DATABASE ON
POLITICAL RESPONSIVENESS
PRESENTATION BY LUANA RUSSO
CO-PIS
CHRISTINE ARNOLD
MARK FRANKLIN
CHRISTOPHER WLEZIEN
THE TEAM
DATABASE ON POLITICAL RESPONSIVENESS
¢  Co-PIs
Christine Arnold
Mark Franklin
Chris Wlezien
¢  Post-Docs
Luana Russo Chris Williams
Hossein Rahmani Eliyahu Sapir
This project was funded by Netherlands Organisation for Scientific Research
(NWO)
GOALS OF THE PROJECT
¢ 
To unite currently separate sources of data on
— 
— 
— 
public opinion
democratic institutions
outcomes
into a single comprehensive and cohesive database
¢  To standardize
— 
— 
— 
codes for opinion surveys,
governments
Legislatures
in an open format that would become an industry standard for
cross-national data analysis.
¢  To replace existing static, flat-file datasets with a dynamic,
relational database that would contain information in a more
efficient, flexible, and precise format.
¢  To allow open access to the database
THE WHOLE IS GREATER THAN THE SUM OF ITS PARTS:
CREATING A DATABASE ON POLITICAL RESPONSIVENESS
¢ 
Currently, many different data sources:
—  Public Opinion data
—  Party Manifesto / Party Expert data
—  European / National Legislation
¢ 
But no single collection that combines all these data in one
single common framework
¢ 
Increasing in the last years efforts to do so (Comparative
Agenda Project), we can build upon this momentum -more countries, more coverage of variables...
NEW APPROACH TO MODELING
1)  Historically unprecedented volume of data
available for research.
2)  Mature tools from computer science
and advanced analytical methods.
3)  Cross-linked and harmonized data:
PolicyVotes project
Data allows to scrutinize representational gaps across
ü  Policy areas
ü  Countries
ü  Time
MOTIVATION FOR THE DATABASE
Degree and Quality of Political Representation across
ü  Countries (comparative perspective)
ü  Time (temporal perspective)
ü  Levels of Governance (spatial perspective)
Is there effective political representation in Europe?
Specific aspects of political representation:
ü  Election campaigns of Parties
Electorate
ü  Policy agendas and Legislation
Representation
ü  Differences across Policy areas, Countries, Time
MOTIVATION: ISSUE CONGRUENCE
Policy
Representation
Electorate
(Issue Salience
and Policy Preferences)
Cueing
Strategies
Parties
(Issue Salience
and Policy Position)
MOTIVATION: RECIPROCAL LINK
Policy
Representation
Public Opinion
(Relative preferences for
more or less policy)
Public
Responsiveness
EU / National
Policy Output
(higher or lower)
DATA SOURCES FOR EU-15 COUNTRIES
Political Parties
ISSP
Comparative
Party Manifestos
P
Eurobarometer
Expert surveys
European
Election Studies
CSES
European
Election Studies
CSES
G
E
Electorate
Legislative Acts
Public Spending
Government
KEY STEPS IN THE PROCESS
① 
Web-Harvesting
② 
Harmonization
Ø  Intra-Study
Harmonization: Different waves of the same
studies are pooled into a single dataset.
Ø  Cross-Study Harmonization: All data holdings of the
same units of analysis are harmonized (example:
parties)
③ 
Data Linking
Ø  Inter-unit
“downwards”: copying all upper-level
data to all matching lower level of observation
Ø  Inter-unit “upwards”: aggregating selected data
to selected upper-level nesting groups.
METHOD OF COLLECTING SOURCES:
NATIONAL / EU LEGISLATION
Web-Harvesting:
¢  Use customized scripts to collect legislative data
¢  Objective:
ü  To collect for each document all the meta-information
ü  But: databases typically only make available some of the
documents for specified queries
ü  Solution: Automated Query enumerates all permutations
¢  Advantages:
ü  Time efficient
ü  Volume of collected data
ü  Parallel processes of fetching data
ü  Easier to update data collection in the future
ü  Documents with all meta-information allow for great
flexibility when using the data
METHOD OF COLLECTING SOURCES:
HARMONIZATION (EB, CSES, ESS, ISSP)
Harmonization:
¢  Through
survey sources:
ü  Country codes
ü  Party affiliation
ü  Missing codes
¢  Within
each survey source:
ü  Variable names
ü  Variable answer categories
EXAMPLE: HARMONIZATION OF THE
EUROBAROMETER - OVERVIEW
¢  Harmonization
— 
— 
of Standard EB 1970-2011
1970-1974 = 1 EB per year
1975: 2011 = 2 EB per year
¢  Subset
of 124 variables
EXAMPLE: HARMONIZATION OF THE
EUROBAROMETER - PRINCIPLES
¢  Harmonization:
— 
— 
two opposite tendencies
Maximum homogenization
Keeping richness of information
¢  Effort
to combine both in the most efficient way
¢  Efficient
& easily accessible organization of the
information
Codebook in excel format (one file)
— 
— 
— 
Availably of variables per edition
Organization of variables per topic
Information on change per variable
EXAMPLE: HARMONIZATION OF THE
EUROBAROMETER – CODEBOOK (2/3)
— 
Availably of variables per edition
EXAMPLE: HARMONIZATION OF THE
EUROBAROMETER – CODEBOOK (1/3)
— 
Organization of variables per topic
EXAMPLE: HARMONIZATION OF THE
EUROBAROMETER – CODEBOOK (1/3)
— 
Information on change per variable
DATA STRUCTURE
DATA LINKING
Political Parties
ISSP
Comparative
Party Manifestos
P
Eurobarometer
Expert surveys
European
Election Studies
CSES
European
Election Studies
CSES
G
E
Electorate
Legislative Acts
Public Spending
Government
Thank you for listening!
Questions?
For specific questions please feel free to contact
-  Christine Arnold: [email protected]
-  Luana Russo: [email protected]