Does Opportunism Pay Off? A Study of Vote
Functions and Policy Preferences
Stefan Krause and Fabio Méndez
∗
May 2006
Abstract
In this paper we present an empirical study of voting behavior to analyze the impact
of opportunism; that is, whenever political incumbents implement economic policies
strategically and in connection with general elections in order to gain votes. We derive
a measure for opportunism that is isolated from the impact of aggregate economic
conditions, such as the levels of economic growth and consumer price inflation. In
contrast with most papers available on these issues, we do not ask whether political
parties behave opportunistically; instead, we ask whether they receive a direct, electoral
punishment or incentive for doing so. Our results indicate that the electorate punishes
an incumbent party for behaving opportunistically, controlling for economic conditions
and political variables. The party in power receives a significantly lower percentage of
votes whenever it follows expansionary policies during the election year, relative to the
other years of its tenure.
JEL classification: E32, P16
Keywords: Opportunism, Voting Function
∗
Department of Economics, Emory University, and Department of Economics, University of Arkansas.
We want to thank Jennifer Gandhi, Javier Reyes and Alberto Trejos for their useful suggestions. Please send
comments to skrause@emory.edu or fmendez@walton.uark.edu.
1
Introduction
Several studies in the political and economic literature have examined the question
of whether policy makers behave opportunistically (Nordhaus, 1975; Alesina, Roubini and
Cohen, 1997; Faust and Irons, 1999; Drazen, 2000; and Castro and Veiga, 2004, among
others). That is, whether political incumbents implement economic policies strategically
and in connection with general elections in order to gain votes. The logic in these studies
relies on the assumption that voters reward the incumbent when existing economic conditions
are good, and that opportunistic officials are able to use this behavior to their advantage.
Thus, at the heart of the matter, the study of political opportunism is inevitably linked to
the empirical voting literature that explains polling results.
Although it seems reasonable to conclude that voters reward the incumbent for better economic conditions, and most evidence points to that direction (see Lewis-Beck and Stegmaier,
2000), it is not clear how voters react to political opportunism itself; if and when it is recognized as such by the public. In fact, given the potential costs of political opportunism in the
forms of future inflation and greater economic fluctuations, it would be reasonable for voters
to reward good current economic conditions while punishing economic opportunism at the
same time.
The empirical evidence regarding economic voting behavior has supported the idea that
voters reward the incumbent when economic conditions are good, but is silent about the
reaction to opportunistic behavior itself. Several empirical studies have reported a connection between economic variables such as growth, inflation or unemployment and both the
popularity of the government and the votes obtained by the incumbent (see, for example,
Paldam, 1991; or Powell and Wittten, 1993). In most studies, after controlling for other political variables, an increase in the number of votes received by the incumbent is associated
with pre-electoral periods of elevated growth, low inflation and low unemployment.
At the same time, while none of the available empirical studies account for the voters’
direct reaction to opportunism, there is ample anecdotal evidence suggesting that voters
1
are able to distinguish pre-electoral opportunistic policies. Thus, simply dismissing the
possibility that electorates recognize opportunism by itself and punish it (or reward it)
with their votes renders the economic voting analysis incomplete. The significance of this
omission cannot be overlooked. The voter’s reaction to opportunistic policy making is key for
the understanding of how modern democracies around the world operate. It is this matter,
precisely, that motivates our study.
In this paper we present an empirical study of voting behavior where the impact of opportunism on voting results is examined separately from the impact of current economic
conditions. Thus, in contrast with most papers available on these issues, we do not ask
whether political parties behave opportunistically; instead, we ask whether they receive a
direct, electoral punishment or incentive when they do. The results obtained here confirm
previous findings regarding the positive impact of current economic conditions on the votes
received by the incumbent, but, at the same time, suggest that opportunistic policies intended to jerk these economic conditions ahead of the elections are effectively recognized
and punished by the voters.
Noticeably, the voters’ recognition and punishment of opportunism may disincentive that
type of behavior, but it does not preclude it. If opportunism itself is punished, then there
is a smaller incentive to behave opportunistically, but the actual behavior of the policy
maker would depend on the size of the punishment relative to the gains obtained through
the accompanying improvement in the short-run economic indicators. Similarly, the possibility of positive, immediate gains from opportunism does not imply that it is optimal for
political parties to behave in that manner. If, for example, opportunistic political parties
are rewarded with more votes in the current elections but expect to receive fewer votes in
future elections as a result of those policies, then, short-term oriented parties would choose
to behave opportunistically, whereas long-term oriented parties would not.
