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Political Uncertainty and Exchange Rate Volatility in Parliamentary Democracies
William Bernhard
University of Illinois at Urbana-Champaign
Department of Political Science
361 Lincoln Hall
702 S. Wright St.
Urbana, IL 61801
bernhard@uiuc.edu
David Leblang
University of North Texas
Department of Political Science
Box 305304
Denton, TX 76203-5340
dleblang@unt.edu
August 1998
Abstract: Cabinet dissolutions create uncertainty about economic policy. Consequently, market
expectations of a cabinet dissolution (through an election or a government collapse) increase exchange
rate volatility. Further, the effect of a cabinet event (i.e., continued survival or dissolution) on exchange
rate volatility varies according to whether markets anticipated the event. If the cabinet falls (survives)
when expectations of a dissolution are low (high), exchange rate volatility will be higher than if the
cabinet had survived (ended). A discrete time survival model is used to estimate the probability that a
cabinet will dissolve in any given month for 16 parliamentary democracies from 1970-1995. The
predicted values are then used as a proxy for market expectations in a model of exchange rate volatility.
Even controlling for other determinants of exchange rate volatility, expectations of a cabinet dissolution
increase volatility. Additionally, unanticipated cabinet events produce higher exchange rate volatility.
We thank Mary Bange, D. Scott Bennett, Torben Iversen, William Keech, Christopher Way, and Chris
Zorn for helpful comments. We also thank Sang-Hyun Lee for research assistance.
“On Tuesday, the [Belgian] Government will face a no-confidence vote in Parliament… [Consequently],
the Central Bank felt forced to intervene in the market to stop a run on Belgium’s currency.”
--New York Times, 25 April 1998.
“Turmoil within the ruling coalition pushed the Indian rupee to a record low, prompting the central bank
to pump $100 million into the foreign currency market to stem the slide.”
--AP Wire Reports, 13 August 1998.
Much of the recent political economy literature argues that international markets constrain
domestic policy choices, forcing policymakers to pursue “market-friendly” economic policies.1 This
literature asserts that the anticipation of a market response to a political event will induce actors to alter
their political behavior. Nevertheless, even a cursory examination of the evidence reveals considerable
variation in how markets respond to political events. In some instances, markets react calmly to political
changes. In others, political events touch off frenetic market activity. Empirical studies linking
elections to exchange rates, for example, provide no clear consensus. Bachman (1992) and Bloomberg
and Hess (1996) argue that elections influence exchange rates. Freeman (1996, 1997a, 1997b), on the
other hand, finds little evidence to support this relationship.2
We examine how political events influence market behavior. As a measure of market behavior,
we focus on the volatility of a country’s nominal foreign exchange rate. The level of a country’s
exchange rate reflects, in part, market expectations about the country’s future economic performance.
Volatility in the exchange rate, therefore, represents an indicator of market uncertainty about the
country’s economic future. This volatility can negatively affect the external economic environment,
making international trade and investment riskier.
Exchange rate volatility varies considerably across countries and across time. We measure
exchange rate volatility as the monthly standard deviation of the daily nominal exchange rate of local
1
A sampling of this literature includes: Evans (1997); Freeman (1997a); Frieden (1991); Garrett (1995);
Goodman and Pauly (1993); Maxfield (1997); Quinn (1997); Quinn and Inclan (1997); Strange (1996).
Block (1977) and Lindblom (1977) make similar arguments about the relationship between markets and
politicians.
Bachman (1992) uses a variant of Krasker’s (1980) “peso problem” model to assess the effect of
elections on the observed bias in forward exchange rates. Operating under the assumption that “elections
provide investors with news about the country’s probability of adopting different economic policies,”
Bachman finds that elections in the United States, Britain, France, and Canada significantly influenced
the forward bias (1992, p.209, italics in original). Similarly, Bloomberg and Hess (1996) explore the
relationship between elections and exchange rate forecasts in the Britain and Germany. Their work uses
pre-election polls to capture voter uncertainty about a given election. Bloomberg and Hess find that
changes in the partisan composition of government after an election influence the level of the exchange
rate. Freeman (1997a, 1997b) tests for structural breaks in bilateral real exchange rates among a number
of OECD countries. He finds only minor evidence that elections had any effect on exchange rates.
2
2
currency versus the U.S. dollar. For our entire sample, this volatility ranges from 0.00 to 16.83. The
mean exchange rate volatility is 0.274, with a standard deviation of 0.794. To illustrate some of this
variation, Figure 1 plots the monthly exchange rate volatility of the German, French, and British
currencies from January 1972 through December 1995. For Germany and France, volatility was highest
in the months surrounding the Plaza Accord in September 1985. For Britain and many other countries in
the European Union, the September 1992 crisis in the European Monetary System caused a high degree of
volatility. Aside from these discrete events, however, there remains considerable variation in nominal
exchange rate volatility.
We argue that this variation reflects, in part, how economic markets react to political uncertainty.
In particular, we examine the extent to which cabinet dissolutions in parliamentary democracies affect
exchange rate volatility. We argue that the market expectations of a cabinet coming to an end (through
either an election or a government collapse) will influence exchange rate volatility. As markets come to
expect a cabinet dissolution, exchange rate volatility increases. Additionally, expectations of a cabinet
dissolution will condition how the market responds to an actual cabinet event (i.e., continued survival or
dissolution). Unanticipated events will produce higher exchange rate volatility than anticipated events.
For example, if the cabinet falls when expectations of a dissolution are low, exchange rate volatility will
be higher than if the cabinet had survived. If the cabinet survives when expectations of a dissolution are
high, exchange rate volatility will be higher than if the cabinet had dissolved.
The next section develops the logic of our argument. In the second section, we create a measure
of political uncertainty by estimating a model of cabinet duration, using data on 16 countries from
1970-1995. This model provides monthly probabilities that the cabinet will end, either through a
government collapse or mandatory elections. In the third section, we model exchange rate volatility,
using our predictions from the cabinet duration model as a measure of expectations. Section 4 concludes
by examining the implications of the argument for sectoral support of political reform.
Cabinet Dissolution, Market Expectations, and Exchange Rate Volatility
Recent literature on the political business cycle and partisan differences in macroeconomic
performance assumes that economic agents have rational expectations (See, e.g., Alesina and Sachs 1988;
Alesina and Rosenthal 1995; Alesina and Roubini 1997; Rogoff 1990; Rogoff and Sibert 1988;
Cukierman and Meltzer 1986). According to this assumption, actors incorporate information about the
economy from a variety of sources into their expectations. This information may include political
information, such as the timing of elections or the policy preferences of governing parties.
We extend the type of political information that influences market expectations by focusing on
parliamentary democracies. In these systems, one of the key pieces of information is whether or not the
3
current cabinet is going to survive. Cabinets end for two reasons: a loss of confidence or an election.3
First, in parliamentary democracies, the government must maintain the support of a legislative majority to
remain in office. Cabinets may end due to losing legislative support or, in a multiparty government,
parties may withdraw from the coalition. If the cabinet dissolves, parties must negotiate to form a new
cabinet. In some instances, politicians may call for new elections in response to the crisis.
Cabinets may also end due to constitutionally mandated elections. In systems with exogenous
electoral timing, elections occur at regular intervals. In many parliamentary systems, however, electoral
timing is endogenous. That is, government leaders can call for an election at any time within a
constitutionally mandated electoral term. As the end of a term approaches, government leaders will
attempt to time the election to coincide with opportune conditions.
Cabinet dissolution can create tremendous uncertainty about the identity of the next cabinet and,
as a result, the future course of economic policy. In some cases, a cabinet dissolution will result in new
elections. These elections may change the distribution of legislative seats, and new parties may be
tapped to form the next government. Even without a new election, however, a cabinet dissolution may
produce a new government, composed of parties whose policy priorities differ from those of the previous
government.
