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© 2015 Nicolaus Copernicus University. All rights reserved.  
http://www.dem.umk.pl/dem 

D Y N A M I C  E C O N O M E T R I C  M O D E L S  
DOI: http://dx.doi.org/10.12775/DEM.2015.008  Vol. 15 (2015) 157−165 

Submitted October 24, 2015  ISSN (online) 2450-7067 
Accepted December 15, 2015 ISSN (print) 1234-3862 

Tomasz Groszkowski, Tomasz Stryjewski* 

The Econometric Analysis of Entrepreneurship  
Determinants in Polish Voivodeships  

in the Years 2004–2013 

A b s t r a c t. This article presents research results and describes and clarifies differences in 
a level of the entrepreneurship index observed between Polish voivodeships in the period 
from 2004 to 2013. The expected results were confirmed with the fixed effect (FE)/random 
effect (RE) panel data model. The analysis showed that the level of entrepreneurship varies 
depending on a voivodeship, and that this differentiation is structural and permanent. The 
applied model also confirmed the expected determinants of entrepreneurship. 

K e y w o r d s: entrepreneurship, development factors, panel data model, FE/RE model.  

J E L Classification: C23, C26, L26, R12. 

Introduction  

 Entrepreneurship is undoubtedly one of the fundamental economic de-
velopment factors, through its influence on development of new products, 
new markets, creating jobs, and raising the social welfare in general. It is 
noticeable that some areas are growing fast through establishment of new 
companies, when others remain far behind. The concept of entrepreneurship 
is a complex issue, and it is permanently inscribed into different branches of 
science. A wide range of studies shown that a large and continuously in-
creasing number of classifications and descriptions of entrepreneurship re-

                                                 
* Corresponding author: Tomasz Stryjewski, The University of Computer Science and 

Economics in Olsztyn, e-mail: tomasz.stryjewski@erbud.pl; Tomasz Groszkowski, The Uni-
versity of Computer Science and Economics in Olsztyn. 



Tomasz Groszkowski,Tomasz Stryjewski 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 157–165 

158 

sults in a lack of a uniform theory of entrepreneurship and associated 
measures, implying possible difficulties in their assessment  (Szarecki, 2008, 
p. 181). Entrepreneurship is multidimensional and consists of many elements 
that should be considered when determining the characteristics of entrepre-
neurship (Kalkan, Kaygusuz, 2012). 
 A management sphere pays particular attention to this aspect, mainly for 
functional reasons, describing it as the process of organizing and running 
a business in conditions of risks associated with those activities (Gryffin, 
1997, p. 730–731).  
 Entrepreneurship can therefore be considered as a process creating or 
identifying opportunities, which later are frequently used regardless of re-
sources possessed at a given time. Entrepreneurship understood this way is 
a stimulating factor for a creative entrepreneur who finds energy to establish 
and build a company or organization. Therefore, the entrepreneur is more 
than just a passive observer of the situation in which he is positioned (Tim-
mons, 1990, p. 5). Entrepreneurship can therefore be described as an orga-
nized process consisting of successive phases, occurring in specific condi-
tions and aiming at using innovative ideas to achieve certain benefits, while 
considering risks involved in this process (Adamczyk, 1995, p. 9–10). 
 Entrepreneurship is one of the basic factors of economic, social and cul-
tural development. It is associated with activities of different types of busi-
nesses, from microenterprises, throughout the whole SME sector, up to large 
domestic and international enterprises. Certainly, companies play an im-
portant role in the Polish and in the European economies. They create 
a space where human skills and entrepreneurial attitudes are revealed and 
concentrated. Furthermore, they are a source of innovations and employ-
ment. The experience of well-developed countries shows that entrepreneur-
ship plays a significant role in the economy, stimulating economic growth, 
influencing employment, and providing goods to the market. Therefore, 
companies are a very important factor for economic growth of a relevant 
area. Entrepreneurship and businesses are also stimulating factors for 
growth, and the number of enterprises is frequently considered to be an indi-
cator of economic development. 

