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The Role of Openness inRegional Economic Growth.
The Case of Polish and Spanish NUTS-2 Regions2
With the use panel data techniques, we estimate an empirical growth model for
Polish and Spanish NUTS-2 regions –two similar-in-size European economies with
the inferior initial level of development and at the same time major recipients of EU
structural funds. The analysis is carried out for 16 Polish voivodeships and 19 NUTS-2
level municipalities, provinces and autonomous communities observed over the period
2000–2014. Within the joined group of regions, we observe aclear beta-absolute and
sigma-convergence. Within countries, the evidence points todivergence. The level of
regional sigma convergence is similar. Of particular interest tous is the assessment
of the role of broadly dened economic openness inthe process of regional economic
growth. The initial analysis points tothe bidirectional relationship. We then estimate
adynamic panel data model with the use of GMM due tonon-stationary nature of the
key variables. We control for potential interactions of openness with regional human
capital endowments as well as other major determinants postulated by theoretical
models. The obtained results are inline with theoretical predictions.
Keywords: regional development, economic growth, panel data, Poland, Spain
JEL Classication Codes: C23, R11, F43, O18, O4
1 Instytut Rozwoju, Sopot; Uniwersytet Gdański, Wydział Ekonomiczny, Katedra Ekono-
miki Integracji Europejskiej; e-mail: email@example.com
2 Acknowledgments: The study has been supported by agrant from the National Science
Centre [grant number 2015/19/B/HS4/01704] ‘Regional Exporting Activity. Assessment of
Determinants inLight of Contemporary Foreign Trade Theory for Poland and Spain’ super-
vised by Prof. Stanislaw Umiński and carried out by Institute for Development. The data for
the regional trade of Spain have been retrieved by Marcin Skurczyński and Anna Fornal-
ska-Skurczyńska. We appreciate the comments from two peer reviewers as well as the com-
ments from the participants of the conference at Warsaw School of Economics on 12 May
2017 and the comments and suggestions from Stanisław Umiński, Jarosław Nazarczuk and
44 Tomasz Brodzicki
With ongoing globalisation, the degree of openness of national and thus
regional economies is increasing, even inthe aftermath of the global nancial
crisis. Liberalisation of trade ows and greater mutual openness lead todefrag-
mentation of production and emergence of the so-called global value chains,
which increases mutual interdependence of economies. Greater openness
increases exposure tointernational shocks, which could be considered as one
of its major costs.
Despite the dominance of the viewpoint of the benecial impact of open-
ness (liberalisation) on economic growth, the review of both theoretical and
empirical literature does notbring clear results. Nonetheless, the signicance
and direction of causality inthe relationship between openness and economic
growth is an important issue both intheoretical and empirical economic liter-
ature. On the theoretical ground, asignicant progress was made inthe 80 s
and 90 s,with the emergence of the new growth theory and the new economic
geography. On the empirical ground, the major development was ashift from
standard cross-sectional regressions (ala Barro) tomore sophisticated panel
data, including dynamic panel data models. Most of the empirical studies sofar
have been carried out at national economies level and notthe within the coun-
try or inter-country regional level of analysis.
The purpose of the paper is toidentify the role of trade openness indeter-
mining the growth of Polish and Spanish NUTS-2 regions, controlling for other
signicant factors affecting economic growth process. With the use panel data
techniques, we estimate an empirical growth model for NUTS-2 regions of Poland
and Spain –two similar-in-size European economies with the inferior initial
level of development and at the same time major recipients of the EU structural
funds. The analysis is carried out for 16 Polish voivodeships and 19NUTS-2 level
municipalities, provinces and autonomous communities of Spain, observed over
the period 2000–2014. Assuming that Spanish and Polish regions share the same
steady-state point inthe long run and noting the higher present mean level of
development of Spanish regions, we can learn alot from their experience inthe
EU (Spain entered the EU –then EEC back –in1986).
The remainder of the paper is constructed as follows. Section 2 reviews the
theoretical literature, while Section 3 reviews the empirical literature. Section4
describes the data. Section 5 discusses the dependent variable, potential beta
and sigma convergence of income per capita, the changes inthe openness rate
45The Role of Openness inRegional Economic Growth. The Case of Polish and Spanish...
as well the relation between the two. Section 6 presents the results of an empir-
ical model. The nal sectionconcludes.
2. Openness ineconomic growth
–review of theoretical literature
In the neoclassical growth theory (Solow3&Swan4) openness does notmat-
ter inthe long-run, as growth is independent of economic policy. It could only
lead tothe level effects. In the short-run, capital deepening is the major source
of growth –as income per capita is proportional tocapital per capita. The level
of real income per capita inthe steady state is apositive function of the rate of
saving (investment), anegative function of the population growth rate n and
depreciation of capital δ. Technological progress affects the level of real GDP per
capita positively. The only factor affecting the long-run growth rate is the rate
of exogenous technological progress. In this setting the impact of an increase
inopenness due totrade policy on economic growth is temporary.
