Download Public Opinion Shocks and Government Termination Lanny W. Martin Visiting Assistant Professor

Public Opinion Shocks and Government Termination∗
Lanny W. Martin
Visiting Assistant Professor
Department of Political Science
University of Houston
Houston, TX 77204
E-mail: [email protected]
Phone: (713) 743-3893
Fax: (713) 743-3927
November 1999
Abstract. The ability of a government to remain in power depends partially upon its
vulnerability to unexpected changes occurring in the outside political environment. In this paper,
I examine the relationship between government termination and changes in the electoral
expectations of political parties in the legislature, as reflected by shifts in popular support for the
government. I find that the decision to terminate the government is related in complex ways to
changes in public opinion. Governments are more likely to collapse as certain members of the
incumbent coalition expect to gain more ministerial portfolios, and in cases of minority
government, when the opposition expects to gain more legislative seats. Further, I show that
these effects increase with the approach of regularly-scheduled elections.
∗
I would like to thank Bing Powell, Renée Smith, and David Austen-Smith for helpful comments on previous drafts. This
is a work in progress, and all suggestions for improvement are welcome. All analyses were performed using STATA 5.0
and S-Plus.
Introduction
Comparative scholars interested in the question of government termination in
parliamentary democracies have gradually come to recognize that the ability of a government to
stay in power depends in part on its vulnerability to unexpected changes in the larger political,
social and economic environment.1 This observation follows from the idea that the government
that initially emerges from the coalition bargaining process is simply a product of the
circumstances prevailing in the legislature (and the country as a whole) at the time. Over the
course of the government’s tenure, these circumstances can change quite dramatically.
Governments must continually face a number of unexpected events—scandals, wars, international
currency crises, and so on—that have the potential to trigger a premature collapse. In a
parliamentary context, such events, to be critical, presumably would have to reshape the existing
bargaining environment to such a degree that a party whose support had been essential to the
government’s ability to maintain the confidence of a legislative majority instead finds it in its
interest, in the wake of the event, to withdraw its legislative support.
In this paper, I investigate the impact of one class of critical events, public opinion
shocks, on government termination. This focus permits a fresh look at the interaction between
political institutions and the strategic calculations of key legislative actors in parliamentary
democracies. For example, in these systems, constitutional rules, beyond requiring that a
government must retain the confidence of a chamber majority to remain in office, generally
confer asymmetrical power to the prime minister to dissolve the legislature and call new elections
(Huber 1996). One particularly interesting question is whether (and how) prime ministers, when
faced with new information regarding anticipated voter behavior, will exploit the capability
bestowed upon them by these dissolution procedures to gain partisan advantage.
1
Although many observers of day-to-day politics in parliamentary democracies might find this point obvious, political
scientists have only recently begun to take account of the random component of government instability. The liveliest
debate on this subject, pitting approaches that have perceived government stability as essentially deterministic against
1
Public opinion shocks have been a central focus of recent theoretical work on the impact
of critical events on government termination. In particular, Lupia and Strøm (1995) show, in a
game-theoretic model of a three-party legislature, that government coalition parties that stand to
increase their legislative seat share if an election were to be held immediately may either force an
early election, renegotiate the existing coalition agreement or continue to accept the status quo,
depending on the potential opportunity and transactions costs associated with these decisions.
This finding calls into question the intuitive assertion tendered by Grofman and van Roozendaal
(1994) that government parties will always force new elections when they expect to realize
electoral gains. Here, I offer a first empirical test of these conflicting ideas.
In the next section, I offer a brief review of several recent studies of this government
termination, paying particular attention to their treatment of critical events. Building upon the
key insights from this research, I develop three sets of hypotheses relating the electoral
expectations held by political parties to their decision to bring about the collapse of the incumbent
government. I then describe the data and discuss the recently-developed statistical techniques
that will be used to test these hypotheses. Finally, I present the results from my analysis and
highlight several new findings linking recent theoretical research in this field to the real-world
experiences of coalition governments.
Critical Events, Public Opinion Shocks, and Government Termination
A useful first step towards understanding the relationship between government
termination and public opinion shocks is to understand how scholars have come to view the role
of exogenous shocks in general. Several recent studies have made significant contributions to the
body of knowledge on this subject, and these contributions constitute the building blocks of the
analysis in this paper. I begin, then, by presenting some of the more valuable insights from recent
research on critical events and government termination.
those that have modeled it as completely random, can be found in Strøm, Browne, Frendreis and Gleiber (1988). For
later treatments of this subject, see King, Alt, Burns and Laver (1990) and Warwick (1992b, 1994).
2
The first important insight concerns the basic issue of how best to conceive of
government survival and the process underlying it. One approach models government survival as
a deterministic function of a set of attributes of the government, such as its majority status and
ideological make-up, as well as attributes of its bargaining environment, such as the degree of
polarization and fractionalization of the legislature (Laver and Schofield 1990; Strøm 1990;
Powell 1982; Dodd 1976; Warwick 1979, 1992b, 1994; King, Alt, Burns and Laver 1990). A
second approach models government survival purely as a stochastic process whereby
governments are bombarded by a continuous stream of outside random shocks—scandals,
economic crises, etc.—each of which has some probability of bringing down the government
(Browne, Frendreis and Gleiber 1984, 1986, 1988). The basic message from this approach is that
no one government is inherently more durable than another and that the search for systematic
determinants of government termination is therefore pointless.
In an effort to reconcile these two seemingly incompatible views of government survival,
King, Alt, Burns and Laver (1990) showed that it is possible to specify an empirical model in
which government survival is a function of both a systematic and a stochastic component.
Comprising the systematic component in this “unified” model are a set of independent variables,
or covariates, that represent characteristics of the prevailing party system, or of the government
itself, or certain institutional features of the legislature. The stochastic component of the model
adheres to the Browne et al. specification of an exponential hazard rate, which assumes that
random shocks occur at a rate conforming to a Poisson process. Substantively, this translates into
the assumption that the probability that a government will fall at a particular point in time, given
that it has survived up to that time, is constant throughout its term in office. King et al. find that
seven covariates in particular have a systematic impact on government survival: the majority
status of a government, its caretaker status, the number of formation attempts preceding its
formation, the polarization of the legislature, legislative fractionalization, the presence of an
3
investiture rule, and a variable indicating whether a government formed immediately after an
election.
Although the essential point of the “unified” model—that government survival can be
usefully modeled as a function of a set of causal variables as well as a stochastic process that
incorporates the notion of exogenous shocks—remains undisputed in empirical research in this
field, some disagreement has surfaced concerning the correct specification of the unobservable
stochastic component of the model. In particular, Warwick (1992a) and Warwick and Easton
(1992) have argued that the assumption of a constant hazard rate is unwarranted and that, in fact,
government termination rates seem to follow more complex patterns than the King et al. model
allows. As a result, these studies advocate a model that makes less restrictive assumptions about
the shape of the underlying baseline hazard rate. The “partial likelihood” approach employed by
Warwick (1992b, 1994), for example, only makes the assumption that the hazard rate follows
exactly the same pattern over time for each government without placing any restriction on what
this pattern may be.2 Because of the less restrictive assumptions associated with this model,
researchers have since tended to favor this approach over the parametric approach employed in
the King et al. study (Warwick 1994; Alt and King 1994; Martin and Stevenson 1995; Diermeier
and Stevenson 1999a).
Another objection to the King et al. study is that all of the covariates in “unified” model
are fixed for the life of the government and that it therefore neglects to take account of those
systematic factors that vary over the government’s term. This is obviously an important issue for
researchers interested in finding evidence of a systematic empirical relationship between
government survival and the occurrence of exogenous shocks. Offering an alternative approach,
2
This “proportional hazards” assumption is by no means trivial, however. Box-Steffensmeier and Zorn (1998) show that
an incorrect assumption of proportionality can lead to biases in the estimates of the covariates and mistaken inferences.
Diermeier and Stevenson (1999), however, find no evidence of nonproportionality in the data used by the King et al. and
Warwick studies. My own tests of the proportionality assumption for the data set used in this paper also reveal no
evidence of nonproportionality for the Warwick and King variables.
4
Warwick (1992b, 1994) introduces monthly economic trends in inflation and unemployment into
his analysis and finds, as one might expect, that the changing state of the economy has an effect
on the ability of a government to survive in office. He also finds evidence that the ideological
diversity of the government and the “returnability” of cabinet members to office in the event of
government collapse (the main variables of his “ideological diversity” approach) reduces the
substantive importance and statistical significance of several of the King et al. variables,
specifically the number of formation attempts, the effective number of legislative parties
(fractionalization), and legislative polarization (the key variables of what Warwick terms the
“bargaining complexity” thesis of the “unified” model) as well as a government’s caretaker
status. For present purposes, the most important insight from the Warwick studies is that it is
possible within the basic “unified” framework introduced by King et al. to incorporate exogenous
events directly into the systematic component of the empirical model.
