Short . . . Thomas König

Analysing the Process of EU Legislative Decision-Making To Make a Long Story

. Using a data set on 5183 Commission proposals, which were processed in the period between 1984 and 1995, we tested these factors by applying both parametric and non-parametric event history analysis. We also rejected the use of Cox regression because the 'coefficients of the interaction variables were different from zero and highly significant, indicating a clear violation of the proportionality assumption' (Schulz and König, 2000: 662). We therefore presented our results of parametric log logistic analyses, plotting Q versus Log t to check the (linear) relationship, and found that institutional reform may have a substantial impact on decision-making efficiency.

Our study motivated and influenced Golub's research (1999, 2002, 2007). Using 1141 directives, he also began studying the decision-making process, defined as the time lag between the Commission's formal proposal and final adoption of the legislative act. He investigated, in a similar way to Schulz

and me, how a diverse set of factors influences this process; i.e. Council qualified majority voting (QMV) and parliamentary participation unsurprisingly remain the significant effects (Golub, 1999: 750). Golub (1999: 733) concluded that the 'impact of the Luxembourg Compromise has been greatly overestimated, that institutional reforms actually encumbered rather than eased the EC legislative process'. Using a Cox regression in 2002, Golub re-analysed his initial study with a slightly larger sample of 1669 directives and reports to confirm previous findings, even though the effects of three variables changed their direction. Most recently, [START_REF] Golub | Survival Analysis and European Union Decision-Making[END_REF] repeated this analysis on the 1669 directives, now agreeing with our original conclusion that there is a 'trade-off between efficiency and democratic inclusiveness'. The latest version by [START_REF] Golub | How Time Affects EU Decision-Making[END_REF] re-analyses these results and contradicts the previous findings on QMV after the Single European Act (SEA), QMV post Maastricht, co-decision and cooperation after Maastricht, and the Thatcher and backlog effects. Independently of this confusion, the conclusions on enlargement and Thatcher effects remain speculative, as the following analysis will show, owing to the lack of estimators for preferences.

In spite of some similarities in research design, Golub (2007: 156) attracts attention by sharply criticizing previous studies on the EU decision-making process as 'fundamentally suspect'. In 1999, Golub still acknowledged that 'the most sophisticated analysis to date of EC decision making comes from unpublished work by Thomas König and Heiner Schulz', but contended that, 'because the authors do not examine decision making prior to 1984, they are in no position to draw the types of comparative conclusions necessary to assess the impact of institutional reform' (Golub, 1999: 737; see also Golub, 2007: 161). This is very surprising because the intensely discussed reforms in Golub's analyses refer to the SEA and the Maastricht Treaty, which date from 1987 and 1993 respectively, and are thus clearly included in the period of our studies. On the other hand, Golub's widely discussed Luxembourg Compromise occurred in 1966, two years before his longest study begins. But Golub (1999: 737) continued his sharp criticism by stating that 'König and Schulz also lump together decisions, regulations, and directives at various points in their analysis'. My analysis will briefly show that it is better to control for different types of instrument than to assume that any specific type is both better suited for and more representative of the greater body of EU legislation in the analysis of the decision-making process.

More generally, my response attempts to settle some disagreements on scientific grounds by seriously assessing Golub's central and repeated presumptions and thereby concentrates on three aspects: the research design in terms of sample selection and period under study; the theoretical drawbacks in terms of the explanatory variables used; and the statistical method applied, in particular the superiority of a Cox regression with inclusion of time-dependent variables. The results will reveal that Golub's claims are plainly incorrect. With regard to sample selection, the reader will see that the statement that only directives contain 'important, complex and controversial issues rather than merely operational decisions and administrative matters' is simply wrong (Golub, 2007: 166). Secondly, it will become apparent that a repeated econometric analysis and speculation about the nature of control variables, such as historical time or the Thatcher effect, can hardly advance the state of the discipline. Golub's major variable for the hypothesized effects of institutional reforms, enlargement rounds and Thatcher's government is implicitly based on member state preferences, but he fails to construct them explicitly over the years. Instead of concentrating on statistical error when using time-dependent variables, it is more advisable to provide a theoretical foundation (Zorn, 2007: 573). I will exemplify this by showing that the UK position does not reveal the 'Fontainebleau effect' suggested by [START_REF] Golub | How Time Affects EU Decision-Making[END_REF], and the inclusion of member state preferences may explain the curious findings on the effects of institutional variables, which change over time.

The Sample: Directives or all binding instruments

In spite of Golub's general criticism of previous work, a major advantage in this field is the similarity of the research design used by the different authors. All of us commonly use Commission proposals as the unit of analysis and address factors that influence decision-making. For the reader, this similarity facilitates a comparative evaluation of each study's contribution to the state of the discipline and helps in following the discussion. Regarding the unit of analysis, these studies commonly identify formal legislative acts and use the dates of the Commission's formal initiative and of adoption as the central indicators for the investigated process -in other words, the time lag between initiation and adoption. However, a relatively large number of initiatives remain pending because the EU's legislative process is not limited by legislative terms or dissolution (see König, 2007a). 1 Despite these similarities, the study designs differ in two important ways: our work includes the whole body of binding secondary EU legislation with directives, regulations and decisions since 1984, whereas Golub focuses only on Community directives and on the periods of the investigated time since 1974 and 1968, respectively. At first glance, the longer period of study seems to support Golub's claim (2007: 161) that neither [START_REF] Schulz | Institutional Reform and Decision-Making. Efficiency in the European Union[END_REF] nor König (2007b) have anything to say about many issues that Golub addresses: EU enlargement, the Luxembourg Compromise, the effects of institutional changes made by the SEA and Maastricht, formal rules versus informal norms, the effects of legislative backlog or the presence of extremist governments such as that of Thatcher'. During Golub's period of study, the Union experienced several enlargement rounds and treaty changes, but the Luxembourg Compromise in 1966, the Amsterdam (1999) and Nice (2003) treaties as well as Eastern enlargement ( 2004) remain outside the scope of his analyses. From Figure 1, it is easy to see that Golub's criticism is mistaken, because our original studies also cover the time before the Southern enlargement, SEA andMaastricht (1984 to 1994), and König (2007b) additionally includes the Northern enlargement in 1995, allowing us to control for the effect of institutional reforms and enlargement rounds. Note that these variables use dummies only to control for historical time in which several of these events took place. Whether these events, other events during these periods or other confounding variables influenced the decision-making process still remains an open question.

