)a
1996 - 2008
1996 - 2003
2004 - 2008
1996 - 2008
1996 - 2003
2004 - 2008
(1)
(2)
(3)
(4)
(5)
(6)
-0.0684
0,1121
-0.0744
0.7358
1.1087
2,7718
(0.0537)
(0.0827)
(0.0937)
(0.3412)**
(0.6445)*
(1.1379)**
0,0118
-0,0087
0,0130
-0.0778
-0.1196
-0.2941
(0.0059)**
(0.0092)
(0.0102)
(0.0380)**
(0.0717)*
(0.1231)**
Region fixed effects
No
No
No
Yes
Yes
Yes
Time fixed effects
No
No
No
Yes
Yes
Yes
R-squared
0,0200
0.0071
0.0338
0,5640
0,5520
0.5976
F
3.95**
0,90
1,60
8.07***
4.61***
6.62***
F-statistic to test significance of
Region fixed effects
2.78***
1.97**
4.10***
Time fixed effects
10.08***
5.76***
8.27***
Region and time fixed effects
7.16***
4.81***
5.95***
N
156
96
60
156
96
60
a Standard errors are robust standard errors.
*** Significant at the 1 percent level; ** Siginificant at the 5 percent level; * Significant at the 10 percent level.
Unconditional convergence
Conditional convergence
Constant
ln(GDP per capitat-1 )

Page 32
Table 9: Regression analysis of β(beta)-convergence of labour productivity growth (Dependent variable: ∆ln (labor productivity))
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
(standard error)a
(standard error)a
(standard error)a
(standard error)a
(standard error)a
(standard error)a
1997 - 2008
1997 - 2003
2004 - 2008
1997 - 2008
1997 - 2003
2004 - 2008
(1)
(2)
(3)
(4)
(5)
(6)
0.4371
0.7131
0.6451
2,4700
4.3743
4.907
(0.1063)***
(0.1695)***
(0.1496)***
(0.6039)***
(0.9211)***
(0.9065)***
-0.0405
-0,0690
-0.0607
-0.2445
-0,4380
-0,4830
(0.0107)***
(0.0172)***
(0.0150)***
(0.0613)***
(0.0934)***
(0.0900)***
Region fixed effects
No
No
No
Yes
Yes
Yes
Time fixed effects
No
No
No
Yes
Yes
Yes
R-squared
0.1159
0.2022
0.1738
0.6642
0.7203
0.8182
F
14.36***
16.13***
16.47***
9.19***
8.29***
15.17***
F-statistic to test significance of
Region fixed effects
2.59***
4.39***
6.51***
Time fixed effects
15.88***
5.47***
16.79***
Region and time fixed effects
9.09***
7.11***
13.94***
N
144
84
60
144
84
60
a Standard errors are robust standard errors.
*** Significant at the 1 percent level; ** Siginificant at the 5 percent level; * Significant at the 10 percent level.
Unconditional convergence
Conditional convergence
Constant
ln(labour productivityt-1 )

Page 33
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient
(standard error)a
(standard error)a
(standard error)a
(standard error)a
(standard error)a
(standard error)a
(standard error)a
1997 - 2008
1997 - 2003
2004 - 2008
1999 - 2008
1999 - 2003
2004 - 2008
1999 - 2008
(1)
(2)
(3)
(4)
(5)
(6)
(7)
2,6743
4,5154
4,5818
2,6543
6,2104
4,6070
0,6491
(0.5943)***
(0.9179)***
(0.8859)***
(0.7950)***
(1.6535)***
(0.9002)***
(0.1912)***
-0,2210
-0.4139
-0.4262
-0.2265
-0,5910
-0.4275
-0,0396
(0.0595)***
(0.0922)***
(0.0888)***
(0.0785)***
(0.1579)***
(0.0901)***
(0.0212)*
0.0025
0.0004
-0.0053
0.0048
0.0012
-0.0047
0,0093
(0.0051)
(0.0076)
(0.0071)
(0.0055)
(0.0141)
(0.0076)
(0.0056)*
-0,4140
-0.3627
-0.2421
-0.3525
-0.2996
-0.2411
-0,2455
(0.0934)***
(0.1570)**
(0.1012)**
(0.0994)***
(0.1921)
(0.1023)**
(0.1074)**
0.0074
0.0107
-0.0062
0,0282
(0.0189)
(0.0324)
(0.0262)
(0.0145)*
Region fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
No
Time fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
No
R-squared
0.7074
0.7463
0.8395
0.6036
0.5312
0.8397
0,1244
F
11.25***
14.06***
16.84***
6.89***
3.31***
15.97***
3.21**
F-statistic to test significance of
Region fixed effects
3.44***
4.02***
7.28***
2.56***
2.17**
7.05***
Time fixed effects
15.26***
3.89***
16.16***
11.42***
2,07
16.22***
Region and time fixed effects
9.10***
8.33***
16.28***
6.47***
3.84***
15.21***
N
144
84
60
120
60
60
120
a Standard errors are robust standard errors.
