empirical methodologies to research agglomeration externalities

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Empirical Methodologies to Research Agglomeration Externalities

Frank van Oort02-07-2009

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Content

Empirical methodologies of agglomeration externalities

1. Growth in cities Spatial dependence

Spatial heterogeneity

Related variety

2. Employment-population Causality

3. Scale issues MAUP

Multilevel issues

4. Contexts Locations or networks

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5. Policy analysis Indicators of clusters

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Content




Related variety



Multilevel issues


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“My purpose is to show that cities are primary economic organs” (Jacobs 1969, p.6).

“Development is a process of continuously improving in a context that makes injecting improvisations feasible. Cities create that context. Nothing else does” (Jacobs 1984, p.155).

“The city is not only the place where growth occurs, but also the engine of growth itself” (Duranton 2000, p.291-292).

“Large cities have been and will continue to be an important source of economic growth” (Quigly 1998, p.137).

“Agglomeration can be considered the territorial counterpart of economic growth” (Fujita and Thisse 2002, p.389).

Basic principles – externalities in cities

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Growth and innovation externalities

Spillovers

Agglomeration

Clusters

Regional Innovation System

Knowledge Economy

Knowledge Production Function

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• unpaid externalities,• causes of (cumulative) economic growth,• causes of innovation-diffusion,• in urban contexts: agglomeration-economies,• according to NEG-economists, not measurable,• according to many, not measurable in space,• according to some, related to specialised, diversified and urban localised industries,• according to many scale-free,• embedded in endogenous growth models,• embedded in evolutionary economic theory• often related to innovative firms, knowledge institutions, growing firms or the emergence of new firms,• often interpretable as location factors of firms (clusters), and hence interesting for policy

Knowledge spillovers are:

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Example: the Dutch ICT-sector

Spatial factors (externalities) that econometrically relate to firm growth in ICT-firms

Theory: endogenous growth theory & externalities, evolutionary economic geography

Hypotheses on agglomeration economies: specialisation, diversity, competition (Glaeser et al. 1992)

580 municipalities

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Dynamic externalities

BT-BS (63%), DV (9%), OV (11%), SCV (17%)

Glaeser et al. (1992) – Growth in cities. JPE.

Henderson et al. (1995) – Industrial development in cities. JPE.

Their research results are highly suggestive for the relevance of urban environments for economic

growth processes.

Debate:localisation (Marshallian) economies

versus urbanisation (Jacobs) economies

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Agglomeration hypotheses

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Many Empirical Studies, Different Findings

Source: Rosenthal & Strange (2004)

Lack of robustness across studies implies that different economies can exist next to each other and that not per definition one type of agglomeration externality leads to more concentration or economic growth than the other.

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ICT-sector Netherlands:

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Localised spillovers: spatial dependence

Spatial regimes on urban structures also called spatial heterogeneity or non-contiguous spatial dependence

Spatial proximity based clusteringalso called spatial autocorrelationor contiguous spatial dependence

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Spatial proximity:Moran 1 - employment function ICT-firms (location quotients 1996)

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Spatial proximity:Moran 2 - log employment growth all ICT-firms (1996-2000)

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Spatial proximity:Moran 3 - log new firm formation (1996-2000)

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urban regimes 1: national zoning

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urban regime2: labour-markets

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urban regimes 3: Municipal size

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Conclusions: agglomeration hypotheses

Growth in the Dutch ICT sector tends to be concentrated in urban areas that are already relatively specialized in this sector and that are relatively rich in the presence of other industries. These outcomes do not fully support or contradict any of the four theories of localized knowledge spillovers (MAR, Porter, Jacobs)

Spatial policy should not be restricted to the local or regional environment alone. Spatial externalities relevant in local contexts can be at work at higher levels. Local policy makers should be open to the argument of spillover effects from nearby (not necessarily adjacent) agglomerations instead of promoting ‘own’ ICT-clusters

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Summary Van Oort (2007)

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Why, where and when does it matter?

