INNOVATION IN TRANSITION:
A COMPARISON OF THE INNOVATION POTENTIAL OF
INCUMBENT FIRMS AND INNOVATIVE START-UPS IN
THE WESTERN BALKANS
Working Paper No. 106
August 2009
Stephen Roper
Warwick Business School’s Small and Medium Sized Enterprise Centre
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1
Innovation in Transition:
A comparison of the innovation potential of incumbent firms
and innovative start-ups in the Western Balkans
Stephen Roper
Centre for Small and Medium Enterprises, Warwick Business School,
University of Warwick, Coventry, CV4 7AL, UK
Email: Stephen.Roper@wbs.ac.uk
Abstract:
Innovation can play an important role in helping transition economies accelerate their
development towards a competitive market economy. Alternative theoretical
perspectives, however, suggest contrasting views of the innovative potential of
transition economies, and the relative importance of innovation in existing and new
firms. Here, we draw on the knowledge spillovers model of entrepreneurship to
consider the potential contribution to innovation of existing and start-up companies in
the Western Balkans (WBCs). Innovation production functions estimated using the
Business Environment and Enterprise Performance Survey suggest significant
limitations in the innovation capabilities of incumbent firms in the WBCs and the
weakness of the ‗knowledge filter‘. The result is unexploited knowledge spillovers
which might provide the basis for innovative start-ups given appropriate levels of
entrepreneurial capital. In fact, while the attitudinal elements of entrepreneurial
capital are relatively strong in the WBCs aspects of the business environment for
start-up remain problematic. These are likely to constrain innovative start-up activity
and therefore the contribution of innovative activity to the transition process in the
WBCs emphasising the importance of continued institutional and policy development.
Acknowledgements
I am grateful for valuable comments on an earlier draft from participants in the
American Association of Geographers conference, Las Vegas, April 2009 and from
Kevin Mole (Warwick) and Jim Love (Aston).
JEL Codes: P20, L26, O31, O52
Keywords: Innovation, Balkans, Transition, Spillovers
2
Innovation in Transition: A comparison of the innovation potential of
incumbent firms and innovative start-ups in the Western Balkans
1. Introduction
Innovation can play an important role in economic and social development. For
transition economies, innovation can also help to accelerate development towards a
competitive market economy, and establish a competitive presence in international
markets. In this paper we focus on the determinants of innovation in the Western
Balkans, and in particular on the role that public policy can play in enabling
successful innovation in incumbent and start-up companies. Our emphasis on public
policy as an enabler of innovation reflects recent thinking on created advantage
(Cooke and Leydesdorff 2006), and the role of government in managing and
developing regional and national innovation systems (OECD 1999; Commission
2003). Our analysis has clear policy implications for the Western Balkan countries
(WBCs) themselves. More generally, however, our analysis addresses what Oughton,
Landabaso, and Morgan (2002) call the ‗innovation paradox‘ – the need for less
developed areas to have high levels of innovation to stimulate economic growth and
catch-up but the lack of capability to undertake such innovation. Can countries such
as those in the Western Balkans escape the innovation paradox? Key issues here are
the lack of innovation capability of many established firms in transition economies
(Filatotchev et al. 2003), along with concerns about their limited absorptive capacity
(Rodriguez-Pose 2001). This leads to uncertainty about the value of public
investments in advanced or basic R&D which provides the basis for new technologies,
spin-out companies and innovation (Fernandez 1996; Roper, Hewitt-Dundas, and
Love 2003).
Two alternative perspectives to the innovation paradox suggest more positive views of
the development potentials of transition economies. First, Audretsch (2005) outlines
the knowledge spillovers theory of entrepreneurship in which development is led by
innovative start-up businesses based on otherwise unexploited knowledge. In this
view, the potential for new start-ups is greatest where incumbent firms are poor at
exploiting investments in R&D and new knowledge or, in Audretsch terms, where the
3
knowledge filter is weak1. For transition economies, the knowledge spillover theory of
entrepreneurship therefore suggests an alternative development path, with
development being led by innovative start-up firms rather than innovation in
incumbent companies. The empirical evidence suggests, however, that the potential
for innovative start-ups is greatest where investments in new knowledge are high and
there are significant levels of entrepreneurial capital, i.e. ‗the milieu of agents and
institutions that are conducive to the creation of new firms‘ (Audretsch 2005, p. 49).
A key question therefore is the extent to which either of these conditions is met in
transition economies such as the WBCs.
The second more positive perspective – and one which is potentially complementary
to the knowledge spillovers theory of entrepreneurship - suggests that with external
assistance transition economies, such as the WBCs, may be able to leap-frog or at
least compress some of the development stages which have characterised free market
economies. Williamson (2000), for example, suggests that establishing appropriate
institutions and organisations for economic development might involve a 10-100 year
time horizon in free market economies shaped primarily by endogenous development
processes (Williamson 2000, Fig 1, p. 597). However, where institutional
development is being strongly supported by international capital and knowledge
transfers it may enable transition economies to develop more rapidly (Stone, 2004).
For example, the Stabilisation and Association Agreements (SAAs) between the EU
and individual WBCs, supported by substantial grant assistance, aim to help with
political development alongside developing government institutions and legislation
towards EU norms2. Economic growth has also been encouraged through
Autonomous Trade Measures, allowing access to EU markets for almost all products
from the Western Balkans (EU Commission, 2006).
The contrasting perspectives of the innovation paradox and the spillovers theory of
entrepreneurship suggest two empirical questions. First, is it actually the case that
incumbent firms in the WBCs do have low levels of absorptive capacity and face
particular barriers in innovation? Only if this is the case will the knowledge filter be
1
Audretsch also stresses the importance of proximity in knowledge diffusion, suggesting the potential
of in-country knowledge spillovers and business start-ups.
2
The Stabilisation and Association process for the Western Balkans, covers Albania, Bosnia and
Herzegovina, Croatia, FYROM, Serbia, Montenegro and Kosovo.
4
weak. To address this issue we draw on the 2005 BEEPs data compiled by the World
Bank to investigate the determinants of innovation in incumbent firms in the WBCs.
Inter alia our results provide new information on the determinants of innovation in
transition economies, a topic which has to date received relatively little rigorous
attention to date. The second question relates to whether there is an adequate level of
entrepreneurial capital in the WBCs to support innovative start-ups based on
otherwise unexploited knowledge spillovers? Here, we draw on recent work by the
OECD on enterprise policy in the WBCs undertaken as part of the SME Charter
Process (OECD, 2009)
2. The Study Region
The Western Balkans, the main focus of our analysis includes Albania and six
countries which were formerly part of Yugoslavia: Bosnia and Herzegovina, Croatia,
Kosovo, the former Yugoslav Republic of Macedonia (FYROM), Montenegro and
Serbia (Figure 1)3. Aside from Albania, which experienced destabilising financial
crises in 1997, the recent history of the other WBCs was marked by conflict during
the 1990s, and a subsequent and difficult period of economic, social and political
transition. In more recent years the aspiration of the WBCs to join the EU – formally
recognised by the EU itself in the Thessaloniki Summit of 2003 – has been a
significant influence4. However, despite significant aid and assistance and continued
external support for policy and institutional development, social and economic
development across the region has been uneven. Croatia – the largest of the WBCs
with a population of around 4.4m - has perhaps made most progress towards EU legal
and policy frameworks. Other WBCs have significantly lower levels of per capita
GDP than Croatia and have developed their policy and institutional frameworks more
slowly (Table 1)
More generally, the WBCs continue to undergo a process of transition to a market
economy with the steady privatisation of state-owned companies and unbalanced
business demography. In Serbia, for example, while some larger firms have made a
successful transition from the public to private sectors their medium-sized suppliers
3
The term ‗Western Balkans‘ was coined by the EU in 1999 as part of the establishment of the
Stabilisation and Association Process prior to the Thessaloniki Summit of June 2003 (Batt 2007)
4
Slovenia – also formerly part of Yugoslavia - which joined the EU in May 2004 (Batt 2007).
