“CHANGES WITHIN CABINET IN CONTEMPORARY … · Olías 1994, Heywood 1995; Montabes 1997; Newton...

Jerez and Real, “Changes within…”, V. 040405.1

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“CHANGES WITHIN CABINET IN CONTEMPORARY SPAIN, 1977-2004”

Miguel Jerez-Mir∗∗∗∗

and

José Real-Datoωωωω

Paper presented for the ECPR Joint Sessions of Workshops

Workshop 10, “The Selection and De-Selection of Ministers”

Granada, April 2005

Preliminary draft. Do not quote.

Abstract. This paper is an attempt to continue with the tradition of works on the Spanish cabinet, but from a

different point of view. We are mainly interested in changes within cabinets as a indicator that may reveal the PM

strategic behaviour. This obeys to three main patterns: policy performance, public image, and organizational

equilibrium maintenance. Our hypotheses aim to uncover these patterns in the case of Spanish cabinets, by

studying the causal factors that influences ministers’ hazard of being dismissed from the cabinet. We use Cox’s

non-proportional hazard models to test our hypotheses. Our preliminary results are coherent with the three

different patterns of behaviour we have distinguished, and underline the importance of time as a factor affecting

the PM’s behaviour.

1. INTRODUCTION

Previous political science works on the cabinet in contemporary Spain have

approached the topic from different perspectives. Many of them could be qualified as

basically conventional studies of this institution as a major constituent of the political

system as a whole, and more specifically, of the central government (Bar 1988 and 1989;

Olías 1994, Heywood 1995; Montabes 1997; Newton 1997; Román 2002; Magone 2003).

As a matter of fact, the cabinet is frequently approached in the larger context of the

executive branch. Consequently, this literature is particularly interested in the role of both

the cabinet and its prime minister as the core of the executive power and main source of

political and policy momentum. Although Spain is currently defined as a parliamentary

system –more precisely a parliamentary monarchy-, in comparative terms the government,

and particularly its president, must be regarded as exceedingly powerful and dominant in

the policy process (Guerrero 2000). Even if the cabinet is defined as a collegial-collective

body, the central position occupied by the prime minister (PM), induces the government to

∗

University of Granada (Spain) [email protected] ω

University of Almería (Spain) [email protected]


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act with a strong presidential bias (Bar 1983; Heywood 1991; Pasquino 1995; Lancaster

1996), provided that he has the adequate relationship with the party in office and doesn’t

need to confront extraordinary political challenges, as happened in the case of both Suárez

and Calvo Sotelo during the transition to democracy (Heywood 1995).

A second line of research in this literature focuses on the cabinet´s components.

These are mainly works traditionally included under the label “elite studies”, dealing with

empirical analysis of the composition and patterns of recruitment of the ministers (Parrado

1996; Botella 1997; Linz, Jerez, and Corzo 2003). Studies on leadership which focused

either on the PMs or on a particular minister (Linz, 1993; Prego 2000; Ballart and Ramió

2001; Delgado 2004) are to some extent a variant of these elite studies, and may prove to

be extremely useful when studying cabinet performance.

There is another body of literature which connects both dimensions (macro-political

system and micro-individual). This literature is mainly interested in the dynamics of

cabinet formation and in the processes of selection and de-selection of ministers. Who is

going to be a cabinet member, the way portfolios are allocated or the decision to remove a

minister are not phenomena which depend only on the individual’s characteristics.

Resignations and cabinet reshuffles can be the the result of a series of factors which are far

from the individual’s responsibility. Eventual crisis (i.e. scandals, triggering events, etc.),

changes affecting government support (breakdown of the coalition of government or intra-

party factionalism), or strategic manoeuvring by the Prime Minister, can all affect cabinet

composition (for the case of Spain: Reniu 2002)

Our paper focuses on changes within cabinets in recent democratic Spain. We aim

to offer a wide spectrum of Spanish cabinet dynamics. The study will also include a more

analytical investigation of the causal factors influencing the processes under scrutiny.

We would like to remark that our paper does not focus on portfolio allocation. We

are not interested in the distribution of ministers’ particular features, nor in the factors

which increase individual’s likelihood to be appointed as a minister. On the contrary, in

dealing with intra-cabinet changes our concern is mainly directed to the strategic

dimension of these processes. Recent literature (i.e. Dewan and Dowding 2003; Kam and

Indri∂ason 2004) has emphasized this aspect, focusing on the central role played by PMs


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and their strategic interactions with cabinet members. Authors interested in this subject

have also paid attention to the way institutional variables influence the results of those

interactions. In sum, this literature suggests an array of research questions: in what

circumstances do PMs resort to cabinet reshuffles? Do they react to external variables

(shocks, political scandals, party re-alignments, changes in government coalition or parties

in parliament supporting a particular government, elections at local or European level),

[or] do they anticipate themselves in order to improve their leading position or do they

show no reaction whatsoever? What objectives do PMs aim to achieve when reshuffling?

What kind of trade-offs are considered by PMs when they decide on a cabinet reshuffle?

How do PM calculations affect individual features of new (or re-) appointed ministers?

When do PMs look for ‘policy active’ ministers?

In this sense, minister’s careers within cabinets act as indicators which could be

help us to answer the above questions. A PM’s act of selecting and deselecting ministers

reflects the strategic dimension of those processes. So here we are looking for factors

which influence ministers’ departures from government, in order to find possible patterns

in the PMs’ behaviour.

In order to do so, we have given the paper the following structure. In section 2, we

start by describing basic institutional features. Secondly, we introduce our theoretical

assumptions about the role of the PM in Spanish cabinet, and about the way ministers’

exits may help us to uncover the PM’s patterns of behaviour. Then, we introduce our

research hypothesis.

In order to test them we have constructed a longitudinal database on Spanish

ministers since 1977 until 2004. It is the basis for our analysis. We describe it in section 3,

along with the Cox’s non-proportional hazards model we have chosen as the instrument to

test our hypothesis.

Sections 4 and 5 constitute the core of this paper. In Section 4 we display the results

obtained in our analysis. We pay attention to the extent these results allow us to confirm or

result our hypotheses. In Section 5, we discuss the main findings, and connect them with

our theoretical background on the PM’s patterns of behaviour.


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2. THEORY AND HYPOTHESES

Institutional remarks

As we have mentioned above, the cabinet occupies a prominent role in Spanish

politics. This position derives both from the configuration of the Spanish political

institutions, and from the functioning of the political system itself, at least since the

consolidation of democracy in the early 1980s.

On the one hand, the Constitution establishes the Government as the executive

steering institution at state level. It consists of the President (PM), Vice-Presidents (vPM)

(when appropriate), Ministers, and other members as may be created by law. Thus,

Spanish 1978 Constitution establishes an extended version of the executive peak

organization, which includes the cabinet itself -PM, vPMs, and ministers- and other

possible members, such as State Secretaries. Nevertheless, the definite institutional form of

the government has been limited to the cabinet (Newton, and Donaghy 1997).

