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Appendix A: Data Availability and Description

of the Variables

The lack of statistics for the EU regions, as pointed out by almost all the scholars in

this research field, seriously constrains the possibility of acquiring a deeper under-

standing of socio-economic dynamics. Even if over the last decade remarkable

improvement have been recorded in response to increasing political and academic

attention on regions, the situation is still critical on at least three counts. First, most

statistics only go back a few years thus impeding long-term analyses. A second

issue involves the spatial coverage of existing data as many variables are not

available for some regions or countries (e.g., Swedish R&D expenditure data are

not collected at the regional level). Thirdly the number of variables collected at

regional level is very limited thus making the analysis of broader socio-economic

processes often impossible. Moreover, the statistical picture of the EU regions has

become even more fragmented following the accession of the New Member states

on May 1 2004. For the new ten members of the Union very few data are available

and, in the large majority of the cases, only from 1995 onwards.

When the theoretical concepts outlined in the book have to be empirically tested,

the selection of an appropriate scale of analysis, under the constraint of data

availability, becomes crucial. In the context of our analysis the focus is upon the

“institutionally defined region”, the sub-national level which maximises the level of

internal coherence in terms of socio-institutional features while being associated

with a meaningful political decision-taking level.

By coherently applying such criteria to the EU-25 regions, in our empirical

analyses, we will focus upon NUTS1 regions for Germany, Belgium and the UK

and NUTS2 for all other countries (Spain, France, Italy, the Netherlands, Greece,

Austria, Portugal, Finland, Czech Republic, Hungary, Poland, Slovakia). Countries

without a relevant regional articulation (Cyprus, Denmark, Estonia, Ireland, Latvia,

Lithuania, Luxemburg, Malta and Slovenia) were necessarily excluded from the

analysis.1 Overall nine countries out of 25 are excluded from the analysis, as

1As far as specific regions are concerned, no data are available for the French Departmentsd’Outre-Mer (Fr9). Uusimaa (Fi16) and Etela-Suomi (Fi17) were excluded from the analysis

due to the lack of data on socio-economic variables. Etela-Suomi (Fi17) and Trentino-Alto Adige

(IT31) were excluded from the analysis as they have no correspondent in the NUTS2003 classifi-

cation, thus preventing us from matching data available only in the new NUTS classification.

195

inevitably happens in all empirical studies focusing upon EU regions that apply the

same methodology. However, even if such a limitation should be explicitly

acknowledged, we do not believe this affects the generality of our results, as the

countries included in the analysis cover 95.6% of the total EU population, 95.8% of

total GDP, and 96.9% of total R&D expenditure in the EU (1999 Eurostat data).

In our EU analysis EUROSTAT data (stored in the REGIO databank on which we

largely relied for our empirical analysis) have been complemented with Cambridge

Econometrics (CAMECON) data for GDP. Table A.1 provides a detailed definition

of the variables included in the analysis.

A final consideration concerns the distortions produced by the use of NUTS2

regions as a unit of analysis. As pointed out by Cheshire and Magrini (2000), NUTS

regions may bias regression analyses as their boundaries are often arbitrary and non

homogeneous. This bias should be effectively addressed, as suggested by these

authors, by focusing the analysis on F.U.R. (Functional Urban Regions)3 rather than

on NUTS thereby capturing the functional structure of the regions. Unfortunately,

the lack of available data for many of the relevant explanatory variables – a priori –

has prevented us from considering functional regions in our analysis.

Table A.1 Description of the variables, European Union

Variable Definition

Dependent VariablesDependent variable Annual growth rate of regional GDP (For the EU-15 1990–2004; for the

EU-25 1995–2004).

Dependent variable

(Chap. 6)

Annual growth rate of regional patents applications

InnovationR&D Expenditure on R&D (all sectors) as a % of GDP

Social FilterLife-Long Learning Rate of involvement in Life-long learning – % of Adults (25–64 years)

involved in education and training

Education Employed

People

% of employed persons with tertiary education (levels 5–6 ISCED 1997)

Education Population % of total population with tertiary education (levels 5–6 ISCED 1997)

Agricultural Labour

Force

Agricultural employment as % of total employment

(continued)

Islands (PT2 Acores, PT3 Madeira, FR9 Departments d’Outre-Mer, ES7 Canarias) and Ceuta y

Melilla (ES 63) were excluded from the analysis as time-distance information, necessary for the

computation of spatially lagged variables, is not available.2The Nomenclature of Territorial Units for Statistics (NUTS) was established by Eurostat more

than 25 years ago in order to provide a single uniform breakdown of territorial units for the

production of regional statistics for the European Union and the definition of these regions mainly

served administrative purposes.3The concept of Functional Urban Regions (FURs) have been defined by the literature in order to

minimise the bias introduced by commuting patterns. A FUR thus includes a core city, where

employment is concentrated, and its hinterland, from which people commute to the centre. For a

detailed analysis of this concept see Cheshire and Hay (1989).

