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Abstract This paper describes the general structure of a firm location model developed for the City of Hamilton in Ontario, Canada. We propose a novel framework using three interlinked models that include an input-output model, a multinomial logit model and a simultaneous,auto-regressive (SAR) model. The empirical analysis suggests that distance fr...
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Citations
... Studies of office locations indicate several factors that are critical to companies. Many types of firms prefer to locate their offices in or close to central business districts [32] and transport infrastructure [33] and consider socioeconomic factors, such as income level and population density [34]. However, there is a gap in firm location literature on how the office tenancy mix impacts the firms' decision-making, regarding office locations, particularly for anchor tenants. ...
Mixed-use development is increasingly popular in land use planning and zoning, fostering a combination of residential, commercial, and cultural uses into one space. However, there is a lack of understanding of the vertical mix office space within a high-rise commercial building and the dynamics of the industry mix between buildings. This paper examines the spatiotemporal patterns of industry mix between and within office buildings in Sydney CBD from 2006 to 2017, using the data obtained from the City of Sydney floor space and employment surveys. This is the first study that identifies the dynamics of an industrial ecosystem in central Sydney, which has transformed to homophily land blocks, with increasing vertically heterogeneous office buildings, over the past decade. In addition, the study found that the significant clustering of anchor tenants, such as finances, hospitalities, and knowledge-based industries, are apparent.
... Diversas firmas distintas entre sí o similares, incluyendo muchas que están en competencia directa (como las sucursales bancarias), se agrupan en los espacios intraurbanos. 6 Estas aglomeraciones de firmas las explica la economía urbana a partir de las economías de aglomeración (Maoh et al., 2005), que son fuerzas económicas (ahorros, menores costos, gastos no realizados) que actúan sobre las firmas, generándoles ciertas ventajas y beneficios que las incentivan a juntarse en el territorio. Por tanto, si la aglomeración espacial genera a las firmas ciertas ventajas que favorecen un desempeño superior al que tendrían si actuaran de manera aislada, las firmas tendrán un incentivo para aglomerarse. ...
En análisis previos realizados en nuestro país sobre quiénes están excluidos de los sistemas bancario y financiero, se han dejado de lado varios aspectos clave relacionados con la organización espacial de la banca. Destacan tres temas: 1) la estrategia de ubicación de las sucursales; 2) las desigualdades de acceso que genera; y 3) los factores de localización que la determinan. Explorar, estos tres temas en una ciudad concreta permitiría entender mejor la lógica del comportamiento espacial del sistema bancario, derivar lecciones e identificar oportunidades para diseñar políticas públicas que abran más oportunidades de acceso a servicios financieros a la población y a los negocios mexicanos. Esto es precisamente lo que se intenta en este trabajo, tomando como zona de estudio el Área Metropolitana de Toluca (amt).
... In line with the above mentioned, several models have been developed, e.g., Abraham and Hunt (1999); De Bok and Bliemer (2006); De Bok and Sanders (2005); Edwards (1983); Miller (2006, 2007); Elgar et al. (2009); Khan et al. (2002); Maoh et al. (2002); Moeckel (2009); Waddell et al. (2003); Wissen (2000). Increasingly, efforts have been focused on modeling the dynamics in the spatial distribution of economic activity through agent-based representations of firms' demographic processes. ...
This paper addresses an exploratory study about the mediated influence of a set of location and accessibility attributes along with firm types and the effect on office firm closures. We use a proportional hazard modeling approach, examining the probability that an office firm will go out of business in any given year, conditional on the fact that it did not go out of business in the years prior. Variables include urbanization levels and regional effects; accessibility to airports, train stations, shopping centers and the roadway network; availability of parking facilities and schools; demographic and economic aspects; effects of agglomeration economies; rent price; and the type of economic activity of the office firm. Most of them presented significant information to assist in the explanation of the firm closure. Also, interaction effects between covariates and firm types brought a better understanding about the firm closure patterns in relation to urban characteristics. Assessing firm closures is an element of a firm demographic approach and overall dynamics. In turn, this is part of a multi-agent system to simulate the co-evolution of firm dynamics and changes in activity-travel patterns.
