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Homeownership and
entrepreneurship
A regional and industrial analysis of house
prices and startups
Bjorn Berggren,Andreas Fili and Mats Wilhelmsson
Department of Real Estate and Construction Management,
Royal Institute of Technology, Stockholm, Sweden
Abstract
Purpose –The purpose of this paper is to analyze the relationship between housing markets and new firm
formation in six different industries in all 284 municipalities in Sweden.
Design/methodology/approach –The authors have used data from Statistics Sweden and The
Swedish Agency for Economic and Regional Growth to develop a model to analyze the relationship between
house prices and industry-specific new firm formation, with the interaction effect of financial infrastructure.
Findings –In the data, stable high house prices have no effect on entrepreneurship. However, a market with
rising house prices has a positive effect on new firm formation, in retail, construction, business-to-business
services and miscellaneous sectors, but produced no effect in either mining, agriculture and fishing or in
manufacturing. The interaction between rising house prices and financial infrastructure does not change the
positive effect on retail, business-to-business services and miscellaneous sectors, but within the construction
industry, the positive effect on new firm formation disappears. In manufacturing, the authors observe the
opposite –a positive effect, insteadof no effect previously.
Originality/value –The contribution of this study is to provide evidence of how house prices are
associated with entrepreneurship in different industries, as well as analyzing how the interaction between
house prices and financial infrastructure is associated with entrepreneurship. By separating observations in
time, endogeneity is controlled and a causal relationship where higher house prices is postulated, which leads
to an increase in entrepreneurial activity in different industries. By using a spatial Durbin model, the authors
control for spatial dependency.
Keywords Sweden, Entrepreneurship, Industry, Home ownership, Financing, House prices
Paper type Research paper
1. Background
Entrepreneurship and economic development are closely linked (Wiklund et al., 2011;
Storey and Greene, 2013). While old firms grow predominantly by acquisition (Delmar
et al., 2003), a startup typically grows organically, thereby creating new employment
(Van Praag and Versloot, 2007). However, a long-standing argument states that
entrepreneurship is hampered by a lack of funding (MacMillan, 1931;Stiglitz and Weiss,
1981).
Rising house prices might offer one solution to this problem (Berggren et al., 2017).
Many homeowners increase their mortgage for consumption purposes (Branch et al.,
2016), and the same source of funding could be used for starting a business. Rising house
prices could be perceived as an unexpected financial gain. Such gains lead to higher
JEL classification –R11, R31, M13
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456
Received 30January 2018
Revised 29 March 2018
Accepted 7 April2018
International Journal of Housing
Markets and Analysis
Vol. 12 No. 3, 2019
pp. 456-473
© Emerald Publishing Limited
1753-8270
DOI 10.1108/IJHMA-01-2018-0007
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1753-8270.htm
numbers of business starts (Schäfer et al., 2011). However, a high mortgage also
translates as expensive living. Bracke et al. (2013) show that the higher the mortgage, the
less likely the mortgage holder will be an entrepreneur. Blanchflower and Oswald (2013)
show, using USA panel data, that high rates of homeownership actually cause
unemployment in the long run.
Research has shown that another important influence on local business development
is the financial system. King and Levine (1993), among others, suggest that the local
financial system is important for both productivity growth and economic development,
through the evaluation of prospective entrepreneurs. It can be expected that a better
local financial system fosters start-ups in general and differently in the different
industries.
Therefore, we study the effects of rising house prices and the interaction between
house prices and financial infrastructure on entrepreneurship in different industries.
By analyzing the industry level, it is possible to pick up differences that would cancel
each other in an analysis performed on the aggregate level of the total number of
startups. Industry characteristics, such as need for capital, affect the amount of
business starts, and therefore it could be expected that certain industries are more
sensitive to funding.
Elaborating on Jin et al. (2012), the purpose of the present paper is to identify and
estimate an empirical model of the relationship between house prices and business startups
in different industries. We test this proposition on a data set similar to Berggren et al. (2017),
with the addition of industry and financial infrastructure. We discuss the specific economic
context of our data, the multiple channels between house prices and entrepreneurship and
how these channels translate into different industries.
2. Previous studies
Much entrepreneurship is based on innovation (Shane, 2003;Agostini et al.,2015;Love and
Roper, 2015). Though destructive in the short run, such industrial upheaval leads to long-
run growth (Davidsson et al.,1995). There are knowledge spillover effects on the entire
economy (Acs et al.,2012) and locally, as unique knowledge and experience are reinvested
through “entrepreneurial recycling”(Mason and Harrison, 2006). New firms create new
employment (Halilem et al., 2012;Santarelli and Tran, 2012), potentially bringing
unemployed minorities into the socio-economic context (Cowling, 2010;Cowling et al.,2015),
and encourage regional renewal (Gordon and McCann, 2005;Doh and Kim, 2014).
Entrepreneurship generates both competition and cooperation. New patterns of firm
interaction emerge (Mason, 2009), and firms within proximity of each other can form a
cluster (Porter, 2000), with an important economic impact of a long-lasting higher rate of
new business starts (Andersson and Koster, 2010).
Most countries have created policies and strategies for stimulating different forms of new
business starts (Wren and Storey, 2002;Stevenson and Lundström, 2007), notably by
providing better access to the resources needed by startups. Startups are subject to different
conditions, notably pertaining to knowledge, industry characteristics and demand (Shane,
2003). One consequence of knowledge needs has been the direct political intervention in the
form of localization of public agencies such as universities (Acs et al.,2017). Part of industry
characteristics is the need for capital, a need that has prompted the creation of programs
focused on increasing financial resources available to entrepreneurs (Bateman, 2000;Perren
and Jennings, 2005;Storey et al.,2007).
Research on startup finance suggests that all firms in a particular development stage
face similar needs and follow similar financing patterns (Weston and Brigham, 1981;Berger
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and Udell, 1998). This life-cycle view of firm finance has been supported by studies in
Europe (Psillaki and Daskalakis, 2009), China (Newman et al., 2012) and Africa (Abor and
Biekpe, 2009). The typical financial behavior of a startup is not to seek the source of
finance that offers the lowest interest rate. Instead,thechoiceismadebasedoncontrol
aversion. In a firm, there is a general mistrust of outsiders: the more a firm stands to lose
control to outsiders, the less likely it is to submit to that type of financing (Donaldson,
1984;Myers, 1984;Davidsson et al., 2009;Mac an Bhaird and Lucey, 2011). The different
types of funding available to a firm can be ranked by the amount of external control each
source demands, as a stable “pecking order”(Myers and Majluf, 1984). Internally
generated capital is preferred over outsider loans (often bank lending), and loans are
preferred to selling equity to outsiders. Pecking-order theory originated in studies of
large organizations, but the framework is equally useful for studying medium-sized
(Berggren et al., 2000)andsmallfirms (Vanacker and Manigart, 2010;Degryse et al., 2012;
Alon and Rottig, 2013).