Duranton and Puga (2000) paper

• Specialized and diversified cities co-exist

• Larger cities tend to be more diversified

• The distribution of city-sizes and specializations tend to be stable over time

• City growth is related to specialization and diversity

• Relocations are from diversified to specialized cities

• Assumptions: crowding, agents, labour mobility, (endogenous) self-organisation, path-dependency, systems of cities (policentricity).

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Do we measure knowledge spillovers?

• Jacobs’ externalities related variety

• Space, knowledge and growth are more complex related than often thought: spatial dependence, spatial heterogeneity, scale, measurement units, timeframe, definitions

• Localization versus urbanization economies too simple?

• Innovation as source of growth (KPF)

• We did not measure transactions or linkages (yet)!

• Micro-foundations of growth

• Causality

• MAUP

• Contexts of firms (networks, sectors, location)

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Related and unrelated variety

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Variety and urban economic growth

• source externalities = localization: similar firms & products, intra-industry, incremental

innovation -> productivity growth

• source externalities = Jacobs externalities: inter-industry, radical innovation, new markets ->

employment growth

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Hypotheses

1. Jacobs externalities are positively related to employment growth

2. Localisation economies are positively related to productivity growth

3. Unrelated variety is negatively related to unemployment growth

Analysis:• COROP (functional) regions • Netherlands – natural control location factors• 1996-2002 – base year approach• control variables• sensitivity analysis period• standardised variables• spatial econometrics (lag/error and regimes)

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Related and unrelated variety

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Employment growth

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Conclusions

Hypothesis 1 (related/Jacobs/employment) – confirmed

Hypothesis 2 (localisation/productivity) – unconfirmed (traditional)

Hypothesis 3 (unrelated/unemployment) – confirmed (but sensitive)

Classic studies (Glaeser cs.) measure unrelated variety and conclude on related variety

Agglomeration or cities (urbanisation) per se are not enough for stimulating economic growth

Spatial regimes more important than spatial aurocorrelation patterns

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Content




Related variety



Multilevel issues


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Employment-population dynamics in the Dutch Randstad

Frank van OortPecs, 2-7-2009

Urbanisation Act (1976, p.132)

“Little is known on the way spatial development and

social-economic development influence each other.

There exists uncertainty especially on the mutual

dependence of choices for places of living (of people)

and working (of firms)”

Introduction

Employment-population dynamics: “Do people follow jobs, or do jobs follow people?”

International research: Mixed results

Limited Dutch research: “jobs follow people” Levels of analysis: NUTS3 and zip-codes

International research

Marlon Boarnet (1994): “The monocentric model and employment location” Employment is endogenous to population changes

Donald Steinnes (1977): “Causality and intraurban location” Causality runs from residence to employment

Donald Steinness (1982): “Do people follow jobs” A causality issue in urb. ec. Measurement issues!!

Research questions

Research question: What is the relation between population growth and

employment growth at the level of Dutch municipalities?

Special attention for: Industrial composition Influence of policy Spatial differentiation: Randstad Holland

Why important for policy?

Population-employment dynamics is primarily an issue on the municpal level (Nota Ruime) ‘For the accommodation of space for population and employment over Dutch

municipalities, the Dutch government wants municipalities to offer space for the existing population and its growth, as well as the existing population of local firms and their growth potential’.

‘Other services related to spatial planning (governmental, retail, etc.) should be accommodated timely and in the right numbers, related to the local demand of citizens and entrepreneurs’.

No policy regarding spatial heterogeneity: Does the Randstad encounter the same dynamics as municipalities in the

Intermediate Zone and in the National Periphery?

Model

•dPi,t =Xβ + dWEi,t + WEi,t-1 – λP Pi,t-1 + other P-factors

•dEi,t =Yδ + dWPi,t + WPi,t-1 – λE Ei,t-1 + other E-factors

What follows what?

cf. earlier research

But: Especially personal

services follow popualtion Industrial sectors and

business services to a much lesser extent

Distribution does not follow poulation

C.P. other local growth factors population (environment) and jobs growth (clusters)

Jobs follow People

Spatial differentiation

Spatial differentiation

National population-employment dynamics is mainly determined by dynamics in the Randstad (‘jobs follow people’)