On the other hand, the Spanish political system reserves a prominent place for the

cabinet, and particularly its PM. The electoral system rewards the two biggest political

parties, particularly penalizing the small and medium size national parties. As a result

electoral pluralities are magnified for the victorious party in each electoral district which

culminates in its representation in parliament. These biases in the electoral system

“manufactured” absolute parliamentary majorities for the PSOE on three occasions and for

the PP in 2000 (Colomer 2002, 176-177; Gunther et al. 2004, 117). The highly centralized

organization of Spanish political parties, which concentrates a lot of decision-making

power in the hands of the party leadership, and the fact that –starting in late 1982 with

Felipe González- cabinet leadership has been clearly consonant with the ruling party’s,

have placed the majority of government responsibility with the cabinet and its PM

(Colomer 2002, 180-182).

As in any other parliamentary democracy, the cabinet in Spain is a collegial body.

Nevertheless, despite this collective nature, it should be noticed that the PM is not a mere

primus inter pares in this institutional configuration (Montabes 2001). Indeed, the PM


5

should be considered the most powerful figure within the executive branch. Investing

major strength in the position of the president was already a central concern of the Spanish

constitutional architects in 1977-78 (Heywood 1995, 99).

The Spanish constitution establishes the steering role of the Prime Minister, who

“shall direct the Governments' action and coordinate the functions of the other members

thereof, without prejudice to the competence and direct responsibility of the latter in the

discharge of their duties” (Section 98.2). He also proposes to the King the names of the

cabinet members –which is in fact a mere formal act- and their dismissal as well. But his

major attribution is the power to dissolve the Parliament, and to submit their program to a

vote of confidence. Finally, as cabinet members do not require Parliament approval, this

tends to concentrate collective political responsibility on the PM. All these features have

been outlined in order to consider the Spanish executive branch as exhibiting an important

presidential bias.

General information about Spanish cabinets

Since the first democratic election, in June 1977, there have been nine legislative terms,

corresponding to five different PMs. Here, we only consider the cabinets until the seventh

legislature (finished in April 2004). Some basic descriptive features are included in the

following tables and figures:

Table 1. Cabinet’s basic information (1977-2004)

Party in office PM Period Parliamentary

situation

Ministers used during the

period

UCD (Union of the

Democratic Centre) Suárez 5.7.77/5.4.79 (LC) Minority 24

" " 6.4.79/25.2.81 (L I) Minority 35

" Calvo-Sotelo 26.2.81/2.12.82 (L I) Minority 23

PSOE

(Socialist Party) González 3.12.82/25.7.86 (L II) Absolute majority 23

" " 26.7.86/6.12.89 (L III) Absolute majority 21

" " 7.12.89/17.7.93 (L IV) Absolute majority 27

" " 14.7.93/4.5.96 (L V) Minority 21


6

PP (Popular Party) Aznar 5.5.96/26.4.00 (L VI) Minority 18

" " 28.4.00/28.4.00 (L VII) Absolute majority 25

Regarding the evolution of the size of the cabinet the figures are:

Table 2.Average cabinet size

PM Average cabinet size

Suárez 20.9

Calvo 15

González 16.6

Aznar 14.8

Table 3.Average cabinet size by legislative period*

Period LC L1-Suárez L1-Calvo L2 L3 L4 L5 L6 L7

Average

cabinet size 18.7 22.5 15.0 16.0 17.0 17.4 16.0 14.0 15.4

*In L I we have distinguished between both PMs.

Figure 1. Evolution of cabinet size (by event of change)

Cabinet size

by event of change

0

5

10

15

20

25

5.7

.77

24.2

.78

6.4

.79

2.5

.80

9.9

.80

31.8

.81

2.1

2.8

1

12.9

.82

3.1

2.8

2

25.7

.86

12.7

.88

1.5

.90

11.3

.91

15.1

.92

13.7

.93

25.1

1.9

3

2.7

.95

19.1

2.9

5

18.1

.99

30.4

.99

27.4

.00

28.2

.01

27.2

.03

5.9

.03

Date

Min

iste

rs

Basic concepts


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Definition of change within cabinet change: We apply the concept “change within

cabinet” to the following change events:

1. Exits after the end of term, while the same party continues in office

2. Reshuffles: and define reshuffles as any change in ministerial personnel or

responsibilities that affect at least two portfolios and two office-holders (White

2000).

3. Readjusts: any other change in cabinet promoted by the PM which does not fit in

the previous categories.

4. Cabinet crisis: it is a result of the PM resignation. It has only occurred once, when

Adolfo Suárez resigned in February 1981.

5. Resignations.

Thus, we do not consider as “change within cabinet” the situations where ministers exit

from cabinet as a consequence of the access of a new party to office. This cases have been

considered “right censored” in our analysis (see below).

Figure 2 shows an outlook of exit events and their distribution along time:

Figure 2. Exit events by PM (by “century days”)1

Time of change

40000

38000

36000

34000

32000

30000

28000

Exit e

vent

5

4

3

2

1

0

Prime Minister

Aznar

González

Calvo-Sotelo

Suárez

Besides, we consider different results of change or destinations for individual minister

when a change takes place: he may continue with the same portfolio, he may change into

another portfolio, or he may exit. These have been classified in: private affairs, political

career, party office, institutional position, bureaucratic career and Prime Minister (this has

1 The concept “century days” refers to the number of days passed since January 1 1900.

Exit events

1.End of term, 2. Reshuffle, 3. Readjust, 4. Cabinet crisis, 5. Resignation


8

only occurred once, when Calvo-Sotelo was elected PM after Suárez’s resignation). Despite it

could be an interesting subject of research, here we do not deal with ministers’ destinations

after leaving cabinet, but only with exits in a broad sense.

Figure 3. Destinations by PM (by “century days”)

Time of change

40000

38000

36000

34000

32000

30000

28000

Destination

8

7

6

5

4

3

2

1

0

Prime Minister

Aznar

González

Calvo-Sotelo

Suárez

The strategic view of changes within cabinet

In this paper we are mainly interested in processes within cabinet which affect its

composition. This is not merely to describe the type of changes takes place, the moment those

changes occur, or the specific circumstances surrounding them.

Our interest concentrates on a dimension of those processes which we consider of the

highest relevance. As we have mentioned above, most of changes within cabinets imply some

kind of strategic decision made by the PM. Only changes motivated by some unexpected event,

such as a minister’s death or a voluntary resignation, are far from his sphere of activity2. This is

an important assertion, to the extent it assumes that those processes we are interested in are not

a direct result of institutional factors or the situational structure, but they are mediated by the

PM will. This is most important indeed, if we consider the presidential bias of the PM in the

Spanish political system.

2 Nevertheless, even these latter circumstances are not free from the PM’s influence.

Destinations

1.Continue; 2. Continue with another portfolio, 3. Private affairs, 4. Political career,

5. Party office, 6. Institutional position, 7. Bureaucratic career, 8. Prime Minister


9

The PM’s strategic behaviour is linked to a twofold motivation. On the one hand, he

can use his constitutional attributions on minister selection and de-selection in order to

accomplish his political steering functions. So this could be defined as the external dimension

of the PM activity. On the other hand, there is another, internal, dimension, which is related to

the PM’s condition as rational oriented actor which aims to maximize their own payoffs -that

is, increasing their chances of being re-elected (see Dewan and Dowding 2003, Kam and

Indriðason 2004).

