Processes of regional convergence in Poland in the context of the
use of European Union funds
ADAM CZUDEC & RYSZARD KATA
ABSTRACT: The study evaluates the processes of regional income convergence in Poland. This
problem is considered from the angle of the key development gaps separating the regions of Eastern
Poland from other more economically developed regions of the country. The EU Structural Funds
allocated significant resources in 2004-2014 to reduce these gaps, including under the Operational
Programme Development of Eastern Poland (OP DEP 2007-2013), specially designed for this purpose.
It was shown that there has been significant progress in reducing development gaps during the period
in question, including in relation to: expenditure on R&D in relation to GDP, economic activity of the
population and the development of road infrastructure. Progress in this area would not be possible
without the support of EU funds. Even though the scope and scale of positive changes prevented regional convergence, they contributed to inhibiting the processes of further income polarization between regions.
KEY WORDS: regional convergence, β- and σ-convergence, GDP per capita, development gaps,
Eastern Poland, EU funds
1. Introduction
Poland is one of the biggest beneficiaries of EU funds in the European Union (EU). In
2007-2013, through various programmes, Poland received about EUR 67 billion from the EU
budget, and the amount of support in 2014-2020 is expected to reach EUR 72.9 billion. These
funds are intended primarily to improve the competitiveness of the Polish economy and reduce disparities in development between regions in Poland, as well as to reduce the development gap between Polish regions and the most developed regions of the EU.
The largest programmes that used European funds in recent years (2007-2013) were Operational Programmes (OP): OP Infrastructure and Environment, OP Innovative Economy,
OP Human Capital, OP Development of Eastern Poland.
A large part of the funds was allocated to the improvement and development of technical infrastructure, in particular to the construction and modernization of the communications
network (national and regional roads, the Internet), promoting innovation and entrepreneurship and improving the quality of human capital.
Large regional disparities remain an important problem of the Polish economy; they are
visible especially between regions of Western and Eastern Poland. The biggest development
problems have intensified in recent decades in five regions of Eastern Poland, i.e. in the voivodeships: Lubelskie, Podkarpackie, Podlaskie, Świętokrzyskie and Warmińsko-Mazurskie1.
They are manifested mainly in form of the old-fashioned structure of the economy, low investment attractiveness of these regions and intensified migration processes. A special programme – OP Development of Eastern Poland (OP DEP) – was launched to halt these negative trends and reduce disparities between regions; it focused on five regions of Eastern Poland (EP), it was financed by the EU and implemented in 2007-2013, it is continued in the
2014-2020 perspective (under the name OP Eastern Poland).
In connection with the completion of the OP DEP and with the support for regional development in Poland from other EU structural funds in 2014-2020, there arises the question
1
The area of these voivodeships is 31.6% of Polish territory, inhabited by more than 8 million people, representing 21% of the country's population.
about the significance of these funds in the process of interregional convergence. The answer
to this question is a key research problem in this study. The problem is dealt with from the
angle of eliminating development gaps which hinder income convergence in regions of Eastern Poland.
The problems of regional convergence in Poland have been studied by various authors,
and most often the process of convergence was analysed in comparison to regions in Europe
or selected regions of Central and Eastern Europe (Fischer, Stibock 2006; Mikulić et. al.
2013; Smętkowski, Wójcik 2012; Piętak 2015). Researchers generally concluded that the development gap between less developed regions, which included all regions in Poland, and
regions with a high level of economic development, is slowly decreasing. They showed that
there is still a large gap between these two groups of regions and that the process of so-called
convergence clubs are getting stronger2.
The cognitive value of such research consisted in demonstrating the dynamics of interregional convergence, but it were poorly aimed at diagnosing the sources of convergence, i.e.
identifying the main factors that determine dynamics of this process. In addition, the comparative research on a very large number of regions can lead to oversimplification of reasoning, as
is clear from the fact that most regions in Poland are regarded as specialized in agriculture,
although the share of agriculture in GDP in these regions ranges from 3 to 4% (Thiessen et al.,
2013). This happens when, due to the large sample size, it is difficult to determine the real
impact of too many different factors influencing the studied phenomenon. Therefore, the relevance of EU funds in accelerating processes of regional convergence remains an important,
but poorly diagnosed question, especially considered from the angle of development gaps.
Eliminating these gaps, or at least reducing them, is the basic condition for progress in the
process of interregional convergence.
A development gap is understood as a disparity in the level of social or economic resources in the region, during the specified time period, as compared to the regions with the
highest level of the resource or compared to the average level of this resource in a country or
group of countries. Our attention will focus on the qualities of regions (endogenous resources)
that are essential for economic development. Such an approach to development gaps has not
been previously adopted in the context of regional convergence3. In the case of this article, the
difference in the level of development of a given resource will be compared to average values
for Poland.
2. Regional convergence in the light of economic research
The problems of interregional convergence are the subject of many studies. Their
starting point is often to question the validity of the neoclassical growth model, which assumes the disappearance of interregional differences due to the flow of physical capital from
regions with a low rate of return to regions with a higher rate of return (Barro 1997; Mankiw
et. al. 1992). As the most developed regions have high labour costs, the investors, motivated
by low wages, transfer investments to the less developed regions. In turn, according to the
2
Convergence clubs are based on the assumption that development disparities disappear only within certain
groups of regions with similar factors of economic development, which mainly include ethnological and geographical features (Fischer, Stibock 2006, Alexiadis 2013).
