1

Functionalist and Non-Functionalist Logic of Diffusion:

Delegation to Independent Regulatory Agencies in Western Europe

Fabrizio Gilardi, Université de Lausanne

fabrizio.gilardi@iepi.unil.ch

DRAFT – COMMENTS WELCOME

Abstract

Independent regulatory agencies (IRAs) have become the most popular institutionalsolution for the organisation of regulatory policies in Western Europe. The patternof emergence of IRAs suggests that a diffusion process may have been at work, i.e. aprocess where individual decisions of IRA creations have not been independent.This paper tries to investigate this pattern, and draws a distinction betweenspurious, functionalist and non-functionalist diffusion of IRAs. Spurious diffusion issimply due to the concomitant reaction of independent actors to similar pressures.Functionalist and non-functionalist diffusion, on the other hand, explicitly refer tointerdependent actors, but differ on the place the functional properties of IRAs playin the decision to establish them. Schematically, both theories point out that thedecision to establish an IRA at time t should be somehow (positively) related to thenumber of IRAs already existing at time t-1. The paper details the arguments behindthis hypothesis and assesses it empirically using a data set that comprisesinformation for regulators in seven domains (electricity, telecom, financial markets,competition, pharmaceuticals, food safety, and environment) and seventeencountries (EU member states plus Norway and Switzerland). Results show that thenumber of previous adopters does matter in explaining the diffusion of IRAs, butnot always in obvious ways. In general, evidence strongly indicates the presence ofa diffusion process behind the creation of IRAs.

Acknowledgements

Part 3.1 of this study draws on a paper originally presented at the 1st Pan-EuropeanConference on European Union Politics, Bordeaux, 26-28 September 2002. Furtherversions were presented at the Graduate Workshop in Political Science, NuffieldCollege, University of Oxford, 7 November 2002, and at the 12ème Colloqueinternational de la Revue "Politiques et Management Public", Paris, 14-15 November2002. I am indebted to David Levi-Faur for the many generous suggestions anddiscussions. Some of the arguments of the paper are partly drawn from an ongoingproject with him.

Paper for delivery at the workshop "The Politics of Regulation", Universidad Pompeu

Fabra, Barcelona, 29-30 November 2002

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1 Introduction

During the last 15 years or so, independent regulatory agencies (IRAs) have been

created in all west European countries and most regulatory domains. Figure 1 shows the

trend. It can be seen the number of IRAs has increased very sharply since about the mid-

80s, going from less than 20 to more than 90. At first this trend strikes as surprising, but

a closer look reveals that it is all but exceptional. In fact, many phenomena could be

described by a similar curve. These include the spread of technological innovations,

fashions, diseases, social actions such as joining a strike or a riot, and other phenomena

commonly regarded as being subject to diffusion. Indeed, this strongly suggests that a

diffusion process is at work behind the creation of IRAs. The purpose of this paper is to

examine this hypothesis by differentiating between spurious, functionalist and non-

functionalist diffusion. Spurious diffusion is simply due to the concomitant reaction of

independent actors to similar pressures. Functionalist and non-functionalist diffusion,

on the other hand, explicitly refer to interdependent actors, but differ on the place the

functional properties of IRAs play in the decision to establish them. Functionalist

diffusion can be related to herding theory, and non-functionalist diffusion to the

population ecology branch of sociological institutionalism. Schematically, both theories

point out that the decision to establish an IRA at time t should be somehow (positively)

related to the number of IRAs already existing at time t-1.

< Figure 1 about here >

The paper is structured as follows. I will first try to conceptualise the distinction

between functionalist and non-functionalist diffusion, notably by discussing two distinct

yet closely related literatures, namely the population ecology branch of sociological

institutionalism and herding theory. An hypothesis compatible with both theories,

which will have to be refined precisely to discriminate between them, is that the number

of IRAs existing at time t-1 has an impact on the decision to create an IRA at time t. This

hypothesis will be tested in two steps. Firstly, the creation of IRAs will be analysed at

the macro level, by looking at the evolution of the IRA population. Secondly, attention

will be shifted to the micro level to study individual IRA creations through event history

3

analysis. Although the findings cannot discriminate between functionalist and non-

functionalist diffusion, they do indicate that there has been a diffusion process behind

the spread of IRAs across Europe.

