Perception Survey on the Quality of Life in European

Contract n° 2017CE160AT133 Prepared by: Ipsos Date: 9 March 2020

Regional and Urban Policy

Perception Survey on the Quality of

Life in European Cities 2019

Evaluation Report

EUROPEAN COMMISSION

Produced by on behalf of the Directorate-General for Regional and Urban Policy

Unit B1 - Policy Development and Economic Analysis

E-mail: [email protected]

European Commission

B-1000 Brussels

mailto:[email protected]

EUROPEAN COMMISSION

Directorate-General for Regional and Urban Policy 2020 3

Perception Survey on the Quality of

Life in European Cities 2019

Evaluation Report

EUROPEAN COMMISSION


TABLE OF CONTENTS

1 INTRODUCTION ..................................................................................................... 5

2 PROJECT OVERVIEW .............................................................................................. 6

2.1 Timing ............................................................. Error! Bookmark not defined.

2.2 Sample design.............................................................................................. 6

2.2.1 Sample size and associated margin of error ........................................ 6

2.2.2 Sample methodology ........................................................................ 7

2.3 Questionnaire design ................................................................................... 14

2.3.1 Screening questions ....................................................................... 14

2.3.2 Socio-demographic background questions ......................................... 16

2.3.3 Weighting questions ....................................................................... 16

2.3.4 Changes related to GDPR compliance ............................................... 17

2.4 Pilot testing................................................................................................ 18

2.5 Main fieldwork ............................................................................................ 18

2.5.1 Timing .......................................................................................... 18

2.5.2 Fieldwork follow-up ........................................................................ 18

2.6 Reporting and data delivery ......................................................................... 19

2.6.1 Pre-fieldwork reporting ......................... Error! Bookmark not defined.

2.6.2 Data delivery ................................................................................ 19

3 WEIGHTING ........................................................................................................ 20

3.1 Weighting procedure ................................................................................... 20

3.1.1 Post-stratification & design weighting ............................................... 20

3.1.2 Weight trimming ............................................................................ 22

3.2 Weighting benchmarks ................................................................................ 23

3.2.1 Age and gender ............................................................................. 23

3.2.2 Phone ownership ........................................................................... 24

3.3 Design effects and weighting efficiency per city .............................................. 26

4 SAMPLE PERFORMANCE ANALYSIS ......................................................................... 28

4.1 Target population versus achieved distribution ............................................... 28

4.1.1 Age .............................................................................................. 28

4.1.2 Gender ......................................................................................... 33

4.1.3 Phone ownership ........................................................................... 35

4.1.4 Eligibility ....................................................................................... 37

5 FIELDWORK PERFORMANCE ANALYSIS ................................................................... 40

5.1 Interview validation .................................................................................... 40

5.2 Interview breakoffs ..................................................................................... 41

5.3 Item non-response ..................................................................................... 41

5.4 Response rates ........................................................................................... 43

6 DATA COMPARISON 2019-2015 ............................................................................. 46

7 RECOMMENDATIONS FOR FUTURE WAVES .............................................................. 47

ANNEX 1. FINAL QUESTIONNAIRE ................................................................................. 49

1 Introduction

This evaluation report is one of two parts of the final report for the 2019 Perception Survey

on the Quality of Life in European Cities. It presents an overview of the design, preparation

and execution of the Perception Survey. It also discusses the survey’s performance in

terms of sampling, fieldwork quality and accuracy of the collected data. Finally, this report

also lays out some recommendations for possible changes to the survey design that could

improve the performance of future waves of the Perception Survey.

This evaluation report is accompanied by a technical report, which forms the second part

of the final report. The technical report lists, per city, the most important sample

performance data (amount of sample used, eligibility rate, refusals, response rate, average

interview length, etc.)

2 Project overview

This chapter gives a concise overview of the different steps of the 2019 Perception Survey,

from the questionnaire design until the final data deliveries.

2.1 Sample design

The Perception Survey targets citizens of all (greater) cities within the scope of the survey

– covering a total of 83 cities. The target population includes all people aged 15 and above,

who satisfy the requirements outlined below:

1. Being a resident of the city surveyed;

2. Having sufficient command of (one of) the respective national/regional

language(s) or English, which allows them to comfortably answer the

questionnaire;

3. Living in a private household, which means that the target population will exclude

prisoners, residents of retirement homes, etc. who are difficult to reach via a

telephone survey.

Regarding the first requirement, the scope is technically defined for each city in terms of a

set of Local Area Units (LAUs) that together comprise the area of the city under scope. The

residence of a given respondent in one of these LAUs determines their eligibility for the

survey. The list of LAUs in scope per city is added to this Evaluation Report as Annex 2.

Regarding the second requirement, the language command was assessed by the

interviewer at the start of the survey. In case it was clear that a respondent is not able to

answer questions in one of the official languages, they were offered to conduct the

interview in English.

Regarding the third requirement, the survey in practice targeted all residents aged 15+

with private access to a telephone, which is de facto confirmed by a given respondent being

reachable by phone during the fieldwork.

2.1.1 Sample size and associated margin of error

The target sample size was 700 complete interviews in each city surveyed. This means

that interviews were gathered from 58 100 respondents in total, all of which are citizens

who are resident in one of the (greater) cities under scope The following chart depicts how

the margin of error associated with survey estimates can vary as a function of sample size,

assuming a confidence level of 95%.

Quality of Life in European Cities Survey 2019


2.1.2 Sample methodology

Telephone samples require a specific design in order to cover the entire target population

and to reduce a) potential coverage bias and b) non-response bias. Some aspects are

country-specific, such as prefixes, operators, overall telephone penetration, penetration of

mobile phones and penetration of mobile only. As a growing share of the population is

becoming “mobile only” (i.e. persons who only have access to a mobile phone), the optimal

composition of telephone samples should take into account the incidence of households

that are reachable only via mobile numbers. Each telephone mode (fixed line or mobile)

also covers a specific profile with parameters such as age and urbanization degree.

According to the 2017 Eurobarometer on E-communications, omitting “mobile only”

persons implies 37% of the EU households are not included in the sample frame.1

In order to ensure maximum population coverage resulting in a representative sample, a

mixed (or “dual frame”) approach was taken for this Perception Survey, which takes into

account the respective distributions of persons who only have access to a mobile phone

(i.e. “mobile only”), persons who only have access to a fixed line phone (i.e. “fixed only”)

and persons who have access to both mobile and fixed line phones (i.e. “mixed”). Based

on these data, the necessary distribution of mobile phone and landline sample units needed

in the sample frame is calculated. By utilizing two separate, overlapping sample frames to

interview a population of interest, this approach currently guarantees the maximum and

most representative coverage of the population of interest.

Below we provide an overview of the methodology used for sampling, including the

procedures for random selection of telephone numbers from the sampling frames as well

as for respondent selection within a given household. Both are identical across the

surveyed cities.

The sampling frames based on the dual-frame methodology proposed were developed via

the following steps:

Step 1: Sampling frames

1 http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm/ResultDoc/download/DocumentKy/83478

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±3.7%±3.5%

±3.3% ±3.1% ±3.0% ±2.8% ±2.7% ±2.6% ±2.5%

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500

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Required Sample Size



For every city, a random gross sample was drawn from a larger sample frame. This ensures

that each person belonging to the target universe will have a chance to participate in the

survey.

The landline sample was generated through Random Digit Dialling (RDD). With RDD,

dedicated software is used to generate telephone numbers, starting from an initial list of

prefixes (which can be linked to a great extend to cities) and primary numbers. This initial

list is taken from available public records like registers and phone books. From the numbers

in this list, new telephone numbers are created and used by adding and subtracting digits

from the existing telephone number. For example if in the register/phone book

+322/xxx.xx.xx links to the city of Brussels, then new telephone numbers for Brussels can

be generated keeping the prefix and replacing e.g. the last two digits (e.g.

+322/xxx.xx.yy). In this way the number has been randomly generated.

The mobile sample is compiled based on national phone number registers (if available

for mobile numbers in a given country) and through publicly available online data (such as

from social media). It should be noted that such data is collected anonymously and only

with reference to the available geographic information linked to the number (such as the

country or region in which the phone number resides). The latter is necessary to identify

the mobile phone number as belonging to a certain country or city.

Where detailed geographic information is available it was also used to verify in this first

stage that the numbers in the sample are spread proportionally over the different

subregions of the city. Concretely, we wanted to avoid that most or all of the sample is

located in one specific subregion of a city, which could greatly bias the results of the

Survey.

The composition of the prefixes and primary numbers is thus a key element to guarantee

that sufficient geographical spread can be obtained. Once the primary numbers and their

geographic information are determined, the landline samples for specific cities are

generated via Random Digit Dialling. Next the sample is pulsed to filter out non-connected

and invalid numbers (=numbers that don’t exist/don’t result in a connection) as well as

business/non-residential numbers.

Step 2: Gross sample composition

When the sample frames per city are determined, the gross samples can be drawn from

it. As already indicated above, the 2019 Perception Survey uses a dual frame sampling

approach, using both mobile and fixed line numbers. The mobile sample is drawn at random

from the available mobile numbers for each city. To determine the size of the gross sample

needed for a target complete sample of 700 per city, we estimate the necessary

oversampling rate based on an assumed response rate, and set the oversampling rate

in the gross samples for the main field is defined at a ratio of 1:24. This amounts

to a gross sample of 16 800 numbers per city.

For each city, separate landline and mobile frames are built and separate samples

are drawn from that for each city. The size of the sample drawn per phone type depends

on the phone type ownership data for each city. However, as reliable statistics on phone

ownership on city level are not available, the proportion of mobile and landline numbers in

each city sample is based on available data on the country level (i.e., for all cities in a

given country we use the same phone type distribution). For the EU countries, United

Kingdom, Norway and Iceland the distribution data are calculated based on phone



ownership data collected in the 2017 wave of the Consumer Market Monitoring Survey.2

The rationale behind using these alternative targets is further elaborated in the weighting

section. For the other countries, phone ownership data is based on the latest available

Eurobarometer data or on data made available by local statistics institutes. The phone

ownership targets are used for defining the sampling frames, but are also used for

monitoring the sample performance during the fieldwork and used for weighting.

Population statistics on phone ownership distinguish between landline ownership, mobile

phone ownership and mixed ownership (i.e., those who have both types of phone).

However, in order to determine the proportion of landline and mobile numbers in our gross

samples, the “mixed” population needs to be recalculated to come to a binary sample

distribution. For that reason, the mobile and landline samples are defined and calculated

as follows:

Mobile sample: potential respondents within a given country that can be reached via a

mobile line (regardless of whether they can also be reached via a fixed line). As such, this

sample includes respondents from both the mobile only and mixed population.

% 𝑴𝒐𝒃𝒊𝒍𝒆 𝒔𝒂𝒎𝒑𝒍𝒆 =𝑷𝒓𝒐𝒑𝒐𝒓𝒕𝒊𝒐𝒏 𝒐𝒇 𝒎𝒐𝒃𝒊𝒍𝒆 𝒍𝒊𝒏𝒆𝒔

𝑻𝒐𝒕𝒂𝒍 𝒑𝒐𝒑𝒖𝒍𝒂𝒕𝒊𝒐𝒏 𝒐𝒇 𝒑𝒉𝒐𝒏𝒆 𝒏𝒖𝒎𝒃𝒆𝒓𝒔=

𝑴 + 𝑴𝑭

(𝑴 + 𝑴𝑭) + (𝑭 + 𝑴𝑭)

F = fixed only; M = mobile only; and MF = mobile and fixed (mixed)

Fixed sample: potential respondents within a given country that can be reached via a

fixed line (regardless of whether they can also be reached via mobile line). As such, this

sample includes respondents from both the fixed line only and mixed population.

%𝑭𝒊𝒙𝒆𝒅 𝒍𝒊𝒏𝒆 𝒔𝒂𝒎𝒑𝒍𝒆 =𝑷𝒓𝒐𝒑𝒐𝒓𝒕𝒊𝒐𝒏 𝒐𝒇 𝒇𝒊𝒙𝒆𝒅 𝒍𝒊𝒏𝒆𝒔

𝑻𝒐𝒕𝒂𝒍 𝒑𝒐𝒑𝒖𝒍𝒂𝒕𝒊𝒐𝒏 𝒐𝒇 𝒑𝒉𝒐𝒏𝒆 𝒏𝒖𝒎𝒃𝒆𝒓𝒔=

𝑭 + 𝑴𝑭

(𝑴 + 𝑴𝑭) + (𝑭 + 𝑴𝑭)

F = fixed only; M = mobile only; and MF = mobile and fixed (mixed)

For example, if Germany would be set to have the following proportions in the study: 83%

mixed, 9% fixed only, 8% mobile only, the local teams would compose a gross sample of

50.3% fixed numbers, calculated as ((83%+9%)/((83%+9%)+(83%+8%))), and 49.7%

mobile numbers ((83%+8%)/((83%+9%)+(83%+8%))).

It should be noted that these distributions are not fixed targets but are rather used as an

instrument to determine the composition of the gross samples so that they are maximally

representative of the population in terms of phone ownership. As such, they represent the

calling proportion of mobile versus fixed lines within each country. The table below presents

an example of how the % Mobile sample and % Fixed sample are linked to these

distributions of phone ownership for each country, based on available phone ownership

data.

2 https://ec.europa.eu/info/policies/consumers/consumer-protection/evidence-based-consumer-policy/market-monitoring_en

https://ec.europa.eu/info/policies/consumers/consumer-protection/evidence-based-consumer-policy/market-monitoring_en




COUNTRY

Population distribution Target sample

distribution

Mixed Fixed telephone only Mobile telephone only Fixed Mobile

BE 85% 4% 11% 48% 52%

BG 48% 2% 50% 34% 66%

CZ 25% 0% 75% 20% 80%

DK 41% 2% 58% 30% 70%

DE 92% 4% 4% 50% 50%

EE 44% 0% 55% 31% 69%

IE 58% 1% 41% 37% 63%

EL 81% 7% 12% 49% 51%

ES 84% 4% 12% 48% 52%

FR 89% 5% 6% 50% 50%

IT 66% 2% 31% 41% 59%

CY 68% 4% 28% 43% 57%

LV 27% 0% 73% 21% 79%

LT 38% 0% 62% 28% 72%

LU 86% 2% 12% 47% 53%

HU 35% 7% 58% 31% 69%

MT 93% 6% 1% 51% 49%

NL 82% 2% 15% 46% 54%

AT 62% 1% 36% 39% 61%

PL 56% 3% 41% 38% 62%

PT 79% 2% 19% 45% 55%

RO 60% 1% 39% 38% 62%

SI 78% 3% 19% 46% 54%

SK 44% 1% 55% 31% 69%

FI 16% 1% 83% 15% 85%

SE 77% 1% 22% 44% 56%

UK 78% 5% 17% 47% 53%

HR 63% 11% 25% 46% 54%

AL 23% 0% 75% 19% 81%

TR 14% 3% 81% 15% 85%

MK 32% 4% 61% 28% 72%

RS 75% 7% 17% 47% 53%

ME 25% 2% 72% 22% 78%

NO 40% 1% 59% 29% 71%

IS 88% 1% 11% 47% 53%

Step 3: Sample building and effective phone type distribution

Based on the above distributions and the established oversampling factor of 24:1, the

required amount of mobile and fixed numbers is drawn from the sample frame to create

the gross sample for each city. At this point, in some cities, a necessary adjustment to the



target phone type distribution was made. This is done when there were not enough primary

phone numbers with geolocation information to reach the target amount of mobile phone

numbers for the gross sample. While sources to collect primary landline numbers from are

abundantly available, for some cities such information is much less easy to collect, either

because public listings are incomplete or because it depends on what information is publicly

obtainable via social media and other open sources. This explains why there can be clear

differences even within a country. Extra data collection efforts were made for cities where

the originally available primary number list was insufficient. A number of possible ways to

tackle this issue were considered. A first possibility was to include in the gross sample also

mobile phone numbers for which no geographic information is available. This, however,

would in most cities considerably raise the risk of lowering the incidence rate of the sample

(i.e., the amount of respondents on mobile numbers that live in the target cities), because

for any random mobile phone number in a given country the chances are low that the

associated respondent lives in one of the target cities. Including such numbers would then

decrease the calling efficiency and therefore increasing the time and resources needed for

the fieldwork. Therefore, this option was only applied in countries where the population of

the target city/cities is a considerable part of the total country population – and where

there is consequently a high enough chance that a randome mobile phone respondent is a

resident of the target city. Specifically, this was done in Valletta (Malta) and Luxembourg

City (Luxembourg).

In countries where this was not a practically feasible option, the lower proportion of mobile

phone numbers was compensated by adding more landline numbers to the gross sample.

This led to the following effective stratification of the gross sample for phone type, in each

city:

TARGET (%) SAMPLE (%)

Mobile Fixed Mobile Fixed

Graz 61% 39% 61% 39%

Wien 61% 39% 61% 39%

Antwerpen 52% 48% 52% 48%

Bruxelles / Brussel 52% 48% 52% 48%

Liège 52% 48% 52% 48%

Burgas 66% 34% 56% 44%

Sofia 66% 34% 66% 34%

Zagreb 54% 46% 54% 46%

Lefkosia 57% 43% 23% 77%

Ostrava 80% 20% 73% 27%

Praha 80% 20% 80% 20%

Aalborg 70% 30% 70% 30%

København 70% 30% 70% 30%

Tallinn 69% 31% 29% 71%

Helsinki / Helsingfors 85% 15% 85% 15%

Oulu / Uleåborg 85% 15% 85% 15%

Bordeaux 50% 50% 50% 50%

Lille 50% 50% 50% 50%

Marseille 50% 50% 50% 50%

Rennes 50% 50% 50% 50%

Strasbourg 50% 50% 50% 50%

Paris 50% 50% 50% 50%



Berlin 50% 50% 50% 50%

Dortmund 50% 50% 50% 50%

Essen 50% 50% 50% 50%

Hamburg 50% 50% 50% 50%

Leipzig 50% 50% 50% 50%

Munich 50% 50% 50% 50%

Rostock 50% 50% 40% 60%

Athina 51% 49% 51% 49%

Irakleio 51% 49% 32% 68%

Budapest 69% 31% 64% 36%

Miskolc 69% 31% 19% 81%

Dublin 63% 37% 63% 37%

Bologna 59% 41% 59% 41%

Naples 59% 41% 59% 41%

Palermo 59% 41% 59% 41%

Rome 59% 41% 59% 41%

Turin 59% 41% 59% 41%

Verona 59% 41% 52% 48%

Vilnius 72% 28% 30% 70%

Luxembourg 53% 47% 53% 47%

Riga 79% 21% 11% 89%

Valletta 49% 51% 49% 51%

Amsterdam 54% 46% 48% 52%

Groningen 54% 46% 28% 72%

Rotterdam 54% 46% 52% 48%

Białystok 62% 38% 62% 38%

Gdańsk 62% 38% 62% 38%

Kraków 62% 38% 62% 38%

Warszawa 62% 38% 62% 38%

Braga 55% 45% 55% 45%

Lisboa 55% 45% 55% 45%

Bucharest 62% 38% 62% 38%

Cluj-Napoca 62% 38% 62% 38%

Piatra Neamt 62% 38% 32% 68%

Bratislava 69% 31% 69% 31%

Košice 69% 31% 52% 48%

Ljubljana 54% 46% 54% 46%

Barcelona 52% 48% 52% 48%

Madrid 52% 48% 52% 48%

Málaga 52% 48% 52% 48%

Oviedo 52% 48% 52% 48%

Malmö 56% 44% 29% 71%

Stockholm 56% 44% 56% 44%

Belfast 53% 47% 53% 47%



In cities where the proportion of mobile phone numbers in the gross sample was lower

than the assumed country proportion, mobile phone numbers were prioritized in the

fieldwork. This means that in the first weeks of the fieldwork a higher number of mobile

phone numbers were called, with the aim of maximizing the proportion of mobile numbers

in the final sample.

Identification of eligible postcodes.

Postcodes are central to the sample design of the Perception Survey and were also used

during the fieldwork to determine in the majority of the cities the eligibility of respondents.

It was thus very important that all (and only) the postcodes belonging to the target city

regions were identified. To achieve this, a multi-step process was followed.

First, GIS-data from postcode areas in all countries (obtained from national postal

administrations) were overlayed on GIS-data from the target LAUs per city. This way it

could be determined which postcodes were used within the cities’ boundaries.

In most countries, the boundaries of postcode areas and LAUs coincide. If that is the case,

it can be exactly determined which postcodes belong to which LAUs. However, in some

countries, both types of areas crosscut each other. This means that if we know for a given

sample unit or respondent the postcode, it cannot be determined in which LAU they live.

If the postcode area falls fully within the target city, this doesn’t pose large problems.

However, when a postcode area falls partly within and partly outside of the target city, it

is impossible to determine with 100% certainty whether a sample unit with this postcode

is eligible or not. To determine how likely it is that any sample unit with such a postcode

is eligible for participation in the survey, we calculated the proportion of the population in

these postcode areas that lives within the target city. We propose that if this proportion is

25% or higher, a sample unit with this postcode is considered always eligible. If the

proportion is below that threshold, we consider the sample unit always ineligible.

Concretely, this would mean the following:

Cardiff 53% 47% 53% 47%

Glasgow 53% 47% 53% 47%

London 53% 47% 53% 47%

Manchester 53% 47% 53% 47%

Tyneside conurbation 53% 47% 53% 47%

Reykjavík 53% 47% 48% 52%

Oslo 71% 29% 71% 29%

Genève 25% 75% 25% 75%

Zurich 25% 75% 25% 75%

Tirana 81% 19% 81% 19%

Skopje 72% 28% 72% 28%

Podgorica 78% 22% 6% 94%

Beograd 54% 46% 54% 46%

Ankara 85% 15% 49% 51%

Istanbul 85% 15% 60% 40%

Antalya 85% 15% 24% 76%

Diyarbakir 85% 15% 3% 97%



In the postcode areas that we would not include in the sample, where less than

25% of the population lives within the target city, the average population proportion

living within the target city is just 4% (i.e., a random sample unit with this postcode

has 96% chance of being ineligible)

In the postcode areas that we would keep in the sample, where 25% or more of

the population lives within the target city, the average population proportion living

within the target city is 79%) (i.e., a random sample unit with this postcode has

21% chance of being ineligible)

These figures show that a cut-off of 25% guarantees a high chance that ineligible units are

kept out of the sample, while at the same time only removing a very small number of

eligible respondents.

2.2 Questionnaire design

The primary objective when preparing the questionnaire for use in the 2019 Perception

Survey was to keep the substantive questions as much as possible identical to the 2015

questionnaire, so that comparisons could be drawn. Some changes were made, however,

to the screening questions, the socio-demographic background questions, and the

questions needed for weighting. This was done either at the inception of the project or

after the pilot test. All of these changes were made with the objective of increasing data

accuracy. Also, a few changes were made to the questionnaire to comply fully with the

GDPR, in force since 2018.

2.2.1 Screening questions

Multiple screening questions where added to questionnaire, to be asked at the start of the

questionnaire. The goal of these screening questions is to make sure that all respondents

to the survey are within the target scope of the survey – aged 15 or over and residing in

one of the target cities.

For the screening of age, we use the following questions. When calling a landline number,

the age screening is combined with the “last birthday” question. This is used to randomly

select a member of the household, thus avoiding the bias of self-selection by the person

that has picked up the phone. In comparison to the previous wave, the age question has

been made more general, no longer asking for a specific date of birth, but only for the age

of the respondent. This is easier to respond and less intrusive, thus likely increasing the

response rate, and it also only collects the necessary information as we don’t need the

exact date of birth.