b Investment ratio measured as nominal gross fixed capital formation divided by nominal GDP.
c Employment growth measured as ((employment t / employment t−1 ) + 0.05), where 0.05 represents the sum of rate of technological progess and depreciation of capital.
d Education is defined as the number of graduates over 1000 residents of a region
*** Significant at the 1 percent level; ** Siginificant at the 5 percent level; * Significant at the 10 percent level.
ln(share of higher educated persons
in total population)d
Mankiw-Romer-Weil model
Table 10. Regression analysis of β(beta)-convergence of labour productivity growth using Solow model and Mankiw-Romer-Weil model (Dependent variable: ∆ln (labor productivity))
Solow model
Constant
ln(labour productivityt-1 )
ln(investment ratio)b
ln(employment growth)c

Page 34
Table 11. Slovenia: Sources of growth of GDP and labour productivity by regions
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
1997-2003 2004-2007
Osrednjeslovenska
GDP growth (percent)
3,73
3,90
6,88
4,75
4,07
4,58
5,68
4,18
4,79
6,99
6,76
3,95
4,80
5,68
Contribution of labor
-1,11
0,31
1,28
1,22
0,87
3,20
0,91
1,40
1,32
1,76
2,53
2,26
0,95
1,75
Contribution of productivity
4,84
3,59
5,60
3,53
3,20
1,39
4,77
2,77
3,47
5,23
4,23
1,69
3,85
3,92
Contribution of capital
1,94
2,08
2,88
3,29
2,62
2,64
2,57
1,86
1,50
1,75
2,08
1,77
2,57
1,80
Contribution of TFP
2,90
1,51
2,72
0,24
0,58
-1,25
2,21
0,92
1,97
3,47
2,15
-0,08
1,27
2,13
Obalno-kraška
GDP growth (percent)
3,84
3,16
4,21
4,08
1,95
4,94
2,38
3,64
3,00
6,83
8,97
6,18
3,51
5,61
Contribution of labor
-0,57
0,85
1,11
0,85
-0,73
2,21
0,73
-0,54
-1,11
2,38
2,65
2,49
0,64
0,85
Contribution of productivity
4,42
2,31
3,10
3,23
2,69
2,73
1,66
4,18
4,10
4,45
6,32
3,69
2,87
4,76
Contribution of capital
6,49
4,86
2,38
2,02
2,61
2,02
1,77
3,94
1,74
2,41
2,19
3,05
3,16
2,57
Contribution of TFP
-2,07
-2,55
0,72
1,21
0,08
0,71
-0,12
0,24
2,37
2,03
4,13
0,64
-0,29
2,19
Goriška
GDP growth (percent)
5,62
2,08
6,25
3,04
2,78
2,00
0,57
4,36
4,84
5,66
6,59
1,89
3,19
5,36
Contribution of labor
-1,49
0,49
1,08
0,15
0,64
-0,16
-1,17
-0,70
0,52
0,13
1,87
2,18
-0,06
0,46
Contribution of productivity
7,11
1,59
5,17
2,89
2,14
2,16
1,74
5,06
4,32
5,53
4,72
-0,30
3,25
4,91
Contribution of capital