A second major difference between these studies concerns the type of legislative instruments examined. Although Golub analyses only directives, the titles of his publications make more general reference to EU decisionmaking. Golub (2007: 166) [START_REF] Golub | In the Shadow of the Vote? Decision Making in the European Community[END_REF][START_REF] Golub | Institutional Reform and Decision-Making in the European Union[END_REF][START_REF] Golub | Survival Analysis and European Union Decision-Making[END_REF], König (2007b), [START_REF] König | The Efficiency of Legislative Decision Making in the European Union[END_REF] and [START_REF] Schulz | Institutional Reform and Decision-Making. Efficiency in the European Union[END_REF].

Notes: LC = Luxembourg Compromise; Gr = Greece; P/Sp = Portugal and Spain; SEA = Single European Act; GU = German Unification; EThP = End of Thatcher's Period; Maa = Maastricht; A/Fin/Swe= Austria, Finland and Sweden; EMitP = End of Mitterand's Period; Am = Amsterdam; Ni = Nice; EstEn = Eastern Enlargement.

namely regulations, directives and decisions, and this comprehensive sample allows us explicitly to control for the type of legislative instrument used rather than forcing us to make heroic assumptions about their controversial nature.

In this sense, it is also easy to check Golub's justification by using his own data source, the PreLex database, which documents the entry of Commission proposals as either A-or B-items on the Council agenda. This distinction provides a clear indicator for whether a Commission proposal raised controversy among member states: 'In practice, A-items are approved en bloc without prejudice to the provisions on the public nature of proceedings' (European Commission, 2005: 127). The B-items, on the other hand, indicate politically important decisions subject to continued discussion, even if general agreement among the member states is reached in advance.

A quick inspection of Tables 1(a) and 1(b) rejects Golub's second major claim. In absolute numbers, 1266 of the 8475 cases are classified as B-items in the period between 1 January 1984 and 1 February 2003. Even if we control for different periods, regulations raise far more controversies among member states, i.e. 133 regulations versus 20 directives between 1984 and 1986 (before the Southern enlargement), 362 regulations versus 129 directives between the coming into force of the SEA in 1987 and the Maastricht Treaty in 1993, and 159 regulations versus 22 directives after the Northern enlargement in 1995 until the coming into force of the Amsterdam Treaty in 1999. Even relatively speaking, about 17% of all regulations and directives before 1986, about 16% of regulations and 27% of directives between 1987 and 1993, and about 17% of regulations and 10% of directives between 1995 and 1999 do not provide evidence for the purportedly more controversial nature of directives. Unsurprisingly, only decisions are fewer in number and notably less controversial.

Tables 1(a) and 1(b) also point to a major shortcoming in analyses that use only directives for the study of the legislative process. Although the relative number of controversies remains almost stable over time, the absolute number of proposals and conflicts varies greatly across policy areas. These area-specific effects are lacking in Golub's studies, and the numbers for the agricultural sector reveal that most Commission initiatives come in the form of regulations and that almost half of all controversies are in this policy area. This is perhaps less surprising for those scholars familiar with the EU budget, from which about half is allocated to this area. Similarly, the fisheries sector shows a high potential for conflict, whereas the areas of trade and of customs union produce many initiatives but few conflicts. Directives obviously fail to account for this central aspect of EU legislative decision-making: some important sectors, such as fisheries, budget and trade (except for a single case), are not even regulated with directives. Others, such as industry or transport, have relied on directives in some periods, and opted exclusively for ----------------------------------------------------------- ----------------------------------------------------- ----------------------------------------------------------- ----------------------------------------------------- ----------------------------------------------------------- ----------------------------------------------------- ----------------------------------------------------------- ----------------------------------------------------- a regulations and decisions in other periods. This is evidence that using only directives will provide a biased picture of the legislative process in the EU.

The policy area-specific development is an important characteristic of European integration history, which had already been documented in Ernst Haas' early monographs (1958Haas' early monographs ( , 1964)). In the literature, several explanations exist for this area-specific development but, from an institutionalist perspective, this would require the study of the configuration of member state preferences. Do member states generally differ in their preference profiles, or do these preference profiles vary across areas and over time? Regarding this area-specific effect, we provided a theoretical model in our original analysis and found a significant impact of the Union's core domains of agriculture, common rules, the internal market and trade on the duration of the legislative process (Schulz and König, 2000: 663). Later, the impact of member states' area-specific preferences was more rigorously tested and confirmed the predictions of the original model (König, 2007b). This leads to the second aspect of my reply to Golub, namely the theoretical contribution made by each study.

The explanatory variables: History and content

In addition to their common research question, the studies on decisionmaking duration also share some independent variables. In principle, the research designs of all studies are suggestive of the institutionalist school of thought, which focuses on the impact of institutional variables, such as a given procedure or voting rule, as well as actors' preferences, such as the size of the conflict between the actors involved. Even though the studies differ in their level of theoretical sophistication, the conventional assumption is that the size of the core, or the extent of the actors' mutual conflict, determines the length of the process by interacting with the particular institutional settings. This view can be motivated with insights from veto player theory, which stresses the interaction between the distribution of actors' preferences and the particular institutional provisions: the more veto players with diverging preferences, the less likely is a policy change [START_REF] Tsebelis | Veto Players: How Political Institutions Work[END_REF]. Note that further assumptions are necessary for applying veto player theory to the analysis of process time. 2 König and Schulz (1997) and [START_REF] Schulz | Institutional Reform and Decision-Making. Efficiency in the European Union[END_REF] offer a theoretical foundation for the derivation of their hypotheses, whereas Golub lists several relationships, which are sometimes motivated by common wisdom, sometimes related to conventional veto player theory and sometimes a confusing mix of both. For example, Golub (1999: 743) hypothesizes that 'formal rules stipulating majority voting yield faster decision making', but a subsequent hypothesis states that 'the underlying distribution of member state preferences mediates, and potentially negates, the institutional determinants of EC decision making ' (1999: 746). Furthermore, by expecting that 'enlargement slows both majority and unanimous decision making', it turns out that Golub's hypotheses (1999: 744) are based on additional assumptions about member states' preferences: only if the magnitude of conflict among member states increases might one expect slower decision-making, and this clearly requires careful measurement of the preferences at stake. 3 On closer inspection, these studies also differ in the accuracy with which they operationalize their hypotheses. Golub surprisingly draws conclusions from events, such as the Luxembourg Compromise (1966), that are outside the scope of the period under study . By using several control variables for historical time, such as Thatcher or the Greek or other enlargement rounds, Golub controls for some potential omitted variable bias. However, whether the change in EU legislative decision-making in 1990 resulted from the change in the UK government, from German unification or from any other event cannot be satisfactorily answered by such a research design. A major drawback of using only control variables can be identified in [START_REF] Golub | How Time Affects EU Decision-Making[END_REF], showing that the institutional variables themselves are the object of change over time. In other words, why should the application of QMV have a different impact on EU decision-making process before and after 1990?

