Nicholas Charron, Associate Professor

Quality of Government Institute, University

of Gothenburg

Measuring Governance

at the Sub-National

Level in the EU

Measuring ’Governance’ in EU

Lots of indicators:

1. CPI

2. WGI

3. ICRG

4. Freedom

House

5. Eurobarometer

& more…

GROUP Country WGI Score World Rank EU Rank Non-EU Equivilant

DENMARK 2.42 1 1 NEW ZEALAND

SWEDEN 2.22 3 2 NEW ZEALAND

Group 1 FINLAND 2.19 4 3 SWITZERLAND

LUXEMBOURG 2.17 5 4 CANADA

NETHERLANDS 2.17 6 5 CANADA

GERMANY 1.69 16 6 BARBADOS

BELGIUM 1.58 17 7 CHILE

Group 2 UK 1.54 19 8 JAPAN

FRANCE 1.51 20 9 JAPAN

IRELAND 1.50 22 10 JAPAN

AUSTRIA 1.44 23 11 United States

PORTUGAL 1.09 37 12 UNITED ARAB EMIRATES

SPAIN 1.06 41 13 QATAR

CYPRUS 0.96 44 14 BOTSWANA

Group 3 SLOVENIA 0.93 45 15 BOTSWANA

ESTONIA 0.91 46 16 TAIWAN, CHINA

MALTA 0.91 47 17 TAIWAN, CHINA

POLAND 0.51 61 18 COSTA RICA

HUNGARY 0.34 70 19 CUBA

Group 4 CZECH REP. 0.32 71 20 VANUATU

SLOVAK REP. 0.29 72 21 BAHRAIN

LITHUANIA 0.29 73 22 BAHRAIN

LATVIA 0.21 78 23 BRAZIL

CROATIA 0.02 87 24 SOUTH AFRICA

ITALY -0.01 91 25 JORDAN

Group 5 GREECE -0.15 94 26 GEORGIA

BULGARIA -0.17 95 27 PERU

ROMANIA -0.20 96 28 TUNISIA

What about below the country level?

• EU is a community of regions (REGIO,

structural funds, etc.)

• Regional difference in development wider

than states at times:

• If we believe that ’institutions’ explain

cross-country socio-economic differences

, then they should also explain regional

ones…

• For example..

GDP per capita, (PPP) 2012: differences

in countries & regions in EU (source:

Eurostat)

(minus Lux), EU: richest

country (AT) is about 21000

wealther than porest (BG) per

head.

Difference is 21200 euro per

capita between Bolzano/Bozen

& Campania

Gap is 23500 euro per head is

even larger between Bucharest

region and Nord Est

33200

12000

36900

15700

30700

7200

0

10,0

00

20,0

00

30,0

00

40,0

00

Eu

ro p

er

invå

na

re, P

PP

Austria Bulgaria Bolzano (IT) Campania (IT) Bucharsti (RO) Nord-Est (RO)

unemployment %, 2013 (source: Eurostat)

• Similar situation with

unemployment

• Gap between some of EU’s

lowest (Germany) , &

countries hit hardest from the

crisis – IT and HR, is LESS

then high/low regions in

Belgium, Slovakia

• Brussels has 4x greater than

Flanders, which is larger

relative distance than SE to

ES.

Slovakien Belgien

Spanien

6.4

18.5

5.0

11.3

19.216.6

33.9

5.3

8.1

12.2

17.3

26.1

01

02

03

04

0

arb

ets

losh

et %

, 2

01

3

Tysklan

d

Sve

rige

Italie

n

Kro

atien

Spa

nien

Bra

tislavs

ký kra

j

Výc

hodn

é Slove

nsko

Fland

ern

Vallon

Rég

ion

de B

ruxe

lles

Bas

kien

Extre

mad

ura

The ’European Quality of Government Index’ (EQI)

• Starting point: Almost all existing corruption/ QoG data (from the mid-1990s) at national-level

• 2010: 1st (and only) mulit-country, sub national data on QoG to date. Funded by EU Commission (REGIO)

• QoG Composite Index for 172 E.U. regions

• The study is based on a citizen-survey of respondents in EU

• 34,000 respondents in 18 countries (+/- 200 per region). ’consumers’ of QoG

• Repeat in 2013, 2017, (400, 450+ respondents per region)

• 16 QoG-focused (all translated) questions on:

– personal experiences & perceptions

– of the Quality, Corruption & Impartiality…

– …on Education, Health care, and Law Enforcement – plus elections & media

• Formal institutions themselves are not always revealing – it’s about how power is exercised..

tracking formal institutions is not always so informative….