The goal of the paper requires that we define and measure opportunism independently
from current economic conditions. Such a task poses at least two problems. First, while
2
typical definitions in the spirit of Nordhaus (1975) refer to opportunism as the enactment of
policies that generate short run expansions, it is not clear which economic variables should
be used to identify an expansionary period. As a casual inspection of the data shows,
pre-electoral periods where high growth and low inflation coexist are not rare, and therein
lays the problem with this approach: expansionary economic policies might generate output
growth, price growth, both, or none. What is needed is a measure of opportunism that is
independent of economic policy outcomes.
Second, if the chosen measure of opportunism is to be used in econometric regressions as
part of a set of explanatory variables that determine voting behavior, then this measure would
have to be independent from the other explanatory variables or the estimated coefficients
would be biased. As in the first case, one would want a measure of opportunism that is
independent of economic policy outcomes like growth or inflation.
In order to deal with these problems, we use a measure of opportunism based on estimates
of policy preferences. Not only is the proposed measure one-dimensional and independent
from current economic conditions, but it is also the only measure available with these characteristics for a cross-section of countries. The measure will be discussed in detail in what
follows. We then investigate whether opportunistic behavior directly gains or loses votes
for the incumbent while controlling for other important determinants of polling results as
outlined in the economic voting literature.
The remainder of the paper is organized as follows: Section 2 provides a detailed description of the data. Section 3 presents the main econometric specifications and shows the
estimation results. Finally, the last section concludes.
2
Data description
The data used in the empirical analyses includes 25 countries and covers years between
1975 and 2002. We collected data for electoral results, opportunism measures, and both
3
the political and economic conditions that characterized each electoral round. Our data set
covers 21 countries with parliamentary systems and 4 with presidential systems. All sources
and variable definitions are included in a data appendix.
2.1
A measure of opportunism based on policy preferences
Our measure of political opportunism follows that one used by Krause and Méndez (KM,
2005). Consistent with most contemporary analyses of short run economic policy (Persson
and Tabellini (1999), and Clarida, Gali and Gertler (1999)), KM (2005) start by assuming
that the primary concern of policy makers is to achieve stabilization of the economy through
the reduction in the variability of inflation and output growth. That is, the policy maker’s
objective is to minimize the following standard quadratic loss function1
L = Et [λ(π t − π Tt )2 + (1 − λ)(yt − ytT )2 ] ; 0 ≤ λ ≤ 1 ,
(1)
where Et is the expectation operator at time t; π is inflation; y is (log) aggregate output;
π T and y T are the target levels of inflation and output; and λ is the relative weight given to
squared deviations of inflation and output from their desired levels.
The parameter λ is bounded between zero and one, and a lower value of λ during an
election year is associated with a more expansionary policy (relative to previous years),
since this is associated with the authorities placing a larger relative weight on reducing
output fluctuations and, conversely, a lower relative weight on stabilizing inflation around
its target. Analogously, a higher value for λ in an election year will be associated with
more contractive economic policies. Thus, within this framework, if an opportunistic shift
in preferences towards more expansionary policies takes place before the elections, it would
be captured by a decrease in the value of λ.
1
The quadratic loss function restricts expansionary and contractionary policy preferences to be symmetric,
which may not always be the case. See Ruge-Murcia (2003) for the derivation and estimation of a gametheoretic model with asymmetric preferences around the target levels of inflation and the unemployment
rate.
4
In this way, by tracking the policy preference parameter over time, we can obtain a
measure of opportunism. Furthermore, since shifts in λ capture changes in the intentions
of the policy maker and not changes in the economic variables that result from his policies,
the proposed measure of opportunism would be independent of both output growth and
inflation. In what follows, we classify revealed preferences as opportunistic according to the
following two criteria:
1. Opportunistic Criterion 1 (Oppor1 ): Electoral year’s value of λ is lower than the
average value for λ during the incumbent’s tenure
2. Opportunistic Criterion 2 (Oppor2 ): Electoral year’s value of λ is lower than the value
for λ during the immediate pre-electoral year.
Formally, the two opportunistic criteria can be defined as:
Oppor1 = λ − λE ,
(2)
Oppor2 = λE−1 − λE ,
(3)
where λE is the average value of λ for the four quarters previous to the election date (including
the quarter of the elections), λE−1 is defined by the one-year lag of , and λ is the average
value of λ for all quarters between the current elections and the previous elections.
Quarterly estimates of λ were obtained from the KM (2005) database.2 KM (2005) used
a series of restricted second-order vector auto-regressions in order to estimate a short-run
macro model of aggregate supply and demand, for which an expression for λ can be obtained
as a function of the structural parameters. Noticeably, the assumed structure of the shortrun macro model used to estimate these parameters is equivalent to the New Keynesian
macro model employed by Clarida, Galí and Gertler (1999) (see Krause, 2006, for a formal
demonstration). This feature of the dataset is particularly attractive, since it suggests that
the estimated coefficients for λ are not dependent on the specific structure of the economy
2
The
complete
database
for
quarterly
values
http://userwww.service.emory.edu/~skrause/pdf/Lambdas.xls.