1. Entrepreneurship Factors  

 Entrepreneurial individuals, stimulated into action in appropriate condi-
tions, establishing businesses, creating new jobs and additional sources of 
income for themselves and the local population, can be found in every com-
munity, also at a local level. 



The Econometric Analysis of Entrepreneurship Determinants… 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 157–165 

159

 However, an answer to a question about factors influencing entrepre-
neurial behavior is not so clear and obvious. In numerous studies on entre-
preneurship, selection of factors that affect the level of entrepreneurship is 
a frequently encountered problem. This article focuses on economic factors, 
describing the structural and economic determinants of entrepreneurship and 
quantitative aspects of that issue. 
 Entrepreneurship is determined by the certain socio-economic and politi-
cal factors of intensity varying in time and space. Those factors may act in 
two ways: by bringing positive effects to the economy in form of incentives 
for business and economic growth stimulators or, on contrary, as means re-
straining or even inhibiting creation of new and development of already ex-
isting business entities. These factors are classified according to various 
criteria (Chrapek, 2009, p. 321). 
 Their effects on a recovery of a given area can either be positive or ad-
verse, through their potential to create conditions for formation and devel-
opment of entrepreneurship. Providing suitable conditions for development 
of entrepreneurship should therefore be one of the most important priorities 
for the regional development for authorities at any level (Jezierska-Thole, 
2010, p. 129). 
 Processes associated with entrepreneurial behavior occur in specific so-
cio-economic circumstances. GDP, an economic situation, revenues or in-
vestments are just examples of certain macroeconomic indicators affecting 
development of enterprises. 
 Considering the entrepreneurship development conditions, it should be 
noted that every company operates in a certain environment with which it 
interacts. In entrepreneurial research, one of the common problems is identi-
fying factors determining an entrepreneurial activity. A type of analyzed 
data, constrained a limitations in very narrow definition of entrepreneurship 
as the number of formal registered companies.   
 In this study, it was decided to apply a division into regions defined as 
established territorial units of a relatively large surface area and population, 
and with a specific economic policy prevailing. 
In Polish economic reality, this region can correspond to a voivodeship. This 
approach is based on factors including their independently established poli-
cies, independent local authorities, and own budget. With specifics of each 
voivodeship, an endogenous development potential can be developed, influ-
encing opportunities and barriers to the development and growth of enter-
prises in that region. 
 According to the subject of this article, individual features of the region, 
having a direct influence on the investment level and the profitability of the 



Tomasz Groszkowski,Tomasz Stryjewski 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 157–165 

160 

business should be emphasized  (Godlewska, 2001, p. 14). If the region is 
understood as an environment consisting of humans, other companies and 
institutions, having certain features that may or may not be attractive to new 
businesses, then it is important to communicate conditions prevailing in that 
region, which will affect the company's position, and to ensure future entre-
preneurs are familiar with and have access to that information. 

2. Research Methodology and Analysis Results  

 In this paper, the main part of the space-time analysis of entrepreneur-
ship in Poland aimed at finding information about factors determining spe-
cific indicators, and reasons why these indicators vary in individual 
voivodeships. A panel approach of the fixed effect/random effect (FE/RE 
hereafter) type is suitable for such kind of modeling (Greene, 2005). 
 The analysis covered annual data for a period from 2004 to 2013 for 16 
Polish voivodeships. 15 factors possibly affecting the entrepreneurship de-
velopment in Poland1 were selected, following a detailed substantive analy-
sis: Gross Domestic Product per capita (GDP per capita), enterprises invest-
ment outlays per capita (inv), retail sales per capita (sprzed), new apartments 
put in use per thousand inhabitants (mieszk), population density (gest), per-
centage of population living in cities (urbanizacja), number of cities in 
a region (miasta), roads outside built-up areas per 100 km2 (drogi), employ-
ment rate (zatr), average monthly disposable income per capita (doch), num-
ber of tourist facilities per capita (turyst), number of people with higher  
education per thousand inhabitants (wykszt), net migration per thousand 
inhabitants (migr1000), and municipal revenues per capita (doch per capita). 
 Starting with a framework of the congruent modeling concept, an inter-
nal structure of specific processes was analyzed during the first stage of the 
study (Talaga, Zieliński, 1989). A congruent panel data model exhibits the 
same harmonic structure on both sides of the equation in dynamic terms. The 
analysis showed the significance of a linear trend in all tested processes and 
autoregressive relationships over time. However, due to a small sample size 
in the researched period, only the first-order autoregression was considered. 
 The main problem associated with the use of models of this class is that 
they belong to a group of static methods. Modeling the dynamic relationship 
with these methods results in both OLS and GLS coefficients bias 
(Wooldridge, 2010). 