AL

AK

AZ

AR

CA

CO

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

ME

MD

MAMI

MN

MS

MOMT

NE

NV

NH

NJNM

NY

NC

ND

OH

OK

OR

PA

RISC

SD

TN

TXUT

VT

VA

WA

WV

WI

WY

most corruption

least corruption

least corruption010

20

30

40

50

Expe

rt p

erc

ep

tion

s

0 10 20 30 40 50

Corruption Risks (State Integrity Index)

tracking formal institutions is not always so informative….

AL

AK

AZ

AR

CA

CO

CT

DE

FL

GA

HI

ID

IL

IN

IAKS

KY

LA

ME

MD

MAMI

MN

MSMO

MT

NE

NV

NH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WAWV

WI

WY

more corruption less corruption

less corruption0

10

20

30

40

50

Co

rrup

tion

Con

vic

tio

ns

0 10 20 30 40 50Corruption Risks (State Integrity Index)

but perceptions and convictions correlate much stronger….

AL

AK

AZ

AR

CA

CO

CT

DE

FL

GA

HI

ID

IL

IN

IAKS

KY

ME

MD

MAMI

MN

MSMO

MT

NE

NV

NH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WAWV

WI

WYless corruption

less corruptionmore corruption010

20

30

40

50

corr

uptio

n c

on

vic

tio

ns

0 10 20 30 40 50Expert perceptions

EQI data, 2010 & 2013

3 ’Pillars’ of EQI • 16 questions on Corruption, Impartialtiy &

quality in several service areas + elections and local media

• For ex. , for corruption, We combine perceptions and experiences

Highlight - Two types corruption of questions:

A. general perceptions questions (0-10, higher = more perceived corruption)

B. Experiences with ’petty corruption’

Example: Corruption perceptions of 3 services

“Corruption is prevalent in my area’s local

public school system”

“Corruption is prevalent in the public health

care system in my area”

“Corruption is prevalent in the police force in

my area”

Corruption experience: By sector and area

0

.05

.1.1

5.2

EU15 non-EU15

education health care

law enforcement other

Respondents with Public Service contact/ Paid a Bribe in the Last 12 Months

0

.05

.1.1

5.2

.25

.3

De

nm

ark

Sw

ede

n

Fin

lan

d

Ire

land

Ne

the

rla

nd

s

Po

rtug

al

Germ

an

y

Sp

ain

UK

Au

stri

a

Be

lgiu

m

Fra

nce

Cze

ch R

ep.

Cro

atia

Po

land

Slo

vaki

a

Se

rbia

Ita

ly

Gre

ece

Bu

lgari

a

Hu

ng

ary

Ko

sovo

Ro

ma

nia

Ukr

ain

e

country estimate 95% c.i.

