Skip to main content
×
Home

Position Taking in European Parliament Speeches

Abstract

This article examines how national parties and their members position themselves in European Parliament (EP) debates, estimating the principal latent dimension of spoken conflict using word counts from legislative speeches. We then examine whether the estimated ideal points reflect partisan conflict on a left–right, European integration or national politics dimension. Using independent measures of national party positions on these three dimensions, we find that the corpus of EP speeches reflects partisan divisions over EU integration and national divisions rather than left–right politics. These results are robust to both the choice of language used to scale the speeches and to a range of statistical models that account for measurement error of the independent variables and the hierarchical structure of the data.

Copyright
References
Hide All

1 Laver Michael, Benoit Kenneth and Garry John, ‘Extracting Policy Positions from Political Texts Using Words as Data’, American Political Science Review, 97 (2003), 311332; Monroe Burt L. and Maeda Ko, ‘Talk’s Cheap: Text-Based Estimation of Rhetorical Ideal-Points’ (presented at the 21st Annual Summer Meeting, Society for Political Methodology, Stanford University, 2004); Hopkins Daniel and King Gary, ‘A Method of Automated Nonparametric Content Analysis for Social Science’, American Journal of Political Science, forthcoming; Diermeier Daniel et al. , ‘Language and Ideology in Congress’ (presented at the annual meeting of the Midwest Political Science Association, 2007); Slapin Jonathan B. and Proksch Sven-Oliver, ‘A Scaling Model for Estimating Time-Series Party Positions from Texts’, American Journal of Political Science, 52 (2008), 705722.

2 Slapin and Proksch , ‘A Scaling Model for Estimating Time-Series Party Positions from Texts’.

3 Attina Fulvio, ‘The Voting Behaviour of the European Parliament Members and the Problem of the Europarties’, European Journal of Political Research, 18 (1990), 557579; Brzinski Joanne Bay, ‘Political Group Cohesion in the European Parliament, 1989–1994’, in Carolyn Rhodes and Sonia Mazey, eds, The State of the European Union (London: Longman, 1995), pp. 6483; Raunio Tapio, The European Perspective: Transnational Party Groups in the 1989–1994 European Parliament (Sudbury, Mass.: Dartmouth/Ashgate, 1997); Kreppel Amie and Tsebelis George, ‘Coalition Formation in the European Parliament’, Comparative Political Studies, 32 (1999), 933966; Hix Simon, ‘Legislative Behaviour and Party Competition in the European Parliament: An Application of Nominate to the EU’, Journal of Common Market Studies, 39 (2001), 663688; Noury Abdul, ‘Ideology, Nationality and Euro-Parliamentarians’, European Union Politics, 3 (2002), 3358; Noury Abdul and Roland Gerard, ‘More Power to the European Parliament?’, Economic Policy, 17 (2002):35, 280310; McElroy Gail, ‘Committee Representation in the European Parliament’, European Union Politics, 7 (2006), 529; Han Jeong-Hun, ‘Analysing Roll Calls of the European Parliament: A Bayesian Application’, European Union Politics, 8 (2007), 479507.

4 Hix Simon, Noury Abdul and Roland Gerard, ‘Dimensions of Politics in the European Parliament’, American Journal of Political Science, 50 (2006), 494511; Simon Hix, Abdul Noury and Gerard Roland, Democratic Politics in the European Parliament (Cambridge: Cambridge University Press, 2007).

5 Carruba Clifford J., et al. , ‘Off the Record: Unrecorded Legislative Votes, Selection Bias and Roll-Call Vote Analysis’, British Journal of Political Science, 36 (2006), 691704.

6 Carruba et al. , ‘Off the Record’, p. 692.

7 In their sample (5th EP, 1999–2000) co-decision votes were significantly under-sampled: only 0.77 per cent of co-decision votes were by roll call, see Carruba et al. , ‘Off the Record’.

8 Carruba et al. , ‘Off the Record’, p. 702.

9 Hix , Noury and Roland , Democratic Politics in the European Parliament, p. 166.

10 McElroy Gail, ‘Legislative Politics as Normal? Voting Behaviour and Beyond in the European Parliament’, European Union Politics, 8 (2007), 433448, p. 437.

