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PIP – Parties, Institutions & Preferences: PIP Collection [Version 2018-02].”

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PIP – Parties, Institutions & Preferences: PIP Collection [Version 2018-02].” Detlef Jahn, Nils Düpont, Sven Kosanke, and Christoph Oberst together with Thomas Behm and Martin Rachuj.1 P PI – Parties, Institutions & Preferences: Codebook Version: 2018-02 Please quote the data as: Jahn, Detlef, Nils Düpont, Sven Kosanke, and Christoph Oberst together with Thomas Behm and Martin Rachuj. 2018. “PIP – Parties, Institutions & Preferences: PIP Collection [Version 2018-02].” Chair of Comparative Politics, University of Greifswald. Please send any paper using this dataset to Detlef Jahn ([email protected]). For comments or further requests contact Sven Kosanke ([email protected]) or Nils Düpont ([email protected]). 1 For full credits see Appendix. Introduction| 1 Introduction P st nd The PI compiles information about parties, governments, 1 & 2 chambers and presidents and combines them with ideological data about parties (e.g. left-right positions). Additionally, various aspects of the European Union (EU) are covered as well, e.g. the composition of the P 2 European Parliament and the Commission. The PI covers 36 countries from 1944 to 2016, namely 23 OECD countries: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and the USA; together with thirteen Central and Eastern European and Mediterranean EU member states: Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Hungary, Poland, Slovakia, Slovenia, Latvia, Lithuania, Malta, and Romania. The ideological indices included are: Left-Right (LR), Green-Growth (GG) and subordinated indices by Jahn (2011; 2016), cohesion of parties on these dimensions based on Jahn and Oberst (2012), RILE and additional indices (Budge et al. 2001), and Left-Right plus related indices by Franzmann and Kaiser (2006).3 Furthermore expert judgements by Benoit and Laver (2006) and the Chapel Hill Expert Survey Series (CHESS; Bakker et al. 2015; Hooghe et al. 2010; Ray 1999; Steenbergen and Marks 2007) can simply be merged. The dataset is available as a quarterly time-series–cross-section-file where each case equals one quarter per party.4 Consequently the information about party ideology can be combined with various information about the composition of governments, parliaments or presidents in a multifaceted way, e.g. to estimate the policy-position of the government (coalition), to determine the median-position of the 1st chamber, to estimate veto player ranges etc. Thus, this dataset can be used primarily to create independent variables for policy-research but also to examine ideology-related research questions concerning (party) politics. 2 For Greece, Spain, Portugal, and the thirteen Central and Eastern European and Mediterranean new EU member states the actual time period is shorter. See Section Two for country-specific details. 3 Some ad hoc-indices based on the CMP data are included as well, e.g. “pro/anti EU” (Warntjen, Hix, and Crombez 2008) or “environmental protection” (Knill, Debus, and Heichel 2010). 4 For data structure and technical notes see page 3. Introduction| 2 About the Codebook The next paragraphs give a clue how the dataset was compiled. The next one reports general steps plus the main sources, while the second and third aim at clarifying the underlying logic and structure. Thereafter the codebook is divided into two sections. The first section (p. 5) gives an overview about the dataset, the variables, their value labels and notes. If one is only interested in estimating policy positions of governments, presidents etc. please refer to “Guide for Replicating the ASPM”. With this information at hand it is easy to start working with the data. However, if a closer look at the data is desired Section Two (p. 17) starts with introductory notes on compiling rules and deals with each country separately, i.e. detailed information about parties, the coding of ministers plus notes on 1st and 2nd chambers, presidents and the EU. Order of Work Nearly all indices which position parties along different dimensions rest upon the data provided by the Manifesto Project (hereafter simply referred to as “CMP”; Budge et al. 2001; Klingemann et al. 2006; Volkens et al. 2013; 2015). Accordingly the CMP data form one major source. We added information about governments based on Woldendorp, Keman, and Budge (2000), which however ended in most parts in the mid-1990s. For the CEE countries we used data from CIRCA’s “People in Power” database (CIRCA) and Rose and Munro (2009). We updated the data – including the categorization of the ministries – using the European Journal of Political Research Political Data Yearbooks (EJPR) for the current years. The main problem arose from the EJPR giving the ministries’ (full) name while Woldendorp, Keman, and Budge (2000, 21–2) used