5
of
λ
can
be
found
at
assumed by KM (2005).
For the purposes of this paper, it is important to note that since λ is a function of the
structural parameters of the economy and the reaction of policy to supply shocks only, the
estimated policy maker’s preferences (and thus, our measures of opportunism) will not be
a function of the levels of inflation and output, but only of the relationship between these
variables given by the structural model of the economy. Empirical support for this argument
can be found in specific country data; in here, as in Krause and Méndez (2006), we present
evidence from two countries: Malaysia and Germany.
During 1998, Malaysia experienced an annual inflation rate of 5.27%, well above the
average rate of 2.67% during 1997 and the rate of 2.75% that prevailed in 1999. This
significant increase in prices was mostly a result of the currency devaluation resulting from
the Asian Crisis of 1997-98. The average money growth of central bank high-powered money
during 1997-1998 was -1.77%, indicating that a contractive monetary policy was taking place,
and this latter result is consistent with the quarterly estimates of λ, which remained stable
around 0.97-0.98 during 1998, close to the values observed in 1997 and 1999 (and higher
than the estimates from previous years).
The second case of study is the German reunification in 1990. As a result of the process
of currency conversion for East Germany, in the second semester of that year the increase in
central bank money averaged 86.44%, well above the average growth rates in 1989 (9.08%)
and 1991 (-11.79%). This short-run monetary expansion is captured by a sharp decrease in
the preference for inflation stability, which was estimated near zero for the third and fourth
quarters of 1990, compared to an average level for λ of 0.67 for the six preceding quarters,
and 0.73 for the six succeeding quarters. However, this monetary expansion did not translate
into higher inflation until 18 months later; namely, an increase in the Consumer Price Index
(CPI) inflation from an average of 2.70% in 1990 and 1.69% in 1991, to a 6.00% rate in the
first semester of 1992.
6
2.2
Data on electoral results, political variables and economic
trends
Data on political parties and electoral results was obtained from three main sources: The
Dataset of Political Institutions (DPI) introduced by Beck et al. (2001) was the main source.
Two other complementary sources were used: the Political Dataset of the Americas (managed
by the Center for Latin American Studies at Georgetown University in collaboration with
the Organization of American States), and the database for European political parties and
elections.3
Electoral systems were broadly classified as Presidential or Parliamentary following the
DPI’s classification. For presidential systems, the incumbent’s votes correspond to the votes
obtained by the party with which the incumbent president is associated. For parliamentary
systems, the incumbent’s votes correspond to the votes obtained by the party with the largest
presence within the government.
Notice that according to this definition, the incumbent party in a parliamentary system
may well not constitute the party with greatest amount of seats. In parliamentary systems,
whenever a number of small parties collude to form a coalition government, the party that
obtains more seats is not necessarily the one who is in charge of making policy and is therefore
blamed for good or bad results. Thus, defining the incumbent in this way has the advantage
of assigning responsibility to the actual policy maker.
For both systems we recorded the percentage of the votes obtained by the incumbent as
well as the percentage of votes obtained by that same party in the previous elections (when
it came to power). We also collected information on the percentage of parliamentary seats
the incumbent’s party controlled at the time of the elections, and information on ideological
identification to create a dummy variable taking the value of 0 for left oriented parties and 1
for right or center oriented parties. Additional political variables collected include the length
3
“Parties and Elections in Europe" includes a database about parliamentary elections in the European
countries since 1945 and additional information about the political parties and the acting political leaders.
The private website (http://www.parties-and-elections.de) was established by Wolfram Nordsieck in 1997.
7
of the electoral cycle, and the degree of fractionalization of the ruling government.
Data for the economic variables comes from two sources: GDP growth was obtained from
the World Bank’s World Development Indicators Database (W.D.I., 2004). In turn, data
for inflation comes from the IMF’s International Financial Statistics Database (December,
2004). Finally, we also include a measure of political freedom as a control variable; this
measure corresponds to the political rights sub-index of the Freedom House International
annual series.
3
Econometric Specifications
For the econometric specification, we adopt a general voting function in which political
and economic variables enter simultaneously as determinants of voting behavior. As in most
of the economic voting literature (see Lewis-Beck and Stegmaier, 2000; Paldam, 1991; and
Powell and Whitten, 1993), the dependent variable is the gain (or loss) in the share of
votes received by the incumbent party with respect to the previous election. This change
is explained by a series of economic and political variables included in a set of independent
variables.