196 Appendix A: Data Availability and Description of the Variables?

Table A.1 (continued)

Variable Definition

Long Term

Unemployment

Long term unemployed as % of total unemployment

Young People People aged 15–24 as % of total population

Social Filter Index The index combines, by means of Principal Component Analysis, the

variables describing the socio-economic conditions of the region

(listed above)

Structure of the local economyMigration rate Net migration was calculated from population change plus deaths minus

births and then standardised by the average population thus

obtaining the net migration rate

Population density Calculated as Average Population (units) in the base year/Surface of the

region (Sq Km)

% regional of national

GDP

Total regional GDP as a percentage of national GDP

Krugman index of

specialisation

The index is calculated as discussed in the text on the basis Regional

employment data classified according to the “Classification of

economic activities – NACE Rev. 1.1 A17” branches

Transport Infrastructure (Chap. 7)Motorways4 (Inhab.)5 Kms of motorways per thousand inhabitants

Motorways (GDP) Kms of motorways per million EUR of GDP

Motorways (Region

area)

Kms of motorways per square-kilometre

D Motorways (Inhab.) Annual change in Kms of motorways per thousand inhabitants

D Motorways (GDP) Annual change in Kms of motorways per million EUR of GDP

D Motorways (Reg.

Area)

Annual change in Kms of motorways per square-kilometre

Other Control VariablesLog of GDPpc Natural logarithm of regional GDP per capita at time t

National growth Annual growth rate of national GDP (for the EU-15 1990–2004; for the

EU-25 1995–2004).

4Definition of Motorway (Eurostat Regio Guide Book 2006): “Road, specially designed and built

for motor traffic, which does not serve properties bordering on it, and which: is provided, except at

special points or temporarily, with separate carriageways for the two directions of traffic, separated

from each other, either by a dividing strip intended for traffic, or exceptionally by other means;

does not cross at level with any road, railway or tramway track, or footpath; is specially sign-

posted as a motorway and is reserved for specific categories of road motor vehicles. Entry and exit

lanes of motorways are included irrespectively of the location of the sign-posts. Urban motorways

are always included.”5Italy: missing data for all regions after the year 2000. Missing have been replaced by means of

comparable ISTAT dataGreece: data are missing from 1996. Greece has been excluded from the

analysisPoland: data are missing in the Eurostat databank for some regions without any explana-

tory note. Data are also missing from the Polish National Statistical Institute databank. By

inspecting a map of motorways in Poland (2004) the Kms of motorways in these regions appears

to be zero.Portugal: missing data for Centro, Lisboa and Alentejo from 1990 to 2002Regional

surface in 2003 has been used to calculate the density of transport infrastructure to avoid

Appendix A: Data Availability and Description of the Variables 197

The US analysis is based upon 266 MSA/CMSAs6 covering all continental US

States (and the District of Columbia), while MSAs in Alaska, Hawaii, or in other

non mainland territories of the US are excluded from the analysis. The lack of sub-

state level data for R&D expenditure was addressed by relying upon Standard &

Poor’s Compustat7 North American firm-level data which provide a proxy for

private R&D expenditure in 145 MSAs out of the total of 266. The proxy was

calculated by summing up firms’ R&D expenditure in each MSA. Though rough,

this is the only measure available and similar proxies have been commonly used

in the literature on the MSA innovative activities (e.g., Feldman 1994). All

other US variables are based on US-Census data included in the USA Counties

1998 CD-Rom

Table A.2 Description of the variables, United States (Chap. 6)

Variable Definition

InnovationR&D Private expenditure on R&D as a % of GDP was calculated

from Standard & Poor’s Compustat North America firm-

level data

Social FilterEducation: bachelor’s, graduate or

professional degrees

Persons 25 years and over – some college or associate degree

as a percentage of total population

Education: some college level

education

Persons 25 years and over – bachelor’s, graduate, or

professional degree as a percentage of total population

Agricultural Labour Force Agricultural employment as % of total employment

Unemployment Rate Rate of unemployment

Young People People aged 15–24 as % of total population

Structure of the local economyDomestic migration Rate of net domestic migration

Population density Calculated as Average Population (units) in the base year/

Surface of the region (Sq Km)

% regional of national GDP Total regional GDP as a percentage of national GDP

Krugman index of specialisation The index is calculated on the basis of the 13 major industry

groups reported by 1990 census classification and

developed from the 1987 Standard Industrial

Classification (SIC) Manual.

generating noise in the density variable due to changes in the calculation of the regional surface.

Regional GDP and average population in 1990 and 1995 have been used to standardize the

variables included in the EU-15 and EU-25 regressions respectively.6The MSA/CMSA list is based on Metropolitan Areas and Components, 1993, with FIPS Codes,published by the Office of Management and Budget (1993).7Standard & Poor’s Compustat North America is a database of financial, statistical, and market

information covering publicly traded companies in the U.S. and Canada. It provides more than 340

annual and 120 quarterly income statements, balance sheets, flows of funds, and supplemental data

items on more than 10,000 active and 9,700 inactive companies.

