Data and Operationalization

For the analysis of the GFCC, I use a data set originally collected by Vanberg (2005) and extended by Krehbiel (2016). The data set contains all published decisions of the GFCC between 1983 to 2010 reviewing the constitutionality of federal and state laws.¹¹ Because the original data set of Krehbiel (2016) excludes decisions in which the court

11I not only include decisions where the status quo of a law is challenged, but also where it is upheld.

This is because Staton and Vanberg (2008) explicitly state that their formal model works in both cases (see footnote 11 in the original paper for more detail).

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does not have discretion over oral hearings, I also added these cases to the data set.¹² I do not include special decisions such as decisions on provisional orders (Einstweilige Anordnungen, §32 Act on the GFCC), or requests to exclude a judge from a case (Befangenheitsanträge, §19 Act on the GFCC). This is because the logic of the formal model studied here cannot be properly applied to these decision types. Furthermore, it is important to note that the analysis is conducted on the decision-level. The GFCC typically groups similar cases (proceedings) together, which could be directed against the same law(s), but also for instance against rulings of lower courts. A decision is included in the data set if a least one if its proceeding is directed against a federal or state law. This is also because the vagueness scores created in Chapter 3 are measured on the decision-level. This way of aggregating information from the proceeding-level to the decision-level is also used in similar work on the GFCC (Engst, 2018, Chapter 4).

In sum, the final GFCC data set contains 372 observations.¹³

For the analysis of the French CC, I use self-collected data obtained from the web page of the CC on all decisions dealing with the abstract review of laws decided between 1974 and 2010.¹⁴ I only use decisions from 1974 onwards because the CC only became a fully established constitutional court after a reform in 1974 (see Hönnige (2009) for more information on the choice of this time period). No decisions after 2010 are used due to the introduction of a new proceeding type in 2010 (QPC, Question prioritaire de constitutionnalité). QPCs allow the a posteriori control of laws by the CC. This new pool of litigants and extended power of the CC might have changed the court-legislator dynamics. For this reason, only decisions after 2010 are excluded. The final data set on the CC includes 558 decisions.

Measuring Decision Vagueness

Staton and Vanberg (2008) do not provide an exact definition of what makes a vague decision. All they write is that “in the context of our model, a perfectly vague opinion is an opinion that attacks the status quo policy as illegitimate, but does not impose any specific demands on the legislature for reforming that policy” (Staton and Vanberg, 2008, 508).

12Therefore, the final data set includes constitutional complaints, concrete reviews, public law disputes, election disputes involving the constitutionality of an electoral law, constitutional disputes between the national and state governments, constitutional disputes within a state, and abstract reviews. It does not include unpublished chamber decisions. Only decisions which deal directly (unmittelbar) with a law are used.

13Note that the original data set of Krehbiel (2016) contains 613 observations, but his unit of analysis is at the proceeding-level. The smaller N in my case is explained by aggregating information on the decision-level, the exclusion of decisions which only indirectly (unmittelbar) deal with a law and removing decisions dealing with provisional orders.

14These are decisions of the type “DC”, so called “Contrôle de constitutionnalité des lois ordinaires, lois organiques, des traités, des règlements des Assemblées”.

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I argue that this concept can be operationalized by my newly introduced concept of judicial policy implementation vagueness in Chapter 3. Judicial policy implementation vagueness was defined as a particular form of linguistic hedging in the context of court decisions and as the intentional choice of words or terms that give the legislator a wide decision leeway and room to maneuver in how it can implement a court decision. Chapter 3 also proposed two different approaches to measure judicial policy implementation vagueness in decisions of GFCC and the CC, namely the usage of an expanded dictionary and a NLP classifier. Both approaches yield to vagueness scores that are tailored for the judicial domain, and are thus superior to the usage of general dictionaries such as LIWC or other approaches commonly used in judicial politics research.

For the GFCC application, I use the vagueness measure that relies on the NLP classifier (the Convolutional Neural Network classifier) because it exhibited the best out-of-sample prediction performance. The German vagueness measure thus describes the proportion of vague sentences in each decision. The mean of the decision vagueness in the German data is 0.19 (with a standard deviation of 0.5). This means that 0.19 percent of all sentences across all decisions texts in the German data are vague. For the CC application, the expanded dictionary measure is used. The French measure thus describes the proportion of vague words in each decision. The mean of the decision vagueness in the French data is 0.42 (with a standard deviation of 0.23). This means that across all words in the French data, 0.42 percent are vague.

At this point, I want to emphasize again that both vagueness scores measure the occurrence of specific words or sentences within long decision documents in a large text corpus. This is why the empirical approximation of these terms is relatively small in absolute numbers. Nevertheless, I have demonstrated in Chapter 3 that even small changes in the wording of a decision can have a considerable influence on its judicial policy implementation vagueness.