5
have often fared less well. This has led to an industrial structure dominated by small
(often micro) firms with relatively few medium and larger companies5. Despite the
difficulties of the transition process the WBCs experienced rapid growth prior to the
2008/09 economic crisis due primarily to growth in telecommunications, wholesale
and retail trades, construction and finance. This economic growth was accompanied
by rapid structural change, with significant inward investment in banking, finance and
other consumer-related services. Fundamental macro-economic issues remain across
the region, however, including significant unemployment, persistent trade deficits and
a continuing dependence on agricultural exports (Table 1).
Structural changes have been accompanied by important developments in SME policy
since the adoption of the European Charter for SMEs by the WBCs in June 2003.
Targeted primarily at combating unemployment and stimulating growth in more
peripheral areas, these developments have focussed on education and training for
entrepreneurship, skills availability, information provision for small firms and
strengthening the technological capacity of existing SMEs. In Serbia, for example, the
Government‘s 2008 Strategy for Developing Competitive and Innovative SMEs and
2009 Action Plan, identifies five key pillars where policy development is necessary:
incubators as an aid to business start-up, skills and human resources, finance and
taxation including the development of equity financing, clusters and business
networks, and the regulatory environment. Promotion of a culture of enterprise has
also been seen as important with broadly based enterprise promotion events being
organised by the Serbian Agency for the Development of SMEs and
Entrepreneurship, including the annual International Trade Fair of Entrepreneurship
―Business Base‖, regional and local enterprise events, and the regular publication of
the widely distributed ―SME News‖.6 Donor organisations also organise enterprise
promotion activities among high-school and university students across the WBCs.
Although SME policy has developed rapidly in recent years across the WBCs there
has until recently been little progress in the development of public support for
5
In 2007, for example, there were only 598 large firms in Serbia, 2752 medium-sized enterprises and
283 640 micro firms (including sole proprietors). Report on Small and Medium-sized Enterprises 2007,
Ministry of Economy and Regional Development, Belgrade, Table 19, page 24.
6
See, for example, the discussion of various enterprise and business plan competitions in the Serbian
Agency for the Development of SMEs and Entrepreneurship, Annual Report 2007.
6
innovation. Such policy initiatives as there have been have tended to be small scale
and focussed on raising the profile of innovation7. More fundamental issues such as
low levels of R&D investment and the particularly low proportion of R&D
undertaken in the corporate sector remain largely unaddressed. In the FYROM in
2004, for example, only 0.25 per cent of GDP was invested in R&D compared to an
EU average of 1.95 per cent. More significant, however, only 5.7 per cent of this
R&D was in the corporate sector compared to 65.3 per cent in the EU15. As a result,
over the period 2002-05, for example, around 80-85 per cent of patents registered in
the FYROM were registered by foreign companies with foreign firms also accounting
for a similar proportion of trademark and design registrations (Polenakovik and Pinto
2009).
3. Conceptual framework
Our focus here is on the process through which new and incumbent firms undertake
innovation on the basis of new – and potentially pre-existing – knowledge. At a
fundamental level, this process can be seen as part of an evolutionary (Lamarkian)
dynamic in which products, processes and services are steadily refined - and
occasionally transformed – and through which firms upgrade their innovation
capabilities through organisational learning (Nelson and Winter 1982). We interpret
the innovation process as potentially ‗open‘, however, reflecting the role of innovation
partnerships and networks and the importance of inter-organisational knowledge
flows (Chesborough 2003, 2006). For incumbent firms this emphasises the
importance of absorptive capacity, and firms‘ ability to identify and absorb external
knowledge which can complement internal knowledge resources (Zahra and George
2002; Roper and Love 2006). For potential start-ups driven by knowledge spillovers
this emphasises opportunity recognition, entrepreneurial commitment (Gundry and
7
For example in Serbia one recent high-profile step in this direction was the
establishment of the Competition for the Best Technological Innovation (2005) by the
Faculty of Technical Sciences in Novi Sad in partnership with the Ministry of Science
and Chamber of Commerce Republic of Serbia. It attracted 188 entries from a wide
range of student groups, university faculty and other individuals, and has led
ultimately to the formation of 35 new start-up companies.
7
Welsch 2001), cognition, and potentially prior entrepreneurial experience (Westhead,
Ucbasaran, and Wright 2005)8.
In both situations, however, the potential for open innovation will be greatest where
firms are operating in knowledge rich environments (Iansiti and Levien 2004). This,
in turn, will depend on public and private knowledge investments through R&D or
other aspects of knowledge creation or diffusion such as training, purchases of
licenses or new software. Any beneficial aspects of firms‘ knowledge environment
may be spatially specific due to the stickiness of knowledge (von Hippel, 1998),
greater ease of translating tacit knowledge over personal rather than IT based
networks (Audretsch, 1998) and also the greater value of local knowledge in its local
context (Gertler, 2004). We therefore anticipate that locational factors are likely to be
important in shaping both the innovation activity of incumbent firms and start-up
enterprises in the WBCs.
For any given level of local knowledge availability, however, the ability to translate
knowledge into innovation will reflect the structure of the wider innovation system
(Edquist 2004; Nelson 1993; Edquist and Hommen 2008) and the capabilities of
incumbent firms. At level of individual firm, however, (Filatotchev et al. 2003) argue
that absorptive capacity will depend on firms‘ prior knowledge and pre-privatisation
experience which is likely to have left firms poorly equipped for coping with market
environment. In particular, managers‘ expertise, flexibility and willingness to take
risky decisions may be limited. Uhlenbruck et al. (2003) make a similar point,
highlighting three reasons for the slow transition to market of incumbent firms in
transition economies: mediocre assets and managers who lack skill, resources and
experience to manage firms in competitive environments; the loss of traditional
markets; and, the scale of change exceeding mangers and employees cognitive
abilities. More recently Kriauciunas and Kale (2006) discuss the essentially similar
notion of ‗socialist imprinting‘ in their study of firms in Lithuania and argue that
privatisation buy-outs are more likely to be associated with higher levels of absorptive
capacity and therefore potentially innovation than other incumbent firms.
Other aspects of firms‘ resource base will also shape their absorptive and innovation
capabilities. In-house R&D, for example, is likely to have a direct role on knowledge
8
Although see also Frankish, Roberts, and Storey (2008).
8
creation and innovation (Crepon et al. 1998) and also a complementary role in
strengthening firms‘ absorptive capacity (Griffith, Redding, and Van Reenan 2003).
Likewise, workforce skills play an important role in both contributing to innovation
capability (Freel 2005) and absorptive capacity (Roper and Love 2006). Information
technology too can play an important part in facilitating network development and
innovation, particularly where management have clear strategic and organisational
goals (Stroeken 2000). Finally, previous studies have suggested that public support
can also have a positive impact on firms‘ innovation outputs in both developed and
transition economies. Czarnitzki and Licht (2006), for example, compare the effects
of public R&D subsidies in Eastern and Western Germany and identify significant
input and output additionality. We therefore expect innovation capability to be
greatest in firms with stronger internal capabilities in terms of R&D, workforce skills,
IT etc. and among those receiving public support.
Filatotchev (2003) also argue that firm governance and ownership may influence
innovation capability with foreign ownership providing access to more diverse
external networks: ‗FDI privatisations are likely to be associated with inter-firm
networks outside traditional networks, raising absorptive capacity‘ (p. 341). Jensen
(2004) in study of Polish food producers, for example, emphasizes the more extensive
international networks of externally-owned firms and the more localized networks of
domestically owned firms. Similar types of absorptive capacity effects are also
evident in the literature on the technology transfer from FDI into transition economies
where the magnitude of productivity spillovers is seen to be contingent on the
characteristics of recipient firms. In their investigation of technology spillovers in
Estonia for example, Sinani and Meyer (2004) find that the size, ownership and
market positioning of recipient firms all influence the productivity benefits derived
from FDI.