Various types of events have the potential to destabilize the legislative bargaining
environment and thus lead to the collapse of the government. Laver and Shepsle (1998) identify
four broad classes of potentially critical exogenous events that represent shocks to those
parameters identified as fundamental variables in most models of coalition formation. These key
parameters concern the decision rule used to sustain a government in office, party positions on
salient policy dimensions, the weights that parties attach to these dimensions, and the distribution
of legislative seats.3 Thus, exogenous events may become critical events if they “sufficiently”
alter the level of support governments must attain to survive votes of no-confidence (decision rule
shocks), force a change in the (electorally-induced) party ideal points on a given set of issues
(policy shocks), rearrange the relative importance of issue dimensions (agenda shocks), or alter
the expectations parties hold concerning the results of the next election (public opinion shocks).
3
As Martin and Stevenson (1999) show, these variables map into several characteristics of a coalition that are theoretically
related to its odds of taking office, such as its majority status and ideological profile, among many others.
5
In this study, I examine the impact of this latter class of events, public opinion shocks, on
government termination. Thus, events here correspond to public opinion polls, indicating the
voting intentions of the electorate, that provide each party with information about the distribution
of legislative seats that would result if an election were held immediately. Presumably, political
parties are able to use this information to forecast their post-election payoffs in terms of seats,
policy, and ministerial portfolios. The relationship between the benefits and costs of terminating
the incumbent government (whether by calling early elections or by a non-electoral reallocation
of power) and the benefits and costs of allowing the government to continue in office determines
whether this event leads to governmental collapse.
Lupia and Strøm (1995) develop this reasoning using a game-theoretic model of coalition
bargaining in a three-party legislature, where a legislative majority is endowed with the power
both to dismiss the government at will and to dissolve the parliament and force early elections.
The occurrence of an exogenous shock changes the electoral expectations of pivotal legislative
actors and thus alters the prevailing bargaining environment. These actors then make decisions
about whether to continue supporting the incumbent government by evaluating new governmental
alternatives against the expected utility of allowing the present government to remain in power.
Parties that once had an incentive to support the government may, in the wake of the exogenous
shock, have reason to withdraw their legislative support.
Lupia and Strøm identify the benefits each party receives from maintaining the status quo
as a simple additive function of its current share of seats and its share of the policy and officerelated benefits (denominated in its share of ministerial portfolios) of membership in the current
coalition. The benefits from bringing down the government are simply the gains in legislative
seat share and share of coalition membership that the party would receive with a post-electoral
distribution of votes or such gains that it is able to extract from other parties with less favorable
electoral prospects. Bringing down the government also carries with it certain transaction costs,
however, such as the costs associated with mounting an election campaign or the costs of
6
engaging in a new round of coalition negotiations. Lupia and Strøm also identify certain
opportunity costs to calling elections, such as “the forfeiture of the policy-making opportunities
or rent-collection opportunities made possible by holding valuable offices” (Lupia and Strøm
1995, 654). The reason these costs exist is that early elections force parties to sacrifice the
current benefits that they would be able to receive if the incumbent government were allowed to
complete its constitutional term in office. An early election in effect “resets the clock” on the
constitutionally-mandated limit on the maximum length of a cabinet’s term.4 The opportunity
costs of calling an early election, then, are exactly the benefits of retaining the status quo
discounted by the time remaining in the constitutional inter-election period (CIEP). One
important result of their model is that anticipated electoral gains are by themselves an insufficient
condition for government dissolution accompanied by parliamentary dissolution and early
elections. That is, “[in coalition] circumstances, a party with favorable electoral prospects will
also consider the option of extracting advantages through non-electoral means (e.g., bargaining
with parties that have less favorable prospects)” (Lupia and Strøm 1995, 655). Another
interesting insight, given the assumption that the opportunity costs associated with bringing down
the current government decrease throughout the CIEP, is that the likelihood of an event
destabilizing a government increases throughout the government’s term.
Unfortunately, the reliance of the Lupia and Strøm approach on the notion of partyspecific transactions costs combined with the lack of any straightforward means of
operationalizing these costs means that it is not yet possible, strictly speaking, to test their model
of government termination. Exclusive of transactions costs, however, it is possible to examine
whether their basic insight—that events relaying information to political parties regarding an
4
Exceptions to this rule are Norway, where early elections and parliamentary dissolution are constitutionally prohibited
(which suggests that government termination in this country cannot adequately be addressed by the Lupia and Strøm
approach), and Sweden, where early elections and parliamentary dissolution do not “reset the clock” for the next
regularly scheduled elections, which always occur every third September (after 1990, every fourth September) regardless
of the occurrence of an unscheduled intervening election.
7
immediate election should have an impact on government termination—is consistent with the
experience of real-world coalition governments. If their logic is sound, it should be the case, for
example, that if a party in a position to bring down the government believes that by taking this
action it can increase its expected utility (which may be cast in terms of its legislative seat share,
portfolios, or policy-making opportunities), then it should do so. It may be able to force early
elections to realize these gains or it may be able to extract these gains through non-electoral
means from other parties in the government or opposition that wish to avoid elections. Either
way, it is the occurrence of the event signaling anticipated gains in this party’s electoral fortunes
that drive the process of coalition termination.5
Another problem with testing the Lupia and Strøm model in its current form is its
assumption that a chamber majority is required for a parliamentary dissolution. In most
parliamentary systems, the legislature is not able to dissolve itself by a majority vote (with
Austria and Israel as the exceptions). Rather, the prime minister is normally vested with the
formal authority to dissolve the legislature. Of course, a chamber majority may vote a prime
minister out of office and subsequently vote in a replacement who will dissolve the parliament—
which means that a prime minister cannot obstruct a chamber majority resolute on dissolution—
but it is also the case that a prime minister may dissolve the parliament against the wishes of the
chamber majority.
Another difficulty with the Lupia and Strøm approach concerns their assumption that the
government controls a majority of legislative seats. That is, they ignore minority single-party and
5
In the language of Lupia and Strøm, while favorable electoral prospects for any party are not sufficient to cause
government termination accompanied by parliamentary dissolution, they are sufficient to cause government termination
either accompanied by parliamentary dissolution or accompanied by a non-electoral redistribution of power
[demonstrated formally by Diermeier and Stevenson (1997)]. Thus, in the Lupia and Strøm model, if the expected utility
of holding early elections for any party (or parties) in a position to bring down the government is greater than the
expected utility of maintaining the current coalition, then the government will fall. This is the conclusion to be tested in
this paper. Whether a parliamentary dissolution and early elections also occur, however, depends upon whether this
party (or these parties) can derive greater utility from a non-electoral reallocation of power than from holding elections.
This depends substantially upon the nature of the unspecified transaction costs associated with these outcomes. Thus, I
do not distinguish between these two modes of termination in this study.
8
coalition governments. Of course, even minority cabinets must maintain the support of a majority
of legislators, but it is not clear if Lupia and Strøm’s logic necessarily extends to the case where a
party in the government support coalition holds no ministerial portfolios. I address this
possibility, as well as the possibility that the electoral expectations of the prime minister may
have a separate impact on government termination, in the hypotheses below.
In short, the analysis in this paper is not a test of the Lupia and Strøm model, per se, but
rather a test of some of the more intuitive insights emerging from their approach, as well as from
the earlier work of Grofman and van Roozendaal, that suggest that expected changes in the
distribution of legislative seats should affect the likelihood of government termination in a
systematic way, and that these effects are dynamic in nature. The present endeavor represents, to
my knowledge, the first empirical cut at these issues in the government survival literature.
The hypotheses below focus on the strategic incentives facing political parties in terms of
three bargaining parameters that are subject to change with the occurrence of elections. The first,
and most obvious, of these parameters is legislative seat share. As I discussed above, the Lupia
and Strøm model assumes that parties are motivated by their desire to control seats in parliament.
In their model, an increase in a party’s legislative seat share always results in a corresponding
increase in its utility, which is an additive function of its seat share and coalition payoffs. As I
also pointed out, different government and legislative actors may have unequal abilities to bring
about a government collapse. Hypotheses 1a and 1b address the purely seat-related incentives of
the prime minister to bring about the termination of the government:
Hypothesis 1a: The greater the expected increase in seat share for the party of
the prime minister in the event of an immediate election, the greater the
likelihood of government termination.
Hypothesis 1b: This effect should increase as the government nears the end of
the CIEP.
Hypotheses 2a and 2b address the primary message from the Lupia and Strøm model,
which suggests that parties in the governing coalition, all of whom have equal power to bring
9
about cabinet termination, will do so the more favorable the seat distribution that would result
from early elections. A government may consist of several parties, of course, but the party most
likely to bring about the termination of the government on the basis of its electoral expectations is
that party with the most to gain from government termination, i. e., that government party with
the most favorable prospects in terms of the expected increase in its seat share:
Hypothesis 2a: The greater the expected increase in seat share for the
government party with the most seat share to gain in the event of an immediate
election, the greater the likelihood of government termination.
Hypothesis 2b: This effect should increase as the government nears the end of
the CIEP.
