A Discrete-Time Hazard Duration Model of A Discrete-Time Hazard Duration Model of SME Business Establishment Survival in SME Business Establishment Survival in

the City of Hamilton, Ontariothe City of Hamilton, Ontario

By

Hanna MAOH and Pavlos KANAROGLOU

Association of American Geographers (AAG)

2005 Annual Meeting, Denver (April 5 – 9)

OutlineOutlineIntroduction

Research objectives

Study Area and Data

Exploring survival

Modeling business establishment failure

Conclusion & Acknowledgments

IntroductionIntroduction

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

Research ObjectivesResearch ObjectivesAdvance 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

The evolutionary process of business establishment population over time

Intra-urban mobile

establishments*

In-migrated establishments

Newly formedestablishments

Establishment population at

time t

Establishment population at

time t + 1

Out-migratedestablishments

Failed establishments

+ +

– –

* A growth/decline will be determined for stayer and intra-urban mobile establishments

Data: Business Register (BR)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

Exploring SurvivalExploring 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

Survival and Hazard Rates, 1991 and 1996 SME cohorts Survival S(t) Hazard h(t)

Time t

1991 cohort

1996 cohort

1991 cohort

1996 cohort

[1-2) 0.84 0.85 0.17 0.17 [2-3) 0.73 0.74 0.14 0.14 [3-4) 0.65 0.67 0.11 0.10 [4-5) 0.59 0.62 0.09 0.07 [5-6) 0.54 0.58 0.09 0.07 [6-7) 0.49 0.54 0.10 0.07 [7-8) 0.45 0.09 [8-9) 0.42 0.06 [9-10) 0.40 0.05 [10-11) 0.38 0.06 [11-12) 0.36 0.05

Survival rates of the 1991 cohort by size class

Survival rates of the 1991 cohort by industrial class

Survival rates of the 1996 cohort by age class

Failure ModelFailure Model

We follow the life trajectory of 1996 SME cohort till 2002 to model the failure process via a discrete time hazard duration model:

Pit(f) = 1/(1 + exp(-t+ xit))

Firm specific variablesAge (+ve)Size (-ve) and Size-squaredGrowth (-ve)Relocation (-ve)

Macro economic variablesUnemployment rate (+ve)Average total income (-ve)

Geography specific variablesLocal Competition (+ve)Agglomeration economies (-ve)Location dummies

Industry specific variablesAverage size of industry (+ve)Industry dummies

Estimation ResultsEstimation ResultsFirm 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

Estimation ResultsEstimation ResultsMacro 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)

ConclusionConclusionFirm, 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

Firm specific model

Industry specific model

Geography Specific model

Macro-economy specific model

Full model

Pseudo R2 0.0710 0.0186 0.0154 0.0309 0.1003 % Explained Right 66.5 58.3 55.3 53.9 69.9

AcknowledgmentsAcknowledgmentsWe 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