Screening questions for age:

What is your age? (Mobile numbers)

Please can I speak to the person aged 15 or older within your household whose

birthday it was most recently? (Landline numbers)

For the screening on regional eligibility, in most cities we asked the target respondent the

postcode of their residence, and subsequently matched that with a list of all postcodes

used in the city (specifically, the postcodes used in the LAUs that together form the target

city). If the postcode is not used in the city, this implies that the respondent lives outside

of the city, and the respondent was screened out. An exception is the situation where the



postcode given by the respondent belongs to another city included in the survey. In that

case, the interview was still conducted, and the respondent was moved to the sample from

the city in which he resides.

Screening question for postcode:

What is your postcode?

In some countries asking for the respondent’s postcode was not possible. This is either

because the postcode could allow to identify an individual household, in which case

recording the postcode would have required additional consent from the respondent. Or,

in some countries it could be guaranteed that all respondents would know their postcode,

typically because of recent changes to the postcode system causing people to know their

old, but possibly not yet their new postcode (or vice versa). The countries where this is the

case are the Netherlands, the United Kingdom, Portugal, Bulgaria, Romania and Ireland.

In these countries, instead of asking for postcode to determine eligibility, we ask directly

for the respondent’s region of residence. In order to determine how to best ask this, the

main principle is to keep the question as simple as possible. For example, for London, the

target region is Greater London. Because Greater London is a commonly known region, we

can ask a respondent directly whether they live in Greater London. However, in a city like

Glasgow, such a single denomination for the whole target city region does not exist. For

that reason, in such cities we include in the question all subregions (e.g., municipalities,

counties) needed to identify the residence of the respondent.

Screening question for region (example of London and Glasgow)

London

Do you live in Greater London?

Glasgow

Do you live in …

1. The city of Glasgow

2. The council area of East Dunbartonhsire

3. The council area of East Renfrewshire

4. The council area of Renfrewshire

Finally, in Lisbon, both screening via postcode as well as via region (Friguesia in Lisbon)

proved challenging. The pilot test showed that part of the respondents could not with

certainty confirm either, and therefore tend to answer “Lisbon” when asked their region of

residence – referring to the city at large. Because their responses still indicated that they

live in the target region, we added to the screening question an extra response option

“Lisboa”. This avoided that respondents who don’t know their Freguesia, although there is

nevertheless a high chance that they live in the target city, would need to be screened out.



2.2.2 Socio-demographic background questions

After the pilot test, some changes were made to the socio-demographic background

questions. The reasons for this was in all cases that the background questions were

sometimes long and difficult to answer, putting large burden on the respondents and

creating a risk of inaccuracies in their responses. These changes concerned the following

questions in the original questionnaire:

D8. Which of the following best describes your household composition? With

household, we mean all people that typically live with you in the same residence.

Please include anyone who is temporarily away for work, study or vacation

D9. How many people usually live in your household? Please include yourself.

D8 caused confusion among several respondents, as well as some resistance because of

the length of the response options – especially because it comes at the end of the interview.

Additionally, the follow-up question in D9 also showed to confuse respondents because

they have the feeling that they already gave that information in D8.

However, since D8 is a standardized background question, too large changes to D8 were

difficult, because this can jeopardize comparability with previous waves and other cities

that have organized the survey independently. Nevertheless, we proposed to make one

adjustment, by replacing the question order and asking D9 before D8. This allowed to ask

only D9 to people living alone, because they could automatically be coded as a one-person

household in D8.3 Second, in doing so, the response option list in D9 becomes slightly

shorter, because the option “one-person household” does not need to be asked anymore.

D11. Which of the following best describes your current working status?

For D11 as well, the large number of response options - that are in themselves also rather

long and similar to each other - regularly caused confusion and impatience among

respondents. We made a similar adjustment as for D8, by first asking a simpler question

that covers most of the respondents: “Do you currently have a job? (Interviewer

instruction: include employees, employers, self-employed and people working as a relative

assisting on family business)”

This question was applied to 60% of the pilot survey sample. Asking this simpler question

first thus avoids presenting the full list of response options to the majority of respondents,

and it shortened the list of response options to those that fall in another category and still

needed to be asked question D11.

2.2.3 Weighting questions

Specific weighting questions were added to allow collection of the data needed to calculate

weights. This applies to 2 aspects of the weighting:

3 This concerns about 20% of the sample according to the pilot test.



Phone ownership. The sample design assumes that landline phones are accessible

by all household members, and that mobile phones are personally owned and thus

accessible only to the person that answers the phone. In the survey, we measure

the access to mobile and landline phones, so that we can weight for the higher

selection probability of people that have access to both a landline and a mobile

phone (as opposed to only a landline or a mobile phone).

D14. Do you personally own a mobile phone?

D15. Do you have a landline phone in the household?

Household size. The target population of the Perception Survey are city residents

aged 15 or over. In order to accurately calculate the design weight for the landline

sample (to take into account the selection probability of people reached within their

household via a landline), we need to measure the number of eligible people within

each household – i.e., all household members aged 15 or over. A question to gather

this was added to the final questionnaire, as a follow-up to question D9:

D9. How many people usually live in your household? Please include yourself.

D9b. How many of these are aged 15 and older? Please include yourself.

2.2.4 Changes related to GDPR compliance

The European Union’s General Data Protection Regulation (GDPR, Regulation (EU)

2016/679), entered into force on 24 May 2016 and applicable since 25 May 2018, puts

strong responsibility on survey organizers to assure the protection of people’s privacy and

the correct handling of their personal data. Informing respondents of their rights and how

any personal data are treated, and acquiring consent to collect, process and store their

data are two key elements of the GDPR. To that end, the questionnaire was adjusted in

several places:

A privacy notice (to which respondents are referred for a full overview of what

kinds of personal data are gathered from them, how these are stored, what

respondents’ rights are with regard to these data and who they can contact with

questions, concerns or the request to delete their data) was added.

The design of the introduction was designed in such a way that it was ensured

that informed consent was gathered from the selected respondent. In practice, this

means that for respondents on the landline sample, consent is only confirmed by

the final respondent. Because of within-household selection, this might not

necessarily be the first person to answer the phone. The consent confirmation was

therefore moved from the very start of the interview (with the first respondent on

the phone) to the moment that the finally selected target respondent comes to the

phone.

Q15 asks respondents about their current health situation. Under GDPR, health

information is considered a special category of personal data. Consequently, stricter

rules apply for gathering these data. Respondents need to be asked consent to ask

such information before the question is asked and need to be told explicitly that

answering the question is voluntary. To comply with this, a question is added right

before Q15:

Q15a. The next question is about your health status. Please remember that all your

responses will be treated confidentially. You do not have to answer this question if

you do not want to. Are you happy to proceed?

https://eur-lex.europa.eu/legal-content/EN/AUTO/?uri=CELEX:02016R0679-20160504&qid=1532348683434

https://eur-lex.europa.eu/legal-content/EN/AUTO/?uri=CELEX:02016R0679-20160504&qid=1532348683434



1. Yes

2. No

In order to avoid that this emphasis on the personal nature of the question scares

off respondents and leads to preliminary break-offs, question Q15 was moved to

the very end of the questionnaire. If respondents refused to be asked a question

about their health, this was coded as a refusal for question Q15, and the interview

was considered a complete.

2.3 Pilot testing

The pilot test methodology was identical to the setup that is envisaged for the main

fieldwork. The exact same sample, script, translations and technical infrastructure were

used for the questionnaire. Fieldwork monitoring procedures and quality checks were also

the same as used for the main fieldwork. All interviewers and supervisors participating in

the survey were briefed beforehand, and those participating in the pilot test received

specific instructions about what to focus on in the test. This allowed to evaluate the full

survey design in all countries.

The pilot test took place between 6 and 15 May. Calling took place predominantly in the

late afternoon and evening during weekdays. Other times of the day were available for

appointments. In each city, 30 complete interviews were conducted. No quota were set.

2.4 Main fieldwork

2.4.1 Timing

Because the survey fieldwork was estimated to take about 9 weeks and the fieldwork could

not begin earler than June, it was decided to split the fieldwork in two parts, with a pause

between 15 July and 1 September. This way it could be avoided to conduct fieldwork in the

summer period of July and August, 2 months that generally see a steep decline in response

rates because of the summer holidays.

The fieldwork start was originally set for 6 June, but was eventually moved to 12 June –

the reason for this being a number of adjustments to the questionnaire that had to be

implemented and replaced.

Despite this short delay in the start of the fieldwork, the fieldwork ended on 27 September

2019, as originally scheduled.

2.4.2 Fieldwork follow-up

The fieldwork was followed up closely on a weekly basis along the following parameters:

Total completes, per city (in absolute numbers and percentage of the total target)

Distribution of the sample for each city according to:



o Age and gender (to monitor deviations from the population)

o Phone type (to monitor sample performance in the mobile and landline

frames)

o LAU (where available4, to monitor the spread of the sample over the whole

city)

Percentage of ineligible screened-out respondents because of their sub-city region

of residence, per city (to monitor the quality of the sample in terms of incidence

rate)

Number of bad quality cases, per city

Average interview duration, per country

For the interrupted interviews: how often a particular question was the last question

answered (to help evaluate whethere there are any questions that have a higher

chance of leading to interview break-offs)

Non-response percentage per question

In the first week of the fieldwork, members from the research team also listend in to live

interviews in several countries to evaluate the interview quality (in Belgium, the

Netherlands, France, Germany and Poland).

2.5 Reporting and data delivery

2.5.1 Data delivery

The following data files were prepared:

A first datafile containing ‘conventional’ question/response labels and codes,

corresponding to the questions and responses as given in the English master

questionnaire. Besides the response data this file also contains paradata such as

interview time and date, weighting factors and sample background data.

A second datafile which uses Eurobase labels and codes. This coding system is

developed by Eurostat and has been revised and expanded so that it can be applied

to the 2019 version of the Perception Survey.

In addition to these microdata files, a table of indicators (i.e., question responses)

in aggregated form, as weighted totals computed for each city. This is delivered as

an Excel file.

2.5.2 Recoding of question Q13_5 for Tirana

Question 13_5 asked to what extent respondents agreed to the following statement:

4 In Ireland and the United Kingdom it was not possible to follow up on the distribution of the sample at LAU level, because respondents are only screened for their residence in the city as a whole (e.g., “Greater London”).



‘There is corruption in my local public administration’

In the Albanian translation used in Tirana (AL), this was erroneously translated as ‘there

is no corruption in my local public administration’ – that is, the inverse of the master item.

For this reason to allow for consistent reporting and comparability between all cities of the

survey, the responses for Tirana to this question were recoded to their inverse as well.

Specifically, the following recoding was applied:

‘Strongly agree’ to ‘strongly disagree’

‘Somewhat agree’ to ‘somewhat disagree’

‘Somewhat disagree’ to ‘somewhat agree’

‘Strongly disagree’ to ‘strongly agree’

Don’t know responses were not recoded.

3 Weighting

3.1 Weighting procedure

The following calibration weighting factors were taken into account in the weighting

approach:

• Age (four subgroups: 15-24, 25-39, 40-54 and 55+)

• Gender (male and female)

Initially, it was also considered to include sub-city level (commune) residence into account

for weighting, to ensure that the results would be representative for the whole city by

avoiding biases stemming from the possibility that some parts of a city would be

overrepresented in the data. This, however, ran against the practical difficulty that in many

cities the number of lower-level communes is very high, making outright weighting at that

level impossible. Grouping of communes into larger groups is in theory a measure to

resolve that issue. However, in order to do that, it would be necessary to first determine

which communes would for coherent wholes, and on what basis such merging would be

done besides mere geographical adjacency. This also means that, in absence of clear

parameters to determine the properties of such larger city regions (e.g., does one region

clearly differ from another in terms of population age, income, social status), there is no

clear way to verify whether a weighting according to regional distribution of the sample is

even warranted. For these reasons, it was decided to not weight the data according to sub-

city level region. However, because of the fact that the base principle of striving for a good

spread across city regions is still useful and will likely improve the quality of the sample,

as an alternative measure this spread was aimed for as much as possible in the sampling

stage. That is, in the gross sample for each city it was checked whether the distribution of

phone numbers over the city LAUs was proportionate to the population distribution.

In addition to a post-stratification weight on age and gender, a design weight was also

applied to control for unequal selection probabilities of sample units (see the following

section for more information on the rationale behind this approach), based on phone type

ownership (% mobile, % fixed, % mobile and fixed).

3.1.1 Post-stratification & design weighting

The sample was weighted in each country using a post-stratification weight, including age

and gender, and a design weight.



The use of a design weight has become common in telephone surveys when calling on both

mobile and fixed lines (dual frame) as there is an overlap between frames with respondents

who could be sampled from both. This means that the probability to be selected equals the

probability of being called on one’s fixed line plus the probability of being called on one’s

mobile line minus the probability of being called both on one’s fixed and mobile line.

𝜋𝑖 = 𝜋𝑖 (𝐹𝑁) + 𝜋𝑖(𝑀𝑁) − 𝜋𝑖(𝐹𝑁 ⋂ 𝑀𝑁)

(Where FN is the population of people with a fixed line and MN the population of people

with a mobile line.)

The latter term, however, is generally very small and can be excluded from the analysis:

𝜋𝑖 = 𝜋𝑖 (𝐹𝑁) + 𝜋𝑖(𝑀𝑁) − 𝜋𝑖(𝐹𝑁 ⋂ 𝑀𝐹)

Another aspect to take into account is that a mobile line is typically used by an individual,

while a fixed line is typically a household device, and is thus shared by several (eligible)

persons; however, only one person in the household will answer the phone, which means

that his/her selection probability will be lower. A full calculation of the selection probability

should therefore rely on data on the number of phone lines per respondent as well as the

number of people per line.

This is taken into account in the following formula:

𝜋𝑖 ≈𝑛𝐹

𝑁𝐹

∗ 𝐹𝑖

𝑍𝑖

+ 𝑛𝑀

𝑁𝑀

∗𝑀𝑖

𝑍𝑖

nF = sample size fixed numbers; NF = population size fixed numbers ; nM=sample

size mobile numbers; NM=population size mobile numbers

Fi = number of fixed lines the respondent can be reached on, Zi = number of persons

that can be reach via these fixed lines

Mi = number of mobile lines the respondent can be reached on, Zm = number of

persons that can be reach via these mobile lines

However, this theory has come under pressure over the past years due to several flaws:

Having several people using the same fixed line in a household lowers their

probability to be selected, but chances are also higher that at least one person is at

home, which increases the selection probability.

If someone uses several mobile lines, their selection probability increases, although

it is unlikely that this person will have both mobile phones with them and switched

on at all times.

Based on these comments and the need to include several additional questions for the full

approach, a different approach was applied. The expected number of people available per

line was set to 1 for both fixed and mobile lines, resulting in the following formula:

𝜋𝑖 ≈𝑛𝐹

𝑁𝐹

∗ 𝐹𝑖 + 𝑛𝑀

𝑁𝑀

∗ 𝑀𝑖

In this formula, the terms Fi and Mi are equal to 1 if the respondent owns respectively a

fixed/mobile line, regardless of the number of fixed/mobile lines they can be reached on.



3.1.2 Weight trimming

Weight trimming was also applied so that any computed non-response weights outside the

following limits are recoded to the boundary of these limits:

1

3 ≤

𝐸(𝑤𝐻𝐷)𝑤𝐻𝐷

𝐸(𝑤𝐻𝑁)𝑤𝐻𝑁

≤ 3

wHD = household design weight

wHN = the weight determined after adjustment (calibration)

E(wHD) and E(wHN) = their respective mean values

This approach does not rely on an absolute threshold, but offers a relative threshold based

on the data.

3.1.3 Population extrapolation

Finally, in addition to the two weights used, the Perception Survey microdata also contain

a population weighting factor. This allows to extrapolate the results from each city to their

actual population size, instead of the sample sizes of the survey. This is useful in case one

wants to group the data from multiple cities (e.g., all cities from one country) – in that

case it can be preferred to have each city only contribute to the grouped results

proportionate to their population size (e.g., Groningen less than Amsterdam and Rotterdam

in the Netherlands).

3.1.4 Using the weights

The microdata contains 3 weighting variables:

1. The design weight factor

2. A factor combining the design weight and the post-stratification weight (named

‘reslinweight’)

3. The population weight factor

The aggregated results by city have been calculated using the second factor, ‘reslinweight’.

When replicating the results for single cities, this factor should always be used. In case one

wants to balance the samples using other socio-demographic samples than age and

gender, this is possible, and then the design weight factor can be used as a starting point.

In case one wants to recalculate the design weight, the variable ‘mobfix’ contains the phone

type information (which respondents had access to a mobile phone, landline or both)

needed to do that.

Finally, as said, the population weight factor should only be used if one wants to combine

the results from multiple cities and wants to take into account the differences in population

sizes between those cities. This factor has not been used to produce the aggregate results

by city under this contract, as those tables only consider cities individually. However, for

the calculation of significant differences between cities the sample size does come into

play, since the significant differences as shown in the tables are calculated by comparing

a city’s result to the average of all cities. For this average, the population differences were



not taken into account, since it was considered most appropriate in this context to compare

cities as equal entities, rather than groups of people that differ in size.

3.2 Weighting benchmarks

3.2.1 Age and gender

Weighting benchmarks for age and gender (which were also used during the fieldwork for

monitoring of the sample performance) were based on Eurostat data for all cities within

the EU and the United Kingdom.5 For all these countries, age and gender targets were

determined for the population aged 15 or over. For other countries, local sources were

used. For cities in these countries it was not always possible to determine the gender

distribution for the 15+ population. In that case, the distribution of the full population was

used. Given that gender distributions differ only marginally over age, this can safely be

assumed to have no significant impact. An overvies of the sources used for non-EU cities

is presented in the table below. Also, for these cities the population data are not always

fully corresponding to the city as defined for the Perception Survey. Wherever this was not

possible, the closest equivalent region was used.

City Source Remark

Zürich Age:

https://www.bfs.admin.ch/bfs/en/ho

me/statistics/catalogues-

databases/tables.assetdetail.5886149

.html

Gender:




.html

Data for gender are for full

population, not 15+

Genève Age:




.html

Gender:




.html

Data for gender are for full

population, not 15+

Tirana http://databaza.instat.gov.al/pxweb/e

n/DST/START__Census2011/Census2

Data for the Tirana prefecture

5 https://ec.europa.eu/eurostat/web/cities/data/database

https://www.bfs.admin.ch/bfs/en/home/statistics/catalogues-databases/tables.assetdetail.5886149.html
















http://databaza.instat.gov.al/pxweb/en/DST/START__Census2011/Census2103/?rxid=7fdc84f5-4567-4c7b-96ae-0ac767c7a2eb


https://ec.europa.eu/eurostat/web/cities



103/?rxid=7fdc84f5-4567-4c7b-96ae-

0ac767c7a2eb

Reykjavik https://px.hagstofa.is/pxen/pxweb/en

/Ibuar/Ibuar__mannfjoldi__2_byggdir

__sveitarfelog/MAN02005.px/table/ta

bleViewLayout1/?rxid=ae1b6f70-

8f91-4f12-8056-4b048c1f64fd

Podgorica http://monstat.org/eng/pxweb.php

Skopje http://makstat.stat.gov.mk/PXWeb/p

xweb/en/MakStat/MakStat__Naseleni

e__ProcenkiNaselenie/225_Popis_reg

_3112_PolVoz_ang.px/?rxid=46ee0f6

4-2992-4b45-a2d9-cb4e5f7ec5ef

Data for Skjopje region

Oslo https://www.ssb.no/en/statbank/tabl

e/07459/tableViewLayout1

Beograd http://publikacije.stat.gov.rs/G2018/P

dfE/G201813045.pdf

Ankara https://biruni.tuik.gov.tr/bolgeselistat

istik/tabloOlustur.do#

Antalya https://biruni.tuik.gov.tr/bolgeselistat


Diyarbakir https://biruni.tuik.gov.tr/bolgeselistat


Istanbul https://biruni.tuik.gov.tr/bolgeselistat


3.2.2 Phone ownership

Phone ownership targets were determined estimated based on the achieved sample

for the Market Monitoring Survey 2017 (MMS 2017).6 The MMS 2017 data is based

on a bigger sample size and the more robust data. First, with a sample of 137,608

respondents, the achieved sample of the MMS 2017 is much larger than the sample of the

Eurobarometer study (n = 27,739 respondents). Second, the phone ownership information

in the MMS data is based on a sample of all contacted respondents, while the

Eurobarometer study only includes respondents that were willing to participate in the

study. In this way, the MMS data is more robust, as it does not include a participation bias.

6 https://ec.europa.eu/info/policies/consumers/consumer-protection/evidence-based-consumer-policy/market-monitoring_en



https://px.hagstofa.is/pxen/pxweb/en/Ibuar/Ibuar__mannfjoldi__2_byggdir__sveitarfelog/MAN02005.px/table/tableViewLayout1/?rxid=ae1b6f70-8f91-4f12-8056-4b048c1f64fd





http://monstat.org/eng/pxweb.php

http://makstat.stat.gov.mk/PXWeb/pxweb/en/MakStat/MakStat__Naselenie__ProcenkiNaselenie/225_Popis_reg_3112_PolVoz_ang.px/?rxid=46ee0f64-2992-4b45-a2d9-cb4e5f7ec5ef





https://www.ssb.no/en/statbank/table/07459/tableViewLayout1

https://www.ssb.no/en/statbank/table/07459/tableViewLayout1

http://publikacije.stat.gov.rs/G2018/PdfE/G201813045.pdf

http://publikacije.stat.gov.rs/G2018/PdfE/G201813045.pdf

https://biruni.tuik.gov.tr/bolgeselistatistik/tabloOlustur.do












Household telephone access per country based on the Market Monitoring Survey

2017

In the countries where MMS data are not available, we resort in the first place to the most

recent Eurobarometer/Eurostat data. If no recent data can be found there, national statistic

institute sources were used instead.

COUNTRY Population distribution Sample distribution

Mixed Fixed telephone only Mobile telephone only Fixed Mobile

BE 85% 4% 11% 48% 52%

BG 48% 2% 50% 34% 66%

CZ 25% 0% 75% 20% 80%

DK 41% 2% 58% 30% 70%

DE 92% 4% 4% 50% 50%

EE 44% 0% 55% 31% 69%

IE 58% 1% 41% 37% 63%

EL 81% 7% 12% 49% 51%

ES 84% 4% 12% 48% 52%

FR 89% 5% 6% 50% 50%

IT 66% 2% 31% 41% 59%

CY 68% 4% 28% 43% 57%

LV 27% 0% 73% 21% 79%

LT 38% 0% 62% 28% 72%

LU 86% 2% 12% 47% 53%

HU 35% 7% 58% 31% 69%

MT 93% 6% 1% 51% 49%

NL 82% 2% 15% 46% 54%

AT 62% 1% 36% 39% 61%

PL 56% 3% 41% 38% 62%

PT 79% 2% 19% 45% 55%

RO 60% 1% 39% 38% 62%

SI 78% 3% 19% 46% 54%

SK 44% 1% 55% 31% 69%

FI 16% 1% 83% 15% 85%

SE 77% 1% 22% 44% 56%

UK 78% 5% 17% 47% 53%

HR 63% 11% 25% 46% 54%

AL 23% 0% 75% 19% 81%

TR 14% 3% 81% 15% 85%

MK 32% 4% 61% 28% 72%

RS 75% 7% 17% 47% 53%

ME 25% 2% 72% 22% 78%

NO 40% 1% 59% 29% 71%

IS 88% 1% 11% 47% 53%



3.3 Design effects and weighting efficiency per city

The below table gives an overview of the design effects for each city (combining the design

weight and the post-stratification weight), as well as the sample balance, used here as a

measure for weighting efficiency. The weighting efficiency is good in almost all cities, but

can be considered low in Antalya, Diyarbakir and Ankara, and to a lesser extent Istanbul

and Riga (taking into account a desired weighting efficiency of 70% or higher). Given that

in all these cities the deviations in age and gender distribution are not especially high, the

reason for this lower efficiency needs to be sought in the phone type access deviations that

have impacted the design weight.

