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Measuring Policy Positions of Veto Players in Parliamentary Democracies

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Reform Processes and Policy Change

Part of the book series: Studies in Public Choice ((SIPC,volume 16))

Abstract

Testing veto players theory in parliamentary systems requires researchers to have accurate estimates of policy preferences of political parties. In empirical studies, however, political scientists use remarkably context-free and invariant estimates of party positions. As a consequence, they must make assumptions about the nature of policy conflict. In the extreme, the lack of accurate estimates reduces sample sizes and increases the risk of selection bias in studies that use the ideological range among veto players as a key explanatory variable. We introduce a new automated method to identify policy relevant content in political texts. Our method “smart tags” sentences in election manifestos using portfolio-specific keywords from a legislative database. Policy positions can then be estimated on the tagged subset of relevant sentences. We apply our method to analyze the legislative positions of political parties for several portfolios in the German parliament. We show that these estimates vary over time and across portfolios – variation which cannot be produced using existing methods. We believe our method simplifies preference estimation and increases construct validity of policy preference measures used to evaluate policy-focused theories.

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Notes

  1. 1.

    Although Veto Players Theory is applicable to all democratic political systems, our focus in this paper is on parliamentary democracies. This is because we are interested in estimating policy portfolio specific positions for partisan veto players.

  2. 2.

    Other prominent expert surveys have been conducted specifically on the issue of European integration, see Rohrschneider and Whitefield (2007), Steenbergen and Marks (2007), and Hooghe et al. (2010).

  3. 3.

    Note that the statistical inference problem in expert surveys is different from that in voter surveys. Whereas in the latter we are interested in inferring the attitudes of a population (i.e., all citizens or voters) from information about a sample, the goal in expert surveys is to “uncover an assumed underlying truth, the spatial location of a party’s policy positions” (Benoit and Laver 2006). Nevertheless, the reasons for expert participation in surveys remain unexplored, as do issues of expert pool contamination due to multiple surveys directed repeatedly at the same experts.

  4. 4.

    Even more problematic are expert surveys that ask experts at point tto “remember” and indicate the positions of parties held at point t− x.

  5. 5.

    We thank George Tsebelis for making the replication data available to us.

  6. 6.

    In cases for which all three indices exist, the average was calculated on the basis of all three; for countries covered by two indices, only the two standardized indices were used. The same logic was applied for cases covered only by one index.

  7. 7.

    The CMP data contain information on 56 categories, indicating the relative frequency of manifesto quasi-sentences that fit into each of these categories. There has been much criticism of this approach on grounds of reliability and misclassification of the CMP codings (e.g., Mikhaylov et al. 2008; Benoit et al. 2009).

  8. 8.

    See, for example, a recent special issue on the topic: The Statistical Analysis of Political Text. Political Analysis16(4), edited by Burt L. Monroe and Philip A. Schrodt.

  9. 9.

    This is different from the CMP measurement approach, which assumes that there is a true coding of quasi-sentences (the CMP “gold standard”) according to which each coder should be evaluated.

  10. 10.

    Only if researchers assign different party documents as reference texts does this approach use different text for different dimensions, but in any case, it does not use policy-specific texts, but the entire manifesto.

  11. 11.

    In essence, we are interested in topic-coding manifestos. However, we differ from computer-based methods of topic-coding (e.g., Hopkins and King 2010; Quinn et al. 2010) in that we use keywords from bills to explicitly link policy-specific information in manifestos to policy portfolios.

  12. 12.

    GESTA is electronically available via the ‘Documentation and Information System for Parliamentary Materials’ (DIP) system (http://dip.bundestag.de).

  13. 13.

    To ensure comparability across time, we matched policy portfolios to the 14 federal ministries in the 16th legislative term of Bundestag. Although the policy portfolios in GESTA closely mirror the ministerial portfolios, the match is not exact because some ministries changed over time. For example, new ministries were added (e.g., the Ministry for the Environment, Nature Conservation and Nuclear Safety following the 1986 Chernobyl disaster) while others were split (e.g., the Ministry for Youth, Family and Health in 1991).

  14. 14.