11 There are other methodological approaches to studying positions of national parties in the European Union, but they do not focus specifically on parliamentary behaviour. These approaches include expert surveys (Hooghe Liesbet and Marks Gary, ‘Chapel Hill 2002 Expert Survey on Party Positioning on European Integration’, http://www.unc.edu/ (2002); Marks Gary, Hooghe Liesbet, Nelson Moira and Edwards Erica, ‘Party Competition and European Integration in the East and West – Different Structure, Same Causality’, Comparative Political Studies, 39 (2006), 155175; Benoit Kenneth and Laver Michael, Party Policy in Modern Democracies (London: Routledge, 2006); Benoit Kenneth and McElroy Gail, ‘Party Groups and Policy Positions in the European Parliament’, Party Politics, 13 (2007), 528; Steenbergen Marco R. and Marks Gary, ‘Evaluating Expert Judgments’, European Journal of Political Research, 46 (2007), 347366); and there are also MEP surveys (Farrell David et al. , ‘EPRG 2000 and 2006 MEP Surveys Dataset’, http://www.lse.ac.uk/collections/EPRG/ (2006)), mass survey research (Hix Simon and Lord Christopher, Political Parties in the European Union (Basingstoke, Hants.: Macmillan, 1997) ), interest group ratings, and European election manifestos (Gabel Matthew J. and Hix Simon, ‘Defining the EU Political Space: An Empirical Study of the European Elections Manifestos, 1979–1999’, Comparative Political Studies, 35 (2002), 934964). However, none of these approaches actually studies the revealed preferences of the MEPs themselves. In addition, the alternative approaches have some methodological problems. For instance, McElroy points out that elite surveys suffer from sample response issues, preference measures on the basis of constituency characteristics are difficult given the weak electoral connection in the European Parliament, and interest group ratings tend to have selective samples, thus potentially exaggerating extreme positions (McElroy , ‘Legislative Politics as Normal?’, p. 437).

12 Corbett Richard, Jacobs Francis and Shackleton Michael, The European Parliament, 5th edn (London: John Harper, 2003), p. 145.

13 Corbett , Jacobs and Shackleton , The European Parliament, 5th edn.

14 See Rule 149 of the EP Rules of Procedure.

15 Judge David and Earnshaw David, The European Parliament (Basingstoke, Hants.: Palgrave Macmillan, 2003), p. 239.

16 Corbett , Jacobs and Shackleton , The European Parliament.

17 To automate this task, we wrote a computer script which automatically extracted the agenda item and the number of speeches from the information available on the EP website.

18 Kreppel and Tsebelis , ‘Coalition Formation in the European Parliament’; Marks Gary, Wilson Carole and Ray Leonard, ‘National Political Parties and European Integration’, American Journal of Political Science, 46 (2001), 585594; Gabel and Hix , ‘Defining the EU Political Space’; Aspinwall Mark, ‘Preferring Europe: Ideology and National Preferences on European Integration’, European Union Politics, 3 (2002), 81111; Hix , Noury and Roland , ‘Dimensions of Politics in the European Parliament’; Hix, Noury and Roland, Democratic Politics in the European Parliament.

19 Hix , Noury and Roland , ‘Dimensions of Politics in the European Parliament’; Hix, Noury and Roland, Democratic Politics in the European Parliament.

20 Benoit Kenneth, Laver Michael and Mikhaylov Slava, ‘Treating Words as Data with Error: Uncertainty in Text Statements of Policy Positions’, American Journal of Political Science, 53 (2009), 495513.

21 We thank an anonymous referee for pointing this out to us.

22 Hix , Noury and Roland , ‘Dimensions of Politics in the European Parliament’.

23 Budge Ian, Klingemann Hans-Dieter, Volkens Andrea, Bara Judith and Tanenbaum Eric, Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945–1998 (Oxford: Oxford University Press, 2001).

24 Benoit and McElroy , ‘Party Groups and Policy Positions in the European Parliament’, p. 22.

25 Slapin and Proksch , ‘A Scaling Model for Estimating Time-Series Party Positions from Texts’. Wordfish is implemented in R and available at www.wordfish.org.

26 Laver et al., ‘Extracting Policy Positions from Political Texts Using Words as Data’; Proksch Sven-Oliver and Slapin Jonathan B., ‘Institutions and Coalition Formation: The German Election of 2005’, West European Politics, 29 (2006), 540559; Slapin and Proksch , ‘A Scaling Model for Estimating Time-Series Party Positions from Texts’; Hug Simon and Schulz Tobias, ‘Left–Right Positions of Political Parties in Switzerland’, Party Politics, 13 (2007), 305330.