In an augmented model of Mankiw et al.
inaddition, the human capital
endowment is taken into account. The augmented neoclassical model by Brodzicki
takes further the impact of infrastructure into account. In the model, inaccord-
ance with Mincerian tradition, the average level of education may be specied
as afunction of average years of schooling and average years of experience7.
The emergence of the endogenous growth and new trade theories (Lucas8,
Romer9) has led tothe reopening of the debate on the role of trade, and more
general, the degree of openness indetermining economic growth inthe medium
3 SolowR. (1957), AContribution tothe Theory of Economic Growth, QJE 70 (1)/1956,
65–94; idem, Technical Change and the Aggregate Production Function, Review of Econom-
ics and Statistics 39, 312–320.
4 SwanT. (1956), Economic Growth and Capital Accumulation, Economic Record 32,
MankiwG., Romer D., WeilD. (1992), AContribution tothe Empirics of Economic
Growth, Quarterly Journal of Economics 107 (2), 407–437.
BrodzickiT. (2015), Shallow determinants of growth of Polish regions. Empirical analy-
sis with panel data methods, Collegium of Economic Analysis Annals 39, 25–40.
7 BilsM., KlenowP. J. (2000), Does Schooling Cause Growth?, AER 90, 1160–1183.
8 LucasR. (1988), On the mechanics of economic development, Journal of Monetary Eco-
nomics 22 (1), 3–42.
9 RomerP. M. (1986), Increasing Returns and Long-run Growth, JPE 94/1986, 1002–1037;
idem, Endogenous Technological Change, JPE 1990, 98 (5), 71–102.
46 Tomasz Brodzicki
and long run. The models of the rst and second generation endogenized the
rate of growth of technology, either by allowing for the impact of human cap-
ital or introducing aseparate R&D sector purposefully producing knowledge
inthe form of patents. It is worth topoint out, however, that even inasemi-en-
dogenous model of Ben-David&Romer10, openness totrade through its impact
on the process of accumulation of knowledge and technology transfer leads
toendogenization of economic growth.
The new growth theory models of Rivera-Batiz&Romer
man12 differ –apolicy shift leading toagreater extent of openness, could lead
toapermanent effect –long-run growth rate could be affected but notonly pos-
itively, an adverse impact is also possible. In brief, the balance of costs and ben-
ets of greater openness (liberalisation) depends on the nature and the exact
product structure of trade.
Greater openness totrade affects the rate of accumulation of knowledge
mostly through imports. They work as achannel allowing absorption of more
advanced knowledge positively affecting overall efciency and thus growth
rates. Rivera-Batiz&Romer13 show however that whether the effect is positive
or adverse depends on the distance of economy from global technology fron-
tier and the nature of diffusion of knowledge (perfect versus imperfect). Imper-
fect knowledge ows coupled with openness can actually harm underdeveloped
states or regions.
From atheoretical standpoint, openness affects growth through anumber
of channels. First of all, it leads toreallocation of factors of production tomore
productive sectors and thus tospecialisation inaccordance with the compara-
tive or competitive advantage thus resources are allocated efciently. Secondly,
it leads toincreased diffusion and accelerated absorption of knowledge and tech-
nology (technology transfer) inparticular through imports14 or inow of FDI15.
10 Ben-DavidD., LoewyM. B. (2002), Trade and the Neoclassical Growth Model, Journal
of Economic Integration, 18, 1–16.
11 Rivera-BatizL., RomerP. M. (1991), Economic Integration and Endogenous Growth,
QJE 106 (2), 531–555.
12 GrossmanG. M., HelpmanE. (1991), Innovation and Growth inthe Global Economy,
MIT Press, Cambridge MA.
GrossmanG. M., Helpman E. (1992), Innovation and Growth: Technological Competition
inthe Global Economy, MIT Press, Boston.
13 Rivera-BatizL. et. al. (1991), op.cit.
14 CoeD. T., HelpmanE. (1995), International R&D spillovers, EER 39 (5), 859–887.
15 BranstetterL. (2006), Is foreign direct investment achannel of knowledge spillovers?
Evidence from Japan’s FDI inthe United States, Journal of International Economics 68 (2),
47The Role of Openness inRegional Economic Growth. The Case of Polish and Spanish...
Thirdly, it stimulates the rate of innovation as it is frequently associated with an
increase inthe expenditures on research and development. Fourthly, it allows
better utilisation of scale economies and agglomeration externalities as aresult
of greater specialisation. At the same time, it leads toenhanced accumulation
of factors of production. Finally, it stimulates competition innational and inter-
national markets thus forcing companies tobe more innovative.
It is worth stressing that Rodrik16 perceives openness or the extent of inte-
gration as one of three fundamental deep determinants of economic growth
alongside the quality of institutions and geographical conditions.
Afurther insight can be brought by the new economic geography literature.