2 Functionalist and Non-Functionalist Logic of Diffusion of IRAs

The functionalist logic of delegation to IRAs is best illustrated by the principal-agent

framework, where a principal delegates some tasks to an agent because of the functions

the latter can perform while the former cannot (or not so efficiently). A whole range of

functions can be linked to IRAs, but the most convincing is credible commitment

capacity (Majone 1997; Gilardi 2002a). When opening markets to competition, as in the

case of telecom or electricity, governments need to be able to convince investors that the

regulation of the market will not be biased in favour of the former state-owned

incumbent. By delegating regulatory powers to IRAs, governments can improve the

credibility of this commitment to fair regulation. The reason why this is a particularly

strong explanation is that, unlike most others, it addresses the real original feature of

IRAs, namely their independence from politics. A second explanation that goes in the

same direction is the political uncertainty hypothesis (Moe 1990), claiming that

politicians insulate policy from politics to make the former last beyond their term of

office, but empirical evidence for it is less convincing1. In both cases, "the function

creates the organ": the need for credibility and the desire to solve the political

uncertainty problem make politicians grant independence to regulators. The spread of

IRAs, in this case, is driven by the (concomitant) evolution of these functional pressures.

A metaphor for this could be the process whereby when it starts raining, everybody

opens an umbrella at more or less the same time. In this case, however, the process

cannot be conceptualised as diffusion, because actors act independently, while diffusion

implies communication among them (Rogers 1995: 5), or, in other words, implies that

actors are interdependent (Brune and Garrett 2000). This is way I suggest that this

process can be characterised as spurious diffusion.

1 For a presentation of the credibility and political uncertainty arguments, see Gilardi (2002b).

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Non-spurious diffusion of IRAs is composed of functionalist and non-functionalist

diffusion. The functionalist logic of diffusion has been conceptualised as competitive

emulation, learning (Brune and Garrett 2000; Simmons and Elkins 2001), or competitive

and coordinative regulatory interdependence (Lazer 2001). Here actors are explicitly

modelled as interdependent. Competitive emulation and competitive interdependence

refer to the strategic response to the behaviour of competitors. Learning is the process

through which preferences are updated when new information about the effects of

different policies is revealed. Coordinative interdependence, finally, leads to diffusion

when there are benefits from having compatible regulations. The similarity with the

arguments developed above is that here too the function (e.g. survival in a competitive

environment) creates the organ (i.e. adoption of same or similar practices).

A more sophisticated functionalist argument of diffusion can be found in herding

theory (Levi-Faur 2002a), where it is argued that actors observe the behaviour of others

to learn about the best thing to do. At the extreme, it can be optimal for an individual to

follow the behaviour of those who are ahead of him/her, disregarding his/her own

information (Bikhchandani, Hirshleifer and Welch 1992: 992). In this respect, the theory

is similar the more common concept of learning, especially in the perspective developed

my Meseguer (2002), where actors are modelled as baysean learners that update their

information on the basis of the experience of others. The difference is that in herding

theory actors are assumed to have thresholds, which can be defined as the proportion of

the relevant group of reference that must have made a decision before the actor decides

to do the same (Granovetter 1978). In herding theory, then, actors decide on the basis of

their preferences, their thresholds, and the information they can gather from the

behaviour of others, while in learning only preferences and information matter. This

explanation of diffusion remains functionalist, in my view, although it is definitely more

sophisticated than the simple "diffusion-through-common-pressures" model. Here

actors are interdependent, and thus we are in presence of a diffusion process rather than

merely a diffusion outcome.

The non-functionalist logic of diffusion of IRAs, on the other hand, is completely

different. Here, IRAs are not set up because of the function they can perform, but as a

result of the socially-embedded behaviour of actors, who look at each other before

deciding whether to establish an IRA or not.

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This argument has been developed in great detail in the population ecology branch of

sociological institutionalism (Meyer and Rowan 1977; Hannan and Carroll 1992; Baum

and Oliver 1992), which contends that norms, and not functions, drive the adoption of

organisational forms. Over time, some forms become the most widespread solution in a

given context. As a result, potential new adopters will tend to see those forms as the

natural or obvious way to deal with a given problem, and tend to unconsciously exclude

other forms that could also be viable in principle. The outcome of this process is that

new adopters tend to choose the dominant form, which then becomes even more

dominant. Technically, an organisational form that reaches this stage is said to be

"taken-for-granted", and it has been argued that "taken-for-grantedness" (TFG) is

directly related to the total number of similar organisations that exist. This is called

"organisational density" and is the most popular measure of the extent to which a given

organisational form is taken for granted. The main hypothesis that is derived from these

arguments is that the relationship between organisational density and the number of

new adoptions has an inverted-U shape: the impact of density on new adoptions is

firstly positive (because of TFG), but then becomes negative (because of the increased

competition between organisations). This pattern has been tested empirically and is

extremely robust, as it can be found in a wide range of organisations, including public

schools (Rowan 1982), the multidivisional form of management structure (Fligstein

1985), health care organisations (Ruef 2000), newspapers (Carroll and Hannan 1989),

and banks (Ranger-Moore et al. 1991), just to name a few.