For new start-up firms without any established internal resource base or market
position, internal and external resources and networks are potentially even more
important than for incumbent firms (Colombo, Grilli, and Piva 2006). Discussions of
high-tech entrepreneurship in Israel for example, have emphasised the complementary
role of organisational infrastructure (incubators) and venture capital (Avnimelech,
Schwartz, and Bar-El 2007) while studies of sustained innovation in countries like
9
Finland have tended to emphasise labour quality, social norms and interorganisational collaboration (Simonen and McCann 2008). Uhlenbruck et al. (2003)
argue, however, that in many transition economies formal legal and institutional
frameworks and the factor markets necessary to support organisational transformation
and innovation have been slow to develop (Uhlenbruck, Meyer, and Hitt 2003, p.258).
Our expectation therefore is that institutional weaknesses in entrepreneurial capital in
the WBCs may be reducing levels of innovative start-up activity.
To operationalise the relationships between these firm-specific and environmental
factors and innovation in existing firms we make use of the notion of an innovation or
knowledge production function (Griliches 1992; Love and Roper 1999). This relates
innovation outputs to indicators of the knowledge inputs to innovation and the
effectiveness of the innovation process. For firm i this can be written as:
I i  0  1 FCi  2 LM i  3 PS i  4ODi   i
(1)
Where Ii is an innovation output indicator, FCi is a set of firm-specific characteristics,
LMi is a set of location and market indictors, PSi indicates public support, ODi are
operating difficulties and εi is the error term. To assess the innovation potential for
new start-up companies in the WBCs data constraints necessitate a less formal
analytical approach.
3. Data and Methods
For incumbent firms our analysis is based on data taken from the 2005 Business
Environment and Enterprise Performance Survey (BEEPS). Financed jointly by the
European Bank for Reconstruction and Development and the World Bank, this was
the third wave of the BEEPS survey with previous waves undertaken in 1999-2000,
and 2002. The 2005 BEEPS survey used here was undertaken between 10th March –
20th April 2005 and included 28 countries including all of the WBCs with the
exception of Kosovo9. The survey objective was to be broadly representative of the
market-driven sectors of each country reflecting the mix of manufacturing and
9
The 2005 BEEPS covered: Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Croatia, Czech,
Estonia, Serbia and Montenegro, FYROM, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia,
Lithuania, Moldova, Poland, Romania, Russia, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine
and Uzbekistan.
10
services activity, but excluding those sectors of each economy subject to government
price regulation and prudential supervision (i.e. banking, electric power, rail transport,
and water and waste water)10. The target population comprised enterprises which were
established prior to 2002, and had between 2 and 10,000 employees11. The survey
achieved an overall response rate of 36.8 per cent with significantly higher response
rates achieved in each of the WBCs (Synovate 2005)12. In each country sampling
frames were constructed from official government company registers, Chamber of
Commerce membership lists and commercial sources such as the Yellow Pages.
To date the main use of the BEEPS data has been to profile aspects of the business
environment in each country and to examine the impact of criminal activity and
corruption on business development. Krkoska and Robeck (2006), for example,
demonstrate the deterrent impact of crime on FDI inflows and job creation. The
BEEPS survey, however, also includes four variables reflecting different dimensions
of firms‘ product, process and organisational innovation. Two questions relate to
whether firms have ‗developed successfully a major new (or upgraded) product line or
service over the last 3 years‘. Process innovation is reflected by whether a firm has
‗acquired new production technology over the last 36 months‘. Finally, reflecting
organisational or hidden innovation the survey asks whether a firm has had ‗a
completely new organisational structure‘ or ‗had a major reallocation of responsibility
and resources between departments‘ over the last 36 months (NESTA 2007.). Overall,
34.9 per cent of BEEPS respondents reported new product innovation with 32 per cent
reporting acquiring new process technologies. Organisational innovation was less
common, reported by only 15 per cent of firms (Table 2). Firms in the WBCs were
marginally more likely to be innovative than BEEPs respondents as a whole, with
40.0 per cent introducing new product innovations and 39.1 per cent undertaking
process innovation. Comparing these proportions to other innovation survey results is
difficult due to differences in definition but for manufacturing firms at least
10
Some other quota restrictions relating to size, ownership, exporting and location
were also imposed but probably limited in effect (Synovate 2005, p. 4).
11
This figure reflects the overall response rate to all three elements of the BEEPS 2005 survey (i.e. the
random sample, panel sample and manufacturing overlay). Overall, 26249 firms were contacted with
9655 completed interviews (36.8 per cent).
12
For the Western Balkan countries there was no manufacturing uplift and response rates were:
Albania 48.1 per cent; BiH, 46.0 per cent; 50.6 per cent; Croatia, 45.8 per cent; Serbia and Montenegro,
45.8 per cent. (Synovate 2005).
11
innovative activity among BEEPs respondents are below those reported by Arvantis
and Roper (2009) for Ireland and Switzerland (63-68 per cent for product innovation,
58 per cent for process innovation).
The BEEPS dataset also contains a rich set of other variables which give an indication
of firms‘ operating environment and in-house knowledge resources. To reflect
potential differences in national market conditions we identify firms in the ‗EU-plus‘
region and those in the WBCs (see Annex 1). In addition, to reflect the fact that
external knowledge resources – university or research institutes, skilled labour,
specialist support services – are more likely to be more readily available in urban
areas we identify whether firms are located in large city or capital or medium-sized
city (Asheim and Isaksen 1996). Unsurprisingly, manufacturing respondents are less
likely to be located in a large or capital city (33.7 per cent) than services firms (40.6
per cent), with firms in the WBCs more concentrated in larger urban areas (41.6 per
cent) than those in the sample as a whole (37.3 per cent).
The BEEPS dataset also provides details of the ownership profile of the firm, its
privatisation history and group membership, each of which may also impact on
innovation capability (Table 2). Firms in the WBCs were more likely to be single
proprietorships (51.2 per cent) than those in whole sample (37.2 per cent) and,
correspondingly, less likely to be partnerships or limited companies. There was,
however, less difference between BEEPS respondents in the WBCs and elsewhere in
terms of their privatisation history (Table 2). Multi-nationality is also included in the
innovation production functions to reflect the potential for intra-firm knowledge
transfer between national markets and plants (Jensen 2004). Other factors are
included to reflect the strength of firms‘ internal resource base. Firm vintage, for
example, is intended to reflect the potential for cumulative accumulation of
knowledge capital by older establishments (Klette and Johansen 1998), or plant lifecycle effects (Atkeson and Kehoe 2005). Firm size (employment), R&D and skills
reflect aspects of absorptive capacity. Firms in the WBCs were generally larger
(average 111 employees) than those in other countries covered by BEEPS (100
employees) with higher levels of intermediate (high school qualifications) but lower
levels of graduate employment (Table 2).
12
Literature on publicly funded R&D has also suggested repeatedly, that government
support for R&D and innovation can have positive benefits for firms‘ innovation
activity both by boosting levels of investment (Griliches, 1995) and through its
positive effect on organisational capabilities (Buiseret, Cameron, and Georgiou
1995)13. In the BEEPS dataset we have an indication of whether firms received public
subsidies from regional or national agencies or the EU although these are not
innovation specific and could therefore be related to any aspect of firms‘ operations14.
Finally, the BEEPS dataset also includes a number of subjective indicators of the
general operating difficulties which firms perceive relating to finance, skills,
regulation and corruption (Table 2). Finance, macro-economic and regulatory
uncertainty and corruption were reported as important operating difficulties more in
the WBCs than in the sample as a whole and may be barriers to innovation (Roper et
al. 2008).
To assess the factors shaping innovation in new start-up companies in the WBCs we
draw on the SME Policy Index 2009 which profiles the business environment and
developments in SME policy (OECD, 2009). A follow-up to the earlier 2007 exercise,
the 2009 SME Policy Index reports on SME policy development in sixty individual
policy areas covering the ten policy dimensions in the European Charter for Small
Enterprises. Key themes are education and training for entrepreneurship, legislation
and regulation of business activities, technological capacity etc. For each policy area
the SME Policy Index reports on the state of policy development using a five level
scale drawn from national governments and confirmatory views by independent
experts15. Here we focus particularly on those elements of the SME Policy Index
relevant to innovative SMEs; the business environment, the availability of finance and
support measures for innovative enterprises (Roper, 2009).