As Breinlich et al.17 stress, NEG theory is based on trade theory, and thus the
relationship between external trade, internal economic geography, and regional
disparities, is at its core. Fujita et al.18 suggest that openness could work todis-
perse manufacturing industry as awhole but also lead tothe spatial clustering
of specic industries. External trade thus affects spatial patterns of activity by
changing market access considerations19.
It is also worth addressing the direction of causality between trade openness
and economic growth. If openness affects growth than we deal with export-led
growth process through the channels described above. On the other hand, the
causality could go from growth toopenness. High productivity inthe larger
domestic market (home marker effect) translates into greater international com-
petitiveness and increase inexports. At the same time, larger domestic economy
creates alarger demand for imports. Thus abidirectional relationship is likely
toexist if these two are allowed tohold simultaneously20.
16 RodrikD. (2003), Institutions, Integration and Geography: In Search of the Deep De-
terminants of Economic Growth, in: Search for Prosperity: Analytic Narratives on Economic
Growth, Princeton University Press, Princeton.
BreinlichH. et al. (2013), Regional growth and regional decline, CEP Discussion
FujitaM. et al. (1999), The spatial economy: cities, regions and international trade, MIT
Press, Cambridge MA.
HansonG. (1996), Localization Economies, Vertical Organization and Trade, AER, 86 (5),
20 LiuX., SongH., RomillyP. (1997), An empirical investigation of the causal relationship
between openness and economic growth inChina, Applied Economics 29 (12), 1679–1686.
48 Tomasz Brodzicki
3.Review of empirical literature
In the empirical literature, two strands dominate –macro approach –mostly
cross-sectional analysis of global or more homogeneous groups and the micro
approach –analysis for individual countries based on sectoral or rm level data.
Many variables are utilised as proxies of openness, nonetheless, the openness
ratio is the most popular.
Barro21 identied apositive and statistically signicant impact of the level
of openness on economic growth inacross-sectionof countries. Dollar
apotential bias inthe index, utilised an index of exchange rate disturbances,
nding it toadversely affect economic growth. The result was conrmed by
Easterly et al.23 and Lee24 using similar approaches. Sachs &Werner25 utilised
adichotomous index of openness, conditional on meeting 5 criteria nding open-
ness tomatter for growth inacross-sectionof countries. It was also utilised by
Gallup et al.26 leading tosimilar result even if deep-rooted geographical factors
were taken into account and Vamvakidis27 nding positive and statistically sig-
nicant effects of multilateral economic integration. On the other hand, Wac-
ziarg&Welch28 found the studies applying Sachs-Werner index tobe sensitive
tothe period under analysis.
(1998) inhis seminal study analysed the impact of 9 different
indices of openness/disturbances inthe exchange rate on TFP and thus indi-
rectly on real GDP per capita inacross-sectionof 93 countries. The impact was
21 BarroR. J. (1991), Economic Growth inaCross Section of Countries, QJE 106/1991,
DollarD. (1992), Outward-oriented Developing Economies Really To Grow More Rapidly:
Evidence from 95 LDCs, 1976–1985, Economic Development and Cultural Change, 523–544.
23 EasterlyW. et al. (1993), Good Policy or Good Luck?, Journal of Monetary Economics
32 (3), 459–483.
24 LeeJ. W. (1993), International Trade Distortions and Long-run Growth, IMF Staff Pa-
pers, 40 (2), 299–328.
25 SachsJ. D., WarnerA. (1995), Economic Convergence and Economic Policies, NBER
Working Paper 5039.
GallupJ. L., SachsJ. D., MellingerA.D (1999), Geography and Economic Development,
International Regional Science Review 22 (2), 179–232.
VamvakidisA. (1999), Regional Trade Agreements or Broad Liberalization: Which Path
Leads toFaster Growth?, IMF Staff Papers 46 (1), 42–68.
28 WacziargR., WelchK. H. (2003), Trade Liberalization and Growth: New Evidence, Re-
search Paper 1826.
29 EdwardsS. (1998), Openness, Productivity and Growth: What Do We Really Know?,
The Economic Journal 108, 383–398.
49The Role of Openness inRegional Economic Growth. The Case of Polish and Spanish...
found tobe positive however its magnitude was found tobe inferior incompar-
ison tothe initial level of GDP per capita or the initial level of human capital.
Due topotential endogeneity IV approach is frequently utilised. For instance,
proposed an instrumental variable based on geographical
factors that determine toalarge extent trade while having exogenous nature
inrelation tothe level of income. The impact of openness proved tobe insignif-
icant intwo large cross-sections. In contrast, Irwin&Tervio31 reiterated the test
by Frankel&Romer32 inaslightly modied manner inapanel of countries. The
results pointed toapositive relationship between the intensity of trade and the
level of GDP per capita. Romalis33 found similar results using the instrumental
variable approach inalarge panel of countries (135) observed over aperiod of
40 years (1960–2000).
tested six different measures of openness inalonger time period
(1920–1999) nding that the positive relationship between openness and growth
exists only after 1970, which could be related tooverall higher openness with
increasing extent of globalisation.