In the Intermediate Zone complex dynamics (‘jobs follow people’ and ‘people follow jobs’)

Little dynamics in the National periphery (and when so, ‘people follow jobs’)

Conclusions

In general: ‘jobs follow people’

However, differences for industries, policy and density zones: Personal service jobs follow people Jobs in the North-wing of the Randstad follow people In VINEX-municipalities jobs follow people

Population-employment dynamics in the Netherlands: Restrictions on location choice of people (Vinex

municipalities in the North-wing Randstad) Outside the Randstad more opportunities for choice

(where people follow jobs more easily)

Conclusions

Dutch jobs follow people; but only

because the latter have no choice

Policy implications

• Attracting population outside the North-wing of the Randstad is no guarantee for job growth.

• In the National Periphery, policy aiming at only employment growth, e.g. by providing business sites, only to a very limited (to no) extent leads to population growth. Policy aimed at only population growth does not lead to employment growth.

• How about shrinking regions!

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Content




Related variety



Multilevel issues


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Scale-dependency

• Traditional: functional region unit of observation• The choice of this level as spatial unit of analysis

is arbitrary and foremost a result of data limitations.

• Other problems:– Agglomeration externalities may well reach

beyond the regional level or be present at a lower scale

– Most often agglomeration externalities are treated as spatially fixed (agglomeration externalities as club good); this is unsatisfactory

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Analysis

• Accordingly, the spatial scope of agglomeration externalities remains opaque

• Moreover, their effects seem to depend on the spatial scale they are studied

• It is the geographical scale and scope of agglomeration externalities that will be the focus of our analysis (holding sector, time, area, and measurement constant)

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Data

• Aggregated plant-level data on employment (N=647000, 5-digit sectors)

• Three spatial levels of analysis

– Neighborhood (N=3957, +/- 9 km2)

– Municipality (N=483, +/- 70 km2)

– Functional Region (N=40, +/- 850 km2)

• Spatial autoregression to evaluate scale and scope of agglomeration externalities

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Very preliminary outcomes

Textiles, Apparel & Leather Industry

Municipality Functional Region

Localization Economies ++ --

Urbanization Economies -- +

Jacobsean Economies 0 0

Consumer Electronics

Municipality Functional Region

Localization Economies ++ --

Urbanization Economies -- +

Jacobsean Economies 0 0

Dependent Variable: Absolute Employment Growth (1996-2004), estimated with constant, control variables (wage, competition, investments), controlled for fixed and random effects, and spatial dependency. To control for endogeneity, we used lagged levels of past conditions.

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Synthesis

If agglomeration externalities turn out to be scale- and scope-dependent:

• Question the external validity of past studies on agglomeration externalities

• Focus on the micro-foundations of agglomeration externalities

• Take the firm or plant seriously in analysis by

taking it as unit of analysis.

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Content




Related variety



Multilevel issues


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Agglomeration economies and the performance of new firms

Frank van Oort, Utrecht UniversityMartijn Burger, Erasmus University Rotterdam

Pecs, 2-7-2009

Agglomeration economies: definition

• Dates back to the work of Marshall (1890), Hoover

(1948) and Isard (1956).

• Cost-saving benefits or productivity gains external to a

firm, from which a firm can benefit by being located at

the same place as one or more other firms.

• Uncontrollable for a single firm.

• Immobile or spatially constrained.

Agglomeration economies: definition

• Localization Economies

– Agglomeration economies stemming from

concentration of specialized economic

activities, internal to industry

• Urbanization Economies

– Agglomeration economies stemming from

market size, external to industry

Empirical literature on agglomeration

• Empirical studies show that the elasticity of productivity

to city and local industry size typically ranges between

3% and 8% (Rosenthal and Strange, 2004)

• However, results vary over the sectors, regions and time

period under observation.

• Agglomeration externalities may differ with respect to

their reach and the scale on which they are present.

The firm in Agglomeration Economics

• At the same time, little is known about the importance of

agglomeration economies for the performance of firms.

• Most empirical research on agglomeration uses

aggregated data with cities or city-industries as basic

reference unit.

• These studies provide only limited insights and weak

support for the effect of agglomeration economies on

firm performance due to ecological fallacy and spatial

selection.