... Firms need to choose a location when they decide to move from the current location or when a new firm is established. When researchers tackle firm location behavior, they usually use the framework of multinomial logit models (MNL) (3)(4)(5). Usage of the MNL form implies a number of assumptions by the researcher, such as a maximizing behavior by the firm and that the firm possesses perfect information about commercial real estate market conditions. Some theoretical and empirical papers dealing with firm location suggest that these assumptions are, at best, partially correct when applied to firm location issues (6,7). ...
... Most firm location modeling efforts (as well as most household location models) take the CL form when tackling location decisions. Examples of CL models for location can be found elsewhere (4,5,(12)(13)(14). ...
... Despite the wide use of CL models in estimating location models Maoh et al. (5) state that it seems as though little effort has gone into understanding the effects of a different number of alternatives on the estimated model. One exception is a paper by Nerella and Bhat (15), which analyzed the effect of a different sample size of alternatives and concluded that better estimation would be achieved with more alternatives, because it will increase the accuracy of the coefficients in the model. ...
Firm location models constitute a significant element of integrated land use and transportation models because the behavior of individuals and households is influenced by employment location. This paper examines office firm location models, both for new and relocating firms, to identify ways to improve their estimation and application. The results of the paper suggest that introduction of anchor points, such as the firm current location and the owner household location, significantly improve the estimation of office location models. A generation procedure for choice sets is then shown to improve the model's ability to replicate the decision behavior of office firms and thus provides a way to increase efficiency in the application of the model.
... Diversas fi rmas distintas entre sí o similares, incluso muchas que están en competencia directa, se ubican juntas en los espacios intraurbanos. Estas aglomeraciones de fi rmas las explica la economía urbana a partir de las economías de aglomeración (Maoh et al., 2005). Las economías de aglomeración se han estudiado sobre todo para las fi rmas del sector manufacturero (Porter, 1998) y cuando se incorporan al estudio del sector terciario se consideran particularmente las fi rmas de servicios orientadas a la producción (Daniels, 1985). ...
En este trabajo se propone un método basado en el análisis de flujos peatonales para delimitar el centro tradicional de comercio y servicios de la Zona Metropolitana de Toluca. Este método (flujoscopia peatonal) permitió establecer las fronteras espaciales del centro e identificar zonas en función de las oportunidades de negocio. Esto puede resultar útil para orientar las políticas públicas en el centro y para la toma de decisiones locacionales de las firmas. Adicionalmente, el hecho de contar con información desagregada sobre la vitalidad del centro permitirá monitorear mejor su evolución y desempeño en los próximos años.
... Diversas fi rmas distintas entre sí o similares, incluso muchas que están en competencia directa, se ubican juntas en los espacios intraurbanos. Estas aglomeraciones de fi rmas las explica la economía urbana a partir de las economías de aglomeración (Maoh et al., 2005). Las economías de aglomeración se han estudiado sobre todo para las fi rmas del sector manufacturero (Porter, 1998) y cuando se incorporan al estudio del sector terciario se consideran particularmente las fi rmas de servicios orientadas a la producción (Daniels, 1985). ...
In this work a method based upon the analysis of pedestrian fl ows to outline the traditional downtown commercial and service area of the Metropolitan Zone of Toluca is proposed; the method (pedestrian fl uxscopy) allowed establishing spatial boundaries in function of the opportunities of doing business. This might turn out useful to orient public policies in downtown and to make decisions on the location of the brands. Additionally, the fact of having disaggregated information on the vitality of downtown allowed better monitoring its evolution and performance in the years to come.
... A number of locational factors affect the destination choice of the firm. Within the model, locational factors such as population size and agglomeration economy per census tract were among the significant factors instrumental in this analysis (Maoh et al., 2005). Growth in the total number of households and the total number of firms in the study area are defined exogenously by inputting a distribution of new dwellings by census tract and by defining a matrix of final demand per industry. ...
... CO is a greenhouse gas; HC is a respiratory health hazard; and NOx is linked to cardiovascular ailments. For more information Anderson et al. (1994) provide a detailed overview of IMULATE; Anderson et al. (1996) discuss implementation details of MOBILE5.C; and Maoh et al. (2005) offer details pertaining to EMPLOC. In addition, energy consumption is calculated with a formula that relates the amount of fuel consumption to the total flow and average congested speed on the link. ...