3
In literature, the concept of the development gap is identified with the sphere of business management, in the
context of competitive advantages of the company (technological gap, digital gap, innovation gap, etc.). (Bełz et.
al., 2013). In terms of economic development of regions or countries, the notion of a technological gap determines the differences in technological development between the countries/regions (Grabicz 2012; Kubielas
2009). The concept of the development gap also refers to the overall difference in the socio-economic development between countries (Tusińska 2012; Mucha-Leszko 2013).
2
neoclassical model, the working population moves to regions with higher wages, which balances the levels of economic development between regions4.
Empirical studies generally do not support these assumptions – the regional imbalances
in most regions in Europe have increased in the last decades of the twentieth century. The
importance of this problem has taken on a new dimension at the time of EU enlargement and
integration with the countries of Central and Eastern Europe, which are characterized by a
lower level of economic development, as compared to the “old Union” (Geodecki 2006). In
the first decade of the twenty-first century, at Member State level, the convergence was more
evident in the countries newly admitted to the EU than in the “old” EU-15. This was due to
significantly slower growth of GDP in the EU-15 regions than in the regions in the new countries. However, if we look at regions in isolation from groups of countries, we can observe the
increasing dispersion of income between regions of the EU. Most countries of Central and
Eastern Europe (CEE) were characterized by divergence, i.e. the richer the region, the higher
the GDP growth (Geodecki, 2006: 85-86; Nazarczuk 2014: 28-30). In this way, the interregional development gap within the EU, and especially in CEE countries, is widening, i.e.
there are processes of income polarization. This raises the question about the effective methods of reducing regional disparities between regions in the enlarged EU.
In recent years, both the economic theories (new theories of growth, new economic
geography) and regional policies strongly emphasize the importance of endogenous factors of
regional development, which should lead to the new dynamics of economic development at
local and regional level. In addition to improving the use of such factors as natural resources,
including the values of the natural environment, the endogenous factors include, above all,
investments in research and development and improving the quality of human capital (Pardo
2005, Próchnik 2009, Nazarczuk 2014).
From a research point of view, it is important to consider the issue of the methods for
evaluating the process of regional convergence. The literature in this field presents two fairly
common approaches. In the first approach, convergence is evidenced by decreasing differences between the indicators determining the level of economic development5, while the second approach compares the rate of development, or the rate of economic growth 6. In econometric analyses, these two approaches are named σ (sigma) and β (beta) convergence (Geodecki 2006; Bal-Domańska 2011). Each of these measures allows for assessing the scale of
convergence, and β convergence is a prerequisite for σ convergence, but it is not a sufficient
condition (Barro, Sala-i-Martin 1992; Raluca 2015).
The category of β convergence has two variants:
 absolute convergence – occurs when regions are converging in terms of GDP per capita,
regardless of the initial conditions (starting point), which means that the poorer regions
grow faster than rich regions, and this leads to the disappearance of differences in development;
 conditional convergence – it assumes that regions become similar to each other only if
they have similar structural characteristics (structure of the economy, level of education,
transport infrastructure, etc.)7.
4
In the classical model, convergence driven by diminishing marginal rate of return on capital is conditional, as it
depends on: savings rate, depreciation rate of capital, changes in the number of employees and productivity, i.e.
growth factors according to Solow (1956).
5
Decrease in dispersion of income per capita over time (σ convergence).
6
Poorer economies achieve faster growth of GDP per capita regardless of the state of equilibrium characteristic
for each of them (absolute β convergence).
7
Conditional convergence is based on the assumption on the prevalence of multiple paths of sustainable growth
with different values of the structural parameters. Therefore, each economy follows the individual path of sustainable growth (Barro 1992).
3
The basic and widely used measure of assessing convergence between regions is GDP
per capita in relation to the different characteristics of the regions which have an impact on
the disappearance of regional imbalances. The characteristics include:
 migration processes (Ozgen et. al. 2010),
 specifics of the labour market (Crystina 2015),
 structure of the economy (Firgo, Huber 2014; Mora 2007; Mikulić et al. 2013),
 qualities of the natural environment (Salvati 2014),
 provision of infrastructure (Del Bo et. al. 2010),
 quality of life in rural areas (Spellerberg et. al. 2007),
 use of instruments of regional policy in the form of financial support for regions (Alecke et. al. 2013).
The latter issue seems particularly important, because every country of the EU allocates
considerable funds for support to the regions. It is therefore legitimate to examine how these
measures accelerate the processes of regional convergence.
The researchers showed that in 1994-2006 in Germany, there was a reduction in disparities between the less developed regions (mainly in the eastern part of the country) and regions
with a high level of economic development. This was possible with the use of special subsidies for the less developed regions (Alecke et. al. 2013).
In Poland (as in other countries of Central and Eastern Europe), support for the less developed regions from public resources has a relatively short history. All the effects of using
public funds to support the objectives of regional convergence will become apparent in the
long term. At present, i.e. after a few years since the implementation of the programmes, it is
not possible to fully evaluate this support. In these circumstances, the examination of convergence processes should focus primarily on the assessment of the role of public funds in bridging development gaps, which are the main barriers to accelerating the process of regional
convergence.
Based on the literature (Filipiak i in. 2005; Czudec 2009, Jabłoński 2012; Nazarczuk
2014), it can be assumed that the main development gaps in Eastern Poland include:
 poorly advanced technical progress and low propensity to innovate,
 low quality of human capital in the context of economic activity, entrepreneurship and
dealing with constraints on the labour market,
 poor technical infrastructure, especially the components that determine the territorial
availability of regions.
These gaps are a major barrier to the inflow of physical capital and the growth of business investment, which is a key condition for β income convergence.