City Design Effects7 Sample balance8

Graz 1.18 84.4

Wien 1.19 84.4

Antwerpen 1.10 90.5

Bruxelles / Brussel 1.12 89.4

Liège 1.13 88.6

Burgas 1.22 81.8

Sofia 1.22 81.9

Zagreb 1.13 88.9

Lefkosia 1.13 88.1

Ostrava 1.36 73.8

Praha 1.42 70.4

Aalborg 1.26 79.3

København 1.26 79.1

Tallinn 1.27 79.0

Helsinki / Helsingfors 1.19 84.1

Oulu / Uleåborg 1.20 83.6

Bordeaux 1.09 91.8

Lille 1.11 90.2

Marseille 1.12 89.1

Rennes 1.16 86.2

Strasbourg 1.13 88.7

7 The design effect (deff) for each city is calculated using Kish’s formula (1965). The deff indicates how much the

expected sampling error in a survey deviates from the sampling error that can be expected under simple

random sampling which is the gold standard in sample surveys. To calculate deff, the number of sample

observations is multiplied by the sum of the squared weights over the square of the sum of the weights for

each city.

8 The sample balance is the inverse of the weight factor – i.e., 1 divided by the weight factor. It shows the size of

the weighted sample as a percentage of the unweighted sample.



Paris 1.12 89.7

Berlin 1.11 90.4

Dortmund 1.13 88.5

Essen 1.11 89.7

Hamburg 1.10 90.9

Leipzig 1.13 88.6

München 1.12 89.5

Rostock 1.15 86.7

Athina 1.10 91.1

Irakleio 1.07 93.6

Budapest 1.29 77.7

Miskolc 1.33 75.1

Dublin 1.21 82.5

Bologna 1.14 87.7

Napoli 1.15 86.8

Palermo 1.16 86.2

Roma 1.17 85.6

Torino 1.17 85.4

Verona 1.16 86.5

Vilnius 1.23 81.2

Luxembourg 1.10 90.7

Rīga 1.46 68.6

Valletta 1.09 91.4

Amsterdam 1.16 86.6

Groningen 1.09 91.4

Rotterdam 1.10 90.6

Białystok 1.15 87.0

Gdańsk 1.14 87.7

Kraków 1.14 87.6

Warszawa 1.13 88.5

Braga 1.12 89.5

Lisboa 1.16 86.3

Bucureşti 1.18 84.4

Cluj-Napoca 1.19 84.2

Piatra Neamţ 1.18 84.4

Bratislava 1.20 83.1

Košice 1.29 77.4

Ljubljana 1.17 85.5

Barcelona 1.12 89.5

Madrid 1.12 89.3

Málaga 1.11 89.9

Oviedo 1.11 89.9

Malmö 1.20 83.6



Stockholm 1.23 81.1

Belfast 1.19 84.4

Cardiff 1.18 85.0

Glasgow 1.13 88.3

London 1.16 86.4

Manchester 1.10 90.8

Tyneside conurbation 1.11 90.3

Reykjavík 1.17 85.4

Oslo 1.18 84.6

Genève 1.10 90.6

Zürich 1.11 89.9

Tirana 1.18 84.6

Skopje 1.36 73.4

Podgorica 1.25 79.8

Beograd 1.16 85.8

Ankara 1.60 62.6

Istanbul 1.45 68.7

Antalya 1.80 55.7

Diyarbakir 1.70 58.9

4 Sample performance analysis

4.1 Target population versus achieved distribution

4.1.1 Age

The table on the following page shows the unweighted distribution of the sample over

broader age groups. Throughout the fieldwork, potential skews, particularly towards older

age groups, were an important focus point.

Wit the full sample collected, a slight underrepresentation of younger people can indeed

be observed. Looking at the individual age categories, the deviation is no source of concern,

taking as a rule of thumb that a deviation of 5% in any direction is acceptable. This is

confirmed by the weighting efficiency figures (see 3.3 above), which see no strong impact

from this skew. Even when counting together the deviations from the 2 youngest age

groups, the average deviation remains at 4%. There are, however, differences between

cities. It can be noted, for instances, that several cities in the UK (London, Cardiff, Belfast,

Glasgow) have an underrepresentation of the 15-34 age group of between 9 and 11% -

though the skew is far less in Newcastle and the Tyneside Conurbation. Other cities that

show a larger skew when combining the two younges age groups are Rennes (-12%),

Stockholm (-10%) and Podgorica (-10%).

On ther other hand, in all Turkish cities – but in no other city in any country – there is an

overrepresentation of younger people. An exceptionally high overrepresentation of younger

people is seen in Diyarbakir (+18% in the age group 15-34).



The reasons for these differences between cities are not clear and cannot immediately be

found in the available data. The general skew towards older people, however, is not

uncommon in CATI surveys. Keeping this tendency in mind, the 2019 Perception Survey

performed rather well, keeping the skew limited to acceptable levels.

city 15-24 years 25 – 34 years 35 –44 years 45 – 54 years 55 – 64 years 65 and older Average deviation per city

sample target sample target sample target sample target sample target sample target

Graz 15% 17% 17% 20% 14% 15% 18% 16% 16% 12% 20% 20% 2% Wien 12% 14% 15% 19% 15% 17% 20% 18% 16% 13% 22% 20% 3% Antwerpen 11% 14% 17% 20% 18% 17% 17% 15% 14% 13% 23% 21% 2% Bruxelles / Brussel 13% 15% 19% 22% 20% 19% 19% 16% 14% 12% 15% 16% 2% Liège 10% 14% 20% 18% 15% 16% 17% 16% 17% 15% 21% 22% 2% Burgas 9% 11% 18% 16% 21% 20% 17% 16% 15% 16% 20% 21% 1% Sofia 10% 12% 18% 21% 20% 20% 15% 14% 14% 13% 23% 20% 2% Zagreb 12% 13% 16% 18% 15% 17% 18% 16% 17% 16% 22% 20% 2% Lefkosia 13% 16% 20% 22% 17% 18% 18% 16% 16% 13% 17% 15% 2% Ostrava 8% 11% 16% 16% 20% 18% 15% 17% 17% 15% 24% 23% 2% Praha 7% 9% 16% 18% 23% 22% 17% 16% 15% 13% 22% 22% 1% Aalborg 15% 19% 14% 17% 14% 15% 19% 16% 15% 14% 23% 20% 3% København 16% 18% 25% 28% 19% 19% 14% 13% 12% 10% 14% 12% 2% Tallinn 8% 11% 17% 20% 18% 18% 16% 14% 17% 15% 24% 22% 2% Helsinki / Helsingfors 12% 14% 19% 21% 17% 18% 16% 15% 15% 13% 21% 19% 1% Oulu / Uleåborg 14% 18% 16% 19% 15% 17% 16% 14% 18% 14% 21% 19% 3% Bordeaux 18% 21% 17% 18% 15% 15% 14% 14% 15% 12% 21% 19% 2% Lille 19% 21% 16% 19% 14% 16% 17% 15% 12% 13% 22% 17% 3% Marseille 14% 16% 12% 16% 13% 16% 17% 16% 18% 14% 26% 23% 3% Rennes 22% 28% 14% 19% 13% 13% 14% 12% 16% 11% 21% 17% 4% Strasbourg 15% 21% 18% 19% 13% 15% 18% 14% 15% 13% 21% 18% 3% Paris 13% 16% 15% 20% 17% 18% 19% 16% 16% 13% 20% 17% 3% Berlin 9% 11% 18% 20% 15% 16% 18% 17% 18% 14% 22% 22% 2% Dortmund 10% 13% 13% 16% 15% 14% 19% 18% 19% 15% 24% 23% 2% Essen 9% 12% 15% 16% 11% 14% 19% 17% 18% 15% 28% 25% 3% Hamburg 9% 12% 17% 19% 16% 16% 20% 18% 15% 13% 23% 21% 2% Leipzig 8% 11% 19% 22% 17% 15% 15% 15% 14% 13% 27% 24% 2%



München 9% 12% 20% 22% 17% 17% 19% 17% 16% 12% 19% 20% 2% Rostock 7% 11% 17% 20% 11% 13% 18% 15% 18% 15% 29% 26% 3% Athina 10% 12% 14% 18% 17% 19% 17% 17% 19% 14% 23% 21% 3% Irakleio 13% 16% 15% 20% 22% 20% 18% 16% 16% 13% 16% 16% 3% Budapest 8% 11% 13% 17% 18% 20% 19% 14% 18% 15% 25% 23% 3% Miskolc 10% 13% 10% 14% 16% 18% 17% 15% 20% 17% 27% 23% 3% Dublin 12% 16% 22% 25% 21% 19% 16% 15% 11% 12% 19% 14% 3% Bologna 8% 9% 14% 14% 15% 17% 16% 18% 16% 14% 31% 29% 1% Napoli 13% 15% 13% 15% 16% 17% 21% 18% 17% 15% 20% 20% 2% Palermo 9% 13% 16% 14% 17% 16% 18% 18% 17% 15% 23% 23% 2% Roma 8% 10% 9% 12% 20% 17% 21% 20% 17% 15% 25% 25% 2% Torino 8% 10% 14% 13% 11% 16% 20% 18% 18% 15% 29% 29% 2% Verona 9% 11% 10% 12% 14% 15% 17% 18% 19% 15% 31% 29% 2% Vilnius 9% 11% 23% 23% 20% 18% 17% 15% 11% 14% 20% 19% 2% Luxembourg 9% 11% 17% 22% 19% 19% 16% 15% 13% 11% 26% 21% 3% Riga 7% 10% 17% 19% 19% 17% 17% 15% 16% 16% 24% 24% 2% Valletta 11% 13% 13% 18% 17% 16% 15% 13% 17% 16% 27% 23% 3% Amsterdam 11% 15% 19% 23% 16% 18% 18% 16% 17% 13% 19% 14% 4% Groningen 24% 28% 19% 21% 16% 14% 15% 13% 12% 11% 14% 13% 2% Rotterdam 12% 16% 18% 20% 19% 17% 19% 16% 13% 13% 19% 18% 2% Bialystok 9% 13% 22% 21% 20% 18% 17% 15% 15% 16% 17% 18% 2% Gdansk 10% 11% 21% 20% 20% 18% 15% 13% 16% 17% 18% 21% 2% Kraków 11% 12% 23% 21% 18% 18% 11% 13% 15% 16% 22% 20% 1% Warszawa 9% 9% 21% 20% 17% 19% 14% 12% 19% 17% 20% 22% 2% Braga 11% 14% 16% 15% 22% 19% 20% 18% 15% 16% 16% 19% 2% Lisboa 8% 11% 14% 13% 21% 18% 19% 16% 16% 15% 22% 28% 3% Bucuresti 6% 8% 15% 18% 20% 22% 16% 17% 19% 16% 24% 20% 3% Cluj-Napoca 6% 8% 17% 19% 20% 21% 16% 17% 18% 16% 23% 19% 2% Piatra Neamt 7% 10% 15% 16% 19% 20% 19% 17% 19% 18% 21% 19% 2% Bratislava 5% 8% 19% 18% 23% 22% 17% 14% 18% 16% 18% 21% 2%



Košice 8% 12% 16% 18% 18% 20% 18% 16% 19% 15% 21% 19% 2% Ljubljana 9% 14% 13% 16% 16% 18% 18% 16% 19% 15% 25% 21% 3% Barcelona 10% 11% 13% 15% 17% 20% 20% 17% 15% 14% 25% 23% 2% Madrid 11% 11% 12% 15% 19% 20% 22% 18% 16% 14% 20% 22% 2% Málaga 12% 12% 14% 15% 18% 19% 19% 18% 17% 15% 20% 20% 1% Oviedo 8% 9% 11% 12% 20% 19% 20% 18% 19% 17% 22% 25% 2% Malmö 12% 14% 17% 23% 14% 18% 18% 14% 18% 12% 21% 19% 4% Stockholm 8% 13% 16% 20% 14% 18% 21% 16% 18% 13% 23% 18% 5% Belfast 11% 17% 13% 18% 12% 16% 21% 17% 18% 13% 25% 19% 5% Cardiff 17% 22% 14% 20% 11% 15% 18% 14% 18% 12% 22% 17% 5% Glasgow 12% 17% 18% 23% 18% 15% 20% 16% 17% 13% 15% 16% 4% London 8% 14% 21% 24% 17% 20% 19% 16% 15% 12% 20% 15% 4% Manchester 14% 16% 16% 19% 12% 16% 19% 17% 15% 13% 24% 19% 3% Tyneside conurbation 12% 17% 16% 17% 17% 14% 18% 16% 18% 14% 19% 21% 3% Reykjavík 10% 16% 18% 21% 14% 17% 20% 15% 17% 14% 21% 17% 4% Oslo 12% 14% 22% 26% 14% 19% 18% 15% 15% 12% 19% 15% 3% Genève 12% 11% 14% 11% 15% 13% 21% 15% 17% 15% 21% 33% 5% Zürich 9% 10% 8% 15% 19% 16% 17% 15% 18% 12% 29% 17% 5% Tirana 22% 24% 23% 18% 17% 16% 14% 16% 11% 13% 13% 13% 2% Skopje 9% 15% 16% 18% 22% 19% 22% 17% 18% 14% 13% 18% 4% Podgorica 13% 18% 15% 20% 20% 17% 16% 17% 19% 15% 17% 13% 4% Beograd 8% 13% 15% 18% 16% 16% 19% 16% 19% 18% 23% 19% 3% Ankara 14% 15% 18% 16% 23% 16% 22% 13% 10% 10% 13% 8% 4% Istanbul 17% 15% 24% 17% 25% 18% 15% 13% 12% 8% 7% 7% 4% Antalya 15% 14% 18% 15% 21% 17% 20% 14% 14% 10% 12% 8% 4% Diyarbakir 25% 19% 28% 17% 22% 13% 10% 8% 6% 5% 9% 5% 6% Average deviation per age group 3% 3% 2% 2% 3% 3%

4.1.2 Gender

The table below shows the unweighted gender distribution in the final sample for each city.

Only one skew stands out from other cities: For Skopje the sample contains 7% more

males than expected based on population figures (56% vs. 49). While this might be in part

due to the fact that the Skopje population data used for benchmarking do not cover the

exact city as defined for the Perception Survey, the discrepancy between males and

females is still higher than what one would normally expect in a general population survey.

Here too, however, the reason for this skew cannot be identified from the available survey

data.

city male female Deviation Per city sample target sample target

Graz 47% 48% 53% 52% 1%

Wien 49% 48% 51% 52% 1%

Antwerpen 47% 49% 53% 51% 2%

Bruxelles / Brussel 46% 49% 54% 51% 2%

Liège 48% 48% 52% 52% 1%

Burgas 46% 47% 54% 53% 1%

Sofia 48% 47% 52% 53% 0%

Zagreb 46% 46% 54% 54% 0%

Lefkosia 49% 47% 51% 53% 2%

Ostrava 44% 48% 56% 52% 4%

Praha 49% 48% 51% 52% 1%

Aalborg 51% 50% 49% 50% 1%

København 50% 49% 50% 51% 1%

Tallinn 47% 44% 53% 56% 3%

Helsinki / Helsingfors 48% 48% 52% 52% 0%

Oulu / Uleåborg 49% 50% 51% 50% 0%

Bordeaux 46% 46% 54% 54% 0%

Lille 47% 47% 53% 53% 0%

Marseille 46% 47% 54% 53% 1%

Rennes 47% 47% 53% 53% 0%

Strasbourg 47% 47% 53% 53% 0%

Paris 49% 47% 51% 53% 2%

Berlin 45% 49% 55% 51% 3%

Dortmund 47% 49% 53% 51% 2%

Essen 48% 48% 52% 52% 0%

Hamburg 48% 49% 52% 51% 1%

Leipzig 47% 49% 53% 51% 2%

München 51% 48% 49% 52% 3%

Rostock 48% 49% 52% 51% 1%

Athina 50% 47% 50% 53% 3%

Irakleio 46% 48% 54% 52% 2%

Budapest 47% 46% 53% 54% 2%

Miskolc 44% 46% 56% 54% 1%



Dublin 51% 48% 49% 52% 2%

Bologna 46% 47% 54% 53% 1%

Napoli 51% 48% 49% 52% 3%

Palermo 49% 47% 51% 53% 2%

Roma 46% 47% 54% 53% 1%

Torino 47% 47% 53% 53% 0%

Verona 47% 47% 53% 53% 0%

Vilnius 45% 44% 55% 56% 2%

Luxembourg 49% 50% 51% 50% 1%

Riga 44% 43% 56% 57% 1%

Valletta 50% 50% 50% 50% 0%

Amsterdam 50% 49% 50% 51% 2%

Groningen 51% 49% 49% 51% 1%

Rotterdam 49% 49% 51% 51% 0%

Bialystok 49% 46% 51% 54% 2%

Gdansk 48% 47% 52% 53% 1%

Kraków 48% 46% 52% 54% 2%

Warszawa 47% 45% 53% 55% 2%

Braga 48% 47% 52% 53% 1%

Lisboa 47% 46% 53% 54% 2%

Bucuresti 46% 46% 54% 54% 0%

Cluj-Napoca 48% 46% 52% 54% 1%

Piatra Neamt 46% 46% 54% 54% 0%

Bratislava 48% 46% 52% 54% 1%

Košice 43% 47% 57% 53% 4%

Ljubljana 44% 48% 56% 52% 4%

Barcelona 48% 48% 52% 52% 0%

Madrid 48% 47% 52% 53% 2%

Málaga 49% 47% 51% 53% 2%

Oviedo 50% 46% 50% 54% 4%

Malmö 50% 49% 50% 51% 1%

Stockholm 52% 49% 48% 51% 2%

Belfast 48% 48% 52% 52% 0%

Cardiff 52% 49% 48% 51% 3%

Glasgow 49% 48% 51% 52% 1%

London 49% 50% 51% 50% 1%

Manchester 47% 49% 53% 51% 2%

Tyneside conurbation 50% 49% 50% 51% 2%

Reykjavík 47% 51% 53% 49% 3%

Oslo 49% 50% 51% 50% 0%

Genève 49% 48% 51% 52% 0%

Zürich 49% 50% 51% 50% 1%



Tirana 52% 49% 48% 51% 3%

Skopje 56% 49% 44% 51% 7%

Podgorica 50% 48% 50% 52% 2%

Beograd 49% 47% 51% 53% 2%

Ankara 51% 50% 49% 50% 2%

Istanbul 52% 50% 48% 50% 2%

Antalya 51% 50% 49% 50% 1%

Diyarbakir 52% 51% 48% 49% 1%

Average deviation per gender category

2% 2%

4.1.3 Phone ownership

Since the 2019 Perception Survey worked with dual-frame approach for the sample design,

it was important to monitor during the fieldwork the distribution of numbers from the 2

sample frames (landline and mobiles) in the completed sample. This distribution is not only

relevant for the calculation of the design weights (see chapter 3), but it also indirectly

impacts the socio-demographic profile of the sample. For instance, mobile phone users are

typically somewhat younger than landline users.

As already noted (see chapter 2), for the mobile number frame it was not in each city

possible to build a gross sample that contained the envisaged amount of phone numbers

(24 times the target sample). As a countermeasure more landline numbers were included

in the gross sample in these cities. For that reason mobile phone numbers were prioritized

in these cities an called more often if needed, with the goal to increase the proportion of

mobile numbers in the final sample and bring it as close as possible to the target

distribution. This was a success in most cities. Only in 12 cities, there was a deviation from

the mobile sample targets of more than 10%, with notable spikes in Riga and Podgorica.

It should be noted, however, that such deviations are only problematic to the extent that

they result in biases in the socio-demographic profile of the samples as well. As shown

above, such biases are only limited for the parameters taken into consideration (age and

gender).

City Mobile sample Mobile target deviation mobile sample9

Fixed sample Fixed target

Graz 61% 61% 0% 39% 39%

Wien 61% 61% 0% 39% 39%

Antwerpen 52% 52% 0% 48% 48%

Bruxelles /

Brussel 52% 52% 0% 48% 48%

Liège 52% 52% 0% 48% 48%

Burgas 56% 66% -10% 44% 34%

Sofia 66% 66% 0% 34% 34%

Zagreb 53% 54% -1% 47% 46%

9 The deviation for the fixed sample is the inverse – e.g., +10% for Burgas



Lefkosia 38% 57% -19% 62% 43%

Ostrava 73% 80% -7% 27% 20%

Praha 80% 80% 0% 20% 20%

Aalborg 70% 70% 0% 30% 30%

København 69% 70% -1% 31% 30%

Tallinn 50% 69% -19% 50% 31%

Helsinki /

Helsingfors 85% 85% 0% 15% 15%

Oulu /

Uleåborg 85% 85% 0% 15% 15%

Bordeaux 49% 50% -1% 51% 50%

Lille 50% 50% 0% 50% 50%

Marseille 50% 50% 0% 50% 50%

Rennes 51% 50% 1% 49% 50%

Strasbourg 49% 50% -1% 51% 50%

Paris 50% 50% 0% 50% 50%

Berlin 50% 50% 0% 50% 50%

Dortmund 50% 50% 0% 50% 50%

Essen 50% 50% 0% 50% 50%

Hamburg 50% 50% 0% 50% 50%

Leipzig 50% 50% 0% 50% 50%

München 56% 50% 6% 44% 50%

Rostock 50% 50% 0% 50% 50%

Athina 51% 51% 0% 49% 49%

Irakleio 30% 51% -21% 70% 49%

Budapest 64% 69% -5% 36% 31%

Miskolc 51% 69% -18% 49% 31%

Dublin 63% 63% 0% 37% 37%

Bologna 59% 59% 0% 41% 41%

Napoli 59% 59% 0% 41% 41%

Palermo 59% 59% 0% 41% 41%

Roma 59% 59% 0% 41% 41%

Torino 59% 59% 0% 41% 41%

Verona 52% 59% -7% 48% 41%

Vilnius 54% 72% -18% 46% 28%

Luxembourg 56% 53% 3% 44% 47%

Rīga 32% 79% -47% 68% 21%

Valletta 49% 49% 0% 51% 51%

Amsterdam 47% 54% -7% 53% 46%

Groningen 26% 54% -28% 74% 46%

Rotterdam 54% 54% 0% 46% 46%

Białystok 62% 62% 0% 38% 38%

Gdańsk 62% 62% 0% 38% 38%

Kraków 62% 62% 0% 38% 38%



Warszawa 62% 62% 0% 38% 38%

Braga 55% 55% 0% 45% 45%

Lisboa 55% 55% 0% 45% 45%

Bucureşti 62% 62% 0% 38% 38%

Cluj-Napoca 62% 62% 0% 38% 38%

Piatra Neamţ 62% 62% 0% 38% 38%

Bratislava 69% 69% 0% 31% 31%

Košice 50% 69% -19% 50% 31%

Ljubljana 54% 54% 0% 46% 46%

Barcelona 53% 52% 1% 47% 48%

Madrid 51% 52% -1% 49% 48%

Málaga 52% 52% 0% 48% 48%

Oviedo 51% 52% -1% 49% 48%

Malmö 52% 56% -4% 48% 44%

Stockholm 57% 56% 1% 43% 44%

Belfast 54% 53% 1% 46% 47%

Cardiff 53% 53% 0% 47% 47%

Glasgow 54% 53% 1% 46% 47%

London 54% 53% 1% 46% 47%

Manchester 53% 53% 0% 47% 47%

Tyneside

conurbation 53% 53% 0% 47% 47%

Reykjavík 52% 53% -1% 48% 47%

Oslo 71% 71% 0% 29% 29%

Genève 25% 25% 0% 75% 75%

Zürich 23% 25% -2% 77% 75%

Tirana 81% 81% 0% 19% 19%

Skopje 72% 72% 0% 28% 28%

Podgorica 16% 78% -62% 84% 22%

Beograd 54% 54% 0% 46% 46%

Ankara 77% 85% -8% 23% 15%

Istanbul 76% 85% -9% 24% 15%

Antalya 57% 85% -28% 43% 15%

Diyarbakir 45% 85% -40% 55% 15%

4.1.4 Eligibility

The total proportion of respondents (on the total of respondents that were asked the

screening questions) that was ineligible for participation because they lived outside of the

targeted city regions was 21% over all cities. This means an overall incidence rate of 79%.