Looking for rational motivations that might guide PM’s behaviour, in the first place we

find those situations linked to institutional or organizational factors. The PM may use

reshuffles the cabinet as a response to demands of a different share in the cabinet coming from

members of the governmental coalition or party factions (Warwick 2001; Mershon 2001). In

the Spanish case this occurred mainly during the UCD cabinets, due to the high level of

internal fragmentation in that party.

Another situation where the PM may recur to a cabinet change has to do with the

maintenance or amelioration of its position within the party in government. If he controls the

party organization which supports him –as it was the case of José María Aznar and the Popular

Party- he can use his attributions to maintain this position.

Besides, the PM can also intervene in change situations in order to improve his and/or

his party’s electoral chances, in case he is not running for office -as it happened with José

María Aznar in 2004. All these actions aim to protect the PM’s, the cabinet, and the party’s

public image, in order to increase their public support and, thus, their electoral chances. The

PM can expel from cabinet underperforming ministers -i.e. ministers who do not show an

adequate level of policy achievement (Dewan / Dowding 2003). Also, he can move out from

office those ministers who are growing stale in front of the public or whose image is being

damaged by any issue. An extension of this situation occurs when the PM throws out the

“rotten apples”, not executing collective responsibility mechanisms, to make him appear as

responsive or avoiding to be labelled as a “corruption concealer”.

Ministers’ career as indicative of cabinet dynamics


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Ministers’ career can be used as an indicator of this strategic dimension in cabinet

dynamics which we have just emphasised. This means that ministers’ fate in government

reflects PM’s strategic patterns of behaviour, to which we direct our attention. To be more

precise, we intend to address the following dimensions of PM behaviour.

Firstly, if we take policy performance as a variable influencing party’s electoral

chances, policy active ministers can make a difference. The PM may have interest in that

ministers perform as best as possible. If the PM has this “policy performance pattern”, it is

plausible to assume that he may be interested in allocating portfolios according to some kind of

meritocratic criterion (Huber and Martínez-Gallardo (2003, 6) see the problem of “talent

allocation” as central in the process of ministers selection). Besides, assuming that PM

protection may positively affect individual and collective minister’s performance (Dewan and

Dowding 2003, 19), it is in his interest to protect them.

Secondly, ministers “pruning” is also related with cabinet’s public image. On the one

hand, public support is important for policy and electoral purposes. Ministers involved in

scandals or frequently subjected to public criticism, or even those who have just been for a

long time in office, may lessen PM or party’s expectations to repeat the next term. Then the

PM may choose to sack damaged ministers in order to raise public acceptance. Thus, PM’s

“public oriented” pattern is another interesting dimension to deal with.

There is a third, organizational, dimension, which could be called the “pattern of

equilibrium maintenance” within governmental coalition and/or among party factions. In the

Spanish case, as we have shown above, all cabinets have been “one party” cabinets. So

coalition equilibrium is not here a problem to deal with.3 Regarding intra-party relationships,

cabinet composition is supposed to be a reflection of the existing power equilibrium within the

party organization. When the status quo breaks down and conflict arises, it is expected that re-

equilibrium processes within cabinet are set in motion.

Intra-party conflicts may affect, for example, ministers’ parliamentary support or

protection when they face a damaging issue. The manner a PM approaches this task may

3 Nevertheless, ministers’ behaviour sometimes has turned into a problematic issue regarding the

relationships with other parties’ parliamentary support. This was the example during the period Esperanza

Aguirre was Minister of Education (1996-1999)


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depend on factors such as his degree of control on party machinery, or the intensity of intra-

party factionalism. Furthermore, other variables can influence the former, as the type of

parliamentary majority which supports government, or the presence of intra-party scandals

which may weaken internal conflict and opposition. In addition, party quarrels may reflect in

intra-cabinet relationships. When factions form inside cabinet, it demands the PM uses his

balancing functions. This may end in cabinet changes in order to re-establish internal

equilibrium. Intra-cabinet conflict is, thus, another factor to take into account.

The latter dimensions are not independent. They may interact resulting in multiplicative

or reduction effects, even mutual cancellation. . The PM may protect ministers in order to

sustain cabinet credibility. Minor issues affecting a minister -which in normal conditions would

have been deactivated by collective responsibility mechanisms- can be devastating when they

combine with intra-cabinet factionalism, or coincide with a series of damaging issues affecting

other ministers or the cabinet as a whole. On the contrary, those same issues may go unnoticed

when opinion polls are on the cabinet’s side. Dewan and Dowding (2003) have shown how this

is not a straightforward relationship. The level of protection PM’s offers to harmed ministers

vary along time and depending on previous protecting actions.

Then we can conclude that exploring the effect the former dimensions have on

ministers’ risk of exit from government may give us some clues about the strategic patterns

followed by the PM.

Time as independent variable in changes within cabinet

There is evidence which calls for consider time as a variable to be taken into account in

our analysis. It has been shown, for example, that the probability of a reshuffle taken place

varies along time (Kam and Indriðason 2004). Also, temporal patterns in ministerial

resignation have been uncovered (Dowding and Kang 1998).

Time may influence the PM when he has to decide about his ministers’ fate. It could be

interpreted as a proxy of some other hidden causal processes which are taken place in the

meantime. It is plausible to think that the way the PM understands the timing of the term

according to the presence of other variables (institutional, systemic, and stochastic) may affect

his strategic behaviour. That is, in deciding whether to reshuffle or not, the PM may take into


12

account not only the events and circumstances which could provoke it (i.e. pressures from a

party of the governmental coalition, or the emergence of a political scandal affecting a cabinet

member), but also the moment during the term those facts occur.

Thus, when measuring the effect of the above mentioned variables influencing

ministers’ exit risk, we must control for time dependent processes.

Hipotheses

Our goal is to check if there is any pattern in the PM’s behaviour when deciding to effect

any change in cabinet. From the previous pages, we expect that the effect of different variables

varies according to the relevant dimension. Thus our hypotheses are:

A. Policy performance pattern:

Hypothesis 1: Variables such as expertise or previous governmental experience have a

negative significant effect on ministers’ exit risk.

Hypothesis 2: Mechanisms of collective responsibility should be used in order to cope

with crisis. PM protection should be negative and significantly related to ministers’ exit

risk.

B. Public orientation pattern:

Hypothesis 3: Ministerial experience has a significant positive effect on ministers’ risk.

As we have shown, “old” ministers have a higher chance of being damaged, so it is in

the PM’s interest to remove them from cabinet.

Hypothesis 4: Ministers involved in issues which may damage their public image

(scandals, public opposition to policy initiatives) run a higher risk of exit from cabinet.

Hypothesis 5: Ministers’ negative public evaluation should be increase the risk of

leaving cabinet.


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Hypothesis 6: The effect of damaging or critical issues on the risk of being sacked

varies depending on whether the minister is protected by the PM or not.