The analysis of reducing the differences in socio-economic development should therefore be based on analysis of development gaps between poorer and richer regions. This is
about determining whether and how quickly the development gaps decrease, which is a necessary condition for regional convergence. Another important issue is the evaluation of the effectiveness and efficiency (relationship of expenses to effects) of financial support from EU
funds, targeted at reducing development gaps. The role of the EU funds allocated to regional
development in this context is to:
 activate “dormant” resources (e.g. human entrepreneurship, activity on the labour market)
or poorly (inefficiently, not fully) used resources to bring about socio-economic development (innovation, investments in enterprises);
 improve the condition and quality of these resources, which are key to attracting physical,
financial and human capital, attracting investment and creating jobs. This is about infrastructure (transport, information technology, etc.), research and development and education, as well as institutional environment of entrepreneurship (quality of administration).
4
The relationship between the level of income convergence in regions (i.e. balancing the
GDP per capita) and the scale of the reduction in key development gaps is conceptually presented in Figure 1.
Fig. 1. The relationship between the level of income convergence in the region (as measured by GDP
per capita) and the scale of the reduction of key development gap
K t3
Level of income convergence
K t1
K t2
Qt1
Qt2
Qt3
Progress in the reduction of development gap
where: Q = 0  1 (them Q value is closer to 1 - the gap is smaller)
K = 0  1 (them K value is closer to 1 - the convergence of income is higher)
Source: own
The time during which the changes of the analysed values occur is an important dimension in this relationship. Below we present the assumptions of the concept:
The parameter Q measures reduction in the development gap in time [t; tn], for t = 1,2, ... ,n.
The parameter Q is calculated according to the formula:
PZi where: PZi – value of the characteristic for a backward region in period ti
Qti =
PKi
PKi – national average value of the characteristics in period ti
Q =1 means that there is no difference in the level of the characteristic (development gap) between
the backward region and the national average (i.e. no gap).
The parameter K determines the level of income convergence (GDP per capita) for the period [t; tn],
for t =1,2 ,….n. This parameter is in the range of [0; 1]. It is calculated according to the formula:
GDP pc Zi
GDP pcKi
where: GDP pcZi – GDP per capita of the backward region in period ti
Kti =
GDP pc Ki – average domestic GDP per capita in period ti
K=1 is the total convergence of GDP per capita in the backward region in relation to the national
average
As shown in Figure 1, when the development gap is large (low value of parameter – Qt1),
even if it is further decreased (increase in the value of the parameter to Qt2), it does not lead to
progress in terms of convergence (reduction in the value of income convergence from Kt1 to
Kt2). Income differences increase because:
1) more time is needed to bring about significant structural changes that could reverse the
current trend (deepening differences);
5
2) the scale of the reduction in the gap is still small enough that it does not allow for trend
reversal (e.g. external investors still do not see positive changes);
3) there is the effect of institutional hysteresis (the institutional environment has not kept pace
with the changes in the economic and social environment);
4) in addition to the gap that is being reduced, there are other gaps, for which public funds are
not allocated (or are too small), and this slows the pace of positive change.
Only after reaching a “critical point” (Qt2) in reducing the development gap, the drive
for balancing the levels of development of regions begins to have stronger effect. Another
essential element is the “displacement effect”, i.e. the fact that some positive tendencies are
revealed over time. As shown in Figure 1, it takes time and a lot of effort to reduce the gap, to
reverse the negative trend and go beyond point Kt1 in terms of income convergence. If it were
not for the reduction in the development gap, the curve illustrating the income convergence
would drop lower and lower, which means increased polarization. Today, when polarization
factors are very strong, as they are associated with the globalization process (speed and ease
of capital flows searching for “a better place” to achieve high income, network economy, high
labour mobility, etc.), even the reversal of the dominant polarization trend may be considered
success. In addition, it should be noted that different regions may follow different paths of
sustainable growth, and therefore they do not seek balance with the same level of income.
Stopping polarization processes and marginalization of peripheral areas is the first step to start
the process of convergence, either conditional or absolute.
3. Aim of the study, materials and methods
The purpose of the research is to assess the importance of EU funds in supporting regional development in Poland, especially in reducing income disparities between the poorer
regions of Eastern Poland and the rest of the country. The funds do not have a direct impact
on stimulating the development process, but they can be used to a greater or lesser extent to
promote the activities leading to the reduction in development gaps and thus influence the
process of regional convergence. In terms of EU funds, particular attention was paid to the OP
DEP 2007-2013, whose objectives aimed to reduce critical development gaps in Eastern Poland. Another important issue taken in the study is an attempt to answer the question about the
time perspective of public support necessary to achieve the expected effects of convergence.
It can be determined by evaluating the progress in bridging development gaps brought about
with previous support for economically weaker regions, which was implemented in Poland in
2004-2014 using EU funds.
The figures used in research come mainly from the CSO (Central Statistical Office) Local Data Bank and mostly cover the period 2004-2014. This time range was set because of
Poland’s accession to the EU (2004) and gaining access to structural funds for regional development and because of the availability of current data (2014). The study period is long
enough to observe and evaluate changes in the economy of the regions in terms of bridging
development gaps.
The process of reducing development gaps was examined in relation to the three areas
listed in Table 1. The progress in each of these areas is analysed using partial measures (Table
1).