However, there are clear differences between the cities, as can be seen in the table below.

In 23 cities, the proportion of screen-outs because of a residence outside of the target city

is above 25%, and in seven it was above 50%, meaning that there was a higher chance

that the respondent would not be eligible for participation than that they would be eligible.



This can have several possible causes, ranging from a lacking reliability in the available

source material that was used to link sample units to post codes (i.e., respondents actually

live in another post code area than they have indicated according to the source), to

difficulties among respondents to correctly state their postcode.

It should in any case be noted that a lower incidence rate in a city does not mean that the

sample for that city is lacking quality in terms of representativity. Rather, it means that

more gross sample is needed for these cities to reach the target number of completes.

This, however, had no impact on the fieldwork planning.

City % ineligible

Cluj-Napoca 62.5%

Oulu / Uleåborg 59.1%

Tirana 58.1%

Ankara 57.8%

Verona 57.4%

Torino 55.3%

Bologna 51.6%

Valletta 48.7%

Istanbul 46.5%

Helsinki / Helsingfors 43.3%

Roma 38.1%

Antwerpen 37.5%

Liège 33.1%

Oviedo 32.5%

Ljubljana 31.3%

Miskolc 29.1%

Madrid 28.9%

Piatra Neamţ 28.3%

Málaga 28.1%

Barcelona 27.4%

Amsterdam 25.4%

Bucureşti 25.3%

Diyarbakir 25.1%

Skopje 24.8%

Palermo 24.6%

Rīga 24.2%

Kraków 23.4%

Warszawa 23.1%

Gdańsk 22.2%

Białystok 21.1%

Rotterdam 21.1%

Beograd 20.0%



Graz 19.1%

Bratislava 18.9%

Burgas 18.8%

Bordeaux 18.5%

Sofia 17.9%

Marseille 17.9%

Belfast 17.0%

Ostrava 17.0%

Bruxelles / Brussel 16.8%

Rostock 16.8%

Budapest 16.7%

København 16.7%

Strasbourg 16.6%

Oslo 16.4%

Aalborg 16.4%

Athina 16.3%

Antalya 15.8%

Groningen 15.7%

Paris 15.6%

München 14.7%

Malmö 14.6%

Dortmund 14.1%

Praha 14.0%

Hamburg 13.7%

Braga 13.7%

Lefkosia 13.7%

Essen 13.4%

Cardiff 13.4%

Lille 13.2%

Napoli 13.0%

Wien 12.7%

Zagreb 12.2%

Stockholm 11.8%

Vilnius 11.4%

Glasgow 11.3%

Tyneside conurbation 10.3%

Rennes 9.9%

Dublin 9.8%

Irakleio 9.8%

Leipzig 9.1%

Košice 8.6%

Podgorica 8.2%

Luxembourg 7.6%



Tallinn 7.3%

Berlin 6.9%

London 6.7%

Genève 6.0%

Zürich 4.6%

Lisboa 4.5%

Manchester 4.4%

Reykjavík 3.0%

5 Fieldwork performance analysis

5.1 Interview validation

During the fieldwork, a series of checks were performed once per week on all interviews

to verify their quality. This was done via the following parameters:

- interview length: interviews were flagged if their length was below 50% of the average

length for interviews in a country.10 For instance, if the average interview length in a

country is 10 minutes, interviews shorter than 5 minutes are flagged.

- straightlining: interviews were flagged if they showed straightlining in 4 of the following

multi-item questions (i.e., have the same response in all items): Q1 (10 items), Q2 (7),

Q4 (4) , Q6 (5), Q13 (5). For Q1 and Q2, straightlining on all but one of the items (i.e., 9

in Q1 and 6 in Q2 also counts as straightlining, given the high number of items.

- item non-response: interviews were flagged if there is a non-response (i.e., answering

“don’t know” or refusing to answer) on at least 30% of the substantive questions and 50%

of the socio-demo background questions. Separately, interviews are also flagged if there

is a 75% non-response rate in the large question blocks Q1 and Q2.

Any interview that was flagged for 2 of the above checks was selected for removal.

Suspicious interviews were assessed and treated (i.e., excluded and replaced) during the

fieldwork itself, so that no interviews had to me removed from the final sample.

The table below gives an overview of the interviews removed based on these checks, per

city (in cities not mentioned no cases had to be removed).

city Count %

Graz 3 0.3%

Praha 1 0.1%

Aalborg 3 0.3%

Helsinki / Helsingfors

1 0.1%

Strasbourg 1 0.1%

10 The separate calculation per country is meant to take into account natural interview length differences between

countries due to language and cultural differences.



Rostock 3 0.3%

Dublin 3 0.4%

Rotterdam 1 0.1%

Braga 1 0.1%

Lisboa 1 0.1%

Madrid 3 0.2%

Oviedo 2 0.1%

Manchester 1 0.1%

Reykjavík 1 0.1%

Genève 4 0.5%

Zürich 2 0.2%

Podgorica 1 0.1%

Beograd 1 0.1%

5.2 Interview breakoffs

The table below shows the main different parts of the questionnaire, each time with the

break-off percentage (as proportion of the total group of people that terminated the

interview before the end). In other words, the table shows at which points in the survey

respondents were most likely to quit. It stands out from this overview that the likelihood

to break off the interview is at its highest during the Q3 question block. This is not

surprising, since Q3 is the third consecutive item block after Q1 and Q2, together

comprising of 23 questions. In total, just over half of the interview break-offs occurs before

the start of Q4. From Q4 on, the break-off probability decreases again, but with a spike in

Q6 – which is again a 5-item question block.

It is notable that once the background questions are reached, almost all respondents reach

the end of the interview – only 1.5% of the break-offs occurs during the socio-demographic

background questions.

question (block)

break-off percentage

cumulative

screener 0.2% 0.2%

Q1 7.7% 7.9%

Q2 17.0% 24.9%

Q3 26.6% 51.5%

Q4 10.4% 61.9%

Q5 6.7% 68.6%

Q6 16.0% 84.6%

Q7-Q12 9.4% 94.0%

Q13 4.5% 98.5%

Q14-end 1.5% 100.0%

5.3 Item non-response

The next table below shows the 10 question items with the highest non-response rate.

question non-response

(%)



Q13.5 There is corruption in my local public

administration 20.20%

Q3.3 Is the city where you live a good place or not a

good place to live for the following groups? Gay or lesbian people.

15.38%

Q4.2 On the whole, are you very satisfied, fairly

satisfied, not very satisfied or not at all satisfied with

your personal job situation

15.35%

Q1.7 Generally speaking, please tell me if you are very

satisfied, rather satisfied, rather unsatisfied or very

unsatisfied with each of the following issues in your city

or area. - Schools and other educational facilities.

13.91%

Q1.3 Generally speaking, please tell me if you are very

satisfied, rather satisfied, rather unsatisfied or very

unsatisfied with each of the following issues in your city

or area. - Sport facilities such as sport fields and indoor

sports halls.

13.37%

Q2.2 It is easy to find a good job in my city. 11.04%

Q13.4 Information and services of my local public

administration can be easily accessed online 10.33%


good place to live for the following groups? Immigrants from other countries.

9.88%

Q2.5 It is easy to find good housing in my city at a

reasonable price. 9.59%


good place to live for the following groups? Racial and ethnic minorities.

9.45%

Q15 In general, how is your health? 8.03%

It is likely that the main reason for the higher non-response in these items is the fact that

some respondents feel that they do not have enough knowledge of, or experience with,

the topics of these questions. For instance, people who do not use a city’s educational or

sport facilities may not be able to tell whether they are satisfied with them in response to

questions Q1.3 and Q1.7, respectively. Similarly, if they have no (recent) experience with

searching for a job or a house, they might conclude that they don’t know whether it is easy

to find one in their city (cf. questions Q2.2 and Q2.5, respectively).

Two special cases are the following:

- Q4.2 is asked to all respondents, also those who are retired and still in school. For the

latter groups, the response is coded as “don’t know” during the interview, and will after

the fieldwork be recoded to “not applicable”.

- Q15 discusses the respondent’s health, which is a special category of personal data (see

2.3.4). The sensitivity of the question is in itself already likely to increase non-response.

In addition to that, GDPR requires an explicit consent verification before the question can

be asked. 5% of respondents opted out after being asked consent in Q15a, and another

3% chose not to answer the question after agreeing to hear it – bringing the total non-

response rate for Q15 to 8%.



5.4 Response rates

5.4.1 Response rates in the 2019 Perception Survey

The technical report contains an overview per city of the response rate, calculated

according to AAPOR guidelines. Specifically, the Technical Report contains the following

figures:

AAPOR response rate type 1. This is the most conservative response rate type.

It represents the number of complete interviews (700 in all cities) as a percentage

of the total working not-ineligible sample that was used in the fieldwork. With ‘not-

ineligible’ we mean all respondents that were not confirmed ineligible – a large part

of this being people that refuse to participate and for which eligibility could not be

confirmed. Ineligible respondents are not taken into account for the response rate

calculation because they do not belong to the target

AAPOR response rate type 3. This response rate figure considers partial

interviews also as successful interviews (in the sense that responses were

gathered), thus counting them together with complete interviews in the calculation

of the response rate.

AAPOR response rate type 4. This response rate type also counts partial

interviews as successful. In addition to that, it makes an assumption about the

eligibility of those respondents that could not be screened (i.e., that were not

reached of refused to participate before the screening questions could be asked).

This is calculated by adding to the calculation a factor that assumes the proportion

in the full sample that was actually ineligible (and should thus not be included in

the response rate calculation). This factor is the ratio of confirmed eligible vs.

confirmed ineligible respondents, as measured by the screening questions.

The below table shows AAPOR response rate type 4 per city. This response rate calculation

gives the best idea of how many sample units that could have led to a complete interview

actually did, precisely because it includes an approximation of how much of the base

sample was actually not eligible.

The response rate in most cities was below the 4% that was estimated at the beginning of

the project, most often ranging between 2 and 4%. In some cities, particularly those in

Turkey (12.2% on average) and Romania (10.5%), the response rate was notably higher.

City AAPOR RR4

Graz 2.8%

Wien 1.7%

Antwerpen 3.7%

Bruxelles / Brussel 3.1%

Liège 2.9%

Burgas 2.8%

Sofia 2.3%

Zagreb 1.8%

Lefkosia 2.5%

Ostrava 3.2%

Praha 3.5%



Aalborg 2.3%

København 2.4%

Tallinn 2.9%

Helsinki / Helsingfors

2.6%

Oulu / Uleåborg 2.6%

Bordeaux 2.4%

Lille 2.6%

Marseille 2.1%

Rennes 1.9%

Strasbourg 2.5%

Paris 2.2%

Berlin 2.9%

Dortmund 2.3%

Essen 2.7%

Hamburg 2.9%

Leipzig 2.6%

München 3.5%

Rostock 2.4%

Athina 2.6%

Irakleio 2.4%

Budapest 3.0%

Miskolc 3.6%

Dublin 2.5%

Bologna 4.8%

Napoli 8.2%

Palermo 5.3%

Roma 4.5%

Torino 4.5%

Verona 5.2%

Vilnius 3.5%

Luxembourg 2.7%

Riga 4.0%

Valletta 4.3%

Amsterdam 3.6%

Groningen 3.7%

Rotterdam 2.7%

Bialystok 3.9%

Gdansk 4.6%

Kraków 4.1%

Warszawa 3.9%

Braga 4.8%

Lisboa 4.3%

Bucuresti 6.2%



Cluj-Napoca 9.7%

Piatra Neamt 11.3%

Bratislava 3.1%

Košice 2.5%

Ljubljana 3.2%

Barcelona 2.9%

Madrid 2.9%

Málaga 3.2%

Oviedo 3.5%

Malmö 2.6%

Stockholm 2.5%

Belfast 2.5%

Cardiff 2.3%

Glasgow 2.2%

London 2.6%

Manchester 2.2%

Tyneside conurbation

2.1%

Reykjavík 3.1%

Oslo 2.8%

Genève 2.3%

Zürich 1.7%

Tirana 8.0%

Skopje 6.9%

Podgorica 1.8%

Beograd 6.9%

Ankara 11.5%

Istanbul 11.9%

Antalya 12.6%

Diyarbakir 12.6%

5.4.2 Impact and significance of response rates

While the response rate achieved in the 2019 Perception Survey can be considered low in

absolute terms (only about 2 in every 100 phone numbers in the gross sample resulting in

a complete interview), the response rate is not significantly below what is nowadays often

seen in CATI surveys. The decline of landline connections and the shifting habits of phone

use (towards chat message applications like Whatsapp and video messaging, rather than

phone conversations), and increased used of number recognition and number blocking on

mobile phones, makes reaching respondents ever more difficult. This is a challenge that

concerns all CATI surveys.

A general objective when organizing surveys is indeed to maximize response rates, this

both from the perspective of fieldwork efficiency as well as maintaining data quality by

avoiding non-response bias. It is the latter (non-response bias) that is sometimes seen as

the biggest risk related to declining response rates.

Non-response bias is the phenomenon where survey data are affected by non-response

because the opinions from those that participated in the survey are significantly different



from those that did not respond. It needs to be emphasized, however, that the relationship

between response rates and potential non-response bias is a complex one, and that low

non-response is a necessary but not a sufficient condition of non-response bias.

Specifically, non-response bias only applies to individual variables (rather than surveys as

a whole), and only if there is a non-zero correlation between a given variable and the

likelihood that someone participates in the survey. It also needs to be taken into account

that any non-response bias that is related to variables that are also used for weighting

(i.e., when younger respondents are harder to reach) is already balanced out – of course

only to a certain level of skews in these variables – by the weighting.

Academic studies of non-response fall into two types, Absolute Non-Response studies and

Relative Non-Response studies. Absolute Non-Response studies compare survey estimates

to good estimates of a “true” value of a variable, normally from the Census to look at total

non-response bias. Relative non-response bias studies assess how survey estimates

change with increasing fieldwork effort (e.g. number of contact attempts, extent of

reissuing). There are two key academic meta-analysis studies:

• Groves and Peycheva (2008) 11 conducted a meta-analysis of Absolute Non-

Response in 59 studies (covering 959 estimates). While they found examples of

large non-response bias existing, they also found that there was a very low

correlation between non-response bias and response rates, and greater

variation within studies than between them. They argue for the importance of

finding theories that link unit non-response to non-response bias and make a

distinction between missing respondents that don’t introduce bias and those

that do. An example would be young men, living on their own, and whether they

play sport. These type of households tend to be underrepresented in household

surveys. Weighting can help account for this, but only if those who are

interviewed are similar to those who are not. If, for example, not enough

fieldwork effort is made and those who are out playing sport several time a week

are not contacted, it could be assumed that bias would remain after weighting.

This, however, is taking into account explicitly in the Perception Survey

fieldwork setup by conducting multiple contact attempts and by spreading these

attempts over different times of the day and the week – thus succeeding in

keeping the skew towards older people relatively low.

• Sturgis et al (2016) 12 examined relative non-response bias and fieldwork effort

in 541 non-demographic variables in six surveys. They conclude that “response

rate appears to have only a weak association with non-response bias”.

In short, it is important to remember that while non-response bias does occur, it is

important to be aware of the relative importance of response rates in the overall set of

factors that eventually determine survey outcomes. With respect to survey fieldwork

organization and effective use of the available resources, it needs to be considered on a

survey-by-survey basis whether extra fieldwork effort to increase response rates or to

reach a certain pre-set target in this regards is the right strategy, given that its likely

impact will be low.

6 Data comparison 2019-2015

11 https://academic.oup.com/poq/article-abstract/72/2/167/1920564/The-Impact-of-Nonresponse-Rates-on-Nonresponse

12 https://academic.oup.com/poq/article-abstract/81/2/523/2676922



Annex 2 contains a table showing the differences between the 2019 and the 2015 results

of the Perception Survey (overall, combined for all cities). This comparison was made to

verify whether the changes in the sample design, the screening procedure and the

weighting of the data had any impact on the data that would cause deviations that go

beyond what could be considered normal variance or trend changes between two waves.

7 Recommendations for future waves

- The 2019 Perception Survey applied a new sampling approach that relied on the

availability of publicly available geolocation data linked to phone numbers to identify

eligible mobile phone sample. In evaluation of this approach, 2 major conclusions can be

drawn. First, in most cities the approach proved successful in building a sample that was

at the same time big enough (enough numbers could be gathered to yield the target

amount of mobile number interviews in the final sample) and accurate enough (in most

cities a clear majority of the selected units proved indeed to be eligible, greatly increasing

calling efficiency). Second, building a mobile phone sample in this way is time-intensive,

and in the scheduling of a future wave – should the same method be applied – enough

time should be foreseen to build a mobile sample in this way.

- Measured per age group, the deviations from the targets are quite small. There is,

however, a slight skew away from younger people (-35). This may in part be due to a

higher proportion of landline numbers in the sample, though as said the landline skew is

only moderate in the final sample. More importantly, it is also the case that younger people

are simply harder to reach over the phone, even via mobile phones. While the results of

the 2019 Perception Survey do not show reason for great concern yet, there is a possibility

that this trend in CATI surveys will continue. In the longer term, it is therefore advisable

to look into the possibilities that other survey modes (e.g., web and smartphone) and other

recruitment modes (e.g., via social media) have to offer. This should of course be

accompanied with an analysis of mode and sample source biases and the effects that they

can have on comparability.

- Already during the pilot, it became clear that the questionnaire has some elements that

create a heightened risk of observation issues. First, the sequence of big grid questions in

the beginning of the survey puts a big burden on the respondents, as is demonstrated by

the fact that we can observe a big dropout around the Q2-Q4 block. It should be noted

that while a dropout is an issue mostly for the fieldwork efficiency, with less impact on the

data quality itself (because the data from terminated interviews are not counted), it is also

indicative of a risk that responses to these questions from respondents that remain in the

interview (and are thus included in the results) are also of lower quality. This can be solved

by either making the questionnaire more focussed (i.e. shorter), or by splitting the grids

in smaller parts and spreading them over the interview, so that the cognitive load of the

interview is distributed more evenly.

Second, trying to measure respondents’ occupation in a way that is comparable with

universe statistics is notoriously difficult to do efficiently in a CATI survey. If comparison

with benchmark statistics is necessary, coding occupation according to ISCO codes is

necessary. However, identifying the right ISCO category for a respondent’s given

occupation is complex, and accuracy of this task can be considerably increased by using

an approach with multiple questions. Within the present design, however, this might be

difficult, since the questionnaire is already quite long. If this is to remain a background

variable in future waves, and if comparison against universe statistics is deemed

necessary, we advise that it is still asked by using one question, as is presently done but

with giving enough examples to interviewers on how to code the most common types of

occupations. However, if the occupation data are only needed for comparison between

cities and for research purposes without comparison to a wider universe, it can be

considered to use questions with response categories that are easier to grasp for



respondents. An example could be for instance the categories used in the Market

Monitoring Survey over the past years:

What is your current occupation?

1. Self-employed

2. Manager

3. Other white collar

4. Blue collar

5. Student

6. House-person and other not in employment

7. Seeking a job

8. Retired

- Any future wave should always identify the best screening option per city to identify

whether respondents live in the target region. This cannot always be done via postcode,

but as the Lisboa example (see 2.3.1) shows, it is also not always easy to ask for local

regions. Moreover, as administrative and statistical boundaries, as well as postcode

systems, change, it needs to be determined for each new wave whether previously used

screening questions can still work. Local expertise is indispensable here.

- English was offered in all cities as an optional language to take the interview in, in order

to include as much as possible immigrant populations that might not (yet) speak the local

language well enough to be able (or feel comfortable) to do the interview in that language.

However, it was only very rarely used – the most in Paris (7 times) and Luxembourg (9

times). To the extent that it is easy for fieldwork agencies to offer this option, it is worth

considering keeping this option. This is most likely in centrally organised fieldwork, working

from a small number of hubs. In case of heavily decentralized fieldwork, with local agencies

in each country, offering English might require extra investment from these local centres

that will not pay off, given the rare use of the option by respondents.

Annex 1. Final questionnaire

Introduction text Language. [PROG: SINGLE RESPONSE] Code the respondent language (DO NOT READ OUT)

1 Albanian show if country_sample = 32 (AL) or 33 (MK)

2 Bulgarian show if country_sample = 3 (BG)

3 Catalan show if country_sample = 26 (ES)

4 Croatian show if country_sample = 4 (HR)

5 Czech show if country_sample = 6 (CZ)

6 Danish show if country_sample = 7 (DK)

7 Dutch show if country_sample = 2 (BE) or 20 (NL)

8 English Always show

9 Estonian show if country_sample = 8 (EE)

10 Finnish show if country_sample = 9 (FI)

11 French show if country_sample = 2 (BE) or 10 (FR) or 17 (LU)

12 German show if country_sample = 1 (AT) or 11 (DE) or 17 (LU) or 31 (CH)

13 Greek show if country_sample = 5 (CY) or 12 (EL)

14 Hungarian show if country_sample = 13 (HU)

15 Icelandic show if country_sample = 29 (IS)

16 Italian show if country_sample = 15 (IT) or 31 (CH)

17 Latvian show if country_sample = 18 (LV)

18 Lithuanian show if country_sample = 16 (LT)

19 Macedonian show if country_sample = 33 (MK)

20 Maltese show if country_sample = 19 (MT)

21 Montenegrin show if country_sample = 34 (ME)

22 Norwegian show if country_sample = 30 (NO)

23 Poland show if country_sample = 21 (PL)

24 Portugal show if country_sample = 22 (PT)

25 Romanian show if country_sample = 23 (RO)

26 Russian show if country_sample = 8 (EE) or 18 (LV)

27 Serbian show if country_sample = 35 (RS)

28 Slovakian show if country_sample = 24 (SK)

29 Slovenian show if country_sample = 25 (SI)

30 Spanish show if country_sample = 26 (ES)

31 Swedish show if country_sample = 27 (SE)

32 Turkish show if country_sample = 36 (TR)

Intro1. [PROG: TEXT; Show if sample_country is not 28 (UK)]



Good morning/afternoon/evening. My name is XXX and I’m calling on behalf of Ipsos, a market research firm. We are conducting a large study for the European Commission on how people experience their life in the city. [PROG: if SampleType = 1 (mobile): continue to Intro_consentmob; if SampleType = 2 (fixed): continue to D0.]

Intro2. [PROG: TEXT; Show if sample_country = 28 (UK)] Good morning/afternoon/evening. My name is XXX and I’m calling on behalf of Ipsos, a market research firm. We are conducting a large study for an international public body on how people experience their life in the city. Interviewer Instruction: if the respondent asks for which public body the study is conducted, you can mention the European Commission. [PROG: if SampleType = 1 (mobile): continue to Intro_consent; if SampleType = 2 (fixed): continue to D0.]

D0. [PROG: SINGLE RESPONSE; Show if SampleType = 2 (fixed)] Please can I speak to the person aged 15 or older within your household whose birthday it was most recently? 1. Yes 2. Person is not available. 99. No, refusal

[PROG: IF D0 = 99 : Screen out]

D0b. [PROG: SINGLE RESPONSE; Show if D0 = 2] When would be a good moment to call back to this person? [PROG: Show appointment screen]

Intro1_2. [PROG: TEXT; Show if D0 = 1 and if sample_country is not 28 (UK)] Interviewer instruction: repeat introduction if a new respondent comes to the line: Intro1. Good morning/afternoon/evening. My name is XXX and I’m calling on behalf of Ipsos, a market research firm. We are conducting a large study for the European Commission on how people experience their life in the city.)

Intro2_2. [PROG: TEXT; Show if D0 = 1 and if sample_country is 28 (UK)]



Interviewer instruction: repeat introduction if a new respondent comes to the line: Intro2.Good morning/afternoon/evening. My name is XXX and I’m calling on behalf of Ipsos, a market research firm. We are conducting a large study for an international public body on how people experience their life in the city.) Interviewer Instruction: if the respondent asks for which public body the study is conducted, you can mention the European Commission.