It appears clear from this short discussion that both herding theory and population

ecology suggest that the decision to establish an IRA at time t should be somehow

(positively) related to the number of IRAs already existing at time t-1. In the case of

population ecology, the claim is very explicit, because density, i.e. the number of

organisations existing at t-1, is identified as a proxy for TFG and thus as the main

explanatory variable. In the case of herding, things are less clear as the theory does not

give a clear methodological guidance on this point, although it does suggest that the

number of IRAs existing at t-1 should somehow be relevant. The empirical literature on

herding (e.g. Greve 1996; Haveman 1993; Hedström 1994) seems to indicate that, rather

than simply the number of others, what matters is the number of relevant others. How

this can be defined empirically for IRAs is an open question. A second difference could

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be that, in herding processes, only examples that are quite recent matter, while for

population ecology TFG is affected also by more remote decisions. These two points are

related to the functionalist nature of herding theory: actors follow others because their

behaviour supplies information. Naturally, what is needed is not simply information,

but relevant information. This explains why, for herding, actors select the behaviour

they look at. For population ecology, on the other hand, the quality of the information

conveyed by examples is not important. What matters, is the extent to which an

organisational form becomes natural and taken for granted, and each example

contributes to this, no matter when and where it is located.

These differences should be developed further, but, for practical reasons, I will limit

the empirical analysis here to the effect of density, i.e. disregarding the probably

important distinctions sketched above. Also, I will take the evidence as supporting (or

not supporting) the diffusion hypothesis in general, without differentiating between

functionalist (i.e. herding) and non-functionalist (i.e. population ecology) diffusion

processes. This is indeed a major focus of my current research.

3 Empirical Analysis

I will examine the impact of density on the establishment of IRAs in two steps.

Firstly, I follow the methods used in the population ecology literature and analyse the

pattern of creation of IRAs at the macro level, by focusing on the evolution of the

population of IRAs disaggregated into sub-populations (i.e. regulatory domains). This

will constitute a first test of the relevance of density, but some limitations will be

discussed that make this test insufficient. In the second step, then, I shift to the micro

level to examine individual IRA creations through an event history analysis. In both

cases, the analysis2 relies on an original data set comprising information for regulators

in seven regulatory domains (telecom, electricity, financial markets, competition, food

safety, pharmaceuticals, and environment) in seventeen countries (EU member states

plus Norway and Switzerland). Results will show that density does matter, but not

always in easily interpretable ways.

2 The variables, their operationalisation and source are summarised in Appendix 1.

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3.1 Macro Analysis

In the population ecology literature, the evolution of organisational populations is

analysed by looking at how many new organisations of the same type are created in a

given period of time. When sub-types of organisations can be identified, the analysis is

carried out at the sub-population level. In this first step, I embrace this technique and

examine how many new IRAs are created each year in each of the seven regulatory

domains covered by my database. The dependent variable consists of event counts, and

this kind of data can be best analysed through Poisson regression models (King 1988;

Greene 2003: 740-752). The model used here is

E(yi|xi) = exp(βX),

where E(yi|xi) is the expected number of events per period, conditional on the

independent variables, and βX is a vector of the regression coefficients and the

independent variables.

Table 1 shows the results. The three models investigate the pattern of emergence of

IRAs in Western Europe at different levels of analysis. The first model is the most

general and undifferentiated. It does not differentiate between regulatory domains and

studies only the overall IRAs population. The second model divides the overall

population in two subpopulations by considering social and economic regulation

domains separately, but without differentiating between, for example, electricity and

financial markets, or food safety and environment. In the first model, density is scarcely

significant. In the second, overall density has significant positive first-order and

negative second-order effects, as predicted by the theory. Density at the regulatory type

level, on the other hand, is not significant.

< Table 1 about here >

Model 3 tests the main argument of this first step of the analysis, namely that the

creation of IRAs in different regulatory domains is affected by density. It can be seen

that both overall and domain density are significant in the expected direction, namely

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with positive first-order and a negative second-order effect, but not density at the

regulatory type level. Economic regulation, the dummy used to account for functional

pressures for the creation of IRAs, is significant and positive. These results show that

density has an impact at both the overall and the regulatory domain level, but not at the

regulatory type level. In other words, the creation of an IRA for competition policy, for

example, is affected by the existence of other IRAs in the same domain, but also by the

existence of IRAs in general. It does not matter however, whether existing IRAs outside

the specific regulatory domain are of the same regulatory type (i.e. economic or social

regulation). For the creation of an IRA for competition policy, the existence of IRAs for

financial markets or food safety matter equally. Another result is that, in spite of the

relevance of density, functional pressures also play a role in explaining the creation of

IRAs. In effect, economic regulation, where credibility problems are more acute than in

social regulation and thus the incentives to delegate regulatory competencies to IRAs

are higher (Gilardi 2002a), is positively associated to the creation of IRAs after

controlling for the impact of density. Non-functionalist pressures matter, but

functionalist pressures matter too.

