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Accessibility to Knowledge and New Firm Formation in Sweden
Tüzin Baycan
Department of Urban and Regional Planning
Istanbul Technical University, TURKEY
tbaycan@itu.edu.trÖzge Öner
Jonkoping International Business School
SWEDENozge.oner@jibs.hj.se

This paper aims to investigate how accessibility to knowledge affects the new firm formation in
Sweden. Utilizing municipal level data, the paper examines how and to what extent geographic
proximity to establishments that are specialized in formal knowledge creation plays a role for theoverall entrepreneurial milieu in a city region. While measuring accessibility to knowledge at intramunicipal, intra-regional and extra-regional levels, the paper maps out the clustering patterns of new
firms and ranks the municipalities by their performance in creating an entrepreneurial milieu. The
clustering patterns of new firms highlight critical factors in new firm formation and entrepreneurial
performance of regions.

11.

Introduction

Scientific knowledge becomes increasingly important for innovation and new business development astechnologies have grown more sophisticated and emerging industries (i.e. biotechnology andnanotechnology) have become more high-tech and universities play an enhanced role in knowledge
production and innovation both as a human capital provider and a seedbed of new firms (Etzkowitz etal., 2000; Litan et al., 2007a; Mitchell, 2010; Rasmussen et al., 2006). In paralel, the traditional
mission of universities including teaching and research has gradually changed with new perspectives
on the role of the university in the system of knowledge production and has expanded in order to takeover a ‘third mission’ namely commercial activities including patenting, licensing and company
formation (Baldini, 2006; Baycan and Stough, 2012; Owen-Smith and Powell, 2003; Rasmussen etal., 2006). Universities are now expected not only to sustain or to support economic growth but togenerate economic growth through producing new knowledge, human capital, licensing innovations
and creating new companies.
In recent decades, universities have become more involved with their wider surrounding regions byoffering new programmes and by building closer links with business (Boucher et al., 2003; Bramwell
and Wolfe, 2008; Duch et al., 2011; Goldstein and Renault, 2004; Hudson, 2006; Lazzaretti and
Tavoletti, 2005). As an attractor, educator and retainer of students, and shaping them into knowledgebased graduates for firms in the region (Boucher et al., 2003; Bramwell and Wolfe, 2008), universities
have affected the local labour market and contributed to the stock of tacit knowledge to provide formal
and informal technical support. They have also contributed to regional economic development, on the
one hand, by creating new firms, and on the other hand by influencing the region in which they arelocated by knowledge spillover mechanisms to fuel the development of the regional entrepreneurial
culture. Therefore, the presence of a university and its engagement with commercial activities has
played a key role in regional economic development (Baycan, 2012).
Commercialization of knowledge through technology transfer and academic entrepreneurship(university spinoffs) encourages economic development, in particular, local and regional economic
development, generates jobs, and provides innovative products. University spinoffs are especially
important entities for encouraging local economic development (Shane, 2004). While encouraging
local and regional economic development by technology transfer and spinoff companies, knowledgecommercialization affects the local and regional environment as well. The presence of a university, or
its location, affects on the one hand, the creation and the location decisions of new companies, and on
the other hand, the location decisions of spinoff companies. Several studies offer evidence that thepresence of a university is a key factor taken into account by firms when making their locational
decisions (Duch and García-Estévez, 2011; Goldman, 1984; Pressman, 2002; Shane, 2004; Wright etal., 2002).

2

Geographic proximity and networks also play a crucial role in the commercialization of knowledge(Huggins, 2008; Rosenbloom, 2007; Shane, 2004; Uranga et al., 2007; Warren et al., 2008). The
results of many studies show that in industries where new economic knowledge plays an importantrole, innovation tends to cluster geographically. Therefore, it may expect that innovative activity ismore likely to occur within close geographic proximity to the source of that knowledge (Audretsch andFeldman, 1996; Warren et al., 2008).
Against this background, the aim of this paper is to investigate how accessibility to knowledge affects
the new firm formation in Sweden. The paper examines how and to what extent geographic proximity
to establishments that are specialized in formal knowledge creation plays a role for the overall
entrepreneurial milieu in a region. While measuring accessibility to knowledge at intra-municipal, intraregional and extra-regional levels, the paper maps out the clustering patterns of new firms and ranks
the municipalities by their performance in creating an entrepreneurial milieu. The next section
examines how the presence of a university, or its location, affects the creation and the location
decisions of new companies as well as the role of geographic proximity and networks that plays acrucial role in commercialization of knowledge. Using Swedish data at municipal aggregation level,
Section 3 analyzes how accessibility to knowledge affects the new firm formation at intra-municipal,
intra-regional and extra-regional levels and maps out the clustering patterns of new firms. Section 4highlights critical factors in new firm formation and entrepreneurial performance of regions.
2.

