The PopuList 2.0 – Codebook

The PopuList 2.0 data set consists of 1) information about the parties, 2) four dimensions of expert classifications and 3) links to other data sets.

Variable Table

Variable Name Description Variable Type
party_name Name of the party in native language(s) partyinfo
country_name Name of country partyinfo
party_name_english English translation of party name partyinfo
party_name_short Party name abbreviation partyinfo
populist, farright, farleft, eurosceptic Classification of party along expertclass
populist_start, farright_start, etc. Beginning of validity of classification. 1900 here stands for the beginning of the classification period, i.e. 1989; expertclass
populist_end, farright_end, etc. End of validity of classification period. 2100 here stands for the last time the list was updated, i.e. beginning of 2020 expertclass
populist_bl, farright_bl, farleft_bl, etc. Indication of borderline status of classification. expertclass
populist_startnobl, farright_startnobl, etc. Beginning of classification period without borderline cases. expertclass
populist_endnobl, farright_endnobl, etc. End of classification period without borderline cases. expertclass
partyfacts_id Identification number of party in the partyfacts database party id
parlgov_id Identification number of party in the ParlGov database party id
manifesto_id Identification number of party in the Manifesto Project database party id

Most parties can be clearly classified across all dimensions. For some parties the classification is contested or ambiguous. These cases are here labelled as borderline cases. Their borderline status of a classification is indicated by a variable with the same name ending on ‘_bl’. (e.g. populist_bl).

So if you want to keep borderline classifications in the analysis, simply use the main variables (e.g. populist, populist_begin, populist_end) if you want to restrict your analysis to uncontested cases, use the x_endnobl time classifications instead of the x_end column.

The PopuList 2.0 can thus either be used with static classifications, or with the time-dynamic classifications. Moreover user can decide to exclude or keep cases which have less consensus amongst experts.

For an example of how the time dynamic classification can be accounted for in an analysis, see the coding example on this website.