Corruption experience: % of respondents paying any bribe in last 12 months

FR10

FR21FR22

FR23

FR24

FR25FR26FR30 FR41

FR42FR43

FR51

FR52FR53FR61

FR62

FR63

FR71

FR72

FR81

FR82FR83

FR91

FR92

FR93

FR94

BG31

BG32

BG33

BG34

BG41

BG42

PT11PT15PT16PT17PT18

PT20

PT30

DK01DK02DK03DK04DK05SE1

SE2SE3

BE1BE2

BE3

GR1

GR2GR3

GR4

DE1DE2

DE3

DE4DE5

DE6DE7

DE8DE9

DEA

DEBDECDEDDEEDEFDEG

ITC1

ITC2

ITC3

ITC4

ITD1ITD2

ITD3

ITD4

ITD5

ITE1

ITE2

ITE3

ITE4ITF1

ITF2

ITF3

ITF4

ITF5 ITF6ITG1ITG2

ES11

ES12 ES13ES21ES22ES23 ES24ES30 ES41

ES42

ES43ES51

ES52

ES53

ES61

ES62ES70

UKCUKDUKEUKFUKGUKH

UKI

UKJ

UKKUKLUKM

UKN

HU1

HU2

HU3

CZ01

CZ02CZ03

CZ04

CZ05

CZ06

CZ07

CZ08

SK01

SK02

SK03

SK04

RO11

RO12

RO21

RO22

RO31

RO32

RO41

RO42

AT11

AT12

AT13

AT21AT22AT31

AT32AT33

AT34

NL11NL12NL13

NL21NL22NL23NL31NL32

NL33NL34NL41NL42

PL11

PL12

PL21

PL22

PL31

PL32PL33

PL34

PL41

PL42PL43

PL51

PL52

PL61

PL62

PL63

Pearson's: 0.78

Rsq: 0.61

n = 180

010

20

30

40

% e

xp

eri

en

ce 2

013

0 10 20 30 40% experience 2010

Building the EQI 1. Aggregation

Aggregate respondents by region for each of 16 questions

• Using PCA, 3 groups (’pillars’) identified: corruption,

impartialtiy & quality – 16 indicators aggreated to 3 pillars

2. Normalization of Data

• Standardized indicators (z-distribution)

3. Weights

• Equal Weighting

(lots of sensitivity testing)

Individual level Regional level

edqual

helqual

lawqual

media Quality

elections pillar

edimpart1

16 helimpart1

survey lawimpart1 Impartialtiy Regional

questions edimpart2 pillar QoG index

helimpart2

lawimpart2

edcorr Corruption

helcorr pillar

lawcorr

otherscorr

bribe

Regional and National QoG • Combine regional data with latest national level

WGI data

• Set each country’s EQI mean to WGI average of

4 QoG measures

• Aggregate regional scores (population

weighted), around which regional scores show

within-country variation

Why?

• Regional QoG embedded in National Context

• Include countries with no NUTS 2 regions

• Can retroactively adjust when new

regions/countries added/subtracted in future

The EQI: 2010

EQI 2013

Robustness of Data • 2010: Extensive sensitivity

testing (both WGI data

and regional data),

• Alternative aggregation,

weighting, normalization

method, exluding certain

individual charactoristics

by gender, income,

education and age.