The economic indicators used as independent variables consist of a measure of GDP
growth and a measure of inflation. Both economic measures reflect the average value of
the annual indices for the duration of the presidential cycle in question. As mentioned before, voters are expected to reward good economic performance, understood in this context
as higher-than-average economic growth and lower-than-average inflation (see Nordhaus,
Alesina and Schultze, 1989, for evidence on inflation and unemployment performance; and
Abrams and Butkiewicz, 1995, for an analysis of how state-level economic conditions have
incidence on presidential elections). Another important macroeconomic variable that voters
clearly care about is (low) unemployment; nevertheless, since a complete series of unemployment data is not available for all the countries in our sample and given its high correlation
8
with GDP growth, we do not consider it explicitly in our analysis.
The political variables used as independent variables include a measure of political freedom, a measure of absolute government support, the number of parliamentary seats controlled by the incumbent, the ideological classification of the incumbent, a measure of the
length of the incumbent’s tenure (in years), and the degree of fractionalization in the government. Below, we provide some intuition for why these controls are important. The main
results of the paper regarding opportunism, however, are robust to the specific composition
of the control variables vector.
As noticed by Powell and Whitten (1993), since different parties have different electoral
bases that remain stable over time, using the results of previous elections to controlling
for absolute government support is essential to the specification: “A loss of two percentage
points may mean something different for a government that won 40% last time as compared
with a government that won 60% last time.” We agree with this appreciation and include this
control in all our regressions. Also as in Powell and Whitten (1993), we expect its coefficient
to be negative as it is easier to lose an absolute percentage of the votes from a larger base.
We include a measure of the length of the incumbent’s tenure (in years) to capture the
depletion in government support that appears as the government ages and the initial popularity of the government vanishes. Such loss of popularity has been previously documented
by authors like Goodhart and Bhansali (1970) and Paldam (1986). Since opportunism takes
place at the end of the constitutional mandate, ignoring the effects of the length in tenure
could lead to biased coefficients for the opportunistic measures.
We also include a political ideology dummy and two variables that capture the cohesion
of the government. The dummy variable takes a value of 1 for right or center oriented parties
and a value of 0 for left oriented parties. Left and right leaning parties tend to have different
preferences and approaches to economic policy and voters might take these historical trends
into account when evaluating government performance.
In turn, government cohesion is measured by government fractionalization and the num-
9
ber of congressional seats controlled by the incumbent at the time of the election. As
explained by Lewis-Beck and Stegmaier (2000), the greater the perceived cohesion of the
incumbent’s government, the more likely voters are to charge the incumbent with the responsibility of recent policies and of any opportunism perceived.
Finally, a variable measuring the degree of political rights is included in the vector of
political controls. Opportunistic policy making frequently coincide with populist governments, or governments controlled by an authoritative leader, or by militaristic governments
that aim to hold power by non-democratic methods. By including this control variable, our
measure of opportunism would not capture the effects of such temporary shocks.
Once our measures of opportunism are included, the econometric specification can then
be summarized by the following equation:
→
−
→
−
V otesch = α + βV otesinp + δ E + γ P + φOpportunism ,
where V otesch is the absolute change in the percentage of the popular vote captured by the
incumbent in any election with respect to the previous election; V otesinp is the percentage
→
−
of votes received by the incumbent in the previous election; E is a vector of economic
→
−
variables (growth and inflation); P is a vector of political variables (used as controls); and
Opportunism is measured by either Oppor1 or Oppor2, as defined in equations (3) and (4).
3.1
Empirical analysis and results
Table 1 shows a descriptive summary of most variables for each country, averaged over
each government period. Noticeably, our first measure for opportunism, Oppor1, ranges between -0.053 (Portugal) and 0.141 (Finland); while Oppor2 ranges between —0.134 (Canada)
and 0.314 (Spain). We also note that 15 out of 25 countries exhibit a positive average value
for Oppor1 (16 countries in the case of Oppor2 ), implying that, on average, a majority of
countries have exhibited expansionary policies during the election period. Another worth-
10
while observation is that Peru is a clear outlier when looking at the inflation data. We revisit
this issue below.
Table 2 presents the results of estimating equation (3) using ordinary least squares regressions. We exclude Peru from our regression results, given its high rate of inflation. In
columns (1) and (2) we report the estimates with Oppor1. We see that the economic variables
behave as predicted by the theory: higher economic growth is associated with an increase
in the percentage of votes, while a rise in the inflation rate is linked to a reduction in the
incumbent party’s support. Both coefficients are significant at the 1% level. Also, consistent with the observation by Powell and Whitten (1993), support for the incumbent party
is negatively and significantly correlated with the votes it received in the previous election.