C. Equilibrium maintenance pattern:

Hypothesis 7: Cabinet changes should reflect internal disagreements. In this case, it is

expected that minister’s exit risk is positively affected.

Hypothesis 8: Higher the levels of internal factionalism, the higher ministers’ risk to

abandon cabinet.

As we have mentioned above, the PM has different incentives when deciding to protect

or not his ministers before any damaging issue. The PM may protect a minister, a) if he agrees

with his minister’s policies; or b) if he thinks that if he does not protect him, other cabinet

members may feel discouraged to take policy innovative measures. Another possibility is that

the PM privileges public image arguments in order to protect his and his party’s electoral

chances. He may think that it is too costly for him to support the minister’s policies, or simply

consider that it is not worth to defend a corrupt man. In addition, we should take into account

the effect that previous protecting actions have on later ones. Therefore, it is necessary to

consider those possible interactive effects:

D. Control variables

In addition, we need to control for other variables in order to cope with heterogeneity.

Our control variables are:

a. Individual’s characteristics. The risk a minister leaves government may

depend on whether he occupies a leadership position within the

governmental party or his past political curriculum (i.e. previous experience

in political-administrative positions).

b. Institutional and political context variables. Exit risk may be influenced by:

the party in office; whether the cabinet counts with enough parliamentary


14

support; or who is the PM. These variables pick up the political context

surrounding ministers’ career.

c. Exit context. The way exit takes place (end of term, reshuffle, readjust or

resignation) may imply different contextual circumstances, which could

affect ministers’ exit risk. Figure 4 shows how most of exit events -47.6 %-

occur coinciding with the end of the term. Thus, it is necessary to control for

this variable.

Figure 4. Exit events (absolute frequencies)

Resignation

Cabinet crisis

Adjust

Reshuff le

End of term

Fre

quency

80

60

40

20

0

E. Time dependent processes

Time has been mentioned as an important factor to take into account, so we should look

at those variables which change along time and could affect ministers’ exit risk. This is the

case, for example, of resignation issues. The time a resignation issue arises may influence the

PM’s decision about a minister’s fate.

Related to the latter, there is another variable which also changes along time: the

number of ministers involved in damaging issues who have been protected by the PM until that

moment. This variable is mentioned by Dewan and Dowding as linked to the probability a PM

protects a ministers or expel him from the cabinet (2003, 21).


15

Hypothesis 9: The number of ministers involved in a resignation issue who have been

protected by the PM has a significant influence on a minister’s exit risk.

The processes we are dealing with occur during a determinate span of time, what is

called constitutional inter-election period (CIEP). Exit events take place between the times t0

and tCIEP, which in the Spanish case is 4 years. An important question to put here is whether exit

risk varies along this time span. There are arguments that sustain an affirmative answer to this

question (see above). On the one hand, it is plausible to think that cabinet changes (in

particular reshuffles and readjusts) do not take place at the beginning of the term, at least if

there is not internal stability problems. In the Spanish case, the first hundred days after

government comes to office are taken as a “probationary period”, where opposition parties

suspend or soften their critics to the new cabinet. On the other hand, as the end of the CIEP

approaches, it is also reasonable to suppose that the PM prefers to dissolve the parliament and

to call for elections instead of reshuffling or readjusting. If some change must be done, it takes

place when a new term starts. In addition, it is expected that consecutive reshuffles or readjusts

do not take place close to each other.

These arguments make for the following additional hypothesis:

Hipothesis 10: Ceteris paribus, ministers’ exit risk varies along the term.

3. DATA AND METHODS

Data

Given our interest in the effect of a series of covariates in minister’s exit risk along

time, event history analysis (EHA) techniques are the most suitable to perform our analysis.

EHA applies when our dependent variable is categorical, varies along time, and we are

interested in explaining these variations.

In EHA causal effects are represented by the basic concept of hazard rate4 -r(t)- which

can be interpreted “as the propensity to change the state, from origin j to destination k, at [time]

t (…) [where] this propensity is defined in relation to the risk set at t (i.e. the set of individuals

4 It is also called transition rate, instensity rate, transition intensity, risk function, or mortality rate (Blossfeld

and Rohwer 1995, 2001)


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who can experience the event because they have not already had the event before t)” (Blossfeld

and Rohwer 1995, 28 [italics in the original]). In our case, the transition rates refers in general

terms the ministers’ propensity to change from being in office (i) into another state (j) which

we have defined in function of the type of event we want to study. Other concepts of interest is

survivor function, that can be defined as the probability that an event happens later than t.

For our purposes, we have developed a longitudinal database of Spanish ministers since

1977 until 2004. Time is measured in century days. Thus, each minister’s time in office has

been divided into episodes, which constitute our basic units of analysis. We are interested in

changes within term, so every episode corresponds to the period between a minister is

appointed until either any intra-CIEP event change (reshuffle, readjust, cabinet crisis or

resignation) occurs; in case of no intra-CIEP event affects the minister, episodes conclude with

the end of term. As we have mentioned above, ends of term coinciding with a change in the

party in office have been considered right censored (see above) Then, we have 134 ministers

and 248 episodes (45 right censored). Figures 5 and 6 show the distribution of episodes along

legislative periods and by government party respectively:

Figure 5. Number of episodes by legislative period

(including right censored)

L VII

L VI

L VL IV

L IIIL II

L I (Calvo-S

otelo)

L I (Suá

rez)

Constitue

nt

Fre

quency

50

40

30

20

10

0

31

19

24

30

2422

28

45

25

Figure 6. Number of episodes by party in government

(including right censored)


17

PPPSOEUCD

Frequency

100

80

60

40

20

0

50

10098

Variables

They have been elaborated recurring to diverse documentation, mainly official records.

The Centre for Sociological Research (CIS) provided the data on minister’s public evaluation.

Another important source on biographical information has been a Spanish national newspaper,

El País, whose records are accessible from the internet. They have been particularly important

in order to record damaging issues and PM and party protection.

Because of our work is still in a preliminary stage. Thus some variables are

underdeveloped, and have not been used in this paper. For instance, we do not test Hypothesis

8 -regarding the role of party factionalism- for we lack of an adequate measure of this variable.

Along with this situation, there are other variables which only apply to a limited set of cases,

irrespective of further development. Thus, we have built two datasets. The first one comprises

all ministers since 1977, but it allows us to test only a limited number of our hypothesis. The

other dataset includes only PSOE and PP cabinets, but in exchange, it allows us to test the rest

of the hypothesis we have elaborated.

The variables we use are listed below. In parenthesis we refer the corresponding

hypothesis, and asterisk indicates the additional variables included in the PSOE-PP dataset:

Minister’s experience in cabinet (H-1 and 3): Number a days a minister has been a

cabinet member until the moment he is appointed. When there is not previous experience, the

value is 0.

Expertise (H-1): Specific technical or professional knowledge corresponding the

portfolio allocated. We consider previous experience in the same portfolio as experience.

Dichotomic variable.


18

Damaging situation (H-4): Minister experiencing any kind of external shock, public

opposition, scandal, etc. Dichotomic variable

Difference with cabinet mean evaluation* (H-5): Difference between the ministers’

average evaluation and cabinet’s average evaluation during the period. Dichotomic variable.