• Constructed 95%

confidence intervals

around each regional

estimate

-4

-2

0

2

EQ

I

0 50 100 150 200

Rank order of regions and countries by EQI

EQI Estimates and Margins of Error

AT11 - BurAT12 - Nie

AT13 - Wie

AT21 - Kär

AT22 - SteAT31 - Obe

AT32 - Sal

AT33 - Tir

AT34 - Vor

be1 - régi

be2 - vlaa

be3 - régi

BG31 - Sev

BG32 - Sev

BG33 - Sev

BG34 - Yug

BG41 - Yug

BG42 - Yuz

CZ01 - Pra CZ02 - StrCZ03 - Jih

CZ04 - Sev

CZ05 - SevCZ06 - Jih

CZ07 - Str

CZ08 - Mor

de1 - badede2 - baye

de3 - berlde4 - bran

de5 - bremde6 - hambde7 - hessde8 - meck

de9 - nied

dea - nord

deb - rheidec - saar

ded - sach

dee - sach

def - schl

deg - thür

DK01 - Hov

DK02 - Sjæ

DK03 - SydDK04 - MidDK05 - Nor

ES11 - Gal

ES12 - PriES13 - CanES21 - PaíES22 - Com

ES23 - La

ES24 - AraES30 - ComES41 - Cas

ES42 - Cas

ES43 - Ext

ES51 - CatES52 - Com

ES53 - IllES61 - And

ES62 - Reg

ES70 - Can

FR10 - ÎleFR21 - ChaFR22 - Pic

FR23 - Hau

FR24 - CenFR25 - Bas

FR26 - BouFR30 - Nor

FR41 - Lor

FR42 - AlsFR43 - FraFR51 - Pay

FR52 - Bre

FR53 - PoiFR61 - AquFR62 - Mid

FR63 - LimFR71 - Rhô

FR72 - Auv

FR81 - Lan

FR82 - ProFR83 - Cor

FR91 - Gua

FR92 - Mar

FR93 - Guy

FR94 - Réu

gr1 - voregr2 - kentgr3 - atti

gr4 - nisihu1 - koze

hu2 - duna

hu3 - alfoITC1 - Pie

ITC2 - Val

ITC3 - Lig

ITC4 - Lom

ITD1 - ProITD2 - Pro

ITD3 - Ven

ITD4 - Fri

ITD5 - Emi

ITE1 - TosITE2 - UmbITE3 - Mar

ITE4 - Laz

ITF1 - Abr

ITF2 - Mol

ITF3 - Cam

ITF4 - Pug

ITF5 - Bas

ITF6 - CalITG1 - Sic

ITG2 - Sar

nl11 - gronl12 - fri

nl13 - dre

nl21 - ove

nl22 - gelnl23 - flenl31 - utr

nl32 - nor

nl33 - sounl34 - zeenl41 - nornl42 - lim

PL11 - LodPL12 - Maz

PL21 - Mal

PL22 - Sla

PL31 - LubPL32 - Pod

PL33 - Swi

PL34 - Pod

PL41 - WiePL42 - Zac

PL43 - Lub

PL51 - Dol

PL52 - OpoPL61 - Kuj

PL62 - WarPL63 - PomPT11 - Nor

PT15 - Alg

PT16 - CenPT17 - Lis

PT18 - Ale

PT20 - Reg

PT30 - Reg

RO11 - Nor

RO12 - Cen

RO21 - Nor

RO22 - Sud

RO31 - Sud

RO32 - Buc

RO41 - SudRO42 - Ves

SE1 - ÖstrSE2 - SödrSE3 - Norr

SK01 - Bra

SK02 - ZapSK03 - Str

SK04 - Vyc

ukc - nortukd - nort

uke - york

ukf - eastukg - west

ukh - eastuki - lond

ukj - sout

ukk - soutukl - wale

ukm - scotukn - nort

Pearson's correlation: 0.94

Rsq: 0.88

obs: 180

-3-2

-10

12

EQ

I 2

01

3

-3 -2 -1 0 1 2EQI 2010 (adjusted)

Comparison of EQI Scores for Regions in Both Surveys

Within country variation: 2010

Group 1: High QoG

Group 2: Moderate QoG

Group 3: Low QoG

-3-2

-10

12

EQ

I S

core

DK

SE FI

NL

LU

AT

UK IE DE

FR

BE

MT

ES

PT

CY

EE SI

CZ

HU

SK

LV

GR LT

PL IT

BG

RO

EQI Region score Country Score (WGI)

EQI: National Averages and Regional Variation

gr. 1 gr. 2 gr. 3 gr. 4 gr. 5

-3-2

-10

12

3

EQ

I 2

01

3

DK FI

SE

NL

LU

AT

DE

BE

UK IE FR

CY

MT

ES

EE

PT SI

CZ

PL

SK

HU LT

LV IT

GR

HR

TR

BG

RO

RS

EQI (regional estimate) EQI (country estimate)

in rank order and separated by cluster groupings

EQI 2013: National Averages and Regional Variation

Example: 3 countries

NLNL11NL12

NL13

NL21

NL22NL23

NL31

NL32

NL33NL34NL41

NL42

FRFR10

FR21FR22FR23

FR24FR25

FR26

FR30

FR41

FR42FR43

FR51

FR52

FR53FR61FR62

FR63FR71

FR72

FR81

FR82FR83

FR91

FR92

FR93

FR94

Vlaams Gewest (BE2)

Wallonie (BE3)

Brussels (BE1)

BE

-10

12

0 20 40 60 80 100 120 140 160 180EQI rank

95% c.i. NL estimates

BE estimates FR estimates

EQI in NL, BE and FR and Regional Variation

Variation in Italian regions

Validity of citizen perceptions?