I also address the possibility that, in the case of minority government, those parties
constituting the majority opposition are more likely to bring about the termination of the
incumbent government as their electoral prospects become more favorable. Moreover, just as
with parties in the cabinet, because opposition parties derive certain benefits from allowing the
current government to remain in office—although these benefits are perhaps less than for cabinet
members—they also would incur opportunity costs for the loss of these benefits in the event of an
early election. Again, these opportunity costs decrease the closer the end of the parliamentary
term:
Hypothesis 3a: When a minority government is in office, the greater the
expected increase in seat share for the opposition in the event of an immediate
election, the greater the likelihood of government termination.
Hypothesis 3b: This effect should increase as the government nears the end of
the CIEP.
Although Lupia and Strøm assume that legislative seat share is important in and of itself,
they also take account of its instrumental role with regard to its impact on government formation.
In particular, they focus on the impact of a public opinion shock on the expected number of
ministries a party would gain or lose in the aftermath of an immediate election as well as the
expected change in government policy that would result if a new government were to form. I also
10
account for the possibility in the following two sets of hypotheses. The first set concerns
ministerial portfolios:
Hypothesis 4a: The greater the expected increase in portfolio share for the party
of the prime minister in the event of an immediate election, the greater the
likelihood of government termination.
Hypothesis 4b: This effect should increase as the government nears the end of
the CIEP.
Hypothesis 5a: The greater the expected increase in portfolio share for the
government party with the most portfolios to gain in the event of an immediate
election, the greater the likelihood of government termination.
Hypothesis 5b: This effect should increase as the government nears the end of
the CIEP.
Hypothesis 6a: When a minority government is in office, the greater the
expected increase in portfolios for the opposition in the event of an immediate
election, the greater the likelihood of government termination.
Hypothesis 6b: This effect should increase as the government nears the end of
the CIEP.
Finally, the next set of hypotheses concern the expected change in government policy that
would result in the event of early elections:
Hypothesis 7a: The greater the expected increase in policy benefits for the party
of the prime minister in the event of an immediate election, the greater the
likelihood of government termination.
Hypothesis 7b: This effect should increase as the government nears the end of
the CIEP.
Hypothesis 8a: The greater the expected increase in policy benefits for the
government party with the most portfolios to gain in the event of an immediate
election, the greater the likelihood of government termination.
Hypothesis 8b: This effect should increase as the government nears the end of
the CIEP.
Hypothesis 9a: When a minority government is in office, the greater the
expected increase in policy benefits for the opposition in the event of an
immediate election, the greater the likelihood of government termination.
Hypothesis 9b: This effect should increase as the government nears the end of
the CIEP.
11
In the next section, I describe the data set and discuss the set of statistical techniques
required to test these three sets of hypotheses concerning expected changes in seat share,
ministerial portfolios, and government policy that would result in the event of an immediate
election.
Data and Methods
Testing the hypotheses outlined above, all of which focus on the influence of electoral
expectations on the decision to terminate the incumbent government, requires some plausible
measure of the anticipated distribution of legislative seats that would result from calling early
elections. While any number of indicators exist that politicians might use to gauge the mood of
the electorate, probably the most direct and most familiar measure is the public opinion poll.
Because modern survey techniques allow governments to assess the political climate on a
continual basis, the public opinion poll has become an extremely important tool for politicians.
Governments frequently attempt to evaluate the changing mood of society by conducting polls on
every aspect of political and social life. It would not be unreasonable to assume, therefore, that
governments would use their access to public opinion polls to help determine the optimal timing
for terminating the government.
The public opinion data set used in this study consists of popular support data for the
major political parties for the following countries and time periods: Belgium (1977-92),
Denmark (1960-87), Germany (1953-89), Ireland (1977-92), Italy (1976-89), Luxembourg (197992), The Netherlands (1981-92), and Sweden (1970-90). These data provide information on the
percentage of respondents indicating their intention to vote for a particular party based on the
following question: “Which political party would you vote for if there were an election today
(tomorrow)?” All surveys are based on random national samples of about 1,000 to 5,000
respondents each. The data for Denmark, Germany, and The Netherlands are all based on
monthly opinion polls and are provided by Anderson (1995). The monthly poll data from
Sweden were gathered by Sifo Research and Consulting. The popular support data for Belgium,
12
Ireland, Italy, and Luxembourg are estimated monthly values derived from linear interpolations of
opinion poll results collected every six months in the form of Eurobarometer surveys.6
Because parties are interested in possible changes in their seat share, not in their vote
share per se, it is necessary to transform this public opinion data on the expected percentage of
votes into data on the expected percentage of seats. Since all the countries in this study use
proportional representation systems, the percentage of votes a party wins in legislative elections is
roughly equivalent to the percentage of seats it wins in the parliament. No electoral system is
perfectly proportional, however, and the degree of disproportionality across systems differs
according to country-specific features such as assembly size, district magnitude, legal thresholds
of support that parties must meet to gain representation, and the type of electoral formula used to
transform votes into seats (Lijphart 1994). Unfortunately, since the public opinion poll data for
this study is aggregated (that is, poll results were pooled across districts) and since seats typically
are awarded in part according to results at the district level, it is not possible to calculate an exact
transformation of votes to seats. One can account for inter-country differences in proportionality,
however, simply by estimating individual country-by-country regression coefficients for party
vote share as it relates to party seat share in the set of elections in each of the countries and then
use these coefficients to transform expected party vote shares to expected party seat shares.7 As
shown in Table 1, a one-percent increase in a party’s vote share yields approximately a onepercent increase in its seat share in parliament, as one should expect in a proportional
representation system. Moreover, the fit of this simple model is extremely good, as indicated by
6
If P1 and P2 are the percentage values of support for a particular party at survey times t1 and t2, respectively, the
interpolated value of P at time T, where time is measured in months from the date of the prior opinion poll and where
t1<T< t2, is PT = P1 + [(T- t1) (P2-P1)]/(t2- t1). For months in which the Eurobarometer survey was conducted, actual
values, not interpolated values, will be used in the analysis.
7
Electoral systems may also differ within countries over time. Using Lijphart’s (1994) categorization of electoral systems,
I include in these individual country regressions only those elections in which the electoral system in place is the same
electoral system as for the time period covered in my sample. Thus, in terms of Lijphart’s classification, the electoral
systems in these regressions are BEL1 (Belgium: 1946-87), DEN3 (Denmark: 1964-88), GER3 (Germany: 1957-87),
IRE1 (Ireland: 1948-89), ITA3 (Italy: 1958-87), LUX1 (Luxembourg: 1945-89), NET2 (The Netherlands: 1956-89), and
SWE3 (Sweden: 1970-88).
13
the large t-statistics on the votes variables for each country as well as the R2-statistics, all of
which are above 0.98.
<<Table 1 about here>>
After this transformation, it is possible to create measures of the theoretical variables on
expected changes in party seat share by simply subtracting a party’s current seat share from its
expected seat share in the event of an immediate election. Table 2 presents descriptive statistics
of the seats variables for the prime minister, for all parties in the government including the prime
minister, and for the parties in opposition. (For the government seat variable, I present both the
average expected change in government seats and the change for the government party expected
to lose the fewest, or gain the most, seats.)
<<Table 2 about here>>
As the table shows, parties should not, on average, expect to experience large shifts in the
current distribution of legislative seats in the event of an early election. The prime minister and
other members of the government would experience an average loss of their current seat share of
less than one percent, while the opposition would experience an average gain in seat share of
approximately the same magnitude. The range in these variables, however, is fairly large in
substantive terms.
Given these data on the expected distribution of party seat shares, it is also possible to
derive expected changes in party policy benefits as well as expected changes in party portfolios in
the government. The calculation of these latter variables is obviously a rather more difficult
matter, however, as it first requires some idea of which parties are likely to form a new
government if the incumbent government collapses. Fortunately, the models of government
formation discussed in Martin and Stevenson (1999) provide a useful starting point in answering
this question. With estimates of the effects of various coalition characteristics on the probability
that any particular coalition will form the government, it is possible to generate formation
probability estimates for all potential bargaining situations. In Table 3, I present the conditional
14
logit estimates that will be used to calculate these formation probabilities. These results are
derived from estimating Model 12 in Table 3 from Martin and Stevenson (1999) using the full
sample of governments from their model.8 For each coalition, these probability estimates can
then be multiplied by the policy and portfolio payoffs for any particular party (whether in or out
of the coalition in question) and then summed across all potential coalitions in the bargaining
situation to produce the expected policy and portfolios payoffs for this party.
<<Table 3 about here>>
Prior to generating formation probabilities, it is necessary to produce a set of coalition
characteristics for all the potential coalitions in each prospective bargaining situation that would
result in the event of an early election. Because many of these coalition characteristics depend on
the distribution of legislative seats, prospective bargaining situations may differ dramatically
from month to month depending upon the information from public opinion polls regarding
expected seat share. Large swings in seat share could result in substantial shifts in the
distribution of formation probabilities across potential coalitions, which could in turn affect every
party’s expected policy and portfolio payoffs. Figure 1 illustrates the relationship between seat
distribution and the coalition characteristics previously found to affect government formation.