Values: 1 = Evaluation equal or above the cabinet average; 0 = Below average.

PM protection* (H-2 and 6): Categorical variable. Values: 0 = Do not protect; 1 =

Protects,2 = Not involved/Protection not needed (in case the minister has not experienced any

damaging issue).

Intra-Cabinet conflict (H-7): Presence of internal conflict among two or more ministers

within the cabinet. Dichotomic variable.

Number of previous reshuffles (H-10)

In-between reshuffles (H-10): First previous reshuffle before exit.

Ministers previously protected by the PM* (H-9): Number of ministers involved in

damaging situations previously protected by the PM during the term.

Prime Minister: Prime Minister in office. This is a control variable, which functions as

a factor which comprehends hidden contextual variables not considered in our analysis. In our

analysis we have created four dummy variables, one for each PM: 0 = Suárez (reference

category); 1 = Calvo-Sotelo; 2 = Felipe González; 3= José María Aznar.

Party protection*: It registers the position of the party in office on the damaging

situations a minister is involved. Values: 0 = Do not protect; 1 = Protects,2 = Not

involved/Protection not needed (in case the minister has not experienced any damaging issue)

Elected: Minister parliamentary position during the term. Dichotomic variable.

Party leadership position: Minister is member of the Executive Committee of the party

in office. Dichotomic variable.

Politico-administrative experience: Any previous politico-administrative position in

central government (i.e.junior minister). Dichotomic variable.

Absolute majority: Cabinet counts with the support of the absolute majority of the

Parliament. Dichotomic variable.

Appointed at the beginning of the term (H-10) Dichotomic variable.

End of the term (H-10) Ministers who have not exit during the term. Dichotomic

variable.

Party’s terms in office: Number of consecutive terms the same party has been in office.

Methods


19

Firstly, we start by performing some descriptive analysis of the data, in order to find

some clue about the time distribution of events that could help us to decide what kind of event

history analysis technique to use.

Figure 7 and Table 4 offer some initial insight on Spanish ministers’ in-term survival

since 1977.

Figure 7. In term Spanish Ministers’ Survivor Function*

0.00

0.25

0.50

0.75

1.00

0 500 1000 1500century days

Kaplan-Meier Estimation

In Term Ministers' Survivor Function

*Right censored cases are included

Table 4. In-term Spanish ministers’ duration since 1977

mean min median max

648.621 66 521 1453

These data shows that the mean duration of in-term episodes is 649 days, which is

approximately a 44.4 % of maximum CIEP period (1460 days). This distribution is not

homogeneous. Figure 8 and table 5 illustrate how ministers’ survival depends on which

legislative period we are considering.

Figure 8. Ministers’ survivor functions by legislative period* +


20

0.00

0.50

1.00

0.00

0.50

1.00

0.00

0.50

1.00

0 500 1000 1500 0 500 1000 1500 0 500 1000 1500

constituent l i (suárez) l i (calvo-sotelo)

l ii l iii l iv

l v l vi l vii

century daysGraphs by number of legislature (l i separated)

Kaplan-Meier survival estimates, by govleg2

*Right censored cases are included; + L I has been separated by different Prime Ministers (Adolfo Suárez

and Leopoldo Calvo-Sotelo)

Table 5. Maximum minister duration by legislative period*

Legislative period Days

Constituent 639

L I (Suárez) 692

L I (Calvo-Sotelo) 644

L II 1330

L III 1229

L IV 1314

L V 1025

L VI 1453

L VII 1450

Thus, as we mentioned in the previous section, it seems that when testing our

hypotheses we will have to control for those contextual factors reflecting the idiosyncratic

nature of each legislative period.

An example of context effects can be found when looking at the type of parliamentary

majority which supports cabinet. As figure 9 illustrates, ministers’ hazard rates differ

depending on whether party in office has absolute majority of seats in Parliament or it has not.

Besides, different slopes in these functions suggest that hazard rates vary along time, according

to our previous speculations.

Figure 9. Ministers’ Cumulative Hazard Function by Parliamentary Majority*


21

0.0

00.5

01.0

01.5

0

0 500 1000 1500century days

absol = 0 absol = 1

Nelson-Aalen Estimation

Ministers' Cumulative Hazard Function by Parliamentary Majority

*Right censored cases are excluded

Furthermore, there is another point that we would like to underline. Our exploratory

analysis shows the differences existing between types of exit and entry events. Figure 10

illustrates the different smoothed hazard rate functions we obtain depending on whether

ministers arrive at the end of the term or they do not. As we can see, for equal durations the

exit risk varies depending on the type of exit event minister confronts.

Figure 10. Ministers’ smoothed hazard rate function by end of event (Kaplan-Meier

estimation)

0.001

.002

.003

.004

.005

0 500 1000 1500days

cendterm = 0 cendterm = 1

Kaplan-Meier estimation

Ministers' smoothed hazard rate function by end of term


22

Preliminary analysis indicates that not differentiating between exit events distorts the

effects of explanatory variables. When we try to control for exit at the end of term, this variable

reduces or eliminates the effects of other variables.

We would not need to be worried if this change in variable effects were only a

consequence of the different explanatory power of the variables included in the model.

Nevertheless, there are substantive reasons that make this point deserving of a closer scrutiny.

On the one hand, when the PM decides to dissolve Parliament and call for new elections, it is a

decision which has less to do with ministers or cabinet’s particular situation. It may happen not

only when CIEP ends, but in other circumstances as well, i.e. when the PM tries to take

advantage of a peak in popularity, or when a newborn constitution establishes the automatic

dissolution of constituent assembly (although this is not the case here). On the other hand, if

we exclude when the party in office is removed after the voting –that is, right censored cases-

the PM’s decision of maintaining or sacking ministers depends on the new post-election

circumstances (changes in parliamentary support, moves in party as a consequence of the

electoral results, etc.) along with the previous performance of individual ministers.

In conclusion, we think there are reasons to separate exit events in two groups,

differentiating between exits related to reshuffles and readjusts and the rest. This will allow us

to achieve a better understanding of the phenomena surrounding cabinet changes during the

term.

Regarding the type of model to use in our analysis, we are not directly interested in the

form of the probability distribution of events (exits), but in the effect the selected variables on

ministers’ exit risk. Then we use a Cox’s semi-parametric model. To be precise, we use a Cox

non-proportional hazard model (Box-Steffensmeier and Zorn 2001; Box-Steffensmeier, Reiter

and Zorn 2003). We have taken this decision after having tested proportional hazards

assumption on the Cox proportional hazard models we previously applied to our data.5 Those

test confirmed that some covariates had not a proportional effect on hazards along time. So, we

5 Proportional hazards assumption consist in assuming that the effect of a change in the independent variable is to

shift the hazard of the event of interest by a factor of proportionality and that the size of the shift remains constant

over time. In order to test it, we use the techniques suggested by Box-Steffenmeier, Reiter and Zorn (2003), based

on Schoenfeld residuals.


23

have decided to transform our initially Cox’s proportional hazard model in non-proportional,

by including the interaction between those variables and time.