13 Trajtenberg (2001), for example‘ offers more direct evidence on the links between public R&D
support and firms' proprietary knowledge base. In his examination of government support for
commercial R&D in Israel operated by the Office of the Chief Scientist (OCS), he concludes that
‗industrial R&D expenditures are closely linked (with a reasonable lag) to patents, and so are R&D
grants awarded by the OCS'.
14
This differs, for example, from the indicators of government support which are included in the EU
Community Innovation Survey where information is sought on ‗public financial support for innovation
activities‘.
15
This five level scale is as follows: Level 1 – there is no law or institution in place to cover the area
concerned; level 2 – there is a draft law or institution and some signs of government activity; level 3 – a
solid legal and/or institutional framework in place for this area; level 4 – concrete indications of
effective policy implementation; level 5 – significant record of effective policy or institutional
implementation.
13
5. Innovation in existing firms
For existing firms the BEEPS data provides information on four innovation output
indicators relating to new and upgraded products, process and organisational change.
In each case these are binary indictors suggesting the use of bivariate probit models
for the innovation production function. Table 4 reports models for all firms in the 28
countries covered by the BEEPS survey and including sectoral dummy variables16. In
each case the size of the database allows an extensive set of explanatory factors to be
included reflecting the innovation impacts of firm characteristics, location and
markets, public support and perceived operating difficulties.
Innovation of all types is strongly and positively associated with in-house R&D (3- 4
per cent) and the proportion of the workforce with a degree-level qualification. The
presence of an R&D function within a plant may, for example, stimulate innovation
through the type of technology-push process envisaged in linear models of innovation.
R&D staff may also, however, contribute to firms‘ creativity as part of multifunctional groups, or may allow firms to utilise extra-mural networks or information
sources more effectively (Veugelers and Cassiman, 1999; Love and Roper, 2001). It is
also unsurprising given the transitional nature of the economies covered by BEEPS to
find little evidence of any plant vintage effect on innovation outputs emphasising the
potential importance of Schumpeter Mark 1 innovation processes in transition
economies. Unexpectedly weak albeit positive firm size effects may reflect similar
underlying processes.
The ownership structure of firms (i.e. whether the firm was a partnership, limited
company or co-operative) has little effect on the probability of being innovative. More
surprising perhaps is that external ownership also has no significant impact on
innovation although being part of a multi-plant group (6-10 per cent) which creates
potential for intra-group resource and knowledge transfers does have a significant and
positive effect. This is consistent with previous studies which suggest the importance
of positive knowledge or resource transfer effects on innovation (Brugger and Stuckey
1987; Love and Ashcroft 1999). Market effects also prove important, however, with
16
Similar models estimated separately for manufacturing and services firms suggest essentially
identical results. Full estimates of these models are available from the author on request.
14
firms which are exporting also more likely to be innovating. Other aspects of
governance also prove interesting too, however, with women-led enterprises
significantly (3-6 per cent) less likely to be product, process or organisational
innovators. Gundry et al. (2003) suggest that the implementation of new technologies
by women-owned firms depends strongly on their growth ambitions. Other studies
(Carter and Marlow 2007; Sarri and Trihopoulou 2005) have suggested women-led
firms may have less aggressive growth objectives than male-led firms with a
preference for more organic growth reducing their relative levels of innovation.
The privatisation history of firms, i.e. whether they are a private start-up or privatised
subsidiary, also makes little difference to patterns of product or organisational
innovation. Relative to state-owned firms, however, which are the reference group,
privately-owned firms were significantly (14-17 per cent) more likely to have engaged
in process change (Table 4). These results provide partial support for the arguments
made by Filatotchev (2003) suggesting that privatisation and export market exposure
are more important in shaping firms‘ innovation activity than external-ownership per
se.
As expected the probability of undertaking innovation is also influenced significantly
by firms‘ location (Audretsch, 1998; Gertler, 2004) with new product or service and
innovation 8.7 per cent more common in larger cities than in rural areas (Table 4).
Smaller but still significant statistical effects are associated with the impact of urban
locations on organisational innovation, although there is no ‗city‘ effect on process
change. More surprising perhaps given the contrasts noted earlier between the
probability of innovation in the BEEPS data and other EU studies (e.g. Arvantis and
Roper, 2009) is the lower average level of innovative activity in the EU-plus region
(minus 2-6 per cent) than in the reference group (economies outside the EU and
WBCs), and higher than average levels of upgraded product and process innovation in
the WBCs. Ceteris paribus this suggests that firms in the WBCs were 2-11 per cent
more likely to be upgrading products or introducing new process innovations than
firms in the reference group countries and around 9-18 per cent more likely to
innovating than firms in the EU-plus region. Notably, however, these effects are not
uniform across all types of innovation activity with the WBCs performing less
strongly in terms of new (rather than upgraded) products/services and organisational
15
change. The suggestion is that innovation in the WBCs is predominantly incremental
rather than radical and is linked to process but not organisational changes. One
possibility is that this profile reflects investment-led innovation stimulated by firms‘
purchase of new capital equipment rather than new product development initiated by
R&D or new technology (Uhlenbruck, Meyer, and Hitt 2003).
Other environmental factors also prove important for innovation reflecting the wider
literature on the positive effects of public support for R&D and innovation. EU
subsidies have significant positive effects on the probability of innovation (6-13 per
cent), while state and regional supports have positive but generally insignificant
effects. One possibility is that this reflects the more advanced stage of development of
innovation support measures across the EU compared to other BEEPS countries. The
contrast here between the results for those countries covered by BEEPS and other EU
economies, however, emphasises the potential value of further development of more
broadly based public support measures for innovation in the WBCs and beyond. Such
measures may also help to counteract the negative – albeit insignificant - effects of
access to finance on the probability of innovation (Table 4). Few of the other
(subjective) indicators of operating difficulties prove consistently important with, in
particular, little clear evidence that regulation, tax or judicial administration or
corruption were having any consistent and significant effects on the probability of
innovation. In this sense our results contrast somewhat with those of Krkoska and
Robeck (2006) who demonstrate the deterrent impact of crime on FDI inflows and job
creation. This deterrent effect does not seem to carry over in any very significant way
into the innovation activity of firms once they are established.
Building on the exploratory estimation of Table 4, Table 5 reports more parsimonious
innovation production functions for both the whole BEEPS dataset and the WBCs.
Variables are retained where they either proved significant in the exploratory models
(e.g. R&D, graduate level skills) or where they have a major conceptual or substantive
interest (e.g. state or regional subsidies). Several key contrasts emerge between the
determinants of innovation in the WBCs and those among BEEPS respondents more
generally.
16
First, while firms‘ in-house R&D is a consistently positive and significant determinant
of innovation among BEEPS respondents as a whole it plays little role in shaping the
probability of innovation across the WBCs. This probably reflects the point made
earlier relating to the very low level of R&D spend in firms in the WBCs both in
absolute terms and as a percentage of national R&D spending (e.g. 5-10 per cent in
FYROM and Serbia). Second, we see an essentially similar result in terms of graduate
skills, which have positive but insignificant effects on innovation in the WBCs in
contrast to the whole group of BEEPS respondents and other studies of innovation
across the EU and elsewhere (Freel 2005; Leiponen 2005; Mutula and Van Brakel
2007). Third, in common with the more general picture, exporting, being part of a
multi-plant group and the involvement of private capital are significantly linked to
innovation in the WBCs, although external ownership per se is not important (Table 5,
part B). Fourth, there is little evidence that women-led firms in the WBCs are
disadvantaged in terms of innovation. Fifth, locational factors are a less important
influence on innovation in the WBCs than elsewhere, suggesting that urban or
metropolitan areas within the WBCs are conferring on firms few informational or
agglomeration advantages (Asheim and Isaksen 1996; Chai and Huang 2007). Finally,
we find little evidence that public support for firms across the WBCs is having any
significant innovation effect. In particular, in contrast to the results for the BEEPS
respondents as a whole, we find that EU subsidies are having no significant
innovation effects in the WBCs.
The lack of significance of the R&D and graduate skills variables in the innovation
production functions for the WBCs suggest the weakness of many incumbent firms‘
internal technological capabilities and absorptive capacity (Filatotchev et al. 2003).