categories. In order to continue the data as consistent as possible we used the last cabinets provided by Woldendorp, Keman, and Budge and the corresponding issues of the EJPR to gauge our coding instructions.5 For the composition of the 1st and 2nd chamber and presidents we used the EJPR and country related sources, and cross-checked with ParlGov (Döring and Manow 2015). In the end, we added ideological data from Jahn (2011), Jahn and Oberst (2012), Budge et al. (2001), and Franzmann and Kaiser (2006). Expert judgements from Benoit and Laver (2006) and the Chapel Hill Expert Survey Series (Bakker et al. 2015 inter alia) can easily be merged via a Stata do-file. 5 Detailed information about our coding instructions is given in Section Two for each country separately. Introduction| 3 We cross-validated the data within these sources plus, where inevitable, used country specific sources as well. Ultimately, we believe to present a dataset as consistent as possible. Nevertheless, due to fragile chaotic party systems, contradicting sources, matching problems etc. errors are still likely to occur in our data. We therefore welcome comments on irregularities or questions concerning the data treatment in order to improve the dataset. Data Structure and Technical Notes The dataset was compiled using MS Excel for the raw data and Stata for aggregation. The process of aggregation included one step inspired by Cusack’s (2002) SPSS-syntax context_quartely.sps: the “superior context”.6 Each context (e.g. a government or president) “owns” a number of days in each quarter depending on the start and end date. If two (or more) contexts clash in one quarter the context which owns the most days will be the “superior” one. Hence all information related to this context will show up. The data of the subsequent context appears if it becomes the superior one. Our version for STATA creates a time-series by taking the superior context for each quarter into account.7 The basic structure is one observation = one quarter per party. The next table illustrates the data structure. Table: Example of the data structure Iso- Country Year Quarter Party Index Governm p% of Government ... Code ent ministers position 10 A 1960 1 AAA 5 . 2 ... 10 A 1960 1 BBB 2 1 100 2 ... 10 A 1960 1 CCC 3 . 2 ... 10 A 1960 2 AAA 4.5 . 2.5 ... 10 A 1960 2 BBB 2.5 1 100 2.5 ... 10 A 1960 2 CCC 4 . 2.5 ... ... ... ... ... ... ... ... ... ... ... 20 B 1960 1 XXX 10 1 75 9 ... 20 B 1960 1 YYY 6 1 25 9 ... 20 B 1960 1 ZZZ -4 . 9 ... ... ... ... ... ... ... ... ... ... ... Take for example country A, where party BBB took a position on one ideological dimension (column “Index”) equal 2 in the first quarter 1960 and changed slightly in the second quarter. Party BBB is a single-party government. Thus the government position weighted by p% of 6 Included in his PGL-Collection. 7 Note that not all variables are subject to this logic. Refer to page 18 for more details. Introduction| 4 ministers equals 2 and 2.5 respectively. However all cases (i.e. parties) in a quarter contain these values in order to easily generate a yearly time-series by simply calculating the mean value of the year (each quarter has the same amount of cases in each country, even if a party did not exist at that time).8 Due to their nature CMP data and the derived indices are available for election dates only. In order to construct a continuous time-series we interpolated the index values in two different ways indicated by the suffixes “f” or “c”.9 The suffix “o” indicates the original (unmodified) party scores. The other suffixes indicate “flow” or “crisp” values. The former are linear interpolated values, whereas the latter are constant until a new election takes place and the party gets a new value. One can thus choose the interpolation technique which is the most appropriate one for answering his or her research question. The next figure shows the differences for one party in one dimension. Figure: Difference original, flow and crisp values for one party between two elections 5 4 3 2 Index value 1 0 q1 q2 q3 q4 q5 q6 q7 q8 (election) (election) Flow values (linear interpolated) Crisp values Original entry It is helpful to have these technical notes and the data structure in mind when handling the dataset and/or generating new variables. If one is interested in estimating policy positions of governments, median positions of the 1st chamber etc. please read the document “Estimating P Policy Positions using PI ”, because this codebook solely contains information related to the P compilation of the PI dataset. To go on, the next section provides information about the variables, followed by Section Two with country specific information. 8 If so, cases are indicated by variable p118=0. 9 In need of a start- or endpoint in some cases (e.g. party split-ups, mergers, termination etc.) we duplicated original entries.
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