198 Appendix A: Data Availability and Description of the Variables

Appendix B: The Weight Matrix and the

Moran’s I

The Moran’s I is calculated on the basis of the following formula:

I ¼

Pn

i¼1

Pn

j¼1

ðxi � �xÞwijðxj � �xÞPn

i¼1

ðxi � �xÞ

where wij is a sequence of normalised weights that relate observation i to all the

other observations j in the data. Values of I larger (smaller) than the expected value

E(I) ¼ �1/(n�1) signal the presence of positive (negative) spatial autocorrelation.

In our empirical application the element wij of the matrix of the normalised

weights is:

wij ¼1dijP

j

1dij

where dij is the average trip-length (in minutes) between region i and j calculated bythe IRPUD (2000) for the computation of the Peripherality Indicators and made

available by the European Commission.

199

Appendix C: Technicalities of the Principal

Component Analysis and Results for the EU

and the US

The principal component analysis (PCA) is “a statistical technique that linearly

transforms an original set of variables into a substantially smaller set of uncorre-

lated variables that represents most of the information in the original set of vari-

ables: (. . .) a smaller set of uncorrelated variables is much easier to understand

and use in further analysis than a larger set of correlated variables” (Duntenam

1989 p. 9). Through the PCA the original variables (in the case of our analysis

the variables shown in literature as representative of the socio-economic disadvan-

tage of the EU regions) are linearly combined by means of a set of “weights”

(a1, a2, . . ., ak) calculated in order to maximise (under the constraint of that the sum

of the squared weights is equal to one) the variability of the resulting indicator, i.e.,

of the principal component (our Social Factors variable).

Consequently the i-th principal component is:

yi¼ ai1x1 þ ai2x2 þ � � � þaipxp

where (ai1, ai2 aip) are the wights and x1, x2, . . . ,xk are the k variables.

It is possible to calculate as many PCs as the original variables under the

constraint of non-correlation with the previous ones. Anyway the PCs are able to

account for a progressively decreasing amount of the total variance of the original

variables. Consequently, the procedure allow us to concentrate our attention on the

first and limited number of PCs, which are the most representative of the phenome-

non under analysis.

Table C.1 shows the Eigenanalysis of the Correlation Matrix. The first PC alone

accounts for around 43% of the total variance with an Eigenvalue significantly

larger than 1, the second PC accounts for an additional 22% of the total variability

with an Eigenvalue still larger than 1. The first two principal components therefore

explain a significant part of total variability (65%).

Table C.1 EU regions: Eigenanalysis of the Correlation Matrix

Eigenvalue 2.566 1.3311 0.8847 0.6542 0.5381 0.0259

Proportion 0.428 0.222 0.147 0.109 0.09 0.004

Cumulative 0.428 0.65 0.797 0.906 0.996 1

201

The coefficients of the first PC (Table B.2) assigns a large weight to the

educational achievements of the population (0.576) and the labour force (0.551)

and to the participation in Life Long Learning Programmes (0.383). A negative

weight is, as expected, assigned to the agricultural labour force (�0.446) and, with

a smaller coefficient, long-term unemployment (�0.139). The weight of the young

population (0.006) is much smaller but positive. This first principal component

provides us with the “joint measure” for each region’s socio-economic conditions.

Consequently, the first principal component’s scores are computed from the stan-

dardised8 value of the original variables by using the coefficients listed under PC1

in Table C.2.

Table C.2 EU regions: principal components’s coefficients

Variables PC1 PC2 PC3

Education population 0.576 �0.218 �0.043

Education labour force 0.551 �0.318 0.05

Life-long learning 0.383 0.326 0.355

Agricultural labour force �0.446 �0.227 0.068

Long term unemployment �0.139 �0.505 0.802

Young people 0.006 0.662 0.471

Table C.3 US MSAs : Eigenanalysis of the correlation matrix

US

Eigenvalue 1.6979 1.0514 1.0306 0.9499 0.2702

Proportion 0.34 0.21 0.206 0.19 0.054

Cumulative 0.34 0.55 0.756 0.946 1

Table C.4 US MSAs: principal components’ coefficients

Variable PC1 PC2

USPeople with any college level degree 0.413 0.491

People with bachelor degree 0.682 �0.105

Rate of unemployment �0.203 0.856

Agricultural labour force 0.174 0.119

Young people 0.542 0.04

8Standardised in order to range from 0 to 1.

