First Mover Advantage: Measuring and Explaining the

Agenda-Setting Success of the European Commission.

James P. Cross Henrik Hermansson

2015-03-10

Abstract

The European Commission is commonly seen as the predominant agenda-setter in

the European Union (EU) due to its right to initiate legislative proposals, yet there is

significant disagreement about the extent of this power within different institutional

contexts. To date, measuring the impact of this right to propose on legislative outcomes

has proven difficult due to the lack of suitable data. This paper addresses this gap in

the literature by considering the changes between the Commission’s proposals and

the final legislative outcome passed by the Council of Ministers for a large selection of

legislative proposals between 1994 and 2012. It does so by implementing minimum edit

distance algorithms to measure changes between legislative proposals and outcomes.

This new measure of agenda-setting power, when combined with information about the

institutional environment, allows us to empirically evaluate prominent theories of inter-

institutional agenda-setting in the EU. The findings presented suggest that the ability

of the Commission to successfully set the agenda is determined by the institutional

structure in which negotiations take place. Our conclusions contribute to the ongoing

debate on the nature and distribution of executive functions in the EU.

1

1 Introduction

The legislative process in the European Union (EU) has in recent years provided a fertile

ground for the development and testing of formal and informal theories of bargaining and

agenda setting. The formal rules governing the interaction between the different institutional

actors involved in the decision-making process in particular make the EU a perfect setting in

which to test theories of agenda setting. This is because the European Commission occupies a

unique position in relation to other institutional actors as the sole proposer of EU legislation.

This position is thought to give the Commission significant agenda-setting power in the

legislative process. Despite this power, the Council of Ministers and the European Parliament

retain significant abilities to reshape a legislative proposal once it has been introduced by

the Commission by introducing and approving amendments. The balance of power between

the Commission as proposer and the Council and Parliament as amenders is determined

by the rules and procedures that apply to amending a proposal at different stages in the

legislative process (Crombez, 1996, 1997; Garrett and Tsebelis, 1996; Tsebelis and Garrett,

1997; Thomson and Hosli, 2006). Examining how these rules affect the relative ability of each

institutional actor to affect legislative outcomes in the EU is thus an important undertaking

and the central aim of this study.

The main contention of this study is that the success of the Commission in setting the

agenda can be measured by and is inversely related to the amount of successful amendments

made by other institutions to the initial Commission proposal. To justify this contention,

one must first carefully consider what the Commission proposal represents and then consider

what a successful amendment to the proposal put forward by the Commission represents.

We argue that the Commission proposal represents the Commission’s assessment of a piece of

legislation that will pass through the legislative process with as little amendments as possible.

The Commission attempts to anticipate the policy demands of the other institutions and

2

puts forward a piece of legislation that can be agreed upon by a minimum winning coalition

under a particular decision-making rule. Assessing the degree to which the Commission is

successful in putting forward unamended proposals is thus a natural way to examine its

agenda-setting power.

This study thus aims to add to the efforts to assess the impact of legislative rules on

the negotiation process by exploring how the legislative procedures affect the ability of the

Commission to set the agenda, and the ability of the Council and Parliament to amend a pro-

posal once it has been introduced. The paper introduces new measures of the agenda-setting

power of the Commission, based upon the concept of minimum edit distances, and tests the

usefulness of these measures in capturing the Commission’s agenda-setting success using a

significant new dataset of legislative proposals decided upon between 1994 and the 2013.

The findings presented demonstrate that agenda-setting success is influenced by important

subtleties and variations in decision-making rules under different legislative procedures. It

is through consideration of these subtleties that one can begin to unpack the sources of

Commission agenda-setting success.

2 Literature review

The idea that institutional rules affect the relative power of institutional actors in the EU is

not a new one (Aspinwall and Schneider, 2000). Tsebelis (2009) argues that examining the

agenda-setting power of an institutional actor a good way to measure executive dominance

in a political system. The EU represents a special case in this regard, as the relationship

between the Commission, Council and Parliament is more complicated than is the case in a

traditional legislative/executive system.

The idea that institutional rules structure the ability of different institutional actors

to influence legislative outcomes is well acknowledged in the literature. A series of formal

3

models have put forward game-theoretic accounts of the rules of decision making and the

manner in which they empower and constrain different actors at different stages in the

legislative process (Crombez, 1996, 1997, 2001; Tsebelis and Garrett, 2000). These models

make observations about the order in which each institutional actor can move, and then use

backwards induction in order to predict legislative outcomes. Within this literature, there is

some disagreement over the exact order in which each institutional actor can move. This is

due to different interpretations of how the institutional structure empowers different actors

(Steunenberg and Selck, 2006). However, the main finding of this literature is that under the

consultation procedure the legislative game is between the Commission as agenda setter and

the Council as decision maker (Crombez, 1996), while under the co-decision procedure, the

Parliament has a significantly strengthened role, although exactly how power is distributed

between each institution under co-decision is a matter of debate (Tsebelis and Garrett, 2000;

Crombez, 2000).

A number of attempts have been made at assessing the predictive accuracy of the formal

models of the legislative process discussed above. The most well developed of these is the

(Thomson et al., 2006) project that compared and contrasted the predictive accuracy of a

large selection of formal models of the legislative process. The main finding of this study

was that models that sought to explain legislative outcomes as a result of the distribution

of power, policy positions and relative saliency (Achen, 2006) performed better than those

that relied solely on the formal rules of procedure (Steunenberg and Selck, 2006).

A parallel effort at understanding the legislative process has also developed from an em-

pirical perspective by Tsebelis et al. (2001), who track legislative amendments throughout

the legislative process. They examined nearly 5,000 separate amendments to a selection

of 231 examples of EU legislation negotiated under both co-decision (79) and co-operation

(152) between 1988 and 1997. These amendments were tracked over the course of nego-

tiations and coded on an ordinal scale in order to capture the degree to which they were

4

adopted. The main finding of this study was that while the co-decision procedure certainly

empowers the Parliament relative to the other institutional actors, the Parliament also has

important conditional agenda-setting power under the co-operation procedure. Such power

being conditional upon the agreement of the Commission (Moser, 1996).

An important point that Tsebelis et al. (2001) dwell on in their study is that the rela-

tionship between the existence of successful amendments and the power of individual insti-

tutions to affect the decision-making process is not as clearcut as it might first seem. They

point to two potential theoretical objections to measuring agenda-setting power in terms of

amendment success, which stem from assumptions made in the formal theoretical literature

discussed above. In this formal literature, decision making can be assumed to occur under

conditions of complete or incomplete information. If one assumes complete information, then

no amendments to a Commission proposal should be necessary (or indeed proposed), as the

Commission can perfectly anticipate the policy demands of other actors and puts forward a

proposal that subsumes potential policy demands that would be successful into the proposal

in the first place. Similarly, other institutional actors will not make any amendments as

they will be aware that the Commission had perfect information about their policy demands

before making a proposal and utilized this information to put forward a proposal accept-

able to the minimum winning coalition. As a result, they should have no need to propose

amendments. A quick look at the legislative records show that this perfect anticipation of

policy demands does not occur and it is common for both the Council and Parliament to

propose amendments to Commission proposals, and for these amendments to be successful.

The question that then arises is how one should think about such amendments and how they

relate to the formal literature just discussed.

Tsebelis et al. (2001) argue that the existence of amendments can be explained either as an

indicator that incomplete information exists in the legislative game, or that other (nested)

games are concurrently being played besides the legislative one between the Commission,

5

Parliament and Council. Under conditions of incomplete information, amendment attempts

stem from a lack of information about the preferences or payoffs of other actors in the game.