< Figure 2 about here >

Figure 2 depicts graphically the relationship between overall density, i.e. the total

number of IRAs, and the creation of new IRAs3. It can be seen that, as predicted by the

theory, the relationship is nonmonotonic. At low levels of density, the effect of the total

number of IRAs on IRA creation is relatively small, but grows quickly up to the point

where the fact that many of the regulators have become IRAs reduces the likelihood that

new IRAs are created. A second point of interest in Figure 2 is the difference between

the curves for economic and social regulation. The curve for social regulation has always

lower values than that for economic regulation. This reflects the fact that functional

pressures for delegation, notably in terms of credibility, are different between the two

types of regulatory policies.

3 To the values on the y axis, representing predicted foundings, should not be attributed too muchimportance as their value depends strongly on the values at which the independent variables not includedin the graph are kept constant.

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< Figure 3 about here >

These findings are confirmed also for the effect of density at the regulatory domain

level, as shown in Figure 3. There are however two major differences. The first is that

density has a much more immediate impact on IRA creation at the regulatory domain

rather than at the overall level. In effect, it can be seen that the number of predicted

foundings raises steeply as soon as there is one IRA in the subpopulation, whereas the

effect of overall density is more progressive. Secondly, however, the effect of density

quickly becomes negative, actually more quickly than it could have been expected. It is

hardly surprising that when almost all regulators have adopted the IRA form, it

becomes less likely that new IRAs are created, simply because the "population at risk"

becomes smaller. However, it is surprising, tough not necessarily in opposition to the

theory, that density start to have a negative impact on the creation of IRAs already since

in a given regulatory domain there are four or five IRAs.

So far I have discussed the results in terms of predicted IRA foundings, but it is also

useful to see what the model says about the probability that an IRA is created (see Liao

1994). In the analysis above I have discussed the effect of density by keeping all other

variables constant at their overall mean values. This is sensible but not very helpful if

one is interested in the specific values of the predictions. To analyse the effects of

density on probabilities of IRA creation, thus, I will keep variables constant at their

mean values between 1990 and 1995, which are the period during which, as shown in

Figure 1, IRAs became really widespread. Naturally, it would be useful to carry out the

analysis also within other time frames, but due to space constraints I will limit the

discussion here to the 1990-1995 period.

< Figure 4 about here >

Figure 4 shows the predicted probability that at least one IRA is created as a function

of the total number of IRAs. It can be seen that, when keeping all other variables

constant at their 1990-1995 mean values, and by considering only the values of density

that are coherent with the empirical range of the data, the total number of IRAs has a

positive impact on the probability that at least an IRA is created. This holds for both

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economic end social regulatory domains, although, unsurprisingly, the probabilities are

higher for the former. An increase of the overall population from 20 to 40 IRAs boosts

the likelihood that at least an IRA is created for economic regulation domain from under

40% to nearly 80%, and from slightly more than 10% to over 30% for social regulation.

Independently from functional pressures, which exist as this same figure shows, density

can have the effect of doubling the probability that one IRA at least is created in a given

domain. Also, Figure 4 indicates that the probability that an IRA for social regulation is

created at higher levels of density is roughly the same as that of the creation of an IRA

for economic regulation at lower levels of density. To a certain extent, then, density can

compensate for functional pressures in the probability to observe the creation of IRAs.

These results strongly suggest that density is an important factor behind the

establishment of IRAs. However, the methods used in this first step of the analysis have

several problems that limit its usefulness. The first is that the S-shaped pattern of

diffusion shown in Figure 1 can be identified in countless other domains, most of which

have nothing to do with politics. Ironically, the same pattern has been followed by the

diffusion of research on diffusion (Rogers 1995: 45)! It can thus be suspected that the

inverted-U relationship between density and new IRA creations (or other adoptions) is

nothing but a statistical phenomenon that does not require sophisticated explanations.

The second is that the focus on whole populations (or sub-populations) prevents the

researcher from controlling for many potentially important variables. In the case of

IRAs, for example, one cannot account for variables that vary cross-nationally. It is also

difficult to integrate time-varying explanations in a consistent way, because they

typically vary also cross-nationally. The clearest illustration of this problem is the fact

that, in the analysis above, functional pressures for delegation were taken into account

only through a time-invariant dummy for economic and social regulation. Some

improvement could be achieved, but not much.