    We also process the files by removing all numbers, punctuation, other nonalphanumeric characters, and checking for spelling errors. We furthermore exclude words with little semantic value (very short words, articles, pronouns, prepositions, conjunctions, etc.) as well as words without a clear political meaning which appear in almost each GESTA entry (i.e., “proposal”, “measure”, “implementation,” etc.). To do this, we used a dictionary approach implemented in Simstat/WordStat (Davi et al. 2005; Johnson 2007).

  15. 15.

    However, this unique allocation to a policy area is not an essential step in our approach, and researchers may decide to consider multiple policy areas for the same keyword. We use a relative frequency criterion rather than an absolute frequency criterion because the absolute frequency criterion would mean that policy areas in which more legislation is written would also have far more keywords than areas in which less legislation is produced.

  16. 16.

    If a manifesto sentence contains keywords from two or more portfolios, the sentence counts for each of those portfolios. An alternative approach would be to assign each sentence exclusively to one portfolio.

  17. 17.

    We use keyword stems and stemmed manifesto sentences.

  18. 18.

    For example, in some instances a keyword may only occur during one legislative period. Nevertheless, we examine how many times that word was mentioned in the manifestos drafted before and after this period.

  19. 19.

    Wordfish assumes that the frequency with which words occur in documents is generated by a Poisson process, the simplest of the count distributions. The systematic component of this process contains four parameters: document (party) positions, document (party) fixed effects, word weights (discrimination parameters), and word fixed effects. Formally, y ijt ∼ Poisson(λ ijt ), where y ijt is the count of word jin party i’s manifesto at time t. The lambda parameter has the systematic component λ ijt = exp(α it + ψ j + β j ∗ ω it ), with α as a set of document (party-election year) fixed effects, ψ as a set of word fixed effects, β as estimates of word specific weights capturing the importance of word jin discriminating between manifestos, and ω as the estimate of party i’s position in election year t(therefore itis indexing one specific manifesto). For a more detailed description of the estimation process, see Slapin and Proksch (2008).

  20. 20.

    In German, Ladenschluss, Dienstleistungsabend, and Beschäftigungsförderung, respectively.

  21. 21.

    This is also the reason Tsebelis uses multiplicative heteroskedastic regression in his empirical analysis (Tsebelis 2002).

  22. 22.

    The results are based on keyword stems. Only keywords that appear in at least one election manifesto are considered.

  23. 23.

    The CDU–CSU and FDP formed the governing coalition throughout the period of investigation.

  24. 24.

    Moreover, the keyword coverage in manifestos across legislative periods, which is not shown in the table, reveals that for almost all portfolios the majority of keywords appear in at least two legislative periods. In other words, we are not simply selecting keywords that are relevant in one legislative period only. The most stable keyword coverage is in the areas of development and defense, in which more than 70% and 60%, respectively, of keyword stems occur in more than one legislative period. The least stable keyword coverage is in the areas of finances and justice, in which slightly less than 50% occur in at least two periods. We consider this stability to be sufficient to use the keywords to construct a term-document matrix and estimate positions over all three periods simultaneously.

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Acknowledgements

The authors’ names appear in alphabetical order. All authors have contributed equally to all work. A previous version of this chapter was presented at the 67th Annual MPSA National Conference in Chicago, April 2009, and the Veto Players Workshop at the University of Mannheim in May 2009. We thank the participants for helpful comments and suggestions.

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Authors and Affiliations

  1. University of Mannheim, Mannheim, Germany

    Thomas König, Bernd Luig & Sven-Oliver Proksch

  2. University of Houston, Houston, TX, USA

    Jonathan B. Slapin

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  1. Thomas König
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  2. Bernd Luig
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  3. Sven-Oliver Proksch
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  4. Jonathan B. Slapin
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Correspondence to Thomas König .

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Editors and Affiliations

  1. Department of Political Science, University of Mannheim, Mannheim, 68131, Germany

    Thomas König

  2. Mannheim Center for European Research, University of Mannheim, Mannheim, 68131, Germany

    Marc Debus

  3. Dept. Political Science, University of Michigan, S. State St. 505, Ann Arbor, 48109-1045, USA

    George Tsebelis

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König, T., Luig, B., Proksch, SO., Slapin, J.B. (2011). Measuring Policy Positions of Veto Players in Parliamentary Democracies. In: König, T., Debus, M., Tsebelis, G. (eds) Reform Processes and Policy Change. Studies in Public Choice, vol 16. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5809-9_4

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