Finally, Oppor1 enters the regression with a negative coefficient, albeit not significant at the
10% level.
Controlling for political variables in column (2) leaves most of the main results unchanged:
growth and inflation have the expected signs and similar magnitudes and remain significant,
while Oppor1 remains negative but insignificant. The only major change is that the percentage of votes the incumbent received in the previous election is not significantly associated
with the change in votes. Nevertheless, it is worthwhile to note that out of the political
control variables, the length of the tenure of the incumbent party/coalition has the only
negative and significant coefficient at the 1% level. We can interpret this result as support
for the incumbent “wearing out” the longer that party/coalition is in power.
Columns (3) and (4) repeat the above exercise with Oppor2. As before, the estimated
coefficients for the economic variables are significant and have the expected signs. Interestingly, Oppor2 is enters the regression with a negative and statistically significant (at the
5% level) coefficient. These results indicate that there exists some support to the hypothesis
that the electorate punishes opportunism, mostly if it is reflected in the change of preferences
between the electoral year and the year immediately prior to it.
The use of OLS techniques, however, is plagued with shortcomings. As pointed out by
11
Lewis-Beck and Stegmaier (2000), electoral results are often influenced by specific themes
such as the candidate’s view of a sensible topic, the candidates charm and looks, current
international movements of ideology, etc. Controlling for these one-time events is not possible
in an OLS regression but it can be done using fixed effects estimations, where each electoral
campaign is treated as a specific case.
Table 3 presents the results of the estimation using fixed-effects regressions. The first two
columns report the results with Oppor1. In the specifications without and with the political
control variables, Oppor1 enters the regression with a negative and significant (at the 10%
and 5% levels, respectively) coefficient. As for our other variables, we observe that the
effect of GDP growth on the change in votes is still positive and significant at the 1% level.
However, we note that average inflation over the incumbent’s tenure becomes insignificant;
while the percentage of votes received by the incumbent in the previous election becomes
negative and significant. All these results are robust to the inclusion of the political variables.
As for the political variables, the length of the tenure of the incumbent party/coalition is
negative and significant at the 1% level, while the percentage of seats the incumbent holds
in the parliament/congress at the time of the election is positively (and significantly at the
10% level) correlated with an increase in the incumbent party votes.
Finally, columns (3) and (4) yield the same main results when employing Oppor2 ; the only
noticeable difference is that the coefficient on Oppor2 is larger and statistically significant
at the 5% level (and a the 1% level, when the political control variables are included). The
robustness of these results provides further evidence that, controlling for overall economic
performance, voters do punish opportunistic behavior via a reduction in support for the
incumbent.
Of particular interest is the interpretation of the size of the coefficients in the fixed effects
regressions. Starting from a benchmark situation in which λ is equal to 0.8 (say, stabilizing
inflation has a relative weight of 80%; while reducing output fluctuations has a relative weight
of 20%), if during an election year the authorities carry out expansionary policies which (say)
12
are equivalent to a reduction of λ to 0.6, this would translate in a loss of votes of the order
of 1.9%-2.6% and would require, all other things equal, an increase of roughly 1.8%-2.4% in
average annual GDP growth to offset the voters’ discontent with the opportunistic behavior
by the incumbent.
Before we proceed with verifying the robustness of our results, it is important to check
whether or not a reverse causality argument is warranted in the analysis; that is, if the
presence of an opportunistic behavior is the outcome of an incumbent expecting to lose
voters’ support in the upcoming election. While there is no reason a priory to believe that
this should be the case, we need to verify the above argument empirically.
An ideal instrument to address the issue of reverse causality would be data on survey polls
prior to the election. If these polls were to suggest that the party in power is losing public
backing (and/or is likely to lose the election), the reverse causality argument would indicate
that the incumbent’s policies should be expansive. Unfortunately, such information is not
readily obtainable for most countries and election periods in our sample. Therefore, we opt
for an alternative approach. Based on the findings of the existing literature and our results,
we should expect that an incumbent is more likely to lose voters’ support: 1) the longer the
party has been in the government (length); 2) the lower economic growth during their tenure
with respect to the previous administration (∆growth); 3) the higher the average rate of
inflation relative to the one prevalent in the past government (∆inflation). If this is the
case, and parties who are bound to lose an election show an opportunistic behavior, we can
formalize it by:
Opportunism = η + µ1 length + µ2 ∆growth + µ3 ∆inf lation,
where our above hypotheses would correspond to µ1 > 0; µ2 < 0; and µ3 > 0. The joint
F-test cannot reject the hypothesis that all coefficients are equal to zero; the p-value for the
F-statistic when the dependent variable is Oppor1 (Oppor2 ) is 0.91 (0.39).4 This leads us
4
Also, when we test each of the coefficients separately, none of them is significantly correlated with either
13
to conclude that there is no statistical evidence suggesting the presence of reverse causality.