In the second step of the analysis, then, I move to event history analysis. The unit of

analysis is no longer the sub-population/year, but the single regulatory domain in a

single country. Concretely, the information contained in the dependent variable tells

when a single IRA was set up in a given regulatory domain of a given country: for

example, when an IRA for telecom was established in the UK. This shift to the micro

level solves both problems. First, the impact of density is now studied on individual IRA

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creations and not on the evolution of the population. If density is found to be significant,

it will be much more difficult to argue that it is a mere statistical phenomenon common

to all sorts of diffusions. Second, the limits in the capacity to control for variables are

due only to data limitations and not to limitations built into the model. The results of the

event history analysis will be a much stronger test of the relevance of density in

explaining the observed pattern of IRA creations.

3.2 Micro Analysis

Individual IRA creations can be analysed through event history analysis models.

Event history analysis is a statistical technique explicitly devised to study the pattern

and determinants of the occurrence of events (Allison 1984; Yamaguchi 1991). The

establishment of an IRA is the event in the context of this study. I employ here the

widely-used Weibull model, which takes the form (Box-Steffensmeier and Jones 1997:

1428-1429)

h(t) = h α(ht)α-1

and which is estimated through the equation

h(t) = exp(βX + αlnt)

where h(t) is the hazard rate, βX is a vector of the independent variables and their

coefficients, and α is the shape parameter, whose estimate indicates the effect of time.

The hazard rate is a key concept in event history analysis. It is defined as "the rate at

which a duration or episode ends in the interval [t, t + ∆t], given that the duration has

not terminated prior to the beginning of this interval." (Box-Steffensmeier and Jones

1997: 1419). Formally,

h(t) = lim∆t→0 P(t + ∆t > T ≥ t | T ≥ t) / ∆t.

If the baseline rate α and the explanatory variables are taken into account, the hazard

rate becomes

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h(t) = lim∆t→0 P(t + ∆t > T ≥ t | T ≥ t; α; βX) / ∆t.

From these two equations it appears that the hazard rate is basically a probability, and

precisely the instantaneous probability that the event occurs in the interval [t, t + ∆t].

< Tables 2 and 3 about here >

The results of the analysis are presented in Tables 2 and 3; the former examines the

impact of overall density, and the latter that of density at the regulatory type level. It can

readily be seen that event history analysis enabled me to integrate more refined

measures of functional pressures to delegation. While in the Poisson analysis they were

taken into account by a simple dummy for economic and social regulation, here I have

analysed the impact of privatisation and liberalisation (in telecom and electricity), which

are widely believed to be the major drivers of the creation of IRAs, and I have used a

dummy for the two remaining economic regulatory domains (competition and financial

markets). The dummy for economic and social regulation is kept in the model, but it can

be seen that it is not significant. This is due to the fact that more accurate measures of

functional pressures are now present. These play a major role indeed in explaining the

creation of IRAs. As expected, the coefficients privatisation, liberalisation and the

competition/financial markets dummy are positive and significant. This means,

especially for liberalisation and privatisation, that the pattern of creation of IRAs shown

in Figure 1 can partly be explained by the "concomitant pressures" story, which claims

that functional pressures for delegation which arose at roughly the same time can

explain the pattern of emergence of IRAs. Diffusion, however, occurs when actors

behave interdependently, and more specifically when they look at each other before

making a choice – in this case, setting up an IRA or not. An implication is that density,

i.e the number of IRAs existing at time t-1, should positively affect the likelihood of the

creation of an IRA at time t.

The findings shown in tables 2 and 3 support this claim. The former shows that

density at the overall level matter, and the latter confirms this for the regulatory type

level. As the figures are very similar, for simplicity I will limit the discussion here to

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overall density (Table 2). Density, as expected, has a significant and positive effect on

the hazard of IRA creation. When creating IRAs, governments do not simply take

functional pressures into account, but they also look at the decisions that have been

taken elsewhere. This supplies strong additional evidence to the claim that a diffusion

process is at work because, in opposition to Poisson, the analysis here is carried out at

the micro level, which permits to discard the suspicion that the impact of density is a

mere statistical phenomenon with no substantive relevance. Note also that density has

no second order effect, i.e. (density)2 is not significant. This indicates that the inverted-U

shape highlighted by population ecology studies disappears when the focus is shifted to

the micro level. And logically so, since the rationale for this hypothesis was that after a

certain point the number of potential IRA creations is too small to allow for a positive

impact of density4.

< Figure 5 about here >

Figure 5 presents graphically the impact of density and privatisation on the likelihood

of IRA creation. Privatisation has a positive impact on IRA creation, and so has density.