Firms in the WBCs also seem to be deriving few innovation advantages from either
agglomeration or the public support regime. Both suggest the weakness of the
innovation systems within which firms in the WBCs are operating (Uhlenbruck,
Meyer, and Hitt 2003; Polenakovik and Pinto 2009). On the more positive side there
is little evidence that regulation, tax or judicial administration or corruption were
negatively impacting on innovation (Table 4). Overall, our results suggest that the
capability of incumbent firms in the WBCs to absorb and exploit external knowledge,
i.e. the knowledge filter, is relatively weak. In terms of Audretsch (2005), this
enlarges the potential for innovative start-ups to take advantage of otherwise
17
unexploited knowledge spillovers although this will depend on the level of
entrepreneurial capital which encompasses both aspects of entrepreneurial culture as
well as organisational or policy supports for business start-up.
6. Conclusions and Discussion
Based on data from the World Bank BEEPS survey our innovation production
function analysis suggests that across the WBCs incumbent firms have significant
limitations in absorptive capacity (Filatotchev et al. 2003). In particular, in-house
R&D and graduate-level skills make little contribution to firms‘ innovation suggesting
that innovation is only weakly linked to in-house knowledge creation or the
acquisition and implementation of external knowledge (Griffith, Redding, and Van
Reenan 2003; Roper and Love 2006). These internal weaknesses in the innovation
capabilities of incumbent firms are exacerbated by unexpectedly weak innovation
effects of policy support regimes and agglomeration effects (Table 4). Other evidence
suggests the limited extent of university-business linkages across the WBCs with an
academic emphasis on traditional ‗open science‘ rather than more contemporary
‗innovation‘ models based on stronger collaborative relationships (EU 2004). The
suggestion is that across the WBCs the knowledge filter – i.e. incumbent firms‘ ability
to identify and exploit available knowledge - is relatively weak. This creates the
potential for significant unexploited knowledge spillovers to provide the basis for
innovative start-ups.
The extent to which this potential will be realised, however, will depend on the level
of entrepreneurial capital. This has cultural, attitudinal and environmental elements
(Audretsch 2005). An indication of the strength of the environment for innovative
business start-ups is provided by data from the 2009 SME Policy Index (OECD,
2009). Table 6 reports three groups of measures reflecting policy to create an enabling
environment for business start-up, SME finance and policy supports for SME
innovation. In each case the ratings derived from the SME Policy Index suggest the
strength of related policy initiatives with ‗1‘ representing a situation where no policy
is in place through to ‗5‘ where policy is well established and effective. Considerable
variation emerges between the WBCs with the most conducive environments for startup in Croatia, Serbia and FYROM (Table 7). Reflecting points made earlier about
policy development for SMEs and innovation it is also clear that policy supports
18
which aim to create an enabling environment for start-up are better developed than
those for supporting innovative SMEs. The suggestion is that with the exception of
Croatia, and in some areas Serbia, the environmental aspect of entrepreneurial capital
in the WBCs remains under-developed. Particularly important in terms of the
knowledge spillovers theory of entrepreneurship are generally low levels of policy
development in terms of promoting technological dissemination, innovation and
technology cooperation each of which is a potentially important mediating mechanism
for knowledge spillovers (Table 6).
Other aspects of entrepreneurial culture reflect individual and social attitudes to
entrepreneurship. Here there is little consistent evidence across all of the WBCs,
however, evidence from the Global Entrepreneurship Monitor is available for Croatia,
Serbia and FYROM, perhaps the most developed of the WBCs (Table 7). Comparing
these three countries to EU and US benchmarks suggests a relatively positive picture
with relatively high proportions of adults seeing opportunities for entrepreneurship
and feeling that they have the necessary skill base for business start-up. This is also
reflected in levels of new business activity which in all three countries are high by EU
standards (Table 7). It is important to recognise, however, that this relates to all types
of enterprise activity, with no consistent data available on the number of start-ups
involving a significant degree of innovation.
The WBCs therefore present a rather mixed picture in terms of entrepreneurial capital.
Attitudinal elements of entrepreneurial capital seem relatively strong at least in the
more advanced of the WBCs, while the more systemic elements of entrepreneurial
capital, reflecting the environment for business start-up, are weaker particularly in
respect of the support for technology diffusion and innovation cooperation. Linked to
limitations in the availability of risk capital, this is likely to significantly constrain
individual‘s ability to start-up innovative companies and exploit any potential
knowledge spillovers.
Taken together, the limitations in absorptive capacity we have identified in incumbent
firms, together with the environmental challenges facing innovative start-ups in the
WBCs, suggest that knowledge available in the WBCs is unlikely to be fully
exploited. This limits the potential contribution of innovative activity to the transition
19
process in the WBCs emphasising the importance of continued institutional and policy
development. It also suggests that public investments in new knowledge in the WBCs
– at least in so far as they are intended to have economic rather than educational
benefits – need to be carefully targeted. Moves towards the development of
collaborative university-industry research centres are likely to be important here,
based perhaps on proven models such as competence centres (Vinnova 2004).
It is also important, however, to recognise that our arguments apply with different
force to each of the WBCs reflecting differences in their social, economic and policy
development. OECD (2009), for example, acknowledges Croatia as having the most
supportive environment for SME development followed by Serbia, Montenegro,
Albania and FYROM, with more significant policy challenges remaining in Kosovo
and Bosnia and Herzegovina (OECD 2009, p. 16). Across the region, however, policy
support for innovative SMEs remains a development priority.
20
Figure 1: The Western Balkans
21
Table 1: Region Data for the WBCs : 2007
`
Croatia
Albania
Serbia
FYROM
Montenegro BiH
Kosovo
Demographic Indicators
Population, total (millions)
4.44
3.18
3.18
2.04
0.60
3.77
2.13
Population growth (annual %)
-0.1
0.3
0.3
0.0
-0.3
-0.1
1.4
Surface area (sq. km) (thousands)
56.5
28.8
28.8
25.7
14.0
51.2
10.9
Life expectancy at birth, total (years)
76
76
76
74
75
75
69
Fertility rate, total (births per woman)
1.4
1.8
1.8
1.4
1.7
1.2
2.7
Adolescent fertility rate (births per 1,000 women ages 15-19)
14
16
16
21
16
20
Mortality rate, under-5 (per 1,000)
6
15
15
17
10
14
69
Economy
GNI, Atlas method (current US$) (billions)
46.41
10.51
10.51
7.06
3.15
14.29
3.23
GNI per capita, Atlas method (current US$)
10,460
3,300
3,300
3,470
5,270
3,790
GNI, PPP (current international $) (billions)
68.94
23.02
23.02
18.44
7.06
30.25
5.00
GNI per capita, PPP (current international $)
15,540
7,240
7,240
9,050
11,780
8,020
2,300
GDP (current US$) (billions)
51.28
10.83
10.83
7.67
3.48
15.14
4.70
GDP growth (annual %)
5.6
6.0
6.0
5.0
10.7
6.8
7.6
Inflation, GDP deflator (annual %)
4.0
3.2
3.2
5.1
6.8
6.0
4.3
Agriculture, value added (% of GDP)
7
21
21
12
11
10
20
Industry, value added (% of GDP)
32
20
20
30
20
22
20
Services, etc., value added (% of GDP)
61
59
59
59
69
69
60
Time required to start a business (days)
40
36
36
15
24
54
22
Mobile cellular subscriptions (per 100 people)
113
72
72
96
..
65
26
Trade and Investment
Exports of goods and services (% of GDP)
48
28
28
55
51
39
10
Imports of goods and services (% of GDP)
56
54
54
75
94
74
20
Gross capital formation (% of GDP)
33
30
30
23
27
23
26
Foreign direct invest., net inflows (BoP, current US$) (mill.)
4,916
477
477
320
876
2,111
411
Workers' remittances of employees (current US$) (millions)
1,394
1,071
1,071
267
..