... The final group of spatial impacts investigated herein pertain to firm location choices. The EMPLOC module allocates firms to census tracts within IMULATE using a discrete choice framework outlined in Maoh et al. (2005).Table 7 summarizes the results of the simulated firm locations for the City of Hamilton categorized by industry type, growth scenario, and P2G geography. 3 Since the city-wide firm counts are constant between scenarios, it is convenient to present the firm distributions as deviations from the Base Case distribution. ...
Integrated urban models (IUM) are tools that can be used to simulate the long term impacts of transportation and land use policies. In this paper, IMULATE, an IUM for the Hamilton Census Metropolitan Area is used to investigate the impacts of several growth policies outlined in the Province of Ontario's Places to Grow plan. Several intensification strategies are simulated and evaluated with respect to transportation system usage and performance. The simulation results are investigated at the aggregate and disaggregate levels, since some impacts to the system such as pollutant emissions exhibit scale effects. While IUMs have been used to compare growth strategies world-wide, little applied work has been published before in Canada. The results indicate that in the absence of modifications to the transportation system, the proposed intensification strategies are likely to have little net effect in mitigating growing system usage and weakening system performance over the next 25 years. The growth strategies investigated do however impact the spatiality of transport related externalities.
... The matrix input-output of Leontief (1936) allow the quantification of individual and systemic impacts and direct, indirect and induced effects of the creation of one employment in a particular sector (Hazari, 1970). Furthermore, the model input-output capture the linkages between different sectors of the economy and predicts the total number of firms for the different economic sectors in the region (Maoh, et al., 2005). This methodology also has been implemented for the calculation of knowledge spillover using R&D multipliers (Dietzenbacher and Volkerink, 1998; Dietzenbacher and Los, 2000; Dietzenbacher and Los, 2002). ...
Whereas the knowledge spillover theory of entrepreneurship focuses on the diffusion of innovative output and knowledge filter among new firms and industries (Acts, et al., 2005; Audrescht, 2007), it has not been studied the phenomenon of entrepreneurship dissemination or entrepreneurship spillover among sectors. From an adaptation of the model of input-output matrix (Leontief, 1936; Dietzenbacher and Los, 2002) we develop a methodology that allows calculating the concept of entrepreneurship spillover. Besides, using intra-sectorial data from the 73 Spanish sectors, we empirically test the characteristics of the sectors with more entrepreneurship spillover. In short, the results clearly state that higher diversity and competition entails more entrepreneurship spillover. Moreover, the innovation only affects positively entrepreneurship spillover in restricted situations, briefly when the sectors have high competition and/or a high degree of technology.
... Mit Daten der Stadt Hamilton, Kanada, untersuchten Maoh et al. (2005) das demographische Verhalten der Unternehmen in der Provinz Ontario zwischen 1990 und 1997. Dabei konnte festgestellt werden, dass Unternehmen in der Regel aus dem Zentrum wegziehen und im Stadtzentrum auch am meisten Unternehmen gelöscht werden (Maoh und Kanaroglou, 2007). ...
Kurzfassung
In der vorliegenden Arbeit wird die Demographie der zwischen 1991 und 2006 im Handelsregister der Schweizer Kantone St. Gallen und beider Appenzell eingetragenen Unternehmen untersucht. Dabei geht es insbesondere um den Einfluss des Alters, der Größe, der Branche sowie des Standortes der Unternehmen auf deren Migrationsverhalten. Zudem wird auch die aus firmendemographischer Sicht zentrale räumliche Verteilung von Gründungen und Löschungen betrachtet. In einem logit-loglinearen Modell werden die entsprechenden Resultate zusammengefasst und quantifiziert. Das erwähnte Modell ermöglicht die Untersuchung und Schätzung der bisher vernachlässigten Wechselwirkungseffekte sowie der Effekte der einzelnen Einflussgrößen unter Berücksichtigung der Effekte aller anderen Faktoren im Modell. Die Modellschätzungen belegen, dass alle vier untersuchten Einflussbereiche das Verhalten der Unternehmen verändern. Der Vergleich der Variablen Alter und Größe zeigt zudem, dass das Alter in erster Linie das Migrationsverhalten beeinflusst. Die Größe der Unternehmen lässt indes vor allem die Löschungsrate sinken.