4. RESULTS

Table 6 shows the results of our analysis on the entire population of Spanish ministers

since 1977 (n=134) including all kind of exits. Episodes have been split by the time of the first

previous reshuffle during term. Model 1 refers all exit situations, and Model 2 only to those

ministers who exit because of a reshuffle or a readjust.6

Table 6. Cox’s non-proportional hazard models.All ministers (n=134)

MODEL 1- EXIT (ALL EVENTS)

MODEL 2- EXIT (ONLY RESHUFFLES AND

READJUSTS)

Coef. Std. Err. Coef. Std. Err.

Experience in cabinet (days)

-.0002859* 0.000161 0.0002974 0.0002132

Expertise 0.0047731 0.2396096 -0.4448339 0.2947242

Damaging situation -4.936194* 2.789891 0.0355843 0.3219688

Intra-cabinet

conflict

5.758021** 2.418876 1.411202*** 0.3400979

Calvo-Sotelo 4.604218 6.95472 -2.293615*** 0.7250301

González 1.804087 3.769168 -4.947364*** 0.8743638

Aznar 3.974804 4.673395 -3.833016*** 0.8470426

Elected

0.1667427 0.2295181 -0.267775 0.2875125

Party leadership -0.1257306 0.2662283 -.6399805** 0.3190258

Politico-

administrative

experience

0.3031709 0.2470301 0.0660918 0.3154972

Absolute majority 0.3732986 0.4044641 9.615449** 4.258134

Number of previous

reshuffles

-.4491204** 0.2024289 0.1065571 0.2197962

Appointed at the

beginning of the term

-.7688462*** 0.2780207 0.1006014 0.3717069

End of the term 4.354897 3.638695

Party’s tems in ofice .4846812** 0.2136651 .7105531*** 0.2935729

In-between

reshuffles

1.018924*** 0.3006538 11.54324*** 3.606453

TIME

INTERACTIONS

T*Damaging sit. 0.7753658 0.4304457

T*Calvo-Sotelo -0.8962377 1.08476

T*González -0.5048158 0.6208147

T*Aznar -0.9058368 0.7536335

T*End of the term -1.269925** 0.5755643

T*Intra-Cabinet

Conflict

-.7362224* 0.3829563

6 We exclude resignations, because they are considered not a PM decision.


24

T*In-between reshuffles

-1.903359*** 0.5758116

T*Absolute majority -1.26471** 0.6376575

Xi square df Xi square df

203.66 22 105.25 17

No. Obs. 354 No. Obs. 354

Note: * p<0.1, ** p<0.05, *** p<0.01

First thing to notice is that we have partial confirmation for Hypothesis 1. Model 1

suggests that ministers’ experience decrease the risk of being moved out. Nevertheless, neither

ministers’ experience nor expertise this seems to be not important in case of reshuffle or

readjust (Model 2).

Regarding public image dimension (Hypotheses 3 and 4) they are partially confirmed.

On the one hand, there is a surprising and counterintuitive result: ministers involved in

damaging issues decrease their hazard rate compared to those who are “clean”. Nevertheless,

this is only the direct effect, for if we look at the time-interaction it conforms to our previous

assumption that damaging issues increase ministers’ exit risk. This is the interesting point of

including interactions. Thus, when a minister is involved in a damaging issue it is expected that

it runs against him only when term is advanced (see figure 11). It is likely that this direct effect

has to do with what we call the “end of term effect”: because of ministers’ exit at the end of the

term are less frequent than those occurring during the period (see figure 12), this fact may be

reflected in Model 1. Indeed, when we restrict our analysis to only reshuffles, both direct an

interaction effects disappear, what tell us that other covariates than being involved in a

damaging issue influence reshuffles.

Figure 11. Smoothed hazard rate function by damaging issue.

0.0005

.001

.0015

.002

0 500 1000 1500analysis time

exocris = 0 exocris = 1

Smoothed hazard estimates, by exocris


25

Figure 12. Differences between ministers who continue

and those who exit (end of term effect)

End of legislative term

L VII

L VI

L V

L IV

L III

L II

L I

LC

Fre

qu

en

cy

20

10

0

Exit

No

Yes

Furthermore even experience in cabinet positions has not any significant effect on the

dependent variable. Then we reject Hypothesis 3.

Regarding Hypothesis 7 (which corresponds to the organizational equilibrium

maintenance dimension) results confirm it. If the minister is involved in any conflict within

cabinet this fact increases his chances of being expelled from the cabinet in both models.

Nevertheless, here we found another “end of term effect”, which is reflected in the interaction

along time in Model 1. Thus, as the end of the term approaches, those ministers involved in

conflict with other ministers tend to last, maybe for they are the “winners” of the dispute. This

could explain why there is not interaction effect in Model 2.

Regarding the other covariates, they are divided in two main groups. The first group

includes individual characteristics. Neither party leadership nor politico-administrative

experience nor being elected for the corresponding legislative period appear to have any

influence at all on the risk of being moved out of cabinet when we take all kind of exit events.

However, in case of reshuffle, occupying a leadership position in the party in office decreases

the hazard rate significantly.

The other group of covariates tries to cope with contextual circumstances surrounding

ministers’ career along term. Firstly we consider the Prime Minister set of covariates. As we

have noticed above, these variables act as a sort of “catch-all” factor which assembles a series

of latent and period specific influences. Here we find another end of term effect. In Model 1,


26

there is evidence that PMs does not have any significant effect on the ministers’ hazard rate.

However, in Model 2, direct and significant effects suggest that being member of these

cabinets involves a higher exit risk regarding Suárez’s cabinets (which is the category of

reference).

Another interesting result is that time matters (Hypothesis 10): previous in-between

reshuffles along term increase ministers’ hazard rate. Nevertheless, this effect is opposed by the

logic fact that the more the number of previous reshuffles, the less the probability of being

moved out of cabinet.

Also related to the time-dependent dimension of the process we are dealing with is the

fact that being appointed at the beginning of the term reduces a minister’s exit risk in all cases.

Furthermore, the end effect we have mentioned above is present when we control for those

ministers who survive until the end of the term. The effect of this covariate depends on time.

Model 1 shows how as minister’s duration increases, to be a “survivor” also raises his chance

of remaining in cabinet.

Finally, we have detected what we call an “exhaustion effect”. It is related to the

number of consecutive terms a party is in office, for the more the same party is responsible for

the steer of the country, its ministers run a higher risk of being sacked. Furthermore, this

“exhaustion effect” is particularly important in case of reshuffle, as it is shown in Model 2.

This is coherent with the positive significant relationship discovered between minister’s

experience and minister’s exit as a consequence of a reshuffle, which indicates that

“governmental fatigue” may be a factor to take into account.

Table 7 shows the Cox’s models for PP Y PSOE ministers. It will allow us to add some

previously untested hypothesis -2, 5, 6, and 9- to our models. Episodes have been split by the

time of the first previous reshuffle during term, and by the time of the most important

damaging issue affecting the minister.


27

Table 7. Cox’s non-proportional hazard models

PSOE and PP ministers (n=85)

MODEL 3- EXIT (ALL EVENTS)

MODEL 2- EXIT (EXCEPT END OF TERM)

Coef. Std. Err. Coef. Std. Err.