In our original analyses, we had already theorized about the answer and controlled for policy area. The most recent analysis makes progress by presenting area-specific estimators for the preferences of the member states, controlling for interaction effects in the relevant policy areas (König, 2007b). Using the programmatic positions of member state governments, it is now possible to identify the relative importance of institutions and preferences for the process of EU legislative decision-making in a quantitative manner. Moreover, this analysis refutes another of Golub's criticisms of other duration studies, namely their failure to contribute to the discussion of norms. With regard to the ongoing controversy between constructivists and proponents of the theory of spatial voting about the converging or diverging effect of member state positions, the findings reveal that divergence of member state positions significantly determines the duration of the legislative process, particularly in the key domains of EU integration: the greater the distance between the member states' positions, the longer the EU decision-making process takes.

Table 2 summarizes the studies on the legislative process by [START_REF] Golub | In the Shadow of the Vote? Decision Making in the European Community[END_REF][START_REF] Golub | Institutional Reform and Decision-Making in the European Union[END_REF][START_REF] Golub | Survival Analysis and European Union Decision-Making[END_REF], König (2007b), andSchulz &[START_REF] Schulz | Institutional Reform and Decision-Making. Efficiency in the European Union[END_REF]. All studies find that qualified majority voting in the Council (QMV, Rule) speeds up decisionmaking, and that participation by the European Parliament slows down the ----------------------------------------------------------------------------------- ---------------------------------------------------------------------------------- 1974-1995 1968-1998 1968-1998 1984-1994 1984-1999 process (Cooperation, Codecision, Parliament). Note that Schulz and I refrained from distinguishing between the cooperation and co-decision procedures for statistical reasons (only about 18% of the cases indicated any parliamentary participation). Golub almost exclusively uses dummy variables to control for historical time as defined by periods -such as before and after enlargement, treaty changes, Thatcher while prime minister and Agenda (coded as 1 for policy area added by the SEA or Maastricht), whereas Schulz and I controlled for policy areas as well as for each year in our original study (König and Schulz, 1997: 9;Schulz & König, 2000: 658, 664). On closer inspection of the findings on Golub's control variables, the mixed results suggest that the conclusions are biased by periodical selection and omitted variables: the sign of coefficients changes for important variables such as QMV or Thatcher.

Careful analysis of the interaction effects between policy areas and member state preferences reveals that the magnitude of conflict significantly affects the decision-making process in the expected direction (König, 2007b). Except for the area of Common Rules, which is close to zero, member state conflict slows the pace of legislative decision-making, and the effects for qualified majority voting and parliamentary participation confirm our previous results. The most recent study also expands the period under study and painstakingly describes the data-gathering, storing and coding process (for cross-validation of these data, see [START_REF] König | Quantifying European Legislative Research: Using CELEX and PreLex in EU Legislative Studies[END_REF]. This guarantees the reliability of the data and findings, which brings me to the final aspect of my reply to Golub: the choice of an appropriate statistical model. In [START_REF] Golub | Survival Analysis and European Union Decision-Making[END_REF] sweeping words, 'prior studies derive their findings from flawed methods'.

The method: Tests and testing

A third commonality among all studies is the application of (new) techniques of event history analysis, which have become a prominent tool for the study of political processes (for an overview, see [START_REF] Box-Steffensmeier | Event History Modeling: A Guide for Social Scientists[END_REF][START_REF] Zorn | Temporal Change and the Process of European Union Decision-Making[END_REF]. When assessing Golub's most recent focus on statistical innovation, one has to bear in mind that these studies differ in their data sampling, theoretical foundations and identification of the model. It is well known that such differences may have important implications for drawing inferences from empirical research. Most obviously, Golub's data include only directives, whereas our data comprise all binding legislative instruments. Furthermore, the analyses differ in their period of study and their sample of covariates. Although [START_REF] Golub | Survival Analysis and European Union Decision-Making[END_REF] argues for using a specific statistical method and design, the more general question is whether the tests and findings are determined by their different samples or by the applied statistical method. 4 Another general (methodological) problem concerns the completeness of the data; we highly recommend that any user of EU legislative documentation should report the data-gathering process and discuss the (in)completeness of the source of information. For example, because CELEX's official manual states that data are incomplete prior to 1984, we limited our period of study to this date, and only cross-validation with different data sources promises a reduction in problems related to missing data. 5 In this respect, Golub (2007: 161) indicates only that 'extensive use of COM documents and the Official Journal was essential for the pre-1974 period'. Still, a number of conceptual decisions are necessary for preparing these data for statistical purposes. For example, some proposals list more than one reference to the legal basis in the treaty, which means that it might belong to several policy areas and allow for the application of different rules (i.e. qualified or unanimous Council voting). Several proposals can also be collapsed into a single document during the legislative process, whereas others are split into several legislative acts.

All of these conceptual design choices can affect the composition of the resulting sample and thus the selection of the appropriate statistical method. Nevertheless, Golub (2007: 156) generally states that previous research has 'employed [survival analysis] in methodologically inappropriate ways that render their conclusions fundamentally suspect'. Two decisions seem to be of importance to him: the choice of the baseline hazard and the coding of covariates with regard to time dependency. Regarding the choice of the baseline hazard, Golub (2007: 165) strongly advocates using Cox regression because it does not restrict the shape of the baseline and allows 'reliable identification of proportionality violations and readily accommodates non-proportional effects of covariates by incorporating time-interactive terms of the sort B*g(t) where g(t) is some function of survival time (usually ln(t)).' In my view, however, the decisive question is whether this approach of including the interaction with the specific function of time ln(t) remedies a violation of the proportionality assumption.