27 Laver Michael and Benoit Kenneth, ‘Locating TDs in Policy Spaces: Wordscoring Dail Speeches’, Irish Political Studies, 17 (2002), 5973; Laver et al., ‘Extracting Policy Positions from Political Texts Using Words as Data’; Monroe and Maeda, ‘Talk’s Cheap: Text-Based Estimation of Rhetorical Ideal-Points’; Giannetti Daniela and Laver Michael, ‘Policy Positions and Jobs in the Government’, European Journal of Political Research, 44 (2005), 91120; Diermeier et al., ‘Language and Ideology in Congress’.

28 Laver Michael, Benoit Kenneth and Sauger Nicolas, ‘Policy Competition in the 2002 French Legislative and Presidential Elections’, European Journal of Political Research, 45 (2006), 667697.

29 Benoit Kenneth, et al. , ‘Measuring National Delegate Positions at the Convention on the Future of Europe Using Computerized Word Scoring’, European Union Politics, 6 (2005), 291313.

30 McGuire Kevin T. and Vanberg Georg, ‘Mapping the Policies of the U.S. Supreme Court: Data, Opinions, and Constitutional Law’ (prepared for delivery at the Annual Meeting of the American Political Science Association, Washington, D.C., 2005).

31 Slapin and Proksch , ‘A Scaling Model for Estimating Time-Series Party Positions from Texts’.

32 We have applied this model to compare election manifestos from German parties between 1990 and 2005. We found that the technique is able to recover party positions estimated by other techniques (e.g. expert surveys and hand-coding of manifestos). Furthermore, the positions reflect important changes in the party system, in particular a rightward movement of the major social-democratic party, the SPD, in the 1990s. We could produce estimates over time by making the assumption that word weights are time-invariant (see Slapin and Proksch, ‘A Scaling Model for Estimating Time-Series Party Positions from Texts’).

33 Laver et al. , ‘Extracting Policy Positions from Political Texts Using Words as Data’. While the technique has mostly been used to study political manifestos, it has been applied to legislative speeches as well (Laver and Benoit , ‘Locating TDs in Policy Spaces: Wordscoring Dail Speeches’; Laver et al. , ‘Extracting Policy Positions from Political Texts Using Words as Data’; Giannetti and Laver , ‘Policy Positions and Jobs in the Government’). Laver and Benoit use speeches from a confidence debate in the Irish Dáil in October 1991 over the future of the incumbent coalition government. They postulate a ‘pro- versus anti-government’ dimension and use the speech of the prime minister and of the opposition leaders as reference texts. The resulting placement of political parties on a scale of government versus opposition ‘is readily recognisable by any observer of Irish politics’ (Laver et al. , ‘Extracting Policy Positions from Political Texts Using Words as Data’, p. 327).

34 We did validate the Wordfish algorithm presented here with the Wordscores technique. To do so, we anchored the Wordfish dimension in Wordscores by using the speeches from the most extreme parties identified by Wordfish as reference texts. We estimated the Wordscores positions using a slightly updated version of the algorithm (Martin Lanny W. and Vanberg Georg, ‘A Robust Transformation Procedure for Interpreting Political Texts’, Political Analysis, 16 (2008), 93100). As expected, the results correlate very highly across all languages between the two techniques (correlation of 0.91 or higher).

35 This number excludes new member state MEPs joining in 2004 for only a few weeks before the next election, but includes the presidents and vice-presidents of the EP who deliver mostly procedural speeches.

36 Laver et al. , ‘Extracting Policy Positions from Political Texts Using Words as Data’, p. 327.

37 The inferences will only be valid for this total set of speeches and do not necessarily apply for subsets of speeches (e.g. specific policy areas).

38 Hix and Lord , Political Parties in the European Union; Raunio, The European Perspective; Kreppel and Tsebelis, ‘Coalition Formation in the European Parliament’; Kreppel Amie, The European Parliament and Supranational Party System (Cambridge: Cambridge University Press, 2002); Hix Simon, ‘Parliamentary Behavior with Two Principals: Preferences, Parties, and Voting in the European Parliament’, American Journal of Political Science, 46 (2002), 688698; Hix , Noury and Roland , ‘Dimensions of Politics in the European Parliament’; Hix, Noury and Roland, Democratic Politics in the European Parliament.

