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Open Access 14-10-2024 | Special Issue Paper

Business services in regional economies: exploring the co-evolution of supply, demand, and sectoral interactions

Authors: Borje Johansson, Johan Klaesson

Published in: The Annals of Regional Science | Issue 4/2024

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Abstract

The aim of this paper is to demonstrate how economic growth stimulates business-service providers to develop new service varieties, which, in turn, enhance the productivity of business-service buyers. This creates a coevolutionary process where service suppliers and customers interact, leading to an increase in the number of differentiated service offerings. We introduce a framework for local economies, wherein business-service sectors evolve in response to local demand potential, while non-business-service sectors grow based on each economy’s supply potential. Business service growth is more rapid in local economies with higher demand potential, while non-business-service sectors expand faster in areas where the business-service supply potential is greater. A key assumption is that business service firms operate in a monopolistic competition environment, where an increase in business-service capacity leads to an expansion in the variety of services offered. This, in turn, enhances the diversity of service offerings in municipalities with strong demand potential. Additionally, service providers not only deliver innovation-related information to client firms but also unintentionally disseminate knowledge within the region, fostering knowledge spillovers among firms.
Notes

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1 Introduction

In recent decades, economic evolution has increasingly followed paths characterized by rising urbanization and a growing share of business services (BS). The expansion and contraction of BS sectors across different locations can be seen as a response to the demand potential in each area. At the same time, firms in other sectors are drawn to locations based on the size of the available BS supply potential. Our analysis of these demand and supply potentials focuses on local economies, typically municipalities, also referred to as urban areas. These urban areas can be grouped into larger contiguous urban regions, driven by agglomeration processes (Fujita and Thisse 2002).
Each local economy has its unique demand-potential structure that influences the growth or decline of BS categories. We distinguish three broad groups of business services: ordinary business services (OBS), knowledge-intensive business services (KIBS), and very knowledge-intensive business services (VKIBS). Similarly, the location and growth of other sectors are influenced by the supply potential of the local economy.
A municipality’s demand potential refers to its accessible customer base. In our econometric models, we decompose demand potential into local, regional, and extra-regional levels, while also differentiating between the three categories of BS sectors.
The accessibility of BS deliveries to non-BS firms includes options to receive services from OBS, KIBS, and VKIBS firms. For each local economy, the service-supplying firms contribute to the overall service supply potential, representing the opportunities for that economy to source inputs from the three service categories. The supply potential increases as service volumes grow and as the time distance between the seller and buyer decreases.

1.1 Demand potential reflecting customer accessibility

Business-service (BS) deliveries tend to involve more contact-intensive transactions compared to other goods and services. This leads to particularly strong incentives for BS suppliers to locate in large urban agglomerations, which, in turn, stimulates urban growth. Face-to-face interactions are often a key component of BS transactions, significantly impacting delivery costs for both the seller and the buyer. We can identify these delivery costs for various goods and services and rank them according to their sensitivity to distance. When firms in a sector are highly sensitive to distance, their sales tend to decline rapidly as the distance to customers increases. This suggests that BS suppliers will prioritize locations with high demand accessibility.
BS customers are economic actors that drive the demand for business services. Firms across all sectors purchase inputs from BS suppliers. Therefore, we can assume that a typical BS supplier in a local economy expects sales opportunities in both its own area and neighboring urban areas to be proportional to the economic size of each, with appropriate distance decay discounting. Under this incentive structure, BS suppliers are attracted to sites with high customer (demand) accessibility.
In our analysis, the smallest geographic unit considered is the local economy, which is part of a larger urban region, also known as a functional region (Cheshire and Gordon 1998). Consider a firm that resides in the local economy s. This location index is associated with the set \(R(s)\) of all local economies that belong to the same urban region as s—except s itself. In other words,\(R(s)\) identifies those local economies that are members of the region \([R\left(s\right),s].\). The third index set is \(E(s)\) which specifies all local economies which are extra-regional vis-à-vis location s. Thus, the local economy is characterized by (i) its local, (ii) its intra-regional, and (iii) its extra-regional demand potentials—as specified by the 3-component array in formula (1). Our next task is to describe how the three components can be calculated.
Let \({W}_{s}\) be a measure of the economic activity in local economy s. Keeping the focus on s, we can calculate (i) the local demand potential, \({A}_{ss}\text{=}{\text{W}}_{s}\), (ii) the intra-regional demand potential, \({A}_{R(s)}\), and (iii) the extra-regional demand potential, \({A}_{E(s)}\). This gives us a vector of three mutually exclusive potentials (or accessibility measures) such that
$${A}_{s}= \text{ } [{A}_{ss}\text{, }{\text{A}}_{R(s)}\text{, }{\text{A}}_{E(s)}]$$
(1)
We assume that the three components reflect the location attractiveness of the local economy s, where the size of \({A}_{ss}\) and \({A}_{R\left(s\right)}\) positively influences the expansion of business-service capacity, measured by number and diversity of jobs. At the same time, as \({A}_{E(s)}\) expands, this may influence negatively the growth of BS jobs in area s. Thus, the impact of the A-vector on job dynamics corresponds to a “dynamic” home market effect (Quigley 2013; Klaesson and Johansson 2013; Krugman 1980).
The A-vector in (1) describes for a local economy, the economic geography of accessible customers or, in other words, pattern of demand potentials. Economic theory emphasizes the significance of a local economy’s home market, and the diversity generated by monopolistic competition among various service varieties. A central objective of this paper is to assess the “home market effect” for business service providers, which posits that a large demand potential drives capacity expansion among these suppliers. Considering this, we explore how this effect varies when comparing local demand with intra-regional demand potentials.
A second objective is to investigate if there are differences between categories of business services? To highlight this issue, we decompose the BS sector into three groups, which are separated with regard to their education share (knowledge intensity), which is measured as the share of employees with three years or more of university studies. The three groups are labeled OBS, KIBS, and VKIBS:
OBS = ordinary business services with less than 30 percent education share.
KIBS = knowledge-intensive business services, with 30–50 percent education share.
VKIBS = very knowledge-intensive business services, more than 50 percent education share.
By studying the change process of OBS, KIBS, and VKIBS jobs as a response to the size of each local economy’s three demand potentials, we shed new light on urbanization economies, urban growth, outsourcing of service activities, and knowledge-economy phenomena (Andersson 2013; Kolko 2009; Quigley 2013; Hacker et al. 2013).

