The Location of Firms in the City of Hamilton, Canada: A Micro-Analytical Model Approach

1. The Location of Firms in the City of Hamilton, Canada: A Micro-Analytical Model Approach By Hanna Maoh and Pavlos Kanaroglou McMaster University Center for Spatial Analysis (CSpA) School of Geography and Geology 51st Annual North American Regional Science Meeting, Seattle, WA, Nov 11th– 13th, 2004

2. Outline • Introduction • Study Area and Data • Modeling Approach • Results • Conclusion

3. Introduction • Sustainable cities via simulation models • The agent-based Approach • Firm demographic model for the city of Hamilton in Ontario Canada • Location choice component (See diagram)

4. The evolutionary process of business establishment population over time Baseline Establishment population (time t) Stayers, relocating and out-migrants Searching pool Relocating, Newborn and In-migrating In-migrating and new born establishments + Business failure Submodel Location Choice Submodel Stayers Continuer Establishments Relocating Newborn In-migrating Time t+1 Stayers at time t+1 Growth/Decline submodel State transition Submodel Establishment population (time t+1)

6. Data • Statistics Canada Business Register • Maintain annual information about business establishments in Canada; We used 1996 – 1997 and 2001 – 2002 data • Attributes: Establishment size, location (postal code and SGC), SIC code and Establishment Number (EN) • Census population Data for 1996 and 2001 • GIS data: Land use cover, road network, regional malls

7. Modeling Approach • Establishments search the city for the location that will maximize their profit • Searching pool: relocating, new born and in-migrating establishments • Measuring firmographic events: • Continuer establishments: if for two consecutive years, the establishment has the same EN and the Hamilton SGC • Relocating establishments: if a continuer establishment has a different postal code address or coordinates between two consecutive years • Newborn establishments: if the establishment has an EN number in year t + 1 which did not exist in year t • In-migrating establishments: Those with the same EN in two consecutive years, but with a different SGC, and an SGC in Hamilton for the later year

8. Representing space • Bidding process • Establishments in the pool will out-bid each other for a particular location which will be assigned to the highest bidder • The bidding and maximizing profit processes can be modeled using discrete choice models (Martinez, 1992) • Space at the micro-level • Use boundaries of developed land parcels; but postal code addresses has a one-to-many relationship with parcel Alternatively • Divide the city into grid cells of 200 x 200 meters; extract grid cells that correspond to developed commercial and industrial land uses to create the set of alternative locations

9. exp(Vni) Pn(i) = ___________  exp(Vnj) j We model the location choice problem by major economic sectors • We employ a MNL model to handle the location choice decisions: • Creation of Choice set: • Grid cells resulted into a large choice set of 2635 and 2855 alternatives (cells) in the two periods 1996-1997 and 2001 – 2002, respectively Therefore • Random sample of alternatives (McFadden, 1978) : 9 randomly selected cells (locations) in addition to the chosen cell (location) • Linear in parameter systematic utility Vni is a function of: Location characteristics and establishment attributes

10. Model Specification • Model specification is based on information we gathered from the urban economic literature and the available data • Location specific factors included: • Distance to CBD (CBDPRO) • Main road and highway proximity (MRHWYPRO) • Regional Mall proximity (MALLPRO) • Measures of Agglomeration economies (AGGLOn); n is economic sector • Geography classification: Inner suburbs (MOUNTAIN) and outer suburbs (SUBURBS) • Density of new residential development (NEWDEVELOP) • Density of old residential development (OLDDEVELOP) • Population density (POPDENS) and Household density (HHLDDENS) • Household income density (HHLDINCDENS) • Average Housing value density (AVGDWELLVAL) • Percentage of a particular land use at a given location (LANDUSEk); k is type of land use • Firm specific factors included: • Dummies to reflect firmographic event (NEWBORN) and type of industry the firm belongs to (INDUSTRYsic); SIC is 2-digit or 3-digit SIC code

11. Estimation Results • Most firms in Hamilton prefer locating on land far away from the CBD • Central location is important for: • Printing, publishing, and allied manufacturing firms (SIC 28), • Communication and utilities firms SIC(48 – 49) • Food, beverage drug and tobacco wholesale firms, • Finance insurance, business services, accommodation food and beverages and other services • New born manufacturing firms (i.e: incubation plant hypothesis) • Main road and highway proximity is important for all firms except for • All construction firms except for Electrical work firms (SIC 426) • Other product wholesale trade firms (SIC59) • NEWBORN Health and social services AND accommodation food and beverage firms favor land in close proximity to main roads and highways • Land in proximity to Regional Mallsattracts retail trade firms specialized in food, beverage and drugs (SIC60), apparel, fabric and yarn (SIC 61) and general retailing stores (SIC 65). • Construction, communication and transportation firms avoid land in close proximity to regional malls

12. Agglomeration economies is prominent in the city of Hamilton. All firms seems to appreciate the externalities associated with clustering in the local market • All Construction firms except for electrical work firms (SIC 426) favor locating in the inner suburbs above the escarpment. Other services firms show affiliation of location in the inner suburbs area • Wholesale trade and retail trade firms show evidence of suburbanization. This is true for all firms except for food stores (SIC 601), gasoline service station firms (SIC 633), motor vehicle repair shops (SIC 635) and general merchandize stores (SIC641) • Construction, wholesale trade, retail trade, real estate, businesses, and accommodation food and beverage firms favor locations with new residential development • Construction and retail show evidence of avoiding the location with old residential development

13. Manufacturing firms avoid highly populated areas • High Income Locations are attracting services and retail trade firms except for firms specialized in selling shoe, apparel fabric and yarn (SIC 61), household furniture, appliances and furnishing retail (SIC 62) and automotive vehicles parts and accessories sales and services (SIC 63) • Land use variables suggest that: • Construction, communication and transportation firms locate predominantly on open space land • Manufacturing and communication firms favor locations with resource and industrial land use. • Retail trade and services firms show high affiliation with commercial land use • General merchandize Stores (SIC 64) and SIC(65) show affiliation with residential land use areas (i.e: population oriented) • Service firms also show affiliation with governmental and open space land uses

14. Location behavior over time: An Example from the retail sector Estimation results of the 2001 – 2002 models Suggest a consistency in the location choice Behavior over time

15. Conclusion • The research was successful in extending the conventional firm location modeling approach to study location choice behavior at the micro-level • Results suggest that the estimated models have the potential to be used in an agent-based simulation model • Research highlight the potential of using Statistics Canada Business Register to study firm location behavior and to develop agent-based firmographic models

16. Acknowledgments • We would like to thank Statistics Canada for supporting this research through their (2003 – 2004) Statistics Canada PhD Research Stipend program. • We would like to acknowledge the Micro-Economic Analytical Division (MEAD) for providing the corresponding author with office space for one year to facilitate his access to the Business Register data. • Thanks go to Dr. John Baldwin, Dr. Mark Brown and Mr. Desmond Beckstead for their useful discussions, input and assistance. • We are grateful to SSHRC for financial support through a Standard Research Grant and a SSHRC doctoral fellowship