For the log-logistic model, Golub (2007: 167) uses the Grambsch/Therneau test in order to reject the proportionality assumption for his data on directives. This violation of the proportionality assumption by an application of the loglogistic model to his data on directives means that a Cox regression is neither the solution for remedying this effect nor the appropriate model for analysing the entire set of legislative instruments. Unfortunately, Golub does not present the statistical proof and adds only the 'usual' functional form of duration time by ln(t) to his analysis, which leaves open the question of whether ln(t) or any other functional form of g(t) can indeed solve the proportionality problem. The more general problem concerns the specification of g(t), which needs either theoretical foundation or empirical evidence. Both are missing in Golub's analysis.

On closer inspection, [START_REF] Golub | How Time Affects EU Decision-Making[END_REF] questions almost all previous findings by Golub on QMV post SEA, QMV post Maastricht, co-decision and cooperation after Maastricht, and Thatcher and backlog effects. The authors find that using time-interaction variables reveals a more complicated picture than Golub's previous analyses suggested: 'While some covariates may have a positive impact on the hazard rate at some moment in time, leading to a speeding-up of decision-making, this impact may change into a negative one later on. In addition, the combined coefficient for each covariate is insignificant for some period in time' (Golub and Steunenberg, 2007: 559). In contrast to [START_REF] Golub | Survival Analysis and European Union Decision-Making[END_REF], they find that the 'effects of QMV preand post-SEA are indistinguishable for all values of time' (2007: 561); 'the drag from cooperation overwhelms QMV for the first 8 months (not 12), then neutralizes it for the next 8 months, but the QMV effect outweighs the cooperation effect after 16 months'; 'the unanimity norm is even weaker than Golub thought, [. . .] the effect of QMV after the Maastricht treaty [. . .] is not time dependent ' (2007: 562); 'the effects of cooperation and codecision are indistinguishable for all values of t' (2007: 563), 'a large backlog spurs the Council to dispose of new proposals but does not expedite passage of the most controversial pieces of legislation ' (2007: 564), and the Thatcher effect was slightly less than this and, more interestingly, it reversed direction and significantly speeded up the adoption of legislation. The authors finally provide a somewhat curious explanation for one of Golub's most important original findings on the Thatcher effect, namely 'Thatcher's new views' on proposals made after the European Council summit at Fontainebleau in June 1984 (Golub and Steunenberg, 2007: 563). 6 This impressionistic account of Thatcher's preference change and the UK position -which does not consider the time-dependence change of other variables over time -and the troubling results of the re-analysis draw attention to [START_REF] Zorn | Temporal Change and the Process of European Union Decision-Making[END_REF] recommendation on the theoretical foundation for using time-interaction variables. On closer inspection of the UK position, Figure 2 illustrates the governmental positions of the member states in selected core areas as well as on the left/right and EU dimensions over time to provide a more reliable picture on Thatcher's preference change, two aspects are relevant here: (i) whether the UK position did change radically in 1984, and (ii) whether the UK position defines the core of the member states.

The pictures reveal an area-specific development of preferences, according to which the UK position significantly changed on the left/right dimension as well as in the areas of the internal market and agricultural politics when Margaret Thatcher became prime minister in 1979. However, there is hardly any significant change in 1984. Clearly, the most drastic change in the UK position took place in 1997, when Tony Blair entered office. The pictures also reveal that the UK position does not always define the core of member states. Even in EU matters, Margaret Thatcher's government was more moderate most of the time than the narratives suggested. Except for the internal market and the left/right dimension, the UK position has rarely been located at an extreme in trade, common rules or agricultural politics. In accordance with decision-making theories, we find that these preferences change over time as a result of national or European events, such as changes in party government, in the preferences of the political parties themselves or in the size of EU membership in the course of enlargements.

Conclusion: Pitfalls and challenges in research on the EU's legislative process

The study of the EU's legislative process is a challenging task that creates certain methodological problems and threatens several pitfalls. These pitfalls involve data gathering, storage and preparation for the statistical analyses, the identification and construction of the explanatory variables as well as the selection of the appropriate statistical method. Perhaps one of the more general conclusions is that scholars of the legislative process should be less presumptuous and too convinced that a particular approach is superior to other scholars' work. As originally admitted by [START_REF] Golub | In the Shadow of the Vote? Decision Making in the European Community[END_REF], our work was the first to use event history analysis and to perform any sort of diagnostic, and the overview over the past 10 years of duration analyses has demonstrated that our conclusions remain consistent. Finally, we invite any scholar interested in this debate to download the data and run additional analyses to evaluate the respective claims and findings. 7 On closer inspection of studies on the EU's decision-making process, the analysis of controversial legislation shows that directives are a particular instrument that is hardly generalizable to the greater body of EU legislative decision-making. Furthermore, a quick inspection of the periods under study reveals that all studies account for institutional reform and enlargement. On the other hand, it remains an open question whether particular explanatory variables can account for the hypothesized effects. A dummy variable on the period during which Margaret Thatcher was UK prime minister can control only for potentially omitted variables; it is much more labour intensive to specify and construct indicators that help to test for the presumed effects. In my view, this is especially important because the non-proportional effects found in Golub's analyses could very well be due to an omitted variable bias.

Instead of conducting another analysis on directives, the data-gathering process needs documentation and the data have to be cross-validated and published. For any student of the legislative process it would then be easy to cross-check the data and to identify conceptual decisions on censoring, double coding and handling missing values of directives. In recent years, we have carefully improved our method of extracting information from official databases and provided the reader with the necessary information to improve the reliability of the procedural data (see [START_REF] König | Quantifying European Legislative Research: Using CELEX and PreLex in EU Legislative Studies[END_REF]. Furthermore, it has been possible to construct an indicator for the 'missing link' in testing hypotheses derived from the institutional school of thought, namely the areaspecific preferences of the member states, which vary over time and across sectors. Nevertheless, such labour-intensive work requires more collaborative efforts because the preference component has been a major source of error in previous studies on EU legislative decision-making [START_REF] Junge | What's Wrong with Spatial Analysis? The Accuracy and Robustness of Empirical Applications to the Interpretation of the Legislative Process and the Specification of the Preferences[END_REF]. My hope is that such improvements will not only help scholars to test rival approaches and understand the factors influencing the decision-making process, but also motivate them to invest more time in the provision of reliable data and measures.