When actors lack such information, they may put forward proposals or propose amendments

because they believe (erroneously or otherwise) that such proposals or amendments will be

acceptable to the other actors in the legislative game. An unsuccessful amendment to a

Commission proposal can be thought of as the Commission having better knowledge about

the truepreferences and payoffs of the other actors, thus allowing it to successfully identify

the position in the policy space that is acceptable to the minimum winning coalition and set

the agenda accordingly. Following a similar logic, a successful amendment to a Commission

proposal by the Council or Parliament can be thought of as an indicator that the Commission

had imperfect information about the preferences and payoffs of the other actors and so

failed to successfully set the agenda, as it was unable to successfully identify the equilibrium

position within the policy space that was acceptable to a minimum winning coalition.

In contrast to the imperfect information explanation, if one assumes other (nested) games

are being played during legislative negotiations, one can maintain the assumption of com-

plete information and explain attempted amendments as behaviour derived from these nested

games, rather than issues related to incomplete information. One such example of a nested

game would be where a concurrent signalling game is going on between political represen-

tatives and their constituents, with representatives using amendment attempts to signal

policy preferences to constituents in the full knowledge that such amendments will never be

successfully adopted (Meade and Stasavage, 2008; Levy, 2007). In such a situation, both

successful and unsuccessful amendments can be a result of signalling behaviour rather than

Commission agenda-setting power or lack thereof.

Empirically disentangling these two mechanisms is extremely difficult. Tsebelis et al.

(2001) draw on a broad literature of institutional analysis in order to maintain that public

opinion plays little role in EU policy making, and so choose to ignore the possibility of nested

6

games affecting amendment behaviour. Making such an assumption allows one to discount

reputation-based games as being less important in the EU context. We adhere to this

assumption for the purposes of this study, and thus discount reputation based explanations

of amendment behaviour. Such a move allows us to treat amendments and their success

as an indicator of power, rather than cheap-talk signalling. This is of course a simplifying

assumption that future work in this area should examine in more detail.

In a further investigation of the manner in which amendment power is distributed be-

tween different institutional actors, but this time in conciliation committees, Franchino and

Mariotto (2012) examine the relative success of the Council, Commission and Parliament

at getting their internally agreed-upon text reflected in the final legislation decided upon.

They found that on average, the increased utilisation of co-decision over time since its intro-

duction has strengthened the Parliament’s hand vis-a-vis the Commission and the Council.

This rather encouraging finding from the viewpoint of the Parliament is however tempered

by the second finding of the study, which is that when negotiations reach the conciliation

committee, the Parliament finds itself at a disadvantage, as the structure of conciliation

negotiations is biased in favour of the Council.

The interesting thing about the Franchino and Mariotto (2012) study, that contrasts with

previous work in this area, is that it automates the text-analysis process for detecting amend-

ment success by using Slapin and Proksch (2008)’s WORDFISH algorithm. WORDFISH

takes raw political texts and estimates policy positions based on a statistical scaling model

of word frequencies. The major advantage of utilising such a method is that it minimises

reliability problems and allows for easy replication of results. This method has successfully

been applied to problems as diverse as estimating the influence of interest groups on the EU

Commission’s policy position (Kluver, 2009), estimating MEP policy positions from Parlia-

mentary speeches (Slapin and Proksch, 2010), and estimating Japanese party positions on

the basis of election pledges (Proksch et al., 2011).

7

This method is one of a number that have emerged in recent years that automate the

process of analysing political texts to estimate policy positions. Other notable examples

include the Laver et al. (2003) WORDSCORES algorithm that estimates political party

positions from a computer-based analysis of election manifestos. This algorithm takes hand-

coded reference texts (usually expert surveys) identifying the extremes of the policy scale of

interest and then compares word counts of these texts to the texts being analysed to place

such texts on the policy scale of interest.

A second fruitful direction in which computer-based analysis of political texts has pro-

ceeded is in identifying content or topical categories from large corpuses of political text

(Quinn et al., 2010). These models attempt to estimate the the topics around which politi-

cal debate revolve and identify the specific topic(s) of a set of political speeches. They do so

by breaking individual speeches down into vectors of word frequencies, stacking these vectors

into matrices, and then making inferences about the topic of speeches based upon this data

structure. While substantive interpretation of the outputs produced by this methodology is

required, it significantly reduces the costs associated with analysing large amounts of text

in a time efficient and replicable manner.

In this study we introduce a new form of automated text analysis adapted from bio-

informatics and natural language processing. More details of this methodology are given

below, so at this stage we limit the discussion to two broader conclusions that can be drawn

from our discussion of the existing literature on legislative decision-making in the EU. The

first conclusion is that the existing theoretical and empirical literature on the EU legislative

process demonstrates that the influence of each institutional actor varies under the different

legislative procedures. This variation in influence is due to significant changes in the decision-

making rules that apply under different legislative procedures, which limit each institutional

actor’s ability to successfully amend a proposal at different points in time. The second

conclusion is that automated methods of text analysis have an important contribution to

8

make to the study of politics in general and the EU legislative process in particular, as

they allow one to consider significantly more raw text data in a time efficient and replicable

manner. These data in turn can be utilised to provide new insights into the manner in

which different actors approach legislative negotiations. With this in mind we now proceed

to outline the main theoretical arguments put forward in this study.

3 Theory

The theoretical expectations to be explored in this paper emerge from and share the as-

sumptions of the spatial model of politics as applied to the EU legislative process. Within

this framework we assume that all actors are utility maximisers and act under conditions

of incomplete information. That is, they do not possess perfect information about other

actors preferences or payoffs. A further simplifying assumption is that when engaged in

inter-institutional bargaining, the Commission, Council and Parliament act as unified ac-

tors. The unified actor assumption is justifiable because we are interested in the process of

inter-institutional bargaining that can lead to successful amendments of Commission propos-

als, rather than the internal politics of the individual institutions. While we do not elaborate

upon a formal model of the negotiation process, existing models in the literature guide our

theoretical discussion that follows.

Within this spatial modelling framework, the initial Commission proposal put forward

at the outset of negotiations is assumed to represent the point identified by the Commission

in the policy space under negotiation that is closest to the Commission’s most preferred

position, while remaining within the win-set of the other institutions (Shepsle and Weingast,

1987; Crombez, 1996; Tsebelis and Garrett, 2006). This statement implies that the proposal

put forward by the Commission is strategic in nature, rather than representing its’ ‘true’

preferences over outcomes. The assumption of incomplete information means that the Com-

9

mission will not always perfectly predict where this point in the policy space lies. The result

is that in certain situations there will be incentives to propose amendments to a Commission

proposal if an actor (Council or Parliament) feels it has more accurate information about the

truewinset under a particular legislative procedure and can use this information to move the

policy outcome towards its most preferred position. The winset is determined by the rules

that must be adhered to in order to approve amendments at any particular point in time.

The strategic considerations taken into account by the Commission when putting forward a

proposal and the Council and Parliament when proposing an amendment are thus directly

related to the legislative procedure and the manner in which changes in the procedure af-

fect its relative power to set the agenda and other institutional actors ability to amend said

proposal (i.e. –the size of the minimum winning coalition).

Two legislative procedures shall be examined in this study; the consultation procedure

and the co-decision procedure. Each procedure has specific rules when it comes to amending

a Commission proposal for each institution involved in the negotiation process. The consul-

tation procedure, represented in Figure 1, is the simpler of the two so we begin with that.