2,520
Official development assistance and aid (current US$) (millions)
164
305
305
213
106
443
Source: All countries except Kosovo data from the World Development Indicators database, April 2009, available at: http://web.worldbank.org. For Kosovo most figures are
taken from Kosovo in Figures, 2008, Statistical Office of Kosovo, Ministry of Public Service, available at: http://www.ks-gov.net/ESK/eng/. Other data sources as follows:
GNI, GNI per capita and composition of GDP from CIA World Factbook; Fertility rate, UNFPA; Mortality rate, UNICEF; Foreign direct investment, Investing in Kosovo
2008, p.17; Number of days required to start a business from Doing Business in South East Europe 2008, available at
http://www.doingbusiness.org/documents/subnational/DB08_Subnational_Report_SEEurope.pdf.
22
Table 2: Descriptives: Innovation, Firm Characteristics and Markets
Manufacturing
N=3762
Mean
St Dev
Innovation Indicators
New product or service
Upgraded product or service
Process innovation
Organisational innovation
Services
N=4383
Mean
St Dev
Western Balkans
N=1140
Mean
St Dev
Whole Sample
N=9655
Mean
St Dev
0.439
0.592
0.427
0.161
0.496
0.492
0.495
0.368
0.287
0.428
0.221
0.142
0.452
0.495
0.415
0.349
0.399
0.620
0.391
0.142
0.490
0.486
0.488
0.349
0.349
0.502
0.320
0.150
0.477
0.500
0.466
0.357
13.239
130.373
0.577
19.583
504.313
0.494
9.726
73.112
0.486
14.965
300.560
0.500
15.212
110.866
0.518
22.116
326.628
0.500
11.565
99.791
0.527
17.404
399.004
0.499
35.902
29.314
37.864
33.771
50.664
32.558
36.253
31.551
21.548
0.349
0.280
0.023
0.289
0.116
0.346
0.220
0.178
0.174
0.705
23.424
0.477
0.449
0.149
0.453
0.320
0.476
0.414
0.382
0.379
0.456
33.763
0.407
0.253
0.019
0.232
0.106
0.176
0.273
0.219
0.100
0.764
32.776
0.491
0.435
0.136
0.422
0.308
0.381
0.445
0.413
0.300
0.425
23.682
0.512
0.192
0.010
0.179
0.106
0.317
0.370
0.147
0.152
0.706
25.764
0.500
0.394
0.098
0.383
0.308
0.465
0.483
0.355
0.359
0.456
27.790
0.372
0.261
0.020
0.259
0.102
0.234
0.244
0.198
0.137
0.723
29.204
0.483
0.439
0.140
0.438
0.302
0.423
0.429
0.398
0.343
0.447
0.019
0.135
0.012
0.110
0.012
0.110
0.016
0.124
0.031
0.174
0.027
0.163
0.020
0.141
0.027
0.163
Location and Markets
EU plus region
Western Balkans location
Large city or capital
Medium-sized city
Mining
Construction
Manufacturing
Transport
Trade
Real Estate
Hotels and Restaurants
0.475
0.096
0.337
0.424
0.000
0.000
1.000
0.000
0.000
0.000
0.000
0.499
0.295
0.473
0.494
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.470
0.138
0.406
0.358
0.000
0.000
0.000
0.144
0.545
0.190
0.121
0.499
0.344
0.491
0.479
0.000
0.000
0.000
0.351
0.498
0.392
0.327
0.000
1.000
0.416
0.357
0.017
0.089
0.317
0.080
0.294
0.083
0.072
0.000
0.000
0.493
0.479
0.128
0.286
0.465
0.271
0.456
0.277
0.258
0.462
0.118
0.373
0.389
0.010
0.096
0.390
0.065
0.247
0.086
0.055
0.499
0.323
0.484
0.488
0.099
0.295
0.488
0.247
0.432
0.281
0.228
Public support
Subsidies from state
Subsides from region
Subsidies from the EU
0.051
0.015
0.024
0.220
0.123
0.153
0.026
0.021
0.011
0.161
0.143
0.104
0.056
0.021
0.004
0.230
0.144
0.066
0.039
0.022
0.017
0.194
0.145
0.131
Firm characteristics
Plant age in 2005
Employment (2003)
Research and development
Workforce with high school
quals(%)
Workforce with graduate
quals (%)
Single proprietor
Partnership
Cooperative
Limited company
Majority externally owned
Exporting firm
Part of multi-plant group
Women-led enterprises
Privatised state company
Private start-up
Private subsid of former state
Company
Joint venture with external
partner
Notes and Sources: See Annex 1 for variable definitions, Source: World Bank
Business Environment and Enterprise Performance Survey, 2005.
23
Table 3: Descriptives: Operating Difficulties
Access to finance
Cost of finance
Access to land
Title or leasing of land
Tax rates
Tax administration
Customs and trade regulations
Skills and education
Uncertainty about regulation
Macro instability
Functioning of judiciary
Corruption
Manufacturing
N=3762
Mean
St Dev
0.205
0.404
0.274
0.446
0.080
0.271
0.079
0.269
0.367
0.482
0.262
0.440
0.132
0.338
0.122
0.327
0.268
0.443
0.286
0.452
0.146
0.354
0.179
0.383
Services
N=4383
Mean
St Dev
0.150
0.357
0.214
0.410
0.073
0.260
0.083
0.276
0.291
0.454
0.209
0.407
0.095
0.293
0.093
0.290
0.248
0.432
0.233
0.423
0.135
0.341
0.164
0.370
Western Balkans
N=1140
Mean
St Dev
0.216
0.412
0.310
0.463
0.059
0.235
0.070
0.256
0.233
0.423
0.154
0.361
0.130
0.336
0.077
0.267
0.329
0.470
0.317
0.465
0.260
0.439
0.246
0.431
Whole Sample
N=9655
Mean
St Dev
0.178
0.382
0.239
0.426
0.080
0.271
0.085
0.278
0.324
0.468
0.232
0.422
0.104
0.306
0.108
0.310
0.253
0.435
0.248
0.432
0.137
0.344
0.169
0.374
Notes and Sources: See Annex 1 for variable definitions, Source: World Bank
Business Environment and Enterprise Performance Survey, 2005.
24
Table 4: Innovation Production Functions – Probit Models: All Firms
New
product/service
Dy/dx
t-stat
Firm characteristics
Plant age in 2005
Employment (2003)
Research and development
Workforce with high school quals (%)
Workforce with graduate quals (%)
Single proprietor
Partnership
Cooperative
Limited company
Majority externally owned
Exporting firm
Part of multi-plant group
Women-led enterprises
Privatised state company
Private start-up
Private subsidiary of former state co.
