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 interinstitutional 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 proposal 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 empirical 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 negotiations 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 relationship between the existence of successful amendments and the power of individual institutions 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 acceptable 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 t́rueṕreferences 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 complete 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 representatives 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 between 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 introduction 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 amendment 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 Parliamentary 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 handcoded 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 proceeded 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 political 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 bioinformatics 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 decisionmaking 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 assumptions 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 actors. 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 proposals, 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
t́rueẃinset 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 affect 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 consultation 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 requirement 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 proposed 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 Parliament. 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 internal 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 t́rueṕosition, 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 process 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 observed 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 codecision. 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 second 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 amendments 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 collude 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 Búrca, 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; Hörl 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, deletions, 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 diverse as creating accurate spell checkers (Wagner, 1974; Wagner and Fischer, 1974; Wong
and Chandra, 1976), assessing differences between different dialects in computational linguistics (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
1
In the European Union these are summarised here
http://ec.europa.eu/governance/better regulation/documents/legis draft comm en.pdf and here http://eurlex.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 (Levenshtein, 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 consid19
Table 1: Deletion (Levenshtein distance → Levenshtein)
j=0
j=1
Sk
# Levenshtein
i=0
#
0
1
i=1 Levenshtein
1
0
2
1
i=2
distances
Table 2: Addition (Levenshtein → Levenshtein distance)
j=0
j=1
j=2
Sk
# Levenshtein distance
i=0
#
0
1
2
i=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 (represented 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 following formula:
Table 3: Substitution (Levenshtein distance → Levenshtein measure)
j=0
j=1
j=2
Sk
# Levenshtein measure
i=0
#
0
1
2
i=1 Levenshtein
1
0
1
i=2
distances
2
1
2
20
i=0
i=1
i=2
i=3
i=4
i=5
Sk
#
Levenshtein
distances
capture
changes
well
Table 4: Calculating Levenshtein distances
j=0
j=1
j=2
j=3
j=4
j=5
# Changes well captured by Levenshtein
0
1
2
3
4
5
1
2
3
4
5
4
2
3
4
5
6
5
3
4
5
6
7
6
4
3
4
5
6
7
5
4
3
4
5
6



D(i − 1, j) + 1,






D(i, j − 1) + 1,
D(i, j) =


2; if S1 (i) 6= S2 (j),



D(i − 1, j − 1) +



0; if S1 (i) = S2 (j).
j=6
distances
6
5
4
5
6
7
(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 ẃellı́n 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 transposition. 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 solutions 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
2
The key point here is that while the ordering of the text has changed, policy implications have not. This
is well illustrated if one thinks about the manner in which the Starwars character Yoda speaks, where the
order 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 standard 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),
(2)

 D(i − 1, j − 1) + 1; if S1 (i) = S2 (j).
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=1
Sk
# Transposition
i=0
#
0
0
0
i=1 Transposition
0
1
0
i=2
distance
0
0
0
Column max
1
0
Cumulative edits
0
1
Table 6: Addition (Transposition → Transposition distance)
j=0
j=1
j=2
Sk
# Transposition distance
i=0
#
0
0
0
i=1 Transposition
0
1
0
i=2
0
0
0
Column max
1
0
0
1
Cumulative edits
Table 7: Substitution (Transposition distance → Transposition measure)
j=0
j=1
j=2
Sk
# Transposition measure
i=0
#
0
0
0
i=1 Transposition
0
1
0
i=2
distances
0
0
0
Column max
1
0
Cumulative edits
0
1
Table 8: Transposition (Transposition distance of measurement device → measurement device of Transposition distance)
j=0
j=1
j=2
j=3
j=4
j=5
Sk
# measurement device of Transposition distance
i=0
#
0
0
0
0
0
0
i=1 Transposition
0
0
0
0
1
0
i=2
distance
0
0
0
0
0
2
i=3
of
0
0
0
0
0
0
i=4 measurement
0
1
0
0
0
0
i=5
device
0
0
2
0
0
0
Column max
0
1
2
0
1
2
Cumulative 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=6
Sk
# Change well captured by Transposition distance
i=0
#
0
0
0
0
0
0
0
i=1 Transposition 0
0
0
0
0
1
0
i=2
distance
0
0
0
0
0
0
2
0
0
0
0
0
0
0
i=3
captures
i=4
change
0
1
0
0
0
0
0
0
0
2
0
0
0
0
i=5
well
i=6
0
0
0
0
0
0
0
Column max
0
1
2
0
0
1
2
Cumulative 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 additions, 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 calculated. 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.
Agenda-setting success
2204.518
2570.287
Procedure
1.52
0.5
Length to EP decision
428.154
340.704
Length to Council decision
531.246
318.095
EP opinion second reading (yes/no)
0.153
0.36
EP opinion third reading (yes/no)
0.044
0.205
Treaty
1.495
0.684
Legislative instrument
1.207
0.837
Enlargement
1.221
0.893
5
Min.
1
1
1
44
0
0
0
0
0
Max.
17583
2
2863
2610
1
1
2
2
2
N
1516
2733
2187
862
4088
4088
4798
2677
2733
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 proposals 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 amendments. 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∗∗∗
(7.90)
1.001∗∗∗
(4.21)
Nice
+
1.591∗∗∗
(4.32)
1.411∗
(2.30)
Lisbon
+
1.805∗∗
(3.18)
1.895∗∗
(2.72)
Directive
+
0.694∗∗∗
(-4.11)
0.808+
(-1.74)
Regulation
+
0.928
(-0.96)
0.961
(-0.37)
2004 enlargement
+
0.859
(-1.19)
1.012
(0.08)
Year
+
1.000
(0.00)
1.017
(0.59)
EP opinion second reading
+
1.585∗∗∗
(4.59)
EP opinion third reading
+
0.906
(-0.82)
Constant
lnalpha
Constant
Observations
Exponentiated coefficients; t statistics in parentheses
+
p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
32
1367.9
(0.15)
7.83e-13
(-0.47)
1.183∗∗∗
(5.04)
1450
0.982
(-0.29)
671
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)
576
Observations
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 amend34
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 institutions 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 legislative 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 significantly 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 ácceptableṕroposals 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 probability 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.
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