The consultation procedure begins when the Commission introduces a proposal. The next

move in the legislative game is made by the Parliament that must consider the legislative

proposal and provide a non-binding opinion to the Council. The Council then considers this

opinion, and any amendments that Member States wish to put forward and then votes on

the text. Any amendments made or approved of by the Council must be adopted under

the unanimity voting rule. If no amendments are approved, the Council can approve of the

Commission proposal by qualified majority. The broader implications of this institutional

setup is that it makes amending Commission proposals difficult due to the unanimity re-

quirement in the Council, and the minimal formal role of the Parliament. In fact, they only

significant formal power that the Parliament holds under the consultation procedure is that

it can withhold its opinion and thus stall the legislative process at that stage indefinitely.

10

Figure 1: The consultation procedure.

Under the co-decision procedure, represented in Figure 2, the Parliament holds much

greater formal power to influence the decision-making process. This procedure is initiated

when the Commission submits a proposal to the Council and Parliament for negotiation and

decision. Discussions proceed on a parallel basis within both the Council and Parliament

until the Parliament introduces its first reading opinion to the Council, decided upon by

simple majority. If the Council approves of the Parliament’s position by qualified majority,

then the act can be adopted without further debate. If the Council wishes to change the

positions presented by the Parliament, it adopts its own positions by qualified majority,

which are then returned to the Parliament for a second reading. At the second reading,

the act is adopted if the Parliament approves of the position submitted by the Council by

absolute majority. Should the Parliament reject the Council text, then the law fails to be

adopted; if, on the other hand, it modifies the Council text, this modified text is then passed

back to the Council. At this stage, the Council can either approve the text as it stands

by qualified majority or convene a conciliation committee that brings together Council and

Parliamentary representatives who attempt to agree upon a compromise text. If such a text

is agreed upon, it must be approved by both the broader Council and Parliament plenary

11

before it can become law. If such an agreement cannot be reached, the proposal can be

further negotiated, shelved by the Council President, or withdrawn by the Commission.

The first theoretical expectation that emerges from the differences between these two

legislative procedures is that the Commission proposal will see more amendments under the

co-decision procedure than under the consultation procedure. This is due to the argument

that amendments are significantly more difficult to agree upon under consultation as they

need to be agreed upon unanimously in the Council. It should be noted that in cases where

the Parliament and Commission are in agreement, this gives the Parliament a conditional

agenda-setting capability that will have an effect in the opposite direction to the one pro-

posed here (Moser, 1996; Tsebelis, 1996). Unfortunately at this stage we cannot account for

this as we do not have a direct measure of agreement between the Commission and Parlia-

ment. As a result we expect that:

H1: The Commission proposal will see more successful amendments under the co-decision

procedure.

Successful amendment attempts can also be seen as the result of a strategic move made

by an actor at a particular point in the negotiation process based upon demonstrating inter-

nal conflict within an institution. In the literature on bargaining, being able to demonstrate

internal conflict can strengthen one’s bargaining position as it can be reasonable be argued

that internal agreement within an institution will not be possible unless certain concessions

are made (Schelling, 1980). A similar logic is expected to be at work in the bargaining game

between EU institutions as internal conflict can exist within an institution with which it

can extract concessions. Furthermore, internal conflict within an institution can increase

the uncertainty surrounding its trueposition, and can thus lead to Commission mistakes in

identifying the point within the policy space that is closest to its own policy preferences and

12

Figure 2: The co-decision procedure (Adapted from (Thomson and Hosli, 2006)).

13

acceptable to the minimum winning coalition of other actors. Unfortunately at this stage

of our research project we do not have a direct measure of the amendment attempts made

by each institutional actor in the negotiation process, so this aspect of the negotiation pro-

cess cannot be directly investigated. Instead we use a proxy for the internal conflict within

other institutions in terms of the length of time it takes each institution to reach internal

agreement. The intuition here is that the time it takes for each institution to reach internal

agreement reflects the level of disagreement that exists within that institution about the

proposal made by the Commission. The result of this disagreement is that the institution in

question will present amendments to the Commission proposal that are accepted in order to

placate dissenting internal actors.

H2a: The Commission proposal will see more successful amendments as the time it takes

for the Council to reach a common position increases.

H2b: The Commission proposal will see more successful amendments as the time it takes

for the Parliament to reach a reasoned opinion (under consultation) increases or supply a

first reading agreement (under co-decision)

The third theoretical expectation to be explored is that the number of amendments ob-

served under the co-decision procedure will vary significantly depending upon the stage of

negotiations reached. This expectation is justified because of the variation in amendment

rules that exist within each institution over the course of negotiations. According to the

rules of procedure and the formal literature on co-decision, the ability of other institutions

to amend the Commission proposal varies as one moves through the different stages of co-

decision. For instance, the internal amendment rules within the Parliament vary across

each reading (Hagemann and Hoyland, 2010). Within the Parliament, in order to approve

14

the Commission proposal in the first reading stage, the Parliament needs to reach a simple

majority of MEPs present in the chamber at the time of the vote. In contrast, in the sec-

ond reading stage, the Parliament requires an absolute majority of MEPs regardless of who

is present in the chamber at the time of the vote. Hagemann and Hoyland (2010) argue

that this subtle change in majority requirement gives the Council conditional agenda-setting

power, as the Parliament’s power to amend in the second reading stage is conditional on the

actual turnout of MEPs to vote. The Council on the other hand can approve amendments

to the Commission proposal by qualified majority at all stages, which is a lower threshold

than that found under consultation. The implication of this argument that amendments

approved by the Council in its common position become difficult to reverse in the second

reading stage of negotiations in the Parliament. Of course the actual amount of amend-

ments put forward in the common position depends on the preferences within the Council

and their relationship with the Commission proposal. At this stage we do not have the data

to directly test propositions about the interaction between preferences and institutions, so

we assume that the Council demands significant amendments and if negotiations reach a

second reading, these will be hard to reverse. As a result we expect that:

H3: The Commission proposal will see more successful amendments if the second reading

stage is reached under co-decision.

A second subtlety of the co-decision procedure is that the Parliament and Council can col-

lude against the Commission and force negotiations into a conciliation committee where the

Commission has no formal power to withstand amendments (Tsebelis and Garrett, 2000).

The more relaxed amendment requirements in conciliation committees suggest that there

should be more successful amendments to a Commission proposal. Of course as stated

above, actor preferences also play a role, as it could be the case that the Council could

15

agree with the Commission and be opposed to suggested Parliamentary amendments (or

vice versa). Again, unfortunately at this stage we do not have the data to directly test

propositions relating to the policy demands of each individual institution, so here we test

the more limited proposition that:

H4: The Commission proposal will see more successful amendments if the conciliation

stage (3rd reading) is reached under co-decision.

A number of control variables are also included in the analysis as they consistently appear

in the existing literature. We control for the type of legislation under consideration as this

relates to the amount of discretion for member states likely to be included when implementing

a piece of legislation (Thomson and Torenvlied, 2010). The dataset contains a selection of EU

decisions, directives and regulations. Regulations must be directly transposed into national

law as they appear in the text agreed upon, directives in general allow for more discretion

than regulations, and decisions are only relevant to those to whom the are addressed (Craig

and De Burca, 2011). As a result, we expect that regulations will be subject to the most

amendments, directives will be subject to somewhat less amendments, and decisions will be

the least amended of the legislation analysed.