202 Appendix C: Technicalities of the Principal Component Analysis and Results

Appendix D: List of the Regions Included

in the Analysis

Table D.1 EU NUTS Regions

Country NUTS code Name

AT AT11 Burgenland

AT AT12 Niederosterreich

AT AT13 Wien

AT AT21 Karnten

AT AT22 Steiermark

AT AT31 Oberosterreich

AT AT32 Salzburg

AT AT33 Tirol

AT AT34 Vorarlberg

BE BE1 Bruxelles-Brussel

BE BE2 Vlaams Gewest

BE BE3 Region Walonne

CZ CZ01 Praha

CZ CZ02 Stredni Cechy

CZ CZ03 Jihozapad

CZ CZ04 Severozapad

CZ CZ05 Severovychod

CZ CZ06 Jihovychod

CZ CZ07 Stredni Morava

CZ CZ08 Ostravsko

DE DE1 Baden-Wurttemberg

DE DE2 Bayern

DE DE3 Berlin

DE DE4 Brandenburg

DE DE5 Bremen

DE DE6 Hamburg

DE DE7 Hessen

DE DE8 Mecklenburg-Vorpomm.

DE DE9 Niedersachsen

DE DEA Nordrhein-Westfalen

DE DEB Rheinland-Pfalz

DE DEC Saarland

DE DED Sachsen

DE DEE Sachsen-Anhalt

DE DEF Schleswig-Holstein

DE DEG Thuringen

ES ES11 Galicia

(continued)

203

Table D.1 (continued)

Country NUTS code Name

ES ES12 Asturias

ES ES13 Cantabria

ES ES21 Pais Vasco

ES ES22 Navarra

ES ES23 Rioja

ES ES24 Aragon

ES ES3 Madrid

ES ES41 Castilla-Leon

ES ES42 Castilla-la Mancha

ES ES43 Extremadura

ES ES51 Cataluna

ES ES52 Com. Valenciana

ES ES53 Baleares

ES ES61 Andalucia

ES ES62 Murcia

FI FI13 Ita-Suomi

FI FI14 Vali-Suomi

FI FI15 Pohjois-Suomi

FI FI2 Aland

FR FR1 Ile de France

FR FR21 Champagne-Ard.

FR FR22 Picardie

FR FR23 Haute-Normandie

FR FR24 Centre

FR FR25 Basse-Normandie

FR FR26 Bourgogne

FR FR3 Nord-Pas de Calais

FR FR41 Lorraine

FR FR42 Alsace

FR FR43 Franche-Comte

FR FR51 Pays de la Loire

FR FR52 Bretagne

FR FR53 Poitou-Charentes

FR FR61 Aquitaine

FR FR62 Midi-Pyrenees

FR FR63 Limousin

FR FR71 Rhone-Alpes

FR FR72 Auvergne

FR FR81 Languedoc-Rouss

FR FR82 Prov-Alpes-Cote d’Azur

FR FR83 Corse

GR GR11 Anatoliki Makedonia

GR GR12 Kentriki Makedonia

GR GR13 Dytiki Makedonia

GR GR14 Thessalia

GR GR21 Ipeiros

GR GR22 Ionia Nisia

GR GR23 Dytiki Ellada

GR GR24 Sterea Ellada

GR GR25 Peloponnisos

GR GR3 Attiki

(continued)

204 Appendix D: List of the Regions Included in the Analysis

Table D.1 (continued)

Country NUTS code Name

GR GR41 Voreio Aigaio

GR GR42 Notio Aigaio

GR GR43 Kriti

HU HU01 Kozep-Magyarorszag

HU HU02 Kozep-Dunantul

HU HU03 Nyugat-Dunantul

HU HU04 Del-Dunantul

HU HU05 Eszak-Magyarorszag

HU HU06 Eszak-Alfold

HU HU07 Del-Alfold

IT IT11 Piemonte

IT IT12 Valle d’Aosta

IT IT13 Liguria

IT IT2 Lombardia

IT IT32 Veneto

IT IT33 Fr.-Venezia Giulia

IT IT4 Emilia-Romagna

IT IT51 Toscana

IT IT52 Umbria

IT IT53 Marche

IT IT60 Lazio

IT IT71 Abruzzo

IT IT72 Molise

IT IT8 Campania

IT IT91 Puglia

IT IT92 Basilicata

IT IT93 Calabria

IT ITA Sicilia

IT ITB Sardegna

NL NL11 Groningen

NL NL12 Friesland

NL NL13 Drenthe

NL NL21 Overijssel

NL NL22 Gelderland

NL NL23 Flevoland

NL NL31 Utrecht

NL NL32 Noord-Holland

NL NL33 Zuid-Holland

NL NL34 Zeeland

NL NL41 Noord-Brabant

NL NL42 Limburg

PL PL01 Dolnoslaskie

PL PL02 Kujawsko-Pomorskie

PL PL03 Lubelskie

PL PL04 Lubuskie

PL PL05 Lodzkie

PL PL06 Malopolskie

PL PL07 Mazowieckie

PL PL08 Opolskie

PL PL09 Podkarpackie

PL PL0A Podlaskie

(continued)

Appendix D: List of the Regions Included in the Analysis 205

Table D.1 (continued)