When the number of IRAs increases, the likelihood of IRA creation also increases,

controlling for functional pressures. As can be seen in Models 1 and 2 in Table 2,

however, there is no interaction effect between density and privatisation. This means

that the impact of density is the same whether there is privatisation or not. Specifically,

the likelihood of IRA creation is 240% higher when density is 70 than when density is

20. This can be interpreted as the diffusion effect. Conversely, the impact of privatisation

is the same at all levels of density: privatisation increases the likelihood of IRA creation

by nearly 4 times. Yet this is a bit surprising because an interaction effect could have

been expected. The argument is that first privatisation gives an incentive to delegate,

and then density intervenes as a framing factor: when density is high, options other than

IRAs are excluded because IRAs are taken for granted as the obvious way to deal with

privatisation. But this argument is not validated empirically.

4 The argument is however different in the original population ecology studies, where it is argued thatafter a certain point, the presence of many similar organisations increases competition among them, and

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< Figure 6 about here >

An interaction between functional pressures and density, on the other hand, can be

found for liberalisation, as shown in Table 2. The direction of the interaction, however, is

surprising. Figure 6 presents it graphically. Density has a positive impact on the hazard

of IRA creation when there is no liberalisation, but the impact is negative when

governments do liberalise. This means that the argument sketched at the end of the

previous paragraph works actually the other way round. When a government has

functional reasons to delegate, as in the case of liberalisation, the fact that many IRAs

already exist decreases the likelihood that it will establish one itself. And when no such

functional reasons exist, density increases the likelihood of IRA creation. Beyond a

certain density threshold, delegation becomes even more likely without liberalisation.

This does not seem to make much sense, and I have no clear explanation at the moment.

An interaction exists also with political constraints. The concept of political

constraints, similarly to that of veto players (Tsebelis 2002), aims at capturing the extent

of feasibility of policy change by looking at the number of independent veto points in

the political system as well as at the distribution of political preferences both across and

within them (Henisz 2000)5. High levels of political constraints make delegation less

likely, and the effect is stronger in presence of liberalisation, as shown in Figure 6. This

result is consistent with the credibility hypothesis: political constraints are a functional

equivalent of delegation as a means to achieve credible commitment capacity (Gilardi

2002a).

Other aspects could be discussed, such has the strong positive impact of EU directive

97/516 on the hazard of IRA creation, or the substantive meaning of the high value of

the α parameter (see Bennett 1998), which indicates positive dependence on time.

Nevertheless, the important point for the present purposes is that density seems to be a

relevant explanatory factor of the choice to set up IRAs even at the micro level.

this has a negative upon new creations. This hypothesis should survive also an event history analysis test,and it would be very interesting to see whether it actually would.5 I have opted here for political constraints rather than veto players on practical rather thean theoreticalgrounds: Henisz has developed a huge data base where annual values of political constraints are availablefor most countries of the world since at least 1950.6 Directive 97/51/EC of the European Parliament and of the Council of 6 October 1997 amending CouncilDirectives 90/387/EEC and 92/44/EEC for the purpose of adaptation to a competitive environment intelecommunications.

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The event history analysis presented here has examined how individual IRA

creations were affected by density. In other words, I have studied how the decision of

individual governments to set up IRAs in individual sectors was shaped by density. The

findings show clearly that density matters, though not always in obvious ways, and it is

difficult to attribute this to some sort of statistical phenomenon common to the

evolution of all populations, simply because the level of analysis here was not the

population but the individual sector-country.

4 Conclusion

This paper sought to conceptualise and explain the spread of IRAs in Western

Europe. It started from the observation that the empirical pattern of the creation of IRAs

in seven regulatory domains of seventeen European countries has a S-shape that is one

of the main constants of studies of diffusion. This strongly suggests that the creation of

IRAs in Western Europe has been driven by a diffusion process, i.e. by a process where

the main actors, namely governments, act interdependently rather than independently.

The conceptualisation of the diffusion of IRAs put forward here distinguishes

between spurious, functionalist and non-functionalist diffusion. Spurious diffusion occurs

when actors respond to similar functional pressures at roughly the same time, like

people opening umbrellas when it starts raining. In the case of IRAs, such pressures are

best identified with liberalisation, privatisation, and more generally economic

regulation, which are domains where the need to achieve credible commitments give

governments incentives to delegate. Spurious diffusion is to be separated from real

diffusion because actors do not behave in response to each other actions. In functionalist

diffusion, on the other hand, actors are interdependent and do behave in response to

each other's actions. The rationale for the attention to what others do is to be found in

the information this can provide. The logic of diffusion here is functionalist because the

establishment of IRAs (or any other action) is decided because of its (real or perceived)

beneficial effects, which are assessed partly by looking at the experience of others. In

non-functionalist diffusion, finally, IRAs are not set up because of the function they

perform, but because they are they are perceived as the natural institutional solution for

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regulatory policies. Governments choose IRAs and not another options because the

formers have become taken for granted as the obvious way to organise regulatory

policies. In other words, when IRAs are taken for granted, other options are simply not

considered.