We control for treaty changes (from Amsterdam to Nice to Lisbon) which have empowered

the Parliament vis-a-vis the other institutional actors in the negotiation process (Kreppel,

2002; Horl et al., 2005; Rasmussen, 2012). Such treaty changes represent a form of external

shock to the legislative process that have the potential to affect the amount of successful

amendments to Commission proposals. The expectation here is that each treaty change

has further empowered the Parliament and this should increase the amount of successful

amendments to Commission proposals.

We also control for the enlargement round in 2004. We do so, as adding new member

16

states represents another form of external shock to the legislative process that broadens the

variety of interests represented in the Council and Parliament and is thus likely to increase

the demands for and success of attempts to amend Commission proposals (Aleskerov et al.,

2002). As a result we expect that the 2004 enlargement will be associated with increased

levels of successful amendments to Commission proposals.

Finally, we control for an annual time trend within the data in order to capture the

gradual evolution of the legislative process in the EU over time, net of the effects of external

shocks such as enlargement and treaty change.

4 Data and measurement

The dependent variable, capturing the agenda-setting power of the Commission, is based

upon the minimum edit distances between the initial proposal and the final policy outcome

(Levenshtein, 1966). This method has been developed in bio-informatics, computer science,

and natural language processing to measure the number of edit operations (insertions, dele-

tions, or substitutions) required to change one string of characters or words into another

(Wagner, 1974). Minimum edit distances have successfully been applied to problems as di-

verse as creating accurate spell checkers (Wagner, 1974; Wagner and Fischer, 1974; Wong

and Chandra, 1976), assessing differences between different dialects in computational lin-

guistics (Kessler, 1995; Nerbonne and Heeringa, 1997), and assessing genetic alignments in

computational biology (Fitch and Margoliash, 1967; Dayhoff and Schwartz, 1978; Henikoff

and Henikoff, 1992). This method of measuring change between different drafts of legislation

is suitable for task, as the basic structure of the problem of capturing the change between

two text strings in a draft proposal is the same as in the previous applications mentioned.

A number of explicit assumptions must be made when employing minimum edit distances

to measure legislative influence. The first is that changes to a legislative text have substantive

17

meaning in terms of policy outcomes. There are a number of arguments justifying this

assumption. Legislative texts most often consist of definitions of concepts, or the rights and

responsibilities of one party to another. Over time, and in order to reduce legal ambiguities,

the structure, style, vocabulary and grammar of these definitions become subject to very

strong norms and best practices within a polity.1 This formalistic and precision-centered

nature of legislative texts means that there are very few, if any, alternative ways of expressing

the same legislative message. Precision is indeed the guiding principle of legislative drafting.

Ambiguities may of course arise by accident, or be unavoidable, but recent research shows

that they often represent a conscious attempt by the drafter to refer the interpretation to

relevant courts (see for example Wallace (2012)). In other words, even when ambiguities

do arise in legislation they are usually carefully planned and deliberate. In addition, and in

contrast to other written or verbal messages, legislative texts (when entering into force) have

a unique impact on the real world. Any single change may have substantive consequences

for those subject to the law, in terms of their rights and responsibilities. Because of these

singular characteristics of legislative texts, political conflict in legislative bodies is almost

always focused on the exact wording of laws. The legislative rules involved in the writing

of laws, i.e. voting procedures and veto players, further ensures that making changes to

legislative texts is difficult, meaning that spurious or non-salient changes are unlikely to be

made.

Since ‘every word matters’, substantively, legally, and politically, the number of words

that have been edited between versions of a legislative text is a credible indicator of the

number of substantive changes that have been made to the document. But exactly how

accurate is such an indicator? It is difficult to argue strongly, for example, that three edits

represent a three times larger change than if a single edit, at least without knowing anything

1In the European Union these are summarised herehttp://ec.europa.eu/governance/better regulation/documents/legis draft comm en.pdf and here http://eur-lex.europa.eu/en/techleg/index.htm

18

more about the specific words or context. This is especially true for longer documents,

given a certain probability for unplanned ambiguity, misspellings, and grammatical errors

per word. However, if for example one hundred words have been edited it can reasonably be

assumed that this represents a larger, substantive change in the consequences of the law. In

the initial experiments with the measure described above, between 0 and 17,583 edits have

been recorded between the European Commission’s proposals and the final legislative acts

decided upon. In other words, the practical range of the indicator gives confidence that we are

indeed observing large differences in the amount of substantive change to the Commission’s

proposals demanded by the Council and Parliament. We now proceed to describe how one

can calculate the two distinct minimum edit distances to be used in this study.

4.1 Minimum edit distance algorithms

This section describes the implementation of two distinct minimum edit distance algorithms.

The algorithms are described in terms of edit operation on strings with each edit operation

operating on a single element of a string. Here and in the analyses that follow the basic

element of a string is considered to be an individual word.

The classic minimum edit distance algorithm between two strings is calculated as the

minimum number of editing operations required to change one string into another (Leven-

shtein, 1966). Three distinct editing operations are allowed, and each has an assigned weight.

The allowed editing operations are the deletion of a character (weighted 1), the insertion

of a character (weighted 1), or the substitution of a character (weighted 0 if character does

not change and weighted 2 if it does). S1(i) represents the word in string X at position i

and S2(j) is the word at position j. Formally the minimum edit distance D(i, j) between

two strings, X = x1 · · ·xm and Y = y1 · · · yn is the minimum cost of a series of editing

operations required to convert X into Y. The minimum edit distance is computed using a

dynamic programming approach, which is a method for solving larger problems by consid-

19

Table 1: Deletion (Levenshtein distance → Levenshtein)

j=0 j=1Sk # Levenshtein

i=0 # 0 1i=1 Levenshtein 1 0i=2 distances 2 1

Table 2: Addition (Levenshtein → Levenshtein distance)

j=0 j=1 j=2Sk # Levenshtein distance

i=0 # 0 1 2i=1 Levenshtein 1 0 1

ering a larger problem to be the sum of the solutions to a series of sub-problems (Bellman,

1957). A dynamic programming approach allows one to avoid the often high cost associated

with recalculating the solution to sub-problems, as such solutions are stored once calculated

in a process referred to as memoisation.

To compute the minimum edit distance D(i, j) between X and Y , a matrix M (repre-

sented in Table 4) is constructed so that any matrix element Mij represents the minimum

number of edits required to turn x1 · · · i into y1 · · · j. Each Mij is calculated using the fol-

lowing formula:

Table 3: Substitution (Levenshtein distance → Levenshtein measure)

j=0 j=1 j=2Sk # Levenshtein measure

i=0 # 0 1 2i=1 Levenshtein 1 0 1i=2 distances 2 1 2

20

Table 4: Calculating Levenshtein distances

j=0 j=1 j=2 j=3 j=4 j=5 j=6Sk # Changes well captured by Levenshtein distances

i=0 # 0 1 2 3 4 5 6i=1 Levenshtein 1 2 3 4 5 4 5i=2 distances 2 3 4 5 6 5 4i=3 capture 3 4 5 6 7 6 5i=4 changes 4 3 4 5 6 7 6i=5 well 5 4 3 4 5 6 7

D(i, j) =

D(i− 1, j) + 1,

D(i, j − 1) + 1,

D(i− 1, j − 1) +

{2; if S1(i) 6= S2(j),

0; if S1(i) = S2(j).

(1)

As can be seen from the formula, there are three values to be computed at each stage in the

algorithm, and each matrix element mij corresponds to the minimum of these three values.