Banks are reluctant to lend money to new firms. New firms have no formal track
record in form of performance data and legitimacy (De Clercq et al., 2013;Ramlall, 2014)
that could lower the bank’s risk assessment. Instead, banks will demand collateral, and
this poses a significant obstacle to starting new enterprises (Bracke et al., 2013). Denied
bank loans, startups use “bootstrapping”strategies (Bhide, 1992) and rely on insider
finance. Bootstrapping focuses on avoiding costs, for instance, through exchanging
services, buying secondhand or sharing equipment with other firms. Insider finance is the
most important source of finance for startups (Gregory et al., 2005;Revest and Sapio,
2012;Robb and Robinson, 2014). It is defined as the personal funds of the founder, which
include his or her’s personal savings, home mortgage and credit cards (Cassar, 2004;
Storey and Greene, 2013). The main source of collateral is the house of the founder
(Chaney et al., 2012), and higher house prices provide nascent entrepreneurs with more
collateral for a bank loan (Bernanke et al., 1999;Greenspan and Kennedy, 2008;Jin et al.,
2012;Bracke et al., 2013).
On an aggregate level, favorable economic conditions are linked to both higher house
prices (Leamer, 2007) and higher rates of business starts (Kerr and Nanda, 2009). However,
there is also a body of research on the direct link between house prices and home owner
borrowing (Defusco, 2018). In response to house price increases, households withdraw
mortgage equity for consumption purposes (Case et al., 2003;Hurst and Stafford, 2004;
Campbell and Cocco, 2007;Mian and Sufi,2011;Mian et al.,2013). Research shows that
house prices also have an effect on entrepreneurship (Black et al.,1996;Fairlie and
Krashinsky, 2012;Fort et al., 2013;Corradin and Popov, 2015;Kerr et al.,2015;Berggren
et al., 2017). Schmalz et al. (2013) show that in regions with house price appreciation, not only
are homeowners more likely to start a business, but these businesses are also larger than
those started by renters. Regions with larger rises in house prices experienced stronger
growth in employment in small firms, especially in industries with a limited need for capital
(Adelino et al.,2015). Hurst and Lusardi (2004) find evidence of this link only in the very
richest segment of households.
While prior work has investigated different aspects of house prices and mortgage, it is
possible that new knowledge can be acquired by studying entrepreneurship instead. In
particular, there are differences between industries in terms of their sensitivity to house
price increases, as industries differ in terms of need for finance. Some industries are more
capital-intense and require substantial production capacity such as machinery and
equipment, while others require less financial resources, such as service firms relying
primarily on the knowledge of the founder (Adelino et al., 2015). Furthermore, different
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industries have different growth patterns and scalability (Viio and Grönroos, 2014). The
need for capital in different industries affect new business formation, but even more so, it is
affected by demand. Demand conditions, such as market size, growth and segmentation,
have a positive effect on new firm formation (Shane, 2003). For example, in a highly
digitalized service industry, startups will probably target a much larger, possibly
international, market segment than those service firms that are dependent on the physical
delivery of the service, and necessarily cater to local or regional market demand.
Based on these previous studies, we expect to find an industry difference in the number
of businesses started in response to the increase in collateral provided by rising house
prices. First, house prices are associated with consumption (Case et al., 2003), in particular
for old (Campbell and Cocco, 2007) and liquidity-constrained households (Hurst and
Stafford, 2004). Thus, house price increases lead to increased consumption, which should be
exhibited as a growth in the number of new firms in the retail industry. Second, higher
house prices reflect high demand for housing, which attracts construction companies.
Construction is a physical service that is delivered locally, and relies on local demand. Thus,
we should expect that higher house prices lead to an increase in construction-related
entrepreneurship. We also expect to find that the number of business starts increases with
access to financial infrastructure, in that local financial actors help develop local
entrepreneurs. Thus, our hypotheses are formulated as follows:
H1. The number of new businesses started in response to a house price increase will
differ between industries.
H2. The number of new businesses started in response to the interaction between rising
house prices and financial infrastructure, should exhibit the same industry effect as
rising house prices alone, but stronger.
3. Entrepreneurship and housing in Sweden
The impact of the global financial crisis (GFC) was weaker in Sweden than in most
European countries (Claessens, et al., 2010). Even though the GDP decreased by 5 per cent
during 2009, in 2010 Sweden experienced the highest growth in GDP in more than four
decades (Statistics Sweden, 2017), and –as can be seen in Figure 2 –the GFC only made a
small dent in the upward trend of Swedish house prices. The most problematic aspects of
the GFC in Sweden related to two of the largest commercial banks that had to be refinanced
through their owners owing to large losses in their operations in the Baltic States. Most
academics and practitioners would argue that the relatively smooth ride that Sweden
experienced during the GFC was because of the real estate and banking crisis that Sweden
had during the early 1990s. The lessons learned from that crisis, including the importance of
cash flow in lending decisions, made the Swedish financial system more resilient than other
financial systems around the world (Flodén, 2013).
Similar to Austria, Germany and Switzerland, the Swedish financial system is considered
to be one of the bank-oriented financial systems (Levine, 2002,2005). In Sweden, banks play
an important role in financing businesses, and historically, financial–industrial networks
have been of great importance in generating export income (Henrekson and Jakobsson, 2001;
Magnusson, 2002). Unlike market-oriented financial systems, private actors such as
business angels and venture capital funds have been of less importance in financing
startups and growing businesses (Levine, 2005;Beck et al., 2007). In addition, the legal
system is more inclined to favor creditors on behalf of investors (Levine, 2002).
House prices
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459
Reflecting general macroeconomic conditions, the Swedish startup scene has been rather
stable since the Swedish financial crisis. Between 1994 and 2003, there were approximately
34,000-39,000 startups per year. After 2004 the number of startups has steadily increased,
and from 2010 until 2016 the number of startups per year hasbeen around 70,000. The major
reasons for the increase in number of startups are related to changes in the regulatory
requirements for limited companies, such as lower requirements for auditing and capital
Figure 2.
House prices in
Sweden 1981-2016 Source: Statistics Sweden (2017)
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
850
900
950
1,000
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Single family house Holiday house Apartments Consumer prices
Figure 1.
Number of startups in
Sweden 1993-2016
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Source: Statistics Sweden (2017)
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(Braunerhjelm and Henrekson, 2013). As in most Western economies, the bulk of startups in
Sweden (approximately 80 per cent in 2016) is in the service sector (Statistics Sweden, 2017).