Geographic Proximity to Knowledge and New Firm Formation

Geographic or spatial proximity to knowledge plays an important role in the creation and the location
decisions of new firms. Several studies offer evidence that the presence of a knowledge institution is a
key factor taken into account by firms when making their locational decisions (Duch and GarcíaEstévez, 2011; Goldman, 1984; Pressman, 2002; Shane, 2004; Wright et al., 2002). According to a
study by Goldman (1984), 72 percent of the high technology companies in Boston area in the early
1980s were based on technologies originally developed at MIT laboratories. A recent study by Duchand García-Estévez (2011) provides evidence from Spain that the foundation sciences andengineering, and the social sciences and humanities faculties have had a marked impact on thecreation of firms. Another study by Duch et al. (2011) that examines the main contributions ofuniversities to the economic growth of Spanish regions shows that the growth of regional GVA is
positively correlated with both the human capital created by universities and the stock of university
patents.
The location of a knowledge institution also affects the location decisions of spinoff companies.
According to Pressman (2002), in the U.S., 80 percent of all spinoffs operate in the same state as theinstitution that they came from. A similar pattern is also observed in other countries. In Canada, 98
percent of spinoff companies operate in the same province as the university from which they emerged(Pressman, 2002). In the UK, 74 percent of all spinoffs created since 1996 are located in the same3

region as their spawning university (Wright et al., 2002). Given this tendency to cluster, the impact of
university spinoffs on regional economic development is often magnified (Shane, 2004).
Geographic proximity and networks also play a crucial role in the commercialization of knowledge(Huggins, 2008; Rosenbloom, 2007; Shane, 2004; Uranga et al., 2007; Warren et al., 2008). The
results of many studies show that in industries where new economic knowledge plays an importantrole, innovation tends to cluster geographically. Therefore, it may expect that innovative activity ismore likely to occur within close geographic proximity to the source of that knowledge (Audretsch andFeldman, 1996; Warren et al., 2008). The dynamics prior to commercialization of knowledge takeplace at local geographic levels including local clusters, megacentres, and regions (Uranga et al.,2007). Both venture capitalists and universities are important actors within networks of local orregional clusters of knowledge-based activities or systems of regional innovation (Cooke et al., 2004;
Huggins, 2008). Most venture capitalists seek to invest in ventures and activities that are relatively
proximate to their own location, being in the same locality or region (Huggins, 2008). The start-up
companies are predominantly local and the ‘industry helix’ is embedded in local network relationships(Uranga et al., 2007). Collaborations between universities and non-academic agents, andorganizations are localized geographically and according to Frenken and Oort (2004), the capacity tocollaborate and cooperate is often more important than the ability to produce knowledge. Therefore,
as mentioned also by Huggins (2008), the ability to access transparent and visible networks andcommunication channels linking the academic, financial and intermediary sectors is a key factor to
successful regional knowledge commercialization processes.
Geographic proximity is a key factor in developing strong ties within the local and regional networks topromote the transfer of complex knowledge. The argument related to geographic proximity suggeststhat strong ties promote the transfer of complex knowledge, while weak ties promote the transfer ofsimple knowledge (Bathelt et al., 2004; Huggins, 2008). Weak ties or poor network conditions may
also lead to information asymmetries between the potential investors and investees (Shane, 2004).
The lack of existing and effective networks may have a negative effect on the commercialization ofknowledge not just in terms of financing, but also in terms of exchanging expertise and experience(Huggins, 2008). To develop strong ties requires face-to-face interaction and the geographic proximityof network actors facilitates this interaction. Therefore, the existence of established spatially proximate
networks plays an important role in regional knowledge commercialization. In more advanced clusters,
territories, and network drivers are more biased to creating conditions to commercialize knowledge,
and to a lesser extext, to government intervention to foster development (Uranga et al., 2007).
Geographic proximity and the existence of a strong regional innovation system also determine theeffectiveness of technology transfer activities, or in other words, commercialization of knowledge.
Warren et al. (2008), on the basis of their study where the effectiveness of technology transfer activity
in the USA was analyzed, have found that many universities that are geographically isolated fromsupportive innovation systems, exhibit less efficiency in transferring technology. They have stated that4