... The reason for this is probably not based on the importance of the research direction since firm location is considered a very important dimension for activity locations either for trip-or tour based models. Recent firm-demographic studies in land use and transport modelling are provided in Moeckel (2005) and Maoh (2005). Section 4 will elaborate on these approaches in the discussion on micro-simulation of firm-demographic change. ...
... Recently, the application of firm-demographic micro-simulation in the context of an integrated land use and transport model has increasing interest, such as Moeckel (2005) and Maoh (2005). However, many research issues exist that require empirical analysis. ...
... Firm-demography has been applied in micro-simulation on a small scale. This subsection elaborates on firm-demographic micro-simulation and discusses two recent examples of firm-demographic simulation applications in an integrated land use and transport context by Moeckel (2005) and Maoh et al. (2005). ...
Scope and research objective
The construction of significant new transport infrastructure projects, such as the A4-extension from Delft to Rotterdam, has long-term spatial economic effects that are difficult to understand. In general, the planning of investments in new transport infrastructure and the development of new urban areas are important for the quality of our future urban environment. First, urban planning involves decisions that have long term impacts. Next to that, cities are often faced with a scarcity of resources, both in terms of money and in space. The consequences of this difficult task are obvious such as housing shortages and congested infrastructures.
The long term impact of urban plans and the scarcity of resources should be a motivation for urban planners to make a balanced evaluation of alternative urban planning strategies. Integrated land use and transport models can help identify effective or sustainable policy solutions for urban development and are becoming increasingly disaggregate, both in the spatial environment and the behaviour of agents (Timmermans, 2003). The effect of transport infrastructure on firm dynamics is a specific but important issue in the evaluation of spatial policy alternatives.
The design of simulation models that can describe this effect is far from trivial and a number of issues can be identified. First, the existing simulation models that describe infrastructure effects on firm location do not represent individual firm behaviour. Second, the build up of agglomeration economies and spatial externalities are under represented in existing urban simulation models. Third, the geographic detail mostly is too limited to evaluate policy measures at a satisfactory level of detail.
This thesis tries to make a contribution to the development of simulation models that can make more valid projections of the effect of spatial policy options on firm dynamics and firm behaviour. The following objectives are formulated:
1. Develop a simulation model that adequately describes the effects of spatial and transport infrastructure plans on firm dynamics. The model is based on the following assumptions:
a. Firm-specific behaviour should be implemented allowing for heterogeneity in preferences and responses;
b. Accurate representation of spatial externalities by the inclusion of spatial attributes that are specified from urban economic theories;
c. Geographic detail allowing the specification of detailed location attributes and the implementation of detailed zoning schemes.
2. Support the assumptions made in objective 1. with empirical analyses. Many uncertainties exist about the influence of accessibility and agglomeration on the performance and preference of firms, and in particular how these influences relate to internal organisational factors. The driving factors behind firm dynamic developments need to be identified in an extensive set of micro-data on the firm population.
To achieve these objectives, the thesis has develop a dedicated theoretical framework based on a review of most recent theories, and established operational models estimated using empirical data from a large region in The Netherlands.
Approach
This research applies a dynamic micro simulation approach that quantifies the effects of different spatial and transport planning scenarios on the firm population and mobility. The Spatial Firm-demographic Micro-simulation (SFM) model simulates transitions and events in the firm population. These include firm relocation, firm growth, firm dissolution and firm formation. In each time step of a model run, the firm population is processed through the separate components of the firm demographic micro-simulation. Such a firm demographic micro-simulation approach is motivated by the need to improve the representation of firm behaviour in spatial economic models. This improvement is the result of three distinctive features of the spatial firm demographic simulation. First, it represents firm specific behaviour and heterogeneity in responses. Firm behaviour is influenced both by firm internal processes and attributes of the firm’s location. For example, the most important reason for firms to relocate is firm growth (Brouwer et al., 2004; Louw, 1996). Second, it allows distinctive accessibility measures as explanatory variables for each event, such as firm relocation, firm performance, formation or firm dissolution. For instance, some firms perform better in the proximity of motorway on-ramps (Hilbers et al., 1994), while in a relocation decision, accessibility is evaluated in a different way. A third advantage is the possibility to account for path dependency between events. For example, firm growth – e.g., triggered by a new motorway opening – might induce a firm relocation in the following years. By keeping record of the developments at the individual firm level, the life-cycle of a firm and the causality between subsequent events can be modelled endogenously.