In a multiparty system, the bargaining between political parties in the government formation
process creates even greater uncertainty. First, the bargaining process can extend for months, leading to
policy inactivity. Second, bargaining typically occurs behind closed doors, out of the public’s view.
This can make it difficult for economic agents to project the partisan identity of the government. Finally,
it may be unclear what type of coalition bargain is struck. During negotiations, parties may make policy
compromises or trade-off responsibility for different issue dimensions (Laver and Schofield 1990; Laver
and Shepsle 1996; Strom and Leipart 1993). The vague language of public coalition agreements often
does not clarify responsibility for policy.
Cabinet dissolution, therefore, will make economic agents
much less certain about the policy environment.
Although the policy consequences of a cabinet dissolution are not often clear, we argue that
economic agents can have fairly accurate expectations about when a dissolution will occur. Newspaper
and media accounts often report when backbench legislators or, in a multiparty government, coalition
parties are dissatisfied with the current cabinet, creating the conditions for a vote of no confidence.
Economic agents can also recognize when coalition parties have incompatible policy preferences and,
Defining the “end” of a cabinet remains a point of controversy in the literature (Laver and Schofield
1990). Lijphart (1984a), for instance, argues that a cabinet ends only with a change in the party
membership of the cabinet. Other political scientists count a change in the prime minister, a formal
government resignation, and elections as an end to the cabinet.
3
4
thus, be less likely to maintain the coalition. Finally, economic agents are also aware of when
constitutionally mandated elections are due. In systems with endogenous timing, there is often intense
speculation about how long the government will wait to call elections. On the basis of this information,
economic agents will form expectations of when the cabinet is likely to end.
These expectations about political events will, in turn, affect economic behavior. If economic
agents are fairly confident that the cabinet will survive, they can make projections about economic policy.
As economic agents come to believe that a cabinet is likely to collapse, however, they will also have less
certainty about the future of economic policy. This uncertainty will manifest itself in economic behavior.
Economic agents, for instance, may invest less since they are uncertain about what course the government
will take. Likewise, speculators may be less willing to hold government debt. This uncertainty about
economic policy will be evident in the behavior of the country’s exchange rate. The value of a country’s
exchange rate reflects, in part, market expectations of future economic policies. As economic agents are
less certain of future policies, they are likely to trade the currency. Higher levels of exchange rate
volatility, therefore, reflect market uncertainty about the future of economic policy. Consequently, we
argue that as markets become more confident that the government is going to fall, then exchange rate
volatility will increase.
H1: Exchange rate volatility will increase as the probability of the cabinet ending increases.
Drawing on the rational expectations literature, we argue that the effect of an actual cabinet event
on exchange rate volatility will vary according to whether the event was anticipated. In each period, two
events may occur: a cabinet will survive or it will end. For both these events, we expect exchange rate
volatility to be higher if the event is unanticipated than if it is anticipated.
First, cabinets may survive even when markets anticipate their collapse. If economic agents
expect the cabinet to end, either through a collapse or an election, then they will already be uncertain
about economic policy. Their economic behavior will reflect that uncertainty. If the cabinet continues
to survive, economic agents will remain uncertain—and continue to create high levels of exchange rate
volatility. Once the cabinet does indeed dissolve, however, we expect a decrease in exchange rate
volatility since markets have already anticipated the event.
Second, consider a cabinet dissolution. Predicting a cabinet collapse or the date of elections is an
imperfect science. A variety of political shocks may cause the sudden collapse of a coalition: scandal, a
foreign policy crisis, a change in party leadership, death of a minister, snap elections, etc. A cabinet
dissolution, therefore, may surprise economic agents. If economic agents have not anticipated the
cabinet dissolution, they will suddenly become uncertain about the future of economic policy and change
5
their behavior. Therefore, if a cabinet ends when markets did not anticipate a dissolution, we expect that
event to increase exchange rate volatility.
H2: The effect of a cabinet event (i.e., continued survival or dissolution) on exchange rate
volatility will vary according to whether the event was anticipated. An unanticipated event will
produce higher exchange rate volatility than an anticipated event.
We argue that market expectations of a cabinet collapse will affect the behavior of the exchange
rate. Our next step is to provide an operationalization of those expectations. To do this, we employ a
simple model of cabinet dissolution. This model provides a predicted probability of cabinet dissolution
for each month in our sample series. These probabilities serve as a proxy of market expectations of a
cabinet dissolution.
Estimating Political Uncertainty in Parliamentary Democracies
We draw on the extensive literature on cabinet durability to model the probability that the cabinet
will end.4 Typically, political scientists have compared the durability of governing coalitions based on
coalition attributes (the majority status of the government, the number of parties in the government),
regime attributes (fragmentation of the political system, political polarization, etc.), and bargaining
situation (number of formation attempts). Lijphart (1984a and 1984b), for instance, compares the
durability of governments based on their coalition attributes. He finds that single party majority cabinets
are the most durable and minimum winning coalitions are slightly less durable. Minority and oversized
cabinets tend to have the shortest life-spans.
We argue that the probability that the cabinet will end is a function of five sets of variables: the
duration of the cabinet to that point, the time remaining before constitutionally mandated elections,
whether the system has exogenous electoral timing, government type, and party system attributes.
First, consider the duration of the cabinet to that point. Coalition bargains tend to be fragile in
the months just after cabinet formation. As cabinets survive over time, however, they are less likely to
fall over a policy disagreement (King, Alt, Burns, and Laver 1990; Alt and King 1994; Warwick 1994).
That is, cabinets that have survived 24 months are very likely to survive another month, while cabinets
that have survived only a month are not as likely to make it to a second month. Therefore, we include a
variable for cabinet duration, which counts the number of months the cabinet has existed to that point.
We also include a square of that term. We expect the overall effect of these variables to have negative
probability on cabinet dissolution.
Second, we also count elections as instances of cabinet dissolution. Most parliamentary systems
have endogenous electoral timing. As constitutionally mandated elections approach, government leaders
4
Laver and Schofield 1990 and Warwick 1994 provide reviews.
6
will attempt to dissolve the government at the most optimal time. We include a variable, electoral clock,
counting down the time to when an election must, by law, be called.5 We also include a square of this
term. The time until mandated elections will have a higher probability of a cabinet dissolution as it
approaches zero. Consequently, we expect the square term to have a positive estimate.
Third, we include a dummy variable for systems with exogenous electoral timing. In these
systems, electoral timing is constitutionally mandated. Governing politicians cannot call elections at
opportune times. Since politicians know that they must work within the distribution of legislative seats,
they will be less likely to dissolve the cabinet. Exogenous electoral timing, therefore, is likely to prolong
cabinet durability.
Fourth, we include dummy variables for government type: single party majority, minimum
winning coalition, oversize coalition, single party minority, and coalition minority. Following the
literature, we expect that single party majority governments will be most durable, minimum winning
coalition slightly less durable, and oversize and minority governments least durable. We also expect that
the government type will have an interactive effect with the time-dependent variables (duration, electoral
clock). For instance, the probability of a cabinet dissolution with single party majority governments will
be very low and relatively constant throughout most of the term. This probability will increase sharply
as mandated elections approach. We expect a similar pattern with minimum winning coalitions, except
that the probability of cabinet dissolution will be slightly higher just after the coalition forms. With the
minority and oversize coalitions, the probability of a cabinet dissolution will be high early in the terms.
We include interactive terms to capture these relationships.
Fifth, we include two attributes of the party system: fractionalization and polarization. Political
scientists argue that the more fragmented and polarized the political system, the shorter the expected
cabinet duration. We include a variable for party system fractionalization, which measures the number
of effective political parties in the system (Rae 1971). This variable should have a negative effect on
cabinet durability.
Polarization is measured by the electoral support for extremist parties. 6 More
support for extremist parties also implies shortened duration.