remoteness is an important factor as it is indicative of a lack of the required support infrastructures andaccording to the results of their study, technology transfer is currently an ineffective process forgeographically remote universities in the USA. The results of this study show that the efficiency of auniversity technology transfer offices (TTO) depends strongly on whether the institution is locatedwithin a strong and highly networked innovation system. Universities in regions with a highly
developed social structure of innovation are thus more efficient.
A recent study by Rosenbloom (2007) on the geography of innovation commercialization in the USA
also supports the existence of a highly developed regional innovation system. The results of
Rosenbloom’s analysis show that the three measures of innovation commercialization (small business
innovation research (SBIR) and small business technology transfer (STTR) grants, venture capital
investments, and initial public offerings (IPOs)) are substantially more geographically concentrated
and more unequally distributed than is population. The 5 largest cities (San Francisco, Boston,
Denver, San Diego and Austin,) in the nation, which make up one third of the population of the top 50
cities, have received the highest number of grants and account for more than half of all grants. These
results show that city size matters. Larger cities have more resources and thus ought to have more
economic and innovative activity than smaller cities. In other words, bigger cities within a strong
regional innovation system are more efficient. However, Rosenbloom’s study highlights some otherinteresting points, for instance, high rates of idea generation do not necessarily imply high rates ofinnovation commercialization. Nevertheless, investments in innovative capacity and idea generation
do have an important localized effect on the rate of innovation commercialization.
3.

Accessibility to Knowledge and New Firm Formation in Sweden

In this paper we aim to investigate how accessibility to knowledge affects the new firm formation in
Sweden. Using accessibility to knowledge we examine how and to what extent geographic proximity toestablishments that are specialized in formal knowledge creation plays a role for the overall
entrepreneurial milieu in a region/municipality. While measuring accessibility to knowledge at intramunicipal, intra-regional and extra-regional levels, we aim to map out the clustering patterns of new
firms and to rank the municipalities by their performance in creating an entrepreneurial milieu. In thefollowing sub-sections we will first describe our data and methodology and next, we interpret ourempirical analysis and its results.
3.1. Data and Methodology
For the empirical analysis, we employed a unique data set for the year 2008 from Statistics Sweden.
This Swedish micro data provides information at individual level, establishment level, as well as firmlevel for several years. We used this cross sectional data in order to examine the performance ofdifferent municipalities in terms of entrepreneurial milieu with respect to access to knowledge.
Therefore, for our empirical analysis we aggregated the data on a municipal level in order to utilize the5accessibility measures and we calculated access to formal knowledge creation including: i)universities and higher education institutions; ii) R&D natural sciences and engineering; and iii) R&D
social sciences and humanities. Our measure for the entrepreneurial milieu in a region is the numberof new firms in a given year on a municipal level. In Sweden there are 290 municipalities, 81 of which
together with smaller municipalities surrounding them constitute functional regions (FER). These
functional regions have a certain degree of accessibility within its borders, hence are considered to belocal labor markets.
For our empirical analysis, we described five variables: new firms, accessibility to knowledge, humancapital, immigrant share and economic diversity and we employed regression analysis as amethodology. Our variables and their descriptions are given in Table-1.
Table-1: Variables used for the empirical analysis
Variables

Descriptions

New firms

Total number of new firms in a municipality in 2008

Accessibility to knowledge(geographic proximity to formal
knowledge creation)

Intra-municipal access to knowledge: access to knowledge within the

Human capital

Share of population with at least 3 years of higher education

Immigrant share

Share of population that is first generation immigrant to control for push vspull factors

Economic diversity

Number of different 5-digit level industries in a municipality to control
economic diversity

municipal border

Intra-regional access to knowledge: access to knowledge within thefunctional region
Extra-regional access to knowledge: access to knowledge outside of agiven functional region