Joint venture with external partner
Location and Markets
EU plus region
Western Balkans location
Large city or capital
Medium-sized city
Public support
Subsidies from state
Subsides from region
Subsidies from the EU
Operating difficulties
Access to finance
Cost of finance
Access to land
Title or leasing of land
Tax rates
Tax administration
Customs and trade regulations
Skills and education
Uncertainty about regulation
Macro instability
Functioning of judiciary
Corruption
Number of observations
Equation χ2
Pseudo R2
Log Likelihood
Percentage correct predictions
Pearson GOF
Upgraded
Product/service
Dy/dx
t-stat
Process
Innovation
Dy/dx
t-stat
Organisational
Innovation
Dy/dx
t-stat
0.000
0.000
0.035
0.000
0.001
0.078
0.096
0.008
0.094
-0.014
0.114
0.103
-0.034
-0.017
0.001
-0.061
0.015
-1.030
1.920
3.340
-2.200
5.960
1.340
1.640
0.130
1.610
-0.780
8.640
8.370
-2.620
-0.310
0.030
-0.960
0.240
0.000
0.000
0.024
0.000
0.001
0.114
0.163
0.083
0.228
-0.024
0.099
0.095
-0.030
-0.033
-0.028
-0.001
-0.011
0.560
1.480
2.220
-1.100
3.440
1.890
2.710
1.250
3.880
-1.180
7.130
7.300
-2.160
-0.570
-0.490
-0.020
-0.170
0.000
0.000
0.039
0.000
0.001
-0.126
-0.113
-0.169
-0.100
-0.030
0.066
0.069
-0.062
0.171
0.149
0.158
0.193
-0.460
3.360
3.890
-0.880
3.270
-2.120
-1.930
-2.990
-1.720
-1.690
5.210
5.760
-4.880
2.720
2.740
2.110
2.740
0.001
0.000
0.038
0.000
0.001
0.078
0.122
0.100
0.127
0.020
0.038
0.055
-0.025
-0.050
-0.086
-0.053
-0.062
2.280
1.570
5.200
0.230
4.970
2.030
2.940
1.990
3.080
1.590
4.140
6.330
-2.610
-1.600
-2.330
-1.460
-1.930
-0.059
0.028
0.087
0.026
-4.920
1.560
6.110
1.890
-0.068
0.118
0.009
0.006
-5.350
6.200
0.620
0.440
-0.063
0.064
-0.004
-0.004
-5.350
3.640
-0.320
-0.290
-0.017
-0.024
0.031
0.001
-1.940
-1.960
3.050
0.080
0.037
0.019
0.066
1.370
0.510
1.660
0.047
0.045
0.111
1.620
1.170
2.600
0.019
0.033
0.136
0.710
0.920
3.440
0.001
0.075
0.062
0.030
2.790
2.220
-0.017
-1.100
-0.005
-0.370
0.024
1.020
0.071
3.000
0.009
0.640
-0.017
-1.110
0.028
1.540
0.083
4.840
-0.006
-0.410
0.028
1.960
0.031
1.790
0.000
-0.020
9348
810.79
0.067
-5632.07
68.05
9355.56
(ρ=0.314)
-0.024
-1.470
0.026
1.710
0.015
0.590
0.080
3.230
0.001
0.090
0.013
0.790
0.006
0.290
0.096
5.300
0.024
1.540
0.032
2.100
0.003
0.140
-0.005
-0.300
9348
-0.013 -0.890
-0.009 -0.630
0.080
3.340
0.003
0.150
-0.013 -0.970
0.043
2.930
0.038
2.160
0.031
1.900
0.007
0.500
-0.001 -0.100
-0.003 -0.160
-0.007 -0.450
9348
0.019
1.730
-0.002 -0.170
0.030
1.760
-0.009 -0.540
-0.029 -3.080
0.019
1.770
0.022
1.720
0.080
6.410
0.025
2.340
-0.016 -1.610
-0.005 -0.460
0.024
2.120
9348
810.94
0.0626
-6047.07
61.97
9340.01
(ρ=0.355)
756.65
0.0647
-5466.96
69.45
9360.05
(ρ=0.3024)
442.35
0.0567
-3682.81
85.24
9243.91
(ρ=0.6307)
Notes and sources: Values reported are marginal values. All models include industry dummy variables
(not reported) and constant terms. Reference groups - rural enterprises, state-owned firms, other
services. See Annex 1 for variable definitions.
25
Table 5: Restricted Innovation Production Functions: All Firms and the WBCs
New
product/service
Dy/dx
t-stat
A. All Firms
Firm characteristics
Employment (2003)
Research and development
Workforce with graduate quals. (%)
Majority externally owned
Exporting firm
Part of multi-plant group
Women-led enterprises
Privatised state company
Private start-up
Private subsidiary of former state co.
Joint venture with external partner
Location and Markets
EU plus region
Western Balkans location
Large city or capital
Medium-sized city
Public support
Subsidies from state
Subsides from region
Subsidies from the EU
Number of observations
Equation χ2
Pseudo R2
Log Likihood
Percentage correct predictions
Pearson GOF
B. Western Balkan Countries
Firm characteristics
Employment (2003)
Research and development
Workforce with graduate quals. (%)
Majority externally owned
Exporting firm
Part of multi-plant group
Women-led enterprises
Privatised state company
Private start-up
Private subsidiary of former state co.
Joint venture with external partner
Location and Markets
Large city or capital
Medium-sized city
Public support
Subsidies from state
Subsides from region
Subsidies from the EU
Number of observations
Upgraded
Product/service
Dy/dx
t-stat
Process
Innovation
Dy/dx
t-stat
Organisational
Innovation
Dy/dx
t-stat
0.000
0.085
0.004
-0.013
0.316
0.291
-0.077
0.177
0.247
0.073
0.289
1.683
3.036
7.891
-0.252
9.134
8.960
-2.140
2.921
4.734
0.601
2.891
0.000
0.049
0.002
-0.002
0.277
0.272
-0.069
0.322
0.288
0.408
0.366
1.537
1.822
4.815
-0.046
8.033
8.460
-1.998
5.510
5.801
3.491
3.703
0.000
0.108
0.002
-0.069
0.189
0.205
-0.187
0.165
0.168
0.126
0.228
3.415
3.788
4.318
-1.340
5.384
6.213
-5.031
2.723
3.215
1.049
2.255
0.000
0.161
0.004
0.123
0.196
0.264
-0.106
0.152
-0.020
0.096
0.037
1.700
4.758
5.737
2.206
4.930
7.108
-2.381
2.232
-0.333
0.701
0.332
-0.165
0.053
0.239
0.073
-5.162
1.147
6.254
1.987
-0.133
0.266
0.032
0.034
-4.314
5.832
0.880
0.993
-0.181
0.145
-0.004
-0.011
-5.593
3.133
-0.106
-0.297
-0.046
-0.115
0.159
0.027
-1.206
-2.050
3.473
0.610
0.090
1.233
0.058
0.588
0.192
1.831
9356
716.10
0.0593
-5684.09
67.38
8658.56
(ρ=0.0788)
0.115
1.577
0.137
1.432
0.312
2.890
9356
621.59
0.0479
-6174.29
60.22
8563.25
(ρ=0.2314)
0.057
0.780
0.126
1.294
0.386
3.681
9356
691.58
0.0591
-5504.85
69.09
8717.31
(ρ=0.0285)
0.003
0.037
0.324
3.081
0.280
2.489
9356
314.84
0.0403
-3751.35
89.27
8758.51
(ρ=0.0135)
0.000
-0.043
0.002
0.042
0.275
0.374
0.058
0.240
0.221
0.245
0.156
0.602
-0.532
1.241
0.296
2.923
4.375
0.506
1.473
1.491
0.670
0.470
0.000
-0.015
0.001
-0.075
0.206
0.252
0.100
0.751
0.385
1.138
0.727
0.580
-0.190
0.424
-0.517
2.118
2.890
0.867
4.433
2.608
2.590
2.136
0.000
0.130
0.001
0.171
0.055
0.193
-0.167
0.206
0.125
0.478
-0.338
1.233
1.586
0.469
1.171
0.578
2.219
-1.398
1.253
0.841
1.328
-0.941
0.000
-0.023
0.004
0.016
0.146
0.328
-0.360
0.315
0.150
0.352
-0.240
1.584
-0.229
1.745
0.097
1.277
3.144
-2.202
1.587
0.819
0.839
-0.559
-0.100
-0.045
-0.903
-0.416
-0.105
0.062
-0.951
0.572
-0.164
-0.010
-1.470
-0.091
0.204
0.075
1.445
0.531
-0.366 -1.997
0.170
0.625
-0.096 -0.158
1089
0.039
0.821
0.185
2.846
-0.038 -0.213
0.123
0.455
-0.068 -0.112
1089
-0.012
-0.065
0.291
1.006
-0.707
-1.193
1089
1084
26
Equation χ2
Pseudo R2
Log Likihood
Percentage correct predictions
Pearson GOF
77.14
0.0528
-692.43
1039.77
1093.77
(ρ=0.237)
75.27
0.0519
-688.01
1027.56
1027.56
(ρ=0.327)
113.83
0.0783
-669.66
1066.87
1066.87
(ρ=0.096)
49.68
0.057
-410.93
1052.35
1052.35
(ρ=0.1408)
Notes and sources: Values reported are marginal values. All models include industry
dummy variables (not reported) and constant terms. Reference groups - rural
enterprises, state-owned firms, other services. See Annex 1 for variable definitions.