                                                 
1 Understood as the number of companies per 10000 people of employable age (entrepre-

neurship index – wskpz). 



The Econometric

Figure 1.  Entrepreneurship index 
2004–2013 

Unfortunately, dynamics of
terms of ACF and PACF functions)
ble is not allowed in the FE/RE models
priate instrumental variable
that the search for an 
give any meaningful
cial" variable correspond
 An instrumental variable for the entrep
ed with a dynamic panel model based on
(model hypothesis):
 itwskpz α= 0

where: ������� – spatial entrepren
trend, �	, ��, ��, �

Econometric Analysis of Entrepreneurship Determinants… 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 

Entrepreneurship index – time series for voivodeships in the years 
 

dynamics of all processes exhibits strong autocorrelation
terms of ACF and PACF functions). Since use of a lagged dependent vari

in the FE/RE models, it was necessary to select an
priate instrumental variable, so no information was lost. It should be noted 

an instrument within a specified group of variables
meaningful results. Therefore, it was necessary to create an 

corresponding to a character of the real instrument. 
nstrumental variable for the entrepreneurship indicator was 
a dynamic panel model based on a structure of internal variables

: 

ittiti twskpzwskpz ηααα ++++ −− 32,21,10  

spatial entrepreneurship indicator in period t, � 
 – evaluation parameters, η

��
 – random spatial comp

) 157–165 

161

 
time series for voivodeships in the years  

autocorrelation (in 
dependent varia-
select an appro-

It should be noted 
specified group of variables did not 

an "artifi-

reneurship indicator was evaluat-
of internal variables 

(1) 

 – linear 
random spatial compo-



Tomasz Groszkowski,Tomasz Stryjewski 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 157–165 

162 

nent in period t. The following model was developed with a two-step estima-
tion method2 (2-step method). 

Table 1. A dynamic panel model for a spatial entrepreneurship indicator (Blundell 
and Bond estimator) 

Dependent variable: wskpz 

 Coefficient SD z p-value  

wskpz(-1) 0.9835 0.0385 25.5764 <0.00001 *** 
Const. 1.4756 5.0447 0.2925 0.7699  
Time 0.4533 0.1128 4.0188 0.00006 *** 

Sargan over-identification test:  
Chi-square(43) = 15.7061 

Test for AR(1) errors: z = –2.27161 [0.0231] 
Test for AR(2) errors: z = –0.713604 [0.4755] 

 The fitted values from the above model created an instrumental variable 
for a dynamic relationship in FE/RE models. 
 Further research focused on identifying determinants for the spatial en-
trepreneurship indicator and its spatial differentiation described by a neigh-
borhood matrix. For this purpose, a group of potential exogenous variables 
was determined and a pooled OLS model was estimated. Results of this es-
timation are shown in Table 2 (only statistically significant variables). The 
next step of the analysis was verification which approach is correct: pooled 
OLS or FE/RE. Results of this verification are shown in Table 3. 