5
Countries have different constitutionally mandated election periods: 36, 48, or 60 months. We
normalized the electoral clock variable to reflect these different periods. The formula for the electoral
clock variable is: (Number of Months Until Election Must Be Called)/(Constitutional Electoral Period).
The electoral clock variable runs from 1 (Full electoral period remaining) to 0 (No time remaining). A
value of 0.5 indicates that half the electoral period remains before elections must be called.
6
Following Powell (1982), we measure polarization as the percentage of electoral support for extremist
parties. According to Powell, extremist parties exhibit one of the following characteristics: 1) A
well-developed nondemocratic ideology; 2) A proposal to break-up or fundamentally alter the boundaries
7
Finally, following King, Alt, Burns, and Laver (1990), we include a set of nation dummy
variables. The number of cabinet collapses varies substantially across the countries in our sample.
Canada and New Zealand had the fewest governments (10 and 11 each) while Belgium had the most
governments (21). The dummy variables account for country specific factors that influence the
probability of cabinet dissolution.7
Table 1 provides descriptive statistics for those variables.
Sample, Dependent Variable, Methodology
We examine the duration of cabinets in a set of 16 parliamentary democracies over the period
January 1970-December 1995. The countries include Australia, Austria, Belgium, Britain, Canada,
Denmark, Finland, France, Germany, Ireland, Israel, Japan, Netherlands, New Zealand, Norway, and
Sweden.8 We include only cabinets that began on or after January 1970 and that ended before January
1996. Overall, the sample includes 208 cabinets.
The dependent variable, End, is a dummy variable, coded one for each instance of cabinet
dissolution, due either to election or to a change in the composition of the parties in government, and
coded zero, otherwise. Of the 208 cabinets, 127 end with an election, while 81 end without an election.
The data are from Woldendrop, Keman, and Budge and supplemented by annual issues of the European
Journal of Political Research.
Recent work on cabinet duration uses event history analysis. Event history models estimate the
underlying hazard of an event (i.e., a cabinet ending), and also analyze the influence of covariates on the
length of time a cabinet remains in power. Typically, these models have estimated continuous time
survival models of cabinet duration with time-constant covariates (King, Alt, Burns and Laver 1990; Alt
and King 1994; Beck 1997; Warwick 1994). That is, they assume that cabinet duration is a function of
of the nation; or 3) Diffuse protest, alienation, and distrust of the existing political system. We follow
Powell’s (1982) classifications with the exception of including France’s National Front.
7
We have not incorporated other time-constant covariates suggested by the literature (e.g., number of
formation attempts). We did include the variables suggested by King et al. (1990) for those cases which
overlapped with ours. Our finding in this very limited sample was that the nation dummy variables
absorbed much of the variation that was attributable to these other time-constant covariates.
8
In alternative specifications, we ran the models with the Italian case. Italy, however, has both the
highest number of governments for our time frame (25) and the highest level of exchange rate
volatility--for every observation, the standard deviation of the exchange rate for Italy is well above 2.
The only other country with a standard deviation of the exchange rate over 2 is Japan, and then it is only
for a few months. In fact, in September 1992, Italy’s exchange rate had a standard deviation of 83! Our
empirical analysis indicates that the Italian case distorts the exchange rate volatility model, so we dropped
it from the entire analysis. Switzerland was excluded from the analysis due to the permanent oversize
status of their executive council.
8
variables that are measured at the time of cabinet formation. This approach is similar to a cross-sectional
data set where the dependent variable is the number of months that the cabinet has been in power.
Instead, we want to employ a statistical model that allows us to estimate the probability that a
cabinet will end (or survive) in any given month. This probability is a function of both time-constant
covariates (e.g., government type, country dummy variables) and time-varying covariates (e.g., cabinet
duration, electoral clock). Consequently, we use a discrete-time hazard model with a probit specification
(Allison 1984; Beck, Katz, and Tucker 1997). Here, the hazard rate represents the probability that a
cabinet will end at a particular time, given that the cabinet has survived to that point. We observe only
whether a cabinet survives or ends; the actual probability of a cabinet ending in any particular month is
latent. Inclusion of the electoral clock and cabinet duration variables helps control for duration
dependence in the analysis. This model provides predicted probabilities of cabinet dissolution for each
for each month included in the sample.9
Results
Table 2 contains the results of the discrete-time probit model. This model was estimated with a
set of 15 country specific dummy variables, the results of which are not included in the table. A
log-likelihood ratio test rejects the null hypothesis that, as a whole, the model is not statistically different
from zero. The model does a good job predicting when a government is going to survive (98% of the
cases correctly specified) and when a government is going to end (84% of the cases correctly specified).
This is evidence that the model is fairly well specified.10
Maximum likelihood parameter estimates are in column one and robust standard errors (adjusted
for unequal error variances across countries) are in column two. There is extensive collinearity among
many of the independent variables, resulting from the construction of the duration and electoral clock
variables and their interactions with government type variables. Consequently, it is not surprising that
most of the independent variables are individually statistically indistinguishable from zero.11 As a result,
9
We also estimated the cabinet duration model using continuous time duration models, including both
Weibull and Cox models. While it is difficult to compare coefficients across models, parameter
estimates are statistically significant and in the same direction across all three specifications. The
correlation between the predicted hazard from the Weibull model and the probit specification is .87. The
correlation between the predicted hazard from the Cox specification and the probit specification is .84.
Both correlations are statistically significant at the 0.05 level.
10
Given that we observe only 208 observations where a government ends, it would not be surprising if
the model produced skewed results. Therefore, we take a case to be correctly predicted if the estimated
probability is less than or equal to the mean of the dependent variable in the sample.
11
This would be a problem if we argued only that different government types had different intercepts.
Instead, we contend that there is an interaction between government type and the length of time that a
cabinet has been in power.
9
we report a set of log-likelihood ratio tests that test for the joint significance of each government type and
its interaction with the duration and electoral clock variables. These results are presented at the bottom
of table 2 and indicate that we can reject the null hypothesis that none of the sets of variables have any
statistically significant influence on the dependent variable at the .10 level, with the exception of single
party minority governments.
The exogenous electoral timing variable is statistically significant but, contrary to our
expectations, positive. This result probably reflects the fact that only three countries in our sample have
exogenous timing (Sweden, Israel and Norway).
Fractionalization and polarization are not statistically significant. In alternative specifications
that did not include the country dummies, however, polarization was statistically significant, but
fractionalization was not.
Finally, a number of the country dummies are statistically significant. Britain, Austria, Norway,
and Canada have negative and statistically significant country dummies. The dummy variables for
Belgium, France, Japan and Australia are positive and significant. The other country dummy variables
were not significant.
Given that few of the independent variables are individually significant, how can we be confident
that the results meet with our expectations? The discrete-time hazard model using the probit
specification provides predicted probabilities of a cabinet dissolution for each month. We first compare
the average predicted probabilities of a cabinet dissolution by government type (Table 3). As expected,
the average probability of a single party majority cabinet falling is lowest, and the average probability of a
minimum winning coalition falling is only slightly higher. Single party minority governments and
oversize coalitions have higher average probabilities of collapsing while coalition minority governments
have the highest average probability of coming to an end. These results square with the findings
contained in the literature.
The average probabilities reported in table 3, however, are static. Instead, we argue that
government type and the time-dependent variables have an interactive effect with the time-dependent
variables (duration, electoral clock.). To get a sense of this dynamic interaction, we perform a simulation
to see how the predicted probabilities of cabinet dissolution for different government types change over
the course of an electoral period. The results of this simulation, presented in Figure 1, plot the
probability of cabinet collapse (on the y-axis) against the number of months the cabinet has been in power
(on the x-axis). For the sake of presentation, we assume a 48-month electoral clock. These results
conform relatively well with our expectations. The single party majority government, for example, has a
low and stable probability of dissolution throughout most of the term, which then increases sharply at the
10
end of the electoral period. Minimum winning coalitions tend to be least stable at the beginning of their
terms. As they survive, they are less likely to collapse until elections have to be called. Single party
majority governments become more unstable in the medium-term. Oversize coalitions have the highest
probability of collapsing throughout almost the entire term. Overall, this figure suggests that the model
provides a reasonable approximation of our expectations concerning the interaction between government
type, the electoral clock, and the time that a government has already spent in office.
