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Mapping industrial districts by using LMAs:

quality issues and spatial patterns

Silvia Lombardi and Michele D’Alò

Rome, 16.06.2017

Outline

1. Mapping Industrial districts: issues at stake

2. Objective and methodology

3. Spatial dependence and LMAs

4. Semi-parametric model – initial results

5. Conclusions and future developments

Mapping industrial districts by using LMAs: quality issues and spatial patterns. Silvia Lombardi – Rome, 16.06.2017

• The use of labour market areas (LMAs) is an issue at stake in

official statistics given their informative potential in terms of

detection of socio-economic phenomena at the territorial level.

• Mapping Industrial districts (IDs) is an application of LMAs for

the identification of meaningful territorial units in Italy in terms

of manufacturing concentrations of specialized firms.

• Such mapping has provided the quantitative evidence of the

IDs thesis which has risen the attention of policy makers on

the territorial dimension conceived as the basis for economic

and industrial local policies.

Mapping Industrial districts: issues at stake

1


• Economic literature has undergone a widespread debate

around the most accurate methodologies for the mapping of IDs

• Heterogeneity of results may be due to the application of

different methodologies (see, among others, Iuzzolino, 2004;

COGIS, 2005; Alampi et al, 2013; Canello and Pavone, 2015;

see an updated review in Lombardi and Lorenzini, 2017)

• The very beginning of the debate (but not the whole issue) is

whether the use of LMAs as unit of analysis is appropriate or

not:

commuting flows actually change over time, LMAs are not a

good proxy of the socio-economic space; which is the best

mechanism to define the aggregation of communities?

the underpinning of IDs theory is the interaction between

the population of firms and the community of people

2


Industrial Districts (2011)

• Business Census data in

2011 identified a 141 IDs

(out of 611 LMAs) that

absorb 24,5% of total

Italian employment (37,9%

of manufacturing

employment).

• Mainly specialized in

mechanicals (27% of IDs),

textile and clothing (23%),

household goods (17%)

• Hierarchical algorithm in

four steps by using

Location Quotient (LQ)

Textile and clothing

Leather Household goods Jewelry, musical instruments, etc.

Food industry Mechanicals

Metallurgy Chemicals and plastics Polygraphs

No ID

http://www.istat.it/it/archivio/150320 Istat 2015


3

http://www.istat.it/it/archivio/150320

Objective

4

• To provide spatial statistics evidences in order to assess the

robustness of the official mapping methodology of IDs

based on LMAs as well as its capability to detect the IDs

phenomenon.

• Test for the presence of spatial dependence patterns at the

community level


• We take into consideration specialization patterns and spatial

dependence across communities

by using the same indicator of the official mapping

methodology, that is: Location Quotient (LQ) on manufacturing

activities

Testing for spatial autocorrelation patterns on manufacturing

activities by applying a Local index of spatial agglomeration

(LISA) in order to detect clusters of communities

• Compare results with IDs at the community level

• Apply Geo-additive model (GAM) in order to take into

consideration non linearities in the agglomeration pattern (initial

results)

• 2011 Business Census data (persons employed at the

establishment level)

Methodology and data

5


We test agglomeration of manufacturing activities by using spatial

autocorrelation statistics

Moran’s I (1950) synthesizes the information about the degree of

spatial dependence

Positive spatial autocorrelation: similar values tend to cluster in

space, meaning that neighbours are similar: spatial clustering,

values observed at a location depend on values observed at

neighbouring locations

Global/local spatial autocorrelation: Hypothesis of stationary over

space vs local instabilities

Spatial autocorrelation vs LMAs to detect IDs

6


Moran’s I can be decomposed into local values (Anselin, 1995): Local

Moran is a local index of spatial agglomeration (LISA) used to detects

cluster, that is areal units with similar neighbours:

Ii is calculated for each areal unit. We analyse the High-High clusters of

communities

We apply such local index of spatial agglomeration to a set of metrics:

• the share of employment in manufacturing activities

• LQ of manufacturing activities

LQm = ( LMAemp, Nace / ITAemp, Nace ) / ( LMAemp, tot / ITAemp, tot )

• LQ of specialization industries

LQind = ( LMAemp, ind / ITAemp, ind ) / (LMAemp, man / ITAemp, man )

Local Moran’s Index (1/2)

7


Identifying spatial weights matrix

k-Nearest neighbors vs Contiguity–based neighborhood

Data: LQ of manufacturing activities

8

Local Moran’s index (2/2)

Spatial matrix Moran’s I value Expectation P-value

Knn=5 4.30 -1.24 0.000

Knn=7 4.18 -1.24 0.000

Knn=10 0.41 -0.00 0.000

Queen 4.03 -1.24 0.000

There is positive global autocorrelation

We choose Knn=5 as performs the maximun value of Moran’s I


Results - Local Index of Spatial Agglomeration (LISA)

