Measuring Governance at the Sub-National Level in the EU · FR82 - Pro FR83 - Cor FR91 - Gua FR92 -...
Transcript of Measuring Governance at the Sub-National Level in the EU · FR82 - Pro FR83 - Cor FR91 - Gua FR92 -...
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