New firms
We used total number of firms in a municipality for the year 2008 as the dependent variable in the
regression analysis. We also looked at the performance of individual Swedish municipalities when it
comes to new formation to see whether they behave differently when market size (in terms of wagesums) is taken into consideration rather than the geographical proximity (in terms of access toknowledge) is taken into account. This variable is adjusted for market size (in terms of wage sums).
Accessibility to knowledge
Being the variables of interest, we utilized accessibility to knowledge to examine how and to what
extent geographical proximity to establishments that are specialized in formal knowledge creation
plays a role for the overall entrepreneurial milieu in a municipality.

6

We employed each Swedish municipality’s accessibility to total number of establishments that are
specialized in formal knowledge creation (e.g. higher education institutions, universities, research anddevelopment centers) as a measure for ‘geographic proximity to knowledge’. Our calculations are
based on the earlier work of Johansson, Klaesson, and Olsson (2002). In their study, accessibility
measures are combined with the wage sums of municipalities to construct an accessible market size
measure. Following the same ground, we obtained an accessibility measure for knowledge for the
Swedish municipalities. Total accessibility of each municipality is divided into three parts as shown
below:




, = , + , + , , ∀ ,

(1)



In equation 1, , refers to intra-municipal, , to intra-regional and , to extra regional

accessibility for a municipality i in a point of time, t. What meant by intra-regional in this context is theaccessibility from one to the other municipalities within the same functional economic region (FER).
The summation in the equation is not considered to be the relevant measure for an individualmunicipal market. It rather provides a total ‘knowledge accessibility’ for the entire country. The reason
is that different municipalities are assumed to be in competition for the same geographical pattern ofentrepreneurs and consumers. Hence one can utilize the three components of the equation as
separate indicators of the same geographical hierarchy, municipal accessibility being the mostimportant one and decaying as it radiates outwards.
In the research of Johansson et al. (2002), it is argued that the influence of the three components in
the given equation differs for different municipalities. The relative size of these components is used toprovide different types of municipalities. Assuming W= {1,…,n} to be the set containing all
municipalities, n in an economy and R denoting a functional economic region (FER) employing several
municipalities (W) within, we can say that R⊂ W. Then = R \ {m} denoting the municipalities inregion R, while excluding the given municipality m. Finally  = W\R denotes all municipalities in that
economy excluding the ones in R.
{  }Intra-municipal: ,, = , 

Intra-regional:{  }
,, = ∑ , {  }Extra-regional: ,, = ∑ , 

∀ ,

≠ ",

K in the formula, for this particular analysis stands for the total number of establishments in a
municipality that are specialized in formal knowledge creation. Travelling time by car between two
given municipalities are represented by t, and as a time distance sensitivity parameter, λ is used. For
each component of the equation, there exists a different λ value1. The values are found by Johansson
et al. (2003b) by using Swedish commuting data for 1998. A similar approach has previously been1# , for intra-municipality 0.02, # (intra-regional) 0.1, and for the # (extra-regional ) 0.05

7

used by Karlsson and Nyström (2006), where they investigated the impact of these knowledge firms
on different industries.
We expect a positive and significant impact from intra-municipal accessibility to knowledge on the total
number of new firms in a municipality in a given year. Whereas the intra-regional knowledge access is
expected to have a negative impact for the number of firms in a given municipality as a results ofcompetition between city municipalities. If a municipality is surrounded by others with higher access toknowledge, start-ups are expected to outflow towards these municipalities. Finally extra-regional
accessibility to knowledge, is expected to have no or very little impact on the new firm formation in a
municipality.
Human capital
We constructed this variable by taking the share of the population in a municipality with minimum three
years of higher education. Municipalities with higher human capital share is expected to have agreater potential for entrepreneurial spirit.
Immigrant share
Previous studies in relation to pull and push factors for entrepreneurial spirit in a given locationsuggests that places with higher immigrant population has the tendency to reflect greater potential for
entrepreneurship, hence for new firm formation. After all those that are less likely to be employed has
greater incentive to start up a firm. Hence municipalities that are densely populated by immigrants are
expected to reflect a tendency for a push factor for the entrepreneurial milieu in the region. By looking
at the share of the population that is first generation immigrant in a municipality, we aim to control forthe potential push factor in the close surrounding.
Economic diversity
Since the data is aggregated on a municipal level, it is not possible to control for the type of start-ups.
We rather controlled for the overall economic diversity in a given municipality to capture – at least to
some extent- whether the entrepreneurial milieu is dependent on a diverse or a specialized market
setting.
3.2. Empirical Analysis
For the empirical analysis, first we mapped several indicators of entrepreneurship in a region to see if
there is a clear spatial pattern among the Swedish municipalities. Next, we conducted a regression
analysis via which we try to capture the way these indicators have an impact on the overall
entrepreneurial milieu in a city region.