An implication of both functionalist and non-functionalist diffusion is that the

likelihood of establishment of IRAs at time t should be affected by density, i.e. the

number of IRAs already existing at time t-1. Although this hypothesis should be refined,

notably to differentiate between functionalist and non-functionalist diffusion, it has been

at the centre of the present analysis without amendments. The findings show that

density does affect the likelihood of IRA creation, though not always in easily

interpretable ways. This is a strong confirmation of the fact that diffusion processes have

been at work behind the decisions to set up IRAs in West European countries. Future

challenges include the refinement of the conceptual distinction between functionalist

and non-functionalist diffusion, as well as the building of a methodology capable of

empirically discriminate between them.

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19

Table 1. Density and Creation of IRAs: Poisson Regression Coefficients (Maximum LikelihoodEstimation)

Model 1 Model 2 Model 3

overall density 0.06(0.05)

0.11**(0.05)

0.11**(0.05)

(overall density)2 -0.0007*(0.0004)

-0.0009**(0.0004)

-0.0008**(0.0004)

regulatory domain density 0.18(0.13)

(regulatory density)2 -0.024***(0.008)

regulatory type density -0.06(0.08)

-0.07(0.07)

(regulatory type density)2 0.0003(0.0008)

0.001(0.001)

economic regulation 1.32**(0.62)

population age 0.06**(0.03)

0.04(0.03)

0.06**(0.02)

domain age -0.02***(0.01)

regulatory type age 0.02*(0.01)

preceding foundings 0.09(0.08)

0.09(0.08)

0.19*(0.10)

constant -9.33***(1.20)

-8.97***(1.10)

-10.04***(1.21)

Likelihood ratio chi-square 201.82 216.33 237.41degrees of freedom 4 7 10N 81 162 567Note: * p<0.10, ** p<0.05, *** p<0.01; standard errors in parentheses.

20

Table 2. Overall density model – Weibull regression coefficients

Model 1 Model 2 Model 3 Model 4

economic regulation 0.302(0.252)

0.304(0.252)

0.311(0.249)

0.305(0.25)

political constraints -1.807*(1.003)

-1.804*(1.004)

-1.804*(1.006)

-1.75*(1.006)

overall density 0.018**(0.009)

0.018**(0.009)

0.018**(0.009)

0.039(0.038)

(overall density)2 -0.0002(0.0003)

EU directive 97/51 (telecom) 2.09***(0.365)

2.09***(0.365)

2.067***(0.358)

1.995***(0.366)

privatisation 0.593(1.121)

0.75(1.075)

1.365***(0.523)

1.33***(0.511)

privatisation X overall density 0.018(0.035)

privatisation X overall densityX political constraints

0.028(0.045)

liberalisation 3.196***(0.706)

3.112***(0.724)

2.801***(0.696)

2.922***(0.736)

liberalisation X overall densityX political constraints

-0.096***(0.027)

-0.094***(0.027)

-0.083***(0.025)

-0.088***(0.028)

competition / financial markets 0.663**(0.266)

0.66**(0.265)

0.654**(0.297)

0.661**(0.264)

liberal market economy 0.445(0.297)

0.441(0.297)

0.435(0.297)

0.431(0.299)

constant -18.587***(4.466)

-18.601***(4.472)

-18.657***(4.477)

-17.091***(5.474)

α 4.823(1.3)

4.825(1.301)

4.841(1.303)

4.335(1.647)

log likelihood -1.983 -2.016 -2.061 -1.897Wald chi2 74.57 76.00 89.44 84.73d.f. 10 10 9 10N of subjects 109 109 109 109N of observations 3807 3807 3807 3807* p < 0.1, ** p < 0.05, *** p < 0.01; robust standard errors in parentheses.