1.2 Supply potential and agglomeration externalities

Assume now that the capacity of business service sectors in a local economy expands in response to increasing demand potential. This responsive behavior across different local economies leads to a rise in the supply of business services. Given the assumption of monopolistic competition, this supply expansion results in the entry of new business-service varieties into the market, thereby enhancing the diversity of service inputs available to firms.
As suggested by Matsuyama (1995), the increased number of service varieties will enrich the technology opportunities in local economies, and this will imply increasing productivity in service-buying sectors such as Primary, Manufacturing, Public, and Household sectors. Firms in these sectors are attracted to expand their output capacity in locations with a generous supply potential. The latter is calculated as a combination of (i) a local, (ii) a regional, and (iii) an extra-regional component. Component (i) refers to the non-discounted business service capacity of each local economy, while the extra-local components (ii) and (iii) are discounted with the distance to it. Moreover, for each local economy, we observe three categories of business services: OBS, KIBS, and VKIBS, which are studied separately.
The current subsection shifts perspective relative to the former subsection. As discussed earlier, the location of various business services can be modeled as a response to the overall demand potential of each local economy. Once these location decisions are made, we can inspect the supply potential (of OBS, KIBS, and VKIBS) for each local economy. Given this, we can formulate and estimate a model of how non-BS sectors adjust their location in response to the value of each economy’s supply potential of business services.
The importance of business-service supply is evident if we recognize that BS firms bring novelties to the local economies, comprising technical solutions, innovation ideas, and knowledge flows. Shearmur and Doloreux (2008; 2013) emphasize that business-service supply potentials of each local economy indicate the size of renewal opportunities across sectors. This role of knowledge provision is accentuated for the KIBS and VKIBS sectors, and Shearmur and Doloreux (2008; 2013) suggest that the interaction between knowledge providers and their customers bring about location patterns that are compatible with central-place system models as outlined by Tinbergen (1967), Beckmann (1958), and others.
The supply potential analysis belongs to a theoretical framework in which increasing returns to urban development is generated by monopolistic competition between service varieties which are sold as intermediary inputs to other sectors. The productivity of the economy increases as the multiplicity of service input varieties increases (Rivera-Batiz 1988; Fujita and Thisse 2002). The critical mechanism is that increasing supply of business services also implies that the differentiation of accessible services increases, and the extended scope of service varieties enables service-buying firms to enhance their productivity.

1.3 Coevolution of business services and other sectors

The ambition of the present paper is to describe how business-service suppliers are stimulated by economic growth to develop new service varieties, which in turn improve the productivity of business-service buyers. This generates a coevolutionary process, in which service suppliers and service customers interact while increasing the number of differentiated service varieties.
A second objective is to show that a monopolistic competition framework helps to explain how the development of new differentiated business-service varieties brings about growth of economic activities in spatial economies, while further stimulating the emergence of additional service varieties. This framework relies on the features (i)–(iv).
(i)
Business services can be arranged into service groups, where each group consists of distinct business-service varieties. Such varieties are developed over time in response to the size of pertinent demand potentials and their expansion
 
(ii)
A business-service supplier delivers one or several service varieties to accessible customers, where each variety has a negatively sloping demand schedule
 
(iii)
Each service variety is differentiated from other varieties, which explains the presence of a price elasticity greater than one, implying that every service variety has some “market power” such that there exists a profit maximizing price for each variety
 
(iv)
A business-service supplier has incentives to develop varieties that are not produced by any other supplier, which implies that growth of service supply generates increasing supply diversity
 
The presentation is outlined as follows: Sect. 2 describes the categories of business services and introduces the concepts demand potential and supply potential of local economies, while formulating cumulative dynamics for the interaction between demand and supply potentials. Section 3 presents descriptive statistics of sector growth patterns. Section 4 applies OLS techniques as well as fixed-effect panel estimations to depict how the size of economic activities (e.g., GDP or wage sum) influences the change of business-service capacity in every local economy. Section 5 also combines OLS techniques and fixed-effect estimations to depict how the service capacity generates growth of non-BS sectors. Section 6 concludes.

2 Growth in local economies

2.1 Categories of business services, responding to demand potentials

Consider that we can observe a proxy for the economic activity in each of Sweden’s 290 municipalities. The gross regional product (GRP) as well as the wage sum (W) can be applied to measure economic activity in space. Recognizing that information about location in space is far better for wage sum, we have chosen to use the W-variable, where \({W}_{s}\) represents the total wage sum in municipality s. Each municipality is referred to as a local economy. Business-service suppliers in economy s have a vector of three demand potentials \({A}_{s}= \text{ } [{A}_{ss}\text{, }{\text{A}}_{R(s)}\text{, }{\text{A}}_{E(s)}]\), where
$$\begin{gathered} \left( i \right){\text{ }}A_{{ss}} = ~W_{s} {\text{ denotes the local demand potential}} \hfill \\ \left( {ii} \right){\text{ }}A_{{R\left( s \right)}} = \sum\nolimits_{{k \in R\left( s \right)}} {W_{k} \exp \left\{ { - \lambda t_{{ks}} } \right\}} {\text{ denotes the intra - regional demand potential}} \hfill \\ \left( {iii} \right){\text{ }}A_{{E\left( s \right)}} = \sum\nolimits_{{k \in E\left( s \right)}} {W_{k} \exp \left\{ { - \lambda t_{{ks}} } \right\}} {\text{ denotes the extra - regional demand potential~}}~ \hfill \\ \end{gathered}$$
(2)
The variable \({t}_{ks}\) represents the time distance between local economy k and s. Moreover, \(\lambda\) denotes the time sensitivity, and thereby we can interpret \(\mathit{exp}\left\{-\lambda {t}_{ks}\right\}\) as a distance discount factor which describes how the sales opportunities of BS firms in location s shrink as the time distance gets larger. We may observe that the formulas in (2) can be derived from random choice theory (Mattsson 1984; Johansson et al. 2002, 2003), and hence the transaction behavior may be given a probability interpretation. Following Weibull (1976, 1980) we may also let (i)–(iii) signify local, intra-regional and extra-regional customer accessibility.
To capture the monopolistic competition nature of the underlying model, we introduce a frugal description of the microeconomic adjustments that influence market solutions. Let us assume that a typical business-service variety i has a negatively sloping demand schedule such that the output capacity \({x}_{is}={G}_{i}({A}_{s}){p}_{i}^{-{\theta }_{i}}\) depends positively on the size of the demand-potential vector \({A}_{s}\), while the supply capacity shrinks as the price, \({p}_{i}\), is augmented. Observing that \({\theta }_{i}>1\) is the price elasticity of demand and \({v}_{i}>0\) is the variable cost, we can show that the optimal price of variety i is (Andersson and Johansson, 2012)
$${p}_{i}^{*}={v}_{i}[{\theta }_{i}/({\theta }_{i}-1)]$$
(3)
where \({\theta }_{i}/({\theta }_{i}-1)\) is variety i’s markup that can contribute to cover the (fixed) development costs, \({F}_{i}\), of variety i. A feasible location in s satisfies the condition \({p}_{i}^{*}\ge {v}_{i}+{F}_{i}/{x}_{is}\). Let \({n}_{s}\) be the number of varieties that at a given point in time meets this condition so that \({\sum }_{i=1}^{{n}_{s}}{p}_{i}^{*}{x}_{is}^{*}={n}_{s}{G}_{i}\) can represent a sub market equilibrium for location s. This implies that the introduction of a new service variety requires a shift of the demand potential from the level \({\sum }_{i=1}^{{n}_{s}}{p}_{i}^{*}{x}_{is}^{*}={n}_{s}{G}_{i}\) to the level \(\overline{\overline{{G}}}_{s} = (n_{s} + 1)G_{i}\). Hence, increasing the supply capacity also brings about increased diversity.
We observe from the price equation that the variable cost, v, is not location dependent, whereas the fixed cost per unit output is. The model outlined in (2), (3) suggests an adjustment process in which the number of varieties increases as the demand potential, \({A}_{s}\)=\([{A}_{ss}\text{, }{\text{A}}_{R(s)}\text{, }{\text{A}}_{E(s)}]\), gets larger. That is the “hunger for variety” effect that obtains in a monopolistic competition setting with a CES demand specification. This prediction of diversity can be explicitly tested by examining the coevolution of number of firms and total number of jobs in a service sector, in local economies and in regions.
Consider now that we have three BS sectors indexed by \(j\in \left\{OBS,KIBS,VKIBS\right\}\). For each of these service sectors, we observe each local economy, the number of jobs, and indirectly the set of individual varieties. We assume that the demand-potential vector, \({A}_{s}= \text{ } [{A}_{ss}\text{, }{\text{A}}_{R(s)}\text{, }{\text{A}}_{E(s)}]\), determines for the local economy s how BS suppliers’ output capacity is adjusted. This means that we assume that the change of service capacity in location s is influenced by accessible demand in the local, regional, and extra-regional markets. In an OLS specification, this assumption can be illustrated by formula (4), where j indicates a BS sector and \(\tau \ge 0\) denotes a time lag.
$$\mathit{ln}\;\Delta {D}_{s}^{j}(t+\tau )={\alpha }_{0}^{j}+{\alpha }_{1}^{j}\mathit{ln}\;{A}_{ss}(t)+{\alpha }_{2}^{j}\mathit{ln}\;{A}_{R(s)}+{\alpha }_{3}^{j}\mathit{ln}\;{A}_{E(s)}+{\varepsilon }_{s}$$
(4)
The specification presented in Eq. (4) outlines the baseline model focused on demand potentials, which forms the core framework for our analysis.