Wacziarg &Welch35 utilized adifferent approach toanalysing the effects of
cases of signicant trade-policy liberalizations and found that, on average, the
investment rate increased by 1.5 to2 percent, and the share of trade inGDP by
5percent, while the ex-postgrowth rate was higher than ex-ante growth rate
by amean of 1.5 percent.
Using the extreme bounds analysis, Levine&Renelt
found the index of open-
ness tobe one of the variables affecting the growth rate inacross-sectionof
countries indirectly through an impact on the process of accumulation of capi-
tal (rate of investment). The direct impact of openness was rejected. In contrast,
FrankelJ., RomerD. (1996), Trade and Growth: An Empirical Investigation, NBER
Working Paper 5476; FrankelJ., RomerD. (1999), Does Trade Cause Growth?, AER 89 (3),
31 IrwinD., TervioM. (2002), Does trade raise income? Evidence from the twentieth cen-
tury, Journal of International Economics 58, 1–18.
32 FrankelJ., RomerD. (1999), Does Trade Cause Growth? op.cit.
33 RomalisJ. (2007), Market Access, Openness and Growth, NBER Working Paper W13048/
34 VamvakidisA. (2002), How Robust is the Growth-Openness Connection? Historical Evi-
dence, Journal of Economic Growth 7, 57–80.
35 WacziargR., WelchK. H. (2003), op.cit.
LevineR., ReneltD. (1992), ASensitivity Analysis of Cross-country Growth Regressions,
American Economic Review 82, 942–963.
50 Tomasz Brodzicki
Doppelhofer, Sala-i-Martin&Miller37 using the Bayesian Averaging of Classical
Estimates for abalanced panel of 88 countries and 68 variables founding the
time since the opening of the economy (impact of liberalisation) topositively
affect economic growth. The overall openness was found tomatter less.
The studies on the impact of openness on growth at the regional level are
rather rare. In recent years anumber of studies have been performed on Asian
economies. And thus Sun et al.38 show inastudy of Chinese regions at man-
ufacturing industries level that openness totrade (trade orientation and FDI)
have apositive effect on technical efciency. Leong39, analysing the impact of
special economic zones as cases of liberalisation on regional economic growth
inChina and India, found that both FDI and export topositively affect growth.
The presence of SEZs increases regional growth, however, an increase inthe
number of SEZs has anegligible effect on growth. Leong nds greater openness
(wider liberalisation) as aprecondition of further growth. Wei et al.40 inapanel
of Chinese regions over the entire period 1979–2003 proved that FDI inows
were one of the forces behind the observed regional discrepancies ingrowth.
The authors claim however that FDI cannot be blamed for inequality as it was
due tothe uneven distribution of FDI and notthe FDI itself.
Anwar&Nguyen41 using simultaneous equations model found inapanel of
61 provinces of Vietnam from 1996–2005, amutually reinforcing two-way pro-
cess between FDI and regional economic growth. The benets of FDI inow
could be further strengthened by more investments into education and train-
ing, development of the nancial market and reducing technology gap between
foreign and local rms.
, rising spatial disparities inregional devel-
opment inmany developing states are mostly due touneven impact of increased
trade openness and globalisation. It leads toefciency gains mostly due to con-
37 DoppelhoferG., MillerR. I., Sala-i-MartinX. (2000), Determinants of Long-term Gro-
wth: ABayesian Averaging of Classical Estimates (BACE) Approach, NBER Working Paper
SunH. et al. (1999), Economic Openness and Technical Efciency: ACase Study of Chi-
nese Manufacturing Industries, Economics of Transition 7 (3), 615–636.
39 LeongC. K. (2013), Special Economic Zones and Growth inChina and India: An Em-
pirical Investigation, International Economics and Economic Policy 10 (4), 549–567.
WeiK., YaoS., LiuA. (2009), Foreign Direct Investment and Regional Inequality inChina,
Review of Development Economics 13 (4), 778–791.
AnwarS., NguyenL. P. (2010), Foreign Direct Investment and Economic Growth inViet-
nam, Asia Pacic Business Review 16 (1–2), 183–202.
42 KanburR., VenablesA. (2005), Rising Spatial Disparities and Development, UNI-WI-
DER Policy Brief 3.
51The Role of Openness inRegional Economic Growth. The Case of Polish and Spanish...
certation of economic activity inmajor cities and coastal districts, adversely
affecting inland regions. In astudy on Latin America, Serra et al.43 argue that
regional disparities modestly increased, at least temporarily, inthe wake of trade
liberalisation. It was especially marked for Mexico.