39 Benoit and McElroy , ‘Party Groups and Policy Positions in the European Parliament’.

40 Hix , Noury and Roland , ‘Dimensions of Politics in the European Parliament’. There are no independent measures of ideology available at the individual level with the exception of the EPRG survey of MEPs themselves, which suffers from low response rates (Farrell et al., ‘EPRG 2000 and 2006 MEP Surveys Dataset’). If the researchers wish to compare roll-call positions with expert survey positions or CMP data, they must aggregate up to the level of national party.

41 We thank one of the anonymous referees for pointing this out.

42 We exclude new member state MEPs as they were only represented in the 5th European Parliament by nominated members for a few weeks between the date of enlargement (1 May 2004) and the elections to the 6th European Parliament (June 2004).

43 We used Perl scripts to automate this task. The speech archive of the European Parliament is available at http://www.europarl.europa.eu/activities/archives/cre/search.do?language=EN, last consulted in April 2008.

44 Hix , Noury and Roland , ‘Dimensions of Politics in the European Parliament’.

45 We use Will Lowe’s jfreq program, available at http://www.williamlowe.net/software/.

46 The English Wordfish results using words mentioned by at least ten parties correlate with results using words mentioned by at least thirty parties at 0.99.

47 The EU has fewer official languages (twenty-three) than member states (twenty-seven). German is spoken in Germany and Austria, English in the United Kingdom and Ireland, Greek in Greece and Cyprus, and Belgium and the Netherlands share common languages with their neighbouring countries.

48 Corbett , Jacobs and Shackleton , The European Parliament, p. 34; Judge and Earnshaw , The European Parliament, p. 163.

49 These obligatory tasks result in considerable costs in the EU. In 2003, prior to the enlargement, EU institutions spent a combined 549 million euros on translation, and following enlargement to twenty-five members in 2004, the expense rose to an estimated 807 million euros per year, or approximately 1.78 euros per EU citizen (see European Commission Memo 05/10, January 2005, http://europa.eu/rapid/pressReleasesAction.do?reference=MEMO/05/10). In 2005, after enlargement by ten new member states, the EP had over one million pages of parliamentary documents translated. In addition, the EP provided interpretation services totalling 85,340 work days (see European Parliament Budget 2005, http://www.europarl.europa.eu/pdf/budget/rapportpublic2005_en.pdf).

50 There are several reasons to believe that translation may affect the output of computer-based content analysis. The German language has a particular feature that allows the compounding of words to create new ones. For example, the phrase ‘workers’ rights’ is described by two words in English, three in French (‘droits des travailleurs’), but only one in German (‘Beschäftigtenrechte’). Moreover, translation itself possibly adds error to the data, which could lead to different results across language. Translation theorists have suggested that one can view translation as a series of choices that can be modelled as a decision tree (Levý Jiří, ‘Translation as a Decision Process’, in To Honor Roman Jakobson II (The Hague: Mouton, 1967), 11711182). Each language presents the translator with a set of possible choices about which particular translation to choose. A stylistic choice a translator makes at one node may affect how he or she translates the rest of the text. This means that additional error may enter into the data both because different languages offer different choice sets and translators will make different decisions within those choice sets. Thus, we might get different results because some languages use different words and grammatical structures to express exactly the same content and because translators might follow different strategies in translation.

51 Appendix A shows the national party estimates using the English translations. The estimation is based on 4,859 unique words in English, 6,248 unique words in French and 7,369 unique words in German.

52 In contrast, speeches from a party whose native language is not English, German or French, are translated into all three languages.

53 The estimation is based on 4,765 unique words.

54 We can also calculate the average standard deviation of national parties based on the results from the individual level analysis. For those national parties with more than one MEP (n = 71), the average standard deviation of positions is 0.68, which is about two-thirds of the overall standard deviation of the positions (fixed at 1). If we include national parties with one MEP (n = 103), the mean standard deviation of the positions across national parties drops to 0.47. It would be interesting to explore the reasons for the variation of individual-level positions in future research.

55 Hooghe and Marks , ‘Chapel Hill 2002 Expert Survey on Party Positioning on European Integration’; Marks et al., ‘Party Competition and European Integration in the East and West’; Steenbergen and Marks, ‘Evaluating Expert Judgments’.

