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Delegation and varieties of capitalism: Explaining the independence of national competition agencies in the European Union

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Comparative European Politics Aims and scope

Abstract

This article aims to explain why, despite the fact that all national competition authorities (NCAs) in EU member states enforce the same law, relevant differences exist in the degree of independence that these agencies enjoy. The author advances an original theoretical framework according to which the decision on the independence of NCAs depends on the structure of the economic system of a country. In particular, it is hypothesized that the means by which firms operate in the national market affects the tendency of national legislators to delegate more or less independence to the NCA. The statistical analysis carried out shows that both countries with low and high levels of employer density tend to have less independent competition authorities than those of other countries. On the one hand, the findings support the argument, advanced by varieties-of-capitalism scholars, that liberal market economies and coordinated market economies achieve greater efficiency than mixed market economies. On the other, the expectation that all institutional choices should be coherent with the firms’ coordination method is not confirmed.

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Notes

  1. ‘Council Regulation (EEC) No 4064/89 of 21 December 1989 on the control of concentrations between undertakings’.

  2. EC Regulation 1/2003 prescribes that all the European competition authorities have to implement Articles 101 and 102 of the Treaty on the Functioning of the European Union, which prohibit agreements restricting competition and abuses of dominant position.

  3. See, in particular, Article 3 of Regulation 1/2003.

  4. If

    and A=f(B), then:If ∀ Ai:A i =A i, then ∀ B j :B j= B j .

  5. In this article, by the terms ‘legislators’, ‘lawmakers’ and ‘politicians’, I will refer to members both of the parliament and of the government. As a matter of fact, especially in the countries studied here, it is impossible to attribute political decisions either to one body or to the other: in all the EU member states (except Cyprus), the government must have the confidence of the parliament, and distinguishing between the two makes little sense.

  6. As Koop (2011) has shown, regulatory agencies are more accountable (that is, less independent) in highly salient policy fields.

  7. The survey, collected between September and December 2009, was mainly based on Gilardi’s (2002; 2005a) one, which was drawn from that of Cukierman et al (1992). Some adjustments were also suggested by Hanretty and Koop (2012).

  8. The problems connected with the assumptions often made to construct these indices are discussed by Hanretty and Koop (2012). The factor analysis method, like all the others employed by other scholars, assumes that independence is unidimensional throughout the data. The index employed in the statistical analysis and another calculated with Gilardi’s method are correlated at 89 per cent.

  9. Factor analysis has been performed on a data set including all the variables drawn from the survey, using the principal-component factor method. As there were some missing values (and factor analysis by default deletes all the observations with missing values), I needed to impute them using multiple imputation. The original data set contained 99 missing values out of 1053 values (9 per cent). However, it must be considered that eight authorities had nine missing values each because they do not have a board − therefore they could not answer the questions of the survey which regarded the board. If we exclude these 72 ‘inevitable’ missing values, the missing values due to a lack of answer were only 27 (2.5 per cent). The Multiple Imputation command (in PASW 18) has generated five imputed data sets. To obtain the matrix for factor analysis, I have calculated the mean across these five replications for every value in the data sets.

  10. This database was used because it contains data on all the EU member states – a requirement for this analysis.

  11. For some countries, yearly data are not available. However, there is very little variation across time (the average coefficient of variation is very low, 0.04) among all cases. Therefore, missing values are little likely to bias the calculated mean value.

  12. The polarization value for each country is the standard deviation of this distribution, calculated with the formula:

    where LR t is the left–right value, in a scale going from 0 (maximum left) to 10 (maximum right), for each country in year t;μLR is the mean of this value across the period of interest.n is the number of years included in the calculation.The left–right position of each country’s government for each year has been calculated as:

    where S x is the number of seats that party x holds in the parliament in year t;LR x is the left–right position of that party;n is the number of the parties supporting the government in year t.All the data for these indicators have been taken from the ParlGov database (http://www.parlgov.org) (Doering and Manow, 2010). The polarization index is a component of the ‘replacement risk’ indicator developed by Franzese (2002).

  13. The studentized Breusch–Pagan test (Breusch and Pagan, 1979; Zeileis and Hothorn, 2002) reports a statistic of 5.10 with 5 degrees of freedom, which is significant at 0.32.

  14. The Bonferroni outlier test (Fox and Weisberg, 2011) does not indicate the presence of any P-value lower than 0.05 (the smaller Bonferroni P-value is 0.75).

  15. The graph in Figure 2 has been obtained by simulating expected values and standard deviation for each permille of the employer density’s distribution (10 000 simulations for each permille). Simulations have been performed with the programme Zelig (Imai et al, 2009) in R (R Core Team, 2012). For a discussion of the logic and the advantages of simulation over other post-estimation methods, see King et al (2000).

  16. The code for the graph in Figure 3 has been taken from one of the examples presented by Kastellec and Leoni (2007).

  17. Bootstrapped simulations have been performed with the programme Zelig (Imai et al, 2009) in R (R Core Team, 2012).

  18. For the employer density indicator, the three values have been set, respectively, to the 5th, the 50th and the 95th percentile (0.22, 0.6, 0.85). For polarization, the two values have been set at the 5th and 95th percentile (0.45, 2.01). For EU membership, the three values have been set at the minimum, the mean and the maximum (2, 23, 57).

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Acknowledgements

The author wants to thank Adrienne Héritier, Yannis Karagiannis, Igor Guardiancich and three anonymous CEP reviewers for their helpful comments.

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Authors and Affiliations

  1. Collegio Carlo Alberto, Via Real Collegio, 30, 10024, Moncalieri (TO), Italy

    Mattia Guidi

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  1. Mattia Guidi
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Appendices

Appendix A

Table A1

Table A1 Survey on the formal independence of competition authorities
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Appendix B

Table B1

Table B1 Factor loadings of the items derived from the survey
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Guidi, M. Delegation and varieties of capitalism: Explaining the independence of national competition agencies in the European Union. Comp Eur Polit 12, 343–365 (2014). https://doi.org/10.1057/cep.2013.6

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