Some Pairwise correlations

EQI2013 EQI2010

Variable Pearson's p-value Pearson's p-value

PPP p.c. (log, 07-09) 0.68 0.000 0.69 0.000

Economic Inequality -0.48 0.000 -0.44 0.000

Gender Inequality -0.45 0.000 -0.47 0.000

Social Trust (2008) 0.60 0.000 0.56 0.000

Social Trust (2013) 0.48 0.000 0.50 0.000

Education 0.51 0.000 0.50 0.000

Health (infant mortality) -0.59 0.000 -0.62 0.000

Unemployment (25-64) -0.33 0.000 -0.31 0.000

Unemployment (long term) -0.31 0.000 -0.34 0.000

Population Density -0.04 0.500 -0.06 0.440

Outside expert assesments vs. Citizen experiences & perceptions (2013 survey)

Spearman rank

correlations w/ citizen

perceptions:

Experience: 0.83

CPI: 0.88

WGI: 0.87

ICRG: 0.82 242322212019181716151413121110987654321

DK FI

IE NL

UK

SE

DE

AT

PL

TR

BE

ES

FR IT

HU

CZ

PT

BG

RO

GR

SK

HR

RS

UA

Citizen Percep. CPI

WGI ICRG

Citizen Exp.

Split sample comparison • Common critique: not everyone experiences

corruption 1st hand, so why are their perceptions

valid?

• Let’s seperate the percpetions of corruption

between two groups

1. Those respondents who have paid a bribe in the

last 12 months

2. Those who have not

Are the perceptions similar? Do they produce

similar rank orders?

Regional level (weighted by # of ‘experience cases’), 2010

FR10

FR21FR22

FR23

FR24FR25

FR26FR30FR41

FR42 FR43FR51

FR52FR53FR61

FR62

FR63FR71

FR72 FR81

FR82FR83

FR91 FR92FR93

FR94

DE1DE3

DE4DE5DE6

DE7

DE8DE9

DEA

DEB

DECDED

DEE

DEF

DEGITC1

ITC2

ITC3

ITC4

ITD1 ITD2

ITD3ITD4

ITD5

ITE1

ITE2

ITE3ITE4

ITF1ITF2

ITF3

ITF4ITF5

ITF6

ITG1

ITG2

ES11

ES12ES13

ES21

ES22

ES23 ES24 ES30

ES41

ES42ES43

ES51ES52

ES53

ES61ES62

ES70UKCUKDUKE

UKFUKGUKH

UKI

UKJ

UKKUKL

UKMUKN

HU1

HU2HU3

CZ01

CZ02CZ03

CZ04

CZ05

CZ06CZ07

CZ08

SK01

SK02

SK03SK04

PT11

PT15

PT16

PT17

PT20RO11

RO12

RO21RO22

RO31

RO32

RO41

RO42

SE2

DK01

DK02DK04

BE1

BE2

BE3

AT11AT12

AT13AT21

AT22AT31AT32

AT33AT34

NL2

NL3

NL4

PL11

PL12

PL21

PL22

PL31PL32PL33

PL34PL41PL42

PL43

PL51

PL52

PL61

PL62PL63

BG31

BG32

BG33

BG34

BG41

BG42

GR1

GR2

GR3

GR4

Rsq. 0.50

Obs: 164

12

34

56

co

rrup

tion

perc

eptio

ns (

no

expe

rie

nce

)

0 2 4 6 8 10corruption perceptions (with experience)