2,63
3,44
3,70
2,78
2,94
1,20
1,43
1,98
2,05
1,74
2,40
1,83
2,59
2,04
Contribution of TFP
4,48
-1,86
1,47
0,11
-0,80
0,96
0,31
3,08
2,27
3,80
2,31
-2,12
0,67
2,87
Jugovzhodna Slovenija
GDP growth (percent)
7,18
4,80
5,47
4,77
3,10
2,71
2,18
6,44
5,76
6,23
7,25
2,85
4,32
6,42
Contribution of labor
-0,47
0,65
0,33
0,71
0,52
-0,75
-1,09
0,79
0,01
0,26
1,52
1,34
-0,01
0,64
Contribution of productivity
7,65
4,15
5,14
4,06
2,58
3,45
3,27
5,65
5,75
5,97
5,74
1,51
4,33
5,78
Contribution of capital
7,62
3,69
2,22
2,59
2,00
1,78
2,70
3,58
5,20
4,99
2,61
2,74
3,23
4,10
Contribution of TFP
0,03
0,46
2,92
1,47
0,57
1,68
0,57
2,07
0,55
0,98
3,12
-1,23
1,10
1,68
Savinjska
GDP growth (percent)
4,97
2,72
5,44
3,06
0,67
5,16
1,57
4,46
5,27
5,18
5,59
4,97
3,37
5,12
Contribution of labor
-2,07
-0,65
0,91
0,99
-0,40
-0,09
-0,65
-0,30
0,18
1,04
2,05
2,45
-0,28
0,74
Contribution of productivity
7,04
3,37
4,53
2,08
1,07
5,26
2,22
4,75
5,08
4,13
3,55
2,52
3,65
4,38
Contribution of capital
3,77
2,44
6,36
5,31
3,50
3,77
4,47
1,28
1,31
1,45
1,75
1,55
4,23
1,45
Contribution of TFP
3,27
0,93
-1,83
-3,23
-2,43
1,49
-2,25
3,48
3,77
2,69
1,80
0,97
-0,58
2,93
Podravska
GDP growth (percent)
5,21
3,58
6,25
5,20
2,24
5,01
1,82
5,21
3,30
6,37
7,84
3,88
4,19
5,68
Contribution of labor
-1,05
-0,11
1,40
1,52
0,90
1,76
-0,21
-0,39
-1,77
1,34
2,89
2,21
0,60
0,52
Contribution of productivity
6,26
3,70
4,85
3,68
1,35
3,25
2,02
5,60
5,07
5,03
4,95
1,67
3,59
5,16
Contribution of capital
3,23
2,52
2,53
2,24
2,56
2,60
2,31
1,90
2,04
2,91
3,02
2,92
2,57
2,47
Contribution of TFP
3,03
1,18
2,32
1,44
-1,21
0,66
-0,29
3,70
3,03
2,13
1,94
-1,25
1,02
2,70
Gorenjska
GDP growth (percent)
6,59
3,19
3,57
3,84
4,08
3,33
1,44
3,17
4,19
4,74
7,19
2,93
3,72
4,82
Contribution of labor
-1,95
-1,24
0,50
0,12
0,87
0,54
-0,47
0,07
-1,31
0,36
1,66
1,38
-0,23
0,19
Contribution of productivity
8,54
4,42
3,07
3,73
3,20
2,79
1,91
3,10
5,50
4,39
5,53
1,56
3,95
4,63
Contribution of capital
2,85
4,11
2,83
2,43
2,87
1,98
2,61
1,80
2,11
1,88
2,58
1,97
2,81
2,09
Contribution of TFP
5,68
0,31
0,25
1,30
0,34
0,81
-0,70
1,30
3,39
2,50
2,95
-0,41
1,14
2,54
Spodnjeposavska
GDP growth (percent)
5,95
8,10
2,05
5,58
2,66
3,31
-2,50
4,83
6,95
3,39
5,77
5,91
3,59
5,24
Contribution of labor
-1,32
-0,77
-0,06
1,07
-1,30
-0,28