Finally, Table 4 reports these probabilities for periods when a cabinet survives (End=0) and when
a cabinet ends (End=1). As expected, the mean probability of a cabinet dissolution is substantially
higher in periods when the cabinet ends than when it survives. In fact, when a cabinet survives, the
predicted probability of cabinet dissolution never exceeds than 0.22. The predicted probabilities for
when a cabinet dissolves, however, range from (essentially) zero to (essentially) one. Where the predicted
probability of cabinet dissolution is low, the cabinet dissolution is unanticipated.
Discussion
These predicted probabilities are a proxy for market uncertainty in parliamentary democracies.
We use the predicted probabilities from our model of cabinet duration as an independent variable,
Expectations, in our model of exchange rate volatility. Cabinet dissolutions create uncertainty about the
future of economic policy. Consequently, as economic agents anticipate a cabinet dissolution, they are
more likely to be uncertain about economic policy. Increased exchange rate volatility will reflect this
uncertainty. The expectations variable, therefore, will have a positive effect on exchange rate volatility.
Further, we argue that the effect of an actual cabinet dissolution on exchange rate volatility will
be contingent on expectations of a cabinet collapse. We include an interaction between the dummy
variable for a cabinet dissolution and the predicted probability of cabinet dissolution (End*Expectations).
Higher values indicate that the cabinet dissolution was anticipated. Lower values suggest the cabinet
dissolution was a surprise. Since we hypothesize that unanticipated cabinet events have a greater effect
on exchange rate volatility, this interactive term should have a negative estimate.
Exchange Rate Volatility
This section discusses the political determinants of exchange rate volatility to identify control
variables for our model.12 While the literature offers some guidelines, there is no consensus about what
political factors affect exchange rate volatility. Political economists investigating the political
determinants of exchange rates typically include a variety of political variables under the rubric of
12
It is important to note that we do not estimate the influence of political variables on the level of
nominal exchange rates. The literature finds that no model, whether based either on underlying
fundamentals (e.g., an asset market approach) or on surveys of market participants, outperforms a naive
random walk in forecasting exchange rate levels (Branson and Henderson 1985; Frankel and Mussa 1985;
Isard 1988 and 1995; Mussa 1984; Meese 1990; Obstfeld and Stockman 1985; Taylor 1995).
11
“country risk” (e.g., Bailey and Chung 1995). Aliber (1973), for instance, views political risk as a
combination of the existence of capital controls, the structure of taxation, the variability of
macroeconomic policy, and the probability of political change in either a regular or an irregular fashion.
Presumably, countries with higher levels of risk will experience higher levels of exchange rate volatility.
More recent work has emphasized variables such as distributional coalitions (Frieden 1991), political and
socioeconomic institutions (Freeman 1997b), government partisanship (Garrett 1995), and elections
(Bachman 1992; Bloomberg and Hess 1996; Freeman 1997a).
From the literature we identify four sets of control variables for the model of exchange rate
volatility: exchange rate regime and restrictions; monetary policy; domestic political institutions; and
domestic political factors.
Exchange Rate Regime and Restrictions
Exchange Rate Regime: A country’s exchange rate regime will affect the level of exchange rate
volatility. The literature on optimal currency areas argues that a fixed exchange rate will decrease
exchange rate volatility (Mundell 1961, McKinnon 1962). Presumably, reduced exchange rate volatility
enhances the external trading environment. Frankel and Rose (1996) suggest that this type of argument
motivated European policymakers to adopt the European Monetary System.
We distinguish between four exchange rate options: a floating exchange rate, a unilateral peg,
participation in the European Exchange Arrangement (E.E.A.), commonly called the Snake, and
participation in the European Monetary System (E.M.S.).13 Membership in the E.E.A. was limited to
countries in Europe, although none were required to join. France, for example, left the Snake in January
1974, only to rejoin the following year—and then exit a second time in 1976. Participation in the E.M.S.
was further restricted to member states of the European Community, although, again, member states were
not required to join. Indeed, Britain chose not to participate in the E.M.S. until 1990 and then withdrew
in 1992.
We include separate exchange rate regime dummy variables capturing whether a country has a
fixed exchange rate regime, participated in the Snake, or participated in the E.M.S. Countries with
13
The International Monetary Fund identifies at least seven different types of exchange arrangements,
classifying them according to their flexibility. Less flexible arrangements include fixing to a major
currency (e.g., the U.S. dollar or the French franc), or fixing to a composite currency (e.g., the SDR or the
ECU). Arrangements with limited flexibility include those where a currency fluctuates within certain
bands around the target currency. Included in this category are cooperative currency arrangements such
as the E.M.S. and other systems where the exchange rate is adjusted according to a predetermined set of
indicators (e.g., a crawling peg). The most flexible arrangements are managed floating, where the central
bank actively intervenes in foreign exchange markets to maintain the value of the currency, and freely
floating arrangements, where interventions are aimed at moderating the rate of change of the exchange
rate.
12
floating exchange rate regimes are the comparison group. We coded these variables monthly using data
from the International Monetary Fund’s Annual Report on Exchange Arrangements and Exchange
Restrictions (various years). We expect that countries with floating exchange arrangements will
experience higher volatility than countries that either unilaterally peg their currencies or participate in a
multilateral currency arrangement.
Realignments/Devaluations: Countries with a fixed exchange rate periodically adjust their
currency’s parity. The E.M.S., for example, established explicit rules governing currency realignments.
During the first few years of the E.M.S., currency realignments occurred relatively often. As European
economies converged during the 1980s, however, the frequency of realignments declined. Devaluations
and realignments will increase the variability of the exchange rate.
We include a dummy variable for realignments and devaluations of the exchange rate. Data are
from the International Monetary Fund’s Annual Report on Exchange Arrangements and Exchange
Restrictions (various years), Gros and Thygesen (1992) and Cobham (1994). This variable should have a
positive effect on volatility.
Capital Controls: According to the Mundell-Fleming model, countries can obtain only two of the
following three policy objectives concurrently: a fixed exchange rate, capital mobility, or domestic
monetary autonomy. Countries will often limit the movement of short-term capital to maintain domestic
monetary autonomy and exchange rate stability. A sizable literature argues that the desire to limit
volatile short-term capital flows and maintain exchange rate stability is one of the primary motivations
behind the implementation of capital controls (Alesina et al. 1994; Mathieson and Rojas-Suarez 1993;
Leblang 1997).
We include a dummy variable, capital controls, coded one if a country has controls on short-term
capital and coded zero otherwise. Data are from the International Monetary Fund’s Annual Report on
Exchange Arrangements and Exchange Restrictions (various years). The presence of capital controls
should decrease exchange rate volatility.
Monetary Policy
Domestic Inflation: Exchange rates reflect monetary policies and domestic inflation will push the
exchange rate downward. Higher levels of inflation are also associated with higher variability in the
inflation rate, contributing to uncertainty about the future course of monetary policy. Consequently, we
expect higher levels of inflation to increase exchange rate volatility.
13
We include a variable for the monthly rate of inflation.14 Data are from International Monetary
Fund’s International Financial Statistics. We expect a positive relationship with exchange rate
volatility.
Short-term Interest Rates: We also include domestic interest rates, measured in either the money
market or on treasury bills.15 These measures were chosen because policymakers directly control these
rates. Not every country reports this information on a monthly basis, so we use the series for each
country that was available for the longest continuous period of time. We expect that countries with higher
interest rates will have lower exchange rate volatility. The data are from the International Monetary
Fund’s International Financial Statistics.