The quality of locations is measured by advanced accessibility and agglomeration measures. First, accessibility is measured by proximity to infrastructure access points. These measures are directly observed by decision makers and identified in many empirical studies as significant location factors. Second, accessibility is measured by logsums of business and commuting trips from a transport model. These logsums are used to measure the accessibility to the labour and business markets. They measure the value of a choice set and are well founded in utility theory. Agglomeration measures are based on the theories from urban economics and integrate travel time range bands into measures of specialisation and diversification. Specialisation is included as a location factor to test if Marshallian locational externalities are significant, e.g. labour market pooling, input sharing or knowledge sharing within the same industry sector. Diversification is included to test if Jacobs location externalities are significant, in other words if firms benefit from knowledge spill-overs from ideas or innovations in other industry sectors.
The SFM-model features a firm location choice model that accounts for interdependencies between location alternatives that arise from the spatial dimension of the choice context. This spatial dimension distinguishes itself from a-spatial choice in several ways, such as that each decision maker has a spatial location, there are usually a large number of spatial alternatives and alternatives show a complex interdependency following from the continuous spatial dimension (Fotheringham, 1989; Guo, 2004). Therefore, the location choice model is based on a Competing Destinations model (Fotheringham, 1989) that accounts for spatial cluster membership of competing alternatives. It applies individual choice sets with a number of feasible firm locations.
Case study
To test the assumptions in the model, it is applied to a case study for the province of South Holland in The Netherlands. The area contains a firm population of approximately 90.000 firms, which are distributed across 70.000 6-character zip code locations. The SFM model has been developed in three stages: model design, model estimation, and model-validation. The model is estimated on an extensive longitudinal dataset with the full firm population from 1990 to 1996, the LISA data (National Information System of Employment). The model has been validated on data for the succeeding period from 1996 to 2004.
Various accessibility and agglomeration attributes are computed for each year of the calibration and validation periods, and linked to the location of each firm observation. The distance measures to transport infrastructure access points are computed form coordinate information. The logsum are derived from the Dutch National Modelling System (NMS, see Hague Consulting Group, 2000). The agglomeration attributes for diversification and specialisation are computed with travel times from the NMS and the location of all firms in the LISA data.
Estimation results
The calibration of the SFM-model consists of the separate estimation of all firm demographic sub models. The purpose of these estimations is to provide an empirical foundation of the parameters in the SFM model and to gain more insight into the different components of spatial economic development, and the possible interactions between these components.
The relocation probability can mostly be explained from the firm attributes, which is in line with firm demographic literature: bigger firms are less likely to relocate (Carroll and Hannan, 2000; Brouwer et al., 2004) and firms with relatively large growth rates are more likely to relocate (Carroll and Hannan, 2000; Pellenbarg, 1996; Louw, 1996). The relocation probability varies across industry sectors, but accessibility appears to have a limited influence. Agglomeration, however, does have an effect: firms at diverse locations are more likely to relocate. This is interpreted as a pattern of successful firms that leave their breeding areas.
When firms relocate and search for a new location, they have a significant preference for locations in the proximity of their original location. This is interpreted as evidence for keep-factors: a relocating firm strives to maintain its existing spatial network. Moreover, the spatial clustering of location alternatives proves to have a significant influence on the choice behaviour of firms: alternatives that are clustered in space, individually have a smaller choice probability. This is in line with findings by Pelligrini and Fotheringham (2002). Furthermore, outspoken differences in location preference between industry sectors are measured for highway and/or train station proximity. Moreover, some industry sectors have a preference for locations with a good labour market accessibility. The estimations provide strong evidence that firms prefer locations that have a relatively high representation of firms from their own industry sector. This is interpreted as evidence for the existence of Marshall externalities and consistent with the findings of Duranton and Puga (2000) and Holl (2004b).