The changes in each of the areas are evaluated using the Location Quotient, which is
used in regional analyses to examine the concentration of industry and jobs in the various sectors of the economy of the region in comparison to the values describing a larger area of the
economy, e.g. the whole country (Guimaraes et. al., 2009: Nazarczuk 2014: 217-218). The
LQ set for the share of innovative enterprises is expressed by the formula:
6
LQ i 
ei E i
/
e E
where:
ei – number of innovative enterprises in the region
e – total enterprises in the region
Ei – number of innovative enterprises in the country
E – total enterprises in the country
Table 1. Development gaps and partial measures of progress in reducing these gaps in the regions
Development gap
Measures of progress in reducing development gaps
A/ Expenditure on
R&D
and business innovation
 Increased spending on R&D in relation to GDP in 2004-2012 (in %)
 Expenditure on R&D in 2012 (Poland = 100)
 Location Quotient (LQ) of innovative enterprises (industry and services) in
2006 (Poland = 1.0)
 LQ of innovative enterprises in 2014 (Poland = 1.0)
 Change in LQ of innovative companies in 2006 and 2014 (in %)
 Average annual change of registered unemployment in 2004-2012 (in %)
 Increase in employment rates (total and women) between 2004 and 2014
 LQ for the level of total employment in 2014
 Change in LQ for employment levels between 2004 and 2014 (in %)
 LQ for the length of expressways and motorways per 100 km2 in 2005 and
2014
 LQ for the length of railway line operated per 100 km2 in 2005 and 2014
B/ Level of economic
activity
of population
C/ State of technical
infrastructure determining
territorial
availability of regions
The LQ helps in studying the changes in the characteristic over time. The following
formula was used for this purpose:
∆LQ =
LQt – LQt-1
LQt-1
where:
∆LQ – change in location quotient in the period under consideration
LQt – quotient in the final period of analysis
LQt-1 – quotient in the initial period of analysis.
The calculations were performed for the sixteen regions in Poland and the average values for the country were also established.
The following regression function was used to check whether reducing the gaps in development between 2004-2014 brought about the β income convergence (β absolute convergence), indicating that the regions in Poland are converging in terms of GDP per capita
(Próchniak, Rapacki 2009: 148; Głodowska 2013: 43):
1
((ln( yT )  ln( y0 ))   0  1 ln y0
T
where:
y0, yT – GDP per capita in the first (y0) and last year (yT) of the test period,
α0, α1 – parameters of the regression function,
T – number of periods (years)
A negative value of parameter α1 indicates β convergence, which means that regions
with lower levels of GDP per capita (i.e. regions of Eastern Poland) have a higher rate of this
indicator over the period considered.
The σ convergence was evaluated using the standard deviation (sd) of natural log of
GDP per capita (yt). This was the basis for estimating linear trend function in the form (Matkowski, Próchniak 2014: 46):
7
sd(lnyt) =α0+ α1t
The negative and statistically significant (p<0.05) value of α1 indicates σ convergence,
which means that the variation in GDP per capita between regions decreases over time. R.J.
Barro and X. Sala-i-Martin point out that σ convergence is a better criterion than β convergence (Jabłoński 2012: 56-60).
4. Results and discussion
In the context of regional convergence, particular attention should be paid to the Operational Programme Development of Eastern Poland (OP DEP). Its main objective was to support economic transition in the five least developed Polish regions, i.e. the voivodeships:
Warmińsko-Mazurskie, Podlaskie, Lubelskie, Podkarpackie and Świętokrzyskie. It is also the
only EU programme targeted at small and purposefully selected group of regions in one country and can be therefore considered as an experimental operation. There are therefore essential
prerequisites to examine the effects of its implementation. However, due to the short duration
of this programme, it is difficult to expect clear and measurable results in the form of reduced
development disparities measured by GDP per capita in comparison with other regions in Poland. It is necessary to study the role of this and other programmes supporting regional development from EU funds in reducing the development gaps in poorer regions (peripheral),
which is the basic condition for speeding the process of interregional convergence.
The OP DEP 2007-20148 implemented projects worth EUR 2.7 billion, financed by the
European Regional Development Fund (ERDF) and the national budget. Public funds were
allocated to the economic and social areas defined as development gaps in Eastern Poland
(EP). They supported the development of higher education in order to activate the research
and development potential, create a platform for cooperation between universities and business and create innovative potential. The support for different levels of education was aimed
to improve the quality of education, thereby promoting the development of human and social
capital, including better preparation for the labour market and vocational activation of the
population. In support of the innovative potential of businesses and the investment attractiveness of regions, the funds were allocated to research and development projects and the development of technology parks and investment areas. Developing road infrastructure and broadband Internet network was also an important objective aiming to improve the accessibility of
EP regions for current and potential investors and business partners of local companies. This
also stimulates economic activity of the population. Investments in public transport systems
and tourism infrastructure pursued a similar goal. Combined with the support for tourist promotion, they enhance the investment and tourism attractiveness of Eastern Poland.
As is clear from this characteristic, the support of EU funds under OP DEP was largely
used to expand or improve the quality of key resources for socio-economic development of
regions covered by the programme, or to activate endogenous potentials that have been insufficiently used. In this sense, these funds reduce the disparities between regions of Eastern Poland and the rest of the country with regard to resources whose shortage creates development
gaps.
As mentioned earlier, the programme supported regional convergence of eastern voivodeships and is continued in the 2014-2020 financial perspective. The OP Eastern Poland
(OP EP) will be allocated the amount of EUR 2.1 billion (not including national contribution),
which is similar to the previous perspective. The funds in the OP EP are used in several areas
8
The programme concerned the EU financial plan 2007-2013, but some of the projects were funded until the end
of 2015.
