Real Estate Determinants of Firm Relocation in Urban Residential Neighbourhoods

Risselada, Anne; Schutjens, Veronique; Oort, Frank van

doi:10.1111/tesg.12010

Cited by 20 publications

(11 citation statements)

References 42 publications

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“…In addition to agglomeration economies, the literature stresses the importance of localized network externalities in firm relocation. As firms are likely to have established customer, employee and supplier relations in their current location, they often move relatively short distances in order to keep these relations (Risselada, Schutjens, & Van Oort, 2013;Weterings & Knoben, 2013). Therefore, location possibilities that are in close proximity might be more attractive for a firm than locations further away.…”

Section: Introductionmentioning

confidence: 99%

“…The firms' internal characteristics are an important factor in the relocation process (Brouwer, Mariotti, & van Ommeren 2004). Older, larger firms seem to be less likely to relocate than smaller, younger firms as the moving costs are increasing with firm size and older firms often have established long-term customer relations (Brouwer et al, 2004;Risselada et al, 2013). Also, firms that experience a growth or decline in their workforce are more willing to relocate (Kronenberg, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Finally, we note that firm relocation is typically modelled in sequential steps by using a binary logit model to estimate the decision to move or not and a multinomial logit model is used to estimate the location choice given that the firm chooses to move (Kroonenberg, 2013;Brouwer et al, 2004;Risselada et al, 2013). However, by using a mixed logit model approach to estimate both the probability of relocating and where to relocate jointly, it is possible to use the whole data set for model estimation and to deal with possible correlation between alternatives.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Where and why do firms choose to move? Empirical evidence from Norway

Nilsen¹,

Díez-Gutiérrez²,

Andersen³

et al. 2020

JTLU

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A key goal for many public policies is to increase the competitiveness of local industries and make areas more attractive for firm location and development. However, little is known about firm relocations even though they are of crucial importance in understanding economic development within a region and the effectiveness of the policies proposed. This paper contributes to filling this gap by looking further into the firm relocation process by estimating models for the decision to relocate and to where. The studied area consists of four counties on the western coast of Norway that generate about half of Norway’s traditional exports. Changes in firm relocations for approximately 16,500 firms within this study area are analyzed. In addition, interviews with firms that relocated are also carried out to support the findings from the model. The results indicate that the decision to relocate is influenced by a firm’s internal and external characteristics such as agglomeration. The results are relevant for regional planning and development as firms seem to have different preferences regarding what makes a location attractive or not. The results might serve as input in land-use interaction models, where changes in firm location patterns from transport investments are estimated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Where and why do firms choose to move? Empirical evidence from Norway

Nilsen¹,

Díez-Gutiérrez²,

Andersen³

et al. 2020

JTLU

View full text Add to dashboard Cite

show abstract

“…In this article, we will explore the role of residential neighbourhoods as locations for more advanced economic activities, or what Allen Scott (Scott, 2008(Scott, , 2012 has labelled cognitive-cultural activities comprising a wide variety of high-skilled work. Already much research has been done on these kind of economic activities and we can also observe a growing body of literature on economic activities in residential areas (Beckers and Kloosterman, 2014;Daniel et al, 2014;Folmer, 2014;Mason et al, 2015;Risselada et al, 2013;Sleutjes, 2012;Sleutjes and Schutjens, 2013).…”

Section: Introductionmentioning

confidence: 99%

Emerging intra-urban geographies of the cognitive-cultural economy: Evidence from residential neighbourhoods in Dutch cities

Folmer

Kloosterman

2016

Environ Plan A

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Most existing research on advanced economic activities focuses on either inner-city milieus or suburban industrial parks. We contend, however, that residential neighbourhoods constitute a milieu for economic activities which require the input of high-skilled labour or, to follow Allen Scott, cognitive-cultural activities which are characteristic for contemporary urban economies.Based on a longitudinal data set of company-level data, we show that a significant share of economic activities in urban residential neighbourhoods can indeed be classified as cognitivecultural and that this share has been growing over the period [1999][2000][2001][2002][2003][2004][2005][2006][2007][2008]. We present an analysis of the spatiality of the embeddedness of these activities. In particular, we focus on their traded and untraded interdependencies. For this part of the analysis we use survey-data of 370 businesses based in Dutch residential neighbourhoods. Overall, cognitive-cultural activities maintain many untraded interdependencies on a local level, whereas they maintain most traded interdependencies on a supra-local level. They appear to be making frequent use of both local buzz as well as of supralocal 'pipelines', and are thus embedded on various spatial scales. Residential neighbourhoods, then, have to be taken more seriously not just as places of consumption but also as milieus of production for more advanced economic activities.

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“…( ) and Risselada et al . () estimate discrete choice models, Cieślik () and Martínez et al . () estimate count data models .…”

mentioning

confidence: 99%