Country NUTS code Name

PL PL0B Pomorskie

PL PL0C Slaskie

PL PL0D Swietokrzyskie

PL PL0E Warminsko-Mazurskie

PL PL0F Wielkopolskie

PL PL0G Zachodniopomorskie

PT PT11 Norte

PT PT12 Centro

PT PT13 Lisboa e V.do Tejo

PT PT14 Alentejo

PT PT15 Algarve

SK SK01 Bratislavsky

SK SK02 Zapadne Slovensko

SK SK03 Stredne Slovensko

SK SK04 Vychodne Slovensko

UK UKC North East

UK UKD North West

UK UKE Yorkshire and the Humber

UK UKF East Midlands

UK UKG West Midlands

UK UKH Eastern

UK UKI London

UK UKJ South East

UK UKK South West

UK UKL Wales

UK UKM Scotland

UK UKN Northern Ireland

Table D.2 US MSAs

Code MSA Name Code MSA Name

40 Abilene, TX MSA 4080 Laredo, TX MSA

120 Albany, GA MSA 4100 Las Cruces, NM MSA

160 Albany-Schenectady-Troy,

NY MSA

4120 Las Vegas, NV-AZ MSA

200 Albuquerque, NM MSA 4150 Lawrence, KS MSA

220 Alexandria, LA MSA 4200 Lawton, OK MSA

240 Allentown-Bethlehem-Easton,

PA MSA

4240 Lewiston-Auburn, ME MSA

280 Altoona, PA MSA 4280 Lexington, KY MSA

320 Amarillo, TX MSA 4320 Lima, OH MSA

450 Anniston, AL MSA 4360 Lincoln, NE MSA

460 Appleton-Oshkosh-Neenah,

WI MSA

4400 Little Rock-North Little Rock, AR MSA

480 Asheville, NC MSA 4420 Longview-Marshall, TX MSA

500 Athens, GA MSA 4472 Los Angeles-Riverside-Orange County, CA

CMSA

520 Atlanta, GA MSA 4520 Louisville, KY-IN MSA

(continued)

206 Appendix D: List of the Regions Included in the Analysis

Table D.2 (continued)