8
of support, leading to greater competitiveness and innovation of the macro-region. These are
(Ministry of Infrastructure and Development, 2015):
- creating conditions for the development of innovative start-ups,
- support for SMEs sector in the field of innovative activity,
- creating new business models for the internationalization of SMEs,
- improving the efficiency of transport systems and developing sustainable transport in voivodeship cities and their functional areas,
- increasing availability of the macro-region in terms of road and railway infrastructure.
As can be seen, the funds will be mainly allocated to the same development gaps as in
the previous financial perspective under the OP DEP.
One of the key development gaps in the economic structure of regions is the technological gap, resulting, among others, from low expenditures on research and development (R&D),
and low innovativeness of enterprises expressed in percentage of innovative companies in the
region (Kubielas 2009). The results show significant progress in terms of expenditure on
R&D in relation to GDP in Eastern Poland. In 2004-2012, these expenditures increased on
average in the macro-region by 168.8%, while the national average was 58.9%. The increase
was higher than the national average in all voivodeships of Eastern Poland, and by far the
fastest growth in expenditure on R&D was recorded in Świętokrzyskie and Podkarpackie
(Table 2).
Table 2. Expenditure on R&D in relation to GDP (at current prices) and the intensity of innovative
enterprises in the regions (2004-2012)
Specification
Increased spending on
R&D in relation to GDP
in 2004-2012 (in %)
58.9
70.7
59.3
81.8
48.1
37.5
15.0
46.2
125.0
103.2
104.7
117.6
Expenditure on
R&D in 2012
(Poland = 100)
100.0
78.7
48.3
22.5
140.4
100.0
86.5
21.3
121.3
148.3
98.9
41.6
Location Quotient (LQ)* of
innovative enterprises
2006
2014
1.00
1.00
1.10
1.00
0.74
0.86
0.66
0.60
0.76
0.93
0.92
0.97
1.10
1.17
1.02
1.30
1.18
0.98
1.14
1.08
0.82
0.76
0.81
0.86
Poland
Dolnośląskie
Kujawsko-pomorskie
Lubuskie
Łódzkie
Małopolskie
Mazowieckie
Opolskie
Pomorskie
Śląskie
Wielkopolskie
Zachodniopomorskie
Eastern Poland
Lubelskie
121.7
85.4
1.19
1.28
Podkarpackie
251.7
114.6
1.10
1.12
Podlaskie
62.5
43.8
1.11
1.04
Świętokrzyskie
275.0
33.7
1.11
0.84
Warmińsko-mazurskie
133.3
55.1
0.97
0.69
* LQ rate of the national average is 1.0, LQ> 1.0 indicates above-average intensity of the examined phenomenon
in the region, while LQ <1.0 - conversely.
Source: own calculations based on Central Statistical Office data.
Despite the dynamic growth in expenditure on R&D in the macro-region of Eastern Poland, the level of this expenditure in 2012 was lower than the national average (except for
Podkarpackie). On average, the expenditure on R&D in the macro-region in relation to GDP
accounted for 3/4 of the national average (Table 2), but in 2004 it was barely 48% of the national average. The gap in this area has been significantly reduced, but still exists.
In terms of the share of innovative enterprises in the total number of enterprises, each
region was assigned LQ separately for 2006 and 2014. LQ took into account the total share of
9
innovative enterprises in industry and services. The situation of eastern voivodeships varied in
this case. In 2006, four voivodeships in the region had more innovative companies than the
national average (LQ>1.0) and only Warmińsko-Mazurskie reached LQ below 1.0. In 2014,
in addition to Warmińsko-Mazurskie Voivodeship, also Świętokrzyskie Voivodeship reported
LQ below 1.0. In the case of Lubelskie and Podkarpackie, LQ in 2014 increased (by 7.6% and
1.5%) compared to 2006 (Fig. 2). This means that the share of innovative companies in these
regions in the industry and services grew faster than the national average. In the other three
regions, LQ was lower than in 2006, which means increased disparity (gap) in terms of the
number of innovative companies. The largest drop in LQ was recorded in WarmińskoMazurskie and Świętokrzyskie (Figure 2).
Fig. 2. Change in LQ of innovative companies in 2006 and 2014 (in %)
27,2
OPOLSKIE
ŁÓDZKIE
22,4
KUJAWSKO-POMORSKIE
15,9
7,6
LUBELSKIE
7,2
ZACHODNIOPOMORSKIE
6,1
MAZOWIECKIE
5,9
MAŁOPOLSKIE
1,5
PODKARPACKIE
ŚLĄSKIE
-5,4
PODLASKIE
-6,1
-7,2
WIELKOPOLSKIE
-8,9
LUBUSKIE
-9,2
DOLNOŚLĄSKIE
-17,6
POMORSKIE
-24,2
ŚWIĘTOKRZYSKIE
-28,9
WARMIŃSKO-MAZURSKIE
40
30
20
10
%
10
20
30
Source: own calculations based on Central Statistical Office data.
In 2004-2014, there has been a decline in registered unemployment in all regions in Poland. The average rate of decline in the number of registered unemployed in the country was
4.85% year on year. In Eastern Poland, in Świętokrzyskie and Warmińsko-Mazurskie only,
the rate of decline in the number of unemployed was slightly higher than the national average,
while in Podkarpackie and Podlaskie, the rate was 2 times lower than the national average
(Table 3). In the last years of the period, i.e. 2010-2014, when the effects of the impact of EU
funds on the quality of human capital, and in particular on professional activity of the population and job creation could be seen more clearly, the rate of decline in the number of unemployed in Świętokrzyskie and Warmińsko-Mazurskie was significantly (nearly 3-fold) higher
than the national average (Table 3). In other voivodeships of EP, the rate was lower than the
national average, but it was significantly lower only in Lubelskie. It is worth noting that in
2010-2014 unemployment continued to decline in all regions of Eastern Poland, albeit at different speeds, while there were regions in the country where unemployment rate increased
(Table 3). These were the regions where the unemployment rate had been the lowest in the
country for years (Mazowieckie, Małopolskie, Śląskie). This is evidence of the process of
convergence in the area of registered unemployment taking place in recent years. The gap in
this area is still significant, but it is slowly declining, despite the fact that the process shows
significant asymmetry within the macro-region of Eastern Poland.