• Lack of firm-level evidence in the agglomeration

economics literature can mainly be ascribed to data

limitations and confidentiality restrictions

• Absence is nevertheless ‘disturbing’ because the

theories that underlie agglomeration economies are

microeconomic in nature

• Agglomeration economies do not directly foster regional

economic growth, but only indirectly through their effect

on firm performance.


• The region in which a firm is embedded generates opportunities and economic constraints for firms located in that region, e.g. through agglomeration economies and agglomeration diseconomies.

• Firms with more economic opportunities and less economic constraints (proposition 1) tend to perform better in terms of survival chances and productivity growth.

• Regions with better performing firms (proposition 1 and 2) exhibit higher economic growth. Regional performance is here conceptualised as the weighted sum of the firms’ performances.

•Regional Circumstances

•Regional Economic Growth

• Firm Performance

•Firm •Orientations

•1

•2

•3


• Regional growth is a byproduct of interest-seeking firms trying

to optimise their own performance

• The external environment of the firm not only consists of the

location of the firm (physical environment), but also of other

components such as the sector in which the firm is embedded

• Not all opportunities and constraints of firms are related to

macro-level properties

• However, even when constraints and resources are firm-

based, it often remains debatable to what extent their effect is

independent of region and/or sector

From theory to empirics: questions

• We need a model that looks both at micro and macro-level

characteristics when the link between micro and macro levels is

existent – is that the case?

• New types of general questions:

– How is important is the external environment of firms for firm

performance?

– Which types of firms draw more on their external environment?

• New types of more specific questions:

– How important are agglomeration economies for firm

performance?

– Which types of firms profit more from agglomeration economies?

From theory to practice: empirical model

• One way to tackle these questions is by means of

random effects (multilevel analysis), which offers a

natural way to assess to what extent a link between the

micro-level and macro-level is existent

• Two important assets of multilevel analysis:

– Modelling contextuality by means of variance

partitioning

– Modelling heterogeneity by allowing relations to

vary across environments (random slopes).

From theory to practice: empirical model

• We focus on a model of firm survival and employment

growth that is specific to characteristics of the internal

and external environment of the firm

• This external environment may consists of several

components, such as the firm’s location, sector or club

(location-by-sector)

• These environments should simultaneously be assessed

in order to avoid underspecification of the model

• Main interest: effect of agglomeration economies on firm

survival and employment growth.

Data

• Data from employment register (LISA) on of 46,000 new

firms in 2000/2001 in the advanced producer services

sector in the Netherlands

• New firms less constrained by previous decisions, which

influences how they value the marginal worker and

whether new employment is created

• Less influence of a possible spatial sorting effect

Data

• These 46,000 firms are divided over 40 regions (LMA’s)

and 19 subsectors in the advanced producer services

• Dependent variables: firm survival (yes/no) and

employment growth (yes/no) in the first five years of a

firm’s existence

• Independent variables related to the internal and

external environment of the firm

Variables

• Firm: firm survival and employment growth, initial firm

size

• Region-by-Sector (Club): localization economies (own

sector density), competition (turnover)

• Regional: urbanization economies (population density),

R&D expenditures, human capital

• Fixed effects at the sector level

From theory to practice: model

•New •Firms (i)

•Regional Sectors (j)

•Sectors •(k2)

•Regions •(k1)

(1) (2) (3)( 1, 2) ( 1, 2) 0 ( 1, 2)0 0 1 0 2( )ij k k ij k k ij k kj k kprobit X u v v e

•Mixed hierarchical and cross-classified probit model

•DV: Survival / Employment growth (1 if Yes)

•Four classifications:

- Firm

- Regions

- Sectors

- Regions-by-Sectors (Club)