<<Figure 1 about here>>
Given a forecast distribution of legislative seats based on the information from the public
opinion polls, I generate monthly values for each of the variables in Figure 2 and for the
remaining ideological and institutional variables from Table 3. All together, the data set consists
of information on a total of 1,804 months (or prospective bargaining situations) and 247,864
potential coalitions. I generate formation probability estimates for every potential coalition in
every prospective bargaining situation and then, for the prime minister and government party
8
I use Model 12 instead of Model 13 because I am unable to generate Laver and Shepsle’s “strong party” variables, which
would require a program not available to me. I also exclude the electoral pact variables, as it does not seem reasonable
15
variables, I multiply each probability estimate by the corresponding policy and portfolio payoffs
for each of these parties. I discuss the opposition variables below. The policy payoff for any
government party is defined in the same way as the ideological division variable discussed in
Martin and Stevenson (1999), that is, as the distance between the most ideologically distant party
in the potential coalition and the party in question (regardless of whether this latter party is
actually a member of the potential coalition). The portfolio payoffs for any government party
correspond to the percentage of seats the party controls in the potential coalition, which ranges
from 0, in the case where the party in question is not a member of the potential coalition, to 100,
in the case where the potential coalition contains this party and no others.9 After multiplying the
formation probability for each potential coalition by its party-specific policy and portfolio
payoffs, I then sum these products across all potential coalitions in the prospective bargaining
situation to generate party-specific measures of expected policy and portfolio payoffs. I then
create measures of the theoretical variables on expected changes in party policy and portfolio
payoffs by simply subtracting a party’s current policy and portfolio payoffs from its expected
policy and portfolio payoffs in the event of an immediate election. Because of the complicated
nature of the data generation process, I provide a synopsis in Figure 2.
<<Figure 2 about here>>
In the case of the opposition variables, the data generation task is sufficiently massive—
because of the large number of parties in the opposition at any given time for most of the
countries in this sample—that I have to treat the opposition collectively. Specifically, I define the
expected policy payoff for the opposition in a prospective bargaining situation as the sum of the
to assume that pacts that apply to the initial bargaining over government formation would necessarily “carry over” to a
new round of negotiations brought about by a government collapse.
9
This measure assumes that parties entering government will receive an allocation of ministerial portfolios roughly in
proportion to the percentage share of their coalition seats. Sometimes referred to as “Gamson’s Law,” alluding to early
work by Gamson (1961), this proportionality norm is a very good predictor of the allocation of cabinet portfolios.
Browne and Franklin (1973) show that this norm explains about 90 percent of the variation in the real-world allocation
of cabinet seats.
16
seat-weighted policy distances of each opposition party from the most ideologically distant party
in the government multiplied by the probability that the current government will form again.
Thus, the expected policy benefits for the opposition are greater across prospective bargaining
situations the lower the probability the same government will re-form or the smaller the
ideological distance between government and opposition members. I define the expected
portfolios payoffs for the opposition simply as the probability that the current government, or any
subset of parties from the current government, will not form again. Thus, this is the probability
that at least one opposition party will enter the government in a prospective bargaining situation.
As with the government party variables, I calculate the expected changes in opposition policy and
portfolio payoffs by subtracting its current policy and portfolio payoffs from its expected policy
and portfolio payoffs in the event of an immediate election.
Table 4 presents descriptive statistics of these variables for the prime minister, for all
parties in the government including the prime minister, and for the parties in opposition. (Again,
for the government variables, I present both the average expected change for government parties
and the change for the government party expected to lose the fewest, or gain the most, policy and
portfolio benefits.)
<<Table 4 about here>>
These statistics show that the party of the prime minister and its partners can, on average,
expect a slight decrease in their policy benefits (or an increase in their policy costs, accounting
for the positive sign in this table) in the event of an immediate election, while the opposition can
expect a substantially larger increase in its policy benefits. Similarly, government parties on
average can expect to lose portfolio share. The prime minister would lose an average of ten
percent of its ministerial portfolios in the event of an early election, while the average coalition
partner would lose approximately fifteen percent. Meanwhile, the probability that at least one
party currently in the majority opposition will enter the new government in a prospective
bargaining situation is, on average, approximately fifty-five percent.
17
The dependent variable in this study is government termination, a dichotomous variable,
coded monthly, that takes the value “1” if the government collapsed in a given month and the
value “0” if the government remained in office or was censored. Observations are treated as
(right-)censored if they have yet to experience a failure (in this case, government termination) at
the end of the observation period or if this failure is considered by the researcher to be artificially
imposed or “theoretically uninteresting” (Warwick 1992b). For example, both Warwick (1992b,
1994) and King et al. (1990) censored governments that terminated because of the death of illness
of the prime minister, because of legal or constitutional requirements, because they had not
terminated by the end of the observation period (relevant only in the Warwick studies), or
because of the approach of regularly-scheduled elections within the coming year.
The advantage of censoring is that it allows the researcher to incorporate into the
statistical model all available information about a subject’s duration, namely, that it lasted at least
so long as the observed duration, though it could well have lasted longer had the censoring
mechanism not come into play. Accounting for censoring is clearly a superior approach to
assuming (as a regression-based approach would) that the observation actually ended its spell at
the point it was censored or to excluding the censored observations altogether. Both of these
approaches would produce biased estimates of the covariates included in the model (with the
degree of bias depending in part upon the number of censored cases in the sample). One potential
disadvantage of censoring, however, is that if the rule by which observations are censored is
related to the dependent variable, then the standard event history techniques are inappropriate and
will also lead to bias and mistaken inferences (King 1989; Achen 1986). This presents a
potential problem for using the full Warwick and King et al. censoring scheme, which censors
those governments that within one year of the end of the CIEP “voluntarily dissolve themselves
in advance of approaching elections in order to maximize their electoral advantage” (Warwick
1992b, 876). Unless the unobserved factors leading to the voluntary termination decision by a
government in the final year of the CIEP are completely unrelated to those unobserved factors
18
that influence government termination at any other time (i.e., unless the censoring rule and the
dependent variable are stochastically unrelated), then the potential for biases and mistaken
inferences exist, even if researchers are not interested in these particular electorally-related causes
of government termination. Moreover, such terminations are obviously not “theoretically
uninteresting” in the present study but a vital behavioral pattern that needs to be explained. Thus,
in this analysis I have chosen not to censor governments on the basis of this particular type of
termination, though I follow the Warwick and King et al. scheme in all other respects.
I also include the Warwick and King et al. covariates as control variables in my analysis.
These variables are minority status, polarization, effective number of legislative parties, number
of formation attempts, post-election timing, caretaker status, investiture, government ideological
diversity, and “returnability.” The appendix provides descriptive statistics for the control
variables. I also include a dummy variable indicating whether the current government is a
coalition (a value of “1”) or a single-party (minority) government (a value of “0”). This allows us
to distinguish between the effects of the prime minister and government variables (which
necessarily take identical values in the single-party case).
As with the Warwick and King et al. studies, this analysis is concerned with the timing of
government termination, and the most appropriate statistical techniques for analyzing questions
about timing are commonly referred to as survival, duration, or event history models. Event
history analysis allows us to uncover patterns of change in political behavior as well as to
examine possible causes of this change. With event history models, it is possible to predict the
probability of an event occurring given that it has not yet occurred, which is usually referred to as
the hazard rate or hazard function, or in a discrete-time context, simply the hazard probability.
Because of the nature of my data, I have chosen to estimate the probability of government
termination using a discrete-time duration approach, which greatly simplifies the issue of using
time-varying covariates (all of the theoretical variables described above). Although it is possible
to use time-varying covariates in continuous-time models, such as the Cox proportional hazards
19
models used by Warwick (1992b, 1994) or the parametric models used by King et al. (1990) and
Alt and King (1994), the data setup for these models is difficult. As Beck (1996) points out,
however, “time-varying covariates are…not only not difficult for discrete duration models, they
are the natural way to proceed” (18).
The data used in this study is a time-series cross-section with a binary dependent variable
(BTSCS data).10 The analysis of BTSCS data has become more common in political science
applications in the last several years, particularly in the field of international relations.11
Typically, researchers have modeled the binary dependent variable, y, as:
(1)
P(yi,t = 1|xi,t) = 1/[1+exp(-xi,t@
where x is a vector of independent variables, and then performed an “ordinary logit” analysis of
their data. As Beck, Katz, and Tucker (1998) point out, however, BTSCS data is simply a variant
of time-series cross-section (TSCS) data, which is frequently characterized by temporal (and to a
much lesser extent, spatial) dependence. Thus, the use of a straightforward logit (or probit)
analysis, which assumes that the observations are independent, can be highly problematic. In
particular, the use of ordinary binary dependent variable models that ignore temporal dependence
(and thus fail to take advantage of all the information in the data) can lead to inefficient
coefficient estimates, incorrect standard errors, and correspondingly mistaken inferences.12
As a result of this problem, researchers working with BTSCS data have increasingly
begun to turn away from logit-based approaches in favor of event history (or survival) models,
which are constructed specifically to deal with issues of temporal dependence. Bennett (1997,
12) argues, for example, that event history models are superior to the ordinary logit procedure
10
BTSCS models are distinct from binary dependent variable panel models. While the former assume a few fixed units
observed over a long period of time, the latter assume a large sample of units observed over a relatively short period of
time. Beck and Tucker (1996) formalize some of the distinctions between these types of models. Beck (1996) discusses
the suitability of BTSCS models for analyzing data on government termination.