10
Table 3. Changes in registered unemployment and employment rates in 2004-2014
Specification
Poland
Dolnośląskie
Kujawsko-pomorskie
Lubuskie
Łódzkie
Małopolskie
Mazowieckie
Opolskie
Pomorskie
Śląskie
Wielkopolskie
Zachodniopomorskie
Eastern Poland
Lubelskie
Podkarpackie
Podlaskie
Świętokrzyskie
Warmińsko-mazurskie
Source: as Table 2.
Average annual change of
The change in the employLQ for the
registered unemployment
ment rate between 2004 and level of total
(in %)
2014 (in percentage points) employment
in 2014
total
including:
including:
2004-2014
women
2010-2014
-4.85
-0.72
6.9
5.6
1.00
-7.22
-3.63
9.8
7.5
0.98
-4.41
-1.07
5.6
3.6
0.96
-7.19
-5.05
7.5
6.0
0.97
-5.33
-0.31
8.4
7.5
1.04
-3.26
1.35
3.7
2.5
0.99
-3.40
2.16
9.4
7.8
1.12
-5.50
-2.11
6.8
6.1
0.97
-6.00
-0.71
8.4
7.3
0.99
-5.51
0.85
7.9
6.9
0.96
-6.67
-2.71
6.4
4.7
1.04
-6.38
-2.26
6.9
5.1
0.94
-3.38
-2.09
-2.29
-5.03
-4.91
-0.06
-0.57
-0.25
-2.09
-2.11
3.0
2.1
3.7
7.1
4.5
1.7
0.5
5.1
6.4
4.6
0.99
0.92
0.99
0.96
0.90
In the case of another indicator demonstrating the economic activity of the population,
i.e. the employment rate, we see positive changes in 2004-2014 in the macro-region of Eastern Poland. In all voivodeships of the macro-region, as well as across the country, the employment rate in 2014 was higher compared to 2004 and this applied to both total employment
and the employment of women (Table 3). However, the positive changes in this area varied
within the macro-region. Both employment rates grew higher than the national average only
in Świętokrzyskie, in other regions of EP growth was lower than the national average by
about 2-4 percentage points. The lowest growth was in Podkarpacie, where the rate of total
employment increased by only 2.1 percentage points, and in the case of women, by only 0.5
percentage point (both growth rates were the lowest in the country). The LQ calculated for the
total employment rate in 2014 indicates that there is still a gap in this respect between the
eastern regions, the national average and the best regions in the country (Table 3). The smallest gap is in Lubelskie and Podlaskie (LQ=0.99), while the largest in Podkarpackie (LQ=0.92)
and Warmińsko-Mazurskie (LQ = 0.90).
In terms of convergence processes, it is important to know whether the gap in the rate of
employment has been reduced in recent years. The answer to this question is presented in
Figure 3, which shows the change in LQ for the total employment rate in the regions. A positive value of this measure indicates that positive changes were faster in the region than in the
country, while a negative value indicates that the changes (also positive in this case) followed
more slowly than the national average, and therefore a gap in this regard was growing. In the
case of eastern regions, we see that the development gap in the employment rate has decreased only in Świętokrzyskie, and increased in other voivodeships of the macro-region, in
particular in Lubelskie and Podkarpackie.
In the case of another of the development gaps, i.e. the infrastructure determining the
territorial availability of regions, there were positive changes in the length of expressways and
motorways in 2005-2014. The progress in this respect was visible in the whole country: the
length of roads per 100 km2 increased more than 2.5 times (by 269.2%) in 2005-2014. The
growth was much higher in the macro-region of Eastern Poland because only Świętokrzyskie
11
had a section of an expressway in 2005. The disproportion in the development of expressways
and motorways is illustrated by LQ for this characteristic (Table 4).
Fig. 3. Change in LQ for employment levels between 2004 and 2014 (in %)
DOLNOŚLĄSKIE
7,62
POMORSKIE
3,62
MAZOWIECKIE
3,57
3,07
ŚLĄSKIE
ŁÓDZKIE
2,78
LUBUSKIE
1,94
1,18
ŚWIĘTOKRZYSKIE
0,94
ZACHODNIOPOMORSKIE
0,21
OPOLSKIE
WIELKOPOLSKIE
-1,64
-2,41
KUJAWSKO-POMORSKIE
-4,12
WARMIŃSKO-MAZURSKIE
PODLASKIE
-6,68
MAŁOPOLSKIE
-6,68
-8,03
LUBELSKIE
PODKARPACKIE
-9,42
11
9
7
5
3
1
%
1
3
5
7
9
Source: as Table 2.
In 2005, the saturation of these types of roads in Eastern Poland was 15% compared to
the national average and 5% compared to the most developed south-west region. However, in
2014, saturation of expressways and motorways in EP already reached 47% of the national
average and 35% of the value for the south-west macro-region. In case of this characteristic,
the gap between eastern regions and other more developed regions of the country decreased.
However, LQ in 2014 shows that this gap still exists and is significant (Table 4).