Domestic Political Institutions
Central Bank Independence: Recent research argues that independent central banks insulate
monetary policy from political pressures, producing a more stable monetary policy. Empirical research
demonstrates that independent central banks are associated with superior inflation performance (Alesina
1989; Alesina and Summers 1993; Grilli, Masciandaro and Tabellini 1991). To measure central bank
independence, we use the scale developed by Cukierman, Webb, and Neyapti (1992). Central bank
independence should have a negative effect on exchange rate volatility.
Electoral and Legislative Institutions: Bernhard and Leblang (1999) distinguish systems based on
the configuration of electoral and legislative institutions. These institutions are likely to influence the
variability of economic policy. In majoritarian electoral systems, single-party majority governments
usually alternate in office, creating the potential for sharp policy breaks between administrations. With a
proportional representation system, coalition governments are more likely. The process of bargaining
between coalition partners will produce a more stable policy (Rogowski 1987).
The strength of legislative committees also affects policy stability. If legislative committees are
weak, governments have the ability to change policy without interference from the legislature. Where
committees are strong, however, governments must negotiate with the legislature in the policy process.
Consequently, policy is more likely to be stable with strong committee systems.
Policy stability, therefore, should be highest in proportional representation systems with strong
committee systems and lowest in majoritarian systems with weak committee systems. 16 Given that
14
As an alternative, we included the change in the monthly inflation rate. The results were insignificant.
15
We also included a variable that measured the interest rate differential between each country and the
United States. The results were similar.
16
Systems are classified based on their electoral and committee systems (Powell 1989; Strom 1990b).
For the electoral system, we distinguish between majoritarian or proportional systems based on Lijphart
14
policy stability should contribute to lower levels of exchange rate volatility, we expect proportional
representation-strong committee systems to have the lowest volatility; proportional representation-weak
committee systems to have somewhat higher volatility; and majoritarian-weak committee systems to have
the highest volatility. We include dummy variables for proportional representation-weak committee
systems and majoritarian-weak committee systems.
Domestic Political Factors
Government Partisanship: The partisanship literature traditionally assumes that right parties are
more concerned with controlling inflation while left parties place more emphasis on employment and
wealth redistribution (Alesina 1989; Alesina and Sachs 1988; Hibbs 1987; Havrilesky 1987). Garrett
(1995) argues that left parties possess little anti-inflation credibility with financial and capital markets,
contributing to higher risk premia and the possibility of capital flight. Consequently, we should expect
higher levels of exchange rate volatility when left parties hold office.
To examine the relationship between partisanship and exchange rate volatility, we created a
measure of left government strength based on Cameron (1984). The measure multiplies the percentage
of cabinet seats held by left parties by the percentage of a legislative majority held by left parties in the
legislature for each year in each country. Higher values indicate increased left party influence. We
expect this variable to a positive relationship with exchange rate volatility.
Other Controls
Lagged Endogenous Variable: We include a lagged endogenous variable. While the best
predictor of a country’s nominal exchange rate at time t is the value of its nominal exchange rate at t-1,
there is no theory to help us establish a strong prior on either the direction or the magnitude of this
variable. A high degree of exchange rate volatility in the previous period may stimulate policymakers to
intervene on foreign exchange markets to stabilize the currency, suggesting that the sign on the lagged
endogenous variable will be negative. On the other hand, a number of economists have argued that
speculative price changes are not independent over time, but are often characterized by placid and volatile
periods (Mandlebrot 1963; Baillie and Bollerslev 1989). Following this logic, we expect that the lagged
(1984b). To examine opposition influence over policy, we classify systems according to the “strength”
and “inclusiveness” of legislative committees, using a classification developed by Powell and Whitten
(1993) and Strom (1990a). We combined these two measures to characterize different systems:
majoritarian-low opposition influence; proportional-low opposition influence; and proportional-high
opposition influence. There were no cases of majoritarian-high opposition influence in our sample.
Majoritarian-low opposition influence systems include Australia, Canada, France, New Zealand, and
Britain. Proportional-low opposition influence systems include Ireland, Israel, and Japan.
Proportional-high opposition influence systems include Austria, Belgium, Denmark, Finland, Germany,
Netherlands, Norway, and Sweden.
15
endogenous variable to have a positive sign. Inclusion of a lagged endogenous variable also helps
control for first order serial correlation and omitted variable bias.
United States Economic and Political Performance: The dependent variable measures local
currency versus the U.S. dollar. As a check on whether exchange rate volatility reflects changes in the
value of the U.S. dollar, we include three variables. First, we include a variable measuring the
trade-weighted U.S. dollar in real terms to capture changes in the U.S. dollar that are independent of
changes in other currencies. 17
We expect that as the dollar appreciates, currency traders will prefer to
hold dollars. Consequently, exchange rate volatility for other countries will decrease. The data is from
the Federal Reserve Bank in Chicago.
Second, we include a dummy variable, End in U. S., for months when there is change in the
distribution of legislative seats in the U.S. If markets react to political change in the United States, it will
be reflected in the bilateral exchange rates between the US and other countries.
Third, we include a dummy variable, international coordination, for the Plaza accord (September
1985), the Louvre Agreement (February 1987) and the stock market crash of 1987 (October). The
dummy variable is coded one for these events and for the two periods after them. We anticipate that this
coefficient will be positive.
Trend in Exchange Rate Volatility: Dramatic changes in the international economic environment
over the past 20 years provide reason to think that levels of exchange rate volatility in the system as a
whole will vary over the sample period. During the 1970s and 1980s, both technological advances and
regulatory liberalization of the international financial sector dramatically increased the volume of
international capital movement. Indeed, some political economists have argued that international and
domestic capital markets have become so integrated that capital mobility should be considered a structural
component of the international system (Andrews 1992). The increase in the volume of capital
movements implies that exchange rate volatility would increase over time.
Other developments suggest that exchange rate volatility will decrease over time. First, the
establishment of the E.M.S. as a viable system for the management of exchange rates in the European
Community during the 1980s has led to decrease in exchange rate volatility among participating member
states. E.M.S. participants compose a substantial portion of our sample. Earlier attempts at exchange
rate cooperation (e.g., Smithsonian agreement; Snake) had only limited success. Second, domestic
macroeconomic conditions became much more stable over the past 20 years. During the 1970s, the
industrial democracies experienced high levels of stagflation—a crippling combination of inflation and
17
We estimated the model with two alternatives for this measure. First, we used the change in the value
of the trade-weighted U.S. dollar. Second, we used the bilateral exchange rate between the U.S. dollar
and the German Mark. In both these specifications, the variables were statistically insignificant.
16
unemployment. Since controlling inflation in the early 1980s, the industrial democracies have enjoyed
relative economic stability. Moreover, there has been a remarkable economic convergence among
member states of the European Union. Finally, financial deregulation may give economic agents enough
economic opportunities to provide returns without having to hedge currencies.
To capture these changes in the international economic environment, we include a time trend
variable. We expect that this variable will have a negative effect on exchange rate volatility.
Table 5 contains descriptive statistics for the independent and dependent variables.
Estimating Exchange Rate Volatility
Sample, Dependent Variable, Methodology
Our sample includes the same set of 16 parliamentary democracies. The sample begins in
January 1972 since that date marks the collapse of the Bretton Woods system. Not all countries are
observed for the entire sample period, however. For Austria, Denmark, Norway, Finland, and Ireland,
data are available only after 1990; for Israel, data are available after September 1977; and for New
Zealand, data are available after 1985. Consequently, this sample includes 136 instances of a cabinet
dissolution.
The dependent variable is the monthly standard deviation of the nominal exchange rate versus the
U.S. dollar based on daily data. That is, we collected daily foreign exchange rate data for each country
(the local exchange rate versus the U.S. dollar). We then calculated the standard deviation of the
exchange rate for that month. The daily data was the closing price in New York. The daily data are from
the U.S. Federal Reserve Bank, Bloomberg On-Line, the Reserve Bank of New Zealand and the Bank of
Israel.
