Robustness Analyses

I check the robustness of my findings when using different measurements for policy divergence and judicial uncertainty and an alternative dependent variable. A detailed report of all robustness analyses is in Appendix C.4.

First, a central variable in my analyses is the measurement of the degree of policy divergence between court and legislator. For the main analysis, I used the scores from the Comparative Manifesto Project to calculate the ideological position of court and

109

4.5. RESULTS

legislator. These scores are increasingly criticized regarding their spatial and temporal comparability (Lowe et al., 2011; König, Marbach and Osnabrügge, 2013). In order to check whether my findings are robust to the measurement of ideological distance, I replicate my analyses but use the Manifesto Common Space Scores (MCSS) of König, Marbach and Osnabrügge (2013) instead of the original CMP scores. My findings remain robust to the usage of this different ideology measure.

The second robustness check concerns the measurement of judicial policy uncertainty.

For the German analysis, instead of using the complexity of the topic of a decision, I rely on another complexity measure first proposed by Wittig (2016) that measures the length of the case facts of a decision. The case facts can be found at the beginning of a decision of the GFCC and describe the case, the plaintiff’s arguments and other decision-relevant context. Her measure takes into account how long this section is by counting the number of paragraphs. Wittig (2016) argues that the longer the case facts, the more complex the content of a decision is (Wittig, 2016, 102). I repeat my analyses of the GFCC by using the length of case facts as the judicial uncertainty measure.²⁰ I find that while the sign of the respective coefficient is in the expected direction (the longer the case facts, the higher the vagueness of a decision), the coefficient itself is not statistically significant (p>0.1). One possible reason for this could be that the length of case facts is strongly correlated with the overall length of a decision (Pearson’s r is 0.64, p<0.01). Thus, endogeneity could be an issue since the dependent variable is a function of decision length and the number of vague sentences.

For the French analysis, I re-run the main analyses but this time I use a dummy variable indicating whether a case is complex or not (using the same Comparative Agenda Project coding scheme than in the German analysis) instead of the count of the numbers of legal doctrines examined as a measure for judicial uncertainty.²¹ The results remain unchanged when using this alternative measure.

The third robustness test replicates German main analysis but uses a different mea-surement of the dependent variable. Instead of using the proportion of vague sentences in a decision body (the dependent variable based on the CNN classifier), I employ the proportion of vague words (measured by the expanded dictionary established in Chapter 3). For all hypotheses, the coefficients are in the expected directions, but not statistically significant. One reason for this could be that the validation of this measure in Chapter 3.5 showed that the expanded dictionary produces a number of false positives. These, in turn, could affect the statistical analysis since some decisions might be declared as vaguer than they actually are.

20Like Wittig (2016), I cut off the variable at 200 because of an extreme low density above (Wittig, 2016, 104).

21I did not use this measure in the main analysis because not for all decision in my data the CAP coding is provided (only until 2007), and therefore the overall N is smaller.

110

CHAPTER 4. THE VALUE OF VAGUENESS

For the fourth robustness check, I apply repeated random sub-sampling validation to account for unobserved heterogeneity. I re-run my analyses twenty times using only a two-third subset of the data, collect the results and combine the estimates as outlined in King et al. (2001). In the French analysis, the results remain unchanged over the different subsets with respect to size, sign and significance. In the German analysis, I find that the direction and size of the estimates are robust across different subsets, but that the combined estimates are no longer statistically significant, as indicated by larger standard errors. However, this is a result of the rather small N of the German data (N = 372 decisions), so that the larger standard errors are a result of sampling variability.

For the last robustness check, I use bootstrapping as an alternative way to obtain estimates and standard errors (Efron, 1979; Efron and Tibshirani, 1986). In the main analysis, the robust variance estimator outlined in Papke and Wooldridge (1996) was used. In this robustness check, I replicate all main analysis and simulations using boot-strapping. Bootstrapping is a straightforward and easy-to-implement computational procedure to derive estimates of means and standard errors. The strength of boot-strapping is that the sampling distribution of a quantity is approximated by repeatedly taking n samples with replacement from the original data. Like this, less distributional assumptions are required, because mean and standard error are directly calculated from the sampling distribution. In my robustness analysis, I use n = 1, 000 where the size of each bootstrap sample is identical to the original data. These bootstrapped samples are also directly used as the sampling distributions for the simulations. The coefficients and standard errors obtained via the bootstrapping procedure are similar the ones from the main analysis. Also, all simulation results mirror the findings from the main analysis (see Appendix C.5 for a detailed analysis): how courts use vague language is a function of their (limited) policy expertise, the preference divergence with the legislator and the public support of the court. In summary, the evidence of the robustness analysis mostly supports the findings of the main analysis. Future work, though, must assess the robustness of the German analysis using alternative measures and more data in further detail.

4.6 Conclusion

In this chapter, I examined the indirect influence of public support on the choices that judges make. In particular, this chapter is the first one to test the empirical implications of the game-theoretic model of Staton and Vanberg (2008). In essence, this formal model argues that courts strategically vary the vagueness of their decisions as a function of their policy preferences, judicial uncertainty and their fear of potential legislative noncompliance. Nonetheless, the court’s ability to use vagueness ultimately depends on their level of public support. Therefore, this chapter considers both the varying

111

4.6. CONCLUSION

degrees of diffuse support of different courts and the perception of courts as active actors that use the institutional tools at their disposal to manage legislative resistance.

In a comparative study of two constitutional courts in Germany and France using the judicial policy implementation vagueness measure established in Chapter 3, I find mostly support for the central implications of the formal model. Both courts use vague decision language to give discretion to the better-informed legislator. The German judges refrain from doing so if the legislator is ideologically distant, whereas the French judges behave in contrast to the formal model in this regard and write more vague decisions. I also show that courts use decision language differently depending on their public support: The German Federal Constitutional Court, a court with high public support, writes extra-specific decision in the face of potential noncompliance to strategically pressurize the legislator and to induce higher costs for legislative noncompliance. By contrast, the French Conseil Constitutionnel, a court with low public support, uses vagueness as a “defensive mechanism” to hide noncompliance from public view.

These findings have larger implications for the study of judicial politics. If judges strategically write vague decisions to manage the challenges of judicial policy-making, then empirical tests of common separation-of-powers models should take this into account. Studies only using binary measures of decision outcomes (e.g. a law is declared as unconstitutional or not), as currently undertaken in many studies, are likely to underestimate the actual degree of strategic judicial behavior. Therefore, my findings suggest that judicial politics must overcome the binary coding of judicial outcomes by using richer and more fine-grained measures of strategic judicial behavior.²² In addition, considering the language of judicial decisions is an important step to reconcile legal scholarship with political science, since it takes factors into account that most often are ignored in quantitative judicial politics.

My results are in line with a growing part of the judicial literature which suggests that courts and judges take active measures to prevail in the strategic interaction between court, government, and the public (Staton, 2006, 2010; Krehbiel, 2016; Engst, 2018). It shows that courts use the institutional tools at their disposal to deal with potential legislative noncompliance. Finally, because the implications of Staton and Vanberg’s (2008) model can be applied to many other delegation relationships, the findings of this chapter have implications beyond judicial politics and open new avenues for further research. Central banks and other non-majoritarian institutions face the same delega-tion problems as constitudelega-tional courts, but also lack proper enforcement mechanisms.

Further research could thus investigate whether these institutions strategically use vague language in a similar way as suggested in this chapter.

22A remarkable exception of conceptualizing judicial choices as binary choices is Engst (2018).

112

CHAPTER 5