When analysing the nexus between openness and economic growth at
regional level we have tonote the direct or indirect impact of other accompany-
ing variables or processes. For instance, Sachs et al.44 studying σ -convergence
and ß-convergence show that more than 80 percent of the cross-state variation
ingrowth rates among Indian states can be explained solely by an urbanisation
variable. Agglomeration factors are also strongly postulated by NEG theories.
The role of human capital accumulation is clear on theoretical and empirical
grounds. However, the scope of the denition of human capital differs. For exam-
ple, inthe study by Boschma&Fritsch
points inline with Florida toan important
contribution of the so-called creative class for regional growth in7European
countries. They are however notable todetermine whether human capital as
measured by the creative occupation, outperforms standard indicators based
on formal education and whether formal education has astronger impact. The
creative class endowment is positively affected by the regional climate of toler-
ance and openness as well as regional job opportunities.
The economic structure could matter as well including the size and share of
the industrial sector. For instance, the study by Hansen&Zhang46 points tothe
key role of the industrial sector inexplaining the regional variation ingrowth
among Chinese provinces. The result supports the Kaldorian approach toregional
economic growth with cumulative causation between trade liberalisation, the
rise inexport demand, the growth of industrial sector (industrialisation) and its
impact on overall productivity and thus increases ininternational competitiveness.
One of the issues that cannot be overlooked is the issue of path-depend-
ency inregional development. For instance, Felice&Vecchi47 indicate that the
regional North-South variation inItaly was already present the moment the
43 SerraM. I. et al. (2006), Regional Convergence inLatin America, IMF Working Paper
44 SachsJ. et al. (2002), Understanding Regional Economic Growth inIndia, Asian Eco-
nomic Papers 1 (3), 32–62.
45 BoschmaR. A., FritschM. (2009), Creative Class and Regional Growth: Empirical Evi-
dence from Seven European Countries, Economic Geography 85 (4), 391–423.
46 HansenJ. D., Zhang J. (1996), AKaldorian Approach toRegional Economic Growth
inChina, Applied Economics 28 (6), 679–685.
FeliceE., VecchiG. (2015), Italy’s Modern Economic Growth, 1861–2011, Enterprise & So-
ciety 16 (2), 225–248.
52 Tomasz Brodzicki
country was unied and then increased. The explanation of the present vari-
ation involves endogenous factors –natural resources, human capital endow-
ment, and social capital.
In an article Brodzicki
published inthe Annals, attempted toidentify shal-
low determinants of growth of Polish regions as well the sign and magnitude
of macroeconomic’ education –externality and macroeconomic infrastructure
externality. We constructed accordingly an augmented neoclassical growth
model incorporating aMincerian approach tohuman capital accumulation, fur-
ther assuming adirect impact of infrastructure on the overall productivity. The
estimated panel model, accounting for xed region-specic effects, was robust
and explained approx. 90 percent of observed variation inGDP per capita. The
return tothe accumulation of human capital through education and experience
for Polish regions was found tobe statistically signicant, robust and positive.
The macroeconomic infrastructure externality proved tobe, inturn, positive
–however overall insignicant with the impact of quality of railway.
In the empirical partof the paper, we utilise foremost the data from the QoG
EU Regional dataset (Charron et al.
2016). The trade data for Polish and Spanish
regions have been obtained from the Polish Customs Chamber (Izba Celna) and
retrieved from DataComex Español database
. They cover the period 2005–2015.
QoG EU Regional database is adataset consisting of approximately 450 var-
iables covering three levels of European regions NUTS0, NUTS1, and NUTS2.
The data is given intime-series version (from 1990 to2015) and the unit of
analysis is region-year. The data on GDP per capita are available for the period
48 BrodzickiT. (2015), op.cit.
CharronN. et al. (2016), The Quality of Government EU Regional Dataset, version Sep.16,
University of Gothenburg: The Quality of Government Institute, http://www.qog.pol.gu.se.
53The Role of Openness inRegional Economic Growth. The Case of Polish and Spanish...
5.Convergence inregional incomes and the openness ratio
The empirical analysis is carried out for agroup of 16 Polish and 19 Span-
ish NUTS-2 regions within the period 2000 to2014.
The dependent variable is the present study is anatural logarithm of GDP
per capita (ln_y). The other key variable is an openness index of regions is
measured using the standard openness index –the ratio of exports and imports
If we treat two regions jointly the relation of the initial log of GDP per capita
and the mean growth rate of GDP per capita over the observed period is nega-
tive and points tobeta convergence. Polish regions are clearly catching up with
Spanish regions interms of the level of development. If we treat both countries
separately, the data are less conclusive pointing to weak regional divergence
inPoland and weak regional beta-convergence inSpain, however, the results
are notstatistically robust.
We know from economic growth theory that beta-convergence is anecessary
however notsufcient condition for sigma-convergence. Thus the above result
should be indicative of sigma-divergence inboth countries at NUTS 2 level. We
test is by plotting the evolution of standard deviation of the log of GDP per cap-
ita for both countries over the analysed period.