Because we are interested in the variation of exchange rate volatility across both time and space,
we exploit a pooled cross-sectional time-series research design. We use an econometric model endorsed
by Beck and Katz (1995, 1997). This approach suggests estimating the pooled cross-sectional
time-series model via ordinary least squares, but (i) adjusting the standard errors for unequal variation
within panels, (ii) including a lagged endogenous variable, and (iii) correcting for autocorrelation.18 In
addition, this procedure allows us to correct for heterocedastic disturbances across panels. This approach
18
We implement this procedure using the xtgls procedure in STATA. We include the options to
estimate panel corrected standard errors (pcse), a panel-specific autoregressive parameter (psar1), and to
correct for heteroscedastic disturbances across panels (p(h)). We also estimated the model with an
common AR(1) term and with a set of country specific dummy variables. In both of these specifications,
our variables of interest (Expectations, End*Expectations) were statistically significant and correctly
signed.
17
provides unbiased and efficient parameter estimates and permits us to examine the dynamic structure of
exchange rate volatility.
Results
We first estimate two “naïve” models of exchange rate volatility that do not include the
Expectations variable or the interactive term (Table 6). Parameter estimates are ordinary least squares
estimates generated via feasible generalized least squares and corrected for panel-specific first-order serial
correlation. Panel corrected standard errors are reported.
Model I includes only the baseline economic variables and the End variable, which identifies the
month when a cabinet dissolves.19 The residuals of Model I are well behaved; tests for serial correlation
and heteroscedasticity reveal no significant problems. The chi-square statistic allows us to reject the null
hypothesis that, taken together, none of the independent variables are significantly different from zero.
The substantive results of Model I generally meet our expectations. The lagged endogenous
variable is positive and statistically significant. This finding is in line with research in market volatility
that has found that more volatile periods cluster together.
Countries with either unilateral or multilateral exchange rate pegs experience lower volatility than
those with floating exchange rates. The coefficients on the three exchange rate regime dummy variables
are all statistically significant and negative, indicating that these arrangements do encourage lower
exchange rate volatility.
Devaluations and realignments are significantly associated with higher
volatility. The capital controls variable, however, is statistically insignificant.
The monetary policy variables do not reveal a clear pattern. Neither inflation or short-term
domestic interest rates has a statistically significant impact on exchange rate volatility. On the other
hand, the central bank independence variable is statistically significant and in the predicted (negative)
direction. Countries with higher degrees of central bank independence have less exchange rate volatility.
Government partisanship is statistically significant and, contrary to our expectations, negative.
Higher levels of left party influence tend to produce less volatile exchange rates.
We also included variables for political and economic developments in the United States.
Neither the U.S.’s trade weighted dollar variable or the variable for political change in the United States is
statistically significant. The international coordination variable, however, is statistically significant and
positive. Finally, the time trend is negative and statistically significant, indicating that exchange rate
volatility vis-à-vis the US dollar has declined over time.
Finally, the End variable, however, is statistically insignificant indicating that, taken by itself, the
end of a cabinet does not effect the volatility of a country’s exchange rate.
19
Models including only the baseline economic variables had virtually identical results.
18
The second naïve model includes a set of dummy variables for government type (Model II).
(The omitted category is coalition minority governments.) This represents a simplistic method of
accounting for market expectations about cabinet instability. Presumably, the higher durability of single
party majority cabinets and minimum winning coalitions may influence the behavior of the exchange rate.
The results of Model II do not support this. Only single party minority governments have significantly
different exchange rate volatility.
Models I and II suggest that cabinet dissolution and government type do not affect exchange rate
volatility. Instead, we argue that these variables do influence exchange rate behavior, but this influence
occurs through market expectations of political events. That is, markets will use political
information—e.g., the government’s majority status, the number of parties in government, the electoral
clock—to make projections about the occurrence of a cabinet dissolution. These expectations will, in
turn, affect their economic behavior.
The models in Table 7 include the predicted probabilities of cabinet dissolution (Expectations)
and whether the cabinet dissolution was anticipated (Expectations*End). First consider Model III. The
model is, as a whole, statistically significant. The inclusion of the new variables does not change the
direction or magnitude of any of the control variables with the exception of the inflation variable, which is
now statistically significant. Both Expectations and Expectations*End are statistically significant and in
the predicted directions. The Expectations variable has a positive coefficient. As economic agents
believe that a cabinet dissolution is more likely, either through an election or a loss of confidence,
exchange rate volatility increases. The Expectations*End variable is negative, indicating that the less
anticipated the cabinet dissolution, the greater is exchange rate volatility. The End variable, however,
remains insignificant. A chi-squared test indicates that End, Expectations, and Expectations*End, when
taken together, are statistically significant (p<0.05).
Model IV adds a set of institutional controls, based on electoral and legislative institutions.
Again, the Expectations and End*Expectations variables are significant and in the predicted directions.
The configuration of electoral and legislative institutions does influence exchange rate volatility, although
the results do not entirely meet with our predictions. As expected, proportional representation systems
with weak legislative committees have higher exchange rate volatility than proportional representation
systems with strong legislative committees. Surprisingly, however, majoritarian systems have lower
exchange rate volatility. Most of the control variables have the same direction and magnitude as in Model
III, with a few exceptions. First, the dummy variables for a pegged exchange rate and membership in the
E.M.S. become statistically insignifcant. Exchange rate commitments are endogenous to the
configuration of domestic political institutions (Bernhard and Leblang 1999). Consequently, this is not
19
surprising. Second, inflation becomes statistcally insignificant while short-term interest rates become
significant. Third, the partisanship variable loses statistical significance.
Discussion
Figure 3 graphs the relationship between market expectations of a cabinet dissolution
(Expectations) on the X-axis with expected exchange rate volatility along the Y-axis.20 Holding all other
variables at their means, the two lines represent a) expected exchange rate volatility in periods when a
cabinet survives (End = 0), and b) expected exchange rate volatility in periods when a cabinet dissolves
(End = 1).
We argue that exchange rate volatility increases as the probability of a cabinet dissolution
increases. The line (E(volatility|no end)) shows the expected exchange rate volatility for different levels
of expectations when a cabinet survives. When markets have low expectations of cabinet
dissolution—for example, if the probability of a dissolution is equal to 0.1--expected exchange rate
volatility is 0.543. When markets expect the cabinet to dissolve—if the probability of a dissolution is
0.9—expected exchange rate volatility is 2.144. Clearly, as the expectation of government end increases,
so does exchange rate volatility. The line (E(volatility|end)), on the other hand, represents expected
exchange rate volatility for different levels of expectations when the cabinet actually dissolves. This
slope of this line is only slightly positive.
We can evaluate our hypotheses about how unanticipated cabinet events affect expected exchange
rate volatility by comparing the two lines in Figure 3. Consider first the situation where markets
anticipate a cabinet dissolution in a particular period but the cabinet actually survives. Here, we expect
that a cabinet dissolution will decrease exchange rate volatility. And, in fact, this is the case. Indeed,
the highest levels of expected exchange rate volatility occur when economic agents anticipate a cabinet
dissolution but the cabinet survives. When Expectations are 0.22--the highest value that the expectations
variable takes when the cabinet does not end—expected volatility is 0.778. If a cabinet dissolution does
occur when Expectations equal 0.22, however, the expected exchange rate volatility is only 0.387. That
is, a cabinet dissolution when Expectations equal 0.22 causes expected volatility to drop by 0.39.
The second unanticipated event occurs when markets expect a cabinet to survive, but the cabinet
actually dissolves. Here, we predict an increase in volatility if a cabinet dissolution occurs. Again, the
results confirm our prediction. If markets are almost certain that the cabinet will survive—if the
probability of a dissolution is 0.01—and the cabinet does survive, then expected exchange rate volatility
is 0.378. If the cabinet dissolves when markets have these same expectations, expected volatility is
20
This graph uses expected values estimated from Model III. The results from Model IV provide a similar
graph.