2.2 Spatial organization and service supply potentials

Sub sector 2.1 depicts the process of locating business-service suppliers in response to the demand potential, expressed as wage sum in local economies. The present subsection provides a dual perspective, where the location choice of other sectors such as (Pr) Primary, (Pu) Public, (Ma) Manufacturing, and (Ho) Household is assumed to be affected by each location’s service supply potential. Firms in these four sectors benefit from increased access to the distance-discounted supply capacity, and hence they are expected to consider the supply potential of OBS, KIBS, and VKIBS services. Following the same approach as in sub Sect. 2.1, we can define the potentials as in (5)
$$\begin{gathered} ~\left( {\text{i}} \right)~T_{{ss}} {\text{ = ~}}J_{s} {\text{denotes the local supply potential}} \hfill \\ ~\left( {{\text{ii}}} \right)~T_{{R\left( s \right)}} = \sum\nolimits_{{k \in R\left( s \right)}} {J_{k} } \exp \left\{ { - \lambda t_{{ks}} } \right\}{\text{denotes the intra}} - {\text{regional supply potential}}~ \hfill \\ \left( {{\text{iii}}} \right)~T_{{E\left( s \right)}} = \sum\nolimits_{{k \in E\left( s \right)}} {J_{k} } \exp \left\{ { - \lambda t_{{ks}} } \right\}{\text{denotes the extra}} - {\text{regional supply potential}} \hfill \\ \end{gathered}$$
(5)
The three accessibility terms in (5) are calculated for each of the three categories of business-service inputs, where \({J}_{s}\) signifies the number of jobs for a given business-service category. The empirical issue in the present subsection is to investigate the ways in which the output from sector Pr, Pu, Ma, and Ho changes in response to the supply potentials in (5). These output changes are denoted by \(\Delta {S}_{s}^{j}\), where j refers to Pr, Pu, Ma, and Ho, where there are three separate regressions, each using a business-service supply potential \({T}_{s}=[{T}_{ss},{T}_{R(s)},{T}_{E(s)}]\) as vector of explanatory variables. We observe that the first \({T}_{s}\) vector reflects the OBS potential, the second the KIBS potential, and the third the VKIBS potential.
$$\mathit{ln}\;\Delta {S}_{s}^{j}(t+\tau )={\beta }_{0}^{j}+{\beta }_{1}^{j}\mathit{ln}\;{T}_{ss}(t)+{\beta }_{2}^{j}\mathit{ln}\;{T}_{R(s)}+{\beta }_{3}^{j}\mathit{ln}\;{T}_{E(s)}+{\varepsilon }_{s}$$
(6)
The specification outlined in Eq. (6) represents the baseline model concerning supply potentials that serves as the foundation for our analysis, which we will estimate across several slightly modified versions.

2.3 Cumulative dynamics

The framework introduced in this section suggests coevolutionary dynamics, where a local economy’s demand-potential vector determines the growth of business services, and this brings about a new service supply potential for the local economy. This, in turn, generates a richer combination of service varieties, and these bring about an increase of economic activities in the local economy. At this point there are opportunities to further expand the service supply capacity. Parameter constellations decide whether the local economy is growing or declining. Each municipality has a supply potential which describes the service support that sectors in the municipality can obtain.
Intricate dynamics are outlined in Fig. 1. A municipality’s structure of demand potentials consists of (i) local demand potential, (ii) regional demand potential, and (iii) extra-regional demand potential. The compound structure of (i)–(iii) is referred to as municipal demand potential. By analogy, we also consider the municipality supply potential, containing the (i) local, (ii) regional, and (iii) extra-regional supply potential.
We observe for a local economy that growth and decline of OBS, KIBS, and VKIBS services can be interpreted as responses to the size of the BS demand potential. Suppose that BS supply increases and attracts capacity to enter, then we can expect the municipality supply potential to augment, and thereby function as a growth enhancing factor.