The result points toclear sigma-divergence inPoland over the analysed period
and U-shape pattern for Spain –with the initial sigma-convergence and then
divergence inthe aftermath of the nancial and eurozone crises. It seems that
less developed Spanish regions have been more adversely affected by the crises.
The openness ratio increased inmost of the analysed regions from 2005
to2014 (on average by 9 percent). The openness ratio dropped only inthe case
of Mazowieckie, Illes Balears, Canarias and Comunidad de Madrid.
On the other extreme, the highest increases have been reported inAndalucía,
Łódzkie, Dolnośląskie and Opolskie (by more than 15 percent), Lubuskie by
approx. 25 per cent, Región de Murcia 33 percent and Pomorskie by 34.3 percent.
We nowwill investigate the relationship between income per capita and
openness. The correlation between the two is rather weak. We have tonote that
within apanel, non-stationarity and cross-sectional dependence could exist. At
the same time, we deal with aheterogeneous panel data model that is amodel
inwhich all parameters (constant and slope coefcients) vary across regions
analysed (we thus assume conditional convergence tohold).
54 Tomasz Brodzicki
We rst apply Im–Pesaran–Shin test (Im et al.51 2003) as we cannot infer
that all panels share acommon autoregressive parameter. Cultural, other insti-
tutional and deeper rooted factors make this assumption rather feeble. The two
key variables, anamely log of GDP per capita and alog of openness ratio, are
non-stationary and we cannot reject the null hypothesis of nocointegration. In
the further econometric analysis, we thus utilise the standard solution inthe
empirical literature of the subject thus applying adynamic panel data model
estimated with the use of GMM (Arellano-Bover52 & Blundell-Bond53).
The results of Pesaran’s test of cross-sectional independence (29.653,
Pr = 0.0000) indicate that we have to reject the null hypothesis of cross-sec-
tional independence and thus we deal with cross-sectional dependence.
Finally, we analyse whether there exists acausality relationship among the
key variables using the causality test developed by Dumitrescu&Hurlin54. The
authors proposed asimple Granger
non-causality test for heterogeneous panel
data models. Under the null hypothesis of Homogeneous Non-Causality (HNC),
there exists nocausal relationship for any of the cross-sectionunits of the panel.
Under the alternative, one subgroup of cross-section unit is characterised by
causal relationships and the other subgroup indicates nocausal relationship.
The test statistic depends on the individual Wald statistics of Granger non-cau-
sality averaged across the cross-sectionunits. Dumitrescu&Hurlin proposed
ablock bootstrap procedure implemented inSTATA todeal with cross-sectional
The value of panel standardised statistic ZHNC, based on the assumption of
asymptotic moments, allows us toreject the null hypothesis of noGranger-cau-
sality, infavour of the alternative hypothesis that there is Granger-causality
inat least one panel. The results point tobidirectional causality between GDP
per capita and openness inour sample of Polish and Spanish NUTS-2 regions.
This is inline with some of the theoretical postulates described inSection 2 and
empirical results inSection 3.
51 ImS. K., PesaranM., ShinY. (2003), Testing for Unit Roots inHeterogeneous Panels,
Journal of Econometrics 115, 53–74.
52 ArellanoM., BoverO. (1995), Another Look at the Instrumental Variable Estimation of
Error-Components Models, Journal of Econometrics 68 (1), 29–51.
53 BoschmaR. A., FritschM. (2009), Creative Class and Regional Growth: Empirical Evi-
dence from Seven European Countries, Economic Geograph 85 (4), 391–423.
DumitrescuE. I., HurlinC. (2012), Testing for Granger Non-causality inHeterogeneous
Panels, Economic Modelling 29 (4), 1450–1460.
GrangerC. W. (1969), Investigating Causal Relations by Econometric Models and Cross-
-spectral Methods, Econometrica 37 (3), 424–438.
55The Role of Openness inRegional Economic Growth. The Case of Polish and Spanish...
6.The empirical model & discussion of the results
Noting the non-stationarity of the dependent variable, we utilise the dynamic
panel data approach estimated with GMM using the xtdpdsys command. The
command ts dynamic panel-data estimators with the Arellano–Bover/Blun-
dell–Bond system estimator. Noticing problems with one-step GMM (the high
values of the Sargan test of overidentifying restrictions) we apply the two-step
GMM estimator. The initial results are presented inTable 2, where we estimate
the models for ajoint sample of Polish and Spanish NUTS-2 regions. Analyses
are performed for anumber of different specications of the model with avar-
ying selection of explanatory variables.
Our analysis is restricted by the availability of data inour dataset. We, unfor
tunately, have been unable sofar tocontrol the investments rates or regional
physical endowments (apart from transport infrastructure). We control for the
population growth rate (n)as well as the human capital endowment (ln_h –log
of population share with tertiary education).