-2,29
-0,95
0,54
-0,65
-0,21
2,55
-0,71
-0,32
Contribution of productivity
7,27
8,88
2,11
4,51
3,96
3,59
-0,21
5,77
6,42
4,04
5,98
3,36
4,30
5,55
Contribution of capital
3,41
5,27
10,78
7,87
2,92
3,47
4,29
5,49
3,08
1,91
1,85
2,44
5,43
3,08
Contribution of TFP
3,86
3,60
-8,66
-3,36
1,04
0,12
-4,51
0,29
3,33
2,13
4,13
0,92
-1,13
2,47
Koroška
GDP growth (percent)
4,43
4,37
5,15
8,10
2,06
1,40
-0,44
3,56
6,02
2,61
6,38
1,92
3,58
4,64
Contribution of labor
-1,12
-0,46
1,52
0,36
0,10
-1,19
-2,40
-0,35
-0,38
0,64
1,77
0,70
-0,46
0,42
Contribution of productivity
5,56
4,84
3,63
7,73
1,96
2,59
1,96
3,90
6,41
1,97
4,61
1,22
4,04
4,22
Contribution of capital
3,05
2,79
1,85
2,05
1,50
1,13
1,20
2,00
2,33
2,28
3,03
2,82
1,94
2,41
Contribution of TFP
2,50
2,05
1,78
5,69
0,46
1,46
0,76
1,90
4,08
-0,31
1,58
-1,60
2,10
1,81
Notranjsko-kraška
GDP growth (percent)
6,51
3,84
3,08
6,58
1,94
4,89
-0,15
3,46
3,14
4,34
7,93
1,79
3,81
4,72
Contribution of labor
-2,28
-0,82
1,04
0,73
0,32
0,10
-0,68
0,37
-0,32
0,83
1,53
1,68
-0,23
0,60
Contribution of productivity
8,79
4,66
2,03
5,85
1,62
4,80
0,54
3,09
3,47
3,51
6,40
0,11
4,04
4,12
Contribution of capital
4,51
4,49
2,46
2,49
4,42
3,68
2,65
2,47
2,80
4,30
2,97
3,86
3,53
3,14
Contribution of TFP
4,29
0,17
-0,43
3,36
-2,80
1,11
-2,11
0,62
0,67
-0,78
3,43
-3,75
0,51
0,98
Zasavska
GDP growth (percent)
4,74
2,06
4,01
-0,22
-3,15
0,31
-0,06
3,38
2,42
2,68
3,06
1,36
1,10
2,88
Contribution of labor
-2,21
-1,76
0,79
0,34
-2,81
-1,70
-3,02
0,35
-0,37
-1,35
-0,20
-0,28
-1,48
-0,39
Contribution of productivity
6,96
3,81
3,22
-0,56
-0,35
2,01
2,96
3,04
2,78
4,02
3,26
1,64
2,58
3,28
Contribution of capital
1,52
1,96
1,44
1,01
2,27
1,04
1,00
1,21
1,50
1,05
0,66
1,71
1,46
1,10
Contribution of TFP
5,44
1,85
1,78
-1,57
-2,61
0,97
1,96
1,82
1,29
2,97
2,60
-0,07
1,12
2,17
Pomurska
GDP growth (percent)
3,83
2,72
0,23
2,35
2,32
2,18
1,25
3,82
2,12
3,51
5,25
1,88
2,12
3,67
Contribution of labor
-1,13
-0,25
0,05
0,43
-0,46
-1,70
-0,89
-1,15
-2,59
0,19
1,29
-0,04
-0,56
-0,57
Contribution of productivity
4,96
2,98
0,18
1,91
2,78
3,87
2,14
4,97
4,71
3,32
3,96
1,92
2,69
4,24
Contribution of capital
3,67
4,24
2,48
2,64
2,28
2,48
3,46
3,41
3,17
3,73
3,90
3,49
3,04
3,55
Contribution of TFP
1,29
-1,26
-2,30
-0,73
0,49
1,39
-1,32
1,56
1,53