In the same way, house prices in Sweden have risen since the mid-1990s (Statistics
Sweden, 2017), a major cause of concern for policymakers and the Swedish Central Bank
(Swedish Central Bank, 2015;Belfrage and Kallifatides, 2018).
As a result of the continuous growth of house prices, new legislation has been introduced.
Starting from March 1st 2017, all new mortgages need to be amortized and there is a debt-to-
income cap in new mortgages (FI, 2017; SFS, 2016:346). Since autumn 2017, there has been a
significant cool down ofthe Swedish housing market (Swedish Central Bank, 2017).
4. Methodology and data
4.1 Methodology
We estimate a model where new firms per capita over the period 2007-2014 across all
municipalities in Sweden are related to the following six different types of determinants:
(1) establishment structure;
(2) labor market condition;
(3) human capital;
(4) income;
(5) housing market condition; and
(6) financial infrastructure.
We use cross-sectional, aggregated data on the municipality level, for all 284 Swedish
municipalities. The data set is compiled from Statistics Sweden and The Swedish Agency
for Economic and Regional Growth.
There are two potential problems in this type of regional models: endogenous
determinants and spatial dependency. Higher economic growth might result in both
increased bank lending and higher house prices. Hence, the direction between house price
appreciation and start-ups of new firms might be bi-directional (Iacoviello, 2005;Adelino
et al.,2015). We have solved this potential endogeneity problem by estimating a model
where our determinants in the preceding period (2007) are used to explain the variation in
firm formation in different business sectors in the subsequent period (2007-2014), the same
approach used by Armington and Acs (2002),Andersson et al. (2014) and Berggren et al.
(2017). To address spatial dependency, our model comparison and choice of weight matrix
has followed the spatial econometric modeling approach proposed by Elhorst (2010) and
used in Berggren et al. (2017). We estimate an OLS and use LM-tests (Anselin, 1988;Anselin
et al., 1996). If the LM-tests on OLS-residuals are significant, then a spatial Durbin model is
estimated. If these LM-tests suggest that the spatial error model is the best spatial model, the
log likelihood ratio test (LR-test) is used to test the convenience of spatial Durbin model
against spatial error or autoregressive model. To select the spatial weight matrix, we are
comparing the goodness-of-fit (here the AIC measure) of three different types of weight
matrices: inverse distance, binary with a cut-off and five nearest neighbors. All of them have
been used in the spatial econometric literature (Elhorst, 2010).
4.2 Data
The dependent variable is equal to start-ups per capita in different industrial sectors across
all 284 Swedish municipalities, over the period 2007-2014. Our data are divided into the
following six different industrial sectors:
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461
(1) mining, agricultural and fishing;
(2) manufacturing;
(3) construction;
(4) retail;
(5) business to business service; and
(6) miscellaneous sectors.
Our main independent variables consist of two housing market variables, and one variable
capturing financial infrastructure. The first measure is the change in house prices between
2001 and 2007 (Schmalz et al., 2013;Berggren et al., 2017). The second measure is the general
house price level of a municipality in comparison with average national house prices. The
third variable is the existence of a local financial infrastructure in the municipality,
measured as the number of employees in the financial sector multiplied by the change in
house prices between 2001 and 2007 (Mandell and Wilhelmsson, 2015).
The remaining independent variables include human capital, employment, unemployment,
self-employment and in-migration, as well as income and number of employees per
establishment (establishment size) in the municipality. All variables have been used in
Berggren et al. (2017). Human capital is conceived of as the regional level of skill and knowledge
(Armington and Acs, 2002;Adelino et al., 2015;Binet and Facchini, 2015)andismeasuredas
the rate of university degree in the population in 2007. The expected relation of human capital
with new firm formation is positive in less capital-intensive industries, such as retail and
business-to-business services, and negative in more capital-intensive industries, such as
manufacturing industry.
The labor market is described through three different measures: rates of employment,
unemployment and self-employment. Employment rate measures market size and is used
instead of population (Binet and Facchini, 2015) or number of households (Adelino et al.,
2015). The measure total employment captures some of the agglomeration effects (Acs et al.,
1994). Prior work shows that the rate of unemployment is a major determinant of new firm
formation (“necessity entrepreneurship”, see Acs, 2006). The third labor market indicator we
are using is self-employment rates in 2007. It can be regarded as a measure of
entrepreneurial culture (Johannisson, 1984), and the very same measure is used by
Armington and Acs (2002).
Earlier research has suggested that in-migration has an effect similar to necessity
entrepreneurship. As immigrants are new to the socio-economic setting and lack the skills
and networks necessary for finding employment, they become self-employed instead. Hence,
immigrants tend to start more new companies than non-immigrants, but only in less capital-
intensive industries, such as retail and services, and not in manufacturing. Measures of
diversity, such as the “Melting Pot Index”(Lee et al., 2004), have endeavored to capture this
effect. Migration data is used by Adelino et al. (2015), and we include a variable measuring
the percentage of the population that is foreign born in 2007.
Income is measured as the regional average annual income level in 2007. It has been
argued that a high regional income level, by way of increasing market size, will open up
opportunities for new firms (Binet and Facchini, 2015). Hence, we expect to see a positive
effect, with more new firm start-ups in regions with higher regional income.
Finally, we use establishment size as a proxy for the regional industry structure
(Armington and Acs, 2002). We divide the total number for employment in 2007 by the
number of establishments in 2007. The expected relationship is negative as a larger average
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establishment size should be negatively related to the regional start-up rate (Armington and
Acs, 2002;Shane, 2003).
5. Results
As can been observed in Table I, the number of new firms per capita varies both within
municipalities and between sectors. Not surprising, the number of new start-ups per capita
is lowest within the mining and fishing sector and highest within business-to-business
sector. It is also within the later sector the variation between municipalities is highest. The
housing market variables show that the average change in house price is positive and equal
to almost 0.7 per cent. The standard deviation is equal to 0.3. In a third of the municipalities
are the house prices higher than the average house prices in Sweden in 2007. Around 23
per cent of the population has a university degree. The variation across the labor market is,
however, significant. The variation in the variable financial service interacted with change
in house prices is substantial. For example, the standard deviation is larger than the mean
value.
The average size of the municipal labor markets measured as employment is only 15,000,
but the variation is considerable and amounts to 30,000. The average unemployment is
equal to 6.6 per cent with a variation of 2 per cent. We are measuring the industrial structure
with establishment size. The average number of employment per establishment is equal to
eight persons and the standard deviation is equal to 1.7 persons. We are using self-employed
to measure the entrepreneurial climate. We can see that the average rate of self-employed is
9.7 per cent with a variation of 2.5 per cent. The number of immigrants as a percentage of
population is equal to 4.6 per cent, but the variation is large across the labor markets. The
variation in the variable financial service interacted with change in house prices is
substantial. For example, the standard deviation is larger than the mean value.