•Notation: ith firm in the jth club, that is nested in region k1 and sector k2

•Intercept reflecting average probability of firm survival

•Differential intercepts for clubs, regions and sectors; higher level residuals

•Remaining firm differential; firm level residual

Model estimation

- Three-level probit model (with four classifications) with a

random intercept for firms at the lowest level and random

intercepts for regions, sectors-by-regions, and sectors at

the higher levels

- Estimated by means of Restricted Iterated General Least

Squares using the MLWIN statistical package

- For survival and new firm employment growth models a

Mundlak correction to account for endogeneity bias

Model estimation

- First analysis: gives an indication to what extent location

matters by explicitly disentangling the between location

variance from the between firm and between sector

variance

- Second analysis: assesses the effect of agglomeration

economies on new firm survival and growth

- Third analysis: assesses whether the effect of

agglomeration economies on new firm survival and

growth varies across firms

Variance Partition Coefficient

• Variance partition coefficients (VPC) can say something

about the importance of the context

• VPC measures the extent to which the y-values of new

firms in the same club/region/sector resemble each other

as compared to those from new firms in different

clubs/regions/sector

• It may also be interpreted as the proportion of the total

residual variation that is due to differences between

clubs/regions/sectors

VPC’s - survival

• 90.9% of the variance is between-firm variance.• 1.3% of the variance is between-club variance• 3.2% of the variance is between-location variance• 4.5% of the variance is between-sector variance

•New •Firms (i)

•Regional Sectors / Clubs (j)

•Sectors •(k2)

•Regions •(k1)

•90.9%

•1.3%

•4.5%

•3.3%

2 2 2 2

(1) (1) (2) (3)0 0 0 1 0 2

/( 1)j j k k

j u u v vVPC

•Probit distribution for the firm-level residual implies a variance of 1

VPC’s – employment growth

• 90.9% of the variance is between-firm variance.• 1.3% of the variance is between-club variance• 3.2% of the variance is between-location variance• 4.5% of the variance is between-sector variance

•New •Firms (i)

•Regional Sectors / Clubs (j)

•Sectors •(k2)

•Regions •(k1)

•93.7%

•0.8%

•3.0%

•2.5%

2 2 2 2

(1) (1) (2) (3)0 0 0 1 0 2

/( 1)j j k k

j u u v vVPC

•Probit distribution for the firm-level residual implies a variance of 1

Adding predictor variables

• As yet, we only have partitioned the variability in survival

and employment growth of new firms over areas,

sectors-by-areas, sectors, and firms

• However, we can add predictor variables for these

classifications, in order to see to what extent they explain

the partitioned variability

• The predictors we add contain measures related to the

firm characteristics, agglomeration externalities and

sectoral externalities

Adding predictor variables

•New •Firms (i)


•Sectors •(k2)

•Regions •(k1)

•Mixed hierarchical and cross-classified probit model with

• firm-level variables

• club-level variables

• regional-level variables

• sector-dummies

1 1 0 10 1 0 1 0 1 2 1 1 11 1 1

1 1 1 0 1 0 1

( )q r

ijk ijk pijk q q jk r rk k jk pijkj k

k pijk jk k

probit X X X X u X

v X u v

Empirical results

• Localization economies have small positive effect on new firm survival, while it has no impact on employment growth

• Urbanization economies have a positive effect on both the survival opportunities and employment growth of new firms

Adding cross-level interactions

•New •Firms (i)


•Sectors •(k2)

•Regions •(k1)

•Mixed hierarchical and cross-classified probit model with

• firm-level variables

• club-level variables

• regional-level variables

• sector-dummies

• cross-level interactions between firm size and different types of agglomeration economies

1 1 0 10 1 1 0 1 0 1 2 10 1 1 11 1 1 1

10 1 1 1 1 1 1 1 1 1 1 1 0 1 0 11 1

( )q qr

ijk ijk ijk q q jk r rk k q ijk qjkj k j

r

r ijk rk jk ijk k ijk jk kk

probit X X X X X X

X X u X v X u v

Empirical results

• With respect to surviving, larger start-ups profit from

localisation economies and urbanisation economies and

not their smaller counterparts

• With respect to employment growth, smaller start-ups

profit more from localisation economies, and not their

larger counterparts.

empirical methodologies to research agglomeration externalities

Documents

economic growth processes

cumulative economic

agglomeration economies

firm growth

engine of growth

endogenous growth models

agglomeration hypotheses

evolutionary economic