Table 2. The data panel model estimation using pooled OLS  

Dependent variable: wskpz 

 Coefficient SD t-ratio p-value  

Const. 4.6527 3.59303 1.2949 0.1978  
urbanizacja −16.5172 4.61591 –3.5783 0.0005 *** 
turyst(-1) 0.0625 0.0300 2.0804 0.0396 ** 

doch per capita 0.0090 0.0018 4.9687 <0.00001 *** 
Time −0.4339 0.1932 -2.2453 0.0266 ** 

yhat17(-1) 0.9631 0.0297 32.3751 <0.00001 *** 

Mean dependent var 149.7844 S.D. dependent var 25.9606 
R-squared  0.9824 Adjusted R-squared 0.9817 

Log-likelihood −339.4414 Akaike criterion 690.8827 
Schwarz criterion  707.9949 Hannan-Quinn 697.8355 

rho  0.3696 Durbin-Watson 1.0856 

Note: That 17 – instrumental variable from table 1 model. 

  

                                                 
2 See footnote 1. 



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DYNAMIC ECONOMETRIC MODELS 15 (2015) 157–165 

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Table 3. Panel models diagnostics3 

F test (Fixed effect) 

the null hypothesis that the 
pooled OLS model 

is adequate, in favor of the fixed 
effects alternative 

F(15, 107) = 2.6292 p-value = 0.0021 

Breusch-Pagan test (Random effect) 

the null hypothesis that the 
pooled OLS model 

is adequate, in favor of the 
random effects alternative 

LM = 1.7714 
p-value = prob(chi-square(1) > 

1.77137) = 0.1832 

Hausman test  

the null hypothesis that the 
random effects 

model is consistent, in favor of 
the fixed effects model 

H = 14.5289 
p-value = prob(chi-square(5) > 

14.5289) = 0.0126 

 Results presented in Table 3 imply that a fixed effects-FE model is an 
appropriate approach. This means that the spatial differentiation in the entre-
preneurship index level is of a structural and permanent character. 
 Therefore, the next step of the study was to estimate and validate the FE 
model. The model presented in Table 2 became a model hypothesis and the 
estimated (after verification) FE model is presented in Table 4. 

Table 4. The FE model (Dependent variable: wskpz) – LSDV estimator 

 Coefficient SD t-ratio p-value  
Const 31.9812 9.3363 3.4255 0.00086 *** 

doch per capita 0.0091 0.0017 5.4573 <0.00001 *** 
yhat17(-1) 0.7004 0.0728 9.6190 <0.00001 *** 

Mean dependent var  149.7844 S.D. dependent var  25.9606 
Sum squared resid  1127.241 S.E. of regression  3.2012 
LSDV R-squared  0.9868 Within R-squared  0.7427 
LSDV F(17, 110)  484.8430 P-value(F)  4.79e-95 

Log-likelihood −320.8560 Akaike criterion  677.7120 
Schwarz criterion  729.0486 Hannan-Quinn  698.5703 

Rho  0.2742 Durbin-Watson 1.1935 

 The FE model has better statistical properties than the model estimated 
by pooled OLS. In particular, significant reduction in the first-order random 
component autocorrelation was possible in the FE model. However, the 
study also showed that the analysis of the entrepreneurship spatial differenti-
ation using the spatial entrepreneurship indicator is biased and, in conse-

                                                 
3 See footnote 1. 



Tomasz Groszkowski,Tomasz Stryjewski 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 157–165 

164 

quence, has some disadvantages. Particularly, this indicator is strongly auto-
regressive, indicating an autonomous nature of changes in that process. 
It may also imply that variables used in the analysis were unsuitable. 
 However, the results presented in Table 4 confirm the economic theory 
about the entrepreneurship development index. The main determinants of 
entrepreneurship remain the same, being the entrepreneurship indicator from 
the previous period and the municipal revenues per capita. These two varia-
bles positively influence the entrepreneurship rate, although they strengthen 
the autoregressive character of that process. 