3 Descriptive statistics

3.1 Service sector composition

As described in Table 1, business-service activities comprise more than 1/3 of total employment in 2016, and about the same number of jobs is classified as public services. In the period 2007–2016, the employment increased in all sectors, except in manufacturing. The table provides a picture where service production continues to grow, and where manufacturing activities gradually are being outsourced.
Table 1
Employment in sectors in 2007 and 2016 in the whole of Sweden
 
2007
2016
(Pr) Primary
80 120 (1.8)
98 891 (2.1)
(Pu) Public
1426 412 (32.9)
1626 124 (34.5)
(Ma) Manufacturing
840 921 (19.4)
694 791 (14.8)
(Ho) Househ. Services
485 789 (11.2)
540 640 (11.5)
OBS
1138 553 (26.2)
1277 829 (27.1)
KIBS
286 530 (6.6)
391 159 (8.3)
VKIBS
79 167 (1.8)
80 334 (1.7)
Share of total employment in parenthesis
A major location characteristic of business services is the influence of regional size, such that the employment share of business services is higher for municipalities located in large regions than in other regions. To describe this, we divide all municipalities into three groups: those that belong to (i) metropolitan, (ii) medium-sized, and (iii) small regions. Given this we calculate the employment share for OBS, KIBS, and VKIBS services. The result is presented in Table 2.
Table 2
Employment share of business-service sectors in three categories of regions, %
 
Metropolitan
Medium-sized
Small
All municip
OBS
30.9
25.6
22.1
27.1
KIBS
13.1
5.4
3.6
8.3
VKIBS
2.7
1.2
0.5
1.7
Share of total employment
The table shows for each service sector how the sector share is highest in metropolitan regions and lowest in small regions. This observation is compatible with the hypothesis that the supply of business services expands as diversity opportunities grows, implying that the number of service varieties increases faster than the size of the pertinent local economies. The second regularity in the table is that the location intensity of each type of service capacity increases as the region size is augmented.

3.2 Sector growth patterns

Table 3 provides an overview of sector growth paths in the Swedish economy. The absolute change is largest for public services, with almost 200 000 additional employees. OBS services increase by almost 140 000, and KIBS services increase by more than 100 000.
Table 3
Employment growth 2007—2016 in the whole of Sweden
 
2007
2016
Change
Change (%)
(Pr) Primary
80,120 (1.8)
98,891 (2.1)
18,771
23.43
(Pu) Public
1,426,412 (32.9)
1,626,124 (34.5)
199,712
14
(Ma) Manuf
840,921 (19.4)
694,791 (14.8)
 − 146,130
− 17.38
(Ho) Househ
485,789 (11.2)
540,640 (11.5)
54,851
11.29
OBS
1,138,553 (26.2)
1,277,829 (27.1)
139,276
12.23
KIBS
286,530 (6.6)
391,159 (8.3)
104,629
36.52
VKIBS
79,167 (1.8)
80,334 (1.7)
1167
1.47
Share of total employment in parenthesis
We have already concluded that the local supply of business services covaries with the size of the region to which the pertinent local economy belongs. The proportion of business-service supply is larger for municipalities that belong to metropolitan regions (Stockholm, Göteborg and Malmö) than for other municipalities, while the proportion is lowest for municipalities in small regions. These conclusions apply to OBS, KIBS, and VKIBS services.
The descriptive statistics in Table 2 tell us something about the distribution of business-service capacity, suggesting that the local supply of OBS, KIBS and VKIBS capacity display higher values, the larger the region of the local economy is. Do the observations say anything about dynamics? Are the local capacity growth rates higher for municipalities that belong to large regions than for other municipalities? Table 4 sheds light on this question.
Table 4
Employment growth 2007–2016 of business services in three groups of regions, %
 
Metropolitan
Medium-sized
Small
All municip
OBS
15.60
11.37
5.64
12.23
KIBS
39.41
33.69
24.22
36.52
VKIBS
7.86
− 9.78
− 9.68
1.47
Table 4 shows that KIBS services expand on average faster than other business services, and in metropolitan regions, the growth of KIBS is almost 40 percent. For OBS services, the average growth rate is 15.6 percent in metropolitan municipalities, 11.4 percent in municipalities of medium-size regions, and 5.6 percent elsewhere. VKIBS services grow slowly in metropolitan regions and decline slowly in other regions.

4 Business-service expansion in response to the demand potential

In Sect. 4, we examine how the demand potential affects the change of business-service supply capacity in local economies. As a first option in this endeavor, we apply the OLS specification in formula (4) to contrast the fixed-effect panel estimations in sub Sect. 4.2. In which ways do the two specifications bring about coinciding pictures of the location dynamics, and in which respects do the pictures deviate?

4.1 Business-service capacity change in response to the demand potential

The OLS specification is used as a reference picture of how business-service firms respond to the three demand potentials in Table 5. For the VKIBS sector, the local response parameter lacks significance, whereas the intra-regional parameter is significant on the 5 percent level. For ordinary business services and knowledge-intensive services, the estimated model points out the local demand as the dominating dynamic factor, showing that local demand is a capacity change predictor of OBS and KIBS jobs. Moreover, in both these cases the intercept is negative, which reflects the presence of size thresholds.
Table 5
Business-service capacity change and demand potential 2007–2016
 
\(\mathit{ln}\Delta OBS\)
\(\mathit{ln}\Delta KIBS\)
\(\mathit{ln}\Delta VKIBS\)
\(\mathit{ln}{A}_{ss}\)(local demand)
0.0735***
0.0913***
0.0259
 
(8.347)
(4.506)
(0.465)
\(\mathit{ln}{A}_{R(s)}\)(regional demand)
 − 0.000203
0.00463
0.0471**
 
(− 0.0659)
(0.652)
(2.414)
\(\mathit{ln}{A}_{E(s)}\)(extra-regional)
 − 0.0265**
0.0423
 − 0.0228
 
(− 2.121)
(1.471)
(− 0.288)
Constant
 − 0.252*
 − 1.157***
 − 0.455
 
(− 1.679)
(-3.352)
(− 0.480)
Observations
290
290
290
R-squared
0.199
0.098
0.025
t-values in parentheses, and \(\lambda\) = 0.01
*, **, *** indicates significance levels at the 10, 5, and 1 % levels respectively
In summary, Table 5 tells us that there are four significant growth responses: (i) OBS capacity and KIBS capacity change positively in response to a municipality’s local demand potential, (ii) OBS capacity responds negatively to the extra-regional demand potential, and (iii) VKIBS capacity responds positively to the regional demand potential.