Experience in cabinet (days) .008686*** 0.0030866 -.0016293*** 0.0005882

Expertise -0.2604085 0.5112341 -1.542492* 0.9179786

Damaging situation 2.286034** 0.9776567 4.855607** 2.128227

Ministers previously

protected by PM

-1.193962*** 0.3017279 11.25818** 5.147387

Intra-cabinet conflict -0.3456258 0.4897321 -0.9679086 0.7760368

Aznar -38.18653 24.41245 8.631466*** 2.846356

Pmprotects1 -0.0534553 1.135124 -0.3261558 1.618293

Pmprotects2 -2.229421 1.700395 2.276529 .

Partyprotects1 -0.5934393 0.933575 -1.046322 1.251493

Partyprotects2 1.625269 1.451228 -4.006747*** 1.123878

Elected .9795477** 0.468099 0.8643329 0.6910395

Party leadership -1.417704** 0.566572 -2.996612** 1.239355

Difference with cabinet

mean evaluation

.7956442* 0.4435778 1.854106** 0.7954903

Political-administrative

experience

-0.1294169 0.3915206 0.1194781 0.6256002

Absolute majority -37.47304** 17.50279 13.32862*** 3.657389

Number of previous

reshuffles

30.56401 19.35679 -6.529939** 2.777179

Appointed at the beginning

of the term

-2.388751*** 0.8468578 -2.378012 1.677735

Exit at the end of the term 36.1743*** 10.65223

Party’s tems in ofice -14.39515* 7.936947 10.48137*** 2.823851

In-between reshuffles 1.625674 1.110269 -34.81587 32.26967

TIME INTERACTIONS

T*Exit at the end of the

term

-6.242012*** 1.63681

T* Experience in cabinet -.0015607*** 0.0005032

Number of previous

reshuffles

-5.824052* 3.151344

Aznar 6.887126* 3.68023

T*Party’s tems in ofice 2.965427** 1.283341

T*Absolute majority 6.888006** 2.825067

T*Ministers previously

protected by PM

-2.527638** 0.9768548

T*In-between reshuffles 5.299312 5.080493

Xi square df Xi square df

172.33 26 178.53 21

No. Obs. 210 No. Obs. 210

Note: * p<0.1, ** p<0.05, *** p<0.01


28

We can see that Hypothesis 1 is also partially confirmed. Ministers’ experience and

expertise contributes to decrease exit risk in the case of reshuffle. On the contrary, minister’s

exhaustion seems to be more important in case of ministers arrive at the end of the term. Then

we can conclude than in PSOE and PP cabinet, policy performance was an important factor in

order to determine ministers’ survival during term. Nevertheless, Hypothesis 2 must be

rejected, as PM protection does not have any significant effect on hazard rate.

Regarding Hypotheses 3 to 6, we detect an interesting pattern. The significant and

positive coefficient that ministers’ experience shows in Model 3 indicates that, in general

terms, ministers’ exhaustion is taken into account by the PM. In addition, the results in Model

4, allow us to precise this latter assertion, for it indicates that this “exhaustion effect” plays a

role only after the term ends.

In the case of damaging issues have a significant effect in the expected direction in

both models, which permit us to accept Hypothesis 4. Nevertheless, though we did not include

it in our hypotheses, we detect that the pattern of party protection is important during the

PSOE and PP cabinets. To be more precise, party’s indifference to minister’s “troubles” makes

a difference in case of reshuffle.

Besides, regarding Hypothesis 5, we find a very intriguing result, which is that being

evaluated above the cabinet’s average increases ministers’ hazard rate.

We have tried to test Hypothesis 6 by introducing an interaction effect between

damaging situation and PM protection. However, it has been impossible to run the model

because of problems of collinearity. Nevertheless, the results obtained which refer to the time-

dependence of the processes and, more specifically, to the effect of the previous number of

protected ministers (see below) allow us to assert that there is a less direct, but important,

relationship between PM protection and damaging situations.

Regarding organizational equilibrium maintenance hypotheses, we find that intra-

cabinet conflict drops out from Models 3 and 4. A possible interpretation for this fact is that in

PSOE and PP’s cabinets internal conflicts have been less frequent than in the UCD’s cabinets,

or that the relative weight of this type of conflicts compared to external issues has decreased.

We will see this more deeply in the next section. In sum, whatever it has happened with intra-


29

cabinet conflicts, they are no more a variable to take into account, so we have to reject

Hypothesis 7 on the relationship between changes within cabinet and the maintenance of

organizational equilibrium.

Regarding ministers’ individual features, Models 3 and 4 reveal the influence of party

leadership positions. This covariate shows for both models a significant and negative

coefficient, what means that those occupying one of these positions have higher survival

chances. Besides, for Model 3 those who are elected in the term less exposed to abandon

cabinet.

Table 7 also shows evidence of the importance of contextual factors. Thus, a minister in

an Aznar cabinet has a lower hazard rate at the beginning. However, as time goes his risk

increases in the case of reshuffles.

Moreover, Models 4 also confirm the importance of context and pass events. Thus, we

see how the number of previous reshuffles decrease the ministers’ hazard rate in the case of

reshuffles and readjusts.

Also confirming the importance of time, we see how those ministers who are appointed

at the beginning of the term still are less likely to be moved out from the cabinet. But this only

happens in Model 3, whereas in Model 4 this effect disappears.

We finish this section by underlining what we think is the most interesting finding in

those models, which is also related to time-dependency. We have tested Hypothesis 9, which

specifies that the number of ministers previously protected influence ministers’ exit risk. We

have seen how PM protection does not have any significant direct influence. Nevertheless,

Dewan and Dowding (2003) suggest that as the number of previously protected ministers is an

important factor when the PM decides whether to execute mechanisms of collective

responsibility or to leave a minister on his own. According Dewan and Dowding, as the

number of ministers protected by the PM increase, the probability of a new minister to be

protected decreases.

Looking at Model 3, the significant negative coefficient of covariate number of

previously protected ministers seems to deny Dewan and Dowding’s assumption. This means


30

that the more the number of ministers protected, the more the hazard of being sacked is

reduced. However Model 4 contradicts this assertion, showing that we face another end of term

effect. In the latter model we find that the number of previously protected ministers has a

significant positive effect on ministers’ hazard rate. This would confirm Dewan and Dowding

hypothesis, that there is something like a “limited stock of protection” that the PM can offer to

their ministers. Nevertheless, the time-interaction covariate, T*Ministers previously protected

by PM, with his significant negative effect contributes to introduce some confusion to our

conclusion.

5. DISCUSSION

This paper started from the assumption that changes within cabinet can be used as an

indicator of the PM’s behaviour. From the three dimensions we have distinguished -policy

performance, public orientation, and organizational equilibrium maintenance- we have found

evidence supporting these three patterns. However, as models 3 and 4 shows, policy

performance dimension is more important in PP and PSOE cabinets. As we can see in Table 8,

all PMs do prefer high skilled ministers. However, in the UCD’s cabinet, the other dimensions

were prevalent, and particularly organizational equilibrium maintenance. UCD was a very

unstable party, highly factionalised, so that pattern appears to be the most determinant one in

Suarez and Calvo-Sotelo’s decisions on changes within cabinet. Besides, the disappearing of

the influence of this dimension in Models 3 and 4 confirms the latter assertion, for PSOE and

PP had been more structured and united organizations.