As we do notuse xed effects method due tothe utilised econometric approach
(dynamic panel model based on rst differences) we cannot assume that initial
differences inthe level of technology are included inthe region-specic xed
effects. In order toaccount for potential differences, we take into account the
evolution of the ratio of General Expenditures of Research and Development
to GDP (d_gerd).
Similarly toBrodzicki56, we take the quality of infrastructure into account
based on the methodology proposed by Careijo et al 57. The index of infrastruc-
ture quality ICQ relativizes the infrastructure endowment by normalising the
infrastructure endowment by population and land area and simultaneously com-
paring it toabenchmark. In the present article, we take the mean for Polish and
Spanish regions as the respective benchmark. ICQ is calculated inaccordance
with the following formula:
56 BrodzickiT. (2015), op.cit.
57 CareijoE. et al. (2006), Indicadores de Convergencia Real Para los Países Avanzados,
Estudios de la Fundación, FUNCAS, Madrid.
56 Tomasz Brodzicki
where Xr iXB gives the infrastructure endowment of agiven region and the
benchmark (mean for Poland and Spain), while N and Arepresent, respectively,
population and land area.
Our base empirical model ts the data relatively well. The coefcient on
lagged dependent variable is statistically signicant, indicating the presence
of absolute (1) or conditional convergence. In (2) we introduce n and ln_h. In
most of the specications, their impact is statistically signicant and inaccord-
ance with theoretical predictions –the impact of population growth rate is neg-
ative while the impact of the human capital endowment is positive on the level
of regional income per capita. In (3) we introduce and control for variation
inregional R&D potential by the introduction of GERD (d_gerd). The impact
of general expenditure on R&D is statistically signicant, however, adverse.
Finally, in(4) we introduce our key explanatory variable –ln_open. Its impact on
the dependent variable is clearly positive and statistically signicant. Agreater
degree of trade openness boosts the economic growth of Polish and Spanish
regions, ceteris paribus. In models (5) we account for the potential joint effect
of openness and human capital endowment on the level of GDP per capita by
an introduction of an interaction term (open_h). The magnitude of the impact
of openness when we account for the interaction is signicantly stronger, how-
ever, the interaction term is negative and statistically signicant which means
that it decreases inthe human capital endowment. That is an increase inthe
extent of openness brings stronger effects on GDP per capita of regions with
initially lower levels of human capital endowment.
In the last two specications, we control for regional infrastructure endow-
ment and its quality (inicq2 we benchmark against the mean inthe group).
The impact is statistically signicant and positive inline with the results by
Cieślik&Rokicki58 for Poland or the results of Crescenzi&Rodriguez-Pose59 for
As an extension, we could acknowledge the potential spatial correlation
between regions can be included inthe model through the introduction of the
agglomeration effects or the introduction of spatial weighting matrixes inamore
sophisticated spatial econometric approach.
58 CieślikA., RokickiB. (2010), Wpływ inwestycji drogowych narozwój polskich regio-
nów, w:JóźwikB., ZalewaP. (red.), Spójność ekonomiczno-społeczna regionów Unii Euro-
pejskiej, Wydawnictwo KUL, Lublin.
59 CrescenziR., Rodriguez-PoseA. (2008), Infrastructure Endowment and Investment as
Determinants of Regional Growth inthe European Union, European Investment Bank Pa-
57The Role of Openness inRegional Economic Growth. The Case of Polish and Spanish...
With the use dynamic panel data model estimated using two-step GMM, we
have estimated an empirical growth model for Polish and Spanish NUTS-2 regions
over the period 2000–2014 inorder toidentify the dependence of regional growth
on the extent of openness. We rst review theoretical and empirical literature.
Within the joined group of regions, we observe aclear beta-absolute and sig-
ma-convergence. Within countries, the evidence points tosigma-divergence. It
holds inparticular for Spain, after the nancial and euro zone crises. Greater
openness seems overall topositively affect regional economic growth inour
sample. The results of Granger non-causality test point, however, tothe exist-
ence of abidirectional relationship between the variables.
In comparison toour previous article devoted tothe issue of determinants of
regional variation of the growth process inPoland, we have extended the anal-
ysis by using anew dataset, increasing the temporal dimension and cross-sec-
tional dimension by using data for Spanish NUTS-2 regions and nally focusing
on the role of openness totrade. Furthermore, we have utilised amore sophisti-
cated dynamic panel model, estimated with two-step GMM noting the non-sta-
tionary nature of key variables.
Our analysis has several limitations. It is mostly due tothe limited availability
of data at regional level. Nonetheless, we plan toextend our analysis inseveral
dimensions: extending the analysis further toall NUTS 2 regions of the EU28 and
accounting for potential spatial interactions with the use of spatial econometric
techniques, extending the notion of openness by considering the ows of FDIs
as well as by controlling for institutional determinants of regional development.
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61The Role of Openness inRegional Economic Growth. The Case of Polish and Spanish...