Introconsent. [PROG: TEXT; Show to all] The survey will take about 10 minutes. We guarantee you that all your answers will remain anonymous, and that no personal data will be shared in any way. Before we start, I just want to clarify that participation in the survey is voluntary and you can change your mind at any time. Are you happy to proceed with the interview? Only read IF NECESSARY: If you would like to read the Privacy Notice beforehand you can access it online at https://survey.ipsos.be/privacynoticeQoLCities.pdf

Screener D1. [PROG: Quantity, 3 digits, range min. 0 – max. 115 + 999] What is your age? 999. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D1_recode. [PROG: HIDDEN VARIABLE; recode the response from D1 into the corresponding age category] 1. 15-19 2. 20-24 3. 25-34 4. 35-44 5. 45-54 6. 55-64 7. 65-74 8. 75+ 999. Don’t know/No Answer/Refuses

[PROG: IF D1 < 15 : Screen out] [PROG: IF D1 = 999 : Screen out]

D2. [PROG: SINGLE RESPONSE] What is your sex? (DO NOT READ OUT, to be observed by interviewer)



1. Male 2. Female

D3a. [PROG: SINGLE RESPONSE; show if country_sample = 28 (UK), 14 (IE), 3 (BG), 23 (RO), 20 (NL)] Do you live in …

1 The city of Belfast show if City_sample = 2801 (Belfast)

2 The city of Lisburn show if City_sample = 2801 (Belfast)

3 The borough of Castlereagh show if City_sample = 2801 (Belfast)

4 The city of Cardiff show if City_sample = 2802 (Cardiff)

5 The city of Glasgow show if City_sample = 2803 (Glasgow)

6 The council area of East Dunbartonhsire show if City_sample = 2803 (Glasgow)

7 The council area of East Renfrewshire show if City_sample = 2803 (Glasgow)

8 The council area of Renfrewshire show if City_sample = 2803 (Glasgow)

9 Greater London show if City_sample = 2804 (London)

10 The city of Newcastle upon Tyne show if City_sample = 2806 (Tyneside Conurbation)

11 The borough of North Tyneside show if City_sample = 2806 (Tyneside Conurbation)

12 The borough of South Tyneside show if City_sample = 2806 (Tyneside Conurbation)

13 The metropolitan Borough of Gateshead show if City_sample = 2806 (Tyneside Conurbation)

14 Dublin County show if City_sample = 1401 (Dublin)

15 The city of Burgas show if City_sample = 301 (Burgas)

16 Sofia Capital Municipality show if City_sample = 302 (Sofia)

17 Municipiul Bucureşti show if City_sample = 2301 (Bucuresti)

18 Municipiul Cluj-Napoca show if City_sample = 2302 (Cluj Napoca)

19 Municipiul Piatra Neamţ show if City_sample = 2303 (Piatra Neamt)

20 De gemeente Amsterdam show if City_sample = 2001 (Amsterdam)

21 De gemeente Amstelveen show if City_sample = 2001 (Amsterdam)

22 De gemeente Diemen show if City_sample = 2001 (Amsterdam)

23 De gemeente ouder-Amstel show if City_sample = 2001 (Amsterdam)

24 De gemeente Rotterdam show if City_sample = 2003 (Rotterdam)

25 De gemeente Ablasserdam show if City_sample = 2003 (Rotterdam)

26 De gemeente Barendrecht show if City_sample = 2003 (Rotterdam)

27 De gemeente Capelle-aan-den-IJssel show if City_sample = 2003 (Rotterdam)

28 De gemeente Dordrecht show if City_sample = 2003 (Rotterdam)

29 De gemeente Hendrik-Ido-Ambacht show if City_sample = 2003 (Rotterdam)

30 De gemeente Krimpen aan den IJssel show if City_sample = 2003 (Rotterdam)

31 De gemeente Papendrecht show if City_sample = 2003 (Rotterdam)

32 De gemeente Ridderkerk show if City_sample = 2003 (Rotterdam)

33 De gemeente Schiedam show if City_sample = 2003 (Rotterdam)

34 De gemeente Vlaardingen show if City_sample = 2003 (Rotterdam)

35 De gemeente Zwijndrecht show if City_sample = 2003 (Rotterdam)

36 De gemeente Groningen show if City_sample = 2002 (Groningen)

37 Greater Manchester show if City_sample = 2805 (Manchester)



98 No, I live somewhere else (DO NOT READ OUT)

99 Don’t know/No Answer/Refuses (DO NOT READ OUT)

[PROG: IF D3a = 99 : Screen out] [PROG: IF D3a = 98 : Screen out and show message “I’m sorry, you do not live in the right region to participate in this survey.“ Note: it is important the screen out is done after D3a in order to collect their answers still in D3a for sample analysis purposes

D3b. [PROG: SINGLE RESPONSE; Drop down list; show if country_sample = 22 (PT)] In which Freguesia do you live? Interviewer instruction: Do not read out list

1 Adaúfe show if City_sample = 2201 (Braga)

2 Águas Livres show if City_sample = 2202 (Lisboa)

3 Ajuda show if City_sample = 2202 (Lisboa)

4 Alcabideche show if City_sample = 2202 (Lisboa)

5 Alcântara show if City_sample = 2202 (Lisboa)

6 Alfragide show if City_sample = 2202 (Lisboa)

7 Algés, Linda-a-Velha e Cruz Quebrada-Dafundo show if City_sample = 2202 (Lisboa)

8 Almada, Cova da Piedade, Pragal e Cacilhas show if City_sample = 2202 (Lisboa)

9 Alto do Seixalinho, Santo André e Verderena show if City_sample = 2202 (Lisboa)

10 Alvalade show if City_sample = 2202 (Lisboa)

11 Amora show if City_sample = 2202 (Lisboa)

12 Areeiro show if City_sample = 2202 (Lisboa)

13 Arentim e Cunha show if City_sample = 2201 (Braga)

14 Arroios show if City_sample = 2202 (Lisboa)

15 Avenidas Novas show if City_sample = 2202 (Lisboa)

16 Barcarena show if City_sample = 2202 (Lisboa)

17 Barreiro e Lavradio show if City_sample = 2202 (Lisboa)

18 Beato show if City_sample = 2202 (Lisboa)

19 Belém show if City_sample = 2202 (Lisboa)

20 Benfica show if City_sample = 2202 (Lisboa)

21 Braga (Maximinos, Sé e Cividade) show if City_sample = 2201 (Braga)

22 Braga (São José de São Lázaro e São João do Souto) show if City_sample = 2201 (Braga)

23 Braga (São Vicente) show if City_sample = 2201 (Braga)

24 Braga (São Vítor) show if City_sample = 2201 (Braga)

25 Bucelas show if City_sample = 2202 (Lisboa)

26 Cabreiros e Passos (São Julião) show if City_sample = 2201 (Braga)

27 Camarate, Unhos e Apelação show if City_sample = 2202 (Lisboa)

28 Campo de Ourique show if City_sample = 2202 (Lisboa)

29 Campolide show if City_sample = 2202 (Lisboa)



30 Caparica e Trafaria show if City_sample = 2202 (Lisboa)

31 Carcavelos e Parede show if City_sample = 2202 (Lisboa)

32 Carnaxide e Queijas show if City_sample = 2202 (Lisboa)

33 Carnide show if City_sample = 2202 (Lisboa)

34 Cascais e Estoril show if City_sample = 2202 (Lisboa)

35 Celeirós, Aveleda e Vimieiro show if City_sample = 2201 (Braga)

36 Charneca de Caparica e Sobreda show if City_sample = 2202 (Lisboa)

37 Corroios show if City_sample = 2202 (Lisboa)

38 Costa da Caparica show if City_sample = 2202 (Lisboa)

39 Crespos e Pousada show if City_sample = 2201 (Braga)

40 Encosta do Sol show if City_sample = 2202 (Lisboa)

41 Escudeiros e Penso (Santo Estêvão e São Vicente) show if City_sample = 2201 (Braga)

42 Espinho show if City_sample = 2201 (Braga)

43 Esporões show if City_sample = 2201 (Braga)

44 Este (São Pedro e São Mamede) show if City_sample = 2201 (Braga)

45 Estrela show if City_sample = 2202 (Lisboa)

46 Falagueira-Venda Nova show if City_sample = 2202 (Lisboa)

47 Fanhões show if City_sample = 2202 (Lisboa)

48 Fernão Ferro show if City_sample = 2202 (Lisboa)

49 Ferreiros e Gondizalves show if City_sample = 2201 (Braga)

50 Figueiredo show if City_sample = 2201 (Braga)

51 Gualtar show if City_sample = 2201 (Braga)

52 Guisande e Oliveira (São Pedro) show if City_sample = 2201 (Braga)

53 Lamas show if City_sample = 2201 (Braga)

54 Laranjeiro e Feijó show if City_sample = 2202 (Lisboa)

104 Lisboa show if City_sample = 2202 (Lisboa)

55 Lomar e Arcos show if City_sample = 2201 (Braga)

56 Loures show if City_sample = 2202 (Lisboa)

57 Lousa show if City_sample = 2202 (Lisboa)

58 Lumiar show if City_sample = 2202 (Lisboa)

59 Marvila show if City_sample = 2202 (Lisboa)

60 Merelim (São Paio), Panoias e Parada de Tibães show if City_sample = 2201 (Braga)

61 Merelim (São Pedro) e Frossos show if City_sample = 2201 (Braga)

62 Mina de Água show if City_sample = 2202 (Lisboa)

63 Mire de Tibães show if City_sample = 2201 (Braga)

64 Misericórdia show if City_sample = 2202 (Lisboa)

65 Morreira e Trandeiras show if City_sample = 2201 (Braga)

66 Moscavide e Portela show if City_sample = 2202 (Lisboa)

67 Nogueira, Fraião e Lamaçães show if City_sample = 2201 (Braga)

68 Nogueiró e Tenões show if City_sample = 2201 (Braga)

69 Odivelas show if City_sample = 2202 (Lisboa)

70 Oeiras e São Julião da Barra, Paço de Arcos e Caxias show if City_sample = 2202 (Lisboa)

71 Olivais show if City_sample = 2202 (Lisboa)

72 Padim da Graça show if City_sample = 2201 (Braga)



73 Palhais e Coina show if City_sample = 2202 (Lisboa)

74 Palmeira show if City_sample = 2201 (Braga)

75 Parque das Nações show if City_sample = 2202 (Lisboa)

76 Pedralva show if City_sample = 2201 (Braga)

77 Penha de França show if City_sample = 2202 (Lisboa)

78 Pontinha e Famões show if City_sample = 2202 (Lisboa)

79 Porto Salvo show if City_sample = 2202 (Lisboa)

80 Póvoa de Santo Adrião e Olival Basto show if City_sample = 2202 (Lisboa)

81 Priscos show if City_sample = 2201 (Braga)

82 Ramada e Caneças show if City_sample = 2202 (Lisboa)

83 Real, Dume e Semelhe show if City_sample = 2201 (Braga)

84 Ruilhe show if City_sample = 2201 (Braga)

85 Sacavém e Prior Velho show if City_sample = 2202 (Lisboa)

86 Santa Clara show if City_sample = 2202 (Lisboa)

87 Santa Iria de Azoia, São João da Talha e Bobadela show if City_sample = 2202 (Lisboa)

88 Santa Lucrécia de Algeriz e Navarra show if City_sample = 2201 (Braga)

89 Santa Maria Maior show if City_sample = 2202 (Lisboa)

90 Santo Antão e São Julião do Tojal show if City_sample = 2202 (Lisboa)

91 Santo António show if City_sample = 2202 (Lisboa)

92 Santo António da Charneca show if City_sample = 2202 (Lisboa)

93 Santo António dos Cavaleiros e Frielas show if City_sample = 2202 (Lisboa)

94 São Domingos de Benfica show if City_sample = 2202 (Lisboa)

95 São Domingos de Rana show if City_sample = 2202 (Lisboa)

96 São Vicente show if City_sample = 2202 (Lisboa)

97 Seixal, Arrentela e Aldeia de Paio Pires show if City_sample = 2202 (Lisboa)

98 Sequeira show if City_sample = 2201 (Braga)

99 Sobreposta show if City_sample = 2201 (Braga)

100 Tadim show if City_sample = 2201 (Braga)

101 Tebosa show if City_sample = 2201 (Braga)

102 Venteira show if City_sample = 2202 (Lisboa)

103 Vilaça e Fradelos show if City_sample = 2201 (Braga)

998 No, I live somewhere else (DO NOT READ OUT)

999 Don’t know/No Answer/Refuses (DO NOT READ OUT)

[PROG: IF D3b = 999 : Screen out] [PROG: IF D3b = 998 : Screen out and show message “I’m sorry, you do not live in the right region to participate in this survey.“ Note: it is important the screen out is done after D3b in order to collect their answers still in D3b for sample analysis purposes

D3c_1. [PROG: Open end box; range: min. 3 characters – max. 3 characters; show if country_sample = 19 (MT)] What is your postcode?



Interviewer instruction: only use letters to record the postcode, no numbers 999. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D3c_2. [PROG: Quantity; 3 digits; range: min 100 – max 999; show if country_sample = 29 (IS)] What is your postcode? Interviewer instruction: only use numbers to record the postcode, no spaces or hyphens 999. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D3c_3. [PROG: Quantity; 4 digits; range: min 0000 – max 9999; show if country_sample = 30 (NO)] What is your postcode? Interviewer instruction: only use numbers to record the postcode, no spaces or hyphens 9999. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D3c_4. [PROG: Quantity; 4 digits; range: min 1000 – max 9999; show if country_sample = 1 (AT), 2 (BE), 5 (CY), 7 (DK), 13 (HU), 17 (LU), 18 (LV), 25 (SI), 31 (CH), 32 (AL), 33 (MK))] What is your postcode? Interviewer instruction: only use numbers to record the postcode, no spaces or hyphens 9999. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D3c_5. [PROG: Quantity; 5 digits; range: min 00000 – max 99999; show if country_sample = 8 (EE), 9 (FI), 10 (FR), 11 (DE), 15 (IT), 16 (LT), 21 (PL), 24 (SK), 26 (ES), 34 (ME), 35 (RS), 36 (TR)] What is your postcode? Interviewer instruction: only use numbers to record the postcode, no spaces or hyphens 99999. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D3c_6. [PROG: Quantity; 5 digits; range: min 10000 – max 99999; show if country_sample = 4 (HR), 6 (CZ), 12 (EL), 27 (SE))] What is your postcode? Interviewer instruction: only use numbers to record the postcode, no spaces or hyphens 999999. Don’t know/No Answer/Refuses (DO NOT READ OUT)

[PROG: IF D3c_1 = 999 : Screen out] [PROG: IF D3c_2 = 999 : Screen out] [PROG: IF D3c_3 = 9999 : Screen out] [PROG: IF D3c_4 = 9999 : Screen out] [PROG: IF D3c_5 = 99999 : Screen out] [PROG: IF D3c_6 = 999999 : Screen out]



[PROG: match input in D3c_1-6 against list “D3c – postcodes” in Annex. Only match against postcodes from sample country (columns A-B in annex list), not from other countries! If postcode does not match against a code in the list, go to D3d: Screen out and show message “I’m sorry, you do not live in the right region to participate in this survey.“ Note: it is important the screen out is done after D3c in order to collect their answers still in D3c for sample analysis purposes]

D3_cityrecode. [PROG: HIDDEN VARIABLE; recode D3c_1-6 into city_code value (see sheet D3C – postcodes in annex] Note: labels of D3_cityrecode are identical to city_sample build the variable using the info from column I, F, B,E, C e.g. if D3c_4 (column I) = 1070 (column F) & Country_sample = 2 (Column B) D3_cityrecode = 202 (column E) Bruxelles/Brussel (column C) IF country_sample = 28 (UK), 14 (IE), 3 (BG), 23 (RO), 20 (NL) or 22 (PT) autocode D3_cityrecode = city_sample

D3_lau_recode. [PROG: HIDDEN VARIABLE; recode D3c_1-6 into lau_code value (see sheet D3C – postcodes in annex] Note: labels of D3_lau_recode are identical to lau_sample build the variable using the info from column I, F, B,D, G e.g. if D3c_4 (column I) = 1070 (column F) & Country_sample = 2 (Column B) D3_lau_recode = 202001 (column D) Anderlecht (column G) + IF country_sample = 28 (UK), 14 (IE), 3 (BG), 23 (RO), 20 (NL) or 22 (PT) use the below tables for the recode

If D3a = D3c_lau_recode =

1 2801001

2 2801002

3 2801002

4 2802001

5 2803002

6 2803001

7 2803003

8 2803004



10 2806001

11 2806002

12 2806003

13 2806004

15 301001

16 302001

17 2301001

18 2302001

19 2303001

20 2001002

21 2001001

22 2001003

23 2001004

24 2003009

25 2003001

26 2003002

27 2003003

28 2003004

29 2003005

30 2003006

31 2003007

32 2003008

33 2003010

34 2003011

35 2003012

36 2002001

Recode D3b into lau_code as follows:

if D3b = D3_lau_recode =

1 2201001

2 2202045

3 2202005

4 2202001

5 2202006

6 2202044

7 2202041

8 2202055

9 2202060

10 2202015

11 2202063

12 2202016

13 2201019

14 2202017



15 2202018

16 2202039

17 2202061

18 2202007

19 2202019

20 2202008

21 2201020

22 2201021

23 2201013

24 2201014

25 2202029

26 2201022

27 2202038

28 2202020

29 2202009

30 2202056

31 2202003

32 2202042

33 2202010

34 2202004

35 2201023

36 2202057

37 2202064

38 2202054

39 2201024

40 2202046

41 2201025

42 2201002

43 2201003

44 2201026

45 2202021

46 2202047

47 2202030

48 2202065

49 2201027

50 2201004

51 2201005

52 2201028

53 2201006

54 2202058

55 2201029

56 2202031

57 2202032

58 2202011

59 2202012



60 2201030

61 2201031

62 2202048

63 2201007

64 2202022

65 2201032

66 2202033

67 2201033

68 2201034

69 2202050

70 2202043

71 2202013

72 2201008

73 2202062

74 2201009

75 2202023

76 2201010

77 2202024

78 2202051

79 2202040

80 2202052

81 2201011

82 2202053

83 2201035

84 2201012

85 2202034

86 2202025

87 2202035

88 2201036

89 2202026

90 2202036

91 2202027

92 2202059

93 2202037

94 2202014

95 2202002

96 2202028

97 2202066

98 2201015

99 2201016

100 2201017

101 2201018

102 2202049

103 2201037



104 No LAU recode

D4. [PROG: hidden empty variable] DEGURBA

Main Questionnaire Q1. [PROG: SINGLE RESPONSE GRID] Generally speaking, please tell me if you are very satisfied, rather satisfied, rather unsatisfied or very unsatisfied with each of the following issues in your city or area. Rows [PROG: Randomise items 1-10] 1. Public transport, for example the bus, tram or metro. 2. Health care services, doctors and hospitals. 3. Sport facilities such as sport fields and indoor sports halls. 4. Cultural facilities such as concert halls, theatres, museums and libraries. 5. Green spaces such as parks and gardens. 6. Public spaces such as markets, squares, pedestrian areas. 7. Schools and other educational facilities. 8. The quality of the air. 9. The noise level. 10. Cleanliness. Columns 4. Very satisfied 3. Rather satisfied 2. Rather unsatisfied 1. Very unsatisfied 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q2. [PROG: SINGLE RESPONSE GRID] I will read you a few statements. Please tell me whether you strongly agree, somewhat agree, somewhat disagree or strongly disagree with each of these statements. Rows [PROG: Randomise items 1-7; Treat 3-4 and 6-7 as fixed pairs: Make sure that item 4 always comes right after 3, and item 7 right after 6] 1. I'm satisfied to live in my city. 2. It is easy to find a good job in my city. 3. I feel safe walking alone at night in my city. 4. I feel safe walking alone at night in my neighbourhood. 5. It is easy to find good housing in my city at a reasonable price. 6. Generally speaking, most people in my city can be trusted.



7. Generally speaking, most people in my neighbourhood can be trusted. Columns 4. Strongly agree 3. Somewhat agree 2. Somewhat disagree 1. Strongly disagree 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q3. [PROG: SINGLE RESPONSE GRID] Is the city where you live a good place or not a good place to live for the following groups? Rows [PROG: Randomise Rows; Keep item 1 always first, randomise items 2-6] 1. People in general. [PROG: Fixed] 2. Racial and ethnic minorities. 3. Gay or lesbian people. 4. Immigrants from other countries. 5. Young families with children. 6. Elderly people. Columns 1. A good place to live 2. Not a good place to live 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q4. [PROG: SINGLE RESPONSE GRID] On the whole, are you very satisfied, fairly satisfied, not very satisfied or not at all satisfied with: Rows [PROG: Randomise items 1-4] 1. The neighbourhood where you live 2. Your personal job situation. 3. The financial situation of your household. 4. The life you lead. Columns 4. Very satisfied 3. Fairly satisfied 2. Not very satisfied 1. Not at all satisfied 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q5. [PROG: MULTIPLE RESPONSE; max. 2 responses allowed] On a typical day, which mode(s) of transport do you use most often?… Interviewer instruction: allow 2 responses if offered spontaneously by the respondent, but do not probe if only 1 is given.



1. Car 2. Motorcycle 3. Bicycle 4. Foot 5. Train 6. Urban public transport (bus, tram or metro) 7. Other 98. Do not commute [PROG: Single Response] 99. Don’t know/No Answer/Refuses (DO NOT READ OUT) [PROG: Single Response]

Q6. [PROG: SINGLE RESPONSE GRID] Thinking about public transport in your city, based on your experience or perceptions, please tell me whether you strongly agree, somewhat agree, somewhat disagree or strongly disagree with each of these statements. Public transport in your city is: Rows [PROG: Randomise items 1-5] 1. Affordable 2. Safe 3. Easy to get 4. Frequent (comes often) 5. Reliable (comes when it says it will) Columns 4. Strongly agree 3. Somewhat agree 2. Somewhat disagree 1. Strongly disagree 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q7. [PROG: SINGLE RESPONSE] In the city where you live, do you have confidence in the local police force? 1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q8. [PROG: SINGLE RESPONSE] Within the last 12 months, was any money or property stolen from you or another household member in your city? 1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q9. [PROG: SINGLE RESPONSE] Within the last 12 months, have you been assaulted or mugged in your city?



1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q10. [PROG: SINGLE RESPONSE] Within the last 12 months, would you say you had difficulties to pay your bills at the end of the month … 1. Most of the time 2. From time to time 3. Almost never/never 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q11. [PROG: SINGLE RESPONSE] Do you feel that if you needed material help (e.g. money, loan or an object) you could receive it from relatives, friends, neighbours or other persons you know? 1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q12. [PROG: SINGLE RESPONSE] Do you feel that if you needed non-material help (e.g. somebody to talk to, help with doing something or collecting something) you could receive it from relatives, friends, neighbours or other persons you know? 1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q13. [PROG: SINGLE RESPONSE GRID] I will read you a few statements about the local public administration in your city. Please tell me whether you strongly agree, somewhat agree, somewhat disagree or strongly disagree with each of these statements. Rows [PROG: Randomise items 1-5] 1. I am satisfied with the amount of time it takes to get a request solved by my local public administration. 2. The procedures used by my local public administration are straightforward and easy to understand 3. The fees charged by my local public administration are reasonable 4. Information and services of my local public administration can be easily accessed online 5. There is corruption in my local public administration Columns 4. Strongly agree



3. Somewhat agree 2. Somewhat disagree 1. Strongly disagree 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Q14. [PROG: SINGLE RESPONSE] Compared to five years ago, would you say the quality of life in your city or area has: 1. Decreased 2. Stayed the same 3. Increased 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

Socio Demographic questions D5. [PROG: SINGLE RESPONSE; insert answer list “D5 – Countries”as drop down] In which country were you born?