In Figures A1 (Appendix), we are depicting the relationship between start-ups per capita
(2008-2014) in each industry to the change in house prices (2001-2007). We can notice that
the relationship is weak in most industries. However, there is a positive association between
Table I.
Descriptive statistics
Abbreviation Mean SD Min. Max
Start-ups per capita
Mining, fishing Mining 0.001256 0.000819 0.000119 0.004856
Manufacture Manufact 0.001329 0.000462 0.000355 0.003330
Construction Constr 0.003196 0.001158 0.000785 0.007336
Retail Retail 0.003949 0.001049 0.000785 0.008046
B2B service B2B 0.008062 0.004199 0.002809 0.029718
Miscellaneous Misc 0.006860 0.002006 0.002892 0.015951
Independent variables
Human capital Univ 0.23 0.08 0.13 0.59
Employment per establishment Emp/estbl 8.46 1.76 4.21 15.95
High house prices High HP 0.34 0.47 0 1
Change in house prices Change HP 0.69 0.32 0.16 1.62
Self-employment Self 9.73 2.47 4.2 21.2
Employment Emp 15203.5 30722.4 1026 410033
Income Inc 210.42 18.21 178.7 289.1
Unemployment Unemp 6.64 1.96 0.10 0.24
In-migration In-m 4.65 3.32 0.9 24.7
DHP-bank emp Change HP_bank 0.0024 0.0034 0.0004 0.0398
No. of observations 284
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the change in house prices and start-ups per capita in the construction industry, retail sector
and in the business to business service sector, as well is in themiscellaneous sector.
5.1 Spatial dependency
Cross-sectional spatial data need to be tested for spatial dependency, such as spatial
autocorrelation and/or spatial heterogeneity. Before we estimate the final econometric
model, we perform pre-test of the residuals from an OLS model. In Table II, we test for
spatial dependency, as well as for suitable spatial weight matrix (inverse square distance,
binary and five nearest neighbors). If spatial dependency is present, we are also testing
which spatial specification of the econometric model that is preferable (spatial error, lag or
spatial Durbin).
The robust LM-test shows that spatial dependency is present regardless of specification
and spatial weight matrix. Moreover, the test shows that a spatial lag specification is
preferable, compared to a spatial error model, as the test statistics is larger. Spatial
dependency is more pronounced in the spatial distribution of new firms within the mining,
agricultural and fishing industry, and within the retail industry.
The AIC statistics are used to detect preferred spatial weight matrix. Here we are using
the more general spatial Durbin model. The best weight matrix has the lowest AIC value.
We can notice that different weight matrixes are preferred in the different industries. In
mining, agricultural and fishing, in construction and in miscellaneous sectors, the nearest
neighbor constitutes the best spatial weight matrix. In manufacturing, retail and business-
to-business services, the binary variable indicating all the neighbors with the cut-off value,
constitutes the best spatial weight matrix.
5.2 Econometric results
The econometric results are presented in Table III. As spatial dependency is present all
models have been estimated using a spatial econometric modeling approach. The default
model used is the spatial Durbin model. However, if a spatial lag model can be used instead
Table II.
Spatial dependency
diagnostic test
(Robust LM) and
spatial weight matrix
test (AIC)
Mining Manufact. Constr. Retail B2B Misc.
Spatial error
Robust LM
W1 11.509 3.092 0.375 0.366 1.966 2.953
W2 10.053 5.912 0.001 0.672 1.425 1.555
W3 4.030 0.041 0.405 3.093 0.807 2.984
Spatial lag
Robust LM
W1 22.338 5.357 5.534 17.888 4.517 5.771
W2 5.770 7.355 1.937 18.057 2.635 3.299
W3 16.380 0.369 3.396 9.126 5.285 1.446
Spatial Durbin
AIC
W1 325.213 180.397 37.909 116.251 200.910 247.708
W2 335.033 171.632 32.434 -126.609 -207.408 244.946
W3 310.069 182.167 24.303 98.275 199.191 265.777
Notes: W1 = inverse square distance; W2 = binary; W3 = nearest neighbor; Robust LM = robust lagrange
multiplier; SDM = spatial Durbin model; AIC = Aiken Information Criteria; Preferred weight matrix in italic
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Mining Manufact. Constr. Retail B2B Misc.
Change HP 0.101 (0.95) (1.46) 0.146 (1.89) 0.209 (3.85) 0.176 (3.70) 0.136 (2.93)
High HP 0.240 (2.73) 0.057 (0.80) 0.0723 (1.26) 0.056 (1.33) 0.061 (1.66) 0.055 (1.59)
Change HP_bank 5.995 (0.73) 15.335 (2.23) 11.570 (1.21) 238.270 (2.44) 185.096 (2.17) 9.450 (3.06)
Univ 0.149 (0.88) 0.280 (2.00) 0.486 (4.43) 0.026 (0.32) 0.625 (8.63) 0.371 (5.64)
Emp/establ 0.216 (0.67) 0.062 (0.24) 0.315 (1.53) 0.180 (1.20) 0.417 (3.19) 0.475 (3.82)
Self 1.169 (4.49) 0.543 (2.53) 0.808 (4.60) 0.087 (0.69) 0.193 (1.75) 0.077 (0.72)
Emp 0.003 (0.06) 0.035 (0.93) 0.058 (1.97) 0.021 (0.96) 0.020 (1.02) 0.031 (1.72)
Inc 1.149 (1.81) 0.971 (1.93) 0.007 (0.02) 0.713 (2.42) 1.601 (6.17) 0.252 (0.99)
Unemp 0.421 (3.48) 0.004 (0.03) 0.123 (1.26) 0.074 (1.14) 0.092 (1.62) 0.023 (0.39)
In-m 0.202 (3.45) 0.003 (0.05) 0.003 (0.07) 0.078 (2.54) 0.067 (2.50) 0.034 (1.26)
Stockholm 0.002 (0.02) 0.166 (1.56) 0.292 (1.75) 0.056 (0.90) 0.152 (2.80) 0.040 (0.40)
rho 0.480 (8.19) 0.403 (1.34) 0.404 (3.92) 0.647 (1.89) 0.425 (1.27) 0.329 (3.05)
Constant 0.517 (0.14) 62.247 (2.56) 12.856 (2.64) 36.731 (2.62) 34.373 (2.51) 10.437 (3.52)
Model Lag SDM SDM SDM SDM SDM
Weight matrix W3 W2 W3 W2 W2 W3
LR-test (p-value)
SDM vs Lag 0.059 0.030 0.011 0.001 0.011 0.001
AIC 307.207 171.632 24.302 126.609 207.408 265.778
Notes: t-values within parentheses; LR = likelihood ratio test; SDM = spatial Durbin model; Lag = spatial lag model
Table III.