The firm growth model revealed a dominant influence of the growth pattern in the previous years on the expected firm size, consistent with firm demographic literature (Dunne and Hughes, 1994; McCloughan, 1995; Audretsch et al., 2002; Van Wissen and Huisman, 2003). The estimated autoregressive coefficients were smaller than unity for all industry sectors, implying that large firms are expected to grow more slowly than small firms. All autocorrelation coefficients that are estimated have a negative sign, implying a negative correlation between firm growth in subsequent years. In other words, a firm with a relatively substantial growth in one year, is expected to grow less quickly the next year. Infrastructure proximity has a significant effect on firm performance for various industry sectors. Most estimated coefficients for diversity are negative, indicating relatively less growth at diversified locations. The estimation results for the specialisation coefficient reveal a positive influence of specialisation on the expected growth.
Firm dissolution can be explained for the largest part by the firm attributes: age and size. Firm dissolution is high among young firms. This confirms the liability of newness hypothesis (Stinchcombe, 1965). Larger firms have smaller dissolution probabilities, as do firms that are growing in size. Both findings are in line with empirical literature (Hannan and Freeman, 1977; Ekamper, 1996; Van Wissen, 2000; Brouwer, 2004). Next, structural developments within industry sectors are significant as well. Firm dissolution appears to be higher among firms at locations with higher diversity indices. Firm dissolution appears to be a little bit higher in the proximity of highway on-ramps (γ-locations), probably indicating higher dynamics at industrial sites at such locations.
In conclusion, the estimates confirm a complex interdependency between firm demographic events and confirm a significant influence of accessibility and agglomeration throughout the various events. In general, the estimations provide a consistent pattern that can be understood from an agglomeration and life-cycle perspective on firms, similar to Duranton and Puga (2000). The simulation approach that is presented provides a behavioural methodology to better describe and understand the effect of infrastructure and spatial planning scenarios on firm dynamics, taking into account theories on urban economics and industrial organisations.
Validation
To analyse the validity of the estimated models, the coefficients are implemented in the SFM model, and validated with firm data for a succeeding period. Other input to the validation runs consists of the firm population in the base year, the economic development and the real estate development scenario. In base year 1996, the study area hosted 95 thousand firms. The validation runs are based on the observed regional economic development in the study area between 1996 and 2004. The macro economic development is input to the firm growth model and forms constraints to the simulation and influences the outcomes at micro level. The supply of industrial real estate determines the constraints to what locations are available to a firm. Changes in this industrial real estate supply are implemented through synthesized real estate data for the corresponding period.
The validation of the SFM-model across different levels of aggregation yielded interesting results that lead to more questions as well. It is shown that the simulated firm population size follows the trend of the observed population size. However it averages out a more dynamic growth pattern over the years. A sample of microscopic results showed the effect of the stochastic elements in the firm-demographic models. These stochastics prove to lead to very distinct developments at micro level that are representative for the element of coincidence in firm-demographic events. At neighbourhood level, the results indicate a reasonable match between observed and simulated developments, but a systematic flaw was identified in small vs. large neighbourhoods. It is concluded that the presented approach provides reliable estimates of future firm location but further enhancement of the models is desirable. Despite the question raised in this validation, it is argued that this is no reason to reject the approach: the disaggregate nature of the approach raises questions but provides many ways of analysing these issues further.
Final conclusions
The spatial firm demographic micro-simulation model that is presented makes some contributions to the further improvement of models of firm behaviour. First, the approach is based on industrial organisation theory. The approach features individual firm characteristics in the models, and the representation of distinct events that are each differently influenced by accessibility. A second distinctive feature is the inclusion of urban economic theories and advanced agglomeration measures that until now have only been applied marginally in projection models. A number of disaggregate agglomeration attributes are included that measure the level of specialisation or diversification in the composition of the local firm population. The transport dimension is included in these measures through travel times from a transport model.
Policy makers should account for the composition of the firm population in their region: the future demand for firm location is a function of the existing firm population, and complicated developments that can be explained from the perspective of organisational and urban economic theories. In such a way mal-investments can be avoided and optimal urban plans can be identified. In such a way, the regional economy is supported by developing firm locations that better serve the (future) demand of the local firm population.