56 In addition to missing several small parties, the UNC data does not include parties from Luxembourg.

57 Benoit and Laver , Party Policy in Modern Democracies.

58 Benoit and Laver , Party Policy in Modern Democracies, p. 229.

59 Benoit and Laver , Party Policy in Modern Democracies, p. 131. The scales used for these questions range between 1 and 20. The Benoit/Laver survey includes other measures of EU support; however, they all correlate highly and produce the same result.

60 Although most of the missing estimates are for smaller parties, positions for parties from Ireland and France are missing entirely from the survey on these questions.

61 Hix , Noury and Roland , ‘Dimensions of Politics in the European Parliament’.

62 Hix , Noury and Roland , ‘Dimensions of Politics in the European Parliament’; Hix, Noury and Roland, Democratic Politics in the European Parliament.

63 Cook J. R. and Stefanski L. A., ‘Simulation-Extrapolation Estimation in Parametric Measurement Error Models’, Journal of the American Statistical Association, 89 (1994), 13141328.

64 This method corrects for measurement error of the independent variables only. The dependent variable, the positions estimated from word counts in speeches, is also measured with error. Wordfish allows researchers to estimate the fundamental uncertainty surrounding the positions via a parametric bootstrap. We have shown elsewhere through simulations that the confidence intervals of the estimated positions in Wordfish significantly decrease as the number of unique words used in the analysis increases (Slapin and Proksch, ‘A Scaling Model for Estimating Time-Series Party Positions from Texts’). Because we use several thousand unique words to estimate the positions, the confidence intervals of those estimates are rather small (see Appendix B). Moreover, measurement error in the dependent variable will not cause the kind of attenuation bias in the regression coefficients that we worry about. (Poole Keith T., ‘Measuring Bias and Uncertainty in Ideal Point Estimates via the Parametric Bootstrap’, Political Analysis, 12 (2004), 105127). Alternatively, one could apply Bayesian statistical analysis to estimate positions and their uncertainty (Han, ‘Analysing Roll Calls of the European Parliament’).

65 Benoit , Laver and Mikhaylov , ‘Treating Words as Data with Error: Uncertainty in Text Statements of Policy Position’, American Journal of Political Science, 53 (2009), 495513.

66 To estimate the SIMEX model as implemented in R, we use as the measurement error the mean standard deviation of responses across all parties.

67 It is possible to generate uncertainty estimates for Nominate using a parametric bootstrap (Lewis Jeffrey B. and Poole Keith T., ‘Measuring Bias and Uncertainty in Ideal Point Estimates via the Parametric Bootstrap’, Political Analysis, 12 (2004), 105127). Alternatively, one could apply Bayesian statistical analysis to estimate positions and their uncertainty (Han, ‘Analysing Roll Calls of the European Parliament’).

68 Average net contributions per capita for 1999–2003 are operating budgetary balances taken from the 2005 EU Commission report on the allocation of EU expenditures per member state divided by population, p. 138 (http://ec.europa.eu/budget/documents/revenue_expenditure_en.htm). We include those years of the 5th European Parliament for which the budget lists the balances for EU-15 member states only.

69 Steenbergen Marco and Jones Bradford S., ‘Modeling Multilevel Data Structures’, American Journal of Political Science, 46 (2002), 218237, p. 233.

70 Gelman Andrew and Hill Jennifer, Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge: Cambridge University Press, 2007).

71 GDP per capita is significant in the models using the UNC survey data, which excludes Luxembourg. Luxembourg is an outlier on GDP per capita, so excluding it from the analysis alters the results.

72 To preserve space we only report the predicted values for the country-level variables that attain statistical significance in the hierarchical model found in Appendix B.

* Mannheim Centre for European Social Research, University of Mannheim; and Trinity College Dublin, respectively (email: ). The authors thank Ken Benoit, James Honaker, Thomas König, Jeff Lewis, Michael Peress, George Tsebelis, Albert Weale and several anonymous reviewers for their helpful comments and suggestions. A previous version of this article was presented at the Annual Meeting of the Midwest Political Science Association in Chicago and at the Workshop on Estimating Policy Preferences at the Mannheim Centre for European Social Research in 2008. Both authors have contributed equally to all work.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

British Journal of Political Science
  • ISSN: 0007-1234
  • EISSN: 1469-2112
  • URL: /core/journals/british-journal-of-political-science
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Metrics

Full text views

Total number of HTML views: 7
Total number of PDF views: 223 *
Loading metrics...

Abstract views

Total abstract views: 509 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 23rd November 2017. This data will be updated every 24 hours.