Econometric results
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of the spatial Durbin model, we have used the spatial lag model specification. We have used
a LR-test to test if the spatial lag model is nested in the spatial Durbin model (p-values are
shown in the table). Based on the test, we have estimated only one spatial lag models and
five spatial Durbin models. The housing market does have an industry-specific impact of
new firm formation, and in the next section we will look at each industry in turn.
The mining, agricultural and fishing industry is not dependent on the conditions of the
housing market. House price appreciation will not increase the number of new firms per
capita. We can observe that there is a negative effect between high house prices and start-
ups per capita. However, that effect is not causal as it is more an effect of the correlation
between house prices and population density (rural areas are less dense than urban areas).
Moreover, human capital does not have any impact in creation of new firms. The same is
true for establishment size, employment level and income. However, higher unemployment
rates and more self-employment rates increase the number of new firms per capita.
The manufacturing sector is usually a capital-intensive business sector. More capital is
needed than what the house market can generate. In line with this, the results indicate that
the housing market does not have any impact on new firm formation within this sector.
Interestingly, the interaction effect between rising house prices and financial infrastructure
results in a positive effect on new firm formation. Human capital, establishment size,
unemployment rates and the size of the labor market do not have any impact on the
establishment of new firms. However, the income level seems to have a positive effect on
new firm formation. More surprisingly, the same is true for self-employment. More self-
employed people seem to increase the number of firms in this sector.
The construction sector does have a relationship with the housing markets. More firms in
this sector can be observed in regions where house prices have been increased. When the
interaction effect between rising house prices and financial infrastructure is analyzed, there
is suddenly no effect on new firm formation. Besides house prices, human capital has a
negative correlation to new firm formation and the number of self-employed have a positive
correlation.
The firm formation in the retail sector is also positively impacted of house price
appreciation. The impact is larger than in the construction sector. In the retail sector, the
interaction effect between rising house prices and financial infrastructure amplifies the
existing positive effect on new firm formation. Besides housing market condition, income and
migration are the only determinants that are significant. Both those determinants have a
positive effect on new firm’s creation, that is, higher income seems to generate more
companies within the retail sector. The same is true in regions where the rate of in-migration
is higher.
The housing market is very important as a determinant of new firms per capita with the
business-to-business service sector. The change in housing prices has a positive impact on
new firms. Just as in the retail industry, the interaction effect between rising house prices
and financial infrastructure amplifies an already existing positive effect on new firm
Table IV.
A summary of the
results
Mining Manufact. Constr. Retail B2B Misc.
Change HP No effect No effect Positive
effect
Positive
effect
Positive effect Positive
effect
High HP No effect No effect No effect No effect No effect No effect
Change HP interacting
with Banking sector
No effect Positive
effect
No effect Positive
effect
Positive effect Positive
effect
IJHMA
12,3
466
formation. We can also notice that human capital is an important determinant in this sector
together with income level (positive impact), unemployment rates (positive impact),
in-migration (positive impact) and establishment size (negative impact).
6. Conclusions
High house prices per se have no effect on new firm formation at all, in any industries.
Turning to each of the six industries, we find support for H1. There is partial support for
H2. Contrary to expectations, the effect in manufacturing and construction is changed.
The mining, agricultural and fishing industry is completely unaffected by all
independent variables. This is in line with expectations, because of the prohibitively large
needs for capital, that will not be mitigated by financial infrastructure.
Manufacturing is not affected by rising house prices, but when financial infrastructure is
included, the interaction with increased house prices provides a positive effect on new firm
formation. We interpret this as a number of small manufacturing firms that are started in
response to the development aid provided by local banks. Financial infrastructure will have
helped engender entrepreneurial development of new manufacturing firms, that would not
have started operations without that infrastructure.
In the construction industry, rising house prices have a positive effect on new firm
formation. Not surprising, municipalities with large appreciation in house prices will attract
construction of new houses. That is, higher prices is a reflection of high demand for housing,
which means that construction is commenced to meet that demand. However, when
financial infrastructure is added, there is no positive effect. It is not immediately clear how to
interpret this result. If we assume that rising house prices still means high demand for new
construction, our results indicate that financial infrastructure interacts with rising house
prices in a way that deters new construction companies and favors existing firms. This
result is puzzling andwarrants further investigation.
In the last three industries –retail, business-to-business and miscellaneous –there is a
common pattern. There is entrepreneurship in respons to rising house prices, and the
interaction with financial infrastructure amplifies this effect. None of these three industries
require large amounts of capital, which is why we expect to see positive effects from both
rising house prices and financial infrastructure. More new firms within the retail sector can
be explained by mortgage equity withdrawal consumption (retail firms are started to cater
to that demand increase), as well as mortgage collateral effects (higher prices provide
collateral, enables a higher number of new firms). In business-to-business, new firm
formation can arise for the same reasons. In the miscellaneous sector, it is difficult to state a
single channel that encompass all firms, except that the industry is positively associated
with increased access to capital.
There are some implications for policymakers, practice and research. Public initiatives to
foster entrepreneurship through better access to capital need to take industry effects into
account. Not all industries respond the same way. For practice, rising house prices and financial
infrastructure may offer promising business prospects, but the way that different industries
depend on each other suggests that firms need to be wary of how a house price downturn could
propagate through the industries. For research, the present study has contributed to the
literature by the addition of the Swedish case, with an analysis of industry and the interaction
with financial infrastructure. Specifically, future research could focus on understanding the
unexpected results with regard to construction firm formation. On a more general level, further
studies of industrial differences will contribute to an even deeper understanding of the
mechanisms behind home ownership, house price development and entrepreneurship.
House prices
and startups
467
References
Abor, J. and Biekpe, N. (2009), “How do we explain the capital structure of SMEs in Sub-Saharan africa?
Evidence from Ghana”,Journal of Economic Studies, Vol. 36 No. 1, pp. 83-97.
Acs, Z. (2006), “How is entrepreneurship good for economic growth?”,Innovations: technology,
Governance, Globalization, Vol. 1 No. 1, pp. 97-107.
Acs, Z.J., Audretsch, D.B. and Feldman, M.P. (1994), “R&D spillovers and recipient firm size”,Review of
Economics and Statistics, Vol. 76, pp. 336-340.