11
A number of recent methodological studies, such as Beck, Katz, and Tucker (1997), Beck and Katz (1997), and Beck
and Tucker (1996), provide good reviews of BTSCS studies in international relations.
20
since they allow corrections for censoring, heterogeneity and duration dependence. As Beck,
Katz, and Tucker (1998, 4) point out, however, the logit model, once corrected, is exactly an
event history method for BTSCS data, meaning that “logit-oriented BTSCS analysts [can] use
their familiar methods while deriving all the benefits of event history analysis.” The reason for
the equivalence of these seemingly very different estimation methods lies in the fact that BTSCS
data is identical to discrete time, or “grouped,” duration data. In the present study, for instance,
where the termination of a government is the event of interest, the dichotomous dependent
variable takes a value of “1” if a government falls in a particular month and a value of “0” if it
continues its tenure into the next month or is censored; the independent variables are also
measured monthly. As Alt, King and Signorino (1997) show, the dichotomous dependent
variable may be easily represented as a discrete duration variable, which merely counts the
number of months between events. In other words, the information contained in the binary
dependent variable is exactly the length of time between government terminations. As a result,
“[any] BTSCS data can be modeled via event history approaches and any event history data can
be modeled by BTSCS approaches” (Beck and Tucker 1996, 9). Given the equivalence of
BTSCS data and discrete-time duration data, therefore, the analysis in the next section will
employ discrete-time event history models that incorporate corrections for temporal dependence.
In a series of related papers, one group of analysts has recently begun to develop a
solution to the issue of temporal dependence in BTSCS data that employs a variation of the
ordinary logit model (Beck, Katz, and Tucker 1998; Beck and Katz 1997; Beck and Tucker 1996;
Alt, King, and Signorino 1997; Gangl, Grossback, Peterson, and Stimson 1998). In this study, I
adopt the approach of Gangl, Grossback, Peterson, and Stimson (1998) and capture the time
aspect of my data with the method of fractional polynomials. Fractional polynomials estimate a
family of curves that use power terms limited to a small set of integer or fractional powers, which
12
Simulations by Beck and Katz (1997) show that these problems are severe, with standard errors possibly understating
21
are restricted to the set {-2, -1, -0.5, 0, 0.5, 1, 2, 3}. This approach provides a very flexible means
of picking up the correct pattern of duration dependence in survival data. Beck (1996) proposes
an alternative model, a generalized additive model using a cubic smoothing spline, as a
alternative to the fractional polynomial. Both approaches represent a non-linear method of
modeling temporal dependence, and neither is necessarily preferable to the other. I have chosen
the fractional polynomial simply because of its greater ease of use in standard statistical
programs. As a robustness check, though, I re-estimate the results from the final model in the
next section using the techniques suggested by Beck (1996).
Results
In this section, I provide estimates from the logit model with duration dependence. As
these models are non-linear, these estimates will provide information only about the direction and
statistical significance of the relationships and not about the substantive magnitude of the effects.
Here, then, I will concentrate only on the direction and statistical significance of the relationships
in the data and put off until later the discussion of the substantive magnitude of the effects.
<<Table 5 about here>>
To demonstrate the comparability of the logit model results for the present sample to the
results from previous studies of government survival, I present in Table 5 the estimates of the
“bargaining complexity” variables from the King et al. (1990) “unified” model and the
“ideological diversity” variables introduced by Warwick (1992b, 1994). As with all of the
models in following tables, a positive coefficient means that an increase in the value of the
corresponding independent variable yields an increase in the hazard probability of government
termination (and therefore a shorter period of government survival). In Model 1, I show the
effects of the seven King et al. covariates on government termination.13 Even with the
variability by more than fifty percent.
13
For all the models in this paper, I include only those months in which no single party controls a majority of legislative
seats. This reduces the sample size from 1,804 months to 1,707 months.
22
differences in samples and model specification, these estimates mirror the findings of the
“unified” model extremely well. Minority governments are more prone to premature collapse
than majority governments. Caretaker administrations are also more likely to fail, as are
governments that must pass a formal vote of investiture upon taking office. Moreover, cabinets
that form immediately after elections, not surprisingly, last longer than those forming between
elections after a previous government has collapsed. As for the “bargaining complexity”
variables, polarization exhibits a positive effect on government termination of a similar
magnitude as found by the King et al. study, while the number of formation attempts and the
effective number of parties are also in the correct direction but fall below statistical significance
(but only slightly so in the case of formation attempts). These findings of insignificance are
consistent with those of the King and his colleagues.
In Model 2, I introduce the government ideological diversity and “returnability” variables
introduced in Warwick (1992b, 1994). Importantly, these results are consistent with Warwick’s
findings that the inclusion of the ideological diversity measure reduces the influence of
polarization in terms of the magnitude of its effect on government termination and its statistical
significance. Warwick’s explanation for this result is that a large anti-system presence in a
legislature forces the formation of government by pro-system parties that may themselves be
highly divided ideologically. He also expects that because the number of possible coalitions is
reduced in such situations, the probability that any member of the incumbent government will
participate in the next government is greater. His “returnability” measure is designed as a
measure of this propensity as it varies across political systems. The results from Model 2,
however, show that this variable is not a statistically significant determinant of government
termination in the current sample (indeed, its sign is not even in the expected direction). Model 3
provides a re-estimation of Model 2, with the highly insignificant covariates—polarization,
effective number of parties, and “returnability”—excluded. (For each of the models discussed
below, I include these variables in separate estimations but find that their effects remain
23
statistically insignificant and do not perceptibly alter any of the other parameter estimates.)
Because of the near significance of the number of formation attempts covariate, I include it in
Model 3 and all models following. As later results show, this variable does reach statistical
significance with the inclusion of the theoretical variables. Finally, these models provide
estimates of the hazard rate (adjusted for the covariates) in the form of the variables labeled
“Time Period 1” and “Time Period 2.” These estimates indicate that after conditioning for the
covariates a government is more likely to fall the longer it remains in office. In other words, the
hazard probability is increasing, which is consistent with the findings of Warwick (1992a) and
Warwick and Easton (1992).14
<<Table 6 about here>>
Given these plausible estimates of the factors identified by prior research on government
survival, I now proceed to test the theoretical hypotheses discussed in the earlier section of this
paper. First, in Table 6, I examine the effects of expected changes in seat share for the prime
minister, the government as a whole, and (in cases of minority government) the opposition. In
Model 4, I present the effects of the theoretical variables without taking into account the potential
influence of the CIEP; that is, Model 4 represents a test of Hypotheses 1a, 2a, and 3a. As the
coefficients and accompanying t-ratios indicate, none of the these hypotheses can be confirmed at
this point. An expected increase in seats for the prime minister does not lead to a noticeable
impact on the termination of the government, nor does an increase in expected seats for the
government as a whole.15 At first blush, these findings run counter to the expectations of
Grofman and van Roozendaal (1994) and Lupia and Strøm (1995). Similarly, it does not appear
14
Time periods 1 and 2 represent the best fitting fractional polynomials to government duration. Period 1 = x-0.5 and
Period 2 = x3, where x =government duration/10. (The negative coefficient on Time Period 1 indicates an increase in the
hazard rate because of the negative power on x in Period 1.)
15
For each of the government variables in all of the following models, I substitute the average expected increase in seats,
portfolios, and policy costs for all parties in the government for the “pivotal” government member variables referred to
in the previous discussion of the hypotheses. In no case did this substitution make any substantial difference to any of
the findings of this section.
24
that parties in the opposition take advantage of favorable opinion polls that predict an increase in
their legislative seat share in the event of an early election by toppling an incumbent minority
administration.
As Model 5 indicates, though, the inclusion of time effects interacted with the theoretical
variables leads to somewhat different findings. First, the government seats variable is now twelve
times larger than before and statistically significant at the p<.05 (one-tailed) level of significance.
Even more interesting, the interaction of this variable with a log-linear function of remaining time
in the CIEP is in exactly the direction as predicted by the Lupia and Strøm model. As a
government gets closer to end of its constitutionally-allowable term in office, the effects of an
expected increase in seat share for cabinet members in the event of a government termination
become ever greater. This represents the first bit of empirical support for Lupia and Strøm’s
theoretical argument that the extent to which a particular event is “critical” is dependent upon
when the event occurs in the lifetime of the government.
Model 5 also shows that, when time dependency is taken into account, another factor
becomes important when the government controls only a minority of legislative seats.