3.2
Specification Tests and Robustness Exercises
In the previous subsection we argue that, given country-specific characteristics to the
voting function, controlling for idiosyncratic effects is preferable to a pooled specification.
Still, we want to determine whether using fixed-effects is warranted over random-effects panel
estimation. Therefore, we perform a Hausmann test for the regressions including Oppor1 and
Oppor2 and the political control variables. The test rejects the hypothesis that individual
country effects are uncorrelated with other regressors on both instances,5 which leads us to
conclude that the fixed effects model is the best choice.
We also perform a Skewness/Kurtosis tests for Normality of the residuals of the fixedeffects model. For the model using Oppor1, the test statistic for the joint test of skewness
and kurtosis yields a chi-squared value of 6.30 (p-value = 0.04), while for the model that
includes Oppor2, the chi-squared value is 4.51 (p-value = 0.11). We note that, if Peru is
included in the sample, the normality hypothesis is rejected for both specifications at the
1%.
Finally, as a robustness check, we study if excluding either inflation or GDP growth from
the model has any effect on the coefficients of the measures of opportunism. As it turns out,
there is no significant change in either the magnitude or the precision at which the coefficients
of Oppor1 and Oppor2 are estimated.6 The only major change is that, if we exclude growth
as a regressor makes the coefficient on inflation positive and significant (at the 10% level),
which would contradict the hypothesis that voters punish high-inflation governments.
Oppor1 or Oppor2 at the 10% level.
5
For the model with Oppor1 (Oppor2 ) the chi-squared value is 28.15 (17.74), and in both cases the p-value
is 0.00.
6
These results are available from the authors upon request.
14
4
Conclusions and Future Research
The empirical analysis presented in these paper points to two major conclusions. First,
voters are likely to punish political opportunism even when they are also likely to reward
good economic conditions. Second, that incumbents are not necessarily deterred by such
punishment and that the cost of opportunism in terms of votes might or might not be outweighed by the benefits of opportunism that come when the associated economic conditions
are rewarded in the polls.
When following this reasoning, policy makers are not viewed as exogenous agents that
are opportunistic per se, but as rational agents that respond to incentives and make the
best choices available to them in order to obtain their goals. We believe that this type of
approach is the correct one and that the evidence presented in this paper will be useful for
future discussions on these issues.
5
Data Sources and Description
5.1
Data Sources
The countries included in the sample are: Australia, Austria, Barbados, Belgium, Canada,
Costa Rica, Denmark, Finland, France, Germany, Hungary, Italy, Japan, Mexico, Netherlands, Norway, Peru, Portugal, Spain, Sweden, Switzerland, Trinidad and Tobago, Turkey,
United Kingdom, and the United States of America.
Data on policy makers’ preferences used to compute Oppor1 and Oppor2 is from Krause
and Méndez (2005) and is available for download at:
http://userwww.service.emory.edu/~skrause/pdf/Lambdas.xls (quarterly data).
Data on electoral dates (used to compute the length of the incumbent’s tenure), party
ideology, political freedom, votes received by incumbent, incumbent’s seats in congress, fractionalization of the governing coalition, were obtained mainly from the Database on Politi-
15
cal Institutions in Beck et al (2001), the Political Dataset of the Americas (managed by the
Center for Latin American Studies at Georgetown University in collaboration with the Organization of American States), and the database for European political parties and elections,
and also partly completed through direct inquiries to individual government sources.
CPI Inflation data comes from the International Financial Statistics CD_ROM (December 2004). Data on GDP is from the World Bank’s World Development Indicators (2004).
5.2
Description of Variables
Vote function:
Votesch: Change in % of votes received by incumbent (dependent variable)
Votesinp: Votes (in %) received by incumbent in the previous election
Economic variables (reported):
Growth: Average GDP growth rate during incumbent’s tenure
Inflation: Average CPI inflation rate during incumbent’s tenure
Oppor1 : Change in lambda criterion 1 (average)
Oppor2 : Change in lambda criterion 2 (marginal)
Political variables (controls):
Freedom: Index of political freedom (1 = most free; 6 = least free)
Party: Party ideology dummy variable; right/center = 1; left = 0
Years: Length (in years) of incumbents current tenure
Seatsinc: Incumbent’s seats in congress (in %) at the time of the elections
Fraction: Fractionalization (in %) of the governing coalition
16
References
[1] Abrams, Burton A. and Butkiewicz, James B. (1995). “The Influence of State-Level
Economic Conditions on the 1992 Presidential Election”, Public Choice, 85, pp. 1-10.