Acs, Z.J., Audretsch, D.B., Braunerhjelm, P. and Carlsson, B. (2012), “Growth and entrepreneurship”,
Small Business Economics, Vol. 39 No. 2, pp. 289-300.
Acs, Z.J., Audretsch, D.B., Lehmann, E.E. and Licht, G. (2017), “National systems of innovation”,The
Journal of Technology Transfer, Vol. 42 No. 5, pp. 997-1008.
Adelino, M., Schoar, A. and Severino, F. (2015), “House prices, collateral, and self-employment”,Journal
of Financial Economics, Vol. 117 No. 2, pp. 288-306.
Agostini, L., Caviggioli, F., Filippini, R. and Nosella, A. (2015), “Does patenting influence SME sales
performance? A quantity and quality analysis of patents in Northern Italy”,European Journal of
Innovation Management, Vol. 18 No. 2, pp. 238-257.
Alon, I. and Rottig, D. (2013), “Entrepreneurship in emerging markets: new insights and directions for
future research”,Thunderbird International Business Review, Vol.55 No. 5, pp. 487-492.
Andersson, M. and Koster, S. (2010), “Sources of persistence in regional start-up rates—evidence from
Sweden”,Journal of Economic Geography, Vol. 11 No.1, pp. 179-201.
Andersson, M., Klaesson, J. and Larsson, J.P. (2014), “The sources of the urban wage premium by
worker skills: spatial sorting or agglomeration economies?”,Papers in Regional Science, Vol. 93
No. 4, pp. 727-747.
Anselin, L. (1988), Spatial Econometrics: Methods and Models, Kluwer, Dordrecht.
Anselin, L., Bera, A., Florax, R. and Yoon, M.J. (1996), “Simple diagnostic tests for spatial dependence”,
Regional Science and Urban Economics, Vol. 26No. 1, pp. 77-104.
Armington, C. and Acs, Z.J. (2002), “The determinants of regional variation in new firm formation”,
Regional Studies, Vol. 36 No. 1, pp. 33-45.
Bateman, M. (2000), “Neo-liberalism, SME development and the role of business support centres in the
transition economies of Central and Eastern Europe”,Small Business Economics, Vol. 14 No. 4,
pp. 275-298.
Beck, T., Demirguc-Kunt, A. and Peria, M.S.M. (2007), “Reaching out: access to and use of banking
services across countries”,Journal of Financial Economics, Vol. 85 No. 1, pp. 234-266.
Belfrage, C. and Kallifatides, M. (2018), “Financialisation and the new swedish model”,Cambridge
Journal of Economics, Vol. 42 No. 4, pp. 875-900.
Berger, A.N. and Udell, G.F. (1998), “The economics of small business finance: the roles of private
equity and debt markets in the financial growth cycle”,Journal of Banking and Finance, Vol. 22
Nos 6/8, pp. 613-673.
Berggren, B., Fili, A. and Wilhelmsson, W. (2017), “Does the increase in house prices influence the
creation of business startups? The case ofSweden”,Region, Vol. 4 No. 1, pp. 1-16.
Berggren, B., Olofsson, C. and Silver, L. (2000), “Control aversion and the search for external financing
in swedish SMEs”,Small Business Economics, Vol. 15 No.3, pp. 233-242.
Bernanke, B.S., Gertler, M. and Gilchrist, S. (1999), “The financial accelerator in a quantitative business
cycle framework”, In: Taylor J., Woodford M. (Eds) Handbook of Macroeconomics, North
Holland, Amsterdam, Vol. 1.
Bhide, A. (1992), “Bootstrap finance: the art of start-ups”,Harvard Business Review, Vol. 70 No. 6,
pp. 109-117.
IJHMA
12,3
468
Binet, M.E. and Facchini, F. (2015), “The factors determining firm start-ups in french regions and
the heterogeneity of regional labor markets”,Annals of Regional Science, Vol. 54 No. 1,
pp. 251-268.
Black, J., de Meza, D. and Jeffreys, D. (1996), “House prices, the supply of collateral and the enterprise
economy”,Economic Journal, Vol. 106 No. 434, pp. 60-75.
Blanchflower, D.G. and Oswald, A.J. (2013), “Does High Home-ownership Impair the Labor Market?”,
(No. w 19079), National Bureau of Economic Research.
Bracke,P.,Hilber,C.andSilva,O.(2013),“Homeownership and entrepreneurship: the role of commitment
and mortgage debt”, Institute for the Study of Labor (IZA) discussion paper no. 7417, IZA, Bonn.
Branch, W.A., Petrosky-Nadeau, N. and Rocheteau, G. (2016), “Financial frictions, the housing market,
and unemployment”,Journal of Economic Theory, Vol. 164, pp. 101-135.
Braunerhjelm, P. and Henrekson, M. (2013), “Entrepreneurship, institutions, and economic dynamism:
lessons from a comparison of the United States and Sweden”,Industrial and Corporate Change,
Vol. 22 No. 1, pp. 107-130.
Campbell, J.Y. and Cocco, J.F. (2007), “How do house prices affect consumption? Evidence from micro
data”,Journal of Monetary Economics, Vol. 54 No.3, pp. 591-621.
Case, K.E., Quigley, J.M. and Shiller, R.J. (2003), “Home-buyers, housing and the macroeconomy”,
Working paper, UC Berkeley.
Cassar, G. (2004), “The financing of business start-ups”,Journal of Business Venturing, Vol. 19 No. 2,
pp. 261-283.
Chaney, T., Sraer, D. and Thesmar, D. (2012), “The collateral channel: how real estate shocks affect
corporate investment”,American Economic Review, Vol. 102 No. 6, pp. 2381-2409.
Claessens, S., Dell’Ariccia, G., Igan, D. and Laeven, L. (2010), “Cross-country experiences and policy
implications from the global financial crisis”,Economic Policy, Vol. 25 No. 62, pp. 267-293.
Corradin, S. and Popov, A. (2015), “House prices, home equity borrowing, and entrepreneurship”,The
Review of Financial Studies, Vol. 28 No. 8, pp. 2399-2428.
Cowling, M. (2010), “The role of loan guarantee schemes in alleviating credit rationing in the UK”,
Journal of Financial Stability, Vol. 6 No. 1, pp. 36-44.
Cowling, M., Liu, W., Ledger, A. and Zhang, N. (2015), “What really happens to small and medium-sized
enterprises in a global economic recession? UK evidence on sales and job dynamics”,
International Small Business Journal, Vol. 33 No. 5, pp. 488-513.
Davidsson, P., Lindmark, L. and Olofsson, C. (1995), “Small firms, business dynamics and differential
development of economic well-being”,Small Business Economics, Vol. 7 No. 4, pp. 301-315.
