README file
Data Set Title: “PERCEIVE: WP2: Evaluation of EU citizens’ perception of the EU project: Key variables for citizens' identification with EU”
Data subset Author and Contact Person: Pinuccia Calia (Università di Bologna), ORCID 0000-0002-6638-4081; pinuccia.calia@unibo.it 

License: this data subset is distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, https://creativecommons.org/licenses/by/4.0/. 
Publication Year: 2019
Project Info: PERCEIVE (Perception and Evaluation of Regional and Cohesion Policies by Europeans and Identification with the Values of Europe), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 693529; https://www.perceiveproject.eu/.  
Data subset Contents

The data subset consists of:

•	1 tabular quantitative file saved in .csv format 
“PERCEIVE_WP2_D2_4_Classification_Ident_EU.csv”
•	1 README file 
“PERCEIVE_WP2_D2_4_Classification_Ident_EU_README.rtf”
Data subset Documentation
Abstract
This data subset corresponds to quantitative outputs derived from deliverable D24, titled “Report on the probabilistic model of estimation of citizens’ identification with the EU project and ranking of the case study regions”. In this deliverable we develop and apply a probabilistic model which allows us to classify citizens and regions according to their different patterns of identification with EU. We use Latent Class Analysis and multilevel modelling. 

The model builds on previous PERCEIVE’s research results that develop the theoretical framework for the definition and measurement of the level of identification with EU and its drivers.

We use a set of indicators derived from the responses to specific questions in the PERCEIVE Survey: awareness of Cohesion/Regional policies; identification with Europe/country; evaluation of EU membership; perception of effectiveness of EU/country in solving problems of the region; perception of the level of corruption in EU/national institutions, participation to European parliamentary elections, support toward redistribution and aid to poorest European regions. Characteristics at individual and regional levels are used as controls.

The final outcomes of the model are the identification of citizens’ clusters and groups of regions with specific pattern of identification with EU. 

Content of the file 
The file PERCEIVE_WP2_D24_Classification_Ident_EU.csv contains the main variables used to estimate the probabilistic model (indicators, individual and regional characteristics), and the main output variables: the individual prior and posterior cluster’s membership probabilities, the region prior and posterior group’s membership probabilities, and classification of citizens and regions. In particular, the variables used in model estimation are defined this way (table below): 

Variable	Description	Values	
Awareness of EU policies
(awar)	Q1. Have you ever heard about the following EU policies? (EU Cohesion Policy; EU Regional Policy; EU Structural Funds; any EU funded project in your region or area)	0 None of these
1 Only local project
2 SF\Cohesion\ Regional policies	
Identification with Europe 
(recodq9_3)
	Q9. On a 0-10 scale, with ‘0’ being ‘I don’t identify at all’ and ‘10’ being “I identify very strongly”, how strongly you identify yourself with the following: 
Q9_2: Your country; 
Q9_3: Europe	1 Not much (0-3)
2 Somewhat strongly (4-6)
3 Strongly (7-10)	
Identification with Europe vs Country 
(ide_eu_nat)	Comparing Q9_3 to Q9_2 	1 Less 
2 Equal
3 More	
Effectiveness of EU 
(recodq5_1)	Q5. How effective do you think the following institutions will be at dealing with the biggest problem in your region? 
Q5_1: EU; 
Q5_2: National governing institutions; 	1 Not very effective
2 Somewhat effective
3 Very effective	
Effectiveness of EU vs National governing institutions (efct_eu_nat)	Comparing Q5_1 to Q5_2	1 Less
2 Equal
3 More	
Evaluation of EU membership
(q8uk)	Q8. In general, do you think that (YOUR COUNTRY’S) EU membership is a good thing, a bad thing, neither good nor bad?	1 Good
0 Not Good/Neither good or bad/ Not sure	
Corruption in EU 
(recodq16_1)	Q16. On a 0-10 scale, with ‘0’ being that ‘there is no corruption’ and ‘10’ being that corruption is widespread, how would you rate: 
Q16_1: The European union; 
Q16_2: The national government;  	1 Low (0-3)
2 Medium (4-6)
3 High (7-10)	
Corruption in EU vs National government
(crpt_eu_nat)	Comparing Q16_1 to Q16_2	1 Less
2 Equal
3 More	