Code MSA Name Code MSA Name

600 Augusta-Aiken, GA-SC MSA 4600 Lubbock, TX MSA

640 Austin-San Marcos, TX MSA 4640 Lynchburg, VA MSA

680 Bakersfield, CA MSA 4680 Macon, GA MSA

730 Bangor, ME MSA 4720 Madison, WI MSA

740 Barnstable-Yarmouth, MA MSA 4800 Mansfield, OH MSA

760 Baton Rouge, LA MSA 4880 McAllen-Edinburg-Mission, TX MSA

840 Beaumont-Port Arthur, TX MSA 4890 Medford-Ashland, OR MSA

860 Bellingham, WA MSA 4900 Melbourne-Titusville-Palm Bay, FL MSA

870 Benton Harbor, MI MSA 4920 Memphis, TN-AR-MS MSA

880 Billings, MT MSA 4940 Merced, CA MSA

920 Biloxi-Gulfport-Pascagoula, MS

MSA

4992 Miami-Fort Lauderdale, FL CMSA

960 Binghamton, NY MSA 5082 Milwaukee-Racine, WI CMSA

1000 Birmingham, AL MSA 5120 Minneapolis-St. Paul, MN-WI MSA

1010 Bismarck, ND MSA 5160 Mobile, AL MSA

1020 Bloomington, IN MSA 5170 Modesto, CA MSA

1040 Bloomington-Normal, IL MSA 5200 Monroe, LA MSA

1080 Boise City, ID MSA 5240 Montgomery, AL MSA

1122 Boston-Worcester-Lawrence,

MA-NH-ME-CT CMSA

5280 Muncie, IN MSA

1240 Brownsville-Harlingen-San Benito,

TX MSA

5330 Myrtle Beach, SC MSA

1260 Bryan-College Station, TX MSA 5345 Naples, FL MSA

1280 Buffalo-Niagara Falls, NY MSA 5360 Nashville, TN MSA

1305 Burlington, VT MSA 5520 New London-Norwich, CT-RI MSA

1320 Canton-Massillon, OH MSA 5560 New Orleans, LA MSA

1350 Casper, WY MSA 5602 New York-Northern New Jersey-Long Island,

NY-NJ-CT-PA CMSA

1360 Cedar Rapids, IA MSA 5720 Norfolk-Virginia Beach-Newport News,

VA-NC MSA

1400 Champaign-Urbana, IL MSA 5790 Ocala, FL MSA

1440 Charleston-North Charleston,

SC MSA

5800 Odessa-Midland, TX MSA

1480 Charleston, WV MSA 5880 Oklahoma City, OK MSA

1520 Charlotte-Gastonia-Rock Hill,

NC-SC MSA

5920 Omaha, NE-IA MSA

1540 Charlottesville, VA MSA 5960 Orlando, FL MSA

1560 Chattanooga, TN-GA MSA 5990 Owensboro, KY MSA

1580 Cheyenne, WY MSA 6015 Panama City, FL MSA

1602 Chicago-Gary-Kenosha, IL-IN-WI

CMSA

6020 Parkersburg-Marietta, WV-OH MSA

1620 Chico-Paradise, CA MSA 6080 Pensacola, FL MSA

1642 Cincinnati-Hamilton, OH-KY-IN

CMSA

6120 Peoria-Pekin, IL MSA

1660 Clarksville-Hopkinsville, TN-KY

MSA

6162 Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD CMSA

1692 Cleveland-Akron, OH CMSA 6200 Phoenix-Mesa, AZ MSA

1720 Colorado Springs, CO MSA 6240 Pine Bluff, AR MSA

1740 Columbia, MO MSA 6280 Pittsburgh, PA MSA

(continued)

Appendix D: List of the Regions Included in the Analysis 207

Table D.2 (continued)

Code MSA Name Code MSA Name

1760 Columbia, SC MSA 6320 Pittsfield, MA MSA

1800 Columbus, GA-AL MSA 6400 Portland, ME MSA

1840 Columbus, OH MSA 6442 Portland-Salem, OR-WA CMSA

1880 Corpus Christi, TX MSA 6480 Providence-Fall River-Warwick, RI-MA

MSA

1900 Cumberland, MD-WV MSA 6520 Provo-Orem, UT MSA

1922 Dallas-Fort Worth, TX CMSA 6560 Pueblo, CO MSA

1950 Danville, VA MSA 6580 Punta Gorda, FL MSA

1960 Davenport-Moline-Rock Island, IA-

IL MSA

6640 Raleigh-Durham-Chapel Hill, NC MSA

2000 Dayton-Springfield, OH MSA 6660 Rapid City, SD MSA

2020 Daytona Beach, FL MSA 6680 Reading, PA MSA

2030 Decatur, AL MSA 6690 Redding, CA MSA

2040 Decatur, IL MSA 6720 Reno, NV MSA

2082 Denver-Boulder-Greeley, CO

CMSA

6740 Richland-Kennewick-Pasco, WA MSA

2120 Des Moines, IA MSA 6760 Richmond-Petersburg, VA MSA

2162 Detroit-Ann Arbor-Flint, MI CMSA 6800 Roanoke, VA MSA

2180 Dothan, AL MSA 6820 Rochester, MN MSA

2190 Dover, DE MSA 6840 Rochester, NY MSA

2200 Dubuque, IA MSA 6880 Rockford, IL MSA

2240 Duluth-Superior, MN-WI MSA 6895 Rocky Mount, NC MSA

2290 Eau Claire, WI MSA 6922 Sacramento-Yolo, CA CMSA

2320 El Paso, TX MSA 6960 Saginaw-Bay City-Midland, MI MSA

2330 Elkhart-Goshen, IN MSA 6980 St. Cloud, MN MSA

2335 Elmira, NY MSA 7000 St. Joseph, MO MSA

2340 Enid, OK MSA 7040 St. Louis, MO-IL MSA

2360 Erie, PA MSA 7120 Salinas, CA MSA

2400 Eugene-Springfield, OR MSA 7160 Salt Lake City-Ogden, UT MSA

2440 Evansville-Henderson, IN-KY

MSA

7200 San Angelo, TX MSA

2520 Fargo-Moorhead, ND-MN MSA 7240 San Antonio, TX MSA

2560 Fayetteville, NC MSA 7320 San Diego, CA MSA

2580 Fayetteville-Springdale-Rogers, AR

MSA

7362 San Francisco-Oakland-San Jose, CA CMSA

2650 Florence, AL MSA 7460 San Luis Obispo-Atascadero-Paso Robles,

CA MSA

2655 Florence, SC MSA 7480 Santa Barbara-Santa Maria-Lompoc, CA

MSA

2670 Fort Collins-Loveland, CO MSA 7490 Santa Fe, NM MSA

2700 Fort Myers-Cape Coral, FL MSA 7510 Sarasota-Bradenton, FL MSA

2710 Fort Pierce-Port St. Lucie, FL MSA 7520 Savannah, GA MSA

2720 Fort Smith, AR-OK MSA 7560 Scranton–Wilkes-Barre–Hazleton, PA MSA

2750 Fort Walton Beach, FL MSA 7602 Seattle-Tacoma-Bremerton, WA CMSA

2760 Fort Wayne, IN MSA 7610 Sharon, PA MSA

2840 Fresno, CA MSA 7620 Sheboygan, WI MSA

2880 Gadsden, AL MSA 7640 Sherman-Denison, TX MSA

2900 Gainesville, FL MSA 7680 Shreveport-Bossier City, LA MSA

2975 Glens Falls, NY MSA 7720 Sioux City, IA-NE MSA

(continued)

208 Appendix D: List of the Regions Included in the Analysis

Table D.2 (continued)