D(i−1, j) + 1 corresponds to a deletion or a move upwards in the matrix (Table 1), D(i, j−

1) + 1 represents an insertion or move sideways in the matrix (Table 2), and D(i− 1, j − 1)

represents a substitution or diagonal move in the matrix (Table 3). At each iteration of the

algorithm, each element in the matrix is calculated one at a time taking the values from the

previously solved sub-problems as inputs into formula 1 and solving. In this way the larger

problem of converting one string into another is broken down into many separate individual

edit operations, with the minimal path being taken at each iteration of the algorithm. Matrix

M , represented in Table 4 was filled out using formula 1, and the number appearing in the

bottom left corner of the matrix Mmn is the minimum edit distance that represents the cost

of transforming string X into string Y .

A second stage of the algorithm that allows one to determine the actual edits used to

generate the final minimum edit distance score is also possible. This is done by starting at

21

position Mmn and finding which of the three previous possible moves was the least costly, and

working backwards through the matrix. The resulting vector of edit operations is referred to

as the backtrace and is useful as it allows one to determine the edit alignment that translates

string X into string Y . In table 4 one possible first move is thus upwards, representing the

deletion of the word wellın string X. The bold elements in the table represent the backtrace

through the matrix that delivers the minimal edit cost for changing string X into string Y .

It should be noted that there can be more than one path through the matrix that delivers

the minimum edit distance, so a backtrace is not necessarily unique. This is demonstrated

by the fact that there is more than one emboldened path through the matrix in table 4. In

future research we plan to exploit the backtrace to determine exactly what is being added,

removed or substituted from legislation, but for now we put the backtrace to one side.

The Levenshtein distance algorithm places a heavy weight (penalty) on situations where

chunks of text have been moved within a document, what is technically termed a transpo-

sition. This is potentially problematic for legislative texts, as it is quite common for whole

articles to move between different positions within a larger text. In such cases, the article

in question remains, but has been moved. We want to discount such changes, as they do

not entail any policy changes, but instead represent a reorganisation of a legislative text.2

The minimum edit distance described above finds it impossible to see these large jumps as

it focuses on dynamically determining how to edit (delete, insert or substitute) a document

on a word by word basis. In applications of text similarity techniques where copy-paste

and cut-paste type edits are common, for example plagiarism detection, two types of so-

lutions to this blind spot are in use. Both however have significant drawbacks. The first

solution randomly draws sample words from two texts and infers their similarity based on

the similarity of the samples. While suitably insensitive to cut-paste reorganisations of text

2The key point here is that while the ordering of the text has changed, policy implications have not. Thisis well illustrated if one thinks about the manner in which the Starwars character Yoda speaks, where theorder of a sentence changes significantly, but the meaning does not!

22

and computationally highly efficient, the technique is imprecise and best used to identify

possibly similar documents for further similarity checks. The second solution entails the

construction of so called suffix trees, essentially indexes of every possible combination of the

words in each text. The technique is highly accurate, but constructing the suffix trees is

computationally very demanding (see below) and storing them requires significantly more

space than storing the texts, making analysis of large bodies of texts impractical. In order to

avoid the heavy penalisation associated with moving large sections of a string using the stan-

dard Levenshtein distance algorithm, while retaining accuracy and computational efficiency,

a second edit distance algorithm, referred to as the Transposition distance algorithm, has

therefore been developed that can account for such cut-paste and copy-paste type changes.

D(i, j) =

0; if S1(i) 6= S2(j),

D(i− 1, j − 1) + 1; if S1(i) = S2(j).(2)

The first step of the Transposition distance algorithm is very similar to that of earlier

Levenshtein distance algorithm. A matrix is created with the width and height of the longest

of the two strings (in number of words), with an additional row of zeroes at the top and an

additional column of zeroes at the far left to represent the null string of each. Each of the

empty cells in the matrix again represents a meeting point between one string element in

X and one string element in Y . The algorithm then proceeds to fill the empty cells using

a dynamic programming approach, starting from the top left and working row by row. If

the two words which meet in a cell do not match, D(i, j) = 0. If the two words do match,

D(i, j) = D(i− 1, j − 1) + 1.

In this way, continuous strings of matching words will correspond to diagonal segments

of increasing cell values. If the algorithm is applied to two exactly similar texts, the matrix

will be a matrix of zeroes with an unbroken diagonal line of rising cell values. Insertions,

deletions and replacement of words will manifest as gaps (horizontally or vertically) in this

23

Table 5: Deletion (Transposition distance → Transposition)

j=0 j=1Sk # Transposition

i=0 # 0 0 0i=1 Transposition 0 1 0i=2 distance 0 0 0Column max 1 0Cumulative edits 0 1

Table 6: Addition (Transposition → Transposition distance)

j=0 j=1 j=2Sk # Transposition distance

i=0 # 0 0 0i=1 Transposition 0 1 0i=2 0 0 0Column max 1 0Cumulative edits 0 1

Table 7: Substitution (Transposition distance → Transposition measure)

j=0 j=1 j=2Sk # Transposition measure

i=0 # 0 0 0i=1 Transposition 0 1 0i=2 distances 0 0 0Column max 1 0Cumulative edits 0 1

Table 8: Transposition (Transposition distance of measurement device → measurement de-vice of Transposition distance)

j=0 j=1 j=2 j=3 j=4 j=5Sk # measurement device of Transposition distance

i=0 # 0 0 0 0 0 0i=1 Transposition 0 0 0 0 1 0i=2 distance 0 0 0 0 0 2i=3 of 0 0 0 0 0 0i=4 measurement 0 1 0 0 0 0i=5 device 0 0 2 0 0 0Column max 0 1 2 0 1 2Cumulative edits 0 0 1 1 1

24

Table 9: Calculating Transposition distances

j=0 j=1 j=2 j=3 j=4 j=5 j=6Sk # Change well captured by Transposition distance

i=0 # 0 0 0 0 0 0 0i=1 Transposition 0 0 0 0 0 1 0i=2 distance 0 0 0 0 0 0 2i=3 captures 0 0 0 0 0 0 0i=4 change 0 1 0 0 0 0 0i=5 well 0 0 2 0 0 0 0i=6 0 0 0 0 0 0 0Column max 0 1 2 0 0 1 2Cumulative edits 0 0 1 2 2 2

diagonal line, filled with zeroes (see tables 5-7). Such gaps will cause the progression of

rising values to start over from zero. Copy-paste type changes will manifest themselves as

the existence of two (or more) diagonal segments of rising values. This is demonstrated in

table 8.

In the second step, a vector is constructed of the highest values of every column. Those

highest values will be found in the cells of the matrix representing the best match for every

word in the second text (part of the longest unbroken sequence of words in the first text). It

can be easily shown that an unbroken sequence of matching words will result in an unbroken

rising sequence of values in the vector of the highest values in each column. But any addi-

tions, deletions, replacements, copy-paste or cut-paste edits will cause breaks to that rising

sequence, which can be processed by the algorithm to yield the minimum edit distance. In

particular, every zero in that vector (except the very first one) corresponds to a word that

exists in one of the texts, but not the other. In addition, every positive value of the vector

that does not equal the previous value plus one corresponds to a copy-paste or cut-paste edit

of the text. Following these two simple rules, the algorithm counts the instances of zeroes or

broken progression of values to find the minimum number of edits needed to transform text

one into text two.