8

Figure-1 shows the first map that is created to capture the outlook of market size in Swedishmunicipalities. Market size as explained previously is calculated in terms of wage sums. It is a wellknown phenomenon in Sweden that the economic activities, as well as population are clustered in thesouthern part of the country, whereas northern part of the country is occupied by physically large but
sparsely populated municipalities. As can be seen in Figure-1 the three metropolitan regions,
Stockholm, Malmö and Göteborg appear to be a notably higher market density in the southern part of
Sweden.

Figure-1: Swedish municipal market size (Data Source: Statistics Sweden, Map created by authors)

Next, we produced a second map in which the distribution of new firms across Swedish municipalities
that are represented by the green dots (Figure-2). Each dot in the map corresponds to 100 new firms.
Meanwhile the map is shaded with respect to human capital share of the municipalities. Human capitalin this context is considered to be the share of the population in a municipality with at least 3 years ofhigher education. With this map, we aimed to see whether the municipalities with higher human capital9

are the same ones when it comes to the clustering of the start-ups. Looking at the concentration of thegreen dots on the map, as expected from the previous map we see that the southern part of thecountry is highly concentrated in terms of the number of start-ups in 2008. Although southern part of
Sweden appears to be slightly more concentrated for the human capital share, it is hard to detect aclear pattern between the human capital and new firm formation. This is due to the fact that the
number of new firms capture economic activities of all sort, whereas human capital may not be therelevant indicator for the overall entrepreneurial picture. We can assume that this map would appear
to be completely different if we were looking at the new firm formation in a particular sector, which ishigh human capital abundant. Nevertheless our study aims to capture the overall entrepreneurial
profile in these municipalities and so far this map is not indicating a clear pattern for the relation
between human capital and the total number of new firms.

Figure-2: Human capital share and new firm formation in Swedish municipalities in 2008
(DataSource: Statistics Sweden, Map created by authors)

10The map in Figure-3 lays down the university spin-offs over the concentration of establishments thatare specialized in formal knowledge creation. After all we can observe only 42 university spin-offs forthe year 2008, most of which is clustered in the three metropolitan areas in Sweden. (A list foruniversity spin-offs per municipality in 2008 can be found in the appendix.)

Figure-3: Formal knowledge establishments and university spin-offs(Data Source: Statistics Sweden, Map created by authors)

After mapping several indicators of entrepreneurship, next we run a simple regression using numberof firms as a dependent variable and the log transformed municipal wage sums as an explanatory
variable (as a proxy for market potential). With this regression, we obtained the top 10 performing
municipalities in Sweden as listed below in Table-2 from the predicted values. It is not surprising to11see the three metropolitan areas appearing at the top of the list. Following cities, having a greater
market potential, come naturally at this top ten list.
Next, when we employed the same practice for the new firm formation with respect to total number of
establishments where formal knowledge creation takes place, we obtained a new list of the top ten
municipalities as listed below. When look at the impact driven from geographic proximity to knowledge
rather than the market potential we observe that our top ten list changes significantly. For example
Malmö, what is the second best in the former list now comes at the 6thrank. Another exemplaryranking is Umeå, where obviously the impact of proximity to knowledge on the number of new firms isgreater than the market potential when it comes to its rank among the Swedish municipalities in termsof performance.
As an additional practice, we also looked at the same rank by now taking the number of employment
in these establishments where knowledge creation takes place into account instead of number of
establishments. The ranking is once more changed. Lund, which was the 4th rank in the earlierndranking now occupies the 2rank which is suggesting that the scale of these establishments rather

than the number when it comes to new firm formation plays a greater role in Lund in comparison to theother municipalities in Sweden. The same story can be applied for Luleå, where Luleå University witha considerable scale captures most of the employment within one establishment.
Table-2: Over-performing municipalities
Performance of municipalities interms of new firm formation withrespect to market size (in terms ofmunicipal wage sums)

Performance of municipalities interms of new firm formation withrespecttototal
numberofestablishments
whereformalknowledge creation takes place

Performance of municipalities interms of new firm formation withrespect to the number of
employment
intheseestablishments where knowledge
creation takes place

1.