Code MSA Name Code MSA Name

2980 Goldsboro, NC MSA 7760 Sioux Falls, SD MSA

2985 Grand Forks, ND-MN MSA 7800 South Bend, IN MSA

3000 Grand Rapids-Muskegon-Holland,

MI MSA

7840 Spokane, WA MSA

3040 Great Falls, MT MSA 7880 Springfield, IL MSA

3080 Green Bay, WI MSA 7920 Springfield, MO MSA

3120 Greensboro–Winston-Salem–High

Point, NC MSA

8000 Springfield, MA MSA

3150 Greenville, NC MSA 8050 State College, PA MSA

3160 Greenville-Spartanburg-Anderson,

SC MSA

8080 Steubenville-Weirton, OH-WV MSA

3240 Harrisburg-Lebanon-Carlisle, PA

MSA

8120 Stockton-Lodi, CA MSA

3280 Hartford, CT MSA 8140 Sumter, SC MSA

3290 Hickory-Morganton, NC MSA 8160 Syracuse, NY MSA

3350 Houma, LA MSA 8240 Tallahassee, FL MSA

3362 Houston-Galveston-Brazoria, TX

CMSA

8280 Tampa-St. Petersburg-Clearwater, FL MSA

3400 Huntington-Ashland, WV-KY-OH

MSA

8320 Terre Haute, IN MSA

3440 Huntsville, AL MSA 8360 Texarkana, TX-Texarkana, AR MSA

3480 Indianapolis, IN MSA 8400 Toledo, OH MSA

3500 Iowa City, IA MSA 8440 Topeka, KS MSA

3520 Jackson, MI MSA 8520 Tucson, AZ MSA

3560 Jackson, MS MSA 8560 Tulsa, OK MSA

3580 Jackson, TN MSA 8600 Tuscaloosa, AL MSA

3600 Jacksonville, FL MSA 8640 Tyler, TX MSA

3605 Jacksonville, NC MSA 8680 Utica-Rome, NY MSA

3610 Jamestown, NY MSA 8750 Victoria, TX MSA

3620 Janesville-Beloit, WI MSA 8780 Visalia-Tulare-Porterville, CA MSA

3660 Johnson City-Kingsport-Bristol,

TN-VA MSA

8800 Waco, TX MSA

3680 Johnstown, PA MSA 8872 Washington-Baltimore, DC-MD-VA-WV

CMSA

3710 Joplin, MO MSA 8920 Waterloo-Cedar Falls, IA MSA

3720 Kalamazoo-Battle Creek, MI MSA 8940 Wausau, WI MSA

3760 Kansas City, MO-KS MSA 8960 West Palm Beach-Boca Raton, FL MSA

3810 Killeen-Temple, TX MSA 9000 Wheeling, WV-OH MSA

3840 Knoxville, TN MSA 9040 Wichita, KS MSA

3850 Kokomo, IN MSA 9080 Wichita Falls, TX MSA

3870 La Crosse, WI-MN MSA 9140 Williamsport, PA MSA

3880 Lafayette, LA MSA 9200 Wilmington, NC MSA

3920 Lafayette, IN MSA 9260 Yakima, WA MSA

3960 Lake Charles, LA MSA 9280 York, PA MSA

3980 Lakeland-Winter Haven, FL MSA 9320 Youngstown-Warren, OH MSA

4000 Lancaster, PA MSA 9340 Yuba City, CA MSA

4040 Lansing-East Lansing, MI MSA 9360 Yuma, AZ MSA

Appendix D: List of the Regions Included in the Analysis 209

Appendix E: Unit Root Tests (Chap. 7)

Table E.1 EU15: Unit root tests

IPS IPS-trend ADF ADF-trend Phillips-

Perron

Phillips-

Perron

Trend

Regional GDP per capita

(Annual Growth Rate)

�17.683*** �12.595*** 888.473*** 782.099*** 1089.491*** 807.405***

Kms of motorways per

thousand inhabitants

13.291 �1.237* 416.324*** 623.802*** 377.252*** 438.065***

Change in Kms of

motorways per

thousand inhabitants

�15.674*** �14.025*** 1145.003*** 1054.442*** 1697.867*** 1454.49***

Spat.Weigh.Ave of Kms of

motorways/thousand

inhab.