25

By reducing the string matching problem to the analysis of one vector, the algorithm only

requires (if the length of the longest text is n) n2 + 2n computations, very much comparable

to the fastest minimum edit distance algorithm available (Levenshtein, 1960) which requires

(if the length of the texts are n and m, respectively) n ∗m computations. This represents

a large improvement on suffix trees, the other accurate solution to cut-paste edits, which

requires at least (n∗ log(n)+m∗ log(m))+n+m computations, and therefore allows analysis

of larger bodies of text.

A perhaps unfortunate aspect of the algorithm is that it counts pasting over a subset of

a text as only one edit, irrespective of the length of the subset. This can cause the algorithm

to underestimate the semantic change in a text in some very specific and rare circumstances,

notably when a text is changed from being non-repetitive to being repetitive. This downside

has to be weighed against the error inherent in standard minimum edit distance algorithms

which grossly overestimate the changes needed to cut-paste sections of text.

4.2 Data

The raw text inputs (proposal and outcome) inserted into each of these algorithms are

available online from a number of different sources. Here we utilise the legislative observatory

of the European Parliament. Once the relevant texts have been acquired through a process

of web scraping, they are cleaned of extraneous content not directly related to the policy

content of the proposal itself. They are then broken down into their constituent parts

(preambles, articles, and appendices), and these are then matched with the original proposal

and final outcome, to provide the raw text data for creating the measures of change between

a legislative proposal and final outcome. Here we focus solely on the articles in a proposal

as they contain the main content of the legislation of interest. The final dataset utilised

to examine the hypothesis presented above contains 1,454 proposals decided upon between

1994 and 2013.

26

At the time of writing this text, we have successfully calculated all transposition distances

for the proposals under consideration, while not all Levenshtein distances have been calcu-

lated. As a result, we have focused on the transposition distance measure of change between

Commission proposals and final outcomes in the analysis that follows. It is envisaged that

the Levenshtein measure shall be added in the next iteration of the paper and that a full

exploration of the relationship between the Levenshtein and Transpose distance measures

shall be forthcoming in a methodological paper exploring both measures in detail.

The independent variables of interest are measured as follows. The legislative procedure

is accounted for as a dummy variable coded 1 for consultation and 2 for co-decision. The

reading stage within the Parliament is measured using a series of dummy variables coded 0

if the relevant reading stage was not reached and 1 otherwise. The length of time it took for

the Council and Parliament respectively to reach a common position or first reading opinion

respectively was taken from the Hage (2011) dataset, which was collected from the PRELEX

database online. This dataset contained variables capturing the dates that the Commission

introduced a proposal and the dates of the respective agreements within each of the other

institutions. The variable used here is simply the number of days between the introduction

of a proposal and the date a common position and first reading agreement was reached.

The type of legislation is captured by another categorical variable coded 0 for a decision,

1 for a directive, and 2 for a regulation. Treaty changes are captured by a variable that

is coded 0 for the dates the Amsterdam treaty was in force, 1 for the dates that the Nice

treaty was in force and 2 for the dates that the Lisbon treaty was in force. The enlargement

variable is similarly constructed and is coded 0 for the EU-15 enlargement period, 1 for the

EU-25 enlarement period, and 2 for the EU-27 period. Finally, the gradual evolution of the

legislative process over time is captured by a categorical variable for each year represented

in the dataset. Table 1 provides summary statistics for the variables under consideration.

27

Table 10: Summary statistics

Variable Mean Std. Dev. Min. Max. NAgenda-setting success 2204.518 2570.287 1 17583 1516Procedure 1.52 0.5 1 2 2733Length to EP decision 428.154 340.704 1 2863 2187Length to Council decision 531.246 318.095 44 2610 862EP opinion second reading (yes/no) 0.153 0.36 0 1 4088EP opinion third reading (yes/no) 0.044 0.205 0 1 4088Treaty 1.495 0.684 0 2 4798Legislative instrument 1.207 0.837 0 2 2677Enlargement 1.221 0.893 0 2 2733

5 Analysis

The analysis starts with an exploratory analyses of the minimum edit distance measure in

order to demonstrate how it captures changes between the initial Commission proposal and

the final legislative outcome. Figure 3 shows the density of Transposition distances for the

proposals in the dataset. As can be seen, the large majority of Commission proposals have

relatively few successful edit operations carried out on them. This suggest that in general,

the Commission proposals see relatively few changes.

Our second set of analyses considers the relationship between successful amendments and

the institutional environment in which negotiations take place. The model used to capture

these effects is a robust negative binomial regression, as the data is of a count nature. The

coefficients are exponentiated so as to represent incidence rate ratios. The paper describes

three distinct models, summarised in tables 11 and 12. The second column in table 11

summarises the direction of out theoretical expectations. Model one in the third column

looks at the role of the legislative procedure alongside other covariates, while model two in

the fourth column considers only co-decision procedures and includes covariates for the stage

of negotiations reached. Model 1 in table 12 also considers only co-decision proposals, but

looks at the time to internal Council agreement, rather than the time to internal Parliament

28

Figure 3: Density of Transposition distances.

29

agreement. We begin our discussion with model one. The dependent variable in all models is

the minimum edit distance between Commission proposal and final outcome, meaning that

a higher value indicates less successful agenda-setting by the Commission.

As can be seen, our expectations about the manner in which the legislative procedure

affects the likelihood of successful amendments to the Commission proposal does not find

support in the analysis. In fact, the amount of successful amendments to co-decision propos-

als reduces by a factor of 0.84 compared to consultation proposals. This is quite a surprising

finding, given that we expected that the more forgiving amendment rule under co-decision

and the addition of the Parliament as co-legislator would increase the demands for amend-

ments. Perhaps the lower levels of successful amendments under co-decision are due to the

Commission expending more effort in putting forward proposals that are acceptable to the

minimum winning coalition under co-decision, as it is aware that should a proposal proceed

to conciliation committee, the Commission loses the formal power to influence the decision

outcome.

In contrast, the amount of time it takes for the Parliament to make a decision is found to

be significant and in the expected direction. Each extra day that it takes for the Parliament

to reach a decision leads to an increase in the number of successful edits to a Commission

proposal by a factor of 1.001. The substantive size of this effect over the range of the

variable is demonstrated in Figure 4. We see that in situations where the Parliament agrees

on legislation within a day or two, there are predicted to be around 2,000 successful edit

operations, whereas in situations where it took the Parliament 2,500 days to reach a decision

there are predicted to be 22,100 successful edit operations. This suggests that internal conflict

within the Parliament has an important effect on the Commission’s success at setting the

legislative agenda, and we see significant substantive differences between the Commission

proposal and the final outcome when there is internal conflict within the Parliament.

Similarly, the amount of time it takes the Council to reach a decision is also found to have

30

Figure 4: Substantive effects of significant variables.