4.2 Business-service development in a panel model

With a panel estimation, the dependent variable is not change of jobs, but change in the level for the sequence 2006–2016. As described in Table 6, the estimated parameters for all municipalities imply that the local demand potential (\({A}_{ss}\)) stimulates the evolution of OBS, KIBS and VKIBS jobs, whereas the intra-regional demand potential (\({A}_{R(s)}\)) has a negative and dampening effect on OBS jobs. Finally, the extra-regional demand potential (\({A}_{E(s)}\)) has a positive impact on KIBS capacity and a negative impact on OBS and VKIBS capacity.
Table 6
Business-service jobs influenced by local, regional, and extra-regional demand potential for all municipalities 2006–2016
 
lnOBS
lnKIBS
lnVKIBS
\(\mathit{ln}{A}_{ss}\)(local demand)
0.894***
0.533***
0.516**
 
(25.47)
(5.640)
(2.046)
\(\mathit{ln}{A}_{R(s)}\)(regional demand)
 − 0.0818**
0.0894
1.510***
 
(− 2.019)
(0.819)
(5.183)
\(\mathit{ln}{A}_{E(s)}\)(extra-regional)
 − 0.344***
0.116
 − 2.106***
 
(− 8.784)
(1.102)
(− 7.473)
Constant
5.139***
 − 1.303**
11.49***
 
(23.70)
(-2.230)
(7.364)
Observations
2900
2,900
2900
R-squared
0.262
0.072
0.022
Number of municipality codes
290
290
290
Fixed-effect panel estimations, and \(\lambda\) = 0.01
*, **, *** indicates significance levels at the 10, 5, and 1 % levels respectively
The major result in Table 6 shows that the location of OBS, KIBS and VKIBS jobs all respond positively to the local demand potential of a municipality. This result applies to each of the three categories of municipalities (metropolitan, medium-sized, and small regions). We also observe that Tables 5 and 6 bring a consistent message: the development of business services is primarily a response to the size of the local demand potential. Beyond this, we observe that (i) the regional demand potential stimulates VKIBS services to expand their capacity and (ii) the extra-regional demand potential brings about a negative response for OBS and VKIBS service capacity.
The three regressions presented in Table 6 can be repeated for municipalities that belong to metropolitan, medium-sized, and small regions. Let us now repeat the regressions in Table 6, but this time for the subset of metropolitan municipalities. The result is reported in Table 7, and for OBS and KIBS services, we can conclude that metropolitan municipalities display positive, large, and significant response to the local demand potential. This local demand effect is especially strong for metropolitan municipalities. At the same time, there are two negative and significant response parameters. First, the intra-regional demand potential has a negative impact on the development of the OBS service capacity. Second, the extra-regional demand potential has a negative influence on the KIBS service capacity. These are two counteracting or retarding phenomena, reflecting competition from neighboring suppliers. Moreover, Table 7 clarifies that VKIBS display a low correlation with local, intra-regional, and extra-regional demand potentials.
Table 7
Business-service jobs influenced by local, regional, and extra-regional demand potential for metropolitan municipalities 2006–2016
 
lnOBS
lnKIBS
lnVKIBS
\(\mathit{ln}\;{A}_{ss}\)(local demand)
0.713***
1.133***
1.035*
 
(9.290)
(8.447)
(1.733)
\(\mathit{ln}\;{A}_{R(s)}\)(regional demand)
 − 0.780***
0.433
2.266
 
(− 3.502)
(1.111)
(1.306)
\(\mathit{ln}\;{A}_{E(s)}\)(extra-regional)
0.708**
 − 0.973**
 − 3.465
 
(2.539)
(− 1.995)
(− 1.596)
Constant
2.839***
3.364**
10.06
 
(3.114)
(2.110)
(1.417)
Observations
520
520
520
R-squared
0.460
0.431
0.027
Number of municipalities
52
52
52
Fixed-effect panel estimations, \(\lambda\) = 0.01
*, **, *** indicates significance levels at the 10, 5, and 1 % levels respectively.
As a special observation, we note that the explanatory power is much higher for OBS and KIBS services in Table 7 than in Table 6. In this sense, we may classify metropolitan municipalities as more “well behaved” than other municipalities.
The econometric results in Tables 5, 6, and 7 help us to formulate four conclusions about the development of BS service supply in a local economy:
(i)
OBS services in a municipality expand as a positive response to the municipality’s local demand potential,
 
(ii)
The intra-regional as well as the extra-regional demand potentials contribute to negative OBS responses,
 
(iii)
KIBS services in a municipality expand as a response to the municipality’s local demand potential,
 
(iv)
VKIBS response pattern remains unclear
 

5 Sector change and business—service supply potentials

In sub Sect. 5.1, the econometric analysis examines for local economies the size or output capacity of the four sectors Pr, Pu, Ma, and Ho. How can the change of output capacity in each of these sectors be described as a response to the three business supply potentials (i) local supply potential, (ii) intra-regional supply potential, and (iii) extra-regional supply potential, where each of these potentials is specified for the three separate business services, OBS, KIBS, and VKIBS. Sub sector 5.2 investigates the same question, employing a fixed-effect panel estimation technique.

5.1 Business-service supply explaining sector growth in local economies

Sub Sect. 5.1 investigates how economic sectors differ in their reaction to the size and composition of business-service supply, calculated for each local economy. We apply an OLS specification for robust comparison with the panel estimations presented in sub Sect. 5.2. In this context, we classify a parameter as significant if it satisfies p < 0.01.
Table 8, 9, and 10 show that the growth of the Primary sector is not significantly influenced by business-service accessibility—as measured by \({T}_{ss}\), \({T}_{R(s)}\) and \({T}_{E(s)}\). This lack of correlation is compatible with the idea that primary sectors are located in places that offer natural-resource accessibility.
Table 8
Sector change and OBS supply potential in local economies 2007–2016
 
ln \(\Delta\) Primary
ln \(\Delta\) Public
ln \(\Delta\) Man
ln \(\Delta\) Househ
ln \({T}_{ss}\), OBS
0.0144
0.0255***
0.0382***
0.0473***
 
(1.027)
(5.416)
(3.446)
(7.217)
ln \({T}_{R(s)}\), OBS
 − 0.00118
0.00675***
0.0117***
0.00925***
 
(− 0.232)
(3.951)
(2.913)
(3.895)
ln \({T}_{E(s)}\), OBS
 − 0.0301
0.0180***
 − 0.00403
 − 0.00193
 
(− 1.504)
(2.684)
(− 0.255)
(− 0.206)
Constant
0.456*
 − 0.401***
 − 0.595***
 − 0.400***
 
(1.866)
(− 4.891)
(− 3.083)
(− 3.510)
Observations
290
290
290
290
R-squared
0.012
0.237
0.089
0.239
OLS specification, \(\lambda\) = 0.01
*, **, *** indicates significance levels at the 10, 5, and 1 % levels respectively.
Table 9
Sector change and KIBS supply potential in local economies 2007–2016
 
ln \(\Delta\) Primary
ln \(\Delta\) Public
ln \(\Delta\) Man
ln \(\Delta\) House
ln \({T}_{ss}\), KIBS
0.0116
0.0184***
0.0263***
0.0319***
 
(1.144)
(5.461)
(3.291)
(6.729)
ln \({T}_{R(s)}\), KIBS
− 0.00519
0.00815***
0.0135***
0.0107***
 
(− 0.999)
(4.709)
(3.301)
(4.404)
ln \({T}_{E(s)}\), KIBS
− 0.0219
0.0181***
0.000428
0.00184
 
(− 1.257)
(3.123)
(0.0311)
(0.226)
Constant
0.398**
− 0.278***
− 0.489***
− 0.246***
 