Notes 1

For the analysis of process, this poses a methodological problem because pending initiatives might be censored by the date of the researcher's data gathering and thus differ in their likelihood of adoption. In order to cope with this problem, we decided to limit our sample to those initiatives having a similar likelihood of adoption, i.e. by excluding those initiatives that remain pending longer than the median time lag. 2

Although the simple model predicts that the status quo will prevail, member states favouring policy change have an incentive to win over a blocking member state or to offer a compromise on another policy issue in return for the blocking member state's vote. Side-payments and linkage across issues and time are strategies to promote policy change that usually demand more time to resolve differences and to strike a bargain. By contrast, if there is broad agreement, there is no need for such time-consuming strategies, including side-payments and package deals, and this should greatly speed up decisionmaking (Schulz and König, 2000: 657). 3

Additional assumptions are needed about the expectation of a slowing down of speed owing to either backlog effects or the expansion of the EC agenda. 4

Quite apart from (in)correctness, Golub's assumption about the controversial nature of directives has important ramifications because neither the trend for directives nor the trend for controversial legislation can be identical with the trend of the total sample. 5

Comparing the number of directives from cross-validated data sources suggests that Golub's 1141 and 1669 cases are a 'subsample' of the overall sample of directives.

  due to rounding.

Figure 2

 2 Figure 2 Position of UK government between 1973 and 2003.

European Union Politics 9(1) 1 4 8

  attempts to justify this generalization by stating that, '[t]o me, explaining variation in the adoption rate for the most significant and contentious laws constitutes a much more important task'. Our analyses include all binding secondary legislative instruments of the EU,

	König 2007
	Schulz/König 1997, 2000
	Golub 2002, 2007									Regulations Directives
	Golub 1999										Decisions
	L C 1 9 6 6	1 9 6 8	1 9 7 0	G r 1 9 8 1 1 9 7 4	P /S p 1 9 8 6 1 9 8 4	S E A 1 9 8 7	G U /E T h P 1 9 9 0	/S w e /E M it P 1 9 9 5 1 9 9 3 a a M	A m 1 9 9 9	N i 2 0 0 4	E s t E n 2 0 0 5
								in
								A /F
	Figure 1 Study design of

Table 1 (a)

 1 Percentage of Council controversies by instrument, policy areaand period, 1984-1993

	1/7/1987 -31/10/1993	76 months
	1/1/1986 -30/6/1987	18 months
	1/1/1984 -31/12/1986	36 months	-
	Period	Length of period

Table 1 (a)

 1

		1/7/1987 -31/10/1993	76 months
		1/1/1986 -30/6/1987	18 months
		1/1/1984 -31/12/1986	36 months	-
	Continued	Period	Length of period

Table 1 (b)

 1 Percentage of Council controversies by instrument, policy areaand period, 1993-2003

	1/5/1999 -1/2/2003	46 months
	1/1/1995 -30/4/1999	52 months
	1/11/1993 -31/12/1994	14 months	-
	Period summarized	Length of period

Table 1 (b)

 1

		1/5/1999 -1/2/2003	46 months
		1/1/1995 -30/4/1999	52 months
		1/11/1993 -31/12/1994	14 months	-
	Continued	Period summarized	Length of period

Table 2

 2 Study design and results on decision-making process: Variables, models and findings

	König 2007b
	Schulz/König 2000
	Golub 2007
	Golub 2002
	Golub 1999

6 Golub and Steunenberg (2007: 563) finally stress a change in Margaret Thatcher's preferences in explaining the observed change: 'Thatcher became Prime Minister in 1979 and was highly antagonistic for nearly five years until [. . .] an agreement at the June 1984 European Council summit at Fontainebleau.' 7 See http://www.sowi.uni-mannheim.de/lehrstuehle/lspol2/main.html. The data from the Schulz and König analyses can be ordered from heiner.schulz@sas.upenn.edu.

About the author


Jonathan Golub

The Study of Decision-Making Speed in the European Union Methods, Data and Theory

The study of European Union (EU) decision-making speed holds both substantive and theoretical importance. Substantively, it helps identify which factors produce or avert legislative paralysis, an insight that is a necessary precondition for informed debate about potential EU institutional reforms. Indeed, I entered this field in 1994 while at the European University Institute in Florence to test whether the reforms contained in the famous 1987 Single European Act (SEA) had unblocked and expedited legislation. Theoretically, the study of EU decision-making speed helps us assess the utility of the tools we use to understand the EU, such as coalition and spatial models that emphasize formal rules, as well as deliberative and constructivist approaches that privilege informal norms.

As more people gravitated to the study of EU decision-making speed, survival analysis rightly became their method of choice. In a recent article (Golub, 2007), I defended three claims about survival analysis and EU decision-making: first, that all previous survival studies on the topic, including two of my own, suffer from methodological problems that render their findings unreliable; second, that researchers should apply a particular form of survival analysis, a Cox model that accounts for state changes in the data -by using time-varying covariates (TVCs) -and non-proportional covariate effects; third, that we should apply this methodologically superior approach to my 2002 TVC-coded data set of Directives.

.

of Bernard Steunenberg's recognition that time affects EU decision-making in a more complicated manner than I had originally recognized. Christopher [START_REF] Zorn | Temporal Change and the Process of European Union Decision-Making[END_REF], in his contribution, draws particular attention to what he sees as important, understudied temporal processes evident in duration dependence.

In this article I respond to arguments raised by König and Zorn, take stock of what survival analysis has taught us about EU decision-making and discuss where further research should focus. The first section identifies and reinforces a number of uncontested points about survival analysis methodology and the selection and coding of variables. Although these might appear to be esoteric technical concerns, they have significant implications for determining what, if anything, we actually know about EU decision-making speed and how to go about learning more. The second section discusses four main issues for future research: how to deal with non-proportional covariate effects, which cases to count, whether to assign substantive meaning to duration dependence, and how to isolate the effects of rules, preferences and enlargement.