31

Table 11: Determinants of Commission agenda-setting success

(1) (2)Hypothesis Agenda-setting success Agenda-setting success

Procedure + 0.844∗

(-2.56)

Length to EP decision + 1.001∗∗∗ 1.001∗∗∗

(7.90) (4.21)

Nice + 1.591∗∗∗ 1.411∗

(4.32) (2.30)

Lisbon + 1.805∗∗ 1.895∗∗

(3.18) (2.72)

Directive + 0.694∗∗∗ 0.808+

(-4.11) (-1.74)

Regulation + 0.928 0.961(-0.96) (-0.37)

2004 enlargement + 0.859 1.012(-1.19) (0.08)

Year + 1.000 1.017(0.00) (0.59)

EP opinion second reading + 1.585∗∗∗

(4.59)

EP opinion third reading + 0.906(-0.82)

Constant 1367.9 7.83e-13(0.15) (-0.47)

lnalphaConstant 1.183∗∗∗ 0.982

(5.04) (-0.29)Observations 1450 671

Exponentiated coefficients; t statistics in parentheses+ p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

32

Table 12: Determinants of Commission agenda-setting success

(1)Hypothesis Agenda-setting success

Length to Council decision + 1.001∗∗∗

(3.99)

Nice + 1.324(1.59)

Lisbon + 1.871∗

(2.37)

Directive + 0.751∗

(-2.07)

Regulation + 0.922(-0.66)

2004 enlargement + 0.986(-0.08)

Year + 1.013(0.41)

EP opinion 2nd reading + 1.656∗∗∗

(4.18)

EP opinion 3rd reading + 0.790+

(-1.81)

Constant + 4.55e-09(-0.30)

Constant 0.971(-0.46)

Observations 576

Exponentiated coefficients; t statistics in parentheses+ p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

a significant effect on the number of successful edit operations on a Commission proposal

(table 12). Each extra day that it takes for the Council to reach a decision leads to an

33

increase in the number of successful edits to a Commission proposal by a factor of 1.001.

Both of the treaty changes that occurred over the course of the period under consideration

were found to affect the agenda-setting success of the Commission. We found a significant

increase in the number of successful edit operations to Commission proposals when said

proposals were decided upon after the Amsterdam treaty. Moving from Amsterdam to Nice,

the propensity for successful amendments increased by a factor of 1.411-1.591, while moving

from Amsterdam to Lisbon was associated with an increase by a factor of 1.805-1.895. These

findings suggest there has been a gradual rebalancing of power between the EU institutions

with the Commission losing out in this process in terms of its ability to successfully set the

agenda.

The type of legislative proposal was found to be somewhat important in determining the

amount of successful amendments observed. The base category chosen was EU decisions and

it was found that decisions were significantly different to directives but not to regulations.

The number of successful edit operations decreased by a factor of between 0.694-0.808 for

directives relative to decisions. This suggests that the Council and Parliament are less

successful or less interested in amending directives. No significant effect was found when

decisions were compared to regulations.

Both the 2004 enlargement and the yearly time trend variable were not found to have a

significant effect on the number of successful amendments to a proposal in any of the models.

The lack of a significant difference between the pre-2004 and post-2004 enlargement is an

interesting finding as pervious literature on the effects of enlargement have found that it has

been associated with a diversification of interests being represented (Thomson, 2009).

When we started to unpack the subtleties of the co-decision procedure in terms of the

variation in decision rules at different stages of the negotiation process, we arrived at two

testable hypotheses relating to the propensity for successful amendments to Commission

proposals. We first expected there would be an increase in the number of successful amend-

34

Figure 5: The effect of the second reading v first reading.

35

ments to Commission proposals as one proceeded from the first reading to the second. Model

2 in table 11 and model 1 in table 12 show that the number of successful edit operations

increases by a factor of 1.585-1.656 for proposals that reach the second reading relative to

those decided upon at the first reading stage. Figure 5 clarifies the substantive size of this

effect.

In contrast, when negotiations reach a third reading (conciliation), we see no significant

difference to proposals that were agreed upon at the first reading stage in table 11 and a

weakly signifcant negative effect in table 5. This tentative finding suggests that perhaps

conciliation does not disempower the Commission to the degree that some formal models of

the negotiation process have predicted (Tsebelis and Garrett, 2000).

It should be kept in mind that what is lacking from the analysis at this stage is a direct

measure of the policy demands of each institutional actor at each stage in the negotiation

process. As a result we cannot draw strong conclusions about the interaction between insti-

tutions and actor policy demands at this stage. This is of course the natural next step to

take in developing this research project going forward.

6 Conclusions

The analyses presented above have provided a number of interesting insights into the leg-

islative process of the EU. The first of these is that there is significant variation in the

agenda-setting success of the Commission within different institutional settings. Legislative

procedures have an impact upon agenda-setting success, although not always in the expected

direction. The first finding relating to the legislative procedure was that there are signifi-

cantly less successful amendments to a Commission proposal under co-decision compared to

consultation. This is rather surprising, as much of the formal theoretical literature argues

that co-decision significantly empowers the Parliament, and so our expectation was that this

36

would lead to more successful amendments. One plausible explanation of this finding is that

the Commission puts forward proposals more acceptable to a minimum winning coalition

under co-decision, because it is fully aware of its reduced power. This in turn leads to less

need for amendments. In order to test this assertion, we would need a measure of the edit

distance between a Commission proposal and the first reading opinion of the Parliament

and the common position of the Council. Such a measure would allow us to determine if

the Commission does indeed put forward more acceptableproposals under co-decision, as it

would give us a handle on the relative policy positions of each of the institutions involved

in the legislative process. This is a natural next step for the project as it stands, given that

these records are publicly available in many cases.

Our initial findings relating to the influence of different amendment rules at different

stages in the co-decision procedure suggest that this variation significantly affects the proba-

bility of successful amendments to a Commission proposal. Proposals that reach the second

reading stage are significantly more likely to be amended versus those decided upon in the

first reading. Weak evidence was also found that the third reading is different to the first,

although in the opposite direction to what was expected. Again, the implication here is that

variation in amendment rules across different readings seems to matter.

Of our control variables, treaty changes were found to have an important influence on

the agenda-setting success of the Commission, with a significant rebalancing of power to the

amending institutions since Amsterdam. This is in line with previous literature that has

examined how treaty changes have affected the relative distribution of power between the

Commission, Council, and Parliament.

When one looks at the type of legislation, significant differences in agenda-setting success

were found between decision and directives, but not between decisions and regulations.

It must of course be noted that this study represents the very first step in exploring

the usefulness of minimum edit distances for examining political texts and much more work

37

remains to be done. Significant effort must be expended to demonstrate that the assumption

that changes between two legislative texts actually have substantive policy implications

is warranted. This shall be done by comparing our minimum edit distance measure to

existing measures of amendment success in the literature (Tsebelis et al., 2001; Franchino and

Mariotto, 2012). Initial explorations of the correspondence between each of these measures

have provided encouraging results, but much work remains to be done.

A second important undertaking is to extend the use of minimum edit distances to

intermediate draft legislation texts such as the Council common positions and the outcomes

of different Parliament readings. Doing so will allow us to account for the amendment

demands of each institution at each stage of negotiations and thus provide a much more

fine-grained picture of the process through which amendments are introduced and approved.

Accounting for the actual amendment demands of each institution at each stage in the

negotiation process will give us a more direct measure of the policy demands being made by

each institutional actor, which is preferable to the time to agreement proxy used here.

Finally, the minimum edit distance measure provides one with a very fine-grained picture

of the negotiation process and there is significant potential to analyse negotiations on an

article-by-article basis, rather than at the proposal level. Such an undertaking is worthwhile

as it allows one to focus upon specific policy issues contained within individual articles,

rather than reducing all policy demands into a single policy dimension. Previous research has

demonstrated that negotiations in general revolve around a series of distinct issues (Thomson

et al. 2006) and this should be reflected in any analysis of legislative amendment behaviour

and the agenda-setting success of the Commission.

The concurrent explosion in the availability raw data on the legislative process in the

EU, with developments in the analysis of text strings, and ever increasing computing power,

mean that for the first time it is possible to analyse large corpora of text in a time and

resource efficient manner. This study takes advantage of these developments to demonstrate

38

that automated text analysis of the legislative records of the EU is a fruitful approach that

has the potential to significantly improve our understanding of the legislative process. It

is envisaged that further development and refinement of the measures proposed here can

provide new ways to test existing theories of the legislative process at the micro level and

provide the impetus for further theoretical developments based upon the fine-grained picture

of legislative negotiations that such measure can provide.