Variable	Description	Values	
Vote in the EU elections	Q7. Have you voted in either of the last two EU parliamentary elections? 	0 Neither
1 Once
2 Both
99 Don’t know/RF	
Support	Q20: In your opinion, the EU should continue this policy, where wealthier countries contribute more, and poorer EU regions receive more funding?	1 Agree; 
2 Disagree; 
3 D/K	
Gender	Gender	1 Male
2 Female	
Age	Age in years	Continuous	
Education	Level of education	1 Primary and lower secondary school
2 High school
3 Degree, Master, PhD	
Level of income	Net income per month (after taxes)	1 Low
2 Medium
3 High	
Occupation	Occupational status	1 Employed
2 Uunemployed
3 Housewife, pensioner, retired, other
4 Students, trainee	
GDP_14	GDP per inhabitant in Euro at 2014 as percentage of EU average	Numerical value	
Eqi_100	European Index of Institutional Quality at 2013 (Normalized)	Numerical value (0-100)	
Abs_13	Absorption rate of SF expenditures: ratio of SF expenditures up to 2013 to the SF allocation in the 2007-2013 period	Numerical value (0-1)	
SFtot_pc	Total expenditures financed by Structural Fund in years 2007-13 divided by the average population in a region in the period 2007-13	Numerical value (euros)	
Unold_14	Unemployment rate: population 20-64 years	Numerical value	

File specifics
The file is available in a csv format, being the Field delimiter « , » (comma). 

Notes 
The deliverable D24 “Report on the probabilistic model of estimation of the citizens' identification with the EU project and ranking of the case study regions” can be downloaded at:
http://doi.org/10.6092/unibo/amsacta/6113 (AMS Acta)

Methodologies 
Results were obtained using Latent Gold 4.5 

Codebook of variables 
Here are listed the column names used in the file

Variable name	Description	Values	
id	Unique ID	Numeric	
Region_code	Alfanumeric code of the region	String	
region_code_n	Geocode for merging with PERCEIVE regional dataset	Numeric	
age	Age	Numeric	
gender	Gender	0 Male
1 Female	
eqi_100	European Index of Institutional Quality (Normalized 100), 2013	Numeric	
gdp_14	GDP per capita as percentage of EU average, 2014	Numeric	
unold_14	Unemployment rate: population 20-64 years, 2014	Numeric	
abs_2013	Absorption rate of SF expenditures, 2013	Numeric	
sftot_pc	Per capita SF total expenditures, 2007-2013	Numeric	
occup	Employment status	1 Employed
2 Unemployed
3 Housewife, Pensioner, Retired, Other
4 Student, Trainee	
educat	Education	1 Primary and lower secondary school
2 High (secondary) school
3 Degree, Master, PhD	
income	Income	1 Low
2 Medium
3 High
99 (d/k-refused)	
satisf	Satisfied with economic situation in the region	0 Not satisfied
1 Satisfied	

Variable name	Description	Values	
recodq9_3	Identification with Europe	1 Not strongly
2 Somewhat strongly
3 Strongly	
ide_eu_nat	Identification: EU vs National	1 Less
2 Equal
3 More	
recodq5_1	Effectiveness of EU	1 Not so Effective
2 Somewhat effective
3 Very effective	
efct_eu_nat	Effectiveness: EU vs National	1 Less
2 Equal
3 More	
recodq16_1	Perception of corruption: EU	1 Low
2 Medium
3 High	
crpt_eu_nat	Corruption: EU vs National	1 Less
2 Equal
3 More	
vote	Vote in either of the last two EU elections	0 Neither
1 Once
2 Both times
99 (d/k-refused)	
support	Wealthier countries contribute more	1 Agree
2 Disagree
3 d/k	
q8uk	Evaluation of EU country membership (with UK data)	0 Bad thing
1 Good Thing	
awar	Awareness of Cohesion/Regional policies	0 None
1 Only local project
2 SF/Cohesion/Regional policies	
clu_1	Cluster1 posterior membership probability	Numeric	
clu_2	Cluster2 posterior membership probability	Numeric	
clu_3	Cluster3 posterior membership probability	Numeric	
clu_4	Cluster4 posterior membership probability	Numeric	
clu_5	Cluster5 posterior membership probability	Numeric	
clu_6	Cluster6 posterior membership probability	Numeric	
clu_	Cluster number for the most likely (modal) cluster	Numeric	
gclass_	GClass number for the most likely (modal) GClasss	Numeric	
gclass1	GClass1 posterior membership probability	Numeric	
gclass2	GClass2 posterior membership probability	Numeric	
gclass3	GClass3 posterior membership probability	Numeric	
gclass4	GClass4 posterior membership probability	Numeric	
ccl_	Cluster number for the most likely (modal) cluster	Numeric	
ccl_1	Cluster1 prior membership probability	Numeric	

Variable name	Description	Values	
ccl_2	Cluster2 prior membership probability	Numeric	
ccl_3	Cluster3 prior membership probability	Numeric	
ccl_4	Cluster4 prior membership probability	Numeric	
ccl_5	Cluster5 prior membership probability	Numeric	
ccl_6	Cluster6 prior membership probability	Numeric	
gccl_	GClass number for the most likely (modal) GClasss	Numeric	
gccl_1	GClass1 prior membership probability	Numeric	
gccl_2	GClass2 prior membership probability	Numeric	
gccl_3	GClass3 prior membership probability	Numeric	
gccl_4	GClass4 prior membership probability	Numeric