(2.117)
(− 4.435)
(− 3.302)
(− 2.795)
Observations
290
290
290
290
R-squared
0.014
0.248
0.095
0.236
OLS specification, \(\lambda\) = 0.01
*, **, *** indicates significance levels at the 10, 5, and 1 % levels respectively.
Table 10
Sector change and VKIBS supply potential in local economies 2007–2016
 
ln \(\Delta\) Primary
ln \(\Delta\) Public
ln \(\Delta\) Man
ln \(\Delta\) House
ln \({T}_{ss}\), VKIBS
0.00236
0.00953***
0.0184***
0.0167***
 
(0.379)
(4.558)
(3.751)
(5.614)
ln \({T}_{R(s)}\), VKIBS
− 0.00540
0.00948***
0.0141***
0.0128***
 
(− 1.102)
(5.753)
(3.667)
(5.471)
ln \({T}_{E(s)}\), VKIBS
− 0.0249
0.0244***
0.0121
0.0123
 
(− 1.410)
(4.112)
(0.868)
(1.454)
Constant
0.451***
− 0.234***
− 0.490***
− 0.209***
 
(2.711)
(− 4.193)
(− 3.750)
(− 2.630)
Observations
290
290
290
290
R-squared
0.014
0.236
0.100
0.201
OLS specification, \(\lambda\) = 0.01
*, **, *** indicates significance levels at the 10, 5, and 1 % levels respectively.
Inspecting the OBS services in Table 8, we find a strong result which is common for the three sectors Pu, Ma and Ho. These sectors are significantly stimulated to grow (i) by the local supply potential \({T}_{ss}\), and (ii) by the intra-regional supply potential \({T}_{R(s)}\). This reflects the importance of OBS services accessibility. We suggest that high values of the supply potentials (i)–(ii) indicate presence of agglomeration economies, and these economies define the essence of urban region advantages, where OBS service accessibility influence growth of public services, manufacturing, and household services.
In Table 9, the focus is on KIBS services, and we examine the growth stimulation that comes from KIBS accessibility, as measured by KIBS supply potential. Again, a similar result as in Table 8 is obtained: growth of Pu, Ma, and Ho is positively and significantly influenced by the local supply potential \({T}_{ss}\), and (ii) by the intra-regional supply potential \({T}_{R(s)}\). The pertinent parameters are just somewhat larger for OBS supply than for KIBS supply. Growth of Pu, Ma, and Ho is induced by combined effects of high values of local and intra-regional supply potentials. Moreover, the growth of Primary sectors is not significantly influenced by the KIBS supply potential. Thus, the primary sectors benefit very little from improved local and regional supply potentials
Turning to Table 10, we can see that the change of Pu, Man, and Ho is induced by local VKIBS supply and intra-regional VKIBS supply in a similar fashion as in Tables 8 and 9. In addition, extra-regional VKIBS supply has a positive and significant correlation with growing Pu capacity. We also observe that VKIBS jobs are few and very knowledge intensive.
Suppose that we are looking for other systematic similarities between the three Tables 8, 9, and 10. If we want to do so, we may compare the R-square values of the four regression equations. Such a comparison reveals that the growth equations for public services (Pu) and household services (Ho) have much higher R-square values than the Pr and Ma equations. Thus, the growth of Pu capacity and Ho capacity in local economies is clearly more dependent on business-service accessibility than what applies to other sectors. And this difference is present for OBS, KIBS, and VKIBS services.
Tables 8, 9, and 10 examine how the economy can be divided into business services (BS) and the four sectors Pr, Pu, Ma, and Ho, where the sector output expansion in a local economy is responding to the BS supply potentials. For each municipality we identify a potential’s location (local, intra-regional, and extra-regional) and its service category (OBS, KIBS and VKIBS). With this in mind, we derive the following three conclusions from Tables 8, 9, and 10:
(i)
The change of the Pr sector is statistically independent of the BS supply potential, and this holds true for each of the three service categories.
 
(ii)
The Pu sector in a municipality expands in response to the size of the municipality’s local, intra-regional and extra-regional potentials, and this observation applies to all three service categories.
 
(iii)
The Ma and Ho sectors in a municipality expand in response to the size of the municipality’s local and intra-regional potentials—but the size of each extra-regional potential does not bring about increase of the Ma and Ho sectors.
 

5.2 Sector development and business-service supply in a panel model 2007–2016

Section 5.1 has four change observations (\(\Delta \mathit{Pr}, \Delta \text{Pu, }\Delta \text{Ma, }\Delta {\text{Ho}}\)) as dependent variables. In the present section, we employ a fixed-effect panel estimation where the level variables \(\mathit{Pr}(t)\), \({\text{Pu}}(t)\),\({\text{Ma}}(t)\) and \({\text{Ho}}(t)\) are related to the time series of supply potentials. Tables 11, 12, and 13 employ supply potentials calculated for OBS, KIBS, and VKIBS services as alternative explanatory variables. Section 5.2 examines to what extent and in which ways the Panel and OLS models coincide in telling the same story about how service potentials and sectors coevolve. We recognize similarities between explanations from the two types of econometric models as a virtue.
Table 11
Sector development and OBS service supply potential in local economies 2007–2016
 
ln Pr
ln Pu
ln Ma
ln Ho
ln \({T}_{ss}\), OBS
0.0206
0.0705***
0.149***
0.0863***
 
(0.486)
(5.921)
(5.716)
(5.407)
ln \({T}_{R(s)}\), OBS
− 0.0552
0.144***
0.330***
0.0688**
 
(− 0.760)
(7.037)
(7.405)
(2.514)
ln \({T}_{E(s)}\), OBS
2.610***
0.588***
− 1.401***
0.388***
 
(28.61)
(22.94)
(− 25.04)
(11.28)
Constant
− 26.53***
− 1.268***
20.55***
0.513
 
(− 30.18)
(− 5.134)
(38.13)
(1.550)
Observations
2900
2900
2900
2900
R-squared
0.336
0.355
0.211
0.125
No municipalities
290
290
290
290
Fixed-effect panel estimation, \(\lambda\) = 0.01
*, **, *** indicates significance levels at the 10, 5, and 1 % levels respectively.
Table 12
Sector development and KIBS service supply potential in local economies 2007–2016
 
lnPrimary
lnPublic
lnMan
lnHouse
ln \({T}_{ss}\), KIBS
0.0339**
0.0203***
− 0.00865
0.0129**
 
(1.996)
(4.071)
(-0.915)
(2.018)
ln \({T}_{R(s)}\), KIBS
0.00165
0.0120
0.0169
0.0228**
 
(0.0550)
(1.352)
(1.010)
(2.010)
ln \({T}_{E(s)}\), KIBS
1.189***
0.295***
− 0.704***
0.175***
 