Davidsson, P., Steffens, P. and Fitzsimmons, J. (2009), “Growing profitable or growing from profits:
putting the horse in front of the cart?”,Journal of Business Venturing, Vol. 24 No. 4, pp. 388-406.
De Clercq, D., Lim, D.S.K. and Oh, C.H. (2013), “Individual-level resources and new business activity:
the contingent role of institutional context”,Entrepreneurship Theory and Practice, Vol. 37 No. 2,
pp. 303-330.
DeFusco, A.A. (2018), “Homeowner borrowing and housing collateral: new evidence from expiring
price controls”,The Journal of Finance, Vol. 73 No. 2, pp. 523-573.
Degryse, H., de Goeij, P. and Kappert, P. (2012), “The impact of firm and industry characteristics on
small firms’Capital structure”,Small Business Economics, Vol. 38 No. 4, pp. 431-447.
Delmar, F., Davidsson, P. and Gartner, W.B. (2003), “Arriving at the high-growth firm”,Journal of
Business Venturing, Vol. 18 No. 2, pp. 189-216.
Doh, S. and Kim, B. (2014), “Government support for SME innovations in the regional industries: the case of
government financial support program in South korea”,Research Policy, Vol. 43 No. 9, pp. 1557-1569.
Donaldson, G. (1984), Managing Corporate Wealth, The Operation of a Comprehensive Financial Goals
System, Praeger, New York, NY.
House prices
and startups
469
Elhorst, J.P. (2010), “Applied spatial econometrics”,Spatial Economic Analysis, Vol. 5 No. 1, pp. 9-28.
Fairlie, R.W. and Krashinsky, H.A. (2012), “Liquidity constraints, household wealth, and
entrepreneurship revisited”,Review of Income and Wealth, Vol. 58 No. 2, pp. 279-306.
FI (2017), Stability in the Financial System 2017:2, Stockholm.
Flodén, M. (2013), “A role model for the conduct of fiscal policy? Experiences from Sweden”,Journal of
International Money and Finance, Vol. 34, pp. 177-197.
Fort, T.C., Haltiwanger, J., Jarmin, R.S. and Miranda, J. (2013), “How firms respond to business cycles:
the role of firm age and firm size”,IMF Economic Review, Vol. 61 No. 3, pp. 520-559.
Gordon, I.R. and McCann, P. (2005), “Innovation, agglomeration, and regional development”,Journal of
Economic Geography, Vol. 5 No. 5, pp. 523-543.
Greenspan, A. and Kennedy, J. (2008), “Sources and uses of equity extracted from homes”,Oxford
Review of Economic Policy, Vol. 24 No. 1, pp. 120-144.
Gregory, B.T., Rutherford, M.W., Oswald, S. and Gardiner, L. (2005), “An empirical investigation of the
growth cycle theory of small firm financing”,Journal of Small Business Management, Vol. 43
No. 4, pp. 382-392.
Halilem, N., Bertrand,C., Cloutier, J.S., Landry, R. and Amara, N. (2012), “The knowledge value chain as
an SME innovation policy instrument framework: an analytical exploration of SMEs public
innovation support in OECD countries”,International Journal of Technology Management,
Vol. 58 Nos 3/4, pp. 236-260.
Henrekson, M. and Jakobsson, U. (2001), “Where schumpeter was nearly right –the swedish model and
capitalism, Socialism and democracy”,Journal of Evolutionary Economics, Vol. 11 No. 3,
pp. 331-358.
Hurst, E. and Lusardi, A. (2004), “Liquidity constraints, household wealth, and entrepreneurship”,
Journal of Political Economy, Vol. 112 No. 2, pp. 319-347.
Hurst, E. and Stafford, F.P. (2004), “Home is where the equity is: mortgage refinancing and household
consumption”,Journal of Money, Credit, and Banking, Vol. 36 No. 6, pp. 985-1014.
Iacoviello, M. (2005), “House prices, borrowing constraints, and monetary policy in the business cycle”,
American Economic Review, Vol. 95 No. 3, pp. 739-764.
Jin, Y., Leung, C.K.Y. and Zeng, Z. (2012), “Real estate, the external finance premium and business
investment: A quantitative dynamic general equilibrium analysis”,Real Estate Economics,
Vol. 40 No. 1, pp. 167-195.
Johannisson, B. (1984), “A cultural perspective on small business –local business climate”,
International Small Business Journal, Vol. 2 No. 4, pp. 32-43.
Kerr, S., Kerr, W.R. and Nanda, R. (2015), House Money and Entrepreneurship (No. w21458), National
Bureau of Economic Research.
Kerr, W. and Nanda, R. (2009), Financing Constraints and Entrepreneurship (No. w15498), National
Bureau of Economic Research.
King, R.G. and Levine, R. (1993), “Finance, entrepreneurship and growth. Theory and evidence”,
Journal of Monetary Economics, Vol. 32 No. 3, pp. 513-542.
Leamer, E.E. (2007), Housing Is the Business Cycle (No. w13428), National Bureau of Economic
Research.
Lee, S.Y., Florida, R. and Acs, Z. (2004), “Creativity and entrepreneurship: a regional analysis of new
firm formation”,Regional Studies, Vol. 38 No. 8, pp. 879-891.
Levine, R. (2002), “Bank-based or market-based financial systems: which is better?”,Journal of
Financial Intermediation, Vol. 11 No. 4, pp. 398-428.
Levine, R. (2005), “Finance and growth: theory and evidence”,Handbook of Economic Growth, Vol. 1,
pp. 865-934.
IJHMA
12,3
470
Love, J.H. and Roper, S. (2015), “SME innovation, exporting and growth: a review of existing evidence”,
International Small Business Journal, Vol. 33 No. 1, pp. 28-48.
Mac an Bhaird, C. and Lucey, B. (2011), “An empirical investigation of the financial growth lifecycle”,
Journal of Small Business and Enterprise Development, Vol. 18 No. 4, pp. 715-731.
MacMillan, H.P. (1931), “Committee on finance and industry: report, CMND 4811”. His Majesty’s
Stationery Office (HMSO), London.
Magnusson, L. (2002), An Economic History of Sweden, (Vol. 16), Routledge, London.
Mandell, S. and Wilhelmsson, M. (2015), “Financial infrastructure and house prices”,Applied
Economics, Vol. 47 No. 3, pp. 3175-3188.
Mason, C.M. (2009), “Public policy support for the informal venture Capital market in Europe: a critical
review”,International Small Business Journal, Vol. 27 No. 5, pp. 536-556.