Aggregated responses: with vs. without experience

Corruption Perceptions in European Regions: 2010

Regional level (weighted by # of

‘experience cases’), 2013

FR10FR21 FR22

FR23FR24

FR25FR26

FR30

FR41FR42FR43FR51FR52FR53FR61FR62FR63FR71FR72

FR81

FR82FR83

FR91FR92FR93FR94

BG31

BG32

BG33

BG34

BG41

BG42

PT11

PT15

PT16 PT17

PT18

PT20

PT30

DK01 DK05

SE1SE2 SE3

BE1BE2

BE3

HR03HR04

GR1GR2GR3GR4

DE1DE2 DE3 DE4DE5DE6DE7DE8DE9 DEADEB DECDED

DEE

DEFDEG

ITC1

ITC2

ITC3ITC4

ITD1ITD2

ITD3

ITD4

ITD5ITE1ITE2ITE3

ITE4

ITF1

ITF2

ITF3ITF4

ITF5

ITF6ITG1

ITG2 ES11ES12

ES13 ES21ES22ES23

ES24ES30ES41 ES42ES43

ES51 ES52ES53ES61

ES62

ES70

UKCUKD UKEUKFUKG

UKH UKI UKJUKKUKLUKM UKN

HU1HU2HU3

CZ01CZ02

CZ03

CZ04

CZ05CZ06CZ07

CZ08

SK01SK02SK03SK04

RO11

RO12

RO21RO22RO31

RO32RO41RO42

AT11AT12

AT13

AT21AT22AT31

AT32 AT33

AT34

NL11 NL12NL13NL21

NL22NL23NL31NL32NL33NL34NL41NL42

PL11PL12PL21PL22

PL31PL32PL33PL34

PL41PL42PL43

PL51

PL52PL61PL62PL63

FI13 FI18 FI19FI1A

FI20

IE01IE02

TR1

TR2

TR3

TR4

TR5

TR6TR7

TR8 TR9

TRA

TRB

TRC

RS11RS21

RS22RS22

RS23

Kharkov

Zakarpatt

Odessa

CrimeaKievLviv

Rsq: 0.62

obs: 209

02

46

8

perc

eptio

ns o

f th

ose w

ith

ou

t corr

uptio

n e

xp

.

0 2 4 6 8 10perceptions of those with corruption exp.

Aggregated responses: samples with vs. without corruption experience

Perceptions of Corruption in European Regions

France

BulgariaPortugal

Denmark

Sweden

Belgium

CroatiaGreece

Germany

Italy Spain

UK

Hungary

Czech Rep.

Slovakia

Romania

Austria

Netherlands

Poland

Finland

Ireland

Turkey

Serbia

Ukraine

Kosovo

Rsq. 0.64

obs: 25

23

45

6

perc

eptio

ns in

agg

reg

ate

d s

am

ple

with

no e

xp

eri

en

ce

4 5 6 7 8 9

perceptions in aggregated sample with experience

aggregated samples with and without corruption experience

Citizen Corruption Perceptions in 25 European Countries

Comparing our perceptions

measure with objective measure:

country & regional level

AT11

AT12

AT13

AT21AT22

AT31AT32AT33

AT34

BE1BE2

BE3

BG31

BG32

BG33

BG34

BG41

BG42

CZ01

CZ02CZ03

CZ04

CZ05CZ06CZ07

CZ08

DE1DE2

DE3DE4DE5DE6 DE7

DE8DE9DEA

DEBDECDED

DEE

DEF

DEG

DK01DK02DK03DK04 DK05

ES11

ES12

ES13ES22

ES23

ES24 ES30ES41ES42

ES43

ES51ES52

ES53

ES61

ES62

ES70

FI13FI18 FI19FI1A

FR10FR21FR22

FR23

FR24

FR25FR26

FR30

FR41FR42 FR43

FR51

FR52FR53FR61FR62FR63FR71 FR72

FR81

FR82FR83

FR91

FR92 FR93FR94

GR1

GR2GR3

GR4

HU1

HU2

HU3

IE01IE02

ITC1

ITC2

ITC3ITC4

ITD1 ITD2

ITD3

ITD4

ITD5ITE1ITE2

ITE3

ITE4

ITF1

ITF2

ITF3

ITF4ITF5

ITF6

ITG1

ITG2

NL11NL12NL13NL21

NL22NL23

NL31

NL32NL33NL34NL41

NL42

PL11PL12PL21

PL22PL31 PL32PL33

PL34

PL41

PL42PL43

PL51

PL52PL61 PL62PL63

PT11

PT15

PT16PT17

PT18

PT20

PT30

RO11

RO12

RO21

RO22

RO31

RO32

RO41RO42

SE1SE2SE3

SK01

SK02SK03

SK04

UKCUKDUKE

UKFUKGUKHUKI

UKJ

UKKUKLUKM UKN

Pearson's: -0.67

Spearman: -0.71

-3-2

-10

12

EQ

I corr

uptio

n p

illar

0 .1 .2 .3 .4 .5 .6 .7ratio of single bidders

Comparing Corruption Meaures: Perceptions vs. Objective

ATBE

BG

CZ

DE

DK

ES

FI

FR

GR

HU

IE

IT

NL

PL

PT

RO

SE

SK

UK

Pearson's: -.86

Spearman: -0.78

-2-1

01

2

EQ

I pe

rcep

tion

s

0 .1 .2 .3 .4

% of single bidders

Advantages of this approach • Corruption (& related concepts) are latent,

multifaceted, clandestine and can’t completely be observable in total.

• Focus on sub-national level

• More than just corruption

• efficient in data collection, gives policy-makers a ’snap-shot’

• A compliment to measures based on ’expert’ assessments

• Perceptions matter! (stock market, elections, etc. driven by expectations of what others will do…).

• For certain research , a perception/experience

measure is preferred

Drawbacks of this approach

• Difficult for policy-makers to use as a ’benchmark tool’ for progress, etc.

• Over time comparisons require knowledge of margins of error

• Must take representativeness of survey into consideration

• Corruption has many dimensions, citizens (and outside experts) only capable of validly assessing certain types

• Perceptions = ’actual corruption experience’?

EQI 2017 • 3rd round of data to be collected spring 2017

• Similar core questions with some adjustments –

recommendations based on Rauch analysis

(Annoni & Charron 2017)

• Adjust overall scales (odd to even)

• Extend scales on corruption experience question

• Replace 1 corruption perceptions Q

• Check for additional pillar…

Other changes

-450+ respondents per region, 21 countries (incl. UK)

-Hungary NUTS 2, no Turkey, Ukraine or Serbia

-adding some questions on tax authorities

-add experince question – ’have you been

asked/approached to pay…& ’ever’?

Selected Publications on the EQI data

Article:

Charron, Nicholas, Lewis Dijkstra & Victor Lapuente (2014): Regional Governance Matters: Quality of Government within European Union Member States, Regional Studies, vol 48 (1): 68-90

Book:

’Quality of Government and Corruption from a European Perspective’ eds. Charron, Nicholas, Victor Lapuente and Bo Rothstein. 2013. Edward Elgar Publishing

EU Commission Working Paper:

‘Charron, Nicholas, Lewis Dijkstra & Victor Lapuente. 2012. ’Regional Govrnance Matters: A Study on Regional Variation of Quality of Government in the EU

Link: http://ec.europa.eu/regional_policy/sources/docgener/work/2012_02_governance.pdf

EQI questions 4. How would you rate the quality of public education in your area?

5. How would you rate the quality of the public health care system

in your area?

6. How would you rate the quality of the police force in your area?

7. “Certain people are given special advantages in the public

education system in my area.”

8. “Certain people are given special advantages in the public

health care system in my area.”

9. “The police force gives special advantages to certain people in

my area.”

10. “all citizens are treated equally in the public education system in my area”

11. “all citizens are treated equally in the public health care system

in my area”

12. “all citizens are treated equally by the police force in my area”

-.1

-.05

0

.05

.1.1

5

fem

ale

Ed

uc.

(<se

co

nd

ary

)

se

co

nd

ary

tert

iary

+

Ag

e (

18

-29

)

30

-44

45

-59

60

+

Inco

me

(lo

w)

mid

dle

hig

h

Po

pu

latio

n (

<1

0k)

10

k-1

00

k

10

0k-1

m

>1

m

min

ority

la

ng

.

un

em

plo

ye

d

ye

ar

20

13

Marginal effect in EU15 countries

Marginal effect in non-EU15 countries