Table 4. Development of road and railway infrastructure in 2005-2014
Specification
Poland
Dolnośląskie
Kujawsko-pomorskie
Lubuskie
Łódzkie
Małopolskie
Mazowieckie
Opolskie
Pomorskie
Śląskie
Wielkopolskie
Zachodniopomorskie
Eastern Poland
Lubelskie
Podkarpackie
Podlaskie
Świętokrzyskie
Warmińsko-mazurskie
Source: as Table 2.
LQ for the length of expressways
and motorways per 100 km2 in:
2005
2014
1.00
1.00
2.88
1.49
0.77
1.16
0.50
1.73
0.38
2.32
1.62
1.19
0.42
0.70
3.62
0.98
0.81
0.78
3.69
2.47
2.08
1.25
0.85
0.73
0.00
0.00
0.00
0.77
0.00
0.33
0.72
0.17
0.51
0.60
LQ for the length of railway line
operated per 100 km2 in:
2005
2014
1.00
1.00
1.37
1.42
1.14
1.08
1.08
1.06
0.94
0.95
1.12
1.16
0.77
0.76
1.38
1.31
1.06
1.08
2.65
2.58
1.06
1.02
0.82
0.84
0.66
0.85
0.52
0.92
0.77
0.66
0.89
0.52
1.00
0.74
12
In terms of railroads, there was a regression in 2005-2014 in the whole country, as well
as in Eastern Poland. The length of railway lines per 100 km2 decreased by 4.6% in the country, and by 2.7% in the eastern regions. In recent years, however, there are more and more the
investment in the rail network, co-financed from EU funds and aimed at improving the technical condition of railways. Such investments were also carried out in Eastern Poland, although they were not financed by the OP DEP. In general, both in terms of the length of railroads and the state of the infrastructure, the regions of Eastern Poland do not match other regions in the country, and this situation had not changed fundamentally in 2005-2014, as indicated by LQ for the length of railway lines per 100 km2 (Table 4).
Table 5 presents data on the annual rate of growth of GDP per capita (at current prices)
in 2004-2013. The pace of economic growth in the regions of Eastern Poland was slightly
lower than the national average (from 0.1 percentage point in Podkarpackie to 1.0 percentage
point in Świętokrzyskie). Compared to the regions with the fastest economic growth
(Dolnośląskie and Mazowieckie), these differences were slightly higher (Table 5). The total
growth in GDP per capita in the regions of Eastern Poland in the analyzed period did not exceed the national average of 77.1%. This is evidence that no process of interregional income
convergence takes place. Eastern regions grow more slowly than most regions in the country.
It should be noted, however, that compared to the initial years of the period, the differences in
terms of economic growth measured by the growth rate of GDP per capita between the regions of EP and the national average were smaller (except for Świętokrzyskie in 2013). This
may mean that funds allocated for the development of these regions from EU structural funds
produced a positive effect. The peripheral regions achieve economic growth over time, and
the differences between the rate of growth compared to other regions are getting smaller, but
still exist. This means that these regions lag behind the centres of economic growth in the
country.
Table 5. The average annual growth rate of GDP per capita at current prices in 2004-2013
Specification
Poland
Dolnośląskie
Kujawsko-pomorskie
Lubuskie
Łódzkie
Małopolskie
Mazowieckie
Opolskie
Pomorskie
Śląskie
Wielkopolskie
Zachodniopomorskie
Eastern Poland
Lubelskie
Podkarpackie
Podlaskie
Świętokrzyskie
Warmińsko-mazurskie
Source: as Table 2.
The average annual growth
rate of GDP per capita (%
6.6
7.7
5.7
5.8
6.7
6.6
7.3
5.8
6.3
5.7
6.6
5.6
Total growth in GDP per
capita in 2004-2013 (%)
77.1
95.7
64.8
65.8
79.4
77.5
88.6
66.5
73.7
64.9
77.2
63.5
6.3
6.5
6.4
5.6
5.9
77.0
73.2
75.1
63.3
67.2
The results of the statistical analysis of beta (β) and sigma (σ) income convergence are
presented in Table 6. In case of β absolute convergence, the estimation of the regression model did not allow for positive verification of the hypothesis that the poorer regions of Eastern
Poland “catch up” in terms of growth in GDP per capita with better developed regions. The
13
results do not provide statistically significant evidence (probability test p above 0.05) that the
regions are getting closer in terms of economic growth. The results of estimation of the linear
trend function for σ convergence are statistically significant (p <0.05) and indicate the occurrence of divergence – i.e. interregional differentiation increased in the period (2004-2014).
Table 6. The statistical verification of the thesis about the existence of inter-regional income convergence-type β and σ in Poland in 2004-2013
1. Parameters of the linear regression model for the assessment of the â income convergence
The
The function
The value of
estimated
Standard error
p-value
convergence
parameters
t statistics
value
lack of
α0
-0,056785
0,066546
-0,853307
0,407852
statistical
0,011169
0,006655
1,678243
0,115472
significance
α1
R2 = 0,1674; F = 2,816, p<0,11, the estimation error 0,0049
2. The results of the analysis of the linear trend function for the assessment of the ó convergence
The
The function
The value of
estimated
Standard error
p-value
convergence
parameters
t statistics
value
α0
-6,03384
0,951105
-6,34403
0,000222
Nie
0,00310
0,000474
6,55576
0,000177
α1
R2 = 0,8430, F(1,8)=42,978; p<,00018, the estimation error 0,00406
Source: own calculations.