Specifically, an increase in the seat share of parties in the majority opposition leads to a greater
likelihood that the incumbent government will collapse. As with the government seats variable,
the effects of this covariate increase with the approach of regularly-scheduled elections.
Even with the inclusion of time dependency, however, the prime minister seats variable,
even though it is in the right direction, is not statistically discernable from zero. This null result is
interesting only if because recent literature has highlighted the ability of the prime minister to call
early elections as evidence of the importance of his institutional role. Model 6 provides estimates
of the effects of the expected change in seat share without the prime minister variables.
<<Table 7 about here>>
It remains to be seen, of course, whether the positive findings concerning the government
as a whole and the opposition hold up once we take into consideration the indirect effect of public
25
opinion polls on party expectations about the future share of ministerial portfolios and future
government policy. In Table 7, I begin to explore this possibility by incorporating expected
changes in government portfolios into the model. The findings from this analysis are very
revealing. First, a comparison of Models 7 and 8 shows that, just as with the earlier models
involving only seat share, not taking into account possible time dependency dramatically changes
the results. While in Model 7, none of the expected change in portfolios variables is statistically
different from zero, in Model 8, the government portfolios variable is significant and in the
expected direction.16 An expected increase in the share of ministerial portfolios for that party in
the cabinet with the most to gain in these terms in the event of an early election increases the
likelihood of a government collapse. On the other hand, neither the prime minister portfolios
variable nor the majority opposition portfolios variables appear to have any impact on
government termination.
An even more interesting finding from Model 8 is that the incorporation of expected
changes to ministerial portfolios for current government members drastically reduces the role of
expected changes in their seat share. In fact, the seat variable and its corresponding CIEP time
interaction are now in the wrong direction and statistically insignificant. This suggests that an
anticipated increase in seats for “pivotal” government members is important in their decision to
bring down the government only insofar as it affects the number of portfolios they can expect to
receive after a new round of coalition negotiations. Seats, then, appear only to be an instrumental
factor as a means of increasing the number of portfolios for current government members.
Finally, I present a reduced version of Model 8 in Model 9.
<<Table 8 about here>>
16
Unfortunately, the standard errors for the portfolio and policy variables are too small, since they are derived from the
predictions of the conditional logit model in Table 3. The estimates of the covariates are, however, consistent, and even
if the true standard error for the government portfolios variable is twice as large as this biased standard error, the variable
is still significant at the p<0.05 level (one-tailed). Numerical simulations of the standard errors from this model may be
one solution to the problem, but this technology is still at a developing stage.
26
In Table 8, I incorporate policy considerations into the empirical model including the
seats and portfolios variables. First, in Model 10, I include the variables representing the
anticipated increase in policy costs that would result in the event of a governmental dissolution
for the prime minister, the government as a whole, and the opposition. As with previous models,
I find no effects without the incorporation of time interactions. An expected decrease in policy
costs for the prime minister makes no difference to the government termination decision, nor does
this factor matter for other government members or for the opposition. In Model 11, I incorporate
the CIEP-interacted variables, but unlike in previous models these do not cause any of the noninteracted time variables to reach statistical significance.
Policy concerns, then, do not seem to enter the strategic electoral calculus of any of the
actors in a position to bring about the collapse of the government. It is not the case, however, that
policy does not matter at all to government termination. After all, the ideological diversity
variable has remained consistently significant and in the expected direction in all of the models
estimated thus far. Governments that are more ideologically divided, holding constant the
occurrence of public opinion shocks throughout their term, are more likely to fail than
governments that are ideologically compatible. Moreover, policy has an additional indirect on
government termination by way of its effects on the formation probabilities of alternative
coalitions to the incumbent government. As Martin and Stevenson (1999) demonstrated, both the
divisions within prospective governments and (for minority governments) prospective
oppositions have an impact on government formation.
In Model 12, I re-estimate Model 11 excluding these highly insignificant policy variables.
(Model 12 is thus identical to Model 9). As a check on the robustness of these results, I estimate
two additional models examining the validity of two prior modeling choices I made earlier. First,
in Model 13, I estimate effects for the covariates in Model 12 excluding all months for which I
interpolated public support. As these findings suggest, very little changes substantively in terms
of any of the theoretical variables. Both the government portfolios variable (and its time-
27
interaction) and the opposition seats variable (and its time-interaction) remain statistically
significant and in the expected direction with about the same magnitude as with the sample
including interpolated months. Only the control variables exhibit any real changes. Minority
status and investiture both fall to insignificance in this smaller sample. In short, the main effect
of including interpolated months in the model seems to be an increase in model efficiency.
Second, in Model 14, I provide estimates of the covariates in Model 12 using the generalized
additive model suggested by Beck (1996). These results indicate that the findings in this paper do
not depend on the subtleties of alternative models of the hazard rate. Again, the hazard rate,
model by the spline of elapsed time, is increasing for governments in this sample, and the
theoretical variables continue to be robust and exert the expected effects.
<<Figure 3 about here>>
The fractional polynomial model therefore appears to be an adequate specification for
addressing the time dependency in the data. In Figure 3, I provide an illustration of this time
dependency. The solid line in the figure is a prediction of the period within which a government
fails based solely on time. The circles represent the residual between the time-only prediction
and the prediction from the all the covariates in Model 12. As the graph shows, the hazard rate of
government termination is increasing after adjusting for the influence of these covariates. This
increase in the failure rate is especially steep in the first few months of a government’s tenure,
then it increases slightly until approximately three years out, after which the failure rate begins to
increase steadily. Thus, time in office clearly matters for government survival. Time alone,
however, is also a poor predictor of government survival for a large number of governments in
the sample, as indicated by the number of circles far away from the solid line. Most of the circles
far away from this line are above it, indicating that governments with characteristics that increase
their longevity are under-predicted by the time-only model.
<<Figure 4 about here>>
28
In Figure 4, I illustrate the substantive importance of the electoral expectations of current
coalition members, in terms of their anticipated shares of ministerial portfolios, on the probability
of government termination. For this figure, I assume a typical government in the sample, a
majority (non-caretaker) coalition forming immediately after an election after two formation
attempts that has faced an investiture vote. I fix the ideological divisions in this coalition at its
mean value of
–0.11. The difference between “High Expected Increase in Portfolios” and “Low
Expected Increase in Portfolios” is two standard deviations.
The figure illustrates quite nicely the dynamic properties of the expected government
portfolios variable. Specifically, it shows that for the first three years of a government’s term in
office, an expected increase in portfolios for coalition members actually appears to have the
opposite effect than what was predicted; however, this effect is small (less than three percent on
average over this period) and is not statistically significant from zero. After the fortieth month of
a government’s tenure, on the other hand, the relationship between the two hazard probabilities is
significant and in the expected direction. This is a very interesting finding, given the fact that
most governments in this sample face a CIEP time horizon of 48 months. Approximately eight
months before they must face mandatory elections, coalition members become more likely to
withdraw from the government as a result of their expectations about the office-related benefits
early elections may bring. This finding corresponds nicely to the argument of Lupia and Strøm
that “as the parliamentary term approaches its upper bound, election-related opportunity costs
should decrease” (Lupia and Strøm 1995, 656). This is the first empirical confirmation of one of
the central arguments of their strategic model of government termination. Over the remainder of
the period after month forty, a large expected increase in cabinet portfolios for coalition members
versus a small increase in cabinet portfolios increases the likelihood of government termination
by over twenty percent on average.
<<Figure 5 about here>>
29
In Figure 5, I illustrate the effects of the electoral expectations of opposition parties in
periods of minority government regarding their anticipated seat share. All other covariates are
fixed as before, except that the government here is assumed to be a minority single-party cabinet
(the most prevalent type of minority government in the present sample). As before, the difference
between “High Expected Increase in Seats” and “Low Expected Increase in Seats” is
approximately two standard deviations.
This figure, much as the previous one, illustrates the importance of modeling the
dynamics of the theoretical variables. For example, the results from Model 4, where the
interaction with time until CIEP was excluded, suggested that the effect of the expectations of
parties in the majority opposition regarding seat share in the event of early elections was in the
wrong direction and statistically insignificant. The illustration in Figure 5 shows why this was
the case, as for most of the period of a government’s time in office, the effect is in the wrong
direction. For approximately the first three years of their tenure, minority governments are, in
fact, less likely to collapse, by about seven percent on average, when opposition parties can
expect large gains in their current seat share. After this time, however, the effect of the
approaching CIEP begins to become apparent. The average increase in the probability of
government termination after 38 months into a minority cabinet’s tenure is approximately
eighteen percent as the electoral expectations of opposition parties regarding their share of seats
increases from low to high, confirming the importance of the opposition when a minority
government is in power.