Conclusions 

 A study on entrepreneurial activities based on their index led to three 
main conclusions. 
 First, the analysis showed that the problem of entrepreneurship devel-
opment in Poland is a structural problem. This means that differences in the 
levels in individual regions are stable and result from internal conditions, as 
well as from the regional polarization processes. Therefore, this problem 
cannot be solved locally, but only as part of a coherent regional policy at the 
governmental or European level. 
 Second, the entrepreneurship index determinants in the studied period 
were identified. The main determinant is the municipal revenues per capita. 
However, the entrepreneurship index is a strongly autonomous variable and, 
therefore, the autoregressive component is important in the analyzed model. 
The main causes of the dependent variable formation are the regional eco-
nomic situation (autoregressive process) and public revenues. Public reve-
nues affect the dependent variable in two ways: as a determinant (indicator) 
of regional economic situation and as the basis for public expenditures 
which, in turn, influence small local enterprises. 
 Third, an important part of the study is focusing on a selection of suita-
ble explanatory variables. The spatial entrepreneurship indicator is highly 
autocorrelated, and this causes problems with identification of its determi-
nants. In the further research, other measures should be considered for explo-
ration and selection, better describing the investigated process. 

References  
Adamczyk, W. (1995), Ewolucja form i typów przedsiębiorczości (The evolution of forms 

and types of entrepreneurship), Zeszyty Naukowe Akademii Ekonomicznej w Poznaniu, 
236. 



The Econometric Analysis of Entrepreneurship Determinants… 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 157–165 

165

Chrapek, G. (2009), Przedsiębiorczość osób fizycznych na obszarach wiejskich Podkarpacia, 
[w:] Rola przedsiębiorczości w kształtowaniu społeczeństwa informacyjnego (Entre-
preneurship individuals in rural areas of Podkarpacie, [in:] the role of business in 
shaping the information socjety), Seria Przedsiębiorczość – Edukacja, Warszawa-
Kraków. 

Godlewska, H. (2001), Lokalizacja działalności gospodarczej (The location of economic 
activity), Dom Wydawniczy ELIPSA, Warszawa. 

Greene, W. H. (2005), Econometric Analysis, Prentice Hall, New York. 
Gryffin, R. W. (1997), Podstawy zarządzania organizacjami (Fundamentals of organizational 

management), PWN, Warszawa. 
Jezierska-Thole, A. (2010), Zmiany poziomu infrastruktury i jej wpływ na rozwój przedsię-

biorczości na obszarach wiejskich na przykładzie województwa kujawsko-
pomorskiego i pomorskiego (Changes in the level of infrastructure and its impact on 
development of entrepreneurship in rural areas on the example of Kujawsko-
Pomorskie and Pomorskie), Acta Acientum Polonorium, Oeconomia, 9(3). 

Kalkan, M., Kaygusuz, C. (2012), The Psyhology of Enterpreneurship [in:] Entrepreneurship 
– Born, Made and Educated, Ed. T. Burger-Helmchen, Rijeka. 

Szarecki, A. (2008), Przedsiębiorczość jako forma kultury (Entrepreneurship as a form of 
culture), Problemy Zarządzania, 2. 

Talaga, L., Zieliński, Z. (1986), Analiza spektralna w modelowaniu ekonometrycznym (Spec-
tral analysis in econometric modeling), PWN, Warszawa. 

Timmons, J. (1990), New Venture Creation, Irvin, Boston. 
Wooldridge, J. M. (2010), Econometric analysis of cross section and panel data, Massachu-

setts Institute of Technology. 

Ekonometryczna analiza determinant przedsiębiorczości  
w Polskich województwach w latach 2004–2013 

Z a r y s  t r e ś c i. W artykule przedstawiono wyniki badań, opisujące i wyjaśniające różnice 
w poziomie wskaźnika przedsiębiorczości pomiędzy województwami polski w latach 2004– 
–2013. W celu potwierdzenia zakładanych rezultatów, użyto modelu panelowego z efektami 
stałymi i losowymi. Analiza wykazała, że wskaźnik przedsiębiorczości jest zróżnicowany 
pomiędzy województwami, a zróżnicowanie to ma charakter strukturalny i stały. Model po-
twierdził również oczekiwane determinanty przedsiębiorczości.  

S ł o w a  k l u c z o w e: przedsiębiorczość, czynniki rozwoju, model panelowy, model 
FE/RE 




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