Agent-Based Firmographic Models: A
Simulation Framework for the City of
Hamilton
By
Hanna Maoh and Pavlos Kanaroglou
Workshop on modelling and microsimulating firm
demography
University College London (UCL), London, July 2nd, 2005
Research Objectives

Objective: Simulate the evolution of
establishment population between t and t +1
business

Improve upon existing methods used to model firms and
jobs in conventional Land use and transportation models

Adopt the agent-based approach to develop an agentbased microsimulation model

Apply concepts from firm demography to model the
evolutionary process
The Demography of Firms

Firm demography is dedicated to the study of
processes that relates to:
–
–
–
–

Formation of new firms (birth or entry)
Failure of existing firms (death or exit)
Migration of existing firms (local and regional)
Growth and decline of firms
It is concerned with identifying and quantifying
the causes associated with firmographic processes
Evolutionary Process of Business
Establishment Population
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
Modeling Framework
Establishment
population
t
Failure
submodul
e
Establishment
population
t
Survivals
Newly formed
& in-migrating
establishments
t+1
Newly formed
& in-migrating
establishments
t+1
Mobility
submodul
e
Migrants
Location
choice
submodul
e
Growth
submodul
e
Establishment
population
t+1
Firmographic Processes
Assign a
business
to a site
Size of
business
t+1
Establishment
population
t+1
Processes Output
Real Estate Market


A market for industrial and commercial floor
space at the parcel level drives the framework
This market is influenced by:
1. The firmographic events:



Demand for floor-space is generated by the newly formed,
relocating and in-migrating establishments
Failure, departing current location (out-migration or intraurban migration) free up floor space
Growth, decline, merging and splitting also contributes to
change in floor space
2. Development and redevelopment practices influence
the available floor-space
Evolution of the space economy over time, Hamilton 1990 - 1997
Firm Micro-Data: Statistics Canada
Business Register (BR)





Maintain annual information about business
establishments in Canada since 1990
Confidential and can only be used to conduct
statistical analysis
Attributes: Establishment size, location (postal code
and SGC), SIC code and Establishment Number (EN)
BR provides the life trajectory of business
establishments over space and time
BR can be used to measure firmographic events such
as: the formation, migration, location choice, failure,
growth and decline of business establishments
Small and Medium (SME)
Size establishments

SME with less than 200 employees is the target of our analysis

Account for more than 94% of establishments in 1990, 1996 and
2002

Extracted population was constrained to self-owned single
establishments

Establishments that are part of a chain were not included in the
model!

However, the extracted sample is deemed appropriate

Around 80% of SME are with less than 10 employees, 93% of which
are single owned establishments
Modeling Methods
• Use discrete–time hazard duration models to explain the
failure process
• Use multinomial logit models to explain the mobility (stay,
relocate or out-migrate) of business establishments
• Use multinomial logit models to explain the location
choice behavior of intra-urban mobile, newly formed and inmigrating establishments (maximum utility and Bid-rent
concepts)
• Use multivariate regression models to explain the
growth/decline process of business establishments
Failure Submodule
Exploring and modeling survival
and failure of establishments
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
Survival and Hazard Rates, 1991 and 1996 SME cohorts
Survival S(t)
Time
1991
1996
t
cohort
cohort
[1-2)
0.84
0.85
[2-3)
0.73
0.74
[3-4)
0.65
0.67
[4-5)
0.59
0.62
[5-6)
0.54
0.58
[6-7)
0.49
0.54
[7-8)
0.45
[8-9)
0.42
[9-10)
0.40
[10-11) 0.38
[11-12) 0.36
Hazard h(t)
1991
1996
cohort
cohort
0.17
0.17
0.14
0.14
0.11
0.10
0.09
0.07
0.09
0.07
0.10
0.07
0.09
0.06
0.05
0.06
0.05
Survival rates of the 1991 SME cohort by size class
Survival rates of the 1991 SME cohort by industrial class
Survival rates of the 1996 SME cohort by age class
Failure Model

We follow the life trajectory of 1996 SME cohort till 2002
to model the failure process of SME with less than 50
employees via a discrete time hazard duration model:
Pit(f) = 1/(1 + exp(-t+ xit))
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

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
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)
Conclusions on the failure
model

Firm, geography, macro-economy and industry specific
factors can explain failure with firm and macro-economic
being the most influential
Pseudo R2
% Explained Right
Firm
specific
model
0.0710
66.5
Industry
specific
model
0.0186
58.3
Geography
Specific
model
0.0154
55.3
Macroeconomy
specific
model
0.0309
53.9
Full
model
0.1003
69.9

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
Mobility Submodule
Exploration and modeling of mobility
trends
Mobility Trends

7% and 2% of 1996 SME establishments relocated and out-migrated
by 1997, respectively

12% and 3% of 1996 total establishment population relocated and outmigrated by 2002, respectively

Mean employment size of relocating establishments is 15 and mean
relocating distance is 5 kilometres (1996 – 2002)