Table 8. Average proportion of experts

after any cabinet change

Prime Minister Average proportion

Suárez 0,69

Calvo-Sotelo 0,69

González 0,75

Aznar 0,65

Government public image seems to influence PMs’ decisions on cabinet changes, but

this relationship is not as straight as we thought. According to our results, the influence of

damaging issues depends on moment and the context an issue arises and regarding the


31

importance of the other dimensions. Thus, we see that these damaging issues are above all

important in the case of the PSOE and PP’s cabinets. Effect disappears in model 2, maybe

because of the primacy of organizational dimension in the case of UCD’s cabinets.

Nevertheless, the current stage of our research does not allow us to close the debate here, and

more research is needed.

On the other hand, the quite strange result regarding ministers’ public evaluation –that

is, most valued ministers are proner to exit from cabinet-, forces us to be cautious, and

postpone any definitive conclusion in that respect. In our analysis we have considered

ministers’ public evaluation in a very static manner. Maybe exploring other variables, as

ministers’ visibility will offer a most clear result.

Cabinet public image is related to the operation of collective responsibility

mechanisms. It was expected that the PM’s pattern of protection influence ministers’ exit risk.

Nevertheless, we have found no evidence confirming this assertion. We are not saying that this

relationship should be dismissed. What appears in the Spanish case is that there is a tendency

in PMs to publicly defend their ministers when they are involved in damaging situations (see

figure 13). In addition, although our data confirm the existence of some kind of “limited stock

of protection” -in a quantitative sense-, it would also be interesting to test this protection

pattern regarding different types of damaging situations. It is plausible to think that PM

protecting behaviour will differ depending on whether protection is publicly accepted (i.e.

when a minister is criticised by the policies he enacts) or not (i.e. in case of explicit scandal).

Figure 13. PM protection pattern

End of legislative period

L VII

L VI

L V

L IV

L III

L II

Frequency

16

14

12

10

8

6

4

2

0

PMprot

No

Yes

Not involved


32

Regarding the organizational equilibrium maintenance dimension, it is also important.

We have seen how intra-cabinet conflict has a significant influence in ministers’ exit risk.

Unfortunately, we were not able to test the hypothesis on party factionalism. Nevertheless,

Models 3 and 4 may offer indirect confirmation of the importance of factionalism in cabinet

changes. It is known that the UCD was a highly factionalized party, compared to the PSOE

and, above all, the PP. Then, when dropping UCD ministers from our dataset, party’s degree of

factionalism decreases, and this fact may be reflected in the loss of significance of intra-

cabinet conflicts we observe in Models 3 and 4, which only apply to PSOE and PP cabinets.

In our opinion, the most interesting finding in our analysis is the importance of

contextual and situational factors. Events per se do not lead to changes within cabinet,

regardless of whether they are damaging issues or disputes between ministers, but they depend

on when they occur. Context is taken into account by the PM confronts those events. For

example, our data show that the effect of damaging issues increases along time in general

terms. Besides, ministers’ exhaustion has a variable effect, depending of the period. Thus,

Models 1 and 2 show that this factor is important in case of reshuffles. On the contrary, if we

consider only the Gonzalez and Aznar cabinets, the effect reverses, and minister’s experience

becomes a handicap for ministers who survive until the end of the term.

Time is also taken into account by the PM in order to decide the appropriate moment

for reshuffling or readjusting the cabinet. We have detected a significant relationship between

previous reshuffles and ministers’ hazard rate, although it varies depending of the dataset we

consider. Nevertheless, these relationships constitute an evidence of the particular

distributional logic followed by cabinet changes along time. This distribution seems to respond

to the PM’s strategic calculus. For example, in the case of Models 1 and 2 we see that in-

between reshuffles contribute to increase minister’s risk, but that in case of reshuffle this risk

decreases along time. This makes sense as it is likely that the PM’s does not decide to reshuffle

as the constitutional inter-election period approaches to its end. In addition, we can see how the

number of previous reshuffle also contributes to reduce ministers’ hazard rate (Models 1, 3,

and 4), because there is a limited stock of reshuffles at the PM’s disposal during term.

Figures 14 and table 9 confirm this pattern. The first shows the dispersion pattern of

cabinet reshuffles and end of terms by legislative periods. We can see that the time span until a


33

reshuffle take place depend on the legislative period. On the other hand, the average number of

days passed since the last reshuffle until the end of the term is 383.6 days.

Figure 14. Cabinet reshuffles and ends of term, by legislative period*

century days

40000

38000

36000

34000

32000

30000

28000

Days since the b

eginning o

f th

e term

1600

1400

1200

1000

800

600

400

200

0

L VII

L VI

L V

L IV

L III

L II

L I (Calvo)

L I (Suárez)

Const

*Last point in every legislative period corresponds to the end of term.

Table 9. Reshuffles distribution along time (days)

First

reshuffle

Last

reshuffle

Constituent 234 405

L I (Suárez) 285 170

L I (Calvo-Sotelo) 186 126

L II 944 386

L III 716 513

L IV 459 855

L V 295 307

L VI 988 465

L VII 305 226

Mean 490.2 383.6

Figure 15 shows the hazard rate function estimated from the results obtained in Model

1. Its shape proves the relationship between time and the PM’s decision of reshuffling the

cabinet. Thus, we see how hazard rate first increases, until a certain moment where it starts to

decrease in a moderate manner. Then, as a minister’s duration in office increases, hazard rate

drops more rapidly. This drop of the hazard rate at the end also confirms the “end of term

effect” we have mentioned above.


34

Figure 15. Ministers’ estimated hazard rate function (Model 1)

.01

.015

.02

.025

.03

Sm

ooth

ed h

azard

function

0 250 500 750 1000 1250 1500analysis time

Cox proportional hazards regression

6. CONCLUSION

In the previous pages we have intended to offer an explanation of the logic behind

changes within cabinet in contemporary Spain. We contend that the study of the causes of

ministers’ exit during the term can be considered an indicator of the PM’s strategic behaviour.

This is particularly relevant if we take into account the presidential features of the PM in the

Spanish executive.

Our results suggest that, all the three patterns we have distinguished regarding the PM’s

strategic behaviour appear. However, as the case of UCD shows, when party is not united, the

organizational equilibrium maintenance dimension prevails.

In addition, another conclusion is that these factors must be considered in context. In

this sense, time seems to be a major variable influencing the PM’s decisions. Changes in

cabinet are associated to time-dependent elements like the previous changes occurred during

term.

Finally, these pages do not close the topic at all. On the contrary, they just set the scene

for later inquires. As we have signalled above, our data must be improved, and other more

refined measures of our variables should be included, which permit us to deal with other

factors which still remains unexplored. We expect to do this in next paper.


35

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