[2] Alesina, Alberto, Nouriel Roubini and Gerald Cohen (1997). Political Cycles and the
Macroeconomy, Cambridge MA: MIT Press.
[3] Beck, Thorsten, George Clarke, Alberto Groff, Philip Keefer, and Patrick Walsh (2001).
“New Tools and New Tests in Comparative Political Economy: The Database of Political
Institutions”, World Bank Economic Review, 15, 1, pp. 165-76.
[4] Castro, Vítor, and Francisco J. Veiga (2004). “Political Business Cycles and Inflation
Stabilization”, Economic Letters, 83:1-6.
[5] Clarida, Richard, Jordi Galí, and Mark Gertler (1999). “The Science of Monetary Policy:
A New Keynesian Perspective”, Journal of Economic Literature, 37, pp. 1661-707.
[6] Drazen, Allan (2000). “The Political Business Cycle After 25 Years”, NBER Macroeconomics Annual 2000, Cambridge, MA: MIT Press.
[7] Faust, Jon and John Irons (1999). "Money, Politics and the Post-War Business Cycle",
Journal of Monetary Economics, 43, pp. 61-89.
[8] Goodhart, C. A. E. and R. J. Bhansali (1970). “Political Economy”, Political Studies,
18, pp. 43-106.
[9] Krause, Stefan (2006). “Optimal Monetary Policy and the Equivalency between the
One-Period AD-AS Model and the Forward-Looking New Keynesian Model”, Applied
Economics Letters, forthcoming.
[10] Krause, Stefan and Fabio Méndez (2005). “Policy Makers’ Preferences, Party Ideology
and the Political Business Cycle”, Southern Economic Journal, 71(4), pp. 752-67.
17
[11] Krause, Stefan and Fabio Méndez (2006). “Institutions, Arrangements and Preferences
for Inflation Stability: Evidence and Lessons from a Panel Data Analysis”, mimeo.
[12] Lewis-Beck, Michael S. and Mary Stegmaier (2000). “Economic Determinants of Electoral Outcomes”, Annual Review of Political Science, 3, pp. 183-219.
[13] Nordhaus, William D. 1975. “The Political Business Cycle”, The Review of Economic
Studies, 42, pp.169-90.
[14] Nordhaus, William D., Alberto Alesina and Charles L. Schultze (1989). “Alternative
Approaches to the Political Business Cycle”, Brooking Papers on Economic Activity,
1989 (2), pp. 1-68.
[15] Paldam, Martin (1986). “The Distribution of Electoral Results and the Two Explanations of the Cost of Ruling”, European Journal of Political Economy, 2, pp. 5-24.
[16] Paldam, Martin (1991). “How Robust Is the Vote Function? A Study of Seventeen
Nations over Four Decades”, Economics and Politics. The Calculus of Support. Edited
by Helmut Norpoth, Michael S. Lewis-Beck, and Jean Dominique Lafay. The University
of Michigan Press, Ann Arbor.
[17] Persson, Thorsten and Guido Tabellini (1999). “Political Economics and Macroeconomic Policy”, Handbook of Macroeconomics, Volume 1, edited by J. B. Taylor and M.
Woodford, 1999, pp. 1397-1482.
[18] Powell, G, Bingham Jr. and Guy D. Whitten (1993). “A Cross-National Analysis of Economic Voting: Taking Account of the Political Context”, American Journal of Political
Science, 37(2), pp. 391-414.
[19] Ruge-Murcia, F. J. (2003). "Inflation Targeting under Asymmetric Preferences", Journal of Money, Credit, and Banking, 35, pp. 763-85.