Uncontested issues

The contributions to this forum reveal broad agreement on certain aspects of survival methodology, several of the main factors that we expect to influence decision-making speed and the hypothesized direction of their effect. Table 1 presents these uncontested issues. The first column identifies the five separate methodological reasons I gave for previous survival studies being unreliable and why we therefore need to 'wipe the slate clean' and why researchers should employ a Cox model with TVCs and non-proportional covariate effects (Golub, 2007: 161-5). Leading methodologists, including Zorn, have made some of these same arguments (Zorn, 2007: fn8;[START_REF] Box-Steffensmeier | Duration Models and Proportional Hazards in Political Science[END_REF][START_REF] Box-Steffensmeier | Event History Modeling in Political Science[END_REF], and König has never disputed them.

To reinforce the point that a log-logistic model is inappropriate for EU decision-making in general, and not just for my own data, I analysed König's online data set. Figure 1 presents four plots of the log-odds of survival against the log of survival time -for König's time-constant covariates instrument type (i.e. Directives versus non-Directives), agriculture, trade and the internal market. The appearance of straight lines can be misleading (Golub, 2007: 163), but an obvious deviation from parallel lines should lead us to reject the log-logistic model (Collett, 2003: 226). The figure demonstrates that the proportional log-odds assumption does not hold, so we can reject a loglogistic model for König's data. Moreover, since by definition the proportional hazards assumption (or, for the log-logistic model, the proportional log-odds assumption) no longer holds when a model includes TVCs (Collett, 2003: 146, 253;Therneau and Grambsch, 2000: 127;[START_REF] Golub | Survival Analysis and European Union Decision-Making[END_REF], if König were to fix his data set and recode cases properly to reflect the enormous number of state changes, especially the periodic shifts in member state preferences, it would greatly exacerbate violations of the proportionality assumption and render the log-logistic model even less appropriate.

The second column of Table 1 presents currently uncontested theoretical issues. It identifies three basic factors that we all agree bear directly on decision-making speed and our shared view of their hypothetical effects. In short, formal voting rules, the formal powers of the European Parliament and Council preference heterogeneity each affect the size of the core, the status quo win-set and the proportion of winning coalitions, and changes to these stability measures should directly affect decision-making speed (for details, see Golub, 2007: 157-9).

Two key conclusions flow from all these uncontested points. First, much of what we thought we knew about EU decision-making is unreliable since it was derived from studies that applied the wrong survival methodology, often to fundamentally miscoded data. Second, reliable findings about any determinant of decision-making speed can be achieved only with data sets that code TVCs and models that accommodate non-proportional covariate effects and adequately justify their treatment of the baseline hazard.

Issues for future research

Having reached agreement on some key aspects of model specification and data coding, I see four key issues for future research. These are how to deal with non-proportional covariate effects, which cases to count, what to make of duration dependence exhibited by the baseline hazard rate, and how to isolate the effects of rules, preferences and enlargement.

How should we deal with non-proportional covariate effects?

As I noted (Golub, 2007: 164), and as [START_REF] Zorn | Temporal Change and the Process of European Union Decision-Making[END_REF] reminded us in his comment, sometimes the coding of a covariate remains fixed over a case's lifetime, but the effect the covariate has on the likelihood a proposal will be adopted changes with time. How should we handle these situations? In his comment, König does not directly contest the need to deal with such nonproportional covariate effects, but argues that doing so demands a preexisting theoretical foundation and that adding B*ln(t) terms to models of EU decision-making may not adequately remedy proportionality violations [START_REF] König | Analysing the Process of EU Legislative Decision-Making. To Make a Long Story Short[END_REF].

As a long-term goal, scholars should certainly seek to develop a satisfactory theory about the precise nature of non-proportionality in various covariates. But simply ignoring non-proportional effects while we await such a theory does not improve the reliability of previous studies nor does it get us any closer to the objective. Progress must follow the usual scientific process of first identifying the phenomena and then trying to explain them as best we can, through trial, error and incremental refinement. In our piece, Steunenberg and I took a first step by discussing why the effects of some covariates wear off or reverse direction. Naturally, though, we will need to revisit our arguments in light of future research. Fortunately, we have formal tests available to detect proportionality violations and interactive terms to correct them [START_REF] Box-Steffensmeier | Event History Modeling in Political Science[END_REF][START_REF] Box-Steffensmeier | Duration Models and Proportional Hazards in Political Science[END_REF]. These were used in [START_REF] Golub | Survival Analysis and European Union Decision-Making[END_REF] and [START_REF] Golub | How Time Affects EU Decision-Making[END_REF] to select B*ln(t) terms that proved superior to other options such as B*t. The key point is that advances in theory will go hand in hand with the development of new covariates that govern decision-making speed and experimentation with alternative interaction terms.

Which cases should we count?

A second important issue is which cases we should count if we want to draw important inferences about EU decision-making. My view is that we should focus on Directives, since the typical Directive is far more controversial and significant than the typical Regulation or Decision. Counting the latter two instrument types adds considerable background noise to the analysis. An alternative approach, which König supports, is to gather a sample that is representative of the full corpus of EU law.

Those inclined towards assembling a fully representative sample must take care not to lump together the three instrument types. If instrument types are actually that different, then a properly specified survival model would require a battery of interactive terms -of the sort Directive*QMV, Regulation*QMV, etc. (Golub, 2007: 166). Neither of König's survival studies includes such terms, and, remarkably, the models reported in his most recent study do not control for instrument type at all.

The 'include everything' approach is more of a liability than an asset because a fully representative sample is useful only if you subscribe to the far-fetched notion that there are no such things as trivial cases. Which cases, then, should we exclude to improve the signal to noise ratio and obtain a sample representative of significant proposals? König does not provide any criteria for doing this, but he does contest my sampling strategy on the grounds that Regulations, and perhaps even Decisions, are just as likely as Directives to be classified as Council 'B'-items in his data set. In other words, if there is background noise ('A' points), König thinks it is spread evenly across Directives, Regulations and Decisions.