The absence of interregional income convergence does not mean that there is no convergence (reducing disparities in development) in many economic and social areas where development gaps have been identified. According to the concept illustrated in Figure 1, these
processes may not be close enough to reverse the current trend of income divergence. This is
evidenced by indicators of income convergence (K) and progress in reducing the development
gap (Q) established for expressways and motorways (Tab. 7). With regard to many development gaps, including road infrastructure, the distance between poorer regions and wealthier
(more economically developed) regions is becoming smaller (Table 7). However, it is still
large enough that it translates into a process of real income convergence in poorer regions. A
certain asymmetry of the progress in reducing the development gaps that could be observed in
2004-2014 between the regions of Eastern Poland is not without significance. However, the
results indicate some deceleration in processes of income polarization (Table 7), which would
not have been possible without the support of EU structural funds for regional development of
Eastern Poland.
Table 7. The value of the parameter of income convergence (K) and parameter of progress in reducing the development gap (Q) for road infrastructure in Eastern Poland
Voivodeship
The parameter of income convergence (GDP per capita) in:
2004
2009
2013
Kt1
Kt2
Kt3
Lubelskie
0,71
0,69
0,71
Podkarpackie
0,73
0,71
0,71
Podlaskie
0,74
0,73
0,73
Świętokrzyskie
0,79
0,79
0,73
Warmińsko-mazurskie
0,76
0,73
0,72
Source: own calculations based on Central Statistical Office data.
Progress in the reduction of development gap in:
2009
2013
2004
Qt1
0,00
0,00
0,00
0,77
0,00
Qt2
0,00
0,00
0,00
0,57
0,55
Qt3
0,22
0,66
0,18
0,53
0,67
14
Conclusions
This paper focused on the analysis of reducing the gaps in development between the less
economically developed regions of Eastern Poland and other regions in the country and the
impact of these processes on interregional income convergence. We identified three key development gaps addressed by the EU structural funds, in particular, by the OP DEP specifically designed for the five voivodeships of Eastern Poland. Based on the studies, we can make
some conclusions:
1) Development gaps in the observed eastern regions significantly decreased in 2004-2014 (in
particular after 2007), among others with regard to expenditure on R&D in relation to GDP,
economic activity of the population and the development of road infrastructure (expressways and motorways).
2) It would not be possible to achieve positive changes in the areas mentioned above on the
scale observed in 2004-2014 without the support of EU funds, in particular the OP DEP
2007-2013.
3) However, the progress is not large enough to significantly reduce the development gap between regions of Eastern Poland and the most developed regions in the country, although it
can be assumed that EU and national funds reduced the scale and speed of interregional polarization.
4) The current trend of income divergence between regions can be reversed with further support from public funds, which must be precisely directed towards the effective and efficient
reduction of gaps that limit interregional convergence. It is important to take into account
the different situations in different regions, because the experience gained during 2004-2014
in relation to Eastern Poland shows asymmetry in terms of reducing development gaps.
5) Another programme designed for eastern regions (OP EP 2014-2020) continues the objectives pursued by the previous programme. It is reasonable, because the most important development gaps are still not eliminated. The importance of individual gaps varies in different regions. This requires precise control of public funds in territorial and objective terms,
i.e. so that they may help to reduce the most significant barriers to development.
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CORRESPONDENCE ADDRESS:
Adam Czudec, Professor, University of Rzeszow, Faculty of Economics, Cwiklinskiej 2, 35-601
Rzeszow, Poland email: [email protected]
Research interest:
 R1 General Regional Economics
 R12 Size and Spatial Distributions of Regional Economic Activity
 H7 State and Local Government • Intergovernmental Relations
Ryszard Kata, Professor, University of Rzeszow, Faculty of Economics, Cwiklinskiej 2, 35-601
Rzeszow, Poland email: [email protected]
Research interest:
 R5 Regional Government Analysis
 R51 Finance in Urban and Rural Economies
 G2 Financial Institutions and Services
 H7 State and Local Government • Intergovernmental Relations
RECENT PUBLICATION:
Błachut B., Cierpiał-Wolan M., Czudec A., Ślusarz G. (2015) Obszary transgraniczne Polski, Słowacji
i Ukrainy – Czynniki progresji i peryferyzacji, Urząd Statystyczny w Rzeszowie, Rzeszów, pp.
136.
Czudec A. (2015) Finansowanie infrastruktury drogowej przez samorządy gmin i powiatów w
kontekście zasady subsydiarności, Finanse Komunalne, 11, pp. 65-74.
Czudec A., Jablonski L., Potocki T. (2014) Regional well-being - limitations of economic and social
indicators [in:] F.S. Coppola: Challenges for a New World: European Mezzogiorno and Meditarrenean basin - sustainable transitions and reverse visions, Giovani Editori, Napoli, pp. 1-29
(ebook edition).
Czudec A. (2014) Znaczenie transferów zewnętrznych w kształtowaniu stabilności finansowej
jednostek samorządu terytorialnego, Nierówności Społeczne a Wzrost Gospodarczy, 40, pp. 17-30.
Kata R., Czudec A. (2013) Zadłużenie a sytuacja ekonomiczna i ryzyko finansowe jednostek
samorządu terytorialnego, Finanse Komunalne, 5, pp. 5-20.
Czudec A. Kata R. (2012) Metodyczne aspekty oceny gospodarki finansowej jednostek samorządu
terytorialnego, Finanse Komunalne, 10, pp. 5-20.
Kata R. (2012) Determinants of Banks’ Competitiveness in Local Financial Markets, Finansowy
Kwartalnik Internetowy eFinanse, 8(1), pp.1-13.
17