Table 1. Dumitrescu&Hurlin (2012) Granger non-causality test results
Direction of causality WHNC-bar ZHNC-bar ZHNC-bar title
OPEN => Y 2.2386 5.1815***
(p-value = 0.0000)
(p-value = 0.1926)
Y => OPEN 1.8232 3.4439***
(p-value = 0.0006)
(p-value = 0.5686)
Note: ***, **, * determine signicance at 1%, 5%, and 10% level respectively. The approximated critical
values for the average statistic Wwere computed from equation (30) for the case K = 1. The simulated
critical values were computed via stochastic simulations with 50, 000 replications. For N=25, T=10
the simulated critical value is 2.40 (Dumitrescu and Hurlin; 2012; Table 4).
Table 2. Results of estimation of dynamic panel model using two-step GMM
(1) (2) (3) (4) (5) (6) (7)
L.ln_y 0.936*** 0.813*** 0.814*** 0.859*** 0.908*** 0.763*** 0.748***
(0.00508) (0.00653) (0.0124) (0.0160) (0.0289) (0.0237)
–0.0358*** –0.0357*** –0.0422*** –0.0408*** –0.0430*** –0.0426***
(0.00150) (0.00184) (0.00221) (0.00411) (0.00570)
ln_h 0.240*** 0.277*** 0.181*** 0.847*** 0.291*** 0.320***
(0.00932) (0.00991) (0.0160) (0.156) (0.0615) (0.0739)
–0.0416*** –0.0563*** –0.0601***
(0.00862) (0.0127) (0.0205) (0.0111) (0.0254)
ln_open 0.0821*** 0.711*** 0.0669*** 0.0802***
(0.00561) (0.139) (0.00992) (0.00726)
Constant 0.635*** 1.257*** 1.165*** 0.782*** –1.850*** 2.385*** 1.476***
(0.00546) (0.0326) (0.0439) (0.0879) (0.523) (0.511) (0.287)
Observations 490 311 254 169 169 119 119
No of reg_id 35 30 28 26 26 21 21
Sargan test 34.983 28.767 25.415 22.705 24.007 19.007 16.566
AR(1) –3.9494 –2.0051 –1.8532 –2.0451 –2.1912 –1.7245 –1.7704
AR(2) –2.4204 –2.4587 –2.2987 –2.1368 –1.9034 –1.5303 –1.3751
Wald chi(2) 2.09e+06 149166.58 109129.98 44602.91 44252.18 5613.77 43152.18
Note: Source: Standard errors inparentheses *** p<0.01, ** p<0.05, * p<0.1. Estimated in STATA14
62 Tomasz Brodzicki
Figure 1. Beta-absolute convergence inthe sample of Polish and Spanish regions
Source: Own elaboration.
63The Role of Openness inRegional Economic Growth. The Case of Polish and Spanish...
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Figure 2. Sigma-convergence of GDPpc inthe sample of Polish & Spanish regions
Source: Own elaboration.
Principado de Asturias
Comunidad Foral de Navarra
Comunidad de Madrid
Castilla y León
Región de Murcia
Ciudad Autónoma de Ceuta
Ciudad Autónoma de Melilla
Figure 3. The extent of openness of Polish and Spanish NUTS-2 regions in2005 & 2014
Source: Own elaboration on the basis of Polish and Spanish regional trade datasets.
64 Tomasz Brodzicki
* * *
Przy wykorzystaniu metod estymacji modeli panelowych wartykule szacujemy
empiryczny model wzrostu polskich ihiszpańskich regionów poziomu NUTS-2, dwóch
europejskich gospodarek ozbliżonej wielkości, niskim początkowym poziomie roz-
woju, ajednocześnie głównych benecjentów funduszy strukturalnych UE. Analizę
przeprowadzono dla 16 województw Polski i19 prowincji iwspólnot autonomicz-
nych poziomu NUTS-2 Hiszpanii wlatach 2000–2014. Wpołączonej grupie regionów
obserwujemy wyraźną beta-konwergencję rozwojową isigma-konwergencję, podczas
gdy analizy wobrębie krajów wskazują na dywergencję rozwojową. Szczególnym
celem artykułu jest zbadanie wpływu szeroko deniowanej otwartości naproces roz-
woju regionalnego. Wstępna analiza przyczynowości między kluczowymi zmiennymi
wskazuje nawystępowanie zależności dwukierunkowej. Wkolejnym kroku szacujemy
dynamiczny model panelowy za pomocą dwustopniowego estymatora uogólnionej
metody momentów ze względu naniestacjonarny charakter kluczowych zmiennych.
Wprocesie estymacji uwzględniamy potencjalne interakcje otwartości zregionalnymi
zasobami kapitału ludzkiego oraz innymi ważnymi determinantami postulowanymi
przez modele teoretyczne. Uzyskane wyniki sązgodne zpodstawowymi postulatami
Słowa kluczowe: rozwój regionalny, wzrost gospodarczy, dane panelowe, Polska,