1. A Discrete-Time Hazard Duration Model of SME Business Establishment Survival in the City of Hamilton, Ontario By Hanna MAOH and Pavlos KANAROGLOU Association of American Geographers (AAG) 2005 Annual Meeting, Denver (April 5 – 9)

2. Outline Introduction Research objectives Study Area and Data Exploring survival Modeling business establishment failure Conclusion & Acknowledgments

3. Introduction Studying the evolution of Business establishments is important for the future of cities Firm demography approach: concerned with studying processes that relate to: Establishment of new businesses Failure, migration, growth and decline of existing businesses

4. Research Objectives Advance the current state of knowledge on firmographic processes in the urban context Devise behavioral firmographic Decision Support System (DSS) to assess the inter-play between the local economy and Hamilton’s urban form To compare and contrast the micro approach with the conventional macro-approach

5. The evolutionary process of business establishment population over time Intra-urban mobile establishments* In-migrated establishments Newly formed establishments Establishment population at time t Establishment population at time t + 1 Out-migrated establishments Failed establishments + + –– * A growth/decline will be determined for stayer and intra-urban mobile establishments

7. Data: Business Register (BR) BR retains information about all Canadian businesses at the business establishment level that goes back to 1990 Each business establishment has the following attributes: Establishment Number(EN), postal code address, paid workers, operating revenue, 4-digit 1980 Standard Industrial Classification (SIC) code, Standard Geographical Classification SGC code, and Street name and number We make use of self-owned small and medium (SME) size establishments since the BR retains annual information about those businesses

8. Exploring Survival We follow the life trajectory of 1990 and 1996 small and medium size establishments till 2002 We determine the duration of survival and time of failure We explore variation in establishment survival by size, age, industry and geography Non-parametric survival curves suggests that size, age, industry and geography has an influence on the survival rates

10. Survival rates of the 1991 cohort by size class

11. Survival rates of the 1991 cohort by industrial class

12. Survival rates of the 1996 cohort by age class

13. Failure Model We follow the life trajectory of 1996 SME cohort till 2002 to model the failure process via a discrete time hazard duration model: P it (f) = 1/(1 + exp(-  t +  x it )) Firm specific variables Age (+ve) Size (-ve) and Size-squared Growth (-ve) Relocation (-ve) Macro economic variables Unemployment rate (+ve) Average total income (-ve) Geography specific variables Local Competition (+ve) Agglomeration economies (-ve) Location dummies Industry specific variables Average size of industry (+ve) Industry dummies

14. Estimation Results Firm specific variables –Young and small establishments are more susceptible to failure –Growing establishments are more likely to remain in business –Relocation signals a superiority in performance either because it is undertaken to expand or as a reaction to location stress Geography specific variables –Market power (competition) has a positive influence on failure –Market share (agglomeration) has a negative influence on failure –Suburban establishments are less likely to fail compared to those located in the core

15. Estimation Results Macro economic variables –Economic downturn or low demand for services and goods lead to higher rates of failure –High levels of demand for services and goods (purchase power) in the city decrease the propensity of failure Industry specific variables –Small establishments in large industries are more likely to fail –Failure vary by industry (Health and Social Services have the lowest rates of failure; finance insurance services have the highest rates of failure)

16. Conclusion Firm, geography, macro-economy and industry specific factors can explain failure with firm and macro-economic being the most influential The BR can be useful in developing agent-based firm demographic models Extension of the modeling framework to study the failure by economic sector may have a value added

17. Acknowledgments We would like to thank Statistics Canada for supporting this research through their (2003 – 2004) Statistics Canada PhD Research Stipend program. Thanks go to Dr. John Baldwin, Dr. Mark Brown and Mr. Desmond Beckstead from Statistics Canada for their useful discussions, input and assistance. Thanks go to the Social Sciences and Humanities Research Council of Canada (SSHRC) for supporting this research through a Standard Research Grant and Postgraduate Scholarship