27
Table 6: SME Policy Index 2009 – selected items
Croatia
Albania
Serbia
FYROM
Montenegro
BiH
Kosovo
Average
4.5
2.5
4.0
4.0
3.0
2.5
2.5
3.3
5.0
2.5
2.5
3.0
3.0
3.0
2.0
3.0
4.0
3.0
3.0
2.0
2.0
3.0
2.5
2.8
4.5
5.0
2.0
2.0
4.0
4.0
3.0
3.0
3.0
4.0
2.0
3.0
3.0
2.0
3.1
3.3
3.0
4.0
3.0
2.0
3.0
3.0
5.0
4.0
2.0
3.0
2.0
4.0
2.0
1.0
2.9
3.0
4.0
2.0
3.5
3.0
3.0
2.0
2.0
2.8
3.0
2.0
2.5
3.0
2.0
2.5
1.0
2.3
3.5
1.5
3.0
3.0
2.0
2.5
1.0
2.4
4.0
1.5
4.0
3.0
2.0
3.0
2.0
2.8
4.0
1.5
3.5
3.0
2.0
3.5
2.0
2.8
4.5
3.5
4.0
4.0
3.5
3.0
2.0
3.5
4.1
2.2
3.4
3.3
2.7
2.8
1.9
2.9
A. Enabling Environment
National SME promotion events
Range of business services
Quality of business services
Availability and accessibility of
information
Business information centres
B. Finance
Credit guarantee schemes
Venture capital/equity funds
C. Supporting innovative start-ups
Enhancing SME competitiveness
Promote technology dissemination
Innovation and technology centres/
co-operation
Inter-firm clusters and networks
Business incubators
Intellectual property rights
Country Average
Source: OECD (2009)
28
Table 7: Entrepreneurial activity and attitudes
Western Balkan Countries
Croatia Serbia Macedonia
A. Attitudes and Aspirations (% of adult population)
Sees good opportunities for start-up in next 6
months
53
56
47
Fear of failure would prevent start-up
36
28
35
Has the required knowledge for start-up
56
60
52
Entrepreneurship is a desirable career choice
70
72
80
B. Levels of entrepreneurial activity (% of adult population)
Nascent entrepreneurial activity
4.9
4.0
New business owner-manager
2.8
3.6
Early-stage entrepreneurial activity
7.6
7.6
7.2
7.7
14.5
Benchmark Countries
Slovenia Hungary USA
55
33
44
58
26
47
43
48
44
28
48
63
4.1
2.4
6.4
3.8
2.8
6.6
5.9
5.0
10.8
Source: Global Entrepreneurship Monitor, 2008 Executive Report, Tables 1 and 2.
29
Annex 1: Variable definitions
Innovation Indicators
New product or service
Upgraded product or service
Process innovation
Organisational innovation
Firm characteristics
Plant age in 2005
Employment (2003)
Research and development
Workforce with high school
quals. (%)
Workforce with graduate
quals. (%)
Single proprietor
Partnership
Cooperative
Limited company
Majority externally owned
Exporting firm
Part of multi-plant group
Women-led enterprises
Privatised state company
Private start-up
Private subsid of former state
Company
Joint venture with external
partner
Firm ‗developed successfully a major new product line or service over the last
3 years‘ (item 1312)
Firm ‗upgraded an existing product line or service‘ over the last 3 years (item
1313)
Firm has ‗acquired new production technology over the last 36 months‘ (item
1328)
Firm has had ‗a completely new organisational structure‘ or ‗had a major
reallocation of responsibility and resources between departments‘ over the last
36 months (item 1331).
Years since firm first began operations in this country (item 129)
How many full-time employees does your business have now – and how
many did it have three years ago (2003) (item 1355-1359)
Positive spending on R&D including wages and salaries of R&D personnel,
R&D materials, R&D education and R&D related training (item 1257-1263)
What percentage of the workforce of your firm has education levels up to
secondary school (item 1444-1446)
What percentage of the workforce of your firm has education levels up to
university level (item 1447-1449)
Legal status of firm: single proprietorship (items 134-135)
Legal status of firm: partnership (items 134-135)
Legal status of firm: cooperative (items 134-135)
Legal status of firm: corporation, privately held or listed on stock exchange
(items 134-135)
What percentage of the firm is owned by private foreign
individuals/companies/organisations (> 50 per cent ) (item151-153).
Does your firm currently sell its products or services directly to customers
outside the country (item 168)
How many establishments (separate operating facilities) does your firm have
in this country (item 173-174)
Is the principle owner (or one of the principle owners) female? (item 249)
How was your firm established: Privatisation of a state-owned firm (item 250)
How was your firm established: Originally private from the time of start-up
(item 250)
How was your firm established: Private subsidiary of a former state owned
firm (item 250)
How was your firm established: Joint venture with foreign partners; (item
250)
Location and Markets
EU plus region
Western Balkans location
Large city or capital
Medium-sized city
Mining
Construction
Manufacturing
Transport, storage and
communication
Wholesale and Retail trade;
repair of vehicles and
household goods
Includes: Turkey, Slovenia, Poland, Hungary, Czech Rep., Slovakia,
Romania, Bulgaria, Latvia, Lithuania, Estonia
Includes: FYROM, Serbia, Montenegro, Albania, Croatia, BosniaHerzegovina
Located in capital city or other city with population greater than one million
(item city)
Located in city with population greater than 50,000 (item city)
Firm is in ISIC Section C: 10-14 (item ISIC DIVISION)
Firm is in ISIC Section F: 45 (item ISIC DIVISION)
Firm is in ISIC Section D: 15-37 (item ISIC DIVISION)
Firm is in ISIC Section I: 60-64 (item ISIC DIVISION)
Firm is in ISIC Section G: 50-52 (item ISIC DIVISION)
30
Real Estate, renting and
business services
Hotels and Restaurants
Other services
Public support
Subsidies from state
Subsides from region
Subsidies from the EU
Operating difficulties
Access to finance
Cost of finance
Access to land
Title or leasing of land
Tax rates
Tax administration
Customs and trade regulations
Skills and education
Uncertainty about regulation
Macro instability
Functioning of judiciary
Corruption
Firm is in ISIC Section K: 70-74 (item ISIC DIVISION)
Firm is in ISIC Section H: 55 (item ISIC DIVISION)
Firm is in ISIC Section O: includes motion picture and video activities, other
entertainment activities, news agency activities, washing and dry cleaning,
hairdressing, funeral and related activities, other service activities (item ISIC
DIVISION)
Over the last 36 months has your firm received any subsidies from the
national government? (item 1060)
Over the last 36 months has your firm received any subsidies from the
regional/local government? (item 1061)
Over the last 36 months has your firm received any subsidies from EU
sources? (item 1062)
Access to finance (e.g. collateral required or financing not available from
banks) either ‗moderate‘ or ‗major‘ obstacle to the operation and growth of
the business (item 1108)
Cost of financing (e.g. interest rates and charges) either ‗moderate‘ or ‗major‘
obstacle to the operation and growth of the business (item 1109)
Access to land either ‗moderate‘ or ‗major‘ obstacle to the operation and
growth of the business (item 1113)
Title or leasing of land either ‗moderate‘ or ‗major‘ obstacle to the operation
and growth of the business (item 1114)
Tax rates either ‗moderate‘ or ‗major‘ obstacle to the operation and growth of
the business (item 1115)
Tax administration either ‗moderate‘ or ‗major‘ obstacle to the operation and
growth of the business (item 1116)
Customs and trade regulations either ‗moderate‘ or ‗major‘ obstacle to the
operation and growth of the business (item 1117)
Skills and education of available workers either ‗moderate‘ or ‗major‘
obstacle to the operation and growth of the business (item 1120)
Uncertainty about regulatory policies either ‗moderate‘ or ‗major‘ obstacle to
the operation and growth of the business (item 1121)
Macro instability (inflation, exchange rates) either ‗moderate‘ or ‗major‘
obstacle to the operation and growth of the business (item 1122)
Functioning of judiciary either ‗moderate‘ or ‗major‘ obstacle to the operation
and growth of the business (item 1123)
Corruption either ‗moderate‘ or ‗major‘ obstacle to the operation and growth
of the business (item 1124)
31
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