References

Achen, C. (2006). Institutional realism and bargaining models. In Thomson, R., Stokman,

F. N., Achen, C. H., and Konig, T., editors, The European Union Decides, pages 86–123.

Cambridge University Press, Cambridge.

Aleskerov, F., Avci, G., Iakouba, V., and Turem, Z. (2002). European Union enlargement:

Power distribution implications of the new institutional arrangements. European Journal

of Political Research, 41(3):379–394.

Aspinwall, M. and Schneider, G. (2000). Same menu, seperate tables: The institutionalist

turn in political science and the study of European integration. European Journal of

Political Research, 38(1):1–36.

Bellman, R. (1957). Dynamic Programming. Princeton University Press.

Craig, P. and De Burca, G. (2011). EU law: text, cases, and materials. Oxford University

Press.

Crombez, C. (1996). Legislative procedures in the European Community. British Journal

of Political Science, 26(02):199–228.

39

Crombez, C. (1997). The co-decision procedure in the European Union. Legislative Studies

Quarterly, pages 97–119.

Crombez, C. (2000). Spatial models of logrolling in the European Union. European Journal

of Political Economy, 16(4):707–737.

Crombez, C. (2001). The Treaty of Amsterdam and the co-decision procedure. In Schneider,

G. and Aspinwall, M. D., editors, The Rules of Integration. Institutional Approaches to

the Study of Europe, pages 101–122. Manchester University Press, Manchester.

Dayhoff, M. O. and Schwartz, R. M. (1978). A model of evolutionary change in proteins.

Fitch, W. M. and Margoliash, E. (1967). Construction of phylogenetic trees. Science,

155(760):279–284.

Franchino, F. and Mariotto, C. (2012). Explaining negotiations in the conciliation committee.

European Union Politics.

Garrett, G. and Tsebelis, G. (1996). An institutional critique of intergovernmentalism.

International Organization, 50(02):269–299.

Hage, F. M. (2011). The European Union Policy-Making dataset. European Union Politics,

12(3):455–477.

Hagemann, S. and Hoyland, B. (2010). Bicameral politics in the European Union. JCMS:

Journal of Common Market Studies, 48(4):811–833.

Henikoff, S. and Henikoff, J. G. (1992). Amino acid substitution matrices from protein

blocks. Proceedings of the National Academy of Sciences, 89(22):10915–10919.

Horl, B., Warntjen, A., and Wonka, A. (2005). Built on quicksand? a decade of procedu-

ral spatial models on eu legislative decision-making. Journal of European Public Policy,

12(3):592–606.

40

Kessler, B. (1995). Computational dialectology in irish gaelic. In Proceedings of the seventh

conference on European chapter of the Association for Computational Linguistics, pages

60–66. Morgan Kaufmann Publishers Inc.

Kluver, H. (2009). Measuring Interest Group Influence Using Quantitative Text Analysis.

European Union Politics, 10(4):535–549.

Kreppel, A. (2002). Moving Beyond Procedure: An Empirical Analysis of European Parlia-

ment Legislative Influence. Comparative Political Studies, 35(7):784–813.

Laver, M., Benoit, K., and Garry, J. (2003). Extracting policy positions from political texts

using words as data. American Political Science Review, 97(2):311–331.

Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions and rever-

sals. 10:707.

Levy, G. (2007). Decision making in committees: Transparency, reputation, and voting rules.

American Economic Review, 97(1):150–168.

Meade, E. and Stasavage, D. (2008). Publicity of Debate and the Incentive to Dissent:

Evidence from the US Federal Reserve*. The Economic Journal, 118(528):695–717.

Moser, P. (1996). The European Parliament as a conditional agenda setter: What are the

conditions? A critique of Tsebelis (1994). American Political Science Review, pages 834–

838.

Nerbonne, J. and Heeringa, W. (1997). Measuring dialect distance phonetically.

Proksch, S.-O., Slapin, J. B., and Thies, M. F. (2011). Party system dynamics in post-war

Japan: A quantitative content analysis of electoral pledges. Electoral Studies, 30(1):114–

124.

41

Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., and Radev, D. R. (2010). How

to analyze political attention with minimal assumptions and costs. American Journal of

Political Science, 54(1):209–228.

Rasmussen, A. (2012). Twenty Years of Co-decision Since Maastricht: Inter- and Intrainsti-

tutional Implications. Journal of European Integration, 34(7):735–751.

Schelling, T. C. (1980). The strategy of conflict. Harvard university press.

Shepsle, K. and Weingast, B. (1987). The institutional foundations of committee power.

American Political Science Review, pages 85–104.

Slapin, J. B. and Proksch, S.-O. (2008). A scaling model for estimating time series party

positions from texts. American Journal of Political Science, 52(3):705–722.

Slapin, J. B. and Proksch, S. O. (2010). Look who’s talking: Parliamentary debate in the

European Union. European Union Politics, 11(3):333–357.

Steunenberg, B. and Selck, T. J. (2006). Testing procedural models of EU legislative decision-

making. In Thomson, R., Stokman, F. N., Achen, C. H., and Konig, T., editors, The

European Union Decides, pages 54–85. Cambridge University Press, Cambridge.

Thomson, R. (2009). Actor alignments in the European Union before and after enlargement.

European Journal of Political Research, 48(6):756–781.

Thomson, R. and Hosli, M. (2006). Who Has Power in the EU? The Commission, Council

and Parliament in Legislative Decision making*. JCMS: Journal of Common Market

Studies, 44(2):391–417.

Thomson, R., Stokman, F. N., Achen, C. H., and Konig, T. (2006). The European Union

Decides. Cambridge University Press, Cambridge.

42

Thomson, R. and Torenvlied, R. (2010). Information, Commitment and Consensus: A

Comparison of Three Perspectives on Delegation in the European Union. British Journal

of Political Science, 41(01):139–159.

Tsebelis, G. (1996). More on the European Parliament as a Conditional Agenda Setter:

Response to the Moser. American Political Science Review, pages 839–844.

Tsebelis, G. (2009). Agenda Setting and Executive Dominance in Politics. pages 13–24.

Tsebelis, G. and Garrett, G. (1997). Agenda setting, vetoes and the european union’s

codecision procedure. The Journal of Legislative Studies, 3(3):74–92.

Tsebelis, G. and Garrett, G. (2000). Legislative Politics in the European Union. European

Union Politics, 1(1):9–36.

Tsebelis, G. and Garrett, G. (2006). The Institutional Foundations of Intergovernmentalism

and Supranationalism in the European Union. International Organization, 55(2):357–390.

Tsebelis, G., Jensen, C. B., Kalandrakis, A., and Kreppel, A. (2001). Legislative Procedures

in the European Union: An Empirical Analysis. pages 1–29.

Wagner, R. A. (1974). Order-n correction for regular languages. Communications of the

ACM, 17(5):265–268.

Wagner, R. A. and Fischer, M. J. (1974). The string-to-string correction problem. Journal

of the ACM (JACM), 21(1):168–173.

Wallace, D. (2012). Bringing people with us: legislative writing as political rhetoric. Text

and Talk, 32(1):83.

Wong, C.-K. and Chandra, A. K. (1976). Bounds for the string editing problem. Journal of

the ACM (JACM), 23(1):13–16.

43