As with instrument type, those inclined towards König's view of sampling will need to pay greater attention to model specification. All of König's studies lump together 'A' and 'B' proposals with no dummy variable, let alone the necessary interactive terms to distinguish the unique effect of each covariate on 'B' points.

There are powerful reasons, though, for simply ignoring the A/B distinction. Conceptually, it is simply not a proxy for controversy or significance. As Hagemann and de Clerck-Sachsse (2007: 12) observe, 'the records of "A" and "B" points on the Council's agenda do not in any way reflect the level of disagreement or bargaining at the ministerial level', since most proposals eventually wind up as 'A' points by the end of a lengthy back-and-forth process between the Committee of the Permanent Representatives (COREPER) and the Council. What is surprising 'is that "B" points have been adopted at all' (Hagemann and de Clerck-Sachsse, 2007: 12); having been agreed prematurely, as it were, one could even argue that 'B' points are less controversial than 'A' points.

Much better proxies for a proposal's level of significance and controversy are how many rounds of COREPER meetings were needed, and, most obviously, how long it took before the proposal was either adopted or withdrawn. The corpus of EU legislation is full of contentious proposals that are recorded as 'A' points. Regulation 2003/150 on weapons importation taxes, for example, was adopted after 15 years of negotiations. Likewise, there are plenty of trivial proposals that were treated as 'B' points. Regulation 90/836, for example, which extended the period of aid for skimmed milk processing by two months, was adopted in three days. These are not just isolated examples, they are symptomatic of the fact that status as an 'A' or 'B' point tells us nothing about the inherent controversy or significance of a proposal. As shown in Table 2, the typical 'B' point is just as trivial as the typical 'A' point, both requiring only three months of negotiations.

Instead of focusing on 'B' points and privileging thousands of cases such as Regulation 90/836 while demoting thousands of cases such as Regulation 2003/150, the better strategy is to select all proposed Directives because they are typically more controversial, as shown by the survival times in Table 2. Having noted this huge disparity himself (Schulz and König, 2000: 660), it is surprising that König does not address it in his contribution and continues to ignore the implications for case selection.

König's other objections to focusing on Directives are either wrong or overstated. In his comment he contends that this strategy ignores the four policy sectors of transport, trade, the budget and fisheries. In fact, Directives were often used for transport, and not infrequently for trade. They were also used for budgetary policy and, moreover, nearly all legislation in this field is uninteresting from a survival standpoint because it is adopted yearly and under a tight schedule. Fisheries presents the biggest problem, although Directives did address support for fish life, control of fish disease and regulation of fish-processing undertakings.

If we could identify the most important Regulations and Decisions in various EU policy sectors, one way to improve upon my case selection --------------------------------'A method would be to add only these to my Directives data set while still intentionally excluding the vast number of representative but trivial proposals. But this might not be necessary since, arguably, you can generalize from Directives to the universe of significant cases, which would include Regulations such as 2003/150 that also took years to agree. The profile of landmark fisheries Regulations also resembles that of Directives -Regulation 170/83 on the conservation and management of fishery resources took over six years of negotiations, and Regulation 298/83 on multi-annual guidance programmes took over three years.

Should we assign meaning to the baseline hazard?

No study to date has tried to interpret duration dependence evident from the baseline hazard. Like many others, perhaps including König, I regard duration dependence simply as a nuisance. Zorn disagrees. For him, efforts to understand the shape of the baseline would constitute a big step along the path towards more complete models of EU decision-making (Zorn, 2007: 572).

To support this view, Zorn offers a distinction between 'spurious' duration dependence owing to unobserved heterogeneity and omitted covariates, substantively interesting dependence caused by the propensity of the process under study towards self-perpetuation (Zorn, 2007: 574; see also [START_REF] Zorn | Modeling Duration Dependence[END_REF]. I find this distinction unhelpful, since 'true' duration dependence is merely unobserved heterogeneity awaiting successful observation. If one were capable of including all the relevant covariates in the model, the appearance of duration dependence would vanish [START_REF] Golub | Survival Analysis[END_REF]. Note that this is unlike the situation where TVCs cause non-proportional covariate effects. With TVCs, these effects -which we could also call variable-specific duration dependence -will remain, regardless of how many variables we include [START_REF] Golub | Survival Analysis[END_REF].

Although some political scientists might 'have tended to imbue duration dependence with substantive importance' (Zorn, 2000: 369), this produces ambiguous claims. Consider the famous example of international rivalries where the hazard of a rivalry ending rises over time. The theoretical interpretation placed on this 'true' positive duration dependence is that rivals 'tend to wear themselves out and become more willing to settle their disputes over time' (Zorn, 2000: 369). But what does this suggestive phrase actually mean? To have any substance, the twin phenomena 'wear themselves out' and 'become more willing to settle' must be observable and measurable, and thus amenable to inclusion in the model. Of course the same goes for nonproportional covariate effects not caused by TVCs, discussed above, where the eventual objective should be to observe and model why exactly the effects of a covariate 'wear off' over time.

Reaching this objective will be a long and complicated process, but the best way to investigate duration dependence is to employ the particular type of Cox Model I used in my article, since picking an erroneous parametric shape, overlooking TVCs or ignoring non-proportional covariate effects will produce incorrect inferences about the baseline (Zorn, 2000: 370;[START_REF] Golub | Survival Analysis[END_REF]. Moreover, to address Zorn's concerns about heterogeneity and potentially different duration dependence within subsets of the data [START_REF] Zorn | Modeling Duration Dependence[END_REF]2007: 570, 574), one could fit a Cox model with frailty or a stratified Cox model that allows multiple baselines.

How do we isolate the effects of rules, preferences and enlargement?

As mentioned earlier, there is broad agreement that decision-making speed varies with formal voting and Parliamentary rules as well as with Council preferences. Table 3 presents an overview of what we have learned from the various survival analyses about these and other factors. It illustrates that our knowledge is still quite limited and that considerable work remains. Cells marked 'no data' or 'not coded' indicate that a given study is silent about a particular factor. More common is where a study has fitted the wrong sort of model to miscoded data, rendering conclusions about a particular factor 'unreliable'.