Conclusion
The purpose of this paper was to examine the relationship between changing electoral
expectations and government termination in parliamentary democracies. The argument from
recent theoretical research is that if the expected utility of holding early elections for any party (or
parties) in a position to bring down the government is greater than the expected utility of
maintaining the current coalition, then the government is more likely to fall. Furthermore,
30
researchers have suggested that, assuming that the opportunity costs associated with bringing
down the current government decrease throughout the constitutional inter-election period (CIEP),
the likelihood that changes in electoral expectations will destabilize a government should increase
throughout the government’s term.
The analysis in this study represents the first in-depth empirical exploration of these
important new theoretical insights. In general, the findings were confirmatory, though with some
significant qualifications. First, the results indicate that governments are in fact more likely to
fail as members of the current coalition expect favorable electoral prospects, but contrary to the
suggestion made by Grofman and van Roozendaal (1994), a change in expected seats by itself is
irrelevant to the termination decision. Rather, governments are more likely to fail only if
coalition members expect an increase in the number of cabinet portfolios early elections would
bring, which does not necessarily increase monotonically with seat share.17 This is an interesting
finding, as it suggests that parties that hold ministerial portfolios are not willing to lose them
simply to gain more seats in the legislature. Another significant finding is that this expected
portfolios effect is dynamic, as it only begins to appear towards the end of the CIEP. This
represents the first direct empirical support for Lupia and Strøm’s premise that the extent to
which a particular event is “critical” is dependent upon when the event occurs in the lifetime of
the government.
Moreover, I find support for the idea that when a minority government is in power, the
preferences of the parties in the opposition matter. Minority governments are more likely to
collapse the greater the expected seat share for parties in the majority opposition. Again, this
effect becomes stronger the closer a government is to the end of its constitutionally allowable
term in office, indicating that opposition parties are more willing to give up their current benefits
from holding legislative seats, just as government parties are more willing to give up their current
31
share of government portfolios, the closer they are to that point in time where they will have to
give them up anyway.
On the other hand, I do not find any support for the proposition that the electoral
prospects of the prime minister make a difference to the termination decision. This may seem
somewhat surprising given the asymmetrical power of the prime minister to dissolve the
parliament and call early elections at any point in the government’s term, although it is consistent
with the findings from recent research on government formation that show that the party of the
outgoing prime minister enjoys no additional incumbency advantage apart from its membership
in the current government as a whole (Martin and Stevenson 1999). Terminating the present
government, then, would seem to hold no real advantages, and possibly poses additional risks, for
the party of the prime minister.
17
This result corresponds to the findings of Martin and Stevenson (1999) that show that the largest party in the legislature,
if it is not the final formateur, is actually less likely to be in the government.
32
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34
Table 1
The Effect of Votes on Seats in Eight Proportional Representation Systems
Independent
Variables*
Votes
Votes2
Constant
R2
Belgium
Denmark
Germany
Ireland
Italy
Luxembourg
The Netherlands
Sweden
1.165
(30.99)
-0.004
(-2.19)
-0.701
(-4.74)
1.036
(110.94)
-0.000
(-0.66)
-0.016
(-0.43)
1.012
(55.05)
0.000
(0.71)
-0.014
(-0.136)
1.045
(18.15)
0.001
(0.67)
-0.952
(-2.64)
0.988
(48.18)
0.003
(4.97)
-0.405
(-5.52)
1.152
(13.74)
-0.00
(-0.04)
-2.03
(-4.10)
1.012
(85.58)
0.001
(1.96)
-0.082
(-2.06)
1.018
(127.49)
0.000
(1.32)
0.005
(0.08)
0.987
0.999
0.999
0.995
0.999
0.993
0.999
0.999
Estimates are unstandardized regression coefficients with t-ratios in parentheses.
*Note: Votes and Votes2 are based on votes for those parties in national elections whose vote shares exceed the legal thresholds in their respective
systems. Data on votes and seats are taken from Mackie and Rose (1991).
Table 2
Statistics on Expected Change in Seat Share for Prime Minister, Government, and Opposition
N
Average
Std. Deviation
Median
Minimum
Maximum
Prime Minister
1804
-0.48
5.27
-0.8
-18.6
14.8
Government—Average (Coalitions Only)
1277
-0.86
3.24
0.8
-17.6
8.1
Government—Minimum (Coalitions Only)
1277
-0.06
3.71
-0.0
-17.6
10.9
Opposition (Minority Governments Only)
583
0.77
4.38
0.6
-12.3
18.2
All descriptive statistics (except N) are percentages.
Table 3
Conditional Logit Analysis of Coalition Characteristics on Government Formation
Independent Variables
Minimal Winning Coalition
Median Party in Coalition
Ideological Divisions in the Coalition
Number of Parties in the Coalition
Minority Coalition
Minority Coalition with an Investiture Requirement
Largest Party in the Coalition
Incumbent in the Coalition
Previous Prime Minister in the Coalition
Anti-System Presence in the Coalition
Ideological Divisions within Majority Opposition
Log-Likelihood
0.769
(3.04)
0.314
(1.63)
-2.818
(-3.43)
-0.295
(-2.22)
-0.473
(-1.04)
-1.089
(-3.43)
1.039
(4.20)
2.204
(11.80)
0.072
(0.31)
-18.14
(-4.94)
2.590
(3.20)
-597
Entries are unstandardized maximum-likelihood coefficients with t-ratios in parentheses.
Number of formation opportunities=222. Number of potential coalitions=33,272.
Table 4
Statistics on Expected Change in Policy and Portfolios for Prime Minister, Government, and Opposition
(a) Policy
N
Average
Std. Deviation
Median
Minimum
Maximum
Prime Minister
1804
3.20
12.90
-0.01
-42.38
49.97
Government—Average (Coalitions Only)
1277
1.66
11.76
1.99
-50.47
27.28
Government—Maximum (Coalitions Only)
1277
0.22
11.58
1.69
-37.46
21.79
Opposition (Minority Governments Only)
583
-11.26
9.61
-7.66
-38.79
-0.31
N
Average
Std. Deviation
Median
Minimum
Maximum
Prime Minister
1804
-10.88
23.29
-7.10
-88.38
29.71
Government—Average (Coalitions Only)
1277
-15.57
9.20
-15.58
-39.89
8.47
Government—Minimum (Coalitions Only)
1277
-13.34
10.68
-13.11
-39.89
12.02
Opposition (Minority Governments Only)*
583
0.55
0.31
0.48
0.06
0.99
All descriptive statistics (except N) are percentages.
(b) Portfolios
All descriptive statistics (except N) are percentages.
*Note that the opposition portfolio measure is not comparable to the other portfolio measures. See text for description.
Table 5
The Effects of Bargaining Complexity and Ideological Diversity on Government Termination
Independent Variables
Minority Status
Polarization
Effective Number of Parties
Number of Formation Attempts
Post-election
Caretaker Status
Investiture Requirement
Model 1
Model 2
Model 3
0.84
(2.74)
0.02
(2.48)
-0.07
(-0.79)
0.13
(1.40)
-1.33
(-4.93)
2.39
(8.45)
1.00
(3.29)
1.18
(2.73)
0.01
(0.72)
-0.08
(-0.87)
0.17
(1.81)
-1.29
(-4.54)
2.48
(8.56)
1.09
(3.32)
1.09
(1.82)
-0.33
(-0.43)
-1.20
(-4.02)
0.02
(4.19)
-2.28
(-4.02)
-320
1.39
(4.30)
Ideological Diversity
Returnability
Time Period 1
Time Period 2
Constant
Log-likelihood
-1.21
(-4.05)
0.01
(4.09)
-2.24
(-4.10)
-323
0.16
(1.84)
-1.31
(-4.89)
2.44
(8.68)
1.22
(4.06)
1.29
(3.10)
-1.16
(-4.01)
0.02
(4.51)
-2.63
(-5.75)
-321
Entries are maximum-likelihood coefficient estimates with t-ratios in parentheses. N=1707. Loglikelihood statistics for all models are significantly different from the log-likelihood of the fully-restricted
model.
Table 6
The Effects of Expected Change in Seat Share on Government Termination
Independent Variables
Minority Status
Number of Formation Attempts
Post-election
Caretaker Status
Investiture Requirement
Ideological Diversity
Expected Increase in Seats for PM
Model 4
Model 5
Model 6
1.03
(2.65)
0.14
(1.60)
-1.34
(-4.90)
2.53
(8.74)
1.07
(3.34)
1.51
(2.77)
0.00
(0.12)
0.81
(4.40)
0.16
(1.78)
-1.37
(-4.85)
2.54
(8.64)
0.99
(3.03)
1.74
(3.08)
0.07
(0.57)
-0.02
(-0.59)
0.24
(1.84)
-0.08
(-1.85)
0.31
(1.95)
-0.10
(-2.07)
-0.78
(-1.99)
-1.06
(-3.65)
0.02
(4.40)
-1.79
(-2.78)
-316
0.85
(2.10)
0.16
(1.83)
-1.35
(-4.82)
2.55
(8.72)
1.02
(3.14)
1.76
(3.30)
Ln(CIEP time remaining)*PM variable
Expected Increase in Seats for Government
0.02
(0.34)
Ln(CIEP time remaining)*Government variable
Expected Increase in Seats for Majority Opposition
-0.01
(-0.31)
Ln(CIEP time remaining)*Opposition variable
Coalition Government
Time Period 1
Time Period 2
Constant
Log-likelihood
-0.61
(-1.59)
-1.15
(-3.94)
0.02
(4.49)
-1.93
(-3.09)
-319
Entries are maximum-likelihood coefficient estimates with t-ratios in parentheses. N=1707.