-0,41
0,06
-1,58
-0,35
0,69
SLOVENIA
GDP growth (percent)
4,82
3,60
5,41
4,35
2,82
4,04
2,79
4,34
4,47
5,88
6,81
3,74
3,97
5,38
Contribution of labor
-1,32
-0,13
0,96
0,91
0,33
1,07
-0,27
0,22
-0,14
1,05
2,11
1,94
0,22
0,81
Contribution of productivity
6,14
3,73
4,45
3,45
2,49
2,96
3,06
4,12
4,61
4,83
4,71
1,81
3,75
4,57
Contribution of capital
2,48
2,37
2,78
2,79
2,40
2,27
2,45
1,93
1,77
2,00
2,16
2,06
2,51
1,97
Contribution of TFP
3,66
1,35
1,67
0,66
0,09
0,70
0,61
2,20
2,84
2,82
2,55
-0,26
1,25
2,60
Source : Authors' calculations based on data from the Statistical Office of the Republic of Slovenia
Average
(In percentage points, except where indicated otherwise)

Page 35
Table 12. Slovenia: Sectoral patterns of labour productivity growth by region
Between effect Cross effect
(Annual average, percent)
Osrednjeslovenska
1997-2003
3,50
2,88
0,86
-0,24
2004-2007
3,17
3,29
-0,06
-0,05
2008
0,37
0,27
0,14
-0,03
Obalno-kraška
1997-2003
2,68
2,42
0,42
-0,15
2004-2007
4,43
3,69
0,76
-0,03
2008
2,20
1,62
0,57
0,01
Goriška
1997-2003
3,39
2,77
0,76
-0,14
2004-2007
4,77
3,61
1,23
-0,08
2008
-1,51
-2,27
0,95
-0,19
Jugovzhodna Slovenija
1997-2003
4,43
3,61
0,97
-0,15
2004-2007
5,53
4,82
0,74
-0,02
2008
0,59
0,13
0,59
-0,13
Savinjska
1997-2003
3,90
3,31
0,73
-0,15
2004-2007
4,11
3,62
0,54
-0,06
2008
1,10
1,01
0,22
-0,13
Podravska
1997-2003
3,40
2,56
0,98
-0,14
2004-2007
5,01
4,51
0,53
-0,04
2008
0,37
0,10
0,33
-0,06
Gorenjska
1997-2003
4,18
3,25
1,17
-0,24
2004-2007
4,62
4,70
0,10
-0,18
2008
0,62
0,41
0,24
-0,03
Spodnjeposavska
1997-2003
4,74
3,63
1,43
-0,31
2004-2007
5,79
5,21
0,67
-0,08
2008
1,86
1,00
1,09
-0,23
Koroška
1997-2003
4,36
3,76
0,64
-0,05
2004-2007
4,10
4,16
0,08
-0,14
2008
0,59
0,52
0,11
-0,03
Notranjsko-kraška
1997-2003
4,28
3,66
1,00
-0,38
2004-2007
3,90
3,60
0,37
-0,08
2008
-0,91
-2,73
2,84
-1,02
Zasavska
1997-2003
3,39
2,66
1,13
-0,40
2004-2007
3,55
3,96
-0,28
-0,14
2008
1,45
1,11
0,39
-0,05
Pomurska
1997-2003
3,06
1,71
1,59
-0,24
2004-2007
4,62
4,08
0,59
-0,05
2008
1,61
0,84
0,84
-0,07
SLOVENIA
1997-2003
3,75
2,95
0,97
-0,17
2004-2007
4,25
3,92
0,37
-0,04
2008
0,63
0,37
0,30
-0,05
Source : Authors' calculations.
Labour productivity
growth
Of which, contribution of:
Within-sector effect
Reallocation effects
(Percentage points)