21

Table 3. Regulatory type density model – Weibull regression coefficients

Model 1 Model 2 Model 3 Model 4

economic regulation -0.163(0.34)

-0.161(0.34)

-0.157(0.339)

-0.19(0.38)

political constraints -1.767*(1.031)

-1.763*(1.032)

-1.763*(1.034)

-1.74*(1.045)

regulatory type density 0.03**(0.014)

0.03**(0.014)

0.03**(0.014)

0.041(0.048)

(regulatory type density)2 -0.0002(0.0006)

EU directive 97/51 (telecom) 2.015***(0.358)

2.009***(0.357)

1.997***(0.352)

1.969***(0.363)

privatisation 0.832(0.953)

0.994(0.901)

1.373***(0.521)

1.357***(0.516)

privatisation X regulatory type density 0.016(0.04)

privatisation X regulatory type densityX political constraints

0.03(0.06)

liberalisation 3.073***(-0.134)

2.984***(0.676)

2.781***(0.642)

2.819***(0.667)

liberalisation X regulatory type densityX political constraints

-0.134***(0.037)

-0.13***(0.037)

-0.12***(0.034)

-0.123***(0.036)

competition / financial markets 0.664**(0.415)

0.662**(0.28)

0.658**(0.278)

0.662**(0.278)

liberal market economy 0.415(0.3)

0.412(0.3)

0.407(0.3)

0.41(0.302)

constant -18.578***(4.411)

-18.589***(4.416)

-18.616***(4.418)

-18.048

α 4.908(1.257)

4.911(1.257)

4.918(1.258)

4.745(1.574)

log likelihood -1.447 -1.471 -1.491 -1.46Wald chi2 84.05 87.35 101.79 98.16d.f. 10 10 9 10N of subjects 109 109 109 109N of observations 3807 3807 3807 3807* p < 0.1, ** p < 0.05, *** p < 0.01; robust standard errors in parentheses.

22

Figure 1. Creation and Total Number of IRAs in Western Europe (electricity, telecom, financialmarkets, competition, food safety, pharmaceuticals, environment)

0

10

20

30

40

50

60

70

80

90

100

1920 1930 1940 1950 1960 1970 1980 1990 2000

Number of IRAs created Total number of IRAs

23

Figure 2. Predicted IRA creations by overall density

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 10 20 30 40 50 60 70 80 90

Economic regulation Social regulation

Figure 3. Predicted IRA creations by density at the regulatory domain level

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 2 4 6 8 10 12 14 16 18

Economic regulation Social regulation

24

Figure 4. Predicted probability that at least one IRA is created, by overall density

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

20 25 30 35 40

economic regulation social regulation

25

Figure 5. Hazard of IRA creation by overall density and privatisation (1990-1999)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

20 30 40 50 60 70

overall density

privatisation no privatisation

26

Figure 6. Hazard of IRA creation as a function of overall density, conditional on liberalisationand political constraints (1990-1999)

0

0.1

0.2

0.3

0.4

0.5

0.6

20 30 40 50 60 70

overall density

liberalisation – polcon=0.5

liberalisation – polcon=0.8

no liberalisation – polcon=0.5

no liberalisation – polcon=0.8

27

Appendix 1. Summary of variables

Variable Measure Sourcefoundings (dependent variablefor the Poisson analysis)

number of IRAs created in each yearand each regulatory domain

author's data set

creation (dependent variablefor the event history analysis)

dummy (1 for the year when the IRAis created)

overall density overall number of IRAs (lagged oneyear)

author's data set

(overall density)2 overall density squaredregulatory domain density number of IRAs in the regulatory

domain (lagged one year)author's data set

(regulatory domain density)2 regulatory domain density squaredregulatory type density number of IRAs for the same type of

regulation (economic vs. social)(lagged one year)

author's data set

(regulatory type density)2 regulatory type density squared author's data seteconomic regulation dummy (1=economic regulation,

0=social regulation)Economic regulation:electricity, telecom,financial markets,competition. Socialregulation: food safety,pharmaceuticals,environment)

population age years since the creation of the firstIRA

author's data set

domain age years since the creation of the firstIRA in that regulatory domain

author's data set

preceding foundings foundings in the preceding year author's data setpolitical constraints veto players including the judiciary

and sub-federal entitiesVariable POLCON5 inthe Political ContraintsData Set (Henisz 2000)

EU directive 97/51 (telecom) Directive 97/51/EC of the EuropeanParliament and of the Council of 6October 1997 amending CouncilDirectives 90/387/EEC and92/44/EEC for the purpose ofadaptation to a competitiveenvironment in telecommunications.

author's data set

privatisation dummy (1 for years of privatisationin electricity or telecom)

Boylaud and Nicoletti(2000), Levi-Faur(2002b), OECDregulatory database,

liberalisation dummy (1 for years of liberalisationin electricity or telecom)

Steiner (2000), Boylaudand Nicoletti (2000)

competition / financialmarkets

dummy (1 for competition andfinancial markets)

liberal market economy liberal market economies (1 forIreland and the UK)

Hall and Soskice (2001)