20
0.427. Therefore, an unanticipated cabinet dissolution (when Expectations equal 0.01) increases
exchange rate volatility by 0.05.
By comparing the two lines in Figure 3, we can determine the level of Expectations at which the
effect of a cabinet dissolution on expected volatility changes from negative to positive. In fact, cabinet
dissolutions increase expected exchange rate volatility when Expectations < 0.0156. To the left of that
point, the line (E(volatility|end)) is higher than the line (E(volatility|no end)). When
Expectations>0.0156, however, expected exchange rate volatility is higher when the cabinet survives,
rather than when a dissolution occurs.
This constraint may make it seem that a cabinet dissolution increases exchange rate volatility only
under very limited circumstances. In fact, 47.5 percent of the total observations in our sample
(1414/2975) have a value for the Expectations variable that is less than 0.0156. Among these
observations, cabinets actually dissolve 22 times—over 16 percent of the total number of cabinet
dissolutions in this sample. Since these dissolutions were unanticipated, exchange rate volatility should
increase from the previous month. For these 22 cases, average exchange rate volatility in the month just
prior to the dissolution was 0.006. In the months when the dissolutions occurred, average exchange rate
volatility jumped to 0.287—a statistically significant difference (t-statistic = 1.91, p<0.06). Clearly,
unanticipated cabinet dissolutions do increase exchange rate volatility.
Another way to see the influence of unanticipated cabinet events on exchange rate volatility is to
compare cabinet dissolutions associated with an election and those due to a collapse of the coalition.
Unsurprisingly, markets have higher expectations of when a cabinet will end due to an election. The
average value for the Expectations variable when a cabinet dissolves (End=1) is 0.42. The average value
for the Expectations variable when a cabinet dissolution is associated with an election is 0.712. When a
cabinet dissolution is not associated with an election, however, the average of the Expectations variable is
only 0.048. Based on these values, we expect that the exchange rate volatility associated with a cabinet
dissolution not due to an election will be higher than the effect of a cabinet dissolution associated with an
election.
As a baseline, the average exchange rate volatility in periods when a cabinet dissolution does not
occur is 0.258 (Table 8). The average exchange rate volatility in periods when there is a cabinet
dissolution (due to either a collapse or elections) is 0.368, which is significantly different from the
baseline case (t-statistic = 1.65, p< 0.05, one-tailed test). For periods in which a cabinet collapses
without an election, average electoral volatility jumps to 0.487. Given that these events are, on average,
unanticipated, this statistically significant increase over the baseline level of exchange rate volatility is
consistent with our predictions (t-statistic = 2.17, p<0.00). Finally, the average exchange rate volatility
for cabinet dissolutions due to an election is 0.278, which is not statistically different from the baseline
21
case (t-statistic = 0.16, p<0.45). Since markets are usually able to anticipate the timing of elections,
these events are likely to have little impact on exchange rate volatility. This result may help account for
the mixed findings of other scholars who have examined the relationship between elections and exchange
rates.
Conclusion: Exchange Rate Volatility and the Demand for Political Reform
Recent political-economy arguments assume that economic agents use political information to
generate expectations about events that could affect economic policy. We argue that, in parliamentary
democracies, economic agents will develop expectations about the possibility of a cabinet dissolution.
These expectations have consequences for their economic behavior. In particular, expectations of
political change may influence the desire of economic agents to hold the currency. As the probability of
a cabinet dissolution increases, economic agents will have less certainty about the future of economic
policy, contributing to increased exchange rate volatility.
Moreover, the impact of cabinet events on exchange rate volatility varies according to whether
the markets anticipate the event. Unanticipated events increase exchange rate volatility. Given high
expectations of a cabinet dissolution, continued cabinet survival contributes to higher exchange rate
volatility. An actual dissolution decreases predicted exchange rate volatility. Given low expectations of
a cabinet dissolution, however, a dissolution increases volatility. Anticipated events tend to produce
lower levels of exchange rate volatility.
These results provide a mechanism to help link two recent trends in the industrialized countries:
economic internationalization and domestic institutional reform. In particular, our argument suggests
that sectors exposed to the international economy will be in the forefront of demands for political reform.
Unanticipated cabinet events increase exchange rate volatility in parliamentary democracies. Exchange
rate volatility, in turn, makes the external trading environment riskier, hurting international traders and
investors as well as the export-oriented tradable sector (Frieden 1991). For these actors, therefore,
unanticipated political change has direct costs. Consequently, these actors are likely to demand electoral
and political reforms to produce fewer unanticipated cabinet events, including reforms designed to reduce
the number of political parties, to reduce the polarization of the party system, or to limit the ability of
legislators to cause a cabinet collapse. With increasing exposure to international trade and capital
mobility over the past 20 years, these internationally-oriented sectors have gained political influence. As
a consequence, they have been able to place more pressure on politicians to reform the political system,
contributing to the recent wave of institutional reform in the industrial democracies.
22
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Cambridge
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Table 1: Descriptive Statistics for Model of Cabinet Duration
Standard
Variable Name
Mean
Deviation
Minimum
End
0.046
0.201
0
Single Party Majority
0.308
0.462
0
Minimum Winning Coalition
0.284
0.451
0
Oversize Majority
0.171
0.377
0
Single Party Minority
0.166
0.372
0
Coalition Minority
0.701
0.255
0
Electoral Clock
0.583
0.269
0
Electoral Clock2
0.413
0.302
0
Cabinet Duration
15.940
11.97
1
Cabinet Duration2
397.38
521.83
1
Fractionalization
0.673
0.111
0.41
Polarization
0.075
0.079
0
Exogenous Electoral Timing
0.183
0.387
0
N=4512
28
Maximum
1
1
1
1
1
1
1
1
58
3364
0.88
0.299
1
Table 2: Discrete-Time Hazard Model Probit Specification
Dependent Variable—Government End
Variable
Coefficient
Robust Standard Error
Marginal Effect
Constant
-5.17*
2.55
Single Party Majority(d)
0.46
2.40
Minimum Winning Coalition(d)
0.67
2.31
Oversize Coalition(d)
2.14
2.26
Single Party Minority(d)
0.19
2.49
Electoral Clock
-4.68
4.18
Electoral Clock^2
8.96*
2.71
Cabinet Duration
0.25*
0.10
Cabinet Duration^2
-0.004*
0.002
Clock*Single Party Majority
0.84
4.77
Clock*Min. Win. Coalition
3.70
5.16
Clock*Single Party Minority
-0.08
3.75
Clock*Oversize Coalition
0.89
4.60
Clock^2*Single Party Majority
-2.06
3.39
Clock^2*Min. Win. Coalition
-4.84
4.60
Clock^2*Oversize Coalition
-3.30
3.79
Clock^2*Single Party Minority
-0.89
3.36
Duration*Single Party Majority
-0.06
0.09
Duration*Min. Win. Coalition
-0.12
0.11
Duration*Oversize Coalition
-0.14
0.12
Duration*Single Party Minority
0.02
0.13
Duration^2*Single Pty Majority
0.002
0.002
Duration^2*Min. Win. Coalition
0.002
0.002
Duration^2*Oversize Coalition
0.002
0.002
Duration^2*Single Pty Minority
-0.001
0.003
Exogenous Electoral Timing(d)
0.51*
0.23
Fractionalization
-0.74
1.15
Polarization
1.30
1.39
Number of Observations
4512
Correlation (y, y-hat)
%correct when end=0
%correct when end=1
0.684
98%
84%
0.02
0.04
0.34
0.01
-0.06
0.20
0.24
-0.10
0.01
0.05
-0.001
0.008
-0.02
-0.05
-0.02
-0.007
-0.03
-0.06
-0.05
0.003
0.02
0.04
0.02
-0.004
0.04
-0.003
0.004
Log-Likelihood Ratio Tests
P-Value
2
Entire Model
812.63
0.0000
Nation Dummy Variables
57.70
0.0000
Single Party Majority
10.04
0.0741
Minimum Winning Coalition
10.56
0.0608
Oversize Coalition
11.03
0.0507
Single Party Minority
6.44
0.2656
Electoral Clock
509.56
0.0000
Duration
322.07
0.0000
Robust standard errors are based on clustering according to country. Model estimated with a set of nation dummy
variables.