16.138 4.132 206.563 249.137 299.115*** 447.128***

Spat.Weigh.Ave of Change

in Kms of motorways

per thousand

inhabitants

�9.474*** �8.494*** 714.773*** 733.721*** 1547.743*** 1323.908***

Log of GDPpc �4.081*** �9.101*** 38.722 925.186*** 50.357 263.707*

Total intramural R&D

expenditure (all

sectors) as % of GDP

�11.139*** �4.071*** 260.287* 359.048*** 187.576 293.751***

Spat.Weigh.Ave of Total

R&D expenditure

�18.341*** �8.39*** 263.937* 379.222*** 198.743 272.432***

Social Filter Index 7.123 �3.898*** 144.34 311.765*** 115.158 328.813***

% Employed people with

Higher education,

ISCED76 Levels 5–7

5.506 �0.727 96.514 286.352*** 115.94 362.169***

Log of Total GDP (Levels) �2.716*** �8.662*** 29.039 897.83*** 65.681 266.386*

Migration Rate �2.606*** 1.042 448.617*** 258.53* 392.791*** 269.98*

Annual National Growth

Rate

�7.393*** �4.715*** 519.446*** 385.279*** 734.582*** 522.976*

*Significant at 10%; ** significant at 5%; *** significant at 1%

211

Table E.2 EU25: Unit root tests

IPS IPS-trend ADF ADF-trend Phillips-

Perron

Phillips-

Perron-Trend

Regional GDP per

capita (Annual

Growth Rate)

�10.192*** �6.75*** 749.3579*** 832.0173*** 1429.499*** 1138.108***

Kms of motorways per

thousand

inhabitants

10.319 �4.524V 1043.642*** 1032.489*** 676.3293*** 440.2294***

Change in Kms of

motorways per

thousand

inhabitants

�15.594 �10.331*** 859.8299*** 913.1505*** 1385.309*** 1062.309***

Spat.Weigh.Ave of

Kms of

motorways/

thousand inhab.

0.9 �2.842*** 550.951*** 845.0921*** 699.6489*** 608.3887***

Spat.Weigh.Ave of

Change in Kms of

motorways per

thousand

inhabitants

�10.863*** �9.132*** 975.392*** 887.1509*** 1372.871*** 1157.184***

Log of GDPpc �2.505*** �4.561*** 491.6397*** 714.3495*** 355.1378* 293.5088

Total intramural R&D

expenditure (all

sectors) as % of

GDP

�0.995 �0.131 552.1283*** 648.7965*** 809.5998V 532.2063***

Spat.Weigh.Ave of

Total R&D

expenditure

�2.667*** 1.037 615.6541*** 677.3552*** 1262.95V 771.9879***

Social Filter Index 3.395 �2.854*** 271.1387 520.4754*** 228.2082 458.8831***

% Employed people

with Higher

education,

ISCED76 Levels

5–7

0.999 �3.196*** 274.5315 462.6828*** 338.92 549.1543***

Log of Total GDP

(Levels)

�5.3*** �5.143*** 455.2618*** 780.0859*** 349.1037 330.5161

Migration Rate �0.781 1.772 474.7355*** 460.9955*** 497.2357*** 394.0539

Annual National

Growth Rate

�10.666 �3.76 470.7466 1143.498 951.6454 688.7744

*Significant at 10%; ** significant at 5%; *** significant at 1%

IPS – Im-Pesaran-Shin test for unit roots; theW[t-bar] test statistic is standard-normally distributed

under the null hypothesis of non-stationarity

ADF – Augmented Dickey-Fuller Test; combines N independent unit root tests under the null

hypothesis of non-stationarity of all series

Phillips-Perron – Combines N independent unit root tests under the null hypothesis of non-

stationarity of all series

212 Appendix E: Unit Root Tests (Chap. 7)

Appendix F: Spatial Autocorrelation Test

for the Regression Residuals (Chap. 7)

The Moran’s I test does not detect spatial autocorrelation in the residuals of all

regressions included in this book: the combination of “national” variables and

spatially lagged explanatory variables are able to capture a significant part of the

total spatial variability of the data. In all Chapters spatial autocorrelation has been

discussed together with the results of the Moran’s I test. As an additional check we

include a sample of the Moran’s I Scatter Plots computed for all equations and in

the panel data analyses (for each year t): all figures have not been included in the

book as they would take up too much space.

The weight matrix for the computation of the Moran’s I is based on the same

weighting scheme adopted for the calculation of the spatially lagged variables

included in the model (spillovers and social filter conditions of neighbouring

regions). In addition to this weighting scheme (based on distance), first order

contiguity has been also tested delivering similar results.

213

Table F.1 EU-15: Spatial autocorrelation, Moran’s I for the residuals (Eq. 8, Table 7.1a)

UFE1997

W_U

FE

1997

Moran’I = 0.0644

2

1

0

-1

-2

-3 -2 -1 0 1 2 3

UFE1999

W_U

FE

1999

Moran’I = 0.0548

2

0

-2

-4 -2 0 2 4

214 Appendix F: Spatial Autocorrelation Test for the Regression Residuals (Chap. 7)

Table F.2 EU-25: Spatial autocorrelation, Moran’s I for the residuals (Eq. 8, Table 7.1b)

UFE1999

W_U

FE

1999

Moran’I = 0.0260

2

4

0

-2

-4

-6 -4 -2 0 2 4 6

UFF2002

W_U

FF

2002

Moran’I = 0.0138

2

4

0

-2

-4

-6 -4 -2 0 2 4 6

Appendix F: Spatial Autocorrelation Test for the Regression Residuals (Chap. 7) 215