50% of moves happened at short distance within the same municipality

91% of out-migrating establishments moved within a radius of 100
kilometres around Hamilton between 1996 and 2002

57% of out-migrants moved to close by location in the Greater Toronto
Area
Establishment Mobility Model

Objective: Determine if an individual
establishment will choose to Stay (S) at its
current location, Relocate (R) to a different
place within the city or will Leave (L) the city
between 1996 and 1997

We use a MNL model to predict probabilities
P(S), P(R) and P(L)

Mobility is modeled by main economic sector
Utility Specification for
establishment i



Establishment internal factors and location factors are used in the
specification of the Stay, Relocate and move utilities
Internal factors included: Size, Age, Growth rate and dummies
for type of industry industry_d
Location factors included: Geography dummies, a measure for
agglomeration economies (Agglom), distance between old and
new location (Dod) and a measure for location competition
(Lcomp)
Overview of Results

Mobility is more prominent among very small
and very large establishments as depicted by the
Size and Size2 parameters

The Age parameter suggests that young
establishments are more likely to relocate or outmigrate

The need to grow as suggested by the Growth
parameter push manufacturing establishments to
relocate
Overview of Results

The Growth parameter in retail and wholesale
models appear as a proxy for performance since
growing establishments were less mobile

The location dummies suggest decentralization and
suburbanization of establishments in Hamilton

Mobility is more pronounced among the Central
Business District (CBD) establishments
Overview of Results

Agglomeration increases the propensity of
inertia. This effect is more prominent among
retail and service industry establishments

The increase in local competition (location
pressure) will push the establishment to move
long distance

Mobility vary by the type of industry as
discerned from the specified industry dummies
Conclusions on the mobility
model





Mobility is not common place in the urban context
Firm internal factors and location factors are important
determinants of mobility
The research emphasizes the value in using data from
Statistics Canada Business Register to study firmography
in the urban context
More work need to be done to investigate the role of
organizational structure on mobility
Future research is still needed to thoroughly scrutinize the
relation between public policy and establishment mobility
behavior in the urban context; Therefore, enhancing the
attributes of existing firm micro data is required
Location choice submodule
A micro-analytical modeling framework
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
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

We employ a MNL model to handle the location choice
decisions:
exp(Vni)
Pn(i) = ___________ We model the location choice problem by
major economic sectors
 exp(Vnj)
j

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
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

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 Malls attracts 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


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

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
Location behavior over time: An Example from
the retail sector
Variable
HWYMRPRO
AGGLOM5
SUBURBS
NEWDEVLOP
OLDDEVLOP
HHLDINCDENS
LANDUSE4
LANDUSE5
INDUSTRY10 x MALLPRO
INDUSTRY11 x HHLDINCDENS
INDUSTRY12 x LANDUSE4
INDUSTRY13 x SUBURBS
No. of Observations
L(0)
L(B)
Rho 2
Adj. Rho 2
Parameter
1996 – 1997
0.376421
(3.168)
0.022177
(6.829)
0.426167
(2.294)
0.003345
(3.189)
-0.000629
(-2.734)
0.000002
(1.698)
-0.559876
(-2.719)
1.152924
(2.946)
0.650485
(2.890)
-0.000002
(-1.535)
0.643616
(1.888)
-0.425008
(-1.752)
396
-911.824
-812.938
0.10845
0.10544
Parameter
2001 – 2002
0.383930
(2.798)
0.049132
(8.889)
0.389474
(1.845)
0.001438
(1.706)
-0.000925
(-3.287)
0.000002
(1.844)
-0.567610
(-2.192)
0.870484
(2.149)
0.704633
(2.018)
-0.000001
(-0.863)
-0.100075
(-0.255)
-0.494691
(-1.720)
303
-697.6833
-600.3443
0.13952
0.13571
Estimation results of the
2001 – 2002 models
Suggest a consistency
in the location choice
Behavior over time
Conclusion on the location
choice model




The research was successful in extending the
conventional firm location modeling approach
to study location choice behavior at the microlevel
Results suggest a variation in the location
choice behavior among firms from the
different sectors
The modeling approach was able to account
for the heterogeneity in location choice
behavior
Results are consistent with the urban economic
literature
Future Research

Model implementation: Creating a synthetic list of business
establishments (BE) to use as a base year population for any
simulation

Develop a dynamic Geodatabase data model to store, maintain
and update the BE list during simulations. Utilize Unified
Modeling Language (UML) as the basis for the development

Implement a real estate development model to predict the change
in industrial and commercial floor-space

Simulate the inter-play between the local economy and urban
form in Hamilton
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 John Baldwin, Mark Brown and
Desmond Beckstead for providing office space and
access to the BR data. Also I am thankful to them for
their useful discussions, input and assistance.

We are grateful to Social Sciences and Humanities
Research Council of Canada (SSHRC) for financial
support through a Standard Research Grant and a
SSHRC doctoral fellowship