0.20
(1.81)
-0.06
(-1.81)
0.25
(2.11)
-0.08
(-2.29)
-0.77
(-1.99)
-1.07
(-3.70)
0.02
(4.50)
-1.84
(-2.88)
-316
Table 7
The Effects of Expected Change in Seat Share and Portfolios on Government Termination
Independent Variables
Minority Status
Number of Formation Attempts
Post-election
Caretaker Status
Investiture Requirement
Ideological Diversity
Expected Increase in Seats for Government
Ln(CIEP time remaining)*Government seats variable
Expected Increase in Seats for Majority Opposition
Ln(CIEP time remaining)*Opposition seats variable
Expected Increase in Portfolios for PM
Model 7
Model 8
Model 9
0.46
(0.71)
0.15
(1.65)
-1.36
(-4.81)
2.56
(8.57)
1.01
(3.14)
1.87
(3.21)
0.22
(1.83)
-0.07
(-1.83)
0.24
(2.08)
-0.08
(-2.29)
-0.00
(-0.27)
0.47
(0.70)
0.20
(2.11)
-1.63
(-5.30)
2.27
(7.15)
1.16
(3.44)
1.52
(2.62)
-0.13
(-0.80)
0.04
(0.77)
0.49
(3.16)
-0.16
(-3.25)
-0.02
(-1.47)
0.01
(1.45)
0.17
(3.11)
-0.05
(-3.55)
0.49
(0.66)
0.01
(0.59)
-0.76
(-1.35)
-1.24
(-3.91)
0.02
(4.73)
-1.71
(-2.31)
-308
0.80
(1.98)
0.21
(2.31)
-1.66
(-5.66)
2.27
(7.36)
1.09
(3.37)
1.64
(3.10)
Ln(CIEP time remaining)*PM portfolios variable
Expected Increase in Portfolios for Government
-0.01
(-0.45)
Ln(CIEP time remaining)*Government portfolios variable
Expected Increase in Portfolios for Majority Opposition
0.46
(0.65)
Ln(CIEP time remaining)*Opposition portfolios variable
Coalition Government
Time Period 1
Time Period 2
Constant
Log-likelihood
-1.03
(-1.86)
-1.08
(-3.65)
0.02
(4.45)
-1.67
(-2.32)
-315
Entries are maximum-likelihood coefficient estimates with t-ratios in parentheses. N=1707.
0.49
(3.23)
-0.16
(-3.33)
0.14
(3.33)
-0.05
(-3.72)
-0.80
(-1.67)
-1.23
(-4.04)
0.02
(4.84)
-1.67
(-2.61)
-310
Table 8
The Effects of Expected Change in Seat Share, Portfolios and Policy on Government Termination
Independent Variables
Minority Status
Number of Formation Attempts
Post-election
Caretaker Status
Investiture Requirement
Ideological Diversity
Expected Increase in Portfolios for
Government
Ln(CIEP time remaining)*Government
portfolios variable
Expected Increase in Seats for Majority
Opposition
Ln(CIEP time remaining)*Opposition
seats variable
Expected Decrease in Policy Costs for
PM
Ln(CIEP time remaining)*PM policy
variable
Expected Decrease in Policy Costs for
Government
Ln(CIEP time remaining)*Government
policy variable
Expected Decrease in Policy Costs for
Majority Opposition
Ln(CIEP time remaining)*Opposition
policy variable
Coalition Government
Time Period 1
Time Period 2
Model 10
Model 11
Model 12
Model 13
Model 14
0.92
(1.66)
0.21
(2.35)
-1.63
(-5.41)
2.30
(7.35)
1.04
(3.06)
1.57
(2.80)
0.14
(3.37)
-0.05
(-3.68)
0.49
(3.20)
-0.16
(-3.31)
-0.00
(-0.19)
0.80
(1.43)
0.21
(2.36)
-1.59
(-5.28)
2.32
(7.35)
1.09
(3.18)
1.52
(2.70)
0.13
(3.04)
-0.04
(-3.29)
0.45
(2.76)
-0.14
(-2.92)
-0.08
(-1.10)
0.02
(1.10)
0.03
(0.53)
-0.01
(-0.41)
-0.06
(-0.53)
0.02
(0.51)
-0.67
(-1.15)
-1.23
(-3.98)
0.02
(4.73)
0.80
(1.98)
0.21
(2.31)
-1.66
(-5.66)
2.27
(7.36)
1.09
(3.37)
1.64
(3.10)
0.14
(3.33)
-0.05
(-3.72)
0.49
(3.23)
-0.16
(-3.33)
0.09
(0.17)
0.21
(2.05)
-1.41
(-3.86)
2.61
(5.98)
0.44
(0.91)
1.85
(2.16)
0.10
(2.10)
-0.03
(-1.93)
0.38
(2.12)
-0.12
(-2.18)
0.69
(1.77)
0.21
(2.34)
-1.63
(-5.56)
2.28
(7.65)
0.95
(3.07)
1.69
(3.23)
0.13
(3.18)
-0.05
(-3.62)
0.47
(3.05)
-0.15
(-3.13)
-0.80
(-1.67)
-1.23
(-4.04)
0.02
(4.84)
-0.78
(-1.30)
-1.43
(-3.10)
0.01
(2.08)
-0.84
(-1.79)
0.01
(0.68)
-0.01
(-0.22)
-0.64
(-1.10)
-1.23
(-4.03)
0.02
(4.78)
Spline (Elapsed Duration)
Constant
-1.80
-1.81
-1.67
-0.79
(-2.65)
(-2.64)
(-2.61)
(-0.89)
Log-likelihood
-309
-309
-310
-198
Entries are maximum-likelihood coefficient estimates with t-ratios in parentheses. N=1707
(Model 13: N=1179).
0.08
(7.10)
-3.79
(-6.49)
-310
Figure 1
The Effect of Legislative Seat Distribution on Coalition Characteristics Affecting Government Formation
Legislative
Seat Distribution
Across Parties
Set of Potential
Coalitions that
Control only a
Minority of Seats
Number of Parties in
All Potential
Coalitions
Set of Potential
Coalitions that
Contain the Median
Party
Distribution of
Ideological Positions
Across Parties
Set of Potential
Coalitions that are
Minimal Winning
Set of Potential
Coalitions that
Contain the Largest
Party
Figure 2
Summary of the Data Generation Process
Expected (monthly)
Party Vote Share from
Aggregate Public
Opinion Data on
Voting Intentions
Generation of SeatRelated Coalition
Characteristics
(Figure 3.1)
Calculation of
Coalition Formation
Probabilities
(Table 3.3)
Coalition Formation
Probabilities * PartySpecific Policy
(Portfolio) Payoffs
Transformation of
Votes to Seats
(Table 3.1)
Current Party Seat Share
Expected Seat Share
– Current Seat Share
Expected Change in
Seats for Select Parties
(Table 3.2)
Expected (monthly)
Party Policy (Portfolio)
Payoffs
Expected Policy
(Portfolios) –
Current Policy
(Portfolios)
Expected Change in
Policy (Portfolios) for
Select Parties
(Table 3.4)
Summation across
Potential Coalitions
in Each Prospective
Bargaining Situation
Current Party Policy
(Portfolio) Payoffs
Expected (monthly)
Party Seat Share
Figure 3: The Effects of Time on Probability of Government Termination
Fractional Polynomial (-.5 3), adjusted for covariates
Component+residual for fail2
2.58289
-6.52323
1
elapsed
Figure 4
The Effects of Expected Increases in Government Ministries for Coalition Members over Time on the Hazard Rate
0.9
0.8
High Expected Increase
in Portfolios
0.7
Hazard Rate
0.6
0.5
0.4
0.3
0.2
0.1
Low Expected Increase
in Portfolios
0
0
10
20
30
40
Government Tenure (in Months)
50
60
70
Figure 5
The Effects of Expected Increases in Seats for Majority Opposition over Time on the Hazard Rate
1.2
High Expected Increase
in Seats
1
Hazard Rate
0.8
0.6
Low Expected Increase
in Seats
0.4
0.2
0
0
10
20
30
40
Government Tenure (in Months)
50
60
70
Appendix
Descriptive Statistics for Control Variables
Independent Variables
Minority Status
Polarization
Effective Number of Parties
Number of Formation Attempts
Post-Election
Caretaker Status
Investiture Requirement
Ideological Diversity
Returnability
Mean
0.32
8.64
3.70
1.54
0.76
0.05
0.53
-0.12
0.01
S. D.
0.47
11.77
1.35
1.26
0.43
0.21
0.50
0.25
0.19