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Abstract

Interest groups’ policy position documents constitute an important data source for estimating their policy positions and lobbying success. We examine applications of quantitative text analysis to research these documents in the context of the European Commission’s open consultations. We show a considerable degree of incongruity between this method’s assumptions and the text characteristics of EU position documents. We examine how these incongruities affect the validity of position estimates and conduct an empirical analysis of documents submitted in one consultation on CO₂ car emissions. We compare estimates derived on both quantitative and qualitative content analysis and find relatively limited correspondence between the two. These observed differences matter substantively: they result in different findings concerning levels of interest groups’ lobbying success.

Keywords

Content analysis European Union lobbying policy position estimates preference attainment

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References

Abdi

Valentin

(2007) Multiple correspondence analysis. In: Salkind

(ed.) Encyclopedia of Measurement and Statistics, Thousand Oaks, CA: Sage.

Baumgartner

Leech

(1998) The Importance of Groups in Politics and in Political Science, Princeton: Princeton University Press.

Benoit

Laver

(2006) Party Policy in Modern Democracies, London: Routledge.

Beyers

(2008) Policy issues, organizational format and the political strategies of interest organizations. West European Politics 31(6): 1188–1211.

Bunea

(2013) Issues, preferences and ties: Determinants of EU interest groups’ preference attainment in the environmental policy area. Journal of European Public Policy 20(4): 552–570.

Bunea

(2014) Explaining interest groups’ articulation of policy preferences in the European Commission’s Open Consultations: An analysis of the Environmental Policy Area. Journal of Common Market Studies 52(6): 1221–1241.

Caldeira

Wright

(1990) Amici Curiae before the supreme court: Who participates, when and how much? Journal of Politics 52(3): 782–806.

Dür

(2008) Measuring interest group influence in the EU: A note on methodology. European Union Politics 9(4): 559–576.

Diermeier

Godbout

(2012) Language and ideology in Congress. British Journal of Political Science 42(1): 31–55.

10.

European Commission (2007) Communication from the Commission to the Council of the European Union. Results of the review of the Community Strategy to reduce CO2 emissions from passenger cars and light-commercial vehicles (COM(2007)19). Available at: http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52007DC0019&from=EN (accessed 1 January 2015).

11.

Greenacre

(2007) Correspondence Analysis in Practice, Boca Raton, FL: Chapman & Hill.

12.

Grimmer

Stewart

(2013) Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis 21(3): 267–297.

13.

Hayes

Krippendorff

(2007) Answering the call for a standard reliability measure for coding data. Communication Methods and Measures 1(1): 77–89.

14.

Heine JH (2014) Pairwise: Rasch Model Parameters by Pairwise Algorithm. R package version 0.2.5. http://CRAN.R-project.org/package=pairwise (accessed 15 December 2014).

15.

Husson F, Josse J, Le S, et al. (2014) FactoMineR: Multivariate exploratory data analysis and data mining with R. R package version 1.27. http://CRAN.R-project.org/package=FactoMineR (accessed 15 December 2014).

16.

Klüver

(2009) Measuring interest group influence using quantitative text analysis. European Union Politics 10(4): 535–549.

17.

Klüver

(2011) The contextual nature of lobbying: Explaining lobbying success in the European Union. European Union Politics 12(4): 483–506.

18.

Klüver

(2012) Biasing politics? Interest group participation in European policymaking. West European Politics 35(5): 1114–1133.

19.

König

Pöter

(2001) Examining the EU legislative process. The relative importance of agenda and veto power. European Union Politics 2(3): 329–351.

20.

Larsen AG, with contributions from Ellersgaard C and Andrade S (2014). soc.ca: Specific correspondence analysis for the social sciences. R package version 0.7.1. http://CRAN.R-project.org/package=soc.ca (accessed 15 December 2014).

21.

Le Roux B and Rouanet H (2010) Multiple Correspondence Analysis. London: Sage Publications.

22.

Linacre

(1998) Detecting multidimensionality: Which residual data-type works best? Journal of outcome measurement 2(3): 266–283.

23.

Lowe

Benoit

(2013) Validating estimates of latent traits from textual data using human judgment as a benchmark. Political Analysis 21(3): 298–313.

24.

Lowe

(2008) Understanding Wordscores. Political Analysis 16(4): 356–371.

25.

Masters

(1982) A Rasch model for partial credit scoring. Psychometrika 47(2): 149–174.

26.

Proksch

Slapin

(2012) Institutional foundations of legislative speech. American Journal of Political Science 56(3): 520–537.

27.

Rasch

(1960) Probabilistic Models for Some Intelligence and Attainment Tests, Copenhagen: Danmarks pædagogiske Institut.

28.

Rasmussen

Caroll

(2014) Determinants of upper-class dominance in the heavenly chorus: Lessons from European Union online consultations. British Journal of Political Science 44(2): 445–459.

29.

Roberts

(2000) A conceptual framework for quantitative text analysis: On joining probabilities and substantive inferences about texts. Quality and Quantity 34(3): 259–274.

30.

Slapin

Proksch

(2014) Words as data: Content analysis in legislative studies. Oxford Handbook of Legislative Studies, Oxford: Oxford University Press, pp. 126–144.

31.

Slapin

Proksch

(2008) A scaling model of for estimating time-series party positions from texts.’. American Journal of Political Science 52(3): 705–722.

32.

Thomson

Torenvlied

(2010) Information, commitment and consensus: A comparison of three perspectives on in the European Union. British Journal of Political Science 41(1): 139–159.

33.

Yackee Webb

(2006) Sweet-talking the fourth branch: The influence of interest group comments on federal agency rulemaking. Journal of Public Administration Research and Theory 16(1): 103–124.

34.

Wright

(1996) Comparing Rasch measurement and factor analysis. Structural Equation Modeling: A Multidisciplinary Journal 3(1): 3–24.

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Quantitative text analysis and the study of EU lobbying and interest groups

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