Stockholm

1.

Stockholm

1.

Stockholm

2.

Göteborg

2.

Uppsala

2.

Lund

3.

Malmö

3.

Göteborg

3.

Uppsala4.

Uppsala

4.

Lund

4.

Göteborg

5.

Linköping

5.

Solna

5.

Umeå

6.

Västerås

6.

Malmö

6.

Solna

7.

Örebro7.

Linköping

7.

Linköping

8.

Jönköping

8.

Umeå

8.

Huddinge

9.

Helsingborg

9.

Danderyd

9.

Malmö

10.

Nacka

10.

Helsingborg

10.

Luleå

We also produced a map in order to show the relation between the number of establishments that are
specialized in formal knowledge creation and the employment in these establishments (Figure-4). We
found it plausible to exhibit these two indicators on the same map, shading being based on
employment and dots representing the number of these “knowledge” establishments. It is easy toobserve a clear pattern between the two where this relationship decays as we move towards north.

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So, one can say that the variation in terms of establishments is higher in south, whereas we startobserving a big scale of knowledge establishments hosting most of the employment at one place as
we approach north.

Figure- 4: Knowledge establishments and employment in these establishments per municipality in 2008
(Data Source: Statistics Sweden, Map created by authors)

Next, we employed a regression analysis. By employing a regression analysis, we aimed to capture
the direction of the impact driven from our predictors (access to knowledge being the primary one,human capital, immigrant population and economic diversity). The dependent variable is obtained bytaking the total number of new firms in a municipality into account. In order to normalize this number
with respect to market size, wage sums of these municipalities are utilized. Hence the dependent
variable can be considered to be –to some extent- the number of start-ups with respect to market size
(Table-3).

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Table-3: Regression results
Dependent V: New Firms (adjusted for market size)Standardized Beta Coefficientst-stats

Intra-municipal accessibility to knowledge

0.746***

21.281

Intra-regional accessibility to knowledge-0.188***-4.373Extra-regional accessibility to knowledge

0.101***

3.312

Human Capital

0.461***

9.468

Immigrant Share

0.079**

2.180

Economic diversity-0.224***-5.363

2

R : 0.769
Number of jobs: 289*** p<0.01, ** p<0.05, * p<0.1

As explained previously, our variables of interest are the access to formal knowledge creation. Intramunicipal accessibility to knowledge, being the combination of driving distances and the number of
establishments that are specialized in formal knowledge creation has a high statistically significant
positive impact on the number of start-ups, and its impact is higher than the other predictors we
employ in this analysis (Table-3). Second variable, intra-regional access to knowledge, is calculated
likewise by taking driving distances and number of establishments that are specialized in knowledge
creation into account but for the functional region (municipal access to knowledge is discounted so it is
not double counted as explained before). Surprisingly the impact driven from this variable is highly
significant and negative (Table-3). This is suggesting that if a municipality has greater access toknowledge creation within the regional borders, that may lead a competition effect. Meaning start-ups
in small municipalities may rather have the tendency to show up in larger places with higher number ofknowledge establishments. This type of relation however is not captured for the third variable, extraregional access to knowledge, suggesting that the overall access to knowledge creation creates a
positive impact (Table-3). In fact this type of result is well expected given that municipalities within thesame regional border have greater incentive for competition whereas municipalities outside of afunctional region can rather be accounted for the overall impact driven from knowledge creation in the
country.
We also wanted to control for several municipal characteristics in the analysis (Table-3). The first one
is the share of human capital. The variable is obtained by taking the share of population with 3 years
or more higher education. As expected high level of human capital in a municipality has a highly
significant positive impact for the entrepreneurial milieu. Although its impact is not as strong as humancapital, immigrant share in a municipality (being the share of the population that is foreign born) has apositive and significant impact. This may signal two things: due to a sorting mechanism immigrants
may be present in places that have higher level of entrepreneurial spirit, or due to the labor market
conditions there may be some sort of push factors for those that face employment obstacles, hence
ends up by starting a firm. We also wanted to control for the economic diversity in a municipality bytaking the number of different industries (on 5-digit) into account. One other surprising result is