(27.07)
(22.82)
(− 28.74)
(10.57)
Constant
− 7.899***
4.344***
14.94***
4.414***
 
(− 19.76)
(36.95)
(67.10)
(29.32)
Observations
2900
2900
2900
2900
R-squared
0.302
0.257
0.315
0.079
No municipalities
290
290
290
290
Fixed-effect panel estimation, \(\lambda\) = 0.01
*, **, *** indicates significance levels at the 10, 5, and 1 % levels respectively.
Table 13
Sector development and VKIBS service supply potential in local economies 2007–2016
 
lnPrimary
lnPublic
lnMan
lnHouse
ln \({T}_{ss}\), VKIBS
− 0.0180***
− 0.000398
0.00312
0.00255
 
(− 2.874)
(− 0.213)
(0.844)
(1.189)
ln \({T}_{R(s)}\), VKIBS
− 0.0254*
0.000504
0.0157**
− 0.00276
 
(− 1.883)
(0.125)
(1.981)
(− 0.599)
ln \({T}_{E(s)}\), VKIBS
− 1.164***
0.0595***
0.0940**
− 0.141***
 
(− 15.52)
(2.662)
(2.132)
(− 5.494)
Constant
16.72***
7.221***
6.274***
7.927***
 
(23.11)
(33.49)
(14.75)
(32.10)
Observations
2900
2900
2900
2900
R-squared
0.088
0.003
0.003
0.012
No municipalities
290
290
290
290
Fixed-effect panel estimation, \(\lambda\) = 0.01
*, **, *** indicates significance levels at the 10, 5, and 1 % levels respectively.
Table 11 presents regression results when the supply potentials are calculated for ordinary business services (OBS). In case of the Pr sectors, there is only minor positive influences from \({T}_{E(s)}\). For public service sectors, there is a significant influence from \({T}_{ss}\) (local potential), \({T}_{R(s)}\) (intra-regional potential), and \({T}_{E(s)}\) (extra-regional potential). Thus, OBS supply has a positive effect on the expansion of public services, be it local, regional or extra-regional OBS supply. For household services there is a positive effect from \({T}_{ss}\) and \({T}_{R(s)}\) and a pronounced effect from \({T}_{E(s)}.\). In addition, the manufacturing sector expansion is significantly influenced by the local, and intra-regional OBS supply, i.e., by \({T}_{ss}\), and \({T}_{R(s)}\). The extra-regional OBS supply potential reduces manufacturing expansion.
Table 12 describes how a municipality’s KIBS supply potential influences growth of the four sectors Pr, Pu, Ma, and Ho. Local KIBS supply supports growth significantly in the Public service sector. Extra-regional KIBS supply affects positively the development of Pr, Pu, and Ho services, and the development of Ma-sectors negatively. This observation implies relocation of manufacturing.
Table 13 describes how the development of the four sectors Pr, Pu, Ma, and Ho are influenced by the VKIBS supply potentials (local, intra-regional, and extra-regional) of each municipality. VKIBS services are knowledge intensive and hence delivering services to firms which themselves are knowledge intensive. Because of this, Table 13 appears more idiosyncratic and less well-structured than Tables 11 and 12.
The results in Table 13 can be summarized as followed. The local VKIBS supply potential and the extra-regional VKIBS supply potential both reduce the sector growth in the pertinent municipality. The growth of Public services in the same municipality is elevated by the extra-regional VKIBS supply potential. The latter instead reduces the growth of Household services. We also observe that Manufacturing is stimulated to grow as a response to regional and extra-regional VKIBS supply potentials, but at a lower level of significance.
In sub Sect. 5.1, we have recognized growth stimulation patterns which are similar both with respect to types of BS services, accessibility of service supply, and non-BS sectors which are service customers. Here we collect a few observations that help to characterize and compare the estimated response parameters. We first observe that Table 8, 9, and 10, each containing four vectors of response parameters. The first vector consists of insignificant estimates (Pr). The remaining vectors consist of significant estimates of local and intra-local parameters with reference to (Pu, Ma, and Ho). The structural vector similarity extends to include Table 11, but it partly excludes Table 12 and 13.
Table 11 presents the local, intra-regional, and extra-regional response pattern that applies for OBS-based services. We estimate one response vector for each of the four sectors Pr, Pu, Ma, and Ho. Following the same approach for KIBS type of services we obtain Table 12, where the response parameters for the Pr, Pu, Ma, and Ho sectors deviate from those of the OBS-based response parameters. This indicates that KIBS-based accessibility may be less distance sensitive than OBS-based accessibility.

6 Conclusion and reflections on dynamics

In the analyses of the preceding sections, we introduce two alternative econometric models to shed light on the coevolution of BS supply and BS demand. The latter phenomenon refers to how business-service producing firms in a local economy make supply decisions in response to the accessibility of service customers. The BS supply potential shifts perspective by describing how firms in different sectors in a local economy benefit from the economy’s access to supply of business services.

6.1 Spatial BS demand potential

Consider the change process of the OBS and KIBS capacities over time. This gradual phenomenon can be described as a form of response to the size of the local demand potential. There is one potential related to OBS services and another associated with KIBS services. These are the two major significant parameters in the OLS formulation. Similar conclusions apply when a fixed-effect panel model is used. In this case, we have that (i) local demand potential stimulates the elevation of OBS, KIBS, and VKIBS and (ii) regional demand potential generates an additional rise of VKIBS. This means that BS services grow in response to the size of the local economy and the size of the discounted regional economy. Negative significant parameters reflect competition from other locations.