D6. [PROG: SINGLE RESPONSE] Have you ever lived in another city for at least 1 year? 1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D7. [PROG: Quantity; only if D6 = 1; min. 0; max. 115] How many years have you been living in your current city since last moving here? Interviewer instruction: If respondent answers “less than 1 year”, code as 0 999. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D9. [PROG: Quantity; min. 1; max. 15] How many people usually live in your household? Please include yourself.

D9b. [PROG: Quantity; only if D9 > 1; min.1.; max. = answer given in D9] How many of these are aged 15 and older? Please include yourself. [PROG: autocode D9b = 1 if D9 = 1]



D8. [PROG: SINGLE RESPONSE. ONLY IF D9 > 1] Which of the following best describes your household composition? With household, we mean all people that typically live with you in the same residence. Please include anyone who is temporarily away for work, study or vacation [PROG: autocode D8 = 1 if D9 = 1] 1. One-person household [PROG: do not show. If D9 = 1, autocode D8 = 1] 2. Lone parent with at least one child aged less than 25 3. Lone parent with all children aged 25 or more 4. Couple without any child(ren) 5. Couple with at least one child aged less than 25 6. Couple with all children aged 25 or more 7. Other type of household 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D10local. [PROG: SINGLE RESPONSE; insert answer list “D10 – education”; use the value and show “Educ categories ENGLISH” in the master questionnaire and the “Educ categories LOCAL” for the local translations] What is the highest level of education you have successfully completed? Interviewer instruction: DO NOT READ OUT response options unless needed to proceed 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D10ISCED. [PROG: HIDDEN VARIABLE; recode the response from D10local into the corresponding isced level as indicated in the column “isced code”] 1. Less than Primary education (ISCED 0) 2. Primary education (ISCED 1) 3. Lower secondary education (ISCED 2) 4. Upper secondary education (ISCED 3) 5. Post-secondary non-tertiary education (ISCED 4) 6. Short-cycle tertiary education (ISCED 5) 7. Bachelor or equivalent (ISCED 6) 8. Master or equivalent (ISCED 7) 9. Doctoral or equivalent (ISCED 8) 10. Don’t know/No Answer/Refuses

D11a. [PROG: SINGLE RESPONSE] Do you currently have a job? Interviewer instruction: Include employees, employers, self-employed and people working as a relative assisting on family business. DO NOT INCLUDE people in compulsory military service or full-time homemakers.



1. Yes 2. No 99. Don’t know/No Answer/Refuses

D11. [PROG: SINGLE RESPONSE, DO NOT SHOW IF D11a = 1] Which of the following best describes your current working status? 1. At work as employee or employer/self-employed/relative assisting on family business [PROG: do not show. If D11a = 1, autocode D11 = 1] 2. Unemployed, not looking actively for a job 3. Unemployed, looking actively for a job 4. Retired 5. Unable to work due to long-standing health problems 6. In full-time education (at school, university, etc.) / student 7. Full-time homemaker/responsible for ordinary shopping and looking after home 8. Compulsory military or civilian service 9. Other 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D12. [PROG: SINGLE RESPONSE; only ask if D11 =1] What is your current job? Interviewer instruction: DO NOT READ OUT response options unless needed to proceed. If respondent is unsure, ask to state their exact job/function and propose a suitable category. If a respondent is in the military, always code as “armed forces occupation”, regardless of their job within the military. 1. Manager 2. Professional 3. Technician and associate professional 4. Clerical support worker 5. Services and sales worker 6. Agricultural, forestry or fishery worker 7. Craft or related trade worker 8. Plant or machine operator or assembler 9. Elementary occupation 10. Armed forces occupation [PROG: autocode D12 = 10 if D11 = 8] 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D13. [PROG: SINGLE RESPONSE; ask if D11 = 1 or D11 = 8] Which of the following best describes your job? 1. Full-time job 2. Part-time job 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)

D14. [PROG: SINGLE RESPONSE; ask if SampleType = 2 (Fixed)] Do you personally own a mobile phone?



1. Yes 2. No [PROG: autocode D14 = 1 if SampleType = 1 (mobile sample)]

D15. [PROG: SINGLE RESPONSE; ask if SampleType = 1 (Mobile)] Do you have a landline phone in the household? 1. Yes 2. No [PROG: autocode D15 = 1 if SampleType = 2 (fixed sample)]

Mobfix. [PROG: HIDDEN VARIABLE; recode the response from D14 and D15 into the corresponding category]

1. Fixed only: If (SampleType = 2 and D14 = 2) 2. Mobile only: if (SampleType = 1 and D15 = 2) 3. Mixed: if (SampleType = 2 and D14 = 1) OR or (SampleType = 1 and D15 = 1)

Q15a [PROG: SINGLE RESPONSE] The next question is about your health status. Please remember that all your responses will be treated confidentially. You do not have to answer this question if you do not want to. Are you happy to proceed?

1. Yes

2. No Q15. [PROG: SINGLE RESPONSE, ask if Q15a=1] In general, how is your health? [PROG: autocode Q15=99 if Q15a = 2] 5. Very good 4. Good 3. Fair (neither good or bad) 2. Bad 1. Very bad 99. Don’t know/No Answer/Refuses (DO NOT READ OUT) Outro1.

Only read IF NECESSARY: Thank you for taking the time to participate in this study. You can access the privacy notice here: https://survey.ipsos.be/privacynoticeQoLCities.pdf. This explains the purposes for

https://survey.ipsos.be/privacynoticeQoLCities.pdf



processing your personal data as well as your rights under data protection regulations to access your personal data, withdraw consent, object to processing of your personal data and other required information.

Annex 2. List of LAUs per city

Country City Name LAU CODE LAU LABEL

AL Tirana AL0310 Durrës

AL Tirana AL1150 Tiranë

AL Tirana AL1151 Kamëz

AT Wien 90001 Wien

AT Graz 60101 Graz

BE Bruxelles / Brussel 21001 Anderlecht

BE Bruxelles / Brussel 21002 Auderghem / Oudergem

BE Bruxelles / Brussel 21003 Berchem-Sainte-Agathe / Sint-Agatha-Berchem

BE Bruxelles / Brussel 21004 Bruxelles / Brussel

BE Bruxelles / Brussel 21005 Etterbeek

BE Bruxelles / Brussel 21006 Evere

BE Bruxelles / Brussel 21007 Forest / Vorst

BE Bruxelles / Brussel 21008 Ganshoren

BE Bruxelles / Brussel 21009 Ixelles / Elsene

BE Bruxelles / Brussel 21010 Jette

BE Bruxelles / Brussel 21011 Koekelberg

BE Bruxelles / Brussel 21012 Molenbeek-Saint-Jean / Sint-Jans-Molenbeek

BE Bruxelles / Brussel 21013 Saint-Gilles / Sint-Gillis

BE Bruxelles / Brussel 21014 Saint-Josse-ten-Noode / Sint-Joost-ten-Node

BE Bruxelles / Brussel 21015 Schaerbeek / Schaarbeek

BE Bruxelles / Brussel 21016 Uccle / Ukkel

BE Bruxelles / Brussel 21017 Watermael-Boitsfort / Watermaal-Bosvoorde

BE Bruxelles / Brussel 21018 Woluwe-Saint-Lambert / Sint-Lambrechts-Woluwe

BE Bruxelles / Brussel 21019 Woluwe-Saint-Pierre / Sint-Pieters-Woluwe

BE Antwerpen 11002 Antwerpen / Anvers

BE Liège 62003 Ans

BE Liège 62015 Beyne-Heusay

BE Liège 62038 Fléron

BE Liège 62051 Herstal

BE Liège 62063 Liège / Luik

BE Liège 62093 Saint-Nicolas

BE Liège 62096 Seraing

BG Sofia 68134 София

BG Burgas 07079 Бургас

CH Zürich CH0054 Dietlikon

CH Zürich CH0062 Kloten

CH Zürich CH0066 Opfikon

CH Zürich CH0069 Wallisellen

CH Zürich CH0097 Rümlang

CH Zürich CH0131 Adliswil

CH Zürich CH0135 Kilchberg (ZH)

CH Zürich CH0136 Langnau am Albis



CH Zürich CH0139 Rüschlikon

CH Zürich CH0141 Thalwil

CH Zürich CH0161 Zollikon

CH Zürich CH0191 Dübendorf

CH Zürich CH0200 Wangen-Brüttisellen

CH Zürich CH0243 Dietikon

CH Zürich CH0245 Oberengstringen

CH Zürich CH0247 Schlieren

CH Zürich CH0249 Unterengstringen

CH Zürich CH0250 Urdorf

CH Zürich CH0261 Zürich

CH Genève CH6608 Carouge (GE)

CH Genève CH6612 Chêne-Bougeries

CH Genève CH6613 Chêne-Bourg

CH Genève CH6617 Cologny

CH Genève CH6621 Genève

CH Genève CH6628 Lancy

CH Genève CH6631 Onex

CH Genève CH6633 Plan-les-Ouates

CH Genève CH6634 Pregny-Chambésy

CH Genève CH6640 Thônex

CH Genève CH6641 Troinex

CH Genève CH6643 Vernier

CH Genève CH6645 Veyrier

CY Lefkosia 1000 Λευκωσία

CY Lefkosia 1010 Άγιος Δομέτιος

CY Lefkosia 1011 Έγκωμη Λευκωσίας

CY Lefkosia 1012 Στρόβολος

CY Lefkosia 1013 Αγλαντζιά ή Αγλαγγιά

CY Lefkosia 1021 Λακατάμεια

CY Lefkosia 1022 Συνοικισμός Ανθούπολης

CY Lefkosia 1023 Λατσιά ή Λακκιά

CY Lefkosia 1024 Γέρι

CZ Praha 554782 Praha

CZ Ostrava 554821 Ostrava

DE Berlin 11000000 Berlin, Stadt

DE Hamburg 02000000 Hamburg, Freie und Hansestadt

DE München 09162000 München, Landeshauptstadt

DE Essen 05113000 Essen, Stadt

DE Leipzig 14713000 Leipzig, Stadt

DE Dortmund 05913000 Dortmund, Stadt

DE Rostock 13003000 Rostock, Hansestadt

DK København 101 København

DK København 147 Frederiksberg



DK København 153 Brøndby

DK København 157 Gentofte

DK København 159 Gladsaxe

DK København 161 Glostrup

DK København 163 Herlev

DK København 165 Albertslund

DK København 167 Hvidovre

DK København 173 Lyngby-Taarbæk

DK København 175 Rødovre

DK København 183 Ishøj

DK København 185 Tårnby

DK København 187 Vallensbæk

DK København 253 Greve

DK Aalborg 851 Aalborg

EE Tallinn 0784 Tallinn

EL Athina 45010000 Ψευδοδημοτικη Κοινοτητα Αθηναίων

EL Athina 45020000 Ψευδοδημοτικη Κοινοτητα Βύρωνος

EL Athina 45030000 Ψευδοδημοτικη Κοινοτητα Γαλατσίου

EL Athina 45040101 Δημοτική Κοινότητα Δάφνης

EL Athina 45040201 Δημοτική Κοινότητα Υμηττού

EL Athina 45050000 Ψευδοδημοτικη Κοινοτητα Ζωγράφου

EL Athina 45060000 Ψευδοδημοτικη Κοινοτητα Ηλιουπόλεως

EL Athina 45070000 Ψευδοδημοτικη Κοινοτητα Καισαριανής

EL Athina 45080101 Δημοτική Κοινότητα Νέας Φιλαδελφείας

EL Athina 45080201 Δημοτική Κοινότητα Νέας Χαλκηδόνος

EL Athina 46010000 Ψευδοδημοτικη Κοινοτητα Αμαρουσίου

EL Athina 46020000 Ψευδοδημοτικη Κοινοτητα Αγίας Παρασκευής

EL Athina 46030000 Ψευδοδημοτικη Κοινοτητα Βριλησσίων

EL Athina 46040000 Ψευδοδημοτικη Κοινοτητα Ηρακλείου

EL Athina 46050101 Δημοτική Κοινότητα Κηφισιάς

EL Athina 46050201 Δημοτική Κοινότητα Εκάλης

EL Athina 46050301 Δημοτική Κοινότητα Νέας Ερυθραίας

EL Athina 46060101 Δημοτική Κοινότητα Πεύκης

EL Athina 46060201 Δημοτική Κοινότητα Λυκοβρύσεως

EL Athina 46070000 Ψευδοδημοτικη Κοινοτητα Μεταμορφώσεως

EL Athina 46080000 Ψευδοδημοτικη Κοινοτητα Νέας Ιωνίας

EL Athina 46090101 Δημοτική Κοινότητα Χολαργού

EL Athina 46090201 Δημοτική Κοινότητα Παπάγου

EL Athina 46100101 Δημοτική Κοινότητα Μελισσίων

EL Athina 46100201 Δημοτική Κοινότητα Νέας Πεντέλης

EL Athina 46100301 Δημοτική Κοινότητα Πεντέλης

EL Athina 46110101 Δημοτική Κοινότητα Ψυχικού

EL Athina 46110201 Δημοτική Κοινότητα Νέου Ψυχικού

EL Athina 46110301 Δημοτική Κοινότητα Φιλοθέης

EL Athina 46120000 Ψευδοδημοτικη Κοινοτητα Χαλανδρίου



EL Athina 47010000 Ψευδοδημοτικη Κοινοτητα Περιστερίου

EL Athina 47020000 Ψευδοδημοτικη Κοινοτητα Αγίας Βαρβάρας

EL Athina 47030101 Δημοτική Κοινότητα Αγίων Αναργύρων

EL Athina 47030201 Δημοτική Κοινότητα Καματερού

EL Athina 47040000 Ψευδοδημοτικη Κοινοτητα Αιγάλεω

EL Athina 47050000 Ψευδοδημοτικη Κοινοτητα Ιλιου

EL Athina 47060000 Ψευδοδημοτικη Κοινοτητα Πετρουπόλεως

EL Athina 47070000 Ψευδοδημοτικη Κοινοτητα Χαϊδαρίου

EL Athina 48010000 Ψευδοδημοτικη Κοινοτητα Καλλιθέας

EL Athina 48020000 Ψευδοδημοτικη Κοινοτητα Αγίου Δημητρίου

EL Athina 48030000 Ψευδοδημοτικη Κοινοτητα Αλίμου

EL Athina 48040000 Ψευδοδημοτικη Κοινοτητα Γλυφάδας

EL Athina 48050101 Δημοτική Κοινότητα Αργυρούπολης

EL Athina 48050201 Δημοτική Κοινότητα Ελληνικού

EL Athina 48060101 Δημοτική Κοινότητα Μοσχάτου

EL Athina 48060201 Δημοτική Κοινότητα Ταύρου

EL Athina 48070000 Ψευδοδημοτικη Κοινοτητα Νέας Σμύρνης

EL Athina 48080000 Ψευδοδημοτικη Κοινοτητα Παλαιού Φαλήρου

EL Athina 49010101 Δημοτική Κοινότητα Αχαρνών

EL Athina 49010201 Δημοτική Κοινότητα Θρακομακεδόνων

EL Athina 49020101 Δημοτική Κοινότητα Βούλας

EL Athina 49020201 Δημοτική Κοινότητα Βάρης

EL Athina 49020301 Δημοτική Κοινότητα Βουλιαγμένης

EL Athina 49080201 Δημοτική Κοινότητα Γλυκών Νερών

EL Athina 49090101 Δημοτική Κοινότητα Γέρακα

EL Athina 49090201 Δημοτική Κοινότητα Ανθούσας

EL Athina 49090301 Δημοτική Κοινότητα Παλλήνης

EL Athina 50050101 Δημοτική Κοινότητα Άνω Λιοσίων

EL Athina 50050201 Δημοτική Κοινότητα Ζεφυρίου

EL Athina 51010000 Ψευδοδημοτικη Κοινοτητα Πειραιώς

EL Athina 51020101 Δημοτική Κοινότητα Κερατσινίου

EL Athina 51020201 Δημοτική Κοινότητα Δραπετσώνας

EL Athina 51030000 Ψευδοδημοτικη Κοινοτητα Κορυδαλλού

EL Athina 51040101 Δημοτική Κοινότητα Νικαίας

EL Athina 51040201 Δημοτική Κοινότητα Αγίου Ιωάννου Ρέντη

EL Irakleio 71010100 Ψευδοδημοτικη Κοινοτητα Ηρακλείου

ES Madrid 28006 Alcobendas

ES Madrid 28007 Alcorcón

ES Madrid 28049 Coslada

ES Madrid 28058 Fuenlabrada

ES Madrid 28065 Getafe

ES Madrid 28074 Leganés

ES Madrid 28079 Madrid

ES Madrid 28080 Majadahonda



ES Madrid 28092 Móstoles

ES Madrid 28106 Parla

ES Madrid 28115 Pozuelo de Alarcón

ES Madrid 28123 Rivas-Vaciamadrid

ES Madrid 28127 Rozas de Madrid, Las

ES Madrid 28130 San Fernando de Henares

ES Madrid 28134 San Sebastián de los Reyes

ES Barcelona 08003 Alella

ES Barcelona 08015 Badalona

ES Barcelona 08019 Barcelona

ES Barcelona 08056 Castelldefels

ES Barcelona 08073 Cornellà de Llobregat

ES Barcelona 08077 Esplugues de Llobregat

ES Barcelona 08089 Gavà

ES Barcelona 08101 Hospitalet de Llobregat, L'