Measuring Judicial Policy Uncertainty

Judicial policy uncertainty describes a court’s uncertainty about the necessary means to achieve a desired policy outcome. An ideal measure would be, for instance, the training background of each judge for each policy field, a judge’s prior experience with a given topic, or, as a simpler approximation, the policy expertise of at least the rapporteur responsible for a decision. Unfortunately, data on all of this information are not available. For this reason, I argue that the judicial policy uncertainty of judges can be measured by a decision’s complexity. To approximate this complexity, I use the dichotomous coding scheme for complexity following the measurement strategy proposed by Vanberg (2005) and recently used by Krehbiel (2016). According to this coding scheme, decisions involving taxation, budgets, economic regulation, social insurance, civil servant compensation, and party finance are coded as “complex” with

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a value of 0, whereas those involving institutional disputes, family law, judicial process, individual rights, asylum rights, and military conscription are coded as “simple” with a value of 1. In the German data set, this variable is already included in the original data set of Krehbiel (2016) for most of the decisions. For the decisions I manually added, I use the data from the CCDB that contains for each decision the information on the policy area following the Comparative Agenda Project¹⁵ (CAP) to classify these decisions accordingly. The German measure for judicial uncertainty is thus a dummy variable indicating whether a decision is complex or not.

Although a French version of the policy topic coding for each decision is available from the French CAP project, I refrain from using this data in the main analysis because it is only available until 2007. However, I will use it to test the robustness of the findings later in the robustness section. I approximate the case complexity in the French application by the number of legal doctrines the CC has to consider in a decision. In the introduction of the French rulings, the Conseil always quotes the laws, statutes and legal doctrines it has to examine in this particular ruling. I argue that the more laws, statutes or doctrines the Conseil has to consider, the more complex the issue of a ruling is.¹⁶ This variable ranges from 1 to 28, with a mean of 5.2 and a standard deviation of 3.7.

Measuring Preference Divergence

Preference divergence describes the divergence between the ideal point of the court and the policy preferences of the government. I follow the common measurement approach first proposed by Hönnige (2009) by measuring preference divergence as the absolute ideological distance between the court and the government on a common left-right scale using the ideology scores from the Comparative Manifesto Project (CMP) (Laver and Budge, 1992). The position of the government is calculated by weighting the CMP scores by the seats of the governing parties in parliament. This allows for a more nuanced measurement of the government’s policy position than using the raw CMP scores without weighting. In the German analysis, the position of each Senate of the GFCC is measured by assigning each judge the CMP score of the political party that nominated him or her on the given day this judge entered the court. Subsequently, the mean position of each Senate is calculated. In the French application, the same method was applied (but there is only one chamber). Finally, the absolute distance between the government position and the court position is calculated to obtain the ideological distance between court and government.

15https://www.comparativeagendas.net/, accessed 12.04.2019.

16A similar line of argument in the context of judicial decisions can be found in Wittig (2016, 103).

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Measuring Risk of Non-Compliance

The measurement of the perceived risk of non-compliance must approximate the appraisal of the judges whether or not the government will try to evade a decision after the court has delivered a ruling. I follow the measurement strategy of Vanberg (2005) and Krehbiel (2016) and measure the risk of noncompliance by examining whether or not the government whose law is being challenged filed an amicus brief defending the constitutionality of the statute. Filing such a brief can indicate the level of importance of a law for the government, because it requires the legislator to invest resources in such a statement. Furthermore, such a public statement (which will also be published together with the judicial decision) demands the legislator to position itself publicly, and is thus risky for the government’s reputation (Krehbiel, 2016, 997). For both applications, the variable is coded 1 when the challenged government files a brief defending the constitutionality of the law under review and 0 otherwise.

Confounders

For the German application, I control for two potential confounders, namely the public awareness for a decision and the deciding Senate. Vanberg (2005) and Krehbiel (2016) find that the degree of public awareness for a decision affects the behavior of the GFCC.

The court’s decision to write extra vague or specific decisions might be correlated with the salience of a decision. If the public awareness for a decision is high, the court could use this to additionally increase the pressure on the government and write even more specific decisions, because the government’s room for maneuver is smaller than without public awareness. In the same way, case salience may correlate with the government’s decision to file a brief. Therefore, failing to control for existing public awareness of a case could lead to biased results. I use the fact whether the court holds a public hearing or not as a proxy variable of a case’s salience and thus, as an indicator of public awareness. This variable is already used in other studies to approximate public awareness (Vanberg, 2005; Krehbiel, 2016). Unfortunately, the Conseil does not hold such hearings, nor is another measure available that approximates the salience or public awareness for a CC decision in the French application.

I also control for the institutional structure of the GFCC. The GFCC comprises two different Senates with varying persons and jurisdiction. This could result in a systematic variation of the usage of decision language. I address this possibility with the variable Second Senate, which has the value 1 if the Second Senate is concerned with a decision and a 0 if it is the First Senate. Since the French constitutional court only comprises one chamber, there is no need to control for this factor. Summary statistics of all variables used throughout the analyses are in Appendix C.1.

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