6.2 Spatial BS supply potential

Our conclusions in sub Sect. 6.1 reveal that business-service interactions relate to fundamental spatial issues, such as delineation of local economies, measuring density, size of each economy, spatial friction, and time distances. Another spatial consideration is the interaction between alternative BS supply sectors and other (non-BS) sectors in the economy. How can other sectors benefit from accessible BS sectors? The location of BS service capacity is determined by the possibilities to sell the services from that location (municipality), and those possibilities are given by the size of the municipality’s demand potential. A municipality’s supply potential determines the possibility of a firm to acquire advantageous BS service input combinations.
We find that the public sector, manufacturing, and housing sectors are positively influenced by the supply potential of ordinary business services (OBS), knowledge-intensive business services (KIBS), and very knowledge-intensive business services (VKIBS). This underscores a critical insight: an economy’s overall performance tends to improve when all types of business-service capacities continue to expand and evolve. Furthermore, there is a notable advantage when these business-service activities are concentrated in large, densely populated local economies, as they are better positioned to benefit from economies of scale and greater market opportunities. This observation is particularly important when considering that the location requirements for manufacturing tend to be less stringent or dependent on proximity compared to other sectors, which may rely more heavily on local demand and service diversity.
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Literature
go back to reference Andersson ÅE (2013) Knowledge accessibility, economic growth and the Havelmo paradox. In: Klaesson J, Johansson B, Karlsson C (eds) Metropolitan regions—knowledge infrastructures of the global economy. Springer, Heidelberg, pp 73–90CrossRef Andersson ÅE (2013) Knowledge accessibility, economic growth and the Havelmo paradox. In: Klaesson J, Johansson B, Karlsson C (eds) Metropolitan regions—knowledge infrastructures of the global economy. Springer, Heidelberg, pp 73–90CrossRef
go back to reference Andersson M, Johansson B (2012) Heterogeneous distributions of firms sustained by innovation dynamics—a model with empirical illustrations and analysis. J Ind Competition and Trade 12:239–263CrossRef Andersson M, Johansson B (2012) Heterogeneous distributions of firms sustained by innovation dynamics—a model with empirical illustrations and analysis. J Ind Competition and Trade 12:239–263CrossRef
go back to reference Beckmann M (1958) City hierarchies and the distribution of city sizes. Econ Dev Cult Change 6:243–248CrossRef Beckmann M (1958) City hierarchies and the distribution of city sizes. Econ Dev Cult Change 6:243–248CrossRef
go back to reference Cheshire PC, Gordon IR (1998) Territorial competition: some lessons for policy. Ann Reg Sci 32(3):321–346CrossRef Cheshire PC, Gordon IR (1998) Territorial competition: some lessons for policy. Ann Reg Sci 32(3):321–346CrossRef
go back to reference Fujita M, Thisse J-F (2002) Economics of agglomeration, cities, industrial location, and regional growth. Cambridge University Press, CambridgeCrossRef Fujita M, Thisse J-F (2002) Economics of agglomeration, cities, industrial location, and regional growth. Cambridge University Press, CambridgeCrossRef
go back to reference Hacker S, Klaesson J, Pettersson L, Sjölander P (2013) Regional economic concentration and growth. In: Klaesson J, Johansson B, Karlsson C (eds) Metropolitan regions—knowledge infrastructures of the global economy. Springer, Heidelberg, pp 117–140CrossRef Hacker S, Klaesson J, Pettersson L, Sjölander P (2013) Regional economic concentration and growth. In: Klaesson J, Johansson B, Karlsson C (eds) Metropolitan regions—knowledge infrastructures of the global economy. Springer, Heidelberg, pp 117–140CrossRef
go back to reference Johansson B, Klaesson J, Olsson M (2002) Time distances and labor market integration. Pap Reg Sci 81(3):305–327CrossRef Johansson B, Klaesson J, Olsson M (2002) Time distances and labor market integration. Pap Reg Sci 81(3):305–327CrossRef
go back to reference Johansson B, Klaesson J, Olsson M (2003) Commuters’ non-linear response to time distances. J Geogr Syst 5:315–329CrossRef Johansson B, Klaesson J, Olsson M (2003) Commuters’ non-linear response to time distances. J Geogr Syst 5:315–329CrossRef
go back to reference Klaesson J, Johansson B (2013) Urban growth. In: Klaesson J, Johansson B, Karlsson C (eds) Metropolitan regions—knowledge infrastructures of the global economy. Springer, Heidelberg, pp 47–72CrossRef Klaesson J, Johansson B (2013) Urban growth. In: Klaesson J, Johansson B, Karlsson C (eds) Metropolitan regions—knowledge infrastructures of the global economy. Springer, Heidelberg, pp 47–72CrossRef
go back to reference Kolko J (2009), Urbanization, agglomeration and co-agglomeration of service industries. Public Policy Institute of California Kolko J (2009), Urbanization, agglomeration and co-agglomeration of service industries. Public Policy Institute of California
go back to reference Krugman PR (1980) Scale economies, product differentiation and the pattern of trade. Am Econ Rev 70:950–959 Krugman PR (1980) Scale economies, product differentiation and the pattern of trade. Am Econ Rev 70:950–959
go back to reference Mattsson LG (1984) Some applications of welfare maximization approaches to residential location. Pap Reg Sci 55(1):103–120CrossRef Mattsson LG (1984) Some applications of welfare maximization approaches to residential location. Pap Reg Sci 55(1):103–120CrossRef
go back to reference Matsuyama K (1995) Complementarities and cumulative processes in models of monopolistic competition. J Econ Lit 33(2):701–729 Matsuyama K (1995) Complementarities and cumulative processes in models of monopolistic competition. J Econ Lit 33(2):701–729
go back to reference Quigley JM (2013) Agglomeration, regional growth, and economic development. In: Klaesson J, Johansson B, Karlsson C (eds) Metropolitan regions—knowledge infrastructures of the global economy. Springer, Heidelberg Quigley JM (2013) Agglomeration, regional growth, and economic development. In: Klaesson J, Johansson B, Karlsson C (eds) Metropolitan regions—knowledge infrastructures of the global economy. Springer, Heidelberg
go back to reference Rivera-Batiz FL (1988) Increasing returns, monopolistic competition and agglomeration economies in consumption and production. Reg Sci Urban Econ 18:125–153CrossRef Rivera-Batiz FL (1988) Increasing returns, monopolistic competition and agglomeration economies in consumption and production. Reg Sci Urban Econ 18:125–153CrossRef
go back to reference Shearmur R, Doloreux D (2008) Urban hierarchy or local buzz? High-order producer service and (or) knowledge—intensive business service location in Canada, 1991–2001. Prof Geogr 60:333–355CrossRef Shearmur R, Doloreux D (2008) Urban hierarchy or local buzz? High-order producer service and (or) knowledge—intensive business service location in Canada, 1991–2001. Prof Geogr 60:333–355CrossRef
go back to reference Shearmur R, Doloreux D (2013) Innovation and knowledge-intensive business service: the contribution of knowledge intensive business service to innovation in manufacturing establishments. Econ Innov New Technol 22:751–774CrossRef Shearmur R, Doloreux D (2013) Innovation and knowledge-intensive business service: the contribution of knowledge intensive business service to innovation in manufacturing establishments. Econ Innov New Technol 22:751–774CrossRef
go back to reference Tinbergen J (1967) The hierarchy model of the size distribution of centres. Pap Reg Sci Assoc 20:65–80 Tinbergen J (1967) The hierarchy model of the size distribution of centres. Pap Reg Sci Assoc 20:65–80
go back to reference Weibull JW (1976) An axiomatic approach to the measurement of accessibility. Reg Sci Urban Econ 6:357–379CrossRef Weibull JW (1976) An axiomatic approach to the measurement of accessibility. Reg Sci Urban Econ 6:357–379CrossRef
go back to reference Weibull JW (1980) On the numerical measurement of accessibility. Environ Plan A 12:53–67CrossRef Weibull JW (1980) On the numerical measurement of accessibility. Environ Plan A 12:53–67CrossRef
Metadata
Title
Business services in regional economies: exploring the co-evolution of supply, demand, and sectoral interactions
Authors
Borje Johansson
Johan Klaesson
Publication date
14-10-2024
Publisher
Springer Berlin Heidelberg
Published in
The Annals of Regional Science / Issue 4/2024
Print ISSN: 0570-1864
Electronic ISSN: 1432-0592
DOI
https://doi.org/10.1007/s00168-024-01321-x