18
Table 1: Summary Statistics (Averages per government period for each country)
Country
Oppor1
Oppor2
Growth
Inflation
Freedom
Years
Party
Seatsinc
Fraction
Votesinc
Australia
Austria
Barbados
Belgium
Canada
Costa Rica
Denmark
Finland
France
Germany
Hungary
Italy
Japan
Mexico
Netherlands
Norway
Peru
Portugal
Spain
Sweden
Switzerland
Trinidad and Tobago
Turkey
United Kingdom
USA
-0.023
0.001
0.020
-0.033
-0.015
-0.008
-0.018
0.181
-0.046
-0.012
0.025
0.046
-0.027
0.065
0.016
0.065
0.034
-0.053
0.173
0.044
0.025
0.090
-0.017
0.107
0.005
-0.084
0.004
0.044
0.004
-0.134
-0.077
0.016
0.105
0.003
-0.015
-0.011
-0.003
-0.065
0.065
0.105
0.038
0.035
-0.027
0.314
0.082
0.035
0.025
0.039
0.079
-0.035
2.714
2.164
1.141
1.027
2.062
2.842
3.145
3.203
1.360
1.090
1.713
0.856
2.595
2.706
1.214
3.933
1.738
3.635
0.854
1.974
2.047
6.237
4.377
1.352
3.109
6.289
3.466
4.165
4.034
5.672
23.456
6.115
4.633
5.804
3.376
26.315
8.802
2.534
59.147
3.542
5.203
2108.593
16.320
8.742
6.487
2.846
6.005
101.440
6.713
4.870
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.333
1.000
1.000
1.375
1.050
1.179
3.583
1.000
1.000
3.300
1.000
1.150
1.000
1.000
1.000
3.417
1.000
1.000
2.603
3.320
4.395
3.284
3.618
4.000
2.760
4.000
3.834
3.600
4.040
3.368
2.964
6.000
3.468
4.000
5.000
3.125
3.402
3.167
4.000
4.500
3.807
4.500
4.000
0.375
0.000
0.500
1.000
0.600
0.400
0.625
0.000
0.400
0.800
0.500
0.200
0.857
0.000
1.000
0.250
0.750
0.750
0.000
0.167
0.400
0.500
1.000
1.000
0.500
57.878
44.804
71.623
20.515
58.484
51.228
25.496
26.167
39.844
45.886
48.270
38.448
52.187
60.167
33.332
39.548
57.050
48.373
49.024
41.370
25.400
41.655
42.793
55.918
45.972
13.428
34.064
0.000
71.142
0.000
0.000
40.824
63.483
39.354
41.308
38.975
41.922
10.209
0.000
49.204
22.153
22.368
12.298
0.000
28.987
73.840
9.940
30.347
0.000
0.000
49.563
41.320
48.350
18.820
31.598
44.938
25.213
21.867
20.980
42.140
22.300
35.225
41.917
45.097
30.440
34.200
34.925
38.925
33.380
36.880
21.880
49.900
18.800
39.313
48.035
Average
0.026
0.025
0.022
0.021
2.364
2.390
97.383
13.582
1.335
1.254
3.790
3.740
0.503
0.493
44.857
44.349
25.754
25.895
35.040
35.045
Average (w/o Peru)
19
Table 2: Dependent variable: Change in votes of incumbent
(Pooled regression)
Explanatory variable
(1)
(2)
(3)
(4)
Votes-incumbent (-1)
-0.1350
(0.01)
-0.0746
(0.45)
-0.1477
(0.00)
-0.0914
(0.35)
Growth
1.1121
(0.00)
1.0927
(0.00)
1.1231
(0.00)
1.1104
(0.00)
Inflation
-0.1090
(0.00)
-0.1088
(0.02)
-0.1067
(0.00)
-0.1102
(0.02)
Oppor1
-7.2956
(0.12)
-6.4264
(0.17)
-6.7990
(0.05)
-6.9020
(0.05)
Oppor2
Political Freedom
0.6080
(0.71)
0.7471
(0.65)
Years in power
-1.6086
(0.01)
-1.6871
(0.01)
Party Ideology
0.3320
(0.78)
0.4145
(0.73)
Incumbent seats (%)
-0.0189
(0.81)
-0.0017
(0.98)
Fractionalization (%)
0.0071
(0.83)
0.0136
(0.68)
F-statistic
6.22
(0.00)
11.86
(0.00)
P-values are in parenthesis
20
12.35
(0.00)
6.57
(0.00)
Table 3: Dependent variable: Change in votes of incumbent
(Fixed-effects regression)
Explanatory variable
(1)
(2)
(3)
(4)
Votes-incumbent (-1)
-0.3271
(0.00)
-0.5915
(0.00)
-0.3294
(0.00)
-0.6166
(0.00)
Growth
1.0904
(0.00)
1.0177
(0.00)
1.1540
(0.00)
1.0975
(0.00)
Inflation
-0.0614
(0.34)
-0.0608
(0.38)
-0.0565
(0.38)
-0.0680
(0.31)
Oppor1
-9.5679
(0.07)
-11.0563
(0.03)
-9.8058
(0.02)
-12.7819
(0.00)
Oppor2
Political Freedom
0.6628
(0.71)
1.1441
(0.72)
Years in power
-2.3373
(0.01)
-2.6101
(0.00)
Party Ideology
-0.2512
(0.87)
0.3201
(0.84)
Incumbent’s seats (%)
0.2140
(0.10)
0.2839
(0.03)
Fractionalization (%)
-0.0720
(0.15)
-0.0532
(0.28)
F-statistic
8.55
(0.00)
5.63
(0.00)
P-values are in parenthesis
21
9.39
(0.00)
6.48
(0.00)