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generated by this variable, the impact being significant and negative. This result may be generated
due to the fact that we do not control for different types of start-ups in the analysis. Also this may
signal that start-ups favor specialized market places more than diverse market places. This result
requires further investigation.
We acknowledge the fact that by no means this regression analysis clarifies the underlying factors
behind the entrepreneurial potential of a municipality. However it is still signaling a positive andsignificant impact from geographic proximity to the formal knowledge.
4.

Concluding Remarks

In this paper, we investigated how accessibility to knowledge affects the new firm formation in Sweden
and to what extent geographic proximity to establishments that are specialized in formal knowledge
creation plays a role for the overall entrepreneurial milieu in a city region. We found accessibility toformal knowledge plays a significant role for the entrepreneurial milieu in a region. Our analysis at
intra-municipal, intra-regional and extra-regional levels highlighted that: i) intra-municipal accessibility
to knowledge has a high statistically significant positive impact on the number of start-ups, and its
impact is higher than the other predictors; ii) intra-regional access to knowledge is also highly
significant but negative impact on the number of start-ups, suggesting that if a municipality issurrounded by other municipalities that have high access to knowledge within the regional borders,that may lead to a competition effect; iii) extra-regional access to knowledge has a relatively smaller
but positive impact on the number of start-ups, suggesting that municipalities within the same regional
border may have greater incentive for competition and the overall access to knowledge is beneficial
for any municipalities entrepreneurial milieu in Sweden. An overall assessment of our analysis
demonstrated that access to knowledge within municipality plays a more important role and thatmunicipalities with higher knowledge access within its border have greater entrepreneurial potential.
We also examined the clustering patterns of new firms and the performance of municipalities increating an entrepreneurial milieu. Mapping several indicators of entrepreneurship including market
size, human capital, university spin-offs and employment in knowledge institutions highlighted that: i)
economic activities are clustered in southern part of Sweden and the three metropolitan regions,
Stockholm, Malmö and Göteborg appear to be a notably higher market density in the southern part ofthe country; ii) the number of start-ups and the university spin-offs are also clustered in the southernpart of the country, especially in the three metropolitan areas; iii) southern part of the country also
appears to be slightly more concentrated for the human capital share, however it is hard to detect aclear pattern between the human capital and the new firm formation. Regarding these clustering
patterns of economic and entrepreneurial activities in the southern part of the country, especially inmetropolitan areas, it is not surprising that our performance analysis ranked the metropolitan areasand cities having a greater market potential at the top of the list. Nevertheless, our performance
analysis also showed that the impact of geographic proximity to knowledge is higher than the impact
of market potential on entrepreneurial performance. While the number of new firms has a greater

15

impact than the market potential in terms of performance, our descriptive practices signaled that themunicipalities that are over performing in terms of entrepreneurship with respect to market size are not
the same ones that are over performing with respect to potential knowledge hosted within municipal
borders.
An overall evaluation of our findings highlights that geographic proximity to formal knowledge is a
critical success factor in new firm formation as well as the entrepreneurial performance of regions in
Sweden.

References
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Bathelt, H., Malmberg, A., Maskell, P. (2004) “Clusters and knowledge: local buzz, global pipelines
and the process of knowledge creation”, Progress in Human Geography, 28: 31-56.
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Appendix-1: University spin-offs by municipality in 2008 in Sweden
Municipalities

Spin-offs

Stockholm
Växjö
UppsalaVästerås
Upplands Väsby
Västervik
MalmöÖrebroÖstersundLandskrona
SundsvallÅmål
Strängnäs
TäbyÄngelholm
Falkenberg
Sollentuna
Vindeln
Stenungsund
Mark
ArvikaVallentuna
Ystad
BurlövBodenÖsteråker
Håbo
Tomelilla
Vadstena

8
3
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

18