Metric IDs Communities

covered by LISA* LMAs (IDs) covered

by LISA*

v.a. % v.a. %

Manufacturing share (a) 744 33.6 106 75.2

Manufacturing LQ (b) 839 37.9 109 77.3

In common btw (a) and (b) 716 32.4 104 73.8

Textile LQ 213 46.4 30 93.8

Mechanicals LQ 269 32.8 32 84.2

Households goods LQ 86 30.6 21 87.5

Food LQ 34 19.7 7 46.7

Leather LQ 99 62.3 17 100.0

Jewelry, musical instruments, etc. LQ 35 70.0 4 100.0

Chemicals and plastics LQ 34 33.0 4 80.0

Metallurgy LQ 22 33.3 4 100.0

Polygraphs LQ 4 44.4 1 50.0

Coverage: N. of LMAs involving at least one LISA H-H community

by sector is 82.5% on average


Comparison between IDs communities and LISA H-H communities

459

821

281

173 159

50

103

66

9

790

817

691

869

263

86

310

102

141

213

269

86

34

99

35 34 22

4

0

100

200

300

400

500

600

700

800

900

1000

Textile andclothing

Mechanicals Householdgoods

Foodindustry

Leather Jewelry,musical

instruments, etc.

Chemicalsand plastics

Metallurgy Polygraphs

IDs communities

LISA H-H communities

INTERSECTION


10

IDs and LMAs containing LISA H-H communities

32

38

24

15

17

4 5

4

2

30

32

21

7

17

4 4 4

1

0

5

10

15

20

25

30

35

40

Textile andclothing

Mechanicals Householdgoods

Foodindustry

Leather Jewelry,musical

instruments, etc.

Chemicalsand plastics

Metallurgy Polygraphs

IDs ISTAT

IDs & LISA communities


11

IDs vs manufacturing LQ


12

Leather sector: IDs vs LISA H-H clusters


13 100% coverage (LMAs)

Textile sector: IDs vs LISA H-H clusters


14 93.8% coverage

Mechanicals sector: IDs vs LISA H-H clusters


• 84.2% coverage 15

• Spatial dependency of manufacturing employment has been

also analyzed through a non parametric model

Yij= S(latitude; longitude) + εij

• The model highlights areas with higher manufacturing

propensity due to spatial dependency

• The inclusion of smooth terms in the covariates takes into

consideration non-linearities in the relationship between

covariates and manufacturing LQ

GAM – initial results (1/3)

16


LQij= s(ni; ei) + εij

In green:

communities where

geographical conditions

favour manufacturing

agglomeration

GAM Model (2/3)

17


LQij= f(employment rate; active population) +f(ei; ni) + εij

GAM Model (3/3)

18

By adding socio-

economic covariates in

addition to spatial

variable emerge other

factors correlated to

manufacturing propensity

of communities.

Preliminary results show

a slightly more

geopardized map of

manufacturing activities


Conclusions

19

• Results show the presence of a core of LMAs identified as

IDs and including communities detected by agglomeration

patterns applying Local Moran's I

• The analysis will be further developed by anlysing geo-sector

patterns (GAM, SAR) in relation to economic variables in

order to analyse relationship between industry structure and

agglomeration forces as derived from economic literature

Thank you for your attention!


References

Alampi D., Conti L., Iuzzolino G., Mele D. 2013. Le agglomerazioni industriali

italiane nel confronto internazionale. In Omiccioli M. (a cura di), 2013. I sistemi

produttivi locali. Trasformazioni fra globalizzazione e crisi. Roma: Carocci editore

Anselin, L. 1995. Local indicators of spatial association, Geographical Analysis, 27,

93–115.

Canello J., Pavone P. 2015. Mapping the Multifaceted Patterns of Industrial

Districts: A New Empirical Procedure with Application to Italian Data. Regional

Studies, DOI: 10.1080/00343404.2015.1011611

Istat, 2015. I distretti Industriali. Anno 2011. Statistiche report. Roma. 24.02.2015.

Iuzzolino G. 2004. Costruzione di un algoritmo di identificazione delle

agglomerazioni territoriali di attività manifatturiere. In: Economie locali, modelli di

agglomerazione e apertura internazionale. Roma: Banca d’Italia, Atti di convegni.

Lombardi S., Lorenzini F. 2017 (forthcoming). Mapping industrial districts: a

methodological review. Italian Journal of Regional Studies.

Moran P.A.P. 1950. Notes on continuous stochastic phenomena. Biometrika, 37:17


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