Mason, C.M. and Harrison, R.T. (2006), “After the exit: acquisitions, entrepreneurial recycling and
regional economic development”,Regional Studies, Vol. 40 No. 1, pp. 55-73.
Mian, A. and Sufi, A. (2011), “House prices, home equity-based borrowing, and the US household
leverage crisis”,American Economic Review, Vol. 101 No. 5, pp. 2132-2156.
Mian, A., Rao, K. and Sufi, A. (2013), “Household balance sheets, consumption, and the economic
slump”,The Quarterly Journal of Economics, Vol. 128 No. 4, pp. 1687-1726.
Myers, S.C. (1984), “The Capital structure puzzle”,Journal of Finance, Vol. 39 No.3, pp. 574-592.
Myers, S.C. and Majluf, N.S. (1984), “Corporate financing and investment decisions when firms have
information that investors do not have”,Journal of Financial Economics, Vol. 13 No. 2, pp. 187-221.
Newman, A., Gunessee, S. and Hilton, B. (2012), “The applicability of financial theories of Capital
structure to the chinese cultural context: a study of privately owned SMEs”,International Small
Business Journal, Vol. 30 No. 1, pp. 65-83.
Perren, L. and Jennings, P.L. (2005), “Government discourses on entrepreneurship: issues of legitimization,
subjugation, and power”,Entrepreneurship Theory and Practice, Vol. 29 No. 2, pp. 173-184.
Porter, M.E. (2000), “Location, competition, and economic development: local clusters in a global
economy”,Economic Development Quarterly, Vol. 14 No. 1, pp. 15-34.
Psillaki, M. and Daskalakis, N. (2009), “Are the determinants of Capital structure country or firm
specific?”,Small Business Economics, Vol. 33No. 3, pp. 319-333.
Ramlall, I. (2014), “Is there a pecking order in the demand for financial services in Mauritius?”,Journal
of African Business, Vol. 15 No. 1, pp. 49-63.
Revest, V. and Sapio, A. (2012), “Financing technology-based small firms in Europe: what do we
know?”,Small Business Economics, Vol. 39 No.1, pp. 179-205.
Robb, A.M. and Robinson, D.T. (2014), “The Capital structure decisions of new firms”,Review of
Financial Studies, Vol. 27 No. 1, pp. 153-179.
Santarelli, E. and Tran, H.T. (2012), “Growth of incumbent firms and entrepreneurship in vietnam”,
Growth and Change, Vol. 43 No. 4, pp. 638-666.
Schäfer, D., Talavera, O. and Weir, C. (2011), “Entrepreneurship, windfall gains and financial
constraints: Evidence from Germany”,Economic Modelling, Vol. 28 No. 5, pp. 2174-2180.
Schmalz, M.C., Sraer,D.A. and Thesmar, D. (2013), “Housing collateral and entrepreneurship”, Working
paper 19680, National Bureau of Economic Research (NBER), Cambridge, MA.
Shane, S.A. (2003), “A General Theory of Entrepreneurship: The Individual–opportunity Nexus”,
Edward Elgar, Cheltenham.
Statistics Sweden (2017), available at: www.scb.se/en_/Finding-statistics/Statistics-by-subject-area/
Business-activities/
Stevenson, L. and Lundström, A. (2007), “Dressing the emperor: the fabric of entrepreneurship policy”,
Handbook of Research on Entrepreneurship Policy, pp. 94-129.
House prices
and startups
471
Stiglitz, J.E. and Weiss, A. (1981), “Credit rationing in markets with imperfect information”,American
Economic Review, Vol. 71, pp. 393-410.
Storey, D.J. and Greene, F.J. (2013), Small Business and Entrepreneurship, Pearson, Harlow.
Storey, D., Greene, F.J. and Mole, K. (2007), Three Decades of Enterprise Culture?: Entrepreneurship,
economic Regeneration and Public Policy, Springer Verlag.
Swedish Central Bank (2015), Financial Stability Report 2015:1, Stockholm.
Swedish Central Bank (2017), Financial Stability Report 2017:2, Stockholm.
Van Praag, C.M. and Versloot, P.H. (2007), “The economic benefits and costs of entrepreneurship: a
review of the research”,Foundations and Trends® in Entrepreneurship, Vol. 4 No. 2, pp. 65-154.
Vanacker, T.R. and Manigart, S. (2010), “Pecking order and debt capacity considerations for high-
growth companies seeking financing”,Small Business Economics, Vol. 35 No. 1, pp. 53-69.
Viio, P. and Grönroos, C. (2014), “Value-based sales process adaptation in business relationships”,
Industrial Marketing Management, Vol. 43 No. 6, pp. 1085-1095.
Weston, J.F. and Brigham,E.F. (1981), Managerial Finance, 7th ed. DrydenPress, Hinsdale, IL.
Wiklund, J., Davidsson, P., Audretsch, D.B. and Karlsson, C. (2011), “The future of entrepreneurship
research”,Entrepreneurship Theory and Practice, Vol. 35 No. 1, pp. 1-9.
Wren, C. and Storey, D.J. (2002), “Evaluating the effect of soft business support upon small firm
performance”,Oxford Economic Papers, Vol. 54 No. 2, pp. 334-365.
Further reading
Andersson, M. and Koster, S. (2016), “Local industry structure as a resource-base for entrepreneurship”,
in The Empirical and Institutional Dimensions of Smart Specialisation, Taylor and Francis,
pp. 39-56.
Global Entrepreneurship Monitor (2018), available at: www.gemconsortium.org
Mayer, C. (1988), “New issues in corporate finance”,European Economic Review, Vol. 32 No. 5, pp. 1167-1183.
Robson, M.T., (1996), “Housing wealth, business creation and dissolution, in the UK regions”,Small
Business Economics, Vol. 8 No. 1, pp. 39-48.
Statistics Sweden (2016), available at: www.scb.se/en_/Finding-statistics/Statistics-by-subject-area/
Business-activities/
IJHMA
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Appendix
Corresponding author
Bjorn Berggren can be contacted at: bjorn.berggren@abe.kth.se
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Figure A1.
Start-ups per capita
(2008-2014) and
change in house
prices (2003-2008)
0
0.001 0.002 0.003 0.004 0.005
0.5 11.5 2
delta_hp
0
0.001 0.002 0.003 0.004
00.5 11.5 2
delta_hp
0
0.002 0.004 0.006 0.008
00.5 11.5 2
delta_hp
0
0.002 0.004 0.006 0.008
00.5 11.5 2
delta_hp
0
0.01 0.02 0.03
00.5 11.5 2
delta_hp
0
0.005 0.01 .015
00.5 11.5 2
delta_hp
House prices
and startups
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