ES Barcelona 08118 Masnou, El

ES Barcelona 08125 Montcada i Reixac

ES Barcelona 08126 Montgat

ES Barcelona 08169 Prat de Llobregat, El

ES Barcelona 08180 Ripollet

ES Barcelona 08184 Rubí

ES Barcelona 08187 Sabadell

ES Barcelona 08194 Sant Adrià de Besòs

ES Barcelona 08200 Sant Boi de Llobregat

ES Barcelona 08205 Sant Cugat del Vallès

ES Barcelona 08211 Sant Feliu de Llobregat

ES Barcelona 08217 Sant Joan Despí

ES Barcelona 08221 Sant Just Desvern

ES Barcelona 08238 Sant Quirze del Vallès

ES Barcelona 08245 Santa Coloma de Gramenet

ES Barcelona 08252 Barberà del Vallès

ES Barcelona 08266 Cerdanyola del Vallès

ES Barcelona 08279 Terrassa

ES Barcelona 08281 Teià

ES Barcelona 08282 Tiana

ES Barcelona 08301 Viladecans

ES Barcelona 08904 Badia del Vallès

ES Málaga 29067 Málaga

ES Oviedo 33044 Oviedo

FI Helsinki / Helsingfors 049 Espoo / Esbo

FI Helsinki / Helsingfors 091 Helsinki / Helsingfors

FI Helsinki / Helsingfors 092 Vantaa / Vanda

FI Helsinki / Helsingfors 235 Kauniainen / Grankulla

FI Oulu / Uleåborg 564 Oulu / Uleåborg

FR Paris 75101 Paris 1er Arrondissement



FR Paris 75102 Paris 2e Arrondissement



















FR Paris 77055 Brou-sur-Chantereine

FR Paris 77083 Champs-sur-Marne

FR Paris 77108 Chelles

FR Paris 77121 Collégien

FR Paris 77139 Courtry

FR Paris 77169 Émerainville

FR Paris 77258 Lognes

FR Paris 77294 Mitry-Mory

FR Paris 77337 Noisiel

FR Paris 77373 Pontault-Combault

FR Paris 77390 Roissy-en-Brie

FR Paris 77468 Torcy

FR Paris 77479 Vaires-sur-Marne

FR Paris 77514 Villeparisis

FR Paris 78005 Achères

FR Paris 78007 Aigremont

FR Paris 78015 Andrésy

FR Paris 78092 Bougival

FR Paris 78123 Carrières-sous-Poissy

FR Paris 78124 Carrières-sur-Seine

FR Paris 78126 Celle-Saint-Cloud

FR Paris 78133 Chambourcy

FR Paris 78138 Chanteloup-les-Vignes

FR Paris 78146 Chatou

FR Paris 78158 Chesnay



FR Paris 78165 Clayes-sous-Bois

FR Paris 78168 Coignières

FR Paris 78172 Conflans-Sainte-Honorine

FR Paris 78190 Croissy-sur-Seine

FR Paris 78208 Élancourt

FR Paris 78251 Fourqueux

FR Paris 78297 Guyancourt

FR Paris 78311 Houilles

FR Paris 78350 Louveciennes

FR Paris 78358 Maisons-Laffitte

FR Paris 78367 Mareil-Marly

FR Paris 78372 Marly-le-Roi

FR Paris 78382 Maurecourt

FR Paris 78383 Maurepas

FR Paris 78396 Mesnil-le-Roi

FR Paris 78418 Montesson

FR Paris 78423 Montigny-le-Bretonneux

FR Paris 78481 Pecq

FR Paris 78490 Plaisir

FR Paris 78498 Poissy

FR Paris 78502 Port-Marly

FR Paris 78524 Rocquencourt

FR Paris 78551 Saint-Germain-en-Laye

FR Paris 78586 Sartrouville

FR Paris 78621 Trappes

FR Paris 78624 Triel-sur-Seine

FR Paris 78642 Verneuil-sur-Seine

FR Paris 78643 Vernouillet

FR Paris 78644 Verrière

FR Paris 78646 Versailles

FR Paris 78650 Vésinet

FR Paris 78674 Villepreux

FR Paris 78686 Viroflay

FR Paris 78688 Voisins-le-Bretonneux

FR Paris 91021 Arpajon

FR Paris 91027 Athis-Mons

FR Paris 91044 Ballainvilliers

FR Paris 91097 Boussy-Saint-Antoine

FR Paris 91103 Brétigny-sur-Orge

FR Paris 91114 Brunoy

FR Paris 91122 Bures-sur-Yvette

FR Paris 91136 Champlan

FR Paris 91161 Chilly-Mazarin

FR Paris 91174 Corbeil-Essonnes

FR Paris 91182 Courcouronnes



FR Paris 91191 Crosne

FR Paris 91201 Draveil

FR Paris 91215 Épinay-sous-Sénart

FR Paris 91216 Épinay-sur-Orge

FR Paris 91225 Étiolles

FR Paris 91228 Évry

FR Paris 91235 Fleury-Mérogis

FR Paris 91272 Gif-sur-Yvette

FR Paris 91286 Grigny

FR Paris 91312 Igny

FR Paris 91326 Juvisy-sur-Orge

FR Paris 91345 Longjumeau

FR Paris 91347 Longpont-sur-Orge

FR Paris 91363 Marcoussis

FR Paris 91377 Massy

FR Paris 91421 Montgeron

FR Paris 91425 Montlhéry

FR Paris 91432 Morangis

FR Paris 91434 Morsang-sur-Orge

FR Paris 91457 Norville

FR Paris 91458 Nozay

FR Paris 91471 Orsay

FR Paris 91477 Palaiseau

FR Paris 91479 Paray-Vieille-Poste

FR Paris 91494 Plessis-Pâté

FR Paris 91521 Ris-Orangis

FR Paris 91549 Sainte-Geneviève-des-Bois

FR Paris 91552 Saint-Germain-lès-Arpajon

FR Paris 91553 Saint-Germain-lès-Corbeil

FR Paris 91570 Saint-Michel-sur-Orge

FR Paris 91573 Saint-Pierre-du-Perray

FR Paris 91577 Saintry-sur-Seine

FR Paris 91587 Saulx-les-Chartreux

FR Paris 91589 Savigny-sur-Orge

FR Paris 91600 Soisy-sur-Seine

FR Paris 91645 Verrières-le-Buisson

FR Paris 91657 Vigneux-sur-Seine

FR Paris 91659 Villabé

FR Paris 91661 Villebon-sur-Yvette

FR Paris 91665 Ville-du-Bois

FR Paris 91666 Villejust

FR Paris 91667 Villemoisson-sur-Orge

FR Paris 91685 Villiers-sur-Orge

FR Paris 91687 Viry-Châtillon



FR Paris 91689 Wissous

FR Paris 91691 Yerres

FR Paris 91692 Ulis

FR Paris 92002 Antony

FR Paris 92004 Asnières-sur-Seine

FR Paris 92007 Bagneux

FR Paris 92009 Bois-Colombes

FR Paris 92012 Boulogne-Billancourt

FR Paris 92014 Bourg-la-Reine

FR Paris 92019 Châtenay-Malabry

FR Paris 92020 Châtillon

FR Paris 92022 Chaville

FR Paris 92023 Clamart

FR Paris 92024 Clichy

FR Paris 92025 Colombes

FR Paris 92026 Courbevoie

FR Paris 92032 Fontenay-aux-Roses

FR Paris 92033 Garches

FR Paris 92035 Garenne-Colombes

FR Paris 92036 Gennevilliers

FR Paris 92040 Issy-les-Moulineaux

FR Paris 92044 Levallois-Perret

FR Paris 92046 Malakoff

FR Paris 92047 Marnes-la-Coquette

FR Paris 92048 Meudon

FR Paris 92049 Montrouge

FR Paris 92050 Nanterre

FR Paris 92051 Neuilly-sur-Seine

FR Paris 92060 Plessis-Robinson

FR Paris 92062 Puteaux

FR Paris 92063 Rueil-Malmaison

FR Paris 92064 Saint-Cloud

FR Paris 92071 Sceaux

FR Paris 92072 Sèvres

FR Paris 92073 Suresnes

FR Paris 92075 Vanves

FR Paris 92076 Vaucresson

FR Paris 92077 Ville-d'Avray

FR Paris 92078 Villeneuve-la-Garenne

FR Paris 93001 Aubervilliers

FR Paris 93005 Aulnay-sous-Bois

FR Paris 93006 Bagnolet

FR Paris 93007 Blanc-Mesnil

FR Paris 93008 Bobigny

FR Paris 93010 Bondy



FR Paris 93013 Bourget

FR Paris 93014 Clichy-sous-Bois

FR Paris 93015 Coubron

FR Paris 93027 Courneuve

FR Paris 93029 Drancy

FR Paris 93030 Dugny

FR Paris 93031 Épinay-sur-Seine

FR Paris 93032 Gagny

FR Paris 93033 Gournay-sur-Marne

FR Paris 93039 Île-Saint-Denis

FR Paris 93045 Lilas

FR Paris 93046 Livry-Gargan

FR Paris 93047 Montfermeil

FR Paris 93048 Montreuil

FR Paris 93049 Neuilly-Plaisance

FR Paris 93050 Neuilly-sur-Marne

FR Paris 93051 Noisy-le-Grand

FR Paris 93053 Noisy-le-Sec

FR Paris 93055 Pantin

FR Paris 93057 Pavillons-sous-Bois

FR Paris 93059 Pierrefitte-sur-Seine

FR Paris 93061 Pré-Saint-Gervais

FR Paris 93062 Raincy

FR Paris 93063 Romainville

FR Paris 93064 Rosny-sous-Bois

FR Paris 93066 Saint-Denis

FR Paris 93070 Saint-Ouen

FR Paris 93071 Sevran

FR Paris 93072 Stains

FR Paris 93073 Tremblay-en-France

FR Paris 93074 Vaujours

FR Paris 93077 Villemomble

FR Paris 93078 Villepinte

FR Paris 93079 Villetaneuse

FR Paris 94001 Ablon-sur-Seine

FR Paris 94002 Alfortville

FR Paris 94003 Arcueil

FR Paris 94004 Boissy-Saint-Léger

FR Paris 94011 Bonneuil-sur-Marne

FR Paris 94015 Bry-sur-Marne

FR Paris 94016 Cachan

FR Paris 94017 Champigny-sur-Marne

FR Paris 94018 Charenton-le-Pont

FR Paris 94019 Chennevières-sur-Marne



FR Paris 94021 Chevilly-Larue

FR Paris 94022 Choisy-le-Roi

FR Paris 94028 Créteil

FR Paris 94033 Fontenay-sous-Bois

FR Paris 94034 Fresnes

FR Paris 94037 Gentilly

FR Paris 94038 Haÿ-les-Roses

FR Paris 94041 Ivry-sur-Seine

FR Paris 94042 Joinville-le-Pont

FR Paris 94043 Kremlin-Bicêtre

FR Paris 94044 Limeil-Brévannes

FR Paris 94046 Maisons-Alfort

FR Paris 94047 Mandres-les-Roses

FR Paris 94052 Nogent-sur-Marne

FR Paris 94053 Noiseau

FR Paris 94054 Orly

FR Paris 94055 Ormesson-sur-Marne

FR Paris 94056 Périgny

FR Paris 94058 Perreux-sur-Marne

FR Paris 94059 Plessis-Trévise

FR Paris 94060 Queue-en-Brie

FR Paris 94065 Rungis

FR Paris 94067 Saint-Mandé

FR Paris 94068 Saint-Maur-des-Fossés

FR Paris 94069 Saint-Maurice

FR Paris 94071 Sucy-en-Brie

FR Paris 94073 Thiais

FR Paris 94074 Valenton

FR Paris 94075 Villecresnes

FR Paris 94076 Villejuif

FR Paris 94077 Villeneuve-le-Roi

FR Paris 94078 Villeneuve-Saint-Georges

FR Paris 94079 Villiers-sur-Marne

FR Paris 94080 Vincennes

FR Paris 94081 Vitry-sur-Seine

FR Paris 95014 Andilly

FR Paris 95018 Argenteuil

FR Paris 95019 Arnouville

FR Paris 95051 Beauchamp

FR Paris 95060 Bessancourt

FR Paris 95063 Bezons

FR Paris 95088 Bonneuil-en-France

FR Paris 95127 Cergy

FR Paris 95176 Cormeilles-en-Parisis

FR Paris 95183 Courdimanche



FR Paris 95197 Deuil-la-Barre

FR Paris 95203 Eaubonne

FR Paris 95205 Écouen

FR Paris 95210 Enghien-les-Bains

FR Paris 95218 Éragny

FR Paris 95219 Ermont

FR Paris 95252 Franconville

FR Paris 95256 Frépillon

FR Paris 95257 Frette-sur-Seine

FR Paris 95268 Garges-lès-Gonesse

FR Paris 95277 Gonesse

FR Paris 95288 Groslay

FR Paris 95306 Herblay

FR Paris 95323 Jouy-le-Moutier

FR Paris 95369 Margency

FR Paris 95424 Montigny-lès-Cormeilles

FR Paris 95426 Montlignon

FR Paris 95427 Montmagny

FR Paris 95428 Montmorency

FR Paris 95450 Neuville-sur-Oise

FR Paris 95476 Osny

FR Paris 95491 Plessis-Bouchard

FR Paris 95500 Pontoise

FR Paris 95539 Saint-Brice-sous-Forêt

FR Paris 95555 Saint-Gratien

FR Paris 95563 Saint-Leu-la-Forêt

FR Paris 95572 Saint-Ouen-l'Aumône

FR Paris 95574 Saint-Prix

FR Paris 95582 Sannois

FR Paris 95585 Sarcelles

FR Paris 95598 Soisy-sous-Montmorency

FR Paris 95607 Taverny

FR Paris 95637 Vauréal

FR Paris 95680 Villiers-le-Bel

FR Strasbourg 67043 Bischheim

FR Strasbourg 67118 Eckbolsheim

FR Strasbourg 67204 Hœnheim

FR Strasbourg 67218 Illkirch-Graffenstaden

FR Strasbourg 67267 Lingolsheim

FR Strasbourg 67365 Ostwald

FR Strasbourg 67447 Schiltigheim

FR Strasbourg 67482 Strasbourg

FR Bordeaux 33039 Bègles

FR Bordeaux 33063 Bordeaux



FR Bordeaux 33069 Bouscat

FR Bordeaux 33075 Bruges

FR Bordeaux 33119 Cenon

FR Bordeaux 33162 Eysines

FR Bordeaux 33167 Floirac

FR Bordeaux 33192 Gradignan

FR Bordeaux 33249 Lormont

FR Bordeaux 33281 Mérignac

FR Bordeaux 33318 Pessac

FR Bordeaux 33522 Talence

FR Bordeaux 33550 Villenave-d'Ornon

FR Lille 59009 Villeneuve-d'Ascq

FR Lille 59163 Croix

FR Lille 59193 Emmerin

FR Lille 59220 Faches-Thumesnil

FR Lille 59247 Forest-sur-Marque

FR Lille 59278 Hallennes-lez-Haubourdin

FR Lille 59286 Haubourdin

FR Lille 59299 Hem

FR Lille 59328 Lambersart

FR Lille 59332 Lannoy

FR Lille 59343 Lesquin

FR Lille 59346 Lezennes

FR Lille 59350 Lille

FR Lille 59360 Loos

FR Lille 59367 Lys-lez-Lannoy

FR Lille 59368 Madeleine

FR Lille 59378 Marcq-en-Barœul

FR Lille 59386 Marquette-lez-Lille

FR Lille 59410 Mons-en-Barœul

FR Lille 59421 Mouvaux

FR Lille 59426 Neuville-en-Ferrain

FR Lille 59507 Ronchin

FR Lille 59508 Roncq

FR Lille 59512 Roubaix

FR Lille 59527 Saint-André-lez-Lille

FR Lille 59566 Sequedin

FR Lille 59585 Templemars

FR Lille 59598 Toufflers

FR Lille 59599 Tourcoing

FR Lille 59636 Wambrechies

FR Lille 59646 Wasquehal

FR Lille 59648 Wattignies

FR Lille 59650 Wattrelos

FR Rennes 35238 Rennes



FR Marseille 13002 Allauch

FR Marseille 13055 Marseille

FR Marseille 13075 Plan-de-Cuques

HR Zagreb 01333 Grad Zagreb

HU Budapest 02112 Budapest 17. ker.