We know especially little about sector-specific decision-making speed, the effects of member state preference heterogeneity and the interaction between preferences and other covariates. In his comment, König reiterates some of his previous claims about these issues, but they remain unreliable because he did not take the opportunity this forum provided to fix his data and fit a more appropriate model. Nor has he released his data on member state preferences so other people could fix them. Even my recent studies offer quite limited insight into individual policy areas; they show that legislation is adopted just as quickly in new sectors as in traditional ones, but they do not investigate distinctions between traditional sectors. Future work should rectify this.

As for member state preferences, my measures remain crude and indirect, based on a series of dummy variables for four EU enlargements, three time periods and the presence of one notoriously anti-EU political leader, Margaret Thatcher. Party manifestos and Eurobarometer data offer more direct measures of national preferences, but these indicators are useful only if coded as TVCs, because they change with every government reshuffle and each new survey. Ideally, preference TVCs should also code shifts that occur between elections, as a result, for example, of the historic resolution of the budget crisis in 1983, German reunification, the end of the cold war, agreement on the Amsterdam or Nice treaties, or other notable events. An even bigger long-term challenge is to distinguish between the effects of different factors. For instance, changes to the core and changes to the proportion of winning coalitions should both affect speed, so it will also be necessary to retain EU enlargement dummies alongside better TVCs for member state preferences. We must also retain time period dummies to identify the presence or absence of 'consensus', since this norm has supposedly operated throughout much of EU history, independent from changes to Council heterogeneity.

Conclusion

As with any scientific endeavour, the only way to make progress in our understanding of EU decision-making is to learn from our previous mistakes and try not to repeat them. This goes for the types of models we fit, the way we code data and the theories we construct. The follow-up discussion of my original EUP article has revealed important agreement on some of these matters. Nobody contests that researchers must use data sets that employ TVCs to code state changes, must not fit parametric models without adequate justification and must not ignore non-proportional covariate effects. There is also agreement that we expect formal QMV to speed decisions, and formal involvement of the European Parliament as well as Council preference heterogeneity to slow them. These uncontested points have implications for how we proceed in the four areas I have identified where there is less agreement or where we have few reliable findings.

In the area of non-proportional covariate effects, I agree that interpreting them is not straightforward and ascertaining their root cause is even trickier. But at least we know that simply ignoring them produces unreliable results and gets us no closer to a theory of decision-making processes. Steunenberg and I have made a modest start, and a fuller picture will emerge with the discovery of new covariates and better interactive terms.

In the area of case selection, I have argued for a narrow focus on Directives, augmented perhaps with the most important Regulations and Decisions provided that these could be identified. Others might prefer König's 'include everything' approach, which aims for a sample representative of all EU legislation, but if they, too, ignore TVCs, fit the wrong sort of survival model and do not try to filter out background noise, this approach will produce unreliable findings about trivial cases.

Much more work is needed to isolate the effects of rules, preferences and EU enlargement. Progress will come mainly from the construction of better TVCs that capture periodic shifts in member state preferences and terms that interact these preference TVCs with dummies for policy sectors, successive enlargements and periods in which informal norms might have operated.

A final area where work might focus is understanding duration dependence. I remain sceptical about trying to imbue duration dependence with any substantive meaning but, for those who wish to do so, a Cox model with TVCs and non-proportional covariate effects offers the best way to study the baseline hazard.

Figure 1

 1 Figure 1Tests of the proportional log-odds assumption for König's data.

Table 1

 1 Uncontested issues related to EU decision-making speed

	Model choice and data treatment	Theoretical determinants of speed
	Parametric models:	Formal voting rules: QMV shrinks the core,
	• require a prior theory about the	enlarges the win-set, increases the
	baseline hazard	proportion of winning coalitions and thus
	• have faulty diagnostics (e.g.	expedites decisions
	transformed survivor plots)
	• involve the log-logistic which
	requires verification of the
	proportional log-odds assumption
	All models: must accommodate non-	Parliamentary involvement: cooperation
	proportional covariate effects	and co-decision enlarge the core, shrink
		the win-set, decrease the proportion of
		winning coalitions and thus slow
		decision-making
	All data sets: must code cases for	Member state preferences: increasing
	state changes by using TVCs	Council heterogeneity enlarges the core,
		shrinks the win-set and thus slows
		decision-making; decreasing Council
		heterogeneity has the opposite effects

Table 2

 2 Days to adoption for Council 'A' and 'B' points in König's data set

		Regulations and
	All instruments	decisions	Directives
	-

Table 3

 3 Overview of conclusions about factors affecting EU decision-making speed

	Golub Golub & Steunenberg	(2007) (2007)	No effect; no No effect; no consensus	consensus norm norm	Post-SEA effects Post-SEA effects	less than or equal indistinguishable from	to pre-SEA effects pre-SEA effects	Moderately reduced Slightly reduced effects;	effects; mild almost no consensus	consensus norm norm	No data No data		Expedite decisions Expedite decisions	Slows decisions Slows decisions	Indirect measures; Indirect measures;	heterogeneity slows heterogeneity slows	decisions (e.g. Thatcher) decisions (e.g. Thatcher)	No data No data	'New' sector laws not 'New' sector laws not	slower slower	Expedites decisions Expedites decisions
	König	(2007)	Not coded		Not coded			Not coded			No data		Not coded	Unreliable	Unreliable			Not coded	Unreliable		No data
	Schulz & König Golub	(2000) (2002)	Not coded Unreliable		Not coded Unreliable			Not coded Unreliable			No data No data		Not coded Unreliable	Unreliable Unreliable	Indirect & Indirect &	unreliable unreliable		Unreliable No data	Unreliable Unreliable		No data Unreliable
	Golub	(1999)	Luxembourg Unreliable	Compromise	Pre-vs. post-SEA Unreliable	QMV		Pre-vs. Post-Maastricht Unreliable	Treaty QMV		Amsterdam & No data	Nice treaties	EU enlargements Unreliable	Parliament Unreliable	State preferences Indirect &	unreliable		Instrument type No data	Policy sectors Unreliable		Backlog Unreliable

European Union Politics 9(1)

About the author

Jonathan Golub is a Lecturer in the Department of Politics and International Relations, University of Reading, Reading RG6 6AA, UK. Fax: +44 118 975 3833 E-mail: j.s.golub@reading.ac.uk