(d) = dummy variable
a
For a dummy variable, the marginal effect is calculated for a discrete change in the variable. For a continuous
variable, the marginal effect is calculated for a change in 1/2 of one standard deviation.
*two-tailed z-score, p<.05
29
Table 3: Predicted Probabilities of Cabinet Dissolution by Government Type
Standard
Government Type
N
Mean
Deviation
Minimum
Single Party Majority
1390
0.035
0.123
9.76e-10
Minimum Winning Coalition
1281
0.044
0.109
1.92e-06
Oversize Coalition
775
0.058
0.095
0.0003
Single Party Minority
750
0.051
0.142
6.12e-09
Coalition Minority
316
0.077
0.165
1.23e-10
30
Maximum
0.983
0.967
0.815
0.980
0.999
Table 4: Predicted Probabilities of Cabinet Dissolution
Standard
Situation
N
Mean
Deviation
All Periods
4512
0.047
0.122
When Cabinet Survives: End=0
4304
0.029
0.035
When Cabinet Fails: End =1
208
0.426
0.385
31
Minimum
1.23e-10
1.23e-10
5.69e-06
Maximum
0.999
0.220
0.999
Table 5: Descriptive Statistics for Model of Exchange Rate Volatility
Standard
Variable
Mean
Deviation
Minimum
Maximum
Exchange Rate Volatility
0.263
0.794
0.000
16.83
Exchange Rate Volatility (t-1)
0.263
0.794
0.000
16.83
Fixed Exchange Rate Regime
0.149
0.356
0.000
1.000
Member of the Snake
0.084
0.278
0.000
1.000
Member of the E.M.S.
0.318
0.466
0.000
1.000
Floating Exchange Rate Regime
0.436
0.496
0.000
1.000
Exchange Rate Devaluation/Realignment
0.007
0.087
0.000
1.000
Capital Controls
0.369
0.483
0.000
1.000
Inflation
7.490
13.510
-1.443
138.381
Short-Term Interest Rates
8.632
4.079
0.110
82.380
Central Bank Independence
0.343
0.152
0.170
0.690
Exogenous Electoral Timing
0.138
0.345
0
1
P.R.-Strong Committee System
0.483
0.500
0
1
P.R.-Weak Committee System
0.115
0.319
0
1
Majoritarian-Weak Committee System
0.401
0.490
0
1
Partisanship
0.342
0.432
0
1.31
U.S. Trade-Weighted Exchange Rate
100.128
15.667
80.973
158.430
Government End in the US
0.040
0.197
0
1
US Exchange Rate Realignment
0.010
0.100
0
1
End
0.044
0.208
0.000
1.000
Expectations
0.045
0.208
1.09e-09
0.989
End*Expectations
0.018
0.121
0
0.989
N=2975
32
Table 6: Models of Exchange Rate Volatility
Model I
Model II
1.585*
1.137*
(0.314)
(0.374)
Exchange Rate Volatility (t-1)
0.457*
0.438*
(0.048)
(0.049)
End
-0.013
-0.004
(0.038)
(0.036)
Single Party Majority Government
0.232
(0.217)
Minimum Winning Coalition
-0.024
(0.159)
Oversize Coalition Government
0.115
(0.183)
Single Party Minority Government
0.520*
(0.223)
Pegged Exchange Rate Regime
-0.713*
-0.622*
(0.108)
(0.128)
Member of the Snake
-1.070*
-0.859*
(0.130)
(0.157)
Member of the European Monetary System
-0.717*
-0.523*
(0.101)
(0.136)
Exchange Rate Devaluation/Realignment
0.110*
0.110*
(0.037)
(0.356)
Capital Controls
-0.092
-0.188
(0.123)
(0.130)
Inflation
-0.019
-0.012
(0.011)
(0.011)
Short-Term Interest Rates
0.006
0.005
(0.004)
(0.004)
Government End in the U.S.
-0.001
0.006
(0.029)
(0.029)
Central Bank Independence
-1.33*
-1.28*
(0.214)
(0.263)
Partisanship
-0.181*
-0.420*
(0.045)
(0.089)
U.S.Trade-Weighted Dollar Index
0.002
0.003
(0.002)
(0.002)
International Coordination
0.131*
0.128*
(0.061)
(0.061)
Time Trend
-0.003*
-0.002*
(0.001)
(0.001)
Variable
Constant
Number of Observations
Model Chi-Square
Prob>Chi-Square
2975
1120.87
0.0000
2975
1167.44
0.0000
Dependent variable is the standard deviation of monthly nominal exchange rates based on daily data. Parameter estimates are ordinary least
squares coefficients generated via feasible least squares using the xtgls procedure in STATA. Panel Corrected Standard Errors are in
Parentheses. Models are estimated with panel specific AR(1) terms and corrected for heteroscedastic disturbances between panels.
*p<0.05
33
Table 7: Models of Exchange Rate Volatility
Model III
Model IV
1.529*
0.498
(0.322)
(0.350)
Exchange Rate Volatility (t-1)
0.422*
0.340*
(0.048)
(0.053)
End
0.030
0.040
(0.063)
(0.063)
Expectations
1.987*
2.152*
(0.976)
(1.038)
End*Expectations
-1.923*
-2.093*
(0.964)
(1.023)
Pegged Exchange Rate Regime
-0.668*
-0.113
(0.108)
(0.084)
Member of the Snake
-1.097*
-0.361*
(0.132)
(0.100)
Member of the European Monetary System
-0.748*
-0.017
(0.101)
(0.044)
Exchange Rate Devaluation/Realignment
0.118*
0.110*
(0.036)
(0.031)
Capital Controls
-0.123
-0.159
(0.127)
(0.126)
Inflation
-0.018*
-0.013
(0.008)
(0.007)
Short-Term Interest Rates
-0.006
0.007*
(0.004)
(0.004)
Government End in the U.S.
0.003
-0.002
(0.032)
(0.028)
Central Bank Independence
-1.260*
-0.411*
(0.229)
(0.144)
Partisanship
-0.150*
0.055
(0.048)
(0.040)
U.S. Trade-Weighted Dollar
0.001
0.002
(0.002)
(0.002)
International Coordination
0.140*
0.157*
(0.061)
(0.061)
Time Trend
-0.002*
-0.003*
(0.0006)
(0.0007)
Proportional-Weak Committee System
1.034*
(0.131)
Majoritarian-Weak Committee System
-0.135*
(0.027)
Variable
Constant
Number of Observations
2975
2975
Model Chi-Square
1060.66
1082.21
Prob>Chi-Square
0.0000
0.0000
Dependent variable is the standard deviation of monthly nominal exchange rates based on daily data. Parameter
estimates are ordinary least squares coefficients generated via feasible least squares using the xtgls procedure in
STATA. Panel Corrected Standard Errors are in Parentheses. Models are estimated with panel specific AR(1)
terms and corrected for heteroscedastic disturbances between panels.
*p<0.05
34
Table 8: Average Exchange Rate Volatility for Cabinet Dissolutions
Mean
ight Mean
Situation
N Expectation
Volatility
Average—All Periods
2975
0.047
0.263
When Cabinet Survives: End=0
2840
0.029
0.258
When Cabinet Fails: End =1
135
0.419
0.368
When Cabinet Fails Without Election
58
0.050
0.487
When Cabinet Fails With Election
77
0.697
0.278
35