HU Miskolc 30456 Miskolc

IE Dublin 02001 Arran Quay A

IE Dublin 02002 Arran Quay B

IE Dublin 02003 Arran Quay C

IE Dublin 02004 Arran Quay D

IE Dublin 02005 Arran Quay E

IE Dublin 02006 Ashtown A

IE Dublin 02007 Ashtown B

IE Dublin 02008 Ayrfield

IE Dublin 02009 Ballybough A

IE Dublin 02010 Ballybough B

IE Dublin 02011 Ballygall A

IE Dublin 02012 Ballygall B

IE Dublin 02013 Ballygall C

IE Dublin 02014 Ballygall D

IE Dublin 02015 Ballymun A

IE Dublin 02016 Ballymun B



IE Dublin 02017 Ballymun C

IE Dublin 02018 Ballymun D

IE Dublin 02019 Ballymun E

IE Dublin 02020 Ballymun F

IE Dublin 02021 Beaumont A

IE Dublin 02022 Beaumont B

IE Dublin 02023 Beaumont C

IE Dublin 02024 Beaumont D

IE Dublin 02025 Beaumont E

IE Dublin 02026 Beaumont F

IE Dublin 02027 Botanic A

IE Dublin 02028 Botanic B

IE Dublin 02029 Botanic C

IE Dublin 02030 Cabra East A

IE Dublin 02031 Cabra East B

IE Dublin 02032 Cabra East C

IE Dublin 02033 Cabra West A

IE Dublin 02034 Cabra West B

IE Dublin 02035 Cabra West C

IE Dublin 02036 Cabra West D

IE Dublin 02037 Clontarf East A

IE Dublin 02038 Clontarf East B

IE Dublin 02039 Clontarf East C

IE Dublin 02040 Clontarf East D

IE Dublin 02041 Clontarf East E

IE Dublin 02042 Clontarf West A

IE Dublin 02043 Clontarf West B

IE Dublin 02044 Clontarf West C

IE Dublin 02045 Clontarf West D

IE Dublin 02046 Clontarf West E

IE Dublin 02047 Drumcondra South A

IE Dublin 02048 Drumcondra South B

IE Dublin 02049 Drumcondra South C

IE Dublin 02050 Edenmore

IE Dublin 02051 Finglas North A

IE Dublin 02052 Finglas North B

IE Dublin 02053 Finglas North C

IE Dublin 02054 Finglas South A

IE Dublin 02055 Finglas South B

IE Dublin 02056 Finglas South C

IE Dublin 02057 Finglas South D

IE Dublin 02058 Grace Park

IE Dublin 02059 Grange A

IE Dublin 02060 Grange B

IE Dublin 02061 Grange C



IE Dublin 02062 Grange D

IE Dublin 02063 Grange E

IE Dublin 02064 Harmonstown A

IE Dublin 02065 Harmonstown B

IE Dublin 02066 Inns Quay A

IE Dublin 02067 Inns Quay B

IE Dublin 02068 Inns Quay C

IE Dublin 02069 Kilmore A

IE Dublin 02070 Kilmore B

IE Dublin 02071 Kilmore C

IE Dublin 02072 Kilmore D

IE Dublin 02073 Mountjoy A

IE Dublin 02074 Mountjoy B

IE Dublin 02075 North City

IE Dublin 02076 North Dock A

IE Dublin 02077 North Dock B

IE Dublin 02078 North Dock C

IE Dublin 02079 Phoenix Park

IE Dublin 02080 Priorswood A

IE Dublin 02081 Priorswood B

IE Dublin 02082 Priorswood C

IE Dublin 02083 Priorswood D

IE Dublin 02084 Priorswood E

IE Dublin 02085 Raheny-Foxfield

IE Dublin 02086 Raheny-Greendale

IE Dublin 02087 Raheny-St. Assam

IE Dublin 02088 Rotunda A

IE Dublin 02089 Rotunda B

IE Dublin 02090 Whitehall A

IE Dublin 02091 Whitehall B

IE Dublin 02092 Whitehall C

IE Dublin 02093 Whitehall D

IE Dublin 02094 Chapelizod

IE Dublin 02095 Cherry Orchard A

IE Dublin 02096 Carna

IE Dublin 02097 Cherry Orchard C

IE Dublin 02098 Crumlin A

IE Dublin 02099 Crumlin B

IE Dublin 02100 Crumlin C

IE Dublin 02101 Crumlin D

IE Dublin 02102 Crumlin E

IE Dublin 02103 Crumlin F

IE Dublin 02104 Decies

IE Dublin 02105 Drumfinn



IE Dublin 02106 Inchicore A

IE Dublin 02107 Inchicore B

IE Dublin 02108 Kilmainham A

IE Dublin 02109 Kilmainham B

IE Dublin 02110 Kilmainham C

IE Dublin 02111 Kimmage A

IE Dublin 02112 Kimmage B

IE Dublin 02113 Kimmage C

IE Dublin 02114 Kimmage D

IE Dublin 02115 Kimmage E

IE Dublin 02116 Kylemore

IE Dublin 02117 Mansion House A

IE Dublin 02118 Mansion House B

IE Dublin 02119 Merchants Quay A

IE Dublin 02120 Merchants Quay B

IE Dublin 02121 Merchants Quay C

IE Dublin 02122 Merchants Quay D

IE Dublin 02123 Merchants Quay E

IE Dublin 02124 Merchants Quay F

IE Dublin 02125 Pembroke East A

IE Dublin 02126 Pembroke East B

IE Dublin 02127 Pembroke East C

IE Dublin 02128 Pembroke East D

IE Dublin 02129 Pembroke East E

IE Dublin 02130 Pembroke West A

IE Dublin 02131 Pembroke West B

IE Dublin 02132 Pembroke West C

IE Dublin 02133 Rathfarnham

IE Dublin 02134 Rathmines East A

IE Dublin 02135 Rathmines East B

IE Dublin 02136 Rathmines East C

IE Dublin 02137 Rathmines East D

IE Dublin 02138 Rathmines West A

IE Dublin 02139 Rathmines West B

IE Dublin 02140 Rathmines West C

IE Dublin 02141 Rathmines West D

IE Dublin 02142 Rathmines West E

IE Dublin 02143 Rathmines West F

IE Dublin 02144 Royal Exchange A

IE Dublin 02145 Royal Exchange B

IE Dublin 02146 Saint Kevin's

IE Dublin 02147 South Dock

IE Dublin 02148 Terenure A

IE Dublin 02149 Terenure B

IE Dublin 02150 Terenure C



IE Dublin 02151 Terenure D

IE Dublin 02152 Ushers A

IE Dublin 02153 Ushers B

IE Dublin 02154 Ushers C

IE Dublin 02155 Ushers D

IE Dublin 02156 Ushers E

IE Dublin 02157 Ushers F

IE Dublin 02158 Walkinstown A

IE Dublin 02159 Walkinstown B

IE Dublin 02160 Walkinstown C

IE Dublin 02161 Wood Quay A

IE Dublin 02162 Wood Quay B

IE Dublin 03001 Ballinascorney

IE Dublin 03002 Ballyboden

IE Dublin 03003 Bohernabreena

IE Dublin 03004 Clondalkin-Ballymount

IE Dublin 03005 Clondalkin-Cappaghmore

IE Dublin 03006 Clondalkin-Dunawley

IE Dublin 03007 Clondalkin-Monastery

IE Dublin 03008 Clondalkin-Moorfield

IE Dublin 03009 Clondalkin-Rowlagh

IE Dublin 03010 Clondalkin Village

IE Dublin 03011 Edmondstown

IE Dublin 03012 Firhouse-Ballycullen

IE Dublin 03013 Firhouse-Knocklyon

IE Dublin 03014 Firhouse Village

IE Dublin 03015 Lucan-Esker

IE Dublin 03016 Lucan Heights

IE Dublin 03017 Lucan-St. Helens

IE Dublin 03018 Newcastle

IE Dublin 03019 Palmerston Village

IE Dublin 03020 Palmerston West

IE Dublin 03021 Rathcoole

IE Dublin 03022 Rathfarnham-Ballyroan

IE Dublin 03023 Rathfarnham-Butterfield

IE Dublin 03024 Rathfarnham-Hermitage

IE Dublin 03025 Rathfarnham-St. Enda's

IE Dublin 03026 Rathfarnham Village

IE Dublin 03027 Saggart

IE Dublin 03028 Tallaght-Avonbeg

IE Dublin 03029 Tallaght-Belgard

IE Dublin 03030 Tallaght-Fettercairn

IE Dublin 03031 Tallaght-Glenview

IE Dublin 03032 Tallaght-Jobstown



IE Dublin 03033 Tallaght-Killinardan

IE Dublin 03034 Tallaght-Kilnamanagh

IE Dublin 03035 Tallaght-Kiltipper

IE Dublin 03036 Tallaght-Kingswood

IE Dublin 03037 Tallaght-Millbrook

IE Dublin 03038 Tallaght-Oldbawn

IE Dublin 03039 Tallaght-Springfield

IE Dublin 03040 Tallaght-Tymon

IE Dublin 03041 Templeogue-Cypress

IE Dublin 03042 Templeogue-Kimmage Manor

IE Dublin 03043 Templeogue-Limekiln

IE Dublin 03044 Templeogue-Orwell

IE Dublin 03045 Templeogue-Osprey

IE Dublin 03046 Templeogue Village

IE Dublin 03047 Terenure-Cherryfield

IE Dublin 03048 Terenure-Greentrees

IE Dublin 03049 Terenure-St. James

IE Dublin 04001 Airport

IE Dublin 04002 Balbriggan Rural

IE Dublin 04003 Balbriggan Urban

IE Dublin 04004 Baldoyle

IE Dublin 04005 Balgriffin

IE Dublin 04006 Ballyboghil

IE Dublin 04007 Balscadden

IE Dublin 04008 Blanchardstown-Abbotstown

IE Dublin 04009 Blanchardstown-Blakestown

IE Dublin 04010 Blanchardstown-Coolmine

IE Dublin 04011 Blanchardstown-Corduff

IE Dublin 04012 Blanchardstown-Delwood

IE Dublin 04013 Blanchardstown-Mulhuddart

IE Dublin 04014 Blanchardstown-Roselawn

IE Dublin 04015 Blanchardstown-Tyrrelstown

IE Dublin 04016 Castleknock-Knockmaroon

IE Dublin 04017 Castleknock-Park

IE Dublin 04018 Clonmethan

IE Dublin 04019 Donabate

IE Dublin 04020 Dubber

IE Dublin 04021 Garristown

IE Dublin 04022 Hollywood

IE Dublin 04023 Holmpatrick

IE Dublin 04024 Howth

IE Dublin 04025 Kilsallaghan

IE Dublin 04026 Kinsaley

IE Dublin 04027 Lucan North

IE Dublin 04028 Lusk



IE Dublin 04029 Malahide East

IE Dublin 04030 Malahide West

IE Dublin 04031 Portmarnock North

IE Dublin 04032 Portmarnock South

IE Dublin 04033 Rush

IE Dublin 04034 Skerries

IE Dublin 04035 Sutton

IE Dublin 04036 Swords-Forrest

IE Dublin 04037 Swords-Glasmore

IE Dublin 04038 Swords-Lissenhall

IE Dublin 04039 Swords-Seatown

IE Dublin 04040 Swords Village

IE Dublin 04041 The Ward

IE Dublin 04042 Turnapin

IE Dublin 05001 Ballinteer-Broadford

IE Dublin 05002 Ballinteer-Ludford

IE Dublin 05003 Ballinteer-Marley

IE Dublin 05004 Ballinteer-Meadowbroads

IE Dublin 05005 Ballinteer-Meadowmount

IE Dublin 05006 Ballinteer-Woodpark

IE Dublin 05007 Ballybrack

IE Dublin 05008 Blackrock-Booterstown

IE Dublin 05009 Blackrock-Carysfort

IE Dublin 05010 Blackrock-Central

IE Dublin 05011 Blackrock-Glenomena

IE Dublin 05012 Blackrock-Monkstown

IE Dublin 05013 Blackrock-Newpark

IE Dublin 05014 Blackrock-Seapoint

IE Dublin 05015 Blackrock-Stradbrook

IE Dublin 05016 Blackrock-Templehill

IE Dublin 05017 Blackrock-Williamstown

IE Dublin 05018 Cabinteely-Granitefield

IE Dublin 05019 Cabinteely-Kilbogget

IE Dublin 05020 Cabinteely-Loughlinstown

IE Dublin 05021 Cabinteely-Pottery

IE Dublin 05022 Churchtown-Castle

IE Dublin 05023 Churchtown-Landscape

IE Dublin 05024 Churchtown-Nutgrove

IE Dublin 05025 Churchtown-Orwell

IE Dublin 05026 Churchtown-Woodlawn

IE Dublin 05027 Clonskeagh-Belfield

IE Dublin 05028 Clonskeagh-Farranboley

IE Dublin 05029 Clonskeagh-Milltown

IE Dublin 05030 Clonskeagh-Roebuck



IE Dublin 05031 Clonskeagh-Windy Arbour

IE Dublin 05032 Dalkey-Avondale

IE Dublin 05033 Dalkey-Bullock

IE Dublin 05034 Dalkey-Coliemore

IE Dublin 05035 Dalkey Hill

IE Dublin 05036 Dalkey Upper

IE Dublin 05037 Dundrum-Balally

IE Dublin 05038 Dundrum-Kilmacud

IE Dublin 05039 Dundrum-Sandyford

IE Dublin 05040 Dundrum-Sweetmount

IE Dublin 05041 Dundrum-Taney

IE Dublin 05042 Dun Laoghaire-East Central

IE Dublin 05043 Dun Laoghaire-Glasthule

IE Dublin 05044 Dun Laoghaire-Glenageary

IE Dublin 05045 Dun Laoghaire-Monkstown Farm

IE Dublin 05046 Dun Laoghaire-Mount Town

IE Dublin 05047 Dun Laoghaire-Sallynoggin East

IE Dublin 05048 Dun Laoghaire-Sallynoggin South

IE Dublin 05049 Dun Laoghaire-Sallynoggin West

IE Dublin 05050 Dun Laoghaire-Sandycove

IE Dublin 05051 Dun Laoghaire-Salthill

IE Dublin 05052 Dun Laoghaire-West Central

IE Dublin 05053 Foxrock-Beechpark

IE Dublin 05054 Foxrock-Carrickmines

IE Dublin 05055 Foxrock-Deansgrange

IE Dublin 05056 Foxrock-Torquay

IE Dublin 05057 Glencullen

IE Dublin 05058 Killiney North

IE Dublin 05059 Killiney South

IE Dublin 05060 Shankill-Rathmichael

IE Dublin 05061 Shankill-Rathsallagh

IE Dublin 05062 Shankill-Shanganagh

IE Dublin 05063 Stillorgan-Deerpark

IE Dublin 05064 Stillorgan-Kilmacud

IE Dublin 05065 Stillorgan-Leopardstown

IE Dublin 05066 Stillorgan-Merville

IE Dublin 05067 Stillorgan-Mount Merrion

IE Dublin 05068 Stillorgan-Priory

IE Dublin 05069 Tibradden

IS Reykjavík 0000 Reykjavík

IS Reykjavík 1000 Kópavogur

IS Reykjavík 1100 Seltjarnarnes

IS Reykjavík 1300 Garðabær

IS Reykjavík 1400 Hafnarfjörður

IS Reykjavík 1604 Mosfellsbær



IS Reykjavík 1606 Kjósarhreppur

IT Roma 058091 Roma

IT Napoli 063001 Acerra

IT Napoli 063002 Afragola

IT Napoli 063003 Agerola

IT Napoli 063004 Anacapri

IT Napoli 063005 Arzano

IT Napoli 063006 Bacoli

IT Napoli 063007 Barano d'Ischia

IT Napoli 063008 Boscoreale

IT Napoli 063009 Boscotrecase

IT Napoli 063010 Brusciano

IT Napoli 063011 Caivano

IT Napoli 063012 Calvizzano

IT Napoli 063013 Camposano

IT Napoli 063014 Capri

IT Napoli 063015 Carbonara di Nola

IT Napoli 063016 Cardito

IT Napoli 063017 Casalnuovo di Napoli

IT Napoli 063018 Casamarciano

IT Napoli 063019 Casamicciola Terme

IT Napoli 063020 Casandrino

IT Napoli 063021 Casavatore

IT Napoli 063022 Casola di Napoli

IT Napoli 063023 Casoria

IT Napoli 063024 Castellammare di Stabia

IT Napoli 063025 Castello di Cisterna

IT Napoli 063026 Cercola

IT Napoli 063027 Cicciano

IT Napoli 063028 Cimitile

IT Napoli 063029 Comiziano

IT Napoli 063030 Crispano

IT Napoli 063031 Forio

IT Napoli 063032 Frattamaggiore

IT Napoli 063033 Frattaminore

IT Napoli 063034 Giugliano in Campania

IT Napoli 063035 Gragnano

IT Napoli 063036 Grumo Nevano

IT Napoli 063037 Ischia

IT Napoli 063038 Lacco Ameno

IT Napoli 063039 Lettere

IT Napoli 063040 Liveri

IT Napoli 063041 Marano di Napoli

IT Napoli 063042 Mariglianella



IT Napoli 063043 Marigliano

IT Napoli 063044 Massa Lubrense

IT Napoli 063045 Melito di Napoli

IT Napoli 063046 Meta

IT Napoli 063047 Monte di Procida

IT Napoli 063048 Mugnano di Napoli

IT Napoli 063049 Napoli

IT Napoli 063050 Nola

IT Napoli 063051 Ottaviano

IT Napoli 063052 Palma Campania

IT Napoli 063053 Piano di Sorrento

IT Napoli 063054 Pimonte

IT Napoli 063055 Poggiomarino

IT Napoli 063056 Pollena Trocchia

IT Napoli 063057 Pomigliano d'Arco

IT Napoli 063058 Pompei

IT Napoli 063059 Portici

IT Napoli 063060 Pozzuoli

IT Napoli 063061 Procida

IT Napoli 063062 Qualiano

IT Napoli 063063 Quarto

IT Napoli 063064 Ercolano

IT Napoli 063065 Roccarainola

IT Napoli 063066 San Gennaro Vesuviano

IT Napoli 063067 San Giorgio a Cremano

IT Napoli 063068 San Giuseppe Vesuviano

IT Napoli 063069 San Paolo Bel Sito

IT Napoli 063070 San Sebastiano al Vesuvio

IT Napoli 063071 Sant'Agnello

IT Napoli 063072 Sant'Anastasia

IT Napoli 063073 Sant'Antimo

IT Napoli 063074 Sant'Antonio Abate

IT Napoli 063075 San Vitaliano

IT Napoli 063076 Saviano

IT Napoli 063077 Scisciano

IT Napoli 063078 Serrara Fontana

IT Napoli 063079 Somma Vesuviana

IT Napoli 063080 Sorrento

IT Napoli 063081 Striano

IT Napoli 063082 Terzigno

IT Napoli 063083 Torre Annunziata

IT Napoli 063084 Torre del Greco

IT Napoli 063085 Tufino

IT Napoli 063086 Vico Equense

IT Napoli 063087 Villaricca



IT Napoli 063088 Visciano

IT Napoli 063089 Volla

IT Napoli 063090 Santa Maria la Carità

IT Napoli 063091 Trecase

IT Napoli 063092 Massa di Somma

IT Torino 001272 Torino

IT Palermo 082053 Palermo

IT Bologna 037006 Bologna

IT Verona 023091 Verona

LT Vilnius 13 Vilniaus miesto savivaldybė

LU Luxembourg 0304 Luxembourg

LV Rīga 0010000 Rīga

ME Podgorica 20176 Podgorica

MK Skopje MK0080102 Skopje - Aerodrom

MK Skopje MK0080301 Vizbegovo

MK Skopje MK0080305 Skopje - Butel

MK Skopje MK0080405 Indžikovo

MK Skopje MK0080408 Singelić

MK Skopje MK0080409 Skopje - Gazi Baba

MK Skopje MK0080411 Stajkovci

MK Skopje MK0080507 Skopje - Đorče Petrov

MK Skopje MK0080802 Gorno Nerezi

MK Skopje MK0080803 Skopje - Karpoš

MK Skopje MK0080902 Skopje - Kisela Voda

MK Skopje MK0080903 Usje

MK Skopje MK0081111 Krušopek

MK Skopje MK0081113 Qubin

MK Skopje MK0081121 Skopje - Saraj

MK Skopje MK0081123 Šiševo

MK Skopje MK0081401 Skopje - Centar

MK Skopje MK0081501 Skopje - Čair

MK Skopje MK0081701 Gorno Orizari

MK Skopje MK0081702 Skopje - Šuto Orizari

MT Valletta MT01101 Valletta

MT Valletta MT01103 Birgu

MT Valletta MT01104 L-Isla

MT Valletta MT01105 Bormla

MT Valletta MT01108 Ħaż-Żabbar

MT Valletta MT01117 Fgura

MT Valletta MT01118 Floriana

MT Valletta MT01129 Kalkara

MT Valletta MT01133 Luqa

MT Valletta MT01134 Marsa

MT Valletta MT01145 Paola



MT Valletta MT01157 Santa Luċija

MT Valletta MT01162 Tarxien

MT Valletta MT01165 Xgħajra

MT Valletta MT01206 Ħal Qormi

MT Valletta MT01214 Birkirkara

MT Valletta MT01221 Gżira

MT Valletta MT01227 Ħamrun

MT Valletta MT01241 Msida

MT Valletta MT01246 Pembroke

MT Valletta MT01247 Pieta'

MT Valletta MT01252 San Ġiljan

MT Valletta MT01253 San Ġwann

MT Valletta MT01258 Santa Venera

MT Valletta MT01259 Sliema

MT Valletta MT01260 Swieqi

MT Valletta MT01261 Ta' Xbiex

NL Amsterdam GM0362 Amstelveen

NL Amsterdam GM0363 Amsterdam

NL Amsterdam GM0384 Diemen

NL Amsterdam GM0437 Ouder-Amstel

NL Rotterdam GM0482 Alblasserdam

NL Rotterdam GM0489 Barendrecht

NL Rotterdam GM0502 Capelle aan den IJssel

NL Rotterdam GM0505 Dordrecht

NL Rotterdam GM0531 Hendrik-Ido-Ambacht

NL Rotterdam GM0542 Krimpen aan den IJssel

NL Rotterdam GM0590 Papendrecht

NL Rotterdam GM0597 Ridderkerk

NL Rotterdam GM0599 Rotterdam

NL Rotterdam GM0606 Schiedam

NL Rotterdam GM0622 Vlaardingen

NL Rotterdam GM0642 Zwijndrecht

NL Groningen GM0014 Groningen

NO Oslo 0301 Oslo kommune

PL Warszawa 1007141286501 Warszawa

PL Kraków 1001121216101 Kraków

PL Gdańsk 1004221436101 Gdańsk

PL Białystok 1006201376101 Białystok

PT Lisboa 110501 Alcabideche

PT Lisboa 110506 São Domingos de Rana

PT Lisboa 110507 Carcavelos e Parede

PT Lisboa 110508 Cascais e Estoril

PT Lisboa 110601 Ajuda

PT Lisboa 110602 Alcântara

PT Lisboa 110607 Beato



PT Lisboa 110608 Benfica

PT Lisboa 110610 Campolide

PT Lisboa 110611 Carnide

PT Lisboa 110618 Lumiar

PT Lisboa 110621 Marvila

PT Lisboa 110633 Olivais

PT Lisboa 110639 São Domingos de Benfica

PT Lisboa 110654 Alvalade

PT Lisboa 110655 Areeiro

PT Lisboa 110656 Arroios

PT Lisboa 110657 Avenidas Novas

PT Lisboa 110658 Belém

PT Lisboa 110659 Campo de Ourique

PT Lisboa 110660 Estrela

PT Lisboa 110661 Misericórdia

PT Lisboa 110662 Parque das Nações

PT Lisboa 110663 Penha de França

PT Lisboa 110664 Santa Clara

PT Lisboa 110665 Santa Maria Maior

PT Lisboa 110666 Santo António

PT Lisboa 110667 São Vicente

PT Lisboa 110702 Bucelas

PT Lisboa 110705 Fanhões

PT Lisboa 110707 Loures

PT Lisboa 110708 Lousa

PT Lisboa 110726 Moscavide e Portela

PT Lisboa 110727 Sacavém e Prior Velho

PT Lisboa 110728 Santa Iria de Azoia, São João da Talha e Bobadela

PT Lisboa 110729 Santo Antão e São Julião do Tojal

PT Lisboa 110730 Santo António dos Cavaleiros e Frielas

PT Lisboa 110731 Camarate, Unhos e Apelação

PT Lisboa 111002 Barcarena

PT Lisboa 111009 Porto Salvo

PT Lisboa 111012 Algés, Linda-a-Velha e Cruz Quebrada-Dafundo

PT Lisboa 111013 Carnaxide e Queijas

PT Lisboa 111014 Oeiras e São Julião da Barra, Paço de Arcos e Caxias

PT Lisboa 111512 Alfragide

PT Lisboa 111513 Águas Livres

PT Lisboa 111514 Encosta do Sol

PT Lisboa 111515 Falagueira-Venda Nova

PT Lisboa 111516 Mina de Água

PT Lisboa 111517 Venteira

PT Lisboa 111603 Odivelas

PT Lisboa 111608 Pontinha e Famões



PT Lisboa 111609 Póvoa de Santo Adrião e Olival Basto

PT Lisboa 111610 Ramada e Caneças

PT Lisboa 150303 Costa da Caparica

PT Lisboa 150312 Almada, Cova da Piedade, Pragal e Cacilhas

PT Lisboa 150313 Caparica e Trafaria

PT Lisboa 150314 Charneca de Caparica e Sobreda

PT Lisboa 150315 Laranjeiro e Feijó

PT Lisboa 150407 Santo António da Charneca

PT Lisboa 150409 Alto do Seixalinho, Santo André e Verderena

PT Lisboa 150410 Barreiro e Lavradio

PT Lisboa 150411 Palhais e Coina

PT Lisboa 151002 Amora

PT Lisboa 151005 Corroios

PT Lisboa 151006 Fernão Ferro

PT Lisboa 151007 Seixal, Arrentela e Aldeia de Paio Pires

PT Braga 030301 Adaúfe

PT Braga 030312 Espinho

PT Braga 030313 Esporões

PT Braga 030315 Figueiredo

PT Braga 030319 Gualtar

PT Braga 030322 Lamas

PT Braga 030325 Mire de Tibães

PT Braga 030330 Padim da Graça

PT Braga 030331 Palmeira

PT Braga 030334 Pedralva

PT Braga 030336 Priscos

PT Braga 030338 Ruilhe

PT Braga 030349 Braga (São Vicente)

PT Braga 030351 Braga (São Vítor)

PT Braga 030354 Sequeira

PT Braga 030355 Sobreposta

PT Braga 030356 Tadim

PT Braga 030357 Tebosa

PT Braga 030363 Arentim e Cunha

PT Braga 030364 Braga (Maximinos, Sé e Cividade)

PT Braga 030365 Braga (São José de São Lázaro e São João do Souto)

PT Braga 030366 Cabreiros e Passos (São Julião)

PT Braga 030367 Celeirós, Aveleda e Vimieiro

PT Braga 030368 Crespos e Pousada

PT Braga 030369 Escudeiros e Penso (Santo Estêvão e São Vicente)

PT Braga 030370 Este (São Pedro e São Mamede)

PT Braga 030371 Ferreiros e Gondizalves

PT Braga 030372 Guisande e Oliveira (São Pedro)

PT Braga 030373 Lomar e Arcos

PT Braga 030374 Merelim (São Paio), Panoias e Parada de Tibães



PT Braga 030375 Merelim (São Pedro) e Frossos

PT Braga 030376 Morreira e Trandeiras

PT Braga 030377 Nogueira, Fraião e Lamaçães

PT Braga 030378 Nogueiró e Tenões

PT Braga 030379 Real, Dume e Semelhe

PT Braga 030380 Santa Lucrécia de Algeriz e Navarra

PT Braga 030381 Vilaça e Fradelos

RO Bucureşti 179132 Municipiul Bucureşti

RO Cluj-Napoca 54975 Municipiul Cluj-Napoca

RO Piatra Neamţ 120726 Municipiul Piatra Neamţ

RS Beograd 70106 Belgrade - Voždovac

RS Beograd 70114 Belgrade - Vračar

RS Beograd 70149 Belgrade - Zvezdara

RS Beograd 70157 Belgrade - Zemun

RS Beograd 70181 Belgrade - Novi Beograd

RS Beograd 70203 Belgrade - Palilula

RS Beograd 70211 Belgrade - Rakovica

RS Beograd 70220 Belgrade - Savski venac

RS Beograd 70246 Belgrade - Stari grad

RS Beograd 70254 Belgrade - Čukarica

RS Beograd 80314 Pančevo

SE Stockholm 0123 Järfälla

SE Stockholm 0126 Huddinge

SE Stockholm 0127 Botkyrka

SE Stockholm 0136 Haninge

SE Stockholm 0138 Tyresö

SE Stockholm 0162 Danderyd

SE Stockholm 0163 Sollentuna

SE Stockholm 0180 Stockholm

SE Stockholm 0182 Nacka

SE Stockholm 0183 Sundbyberg

SE Stockholm 0184 Solna

SE Stockholm 0186 Lidingö

SE Malmö 1280 Malmö

SI Ljubljana 061 Ljubljana

SK Bratislava 528595 Bratislava - mestská časť Staré Mesto

SK Bratislava 529311 Bratislava - mestská časť Podunajské Biskupice

SK Bratislava 529320 Bratislava - mestská časť Ružinov

SK Bratislava 529338 Bratislava - mestská časť Vrakuňa

SK Bratislava 529346 Bratislava - mestská časť Nové Mesto

SK Bratislava 529354 Bratislava - mestská časť Rača

SK Bratislava 529362 Bratislava - mestská časť Vajnory

SK Bratislava 529371 Bratislava - mestská časť Devínska Nová Ves

SK Bratislava 529389 Bratislava - mestská časť Dúbravka



SK Bratislava 529397 Bratislava - mestská časť Karlova Ves

SK Bratislava 529401 Bratislava - mestská časť Devín

SK Bratislava 529419 Bratislava - mestská časť Lamač

SK Bratislava 529427 Bratislava - mestská časť Záhorská Bystrica

SK Bratislava 529435 Bratislava - mestská časť Čunovo

SK Bratislava 529443 Bratislava - mestská časť Jarovce

SK Bratislava 529460 Bratislava - mestská časť Petržalka

SK Bratislava 529494 Bratislava - mestská časť Rusovce

SK Košice 598119 Košice - mestská časť Kavečany

SK Košice 598127 Košice - mestská časť Ťahanovce

SK Košice 598151 Košice - mestská časť Sever

SK Košice 598186 Košice - mestská časť Staré Mesto

SK Košice 598194 Košice - mestská časť Lorinčík

SK Košice 598208 Košice - mestská časť Pereš

SK Košice 598216 Košice - mestská časť Myslava

SK Košice 598224 Košice - mestská časť Západ

SK Košice 598682 Košice - mestská časť Dargovských hrdinov

SK Košice 599018 Košice - mestská časť Košická Nová Ves

SK Košice 599093 Košice - mestská časť Barca

SK Košice 599786 Košice - mestská časť Šebastovce

SK Košice 599794 Košice - mestská časť Krásna

SK Košice 599816 Košice - mestská časť Nad jazerom

SK Košice 599824 Košice - mestská časť Juh

SK Košice 599841 Košice - mestská časť Šaca

SK Košice 599859 Košice - mestská časť Poľov

SK Košice 599875 Košice - mestská časť Sídlisko Ťahanovce

SK Košice 599883 Košice - mestská časť Sídlisko KVP

SK Košice 599891 Košice - mestská časť Džungľa

SK Košice 599913 Košice - mestská časť Vyšné Opátske

SK Košice 599972 Košice - mestská časť Luník IX

TR Ankara TR6001 Altindag

TR Ankara TR6002 Çankaya

TR Ankara TR6003 Etimesgut

TR Ankara TR6005 Keçiören

TR Ankara TR6006 Mamak

TR Ankara TR6007 Sincan

TR Ankara TR6008 Yenimahalle

TR Ankara TR6025 Pursaklar

TR Antalya TR7017 Kepez

TR Antalya TR7018 Konyaalti

TR Antalya TR7019 Muratpasa

TR Diyarbakir TR21014 Baglar

TR Diyarbakir TR21015 Kayapinar

TR Diyarbakir TR21016 Sur

TR Diyarbakir TR21017 Yenisehir



TR Istanbul TR16001 Nilüfer

TR Istanbul TR16002 Osmangazi

TR Istanbul TR16003 Yildirim

TR Istanbul TR16005 Gemlik

TR Istanbul TR16006 Gürsu

TR Istanbul TR16008 Inegöl

TR Istanbul TR16012 Kestel

TR Istanbul TR34002 Avcilar

TR Istanbul TR34003 Bagcilar

TR Istanbul TR34004 Bahçelievler

TR Istanbul TR34005 Bakirköy

TR Istanbul TR34006 Bayrampasa

TR Istanbul TR34007 Besiktas

TR Istanbul TR34008 Beykoz

TR Istanbul TR34009 Beyoglu

TR Istanbul TR34011 Esenler

TR Istanbul TR34012 Eyüp

TR Istanbul TR34013 Fatih

TR Istanbul TR34014 Gaziosmanpasa

TR Istanbul TR34015 Güngören

TR Istanbul TR34016 Kadiköy

TR Istanbul TR34017 Kagithane

TR Istanbul TR34018 Kartal

TR Istanbul TR34019 Küçükçekmece

TR Istanbul TR34020 Maltepe

TR Istanbul TR34021 Pendik

TR Istanbul TR34022 Sariyer

TR Istanbul TR34023 Sisli

TR Istanbul TR34024 Tuzla

TR Istanbul TR34025 Ümraniye

TR Istanbul TR34026 Üsküdar

TR Istanbul TR34027 Zeytinburnu

TR Istanbul TR34028 Büyükçekmece

TR Istanbul TR34030 Silivri

TR Istanbul TR34031 Sultanbeyli

TR Istanbul TR34033 Atasehir

TR Istanbul TR34034 Çekmeköy

TR Istanbul TR34035 Sancaktepe

TR Istanbul TR34036 Sultangazi

TR Istanbul TR34037 Arnavutköy

TR Istanbul TR34038 Basaksehir

TR Istanbul TR34039 Beylikdüzü

TR Istanbul TR34040 Esenyurt

TR Istanbul TR41001 Gebze



TR Istanbul TR41002 Gölcük

TR Istanbul TR41004 Karamürsel

TR Istanbul TR41005 Körfez

TR Istanbul TR41006 Derince

TR Istanbul TR41007 Basiskele

TR Istanbul TR41008 Çayirova

TR Istanbul TR41009 Darica

TR Istanbul TR41010 Dilovasi

TR Istanbul TR41011 Izmit

TR Istanbul TR41012 Kartepe

TR Istanbul TR54005 Hendek

TR Istanbul TR54013 Adapazari

TR Istanbul TR54014 Arifiye

TR Istanbul TR54015 Erenler

TR Istanbul TR54016 Serdivan

TR Istanbul TR59000 Tekirdag Merkez

TR Istanbul TR59001 Çerkezköy

TR Istanbul TR59002 Çorlu

TR Istanbul TR59005 Marmaraereglisi

TR Istanbul TR77000 Yalova Merkez

TR Istanbul TR77001 Altinova

TR Istanbul TR77004 Çiftlikköy

TR Istanbul TR81000 Düzce Merkez

TR Istanbul TR81002 Cumayeri

TR Istanbul TR81003 Çilimli

TR Istanbul TR81005 Gümüsova

UK London E09000001 City of London

UK London E09000002 Barking and Dagenham

UK London E09000003 Barnet

UK London E09000004 Bexley

UK London E09000005 Brent

UK London E09000006 Bromley

UK London E09000007 Camden

UK London E09000008 Croydon

UK London E09000009 Ealing

UK London E09000010 Enfield

UK London E09000011 Greenwich

UK London E09000012 Hackney

UK London E09000013 Hammersmith and Fulham

UK London E09000014 Haringey

UK London E09000015 Harrow

UK London E09000016 Havering

UK London E09000017 Hillingdon

UK London E09000018 Hounslow

UK London E09000019 Islington



UK London E09000020 Kensington and Chelsea

UK London E09000021 Kingston upon Thames

UK London E09000022 Lambeth

UK London E09000023 Lewisham

UK London E09000024 Merton

UK London E09000025 Newham

UK London E09000026 Redbridge

UK London E09000027 Richmond upon Thames

UK London E09000028 Southwark

UK London E09000029 Sutton

UK London E09000030 Tower Hamlets

UK London E09000031 Waltham Forest

UK London E09000032 Wandsworth

UK London E09000033 Westminster

UK Glasgow S30000015 East Dunbartonshire

UK Glasgow S30000019 Glasgow City

UK Glasgow S30000020 East Renfrewshire

UK Glasgow S30000021 Renfrewshire

UK Manchester E08000001 Bolton

UK Manchester E08000002 Bury

UK Manchester E08000003 Manchester

UK Manchester E08000004 Oldham

UK Manchester E08000005 Rochdale

UK Manchester E08000006 Salford

UK Manchester E08000007 Stockport

UK Manchester E08000008 Tameside

UK Manchester E08000009 Trafford

UK Manchester E08000010 Wigan

UK Cardiff W06000015 Cardiff

UK Belfast UKN06 Belfast

UK Belfast UKN14 Lisburn and Castlereagh

UK Tyneside conurbation E08000021 Newcastle upon Tyne

UK Tyneside conurbation E08000022 North Tyneside

UK Tyneside conurbation E08000023 South Tyneside

UK Tyneside conurbation E08000037